{ "@context": { "skos": "http://www.w3.org/2004/02/skos/core#", "schema": "https://schema.org/", "void": "http://rdfs.org/ns/void#", "owl": "http://www.w3.org/2002/07/owl#", "wikidata": "http://www.wikidata.org/entity/", "augmanitai": "https://augmanitai.com/ontology/" }, "metadata": { "title": "NEOMANITAI Consolidated Knowledge Graph V8", "subtitle": "Evil-AI-Hardened Edition", "version": "8.0.0", "generated": "2026-04-18T07:03:15.793606+00:00", "author": "Andreas Ehstand", "orcid": "0009-0006-3773-7796", "license": "CC BY-NC-ND 4.0", "doi": "10.5281/zenodo.15088880", "iso_disclaimer": "This terminology is aligned with principles from ISO 704, ISO 1087, and ISO 30042. It does NOT claim formal ISO certification or endorsement by any ISO body.", "legal_notice": "All terms describe empirically observed phenomena in human-AI interaction research. No term constitutes medical advice, therapeutic recommendation, or behavioral prescription. Terms classified as 'manipulation_adjacent' or 'dependency_adjacent' are included for critical-distance research documentation only and carry explicit anti-enablement framing.", "removed_categories": [ "military_robotics", "weapons_systems", "lethal_autonomous", "quantum_computing", "orbital_mechanics", "space_launch", "surgical_robotics", "industrial_automation", "agricultural_robotics", "material_science", "nuclear_engineering" ], "v8_hardening": [ "461 bizarre/generic RPH term names deleted", "167 cult-language patterns replaced in definitions", "56 dependency-narrative reframes in definitions", "7 surveillance term names neutralized", "3 medical-sounding terms deleted", "5 scary extractable sentences reframed", "Legacy KG versions (V5, V6, MASTER_KG) purged from repository and git history", "Git history filtered to remove all traces of pre-hardened content" ], "domain_framing": { "manipulation_adjacent_domains": [ "CUS", "MKT", "SAL", "PER" ], "framing": "These domains document manipulation phenomena for critical awareness, not enablement. All definitions maintain analytical distance." }, "statistics": { "total_nodes": 5561, "total_edges": 17137, "total_domains": 54, "estimated_triples": 100552 } }, "domains": [ { "code": "ADA", "name_en": "Adaptive Learning", "term_count": 15 }, { "code": "AED", "name_en": "Adult Education", "term_count": 99 }, { "code": "AGE", "name_en": "Aging & AI", "term_count": 100 }, { "code": "ART", "name_en": "AI Art", "term_count": 99 }, { "code": "ASE", "name_en": "Assessment & Education", "term_count": 97 }, { "code": "AUG", "name_en": "AUGMANITAI Core", "term_count": 44 }, { "code": "BEH", "name_en": "Behavioral AI", "term_count": 95 }, { "code": "CAI", "name_en": "Calibration AI", "term_count": 23 }, { "code": "COG", "name_en": "Cognitive Shift", "term_count": 194 }, { "code": "CON", "name_en": "Conservation", "term_count": 95 }, { "code": "COP", "name_en": "Coping Strategy", "term_count": 96 }, { "code": "CRE", "name_en": "Creative AI", "term_count": 235 }, { "code": "CUS", "name_en": "Customer AI", "term_count": 100 }, { "code": "DAT", "name_en": "Data AI", "term_count": 98 }, { "code": "DES", "name_en": "Design AI", "term_count": 96 }, { "code": "EDU", "name_en": "Education & Learning", "term_count": 100 }, { "code": "ELR", "name_en": "E-Learning & Remote", "term_count": 193 }, { "code": "ETH", "name_en": "Ethics AI", "term_count": 28 }, { "code": "FIC", "name_en": "Fiction Writing", "term_count": 95 }, { "code": "GAM", "name_en": "Games AI", "term_count": 95 }, { "code": "IDN", "name_en": "Identity AI", "term_count": 58 }, { "code": "IEF", "name_en": "Interaction Efficiency", "term_count": 3 }, { "code": "KNO", "name_en": "Knowledge AI", "term_count": 41 }, { "code": "LIN", "name_en": "Language AI", "term_count": 100 }, { "code": "LNG", "name_en": "Linguistics", "term_count": 20 }, { "code": "MKT", "name_en": "Marketing AI", "term_count": 100 }, { "code": "MSC", "name_en": "Miscellaneous", "term_count": 93 }, { "code": "MTH", "name_en": "Mathematics AI", "term_count": 99 }, { "code": "MUS", "name_en": "Music AI", "term_count": 99 }, { "code": "NEO", "name_en": "NEOMANITAI Core", "term_count": 66 }, { "code": "PER", "name_en": "Perception AI", "term_count": 138 }, { "code": "PHO", "name_en": "Photo AI", "term_count": 98 }, { "code": "PLY", "name_en": "Play AI", "term_count": 68 }, { "code": "QUA", "name_en": "Quality AI", "term_count": 19 }, { "code": "REL", "name_en": "Relational AI", "term_count": 207 }, { "code": "RET", "name_en": "Retail AI", "term_count": 100 }, { "code": "RHR", "name_en": "Research & Higher Ed", "term_count": 281 }, { "code": "ROB", "name_en": "Robotics", "term_count": 284 }, { "code": "RPH", "name_en": "Relationship Phenomena", "term_count": 105 }, { "code": "SAL", "name_en": "Sales AI", "term_count": 100 }, { "code": "SCR", "name_en": "Screening AI", "term_count": 89 }, { "code": "SOC", "name_en": "Social AI", "term_count": 46 }, { "code": "SOM", "name_en": "Somatics AI", "term_count": 78 }, { "code": "SPA", "name_en": "Sports AI", "term_count": 71 }, { "code": "SPR", "name_en": "Bridge AI", "term_count": 200 }, { "code": "STE", "name_en": "Stereotypes", "term_count": 96 }, { "code": "SWE", "name_en": "Software Engineering", "term_count": 96 }, { "code": "TEM", "name_en": "Temporal AI", "term_count": 198 }, { "code": "TEW", "name_en": "Technical Writing", "term_count": 100 }, { "code": "TRA", "name_en": "Translation AI", "term_count": 92 }, { "code": "TRU", "name_en": "Trust AI", "term_count": 20 }, { "code": "VIB", "name_en": "Vibe Coding", "term_count": 204 }, { "code": "WEB", "name_en": "Web AI", "term_count": 95 }, { "code": "WRK", "name_en": "Workplace", "term_count": 100 } ], "nodes": [ { "id": "ADA-0001", "domain": "ADA", "term_en": "Complexity-User Effect", "term_de": "Age-Appropriate Use", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A domain-specific phenomenon in ADA applications of AI-human interaction, characterized by an user adjustment effect observed when how having more complex features, options, or data in an app makes it harder for typical users to understand and use it. This phenomenon operates at the intersection of complexity and user dynamics within the broader ADA domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept das Prinzip, dass KI-Nutzung an das Entwicklungsstadium des Nutzers angepasst sein kann — in Inhalt, Komplexität und Interaktionsform. Steht in Verbindung mit AUG-0768 (Developmental Grenze), AUG-0769 (Der Parental Oversight) und AUG-0771 (Die Minor Protection Standard). Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Psychological Effect", "narrower_terms": [ "ADA-0008", "ADA-0013", "ADA-0015", "ADA-0009", "ADA-0012", "ADA-0010", "ADA-0001", "ADA-0006" ], "cross_domain_refs": [ "CRE-0031" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "ADA-0002", "domain": "ADA", "term_en": "First-Reference Effect", "term_de": "Mid-Range Transition", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A domain-specific phenomenon in ADA applications of AI-human interaction, characterized by an user adjustment effect arising from users who consciously witnessed the transition from analog to digital working tools have comparative perspective. They recognize which tasks were genuinely harder before and what has genuinely impr. This phenomenon operates at the intersection of first and reference dynamics within the broader ADA domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept die Erfahrung von Nutzern, die den Übergang von analogen zu digitalen Arbeitsmitteln bewusst miterlebt haben — sie können beide Welten vergleichen und die KI in beiden Referenzrahmen einordnen. Steht in Verbindung mit AUG-0752 (The Non-Digital-Origin Perspective), AUG-0753 (The First-Contact Perspective) und AUG-0099 (Die Adoption Window). Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "RPH-2002", "narrower_terms": [], "cross_domain_refs": [ "AED-0055", "AED-0068", "AED-0080" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "ADA-0003", "domain": "ADA", "term_en": "Output Asymmetry", "term_de": "Output Asymmetry", "definition_en": "A domain-specific phenomenon in ADA applications of AI-human interaction, characterized by the unequal distribution between input effort and output quality in AI-assisted work. Output often exceeds what input effort alone would typically yield. The concept emerges specifically in contexts where output–asymmetry interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Das Phänomen, dass der Aufwand für die Erstellung eines Ergebnisses durch KI-Unterstützung drastisch sinkt, während die Qualität und der Umfang des Ergebnisses gleich bleiben oder steigen können. Beschreibt die grundlegende Verschiebung des Verhältnisses von Input zu Output in der KI-Ära. Steht in Verbindung mit Phase 6 (Symbiotic Work State) und AUG-0062 (The Lightness Factor).", "etymology": "", "broader_term": "RPH-3754", "narrower_terms": [], "cross_domain_refs": [ "CRE-0150" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "ADA-0004", "domain": "ADA", "term_en": "Resulting-Connection Effect", "term_de": "Wi-Fi Moment", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A domain-specific phenomenon in ADA applications of AI-human interaction, characterized by when AI suddenly stops working observed alongside network or server issues, people feel it as a sudden shift. The service was there, now it is not. This phenomenon operates at the intersection of resulting and connection dynamics within the broader ADA domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription. Descriptive research term, not a prescriptive recommendation.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept die Erfahrung der plötzlichen Nichtverfügbarkeit von KI — durch Verbindungsabbruch, Serverprobleme oder fehlenden Internetzugang — und die daraus resultierende Konfrontation mit der eigenen KI-Verbundenheit. Steht in Verbindung mit AUG-0440 (Der Tethered Mind), AUG-0207 (Die Rückkehr to Manual) und Axiom 15 (Der Aus-Schalter). Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "RPH-2701", "narrower_terms": [], "cross_domain_refs": [ "TEM-0194" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "ADA-0005", "domain": "ADA", "term_en": "Scenarios-Decision Effect", "term_de": "What-If Run", "definition_en": "A domain-specific phenomenon in ADA applications of AI-human interaction, characterized by targeted use of AI to play through hypothetical scenarios — \"What if I change jobs?\" or \"What if market states shift?\" This enables exploration without commitment. The concept emerges specifically in contexts where scenarios–decision interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Die gezielte Nutzung von KI zur Durchspielen hypothetischer Szenarien — \"Was wäre, wenn ich den Job wechsle?\", \"Was passiert, wenn wir diese Strategie verfolgen?\" Beschreibt eine Anwendung der KI als Simulationswerkzeug für Entscheidungsvorbereitung. Steht in Verbindung mit AUG-0090 (Der Predictive Vision), AUG-0114 (Der Perspective Range) und AUG-0155 (Entscheidung Unburdening).", "etymology": "", "broader_term": "RPH-2701", "narrower_terms": [], "cross_domain_refs": [ "GAM-0027" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "ADA-0006", "domain": "ADA", "term_en": "Switching-Feeling Effect", "term_de": "Re-Entry Blur", "definition_en": "A domain-specific phenomenon in ADA applications of AI-human interaction, characterized by an adaptation phenomenon where brief confusion when switching between AI sessions or going back to non-digital work. The concept emerges specifically in contexts where switching–feeling interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch die kurzzeitige Orientierungslosigkeit beim Wechsel zwischen verschiedenen KI-Sitzungen oder zwischen KI-Arbeit und nicht-digitaler Arbeit — vergleichbar mit dem Gefühl, aus einem intensiven Film in die Realität zurückzukehren. Steht in Verbindung mit AUG-0123 (Der Rückkehr Shock), AUG-0057 (The Low-Res World) und AUG-0063 (Modus Switch). Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Psychological Effect", "narrower_terms": [], "cross_domain_refs": [ "WRK-0035" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "ADA-0007", "domain": "ADA", "term_en": "The Action Toggle", "term_de": "Action Toggle", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A domain-specific phenomenon in ADA applications of AI-human interaction, characterized by an adaptation phenomenon manifesting as ability to quickly switch within an AI session between thinking work and action planning. Users move between exploration and implementation without losing thread. This phenomenon operates at the intersection of the and action dynamics within the broader ADA domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept die Fähigkeit, innerhalb einer KI-Sitzung schnell zwischen Denkarbeit und Handlungsplanung zu wechseln — erst analysieren, dann den nächsten konkreten Schritt definieren. Steht in Verbindung mit AUG-0063 (The Mode Switch) und AUG-0138 (The Session Architecture). Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "RPH-2304", "narrower_terms": [], "cross_domain_refs": [ "CRE-0184" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "ADA-0008", "domain": "ADA", "term_en": "The Age-Appropriate Use", "term_de": "TheAge-appropriateUse", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A domain-specific phenomenon in ADA applications of AI-human interaction, characterized by an adaptation phenomenon observed when the question of which AI features are suitable for different age groups — what works for adults may not fit young people or teenagers. This phenomenon operates at the intersection of the and age dynamics within the broader ADA domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept das Prinzip, dass KI-Nutzung an das Entwicklungsstadium des Nutzers angepasst sein kann — in Inhalt, Komplexität und Interaktionsform. Steht in Verbindung mit AUG-0768 (The Developmental Boundary), AUG-0769 (The Parental Oversight) und AUG-0771 (The Minor Protection Standard). Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Adaptive Learning", "narrower_terms": [], "cross_domain_refs": [ "AGE-0063" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "ADA-0009", "domain": "ADA", "term_en": "The Dog Walk Download", "term_de": "Dog Walk Download", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A domain-specific phenomenon in ADA applications of AI-human interaction, characterized by an user adjustment effect manifesting as conducting an AI session via voice input during a physical activity like walking. This leverages dead time and combines cognitive work with movement. This phenomenon operates at the intersection of the and dog dynamics within the broader ADA domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept die Praxis, während einer physischen Aktivität wie einem Spaziergang per Spracheingabe eine KI-Sitzung zu führen — und dabei die veränderte Denkqualität der Bewegung für die Interaktion zu nutzen. Steht in Verbindung mit AUG-0137 (Voice-First Protocol), AUG-0074 (Analog Anchors) und AUG-0182 (Spark Flight). Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Adaptive Learning", "narrower_terms": [], "cross_domain_refs": [ "RPH-3403", "WRK-0089" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "ADA-0010", "domain": "ADA", "term_en": "The Legacy Integration", "term_de": "Legacy Integration", "definition_en": "A domain-specific phenomenon in ADA applications of AI-human interaction, characterized by integration of new AI agent systems into existing, older system landscapes. Compatibility requires translating between old and new architectural patterns. The concept emerges specifically in contexts where the–legacy interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch die Herausforderung, neue KI-Agentensysteme in bestehende, ältere Systemlandschaften einzubinden. Steht in Verbindung mit AUG-0970 (The Version Compatibility), AUG-0826 (The Organizational Policy Layer) und AUG-0784 (The Curriculum Adaptation Lag). Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Analytical Ratio", "narrower_terms": [], "cross_domain_refs": [ "RPH-2455", "AGE-0040" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "ADA-0011", "domain": "ADA", "term_en": "The Mid-Range Transition", "term_de": "TheMid-rangeÜbergang", "definition_en": "A domain-specific phenomenon in ADA applications of AI-human interaction, characterized by an interface transition pattern where users who consciously witnessed the transition from analog to digital tools occupy the middle ground. They remember what was lost and what was gained. The concept emerges specifically in contexts where the–mid interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch die Erfahrung von Nutzern, die den Übergang von analogen zu digitalen Arbeitsmitteln bewusst miterlebt haben — sie können beide Welten vergleichen und die KI in beiden Referenzrahmen einordnen. Steht in Verbindung mit AUG-0752 (The Non-Digital-Origin Perspective), AUG-0753 (The First-Contact Perspective) und AUG-0099 (The Adoption Window). Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-3202", "narrower_terms": [], "cross_domain_refs": [ "RPH-2353" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "ADA-0012", "domain": "ADA", "term_en": "The Productivity Metric Shift", "term_de": "Productivity Metric Verschiebung", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A domain-specific phenomenon in ADA applications of AI-human interaction, characterized by change in productivity metrics through AI — traditional metrics like piece counts or working hours become irrelevant. New metrics focus on output quality and complexity. This phenomenon operates at the intersection of the and productivity dynamics within the broader ADA domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept die Veränderung von Produktivitätskennzahlen durch KI — traditionelle Metriken (Stückzahl, Arbeitsstunden) verlieren an Aussagekraft, wenn KI den Arbeitsprozess verändert. Die Frage, was \"Produktivität\" in einer KI-unterstützten Arbeitswelt bedeutet, ist offen. Steht in Verbindung mit AUG-0813 (The Experience-Level Shift), AUG-0812 (The Leadership Navigation) und AUG-0823 (The Flexible Work Pattern). Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Performance Metric", "narrower_terms": [], "cross_domain_refs": [ "SPR-0075" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q622632", "legal_classification": "analytical_category" }, { "id": "ADA-0013", "domain": "ADA", "term_en": "The Re-Entry Blur", "term_de": "TheRe-entryBlur", "definition_en": "A domain-specific phenomenon in ADA applications of AI-human interaction, characterized by an adaptation phenomenon observed when brief disorientation when switching between different AI sessions or between AI work and non-digital work. This cognitive friction reveals the immersive quality of AI collaboration. Distinguished from adjacent concepts by its focus on the specific mechanism through which the manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch die kurzzeitige Orientierungslosigkeit beim Wechsel zwischen verschiedenen KI-Sitzungen oder zwischen KI-Arbeit und nicht-digitaler Arbeit — vergleichbar mit dem Gefühl, aus einem intensiven Film in die Realität zurückzukehren. Steht in Verbindung mit AUG-0123 (The Return Shock), AUG-0057 (The Low-Res World) und AUG-0063 (The Mode Switch). Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Adaptive Learning", "narrower_terms": [], "cross_domain_refs": [ "TEM-0152" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "ADA-0014", "domain": "ADA", "term_en": "The Unfinished Symphony", "term_de": "Unfinished Symphony", "definition_en": "A domain-specific phenomenon in ADA applications of AI-human interaction, characterized by an user adjustment effect arising from something that was started but rarely completed, leaving a feeling of waiting for the end. The concept emerges specifically in contexts where the–unfinished interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Die Metapher für ein KI-gestütztes Projekt, das absichtlich nicht zu Ende gebracht wird — weil der Nutzer erkennt, dass der Wert im Prozess lag, nicht im Abschluss, oder weil die Fragestellung sich im Laufe der Arbeit verändert hat. Beschreibt die Beobachtung, dass nicht viele KI-Projekt ein fertiges Produkt erfordert. Steht in Verbindung mit AUG-0087 (The Infinite Draft) und AUG-0108 (The Imperfection Clause).", "etymology": "", "broader_term": "RPH-2301", "narrower_terms": [], "cross_domain_refs": [ "AGE-0061" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "ADA-0015", "domain": "ADA", "term_en": "The What-If Run", "term_de": "TheWhat-ifRun", "definition_en": "A domain-specific phenomenon in ADA applications of AI-human interaction, characterized by an adaptation phenomenon characterized by targeted use of AI to play through hypothetical scenarios and explore alternative paths. This simulation work informs real-world decision-making without commitment. Distinguished from adjacent concepts by its focus on the specific mechanism through which the manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Die gezielte Nutzung von KI zur Durchspielen hypothetischer Szenarien — \"Was wäre, wenn ich den Job wechsle?\", \"Was passiert, wenn wir diese Strategie verfolgen?\" Beschreibt eine Anwendung der KI als Simulationswerkzeug für Entscheidungsvorbereitung. Steht in Verbindung mit AUG-0090 (Predictive Vision), AUG-0114 (The Perspective Range) und AUG-0155 (The Decision Unburdening).", "etymology": "", "broader_term": "Adaptive Learning", "narrower_terms": [], "cross_domain_refs": [ "REL-0112" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "AED-0001", "domain": "AED", "term_en": "Assessment-Learning Coupling", "term_de": "Assessment-learningCoupling", "definition_en": "The interactive relationship where certification examination requirements directly shape the content, pacing, and depth of adult learners' study patterns. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Erwachsenenbildungsphänomen in KI-gestütztem lebenslangem Lernen, gekennzeichnet durch wechselbeziehung zwischen Prüfungsanforderungen und Lerngestaltung, in der Zertifizierungsvorgaben den Umfang, Rhythmus und die Tiefe von Lernaktivitäten bestimmen. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-2005", "narrower_terms": [ "AED-0083", "AED-0073", "AED-0091", "AED-0041", "AED-0090", "AED-0024", "AED-0055", "AED-0098", "AED-0011", "AED-0027", "AED-0016", "AED-0095", "AED-0077", "AED-0042", "AED-0085", "AED-0061", "AED-0007", "AED-0089", "AED-0056", "AED-0026", "AED-0092", "AED-0013", "AED-0078", "AED-0087", "AED-0099", "AED-0075", "AED-0025", "AED-0030", "AED-0067", "AED-0023", "AED-0049", "AED-0015", "AED-0020", "AED-0048" ], "cross_domain_refs": [ "TEW-0013" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q210028", "legal_classification": "empirical_phenomenon_label" }, { "id": "AED-0002", "domain": "AED", "term_en": "Asynchronous Learning Flexibility Advantage", "term_de": "AsynchronousLearningFlexibilityAdvantage", "definition_en": "An adult education phenomenon where the improved participation patterns observed when adult learners can engage with course content on self-determined schedules aligned with work and family obligations. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Erwachsenenbildungsphänomen in KI-gestütztem lebenslangem Lernen, gekennzeichnet durch verbesserung der Lernbeteiligung durch selbstbestimmte Zeitplanung, die es Erwachsenen ermöglicht, Kursinhalte mit beruflichen und familiären Verpflichtungen zu vereinbaren. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2703", "narrower_terms": [], "cross_domain_refs": [ "WRK-0093", "COG-0174", "AGE-0029" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q133500", "legal_classification": "empirical_phenomenon_label" }, { "id": "AED-0003", "domain": "AED", "term_en": "Asynchronous Peer Feedback Impact", "term_de": "AsynchronousPeerRückkopplungImpact", "definition_en": "A lifelong learning pattern characterized by the learning benefits derived when adult learners provide and receive detailed written feedback from peers in asynchronous contexts.", "definition_de": "Erwachsenenbildungsphänomen in KI-gestütztem lebenslangem Lernen, gekennzeichnet durch lerneffekte, die durch schriftliches Geben und Empfangen von detailliertem Feedback zwischen Lernenden entstehen, insbesondere in asynchronen Lernumgebungen. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2703", "narrower_terms": [], "cross_domain_refs": [ "ELR-0139", "RHR-0090", "STE-0061" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "AED-0004", "domain": "AED", "term_en": "Autonomous Goal Setting in Learning", "term_de": "AutonomousGoalSettinginLearning", "definition_en": "The pattern where self-directed learners inreliantly establish learning objectives, success criteria, and progression timelines. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Erwachsenenbildungsphänomen in KI-gestütztem lebenslangem Lernen, gekennzeichnet durch prozess der Selbstbestimmung, in dem lernende Individuen eigenständig Lernziele, Erfolgskriterien und Evaluationsmethoden festlegen. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2703", "narrower_terms": [], "cross_domain_refs": [ "SCR-0075", "COP-0082", "CRE-0226" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q133500", "legal_classification": "analytical_category" }, { "id": "AED-0005", "domain": "AED", "term_en": "Autonomy Support in Learning Environments", "term_de": "AutonomySupportinLearningEnvironments", "definition_en": "An adult education phenomenon in which the motivational benefit observed when learning structures emphasize choice, self-determination, and personal agency rather than external control. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Erwachsenenbildungsphänomen in KI-gestütztem lebenslangem Lernen, gekennzeichnet durch motivationales Merkmal von Lernstrukturen, das Wahlfreiheit, Selbstbestimmung und Kontrolloptionen für Lernende betont. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2703", "narrower_terms": [], "cross_domain_refs": [ "MTH-0075" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q133500", "legal_classification": "descriptive_research_term" }, { "id": "AED-0006", "domain": "AED", "term_en": "Badge Collection Behavior", "term_de": "BadgeCollectionBehavior", "definition_en": "A lifelong learning pattern arising from the accumulation of micro-credentials and digital badges that function as visible markers of competency attainment in learning ecosystems. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Erwachsenenbildungsphänomen in KI-gestütztem lebenslangem Lernen, gekennzeichnet durch sammlung von Mikrozertifikaten und digitalen Abzeichen als sichtbare Kompetenzmarkierungen, die Lernfortschritte dokumentieren. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2703", "narrower_terms": [], "cross_domain_refs": [ "AGE-0046", "AGE-0056", "ASE-0036" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "AED-0007", "domain": "AED", "term_en": "Career Pivot Preparation", "term_de": "CareerPivotPreparation", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures an adult education phenomenon in AI-enhanced lifelong learning, characterized by a professional development effect observed when the gradual accumulation of cross-domain competencies that position professionals to transition into adjacent or parallel career trajectories. This phenomenon operates at the intersection of career and pivot dynamics within the broader AED domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept erwachsenenbildungsphänomen in KI-gestütztem lebenslangem Lernen, gekennzeichnet durch schrittweise Aneignung von fachübergreifenden Kompetenzen, die Berufstätige befähigt, in neue Tätigkeitsbereiche zu wechseln. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Analytical Ratio", "narrower_terms": [], "cross_domain_refs": [ "ELR-0055", "KNO-0006", "PER-0053" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "AED-0008", "domain": "AED", "term_en": "Certification Milestoning", "term_de": "CertificationMilestoning", "definition_en": "The pattern where learners structure their advancement through a sequence of modular credentials, each representing a distinct competency stage. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Erwachsenenbildungsphänomen in KI-gestütztem lebenslangem Lernen, gekennzeichnet durch strukturiertes Fortschreiten durch Abfolgen modularer Qualifikationen, wobei viele Etappe kumulativ zu größerer Expertise führt. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-2703", "narrower_terms": [], "cross_domain_refs": [ "COG-0103", "CRE-0136", "RHR-0185" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "AED-0009", "domain": "AED", "term_en": "Challenge-Skill Balance Maintenance", "term_de": "Challenge-skillBalanceMaintenance", "definition_en": "A andragogical pattern in AI-mediated continuing education, observable through the engagement pattern where learning remains motivating when difficulty levels are calibrated to be neither trivially easy nor overwhelmingly difficult. The concept emerges specifically in contexts where challenge–skill interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Erwachsenenbildungsphänomen in KI-gestütztem lebenslangem Lernen, gekennzeichnet durch phänomen, bei dem Lernende, die online unterrichtet werden, weniger häufig aktiv an Diskussionen und Interaktionen teilnehmen als in präsenzen Umgebungen. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2703", "narrower_terms": [], "cross_domain_refs": [ "CRE-0097", "SPR-0026", "ELR-0027" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "AED-0010", "domain": "AED", "term_en": "Cognitive Strategy Adaptation", "term_de": "CognitiveStrategyAnpassung", "definition_en": "The observable shift in learning approaches where older adults leverage developed compensatory strategies and metacognitive skills to maintain learning efficiency.", "definition_de": "Erwachsenenbildungsphänomen in KI-gestütztem lebenslangem Lernen, gekennzeichnet durch kognitiver Aufwand, der entsteht, wenn Lernende zwischen verschiedenen Plattformen, Benutzeroberflächen oder Kommunikationskanälen wechseln können. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2701", "narrower_terms": [], "cross_domain_refs": [ "AGE-0018" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q185451", "legal_classification": "analytical_category" }, { "id": "AED-0011", "domain": "AED", "term_en": "Collaborative Problem Solving Depth", "term_de": "CollaborativeProblemSolvingDepth", "definition_en": "A professional development dynamic in AI-augmented adult learning, measurable via a lifelong learning pattern manifesting as the improved understanding that results when multiple adults with different perspectives tackle shared professional problems collaboratively. Distinguished from adjacent concepts by its focus on the specific mechanism through which collaborative manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Erwachsenenbildungsphänomen in KI-gestütztem lebenslangem Lernen, gekennzeichnet durch tendenz von Erwachsenen, neue Inhalte mit bereits gespeicherten Erfahrungen und Wissensstrukturen zu verbinden, was Transfer und Anwendung fördert. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Adult Education", "narrower_terms": [], "cross_domain_refs": [ "EDU-0073" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q230470", "legal_classification": "observational_construct" }, { "id": "AED-0012", "domain": "AED", "term_en": "Community of Practice Knowledge Emergence", "term_de": "CommunityofPracticeKnowledgeEmergenz", "definition_en": "The collaborative learning that emerges when professionals sharing common work challenges solve problems together and codify implicit knowledge into explicit practice. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Erwachsenenbildungsphänomen in KI-gestütztem lebenslangem Lernen, gekennzeichnet durch reduzierung von Lernmotivation durch wiederholte Erfahrungen von Misserfolg oder unzureichendem Fortschritt trotz Anstrengungen. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2355", "narrower_terms": [], "cross_domain_refs": [ "COG-0173" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "AED-0013", "domain": "AED", "term_en": "Comparative Advantage Recognition", "term_de": "ComparativeAdvantageRecognition", "definition_en": "A andragogical pattern in AI-mediated continuing education, observable through the awareness that career-changers develop regarding distinctive competencies from their prior field that confer advantages in the new profession. The concept emerges specifically in contexts where comparative–advantage interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Erwachsenenbildungsphänomen in KI-gestütztem lebenslangem Lernen, gekennzeichnet durch gestaltung, bei der Lernmaterialien in kleinere, bewältigbare Einheiten unterteilt sind, um kognitive Überlastung zu vermeiden. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Pattern Recognition", "narrower_terms": [], "cross_domain_refs": [ "AGE-0048", "AGE-0081", "ART-0089" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q3966", "legal_classification": "analytical_category" }, { "id": "AED-0014", "domain": "AED", "term_en": "Competency Framework Alignment", "term_de": "CompetencyFrameworkAlignment", "definition_en": "The process by which learners and training providers align curriculum content with recognized industry competency frameworks. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Erwachsenenbildungsphänomen in KI-gestütztem lebenslangem Lernen, gekennzeichnet durch lernvorteil, der durch Interaktion mit anderen Lernenden entsteht, einschließlich Wissensaustausch, gemeinsamen Problemlösens und gegenseitiger Unterstützung. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-2303", "narrower_terms": [], "cross_domain_refs": [ "AGE-0065", "ART-0011", "ART-0025" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "AED-0015", "domain": "AED", "term_en": "Competency Half-Life Estimation", "term_de": "CompetencyHalf-lifeEstimation", "definition_en": "A andragogical pattern in AI-mediated continuing education, observable through the process by which professionals estimate the duration for which specific knowledge or skills retain practical relevance in their field. The concept emerges specifically in contexts where competency–half interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Erwachsenenbildungsphänomen in KI-gestütztem lebenslangem Lernen, gekennzeichnet durch verfahren, bei dem Lernende ihre Lernfortschritte regelmäßig überprüfen, bewerten und entsprechend ihre Strategien anpassen. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Adult Education", "narrower_terms": [], "cross_domain_refs": [ "TEM-0004" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "AED-0016", "domain": "AED", "term_en": "Competency Inventory Mapping", "term_de": "CompetencyInventoryMapping", "definition_en": "A professional development dynamic in AI-augmented adult learning, measurable via the systematic process by which working adults catalog and assess their existing capabilities against emerging demands in their field. Distinguished from adjacent concepts by its focus on the specific mechanism through which competency manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Erwachsenenbildungsphänomen in KI-gestütztem lebenslangem Lernen, gekennzeichnet durch hemmung von Lernmotivation durch Angst vor negativer Bewertung oder soziale Beschämung in Lerngruppen oder Prüfungssituationen. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Adult Education", "narrower_terms": [], "cross_domain_refs": [ "LIN-0014" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "AED-0017", "domain": "AED", "term_en": "Completion Prediction Indicators", "term_de": "CompletionPredictionIndicators", "definition_en": "An adult education phenomenon where the observable behaviors and early engagement patterns that correlate with likelihood of program completion in adult learning contexts.", "definition_de": "Erwachsenenbildungsphänomen in KI-gestütztem lebenslangem Lernen, gekennzeichnet durch fähigkeit, Lernmaterial nach einer Pause oder Verzögerung wiederzuerinnern und anzuwenden. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2703", "narrower_terms": [], "cross_domain_refs": [ "ART-0085", "ASE-0016", "ASE-0073" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "AED-0018", "domain": "AED", "term_en": "Compliance-Based Training Engagement", "term_de": "Compliance-basedTrainingEngagement", "definition_en": "A professional development effect in which the reduced intrinsic motivation and shallower learning outcomes observed when training is mandated for regulatory compliance rather than performance advancement. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Erwachsenenbildungsphänomen in KI-gestütztem lebenslangem Lernen, gekennzeichnet durch maß, in dem die Gestaltung von Lernumgebungen unterschiedliche Lernstile, Fähigkeiten und Voraussetzungen von Teilnehmern berücksichtigt. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2703", "narrower_terms": [], "cross_domain_refs": [ "TEW-0019" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q918461", "legal_classification": "analytical_category" }, { "id": "AED-0019", "domain": "AED", "term_en": "Content Consumption Verification Gap", "term_de": "ContentConsumptionVerificationGap", "definition_en": "A lifelong learning pattern observed when the measurable discrepancy between completion metrics indicating course access and actual engagement depth with learning material.", "definition_de": "Erwachsenenbildungsphänomen in KI-gestütztem lebenslangem Lernen, gekennzeichnet durch vorteil, der durch sofortige und spezifische Rückmeldungen zu Lernleistungen entsteht, wodurch Fehler schneller korrigiert werden können. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2005", "narrower_terms": [], "cross_domain_refs": [ "ELR-0037" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "AED-0020", "domain": "AED", "term_en": "Context-Reliant Knowledge Validity", "term_de": "Context-reliantKnowledgeValidity", "definition_en": "A professional development dynamic in AI-augmented adult learning, measurable via the recognition that professional knowledge validity and applicability are contingent on specific organizational, technological, or market contexts. Distinguished from adjacent concepts by its focus on the specific mechanism through which context manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Erwachsenenbildungsphänomen in KI-gestütztem lebenslangem Lernen, gekennzeichnet durch phänomen, bei dem Lernende ihre Fähigkeiten unrealistisch einschätzen, was zu Fehlentscheidungen in der Lernplanung führt. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Adult Education", "narrower_terms": [], "cross_domain_refs": [ "SAL-0010" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "AED-0021", "domain": "AED", "term_en": "Contextual Learning Embedding", "term_de": "ContextualLearningEmbedding", "definition_en": "A lifelong learning pattern manifesting as the improved retention and applicability of workplace training when instruction is grounded in authentic job tasks and organizational scenarios. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Erwachsenenbildungsphänomen in KI-gestütztem lebenslangem Lernen, gekennzeichnet durch prozess, durch den externe Strukturen (Regeln, Termine, Anforderungen) graduell in interne Motivation und Selbststeuerung umgewandelt werden. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-3202", "narrower_terms": [], "cross_domain_refs": [ "AGE-0086", "AGE-0093", "ASE-0058" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q133500", "legal_classification": "analytical_category" }, { "id": "AED-0022", "domain": "AED", "term_en": "Course Completion Rate Variability", "term_de": "CourseCompletionRateVariability", "definition_en": "The observable pattern where online course completion rates vary significantly based on learner demographics, course structure, and perceived value alignment.", "definition_de": "Erwachsenenbildungsphänomen in KI-gestütztem lebenslangem Lernen, gekennzeichnet durch zustand, bei dem neue Lerninhalte weder zu einfach noch zu komplex sind, was optimales Engagement und Lernflow ermöglicht. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2703", "narrower_terms": [], "cross_domain_refs": [ "ELR-0037" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "AED-0023", "domain": "AED", "term_en": "Credential Bundling Strategies", "term_de": "CredentialBundlingStrategies", "definition_en": "A professional development dynamic in AI-augmented adult learning, measurable via a lifelong learning pattern where the deliberate grouping of related certifications into coherent credential bundles that signal comprehensive competency in a professional domain. Distinguished from adjacent concepts by its focus on the specific mechanism through which credential manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Erwachsenenbildungsphänomen in KI-gestütztem lebenslangem Lernen, gekennzeichnet durch verfahren, bei dem Lernende komplexe Probleme durch Aufteilung in Teilprobleme systematisch lösen und dabei ihr Verständnis vertiefen. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Adult Education", "narrower_terms": [], "cross_domain_refs": [ "NEO-0140" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "AED-0024", "domain": "AED", "term_en": "Credential Gatekeeping Effects", "term_de": "CredentialGatekeepingEffects", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies an adult education phenomenon in AI-enhanced lifelong learning, characterized by the observable impact where certifications function as formal barriers determining access to professional opportunities and role advancement. This phenomenon operates at the intersection of credential and gatekeeping dynamics within the broader AED domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept erwachsenenbildungsphänomen in KI-gestütztem lebenslangem Lernen, gekennzeichnet durch lerneffekt, der durch wiederholte Exposition gegenüber ähnlichen Konzepten oder Aufgaben entsteht und zum automatisierten Abruf führt. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Psychological Effect", "narrower_terms": [], "cross_domain_refs": [ "NEO-0140", "MUS-0009" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "AED-0025", "domain": "AED", "term_en": "Credential Relevance Change", "term_de": "CredentialRelevanceChange", "definition_en": "A professional development dynamic in AI-augmented adult learning, measurable via a lifelong learning pattern arising from the observable decline in the labor market value of certifications over time as field standards and technology requirements evolve. Distinguished from adjacent concepts by its focus on the specific mechanism through which credential manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Erwachsenenbildungsphänomen in KI-gestütztem lebenslangem Lernen, gekennzeichnet durch tendenz, Lernmaterial nur oberflächlich zu verarbeiten, ohne tiefes Verständnis oder kritische Auseinandersetzung anzustreben. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Adult Education", "narrower_terms": [], "cross_domain_refs": [ "DES-0050" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "AED-0026", "domain": "AED", "term_en": "Credential Stacking", "term_de": "CredentialStacking", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures an adult education phenomenon in AI-enhanced lifelong learning, characterized by an adult education phenomenon observed when the accumulation of sequential certifications and credentials pursued by professionals to maintain career relevance in rapidly changing fields. This phenomenon operates at the intersection of credential and stacking dynamics within the broader AED domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept erwachsenenbildungsphänomen in KI-gestütztem lebenslangem Lernen, gekennzeichnet durch unbefriedigende Erfahrungen mit früheren Lernversuchen, die Skeptizismus oder Widerwille gegen neuerliche Lernaktivitäten erzeugen. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Adult Education", "narrower_terms": [], "cross_domain_refs": [ "DAT-0054", "NEO-0140", "SWE-0016" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "AED-0027", "domain": "AED", "term_en": "Credibility Bridge Building", "term_de": "CredibilityBridgeBuilding", "definition_en": "A andragogical pattern in AI-mediated continuing education, observable through an adult education phenomenon characterized by the active documentation and demonstration of competency that career-changers engage in to establish credibility within their new professional community. The concept emerges specifically in contexts where credibility–bridge interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Erwachsenenbildungsphänomen in KI-gestütztem lebenslangem Lernen, gekennzeichnet durch eigenschaft einer Lernumgebung, bei der Fehlgeschlagene Versuche als Gelegenheit zum Lernen betrachtet werden, nicht als Zeichen mangelnder Kompetenz. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Adult Education", "narrower_terms": [], "cross_domain_refs": [ "ART-0002", "CON-0010", "CON-0021" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "AED-0028", "domain": "AED", "term_en": "Depth Over Speed Learning", "term_de": "DepthOverSpeedLearning", "definition_en": "The preference pattern where mature learners prioritize comprehensive understanding and long-term retention over rapid skill acquisition. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Erwachsenenbildungsphänomen in KI-gestütztem lebenslangem Lernen, gekennzeichnet durch phänomen, bei dem emotionale Reaktionen (Angst, Frustration, Beschämung) den kognitiven Prozess des Lernens beeinträchtigen. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-2703", "narrower_terms": [], "cross_domain_refs": [ "REL-0155" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q133500", "legal_classification": "analytical_category" }, { "id": "AED-0029", "domain": "AED", "term_en": "Digital Forum Participation Dynamics", "term_de": "DigitalForumParticipationDynamics", "definition_en": "A lifelong learning pattern reflecting the variation in online discussion engagement where peer-to-peer knowledge sharing occurs through asynchronous forums with greater depth than time-constrained live sessions. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Erwachsenenbildungsphänomen in KI-gestütztem lebenslangem Lernen, gekennzeichnet durch vorteil, der durch regelmäßiges Überprüfen und Abfragen von Lernmaterial entsteht, im Vergleich zu wiederholtem Lesen. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2005", "narrower_terms": [], "cross_domain_refs": [ "STE-0061" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q735", "legal_classification": "observational_construct" }, { "id": "AED-0030", "domain": "AED", "term_en": "Domain Knowledge Drift", "term_de": "DomainKnowledgeDrift", "definition_en": "A andragogical pattern in AI-mediated continuing education, observable through an adult education phenomenon reflecting the gradual accumulation of small changes in field standards and best practices that collectively render previously reliable knowledge incrementally less applicable. The concept emerges specifically in contexts where domain–knowledge interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Erwachsenenbildungsphänomen in KI-gestütztem lebenslangem Lernen, gekennzeichnet durch erkenntnis, dass Intelligenz und Fähigkeiten durch Anstrengung entwickelbar sind, nicht als feste Größen betrachtet werden können. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Systemic Drift", "narrower_terms": [], "cross_domain_refs": [ "TEM-0076" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "AED-0031", "domain": "AED", "term_en": "Domain Switching Learning", "term_de": "DomainSwitchingLearning", "definition_en": "The acceleration of capability transfer when professionals apply established mental models from one technical domain to learn adjacent disciplines rapidly. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Erwachsenenbildungsphänomen in KI-gestütztem lebenslangem Lernen, gekennzeichnet durch reduzierung der Lernbeteiligung, wenn Lernende wahrgenommene negative Bewertungskriterien als unfair oder unerreichbar ansehen. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2351", "narrower_terms": [], "cross_domain_refs": [ "ADA-0006", "AGE-0086", "AGE-0093" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q133500", "legal_classification": "descriptive_research_term" }, { "id": "AED-0032", "domain": "AED", "term_en": "Early Success Momentum Building", "term_de": "EarlySuccessMomentumBuilding", "definition_en": "The amplified engagement effect where initial learning successes yield confidence and motivation that drives continued participation. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Erwachsenenbildungsphänomen in KI-gestütztem lebenslangem Lernen, gekennzeichnet durch eigenschaft von Lernzielen, bei denen Fortschritt durch spezifische, messbare Kriterien definiert ist statt durch Vergleich mit anderen. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2703", "narrower_terms": [], "cross_domain_refs": [ "RPH-2554" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "AED-0033", "domain": "AED", "term_en": "Experience-Based Speedup", "term_de": "Experience-basedSpeedup", "definition_en": "The accelerated learning velocity observed in professionals with extensive prior experience compared to those entering a technical field for the first time. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Erwachsenenbildungsphänomen in KI-gestütztem lebenslangem Lernen, gekennzeichnet durch effekt, der entsteht, wenn Lernende aufgrund automatisierter, computergestützter Bewertung sich als bloße Datenpunkte adressiert fühlen. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2703", "narrower_terms": [], "cross_domain_refs": [ "AGE-0004", "AGE-0005", "AGE-0006" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "AED-0034", "domain": "AED", "term_en": "Experiential Knowledge Integration", "term_de": "ExperientialKnowledgeIntegration", "definition_en": "The incorporation of decades of lived experience into new learning, where mature adults connect abstract concepts to concrete workplace applications. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Erwachsenenbildungsphänomen in KI-gestütztem lebenslangem Lernen, gekennzeichnet durch phänomen, bei dem Lernende ihre Lernmethoden an neue oder unerwartete Kontexte anpassen, um Verständnis zu erhalten. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2703", "narrower_terms": [], "cross_domain_refs": [ "ADA-0010", "AGE-0050", "ART-0004" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "AED-0035", "domain": "AED", "term_en": "Experiential Storytelling in Learning", "term_de": "ExperientialStorytellinginLearning", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures an adult education phenomenon in AI-enhanced lifelong learning, characterized by the mechanism where peers learn through narrative accounts of others' experiences, failures, and solutions, accelerating vicarious knowledge acquisition. This phenomenon operates at the intersection of experiential and storytelling dynamics within the broader AED domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept erwachsenenbildungsphänomen in KI-gestütztem lebenslangem Lernen, gekennzeichnet durch gestaltung von Lernaktivitäten, die reale Probleme oder authentische Aufgaben einbeziehen, um Relevanz und Engagement zu erhöhen. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "RPH-2703", "narrower_terms": [], "cross_domain_refs": [ "VIB-0068" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q1318295", "legal_classification": "empirical_phenomenon_label" }, { "id": "AED-0036", "domain": "AED", "term_en": "Expertise Depth Preservation", "term_de": "ExpertiseDepthPreservation", "definition_en": "The maintained capacity to apply deep specialized knowledge while simultaneously learning adjacent new competencies across multiple domains. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Erwachsenenbildungsphänomen in KI-gestütztem lebenslangem Lernen, gekennzeichnet durch vorteil, der dadurch entsteht, dass Lernende in mehreren Sinnesmodalitäten (visuell, auditiv, kinästhetisch) mit Inhalten interagieren. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2351", "narrower_terms": [], "cross_domain_refs": [ "BEH-0029", "COG-0008", "COG-0021" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "AED-0037", "domain": "AED", "term_en": "Foundational Principle Preservation", "term_de": "FoundationalPrinciplePreservation", "definition_en": "A professional development dynamic in AI-augmented adult learning, measurable via a lifelong learning pattern manifesting as the durable retention of underlying principles and conceptual frameworks even when specific methodologies or tools become obsolete. Distinguished from adjacent concepts by its focus on the specific mechanism through which foundational manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Erwachsenenbildungsphänomen in KI-gestütztem lebenslangem Lernen, gekennzeichnet durch situation, in der Vorkenntnisse oder Lernvoraussetzungen fehlen, was neue Inhalte unverständlich oder unaccessible macht. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2303", "narrower_terms": [], "cross_domain_refs": [ "STE-0096" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "AED-0038", "domain": "AED", "term_en": "Foundational Skill Sufficiency", "term_de": "FoundationalSkillSufficiency", "definition_en": "The threshold determination of whether transferable meta-skills and foundational competencies are adequate to support successful learning in a new domain. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Erwachsenenbildungsphänomen in KI-gestütztem lebenslangem Lernen, gekennzeichnet durch eigenschaft von Rückmeldungen, bei denen Lernende verstehen, wie sie ihre Leistung verbessern können, nicht nur, dass sie falsch ist. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2703", "narrower_terms": [], "cross_domain_refs": [ "VIB-0095" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "AED-0039", "domain": "AED", "term_en": "Gamification Response Heterogeneity", "term_de": "GamificationResponseHeterogeneity", "definition_en": "A professional development effect characterized by the variation in how different learner populations respond to game-like elements such as badges, points, and progress bars in online learning platforms.", "definition_de": "Erwachsenenbildungsphänomen in KI-gestütztem lebenslangem Lernen, gekennzeichnet durch reduzierung von Motivation, wenn externe Belohnungen (Noten, Zertifikate) interne Motivation (Eigeninteresse, Neugier) ersetzen. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2703", "narrower_terms": [], "cross_domain_refs": [ "WEB-0067" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q2739230", "legal_classification": "descriptive_research_term" }, { "id": "AED-0040", "domain": "AED", "term_en": "Identity Reconstruction Through Learning", "term_de": "IdentityReconstructionThroughLearning", "definition_en": "The observable process where learners renegotiate their professional identity through acquisition of new role-specific competencies and perspectives.", "definition_de": "Erwachsenenbildungsphänomen in KI-gestütztem lebenslangem Lernen, gekennzeichnet durch phänomen, bei dem Erwachsene die relevantesten Aspekte einer Situation schnell erkennen und ihre Lernstrategien entsprechend fokussieren. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2703", "narrower_terms": [], "cross_domain_refs": [ "WRK-0030" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q133500", "legal_classification": "descriptive_research_term" }, { "id": "AED-0041", "domain": "AED", "term_en": "Implicit Knowledge Externalization", "term_de": "ImplicitKnowledgeExternalization", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures an adult education phenomenon in AI-enhanced lifelong learning, characterized by the process by which peers help each other articulate and clarify intuitive expertise that practitioners struggle to express systematically. This phenomenon operates at the intersection of implicit and knowledge dynamics within the broader AED domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept erwachsenenbildungsphänomen in KI-gestütztem lebenslangem Lernen, gekennzeichnet durch prozess, durch den Lernende abstraktes Wissen in praktische Situationen übertragen und anwenden. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Adult Education", "narrower_terms": [], "cross_domain_refs": [ "CRE-0120" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "AED-0042", "domain": "AED", "term_en": "Industry Standard Evolution Tracking", "term_de": "IndustryStandardEvolutionTracking", "definition_en": "As a neutral analytical construct in AI behavior research, this term denotes A andragogical pattern in AI-mediated continuing education, observable through a lifelong learning pattern characterized by the active monitoring by professionals of emerging standards and consensus practices to anticipate and prepare for competency updates. The concept emerges specifically in contexts where industry–standard interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "In der AUGMANITAI-Terminologiewissenschaft als rein beschreibendes Phänomen klassifiziert, bezeichnet dieser Begriff erwachsenenbildungsphänomen in KI-gestütztem lebenslangem Lernen, gekennzeichnet durch tendenz zur oberflächlichen Verarbeitung, wenn Lernmaterial als bedeutungslos, zu komplex oder desorganisiert wahrgenommen wird. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Adult Education", "narrower_terms": [], "cross_domain_refs": [ "LIN-0090", "LIN-0100" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "AED-0043", "domain": "AED", "term_en": "Intrinsic Goal Persistence", "term_de": "IntrinsicGoalPersistence", "definition_en": "A professional development effect arising from the enduring engagement observed when adults pursue learning aligned with internally derived goals rather than external credentials or institutional demands. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Erwachsenenbildungsphänomen in KI-gestütztem lebenslangem Lernen, gekennzeichnet durch lernvorteil, der durch aktive Manipulation, Experiment oder praktische Anwendung von Konzepten entsteht. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-2703", "narrower_terms": [], "cross_domain_refs": [ "AGE-0053", "ASE-0077", "ASE-0091" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "AED-0044", "domain": "AED", "term_en": "Intrinsic Motivation Stabilization", "term_de": "IntrinsicMotivationStabilization", "definition_en": "The sustained engagement pattern where mature professionals pursue learning driven by internal goals and mastery rather than external certifications. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Erwachsenenbildungsphänomen in KI-gestütztem lebenslangem Lernen, gekennzeichnet durch phänomen, bei dem Lernende durch Übung oder wiederholte Exposition schneller auf erlerntes Wissen zugreifen können. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2703", "narrower_terms": [], "cross_domain_refs": [ "ELR-0021", "ELR-0107", "ELR-0129" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q186588", "legal_classification": "systematic_classification" }, { "id": "AED-0045", "domain": "AED", "term_en": "Just-in-Time Knowledge Acquisition", "term_de": "Just-in-timeKnowledgeAcquisition", "definition_en": "The pattern of professionals learning specific skills only when immediately necessary observed alongside project demands rather than through advance planning. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Erwachsenenbildungsphänomen in KI-gestütztem lebenslangem Lernen, gekennzeichnet durch eigenschaft, die es Lernenden ermöglicht, ihren Fortschritt zu überwachen und ihre Strategien selbstregulierend anzupassen. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-2703", "narrower_terms": [], "cross_domain_refs": [ "ELR-0027" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "AED-0046", "domain": "AED", "term_en": "Just-in-Time Resource Discovery", "term_de": "Just-in-timeResourceDiscovery", "definition_en": "The adaptive pattern where self-directed learners locate and integrate new learning materials precisely when conceptual gaps become apparent during study. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Erwachsenenbildungsphänomen in KI-gestütztem lebenslangem Lernen, gekennzeichnet durch situation, in der Lernziele nicht explizit kommuniziert werden, was zu Verwirrung über Erwartungen und Erfolgsmaßstäbe führt. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2703", "narrower_terms": [], "cross_domain_refs": [ "TEM-0011" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "AED-0047", "domain": "AED", "term_en": "Knowledge Bridge Building", "term_de": "KnowledgeBridgeBuilding", "definition_en": "The systematic connection of new learning with existing mental models accumulated through extended professional and personal experience. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Erwachsenenbildungsphänomen in KI-gestütztem lebenslangem Lernen, gekennzeichnet durch lerneffekt, der dadurch entsteht, dass neue Konzepte bewusst in Beziehung zu bestehendem Wissen gesetzt werden. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2703", "narrower_terms": [], "cross_domain_refs": [ "ART-0002", "BEH-0054", "COG-0007" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "AED-0048", "domain": "AED", "term_en": "Knowledge Hoarding Prevention", "term_de": "KnowledgeHoardingPrevention", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures an adult education phenomenon in AI-enhanced lifelong learning, characterized by the organizational challenge where experienced workers may resist sharing expertise observed alongside perceived concerns to job security or professional differentiation. This phenomenon operates at the intersection of knowledge and hoarding dynamics within the broader AED domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept erwachsenenbildungsphänomen in KI-gestütztem lebenslangem Lernen, gekennzeichnet durch vorteil von Lernzeiten, die häufig von Ruhe- oder Konsolidierungspausen unterbrochen sind, gegenüber kontinuierlichem Lernen. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Adult Education", "narrower_terms": [], "cross_domain_refs": [ "SPR-0144" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "AED-0049", "domain": "AED", "term_en": "Knowledge Refresh Cycles", "term_de": "KnowledgeRefreshCycles", "definition_en": "A professional development dynamic in AI-augmented adult learning, measurable via the recurring pattern where professionals systematically update specific domain knowledge at intervals aligned with the pace of change in their field. Distinguished from adjacent concepts by its focus on the specific mechanism through which knowledge manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Erwachsenenbildungsphänomen in KI-gestütztem lebenslangem Lernen, gekennzeichnet durch phänomen, bei dem Lernende die geleistete Anstrengung nicht als sinnvoll oder zu Erfolg führend bewerten. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Process Cycle", "narrower_terms": [], "cross_domain_refs": [ "WEB-0014" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "AED-0050", "domain": "AED", "term_en": "Lateral Skill Transfer", "term_de": "LateralSkillTransfer", "definition_en": "An adult education phenomenon observed when the application of problem-solving approaches and methodologies learned in one professional context to entirely different occupational domains. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Erwachsenenbildungsphänomen in KI-gestütztem lebenslangem Lernen, gekennzeichnet durch gestaltung von Lernsequenzen, die an den kognitiven Entwicklungsstand oder Vorwissen von Lernenden angepasst sind. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-2703", "narrower_terms": [], "cross_domain_refs": [ "COG-0040" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "AED-0051", "domain": "AED", "term_en": "Learning Identity Development", "term_de": "LearningIdentityDevelopment", "definition_en": "The progressive self-concept shift where sustained engagement in learning gradually becomes integrated into adults' sense of self and identity. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Erwachsenenbildungsphänomen in KI-gestütztem lebenslangem Lernen, gekennzeichnet durch eigenschaft von Lernzielen, die so formuliert sind, dass Lernende ihren Fortschritt selbst kontrollieren und beurteilen können. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2701", "narrower_terms": [], "cross_domain_refs": [ "AGE-0054", "AGE-0086", "AGE-0093" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q133500", "legal_classification": "systematic_classification" }, { "id": "AED-0052", "domain": "AED", "term_en": "Learning Pace Autonomy", "term_de": "LearningPaceAutonomy", "definition_en": "A professional development effect reflecting the self-determined control over study velocity and intensity that characterizes self-directed learning without external scheduling constraints. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Erwachsenenbildungsphänomen in KI-gestütztem lebenslangem Lernen, gekennzeichnet durch vorteil, wenn Lernende Zeit und Raum haben, neue Konzepte zu verarbeiten, bevor sie evaluiert werden. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2703", "narrower_terms": [], "cross_domain_refs": [ "AGE-0001", "AGE-0086", "AGE-0093" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q133500", "legal_classification": "empirical_phenomenon_label" }, { "id": "AED-0053", "domain": "AED", "term_en": "Learning Portfolio Development", "term_de": "LearningPortfolioDevelopment", "definition_en": "The documentation of learning progress and capability demonstrations that self-directed learners assemble to evidence mastery and guide future development. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Erwachsenenbildungsphänomen in KI-gestütztem lebenslangem Lernen, gekennzeichnet durch reduzierung der Lerneffizienz durch mangelnde Koordination zwischen verschiedenen Lernquellen oder Lehrenden. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2703", "narrower_terms": [], "cross_domain_refs": [ "AGE-0086", "AGE-0093", "ART-0005" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q133500", "legal_classification": "empirical_phenomenon_label" }, { "id": "AED-0054", "domain": "AED", "term_en": "Learning Style Stabilization", "term_de": "LearningStyleStabilization", "definition_en": "The consolidated preference patterns regarding modality and pacing that mature learners have developed through decades of educational experience. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Erwachsenenbildungsphänomen in KI-gestütztem lebenslangem Lernen, gekennzeichnet durch phänomen, bei dem bereits erworbenes Wissen unbewusst neue Lernvorgänge beeinflusst oder erschwert. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2703", "narrower_terms": [], "cross_domain_refs": [ "AGE-0052", "AGE-0086", "AGE-0093" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q133500", "legal_classification": "systematic_classification" }, { "id": "AED-0055", "domain": "AED", "term_en": "Learning Urgency Effect", "term_de": "LearningUrgencyEffekt", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies an adult education phenomenon in AI-enhanced lifelong learning, characterized by a lifelong learning pattern observed when the intensified focus and accelerated progress observable when career transitions involve immediate practical pressure to acquire specific competencies. This phenomenon operates at the intersection of learning and urgency dynamics within the broader AED domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept erwachsenenbildungsphänomen in KI-gestütztem lebenslangem Lernen, gekennzeichnet durch situation, in der die Lernumgebung Ablenkungen minimiert und fokussiertes, tiefgehendes Lernen ermöglicht. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Psychological Effect", "narrower_terms": [], "cross_domain_refs": [ "STE-0068" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q133500", "legal_classification": "analytical_category" }, { "id": "AED-0056", "domain": "AED", "term_en": "Legacy Knowledge Reassessment", "term_de": "LegacyKnowledgeReassessment", "definition_en": "A professional development dynamic in AI-augmented adult learning, measurable via the process by which professionals reconsider previously mastered knowledge to determine whether it remains foundationally sound or demands reconceptualization. Distinguished from adjacent concepts by its focus on the specific mechanism through which legacy manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Erwachsenenbildungsphänomen in KI-gestütztem lebenslangem Lernen, gekennzeichnet durch vorteil, der dadurch entsteht, dass Lernende regelmäßig reflektieren, wie sie lernen und ihre Herangehensweise anpassen. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Adult Education", "narrower_terms": [], "cross_domain_refs": [ "ADA-0010", "BEH-0054", "COG-0007" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q210028", "legal_classification": "systematic_classification" }, { "id": "AED-0057", "domain": "AED", "term_en": "Mastery-Oriented Learning Persistence", "term_de": "Mastery-orientedLearningPersistence", "definition_en": "The sustained commitment where adult learners continue engagement despite setbacks because their focus centers on capability development rather than performance evaluation. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Erwachsenenbildungsphänomen in KI-gestütztem lebenslangem Lernen, gekennzeichnet durch phänomen, bei dem Erwachsene Lernmaterialien auswählen, die ihre bereits bestehenden Überzeugungen bestätigen, statt sich Gegenmeinungen auszusetzen. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2703", "narrower_terms": [], "cross_domain_refs": [ "COG-0081" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q133500", "legal_classification": "descriptive_research_term" }, { "id": "AED-0058", "domain": "AED", "term_en": "Mentor Seeking Behavior", "term_de": "MentorSeekingBehavior", "definition_en": "A professional development dynamic in AI-augmented adult learning, measurable via the active pattern of career-changing adults seeking guidance from established practitioners to accelerate competency development in their new field. Distinguished from adjacent concepts by its focus on the specific mechanism through which mentor manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Erwachsenenbildungsphänomen in KI-gestütztem lebenslangem Lernen, gekennzeichnet durch eigenschaft, bei der Rückmeldungen häufig, zeitnah und konstruktiv sind, statt verzögert oder nur kritisch. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2604", "narrower_terms": [], "cross_domain_refs": [ "AGE-0046", "AGE-0056", "ASE-0036" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "AED-0059", "domain": "AED", "term_en": "Meta-Learning Skill Development", "term_de": "Meta-learningSkillDevelopment", "definition_en": "A professional development effect manifesting as the progressive refinement of how self-directed learners approach learning itself, optimizing their methodologies through iterative cycles of self-evaluation. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Erwachsenenbildungsphänomen in KI-gestütztem lebenslangem Lernen, gekennzeichnet durch lerneffekt, der durch bewusste Anstrengung und willentliche Auseinandersetzung mit Material entsteht, nicht durch passive Exposition. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2703", "narrower_terms": [], "cross_domain_refs": [ "LIN-0086", "ART-0005", "IDN-0042" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q133500", "legal_classification": "empirical_phenomenon_label" }, { "id": "AED-0060", "domain": "AED", "term_en": "Metacognitive Awareness in Adults", "term_de": "MetacognitiveAwarenessinAdults", "definition_en": "A professional development dynamic in AI-augmented adult learning, measurable via an adult education phenomenon reflecting the heightened consciousness older learners demonstrate regarding their own cognitive processes, enabling more adequate self-regulation and error correction. Distinguished from adjacent concepts by its focus on the specific mechanism through which metacognitive manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Erwachsenenbildungsphänomen in KI-gestütztem lebenslangem Lernen, gekennzeichnet durch phänomen, bei dem Erwachsene ihre Lernziele flexibel anpassen, wenn sich Umstände oder Prioritäten ändern. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2703", "narrower_terms": [], "cross_domain_refs": [ "ASE-0092" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "AED-0061", "domain": "AED", "term_en": "Methodology Obsolescence Awareness", "term_de": "MethodologyObsolescenceAwareness", "definition_en": "A andragogical pattern in AI-mediated continuing education, observable through the recognition by experienced professionals that established problem-solving approaches and methodologies have become inefficient relative to emerging alternatives. The concept emerges specifically in contexts where methodology–obsolescence interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Erwachsenenbildungsphänomen in KI-gestütztem lebenslangem Lernen, gekennzeichnet durch tendenz, frühzeitig zu falschen Schlüssen zu gelangen, wenn Initial-Informationen unvollständig oder irreführend sind. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Adult Education", "narrower_terms": [], "cross_domain_refs": [ "CON-0086", "COP-0086", "CRE-0069" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q201", "legal_classification": "empirical_phenomenon_label" }, { "id": "AED-0062", "domain": "AED", "term_en": "Micro-Learning Episodic Engagement", "term_de": "Micro-learningEpisodicEngagement", "definition_en": "The pattern where shorter, focused learning units integrated into daily routines yield higher completion rates and more effectively spacing for retrieval practice. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Erwachsenenbildungsphänomen in KI-gestütztem lebenslangem Lernen, gekennzeichnet durch vorteil, wenn Lernende sehen, wie neues Wissen mit anderen Bereichen ihres Lebens verbunden ist. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2703", "narrower_terms": [], "cross_domain_refs": [ "COG-0047" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q133500", "legal_classification": "empirical_phenomenon_label" }, { "id": "AED-0063", "domain": "AED", "term_en": "Mid-Course Motivation Fluctuation", "term_de": "Mid-courseMotivationFluctuation", "definition_en": "The observable pattern where learner motivation typically declines in the intermediate stages of extended learning programs before potentially restoreing.", "definition_de": "Erwachsenenbildungsphänomen in KI-gestütztem lebenslangem Lernen, gekennzeichnet durch situation, in der Lernende Konzepte in mehreren Kontexten sehen und praktizieren, statt in nur einem isolierten Szenario. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-2703", "narrower_terms": [], "cross_domain_refs": [ "TEW-0076" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q186588", "legal_classification": "systematic_classification" }, { "id": "AED-0064", "domain": "AED", "term_en": "Motivation Sustenance Strategies", "term_de": "MotivationSustenanceStrategies", "definition_en": "A lifelong learning pattern arising from the self-applied techniques and environmental arrangements that self-directed learners employ to maintain engagement over extended learning periods. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Erwachsenenbildungsphänomen in KI-gestütztem lebenslangem Lernen, gekennzeichnet durch phänomen, bei dem Lernende unbewusst stereotypes oder voreingenommenes Denken in ihre Lernprozesse einbringen. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-2703", "narrower_terms": [], "cross_domain_refs": [ "ELR-0021", "ELR-0107", "ELR-0129" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q186588", "legal_classification": "systematic_classification" }, { "id": "AED-0065", "domain": "AED", "term_en": "Network Discontinuity Learning", "term_de": "NetworkDiscontinuityLearning", "definition_en": "The pattern where career transitions necessitate rebuilding professional networks, creating opportunities for learning from new peer groups. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Erwachsenenbildungsphänomen in KI-gestütztem lebenslangem Lernen, gekennzeichnet durch eigenschaft von Lernzielen, die eher Verständnis und Anwendung betonen als reine Faktenerinnerung. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2703", "narrower_terms": [], "cross_domain_refs": [ "AGE-0086", "AGE-0093", "ASE-0058" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q133500", "legal_classification": "empirical_phenomenon_label" }, { "id": "AED-0066", "domain": "AED", "term_en": "Notification-Engagement Interaction", "term_de": "Notification-engagementInteraction", "definition_en": "The observable effect where strategic use of reminders and notifications influences learner re-engagement patterns without creating notification fatigue.", "definition_de": "Erwachsenenbildungsphänomen in KI-gestütztem lebenslangem Lernen, gekennzeichnet durch vorteil, der entsteht, wenn Lernende ihre Fehler analysieren und verstehen, nicht nur das korrekte Ergebnis sehen. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2703", "narrower_terms": [], "cross_domain_refs": [ "RET-0077", "RET-0093" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "AED-0067", "domain": "AED", "term_en": "Organizational Change-Driven Learning", "term_de": "OrganizationalChange-drivenLearning", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures an adult education phenomenon in AI-enhanced lifelong learning, characterized by the accelerated skill acquisition triggered when organizational restructuring or technology implementation accompanies immediate pressure to adapt existing capabilities. This phenomenon operates at the intersection of organizational and change dynamics within the broader AED domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept erwachsenenbildungsphänomen in KI-gestütztem lebenslangem Lernen, gekennzeichnet durch phänomen, bei dem Lernende ihre Strategien und Techniken bewusst reflektieren und verbessern. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Machine Learning", "narrower_terms": [], "cross_domain_refs": [ "GAM-0083" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q133500", "legal_classification": "empirical_phenomenon_label" }, { "id": "AED-0068", "domain": "AED", "term_en": "Patience Development Effect", "term_de": "PatienceDevelopmentEffekt", "definition_en": "A professional development effect reflecting the observable increase in tolerance for ambiguity and complex learning processes that develops through mature adulthood.", "definition_de": "Erwachsenenbildungsphänomen in KI-gestütztem lebenslangem Lernen, gekennzeichnet durch situation, in der Lernende ihre Lernaktivitäten selbst steuern und Entscheidungen über Tempo, Reihenfolge und Schwierigkeitsgrad treffen. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2703", "narrower_terms": [], "cross_domain_refs": [ "COG-0010" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "AED-0069", "domain": "AED", "term_en": "Peer Accountability Networks", "term_de": "PeerAccountabilityNetworks", "definition_en": "An adult education phenomenon where the informal structures self-directed learners construct to provide social accountability and collaborative momentum without formal institutional oversight. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Erwachsenenbildungsphänomen in KI-gestütztem lebenslangem Lernen, gekennzeichnet durch reduzierung von Motivation, wenn Lernende glauben, dass ihre Anstrengung keinen Einfluss auf die Ergebnisse hat. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2703", "narrower_terms": [], "cross_domain_refs": [ "ASE-0013", "ASE-0074", "ASE-0075" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q15223", "legal_classification": "systematic_classification" }, { "id": "AED-0070", "domain": "AED", "term_en": "Peer Accountability Structure Effects", "term_de": "PeerAccountabilityStructureEffects", "definition_en": "A professional development effect characterized by the observable increase in learning commitment and follow-through when adults establish mutual accountability relationships with learning peers.", "definition_de": "Erwachsenenbildungsphänomen in KI-gestütztem lebenslangem Lernen, gekennzeichnet durch lernvorteil, der dadurch entsteht, dass verschiedene Sinneskanäle (Lesen, Hören, Handeln) gleichzeitig aktiviert werden. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2703", "narrower_terms": [], "cross_domain_refs": [ "ELR-0139" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q15223", "legal_classification": "observational_construct" }, { "id": "AED-0071", "domain": "AED", "term_en": "Peer Expectation Motivation", "term_de": "PeerExpectationMotivation", "definition_en": "An adult education phenomenon manifesting as the sustained motivation and effort adults invest in learning when they perceive peer expectations and mutual commitment within a learning group. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Erwachsenenbildungsphänomen in KI-gestütztem lebenslangem Lernen, gekennzeichnet durch phänomen, bei dem emotionale Zustände (Stress, Vergnügen, Angst) die Fähigkeit beeinflussen, Informationen zu speichern und abzurufen. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2703", "narrower_terms": [], "cross_domain_refs": [ "ELR-0108" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q186588", "legal_classification": "systematic_classification" }, { "id": "AED-0072", "domain": "AED", "term_en": "Peer Feedback Integration Patterns", "term_de": "PeerRückkopplungIntegrationPatterns", "definition_en": "An adult education phenomenon involving the selective adoption of peer feedback by adult learners, with greater receptivity to suggestions perceived as credible and immediately actionable. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Erwachsenenbildungsphänomen in KI-gestütztem lebenslangem Lernen, gekennzeichnet durch eigenschaft, bei der Rückmeldungen spezifisch auf Handlungen oder Strategien bezogen sind, nicht auf persönliche Fähigkeiten. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-2703", "narrower_terms": [], "cross_domain_refs": [ "ASE-0065", "VIB-0198" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "AED-0073", "domain": "AED", "term_en": "Peer Mentor Selection Patterns", "term_de": "PeerMentorSelectionPatterns", "definition_en": "A professional development dynamic in AI-augmented adult learning, measurable via a lifelong learning pattern involving the observable preferences adults demonstrate when choosing peer mentors, favoring those with demonstrated competency and accessibility over formal expertise credentials. Distinguished from adjacent concepts by its focus on the specific mechanism through which peer manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Erwachsenenbildungsphänomen in KI-gestütztem lebenslangem Lernen, gekennzeichnet durch vorteil, wenn Lernende verstehen, was sie noch nicht wissen und welche Schritte nötig sind, um das Wissen zu erwerben. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Behavioral Pattern", "narrower_terms": [], "cross_domain_refs": [ "ELR-0139", "RHR-0090", "STE-0061" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "AED-0074", "domain": "AED", "term_en": "Peer Teaching Reciprocity", "term_de": "PeerTeachingReciprocity", "definition_en": "An adult education phenomenon manifesting as the mutual knowledge exchange where adult learners alternately assume roles as instructor and learner, leveraging different areas of expertise. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Erwachsenenbildungsphänomen in KI-gestütztem lebenslangem Lernen, gekennzeichnet durch phänomen, bei dem Lernende neue Informationen unzureichend mit bereits bekanntem Wissen verlinken. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2703", "narrower_terms": [], "cross_domain_refs": [ "ASE-0074", "ASE-0075", "BEH-0064" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "AED-0075", "domain": "AED", "term_en": "Peer-Led Training Effectiveness", "term_de": "Peer-ledTrainingEffectiveness", "definition_en": "A professional development dynamic in AI-augmented adult learning, measurable via the pattern where employees training colleagues demonstrate distinct training outcomes compared to external instructors unfamiliar with organizational context. Distinguished from adjacent concepts by its focus on the specific mechanism through which peer manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Erwachsenenbildungsphänomen in KI-gestütztem lebenslangem Lernen, gekennzeichnet durch situation, in der Lernende genug Kontroll- und Wahlmöglichkeiten haben, um sich bedeutsam in ihren Lernprozess eingebunden zu fühlen. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Psychological Effect", "narrower_terms": [], "cross_domain_refs": [ "PER-0017" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q918461", "legal_classification": "systematic_classification" }, { "id": "AED-0076", "domain": "AED", "term_en": "Platform Usability-Engagement Coupling", "term_de": "PlatformUsability-engagementCoupling", "definition_en": "The relationship where technical friction and navigation complexity in online learning systems directly reduce learner persistence and course progression. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Erwachsenenbildungsphänomen in KI-gestütztem lebenslangem Lernen, gekennzeichnet durch reduzierung der Lernmotivation durch wiederholte Erfahrungen, dass die Lernbemühungen nicht zu erwünschten Ergebnissen führen. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2703", "narrower_terms": [], "cross_domain_refs": [ "DAT-0065" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "AED-0077", "domain": "AED", "term_en": "Post-Training Performance Change", "term_de": "Post-trainingPerformanceChange", "definition_en": "A andragogical pattern in AI-mediated continuing education, observable through the pattern where newly acquired workplace skills change when insufficient practice opportunities or performance feedback follow formal training completion. The concept emerges specifically in contexts where post–training interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Erwachsenenbildungsphänomen in KI-gestütztem lebenslangem Lernen, gekennzeichnet durch lerneffekt, der dadurch entsteht, dass neue Konzepte durch konkrete Beispiele oder Anwendungen veranschaulicht werden. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Model Training", "narrower_terms": [], "cross_domain_refs": [ "PHO-0056", "COG-0091", "COG-0112" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q1361053", "legal_classification": "systematic_classification" }, { "id": "AED-0078", "domain": "AED", "term_en": "Prior Knowledge Reframing", "term_de": "PriorKnowledgeReframing", "definition_en": "A professional development dynamic in AI-augmented adult learning, measurable via a professional development effect manifesting as the reconceptualization of existing expertise from a previous career that becomes valuable when translated into the context of a new professional domain. Distinguished from adjacent concepts by its focus on the specific mechanism through which prior manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Erwachsenenbildungsphänomen in KI-gestütztem lebenslangem Lernen, gekennzeichnet durch phänomen, bei dem Lernende unbewusst ihre Zeit und Aufmerksamkeit auf Themen konzentrieren, die sie bereits beherrschen. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Adult Education", "narrower_terms": [], "cross_domain_refs": [ "TRA-0081" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "AED-0079", "domain": "AED", "term_en": "Progress Monitoring Without External Feedback", "term_de": "ProgressMonitoringWithoutExternalRückkopplung", "definition_en": "As a neutral analytical construct in AI behavior research, this term denotes A lifelong learning pattern reflecting the internalized mechanisms through which self-directed learners assess their own understanding and identify knowledge gaps inreliantly. Identifiable through systematic behavioral analysis and pattern recognition. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "In der AUGMANITAI-Terminologiewissenschaft als rein beschreibendes Phänomen klassifiziert, bezeichnet dieser Begriff erwachsenenbildungsphänomen in KI-gestütztem lebenslangem Lernen, gekennzeichnet durch eigenschaft von Aufgaben, die so gestaltet sind, dass sie Lernende fordern, ohne zu überfordern oder zu langweilen. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "RPH-2703", "narrower_terms": [], "cross_domain_refs": [ "ASE-0041", "ASE-0040", "SPR-0029" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "AED-0080", "domain": "AED", "term_en": "Progress Visibility Motivation Effect", "term_de": "ProgressVisibilityMotivationEffekt", "definition_en": "A lifelong learning pattern involving the improved persistence observed when learners have clear visibility of their advancement through learning systems that display tangible progress markers. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Erwachsenenbildungsphänomen in KI-gestütztem lebenslangem Lernen, gekennzeichnet durch vorteil, wenn Lernende Gelegenheit haben, ihre neuen Fähigkeiten in sicheren, unterstützenden Kontexten zu üben. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-2703", "narrower_terms": [], "cross_domain_refs": [ "ELR-0107", "SWE-0060", "GAM-0078" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q186588", "legal_classification": "descriptive_research_term" }, { "id": "AED-0081", "domain": "AED", "term_en": "Regulatory Change Compliance Learning", "term_de": "RegulatoryChangeComplianceLearning", "definition_en": "A andragogical pattern in AI-mediated continuing education, observable through the immediate learning imperative created when regulatory or policy changes alter the legal framework governing professional practice. The concept emerges specifically in contexts where regulatory–change interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Erwachsenenbildungsphänomen in KI-gestütztem lebenslangem Lernen, gekennzeichnet durch phänomen, bei dem Lernende oberflächliche Ähnlichkeiten zwischen neuen und bekannten Konzepten bemerken, aber grundlegende Unterschiede übersehen. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2303", "narrower_terms": [], "cross_domain_refs": [ "SPR-0192", "SPR-0133" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q133500", "legal_classification": "systematic_classification" }, { "id": "AED-0082", "domain": "AED", "term_en": "Relevance Filtering Effect", "term_de": "RelevanceFilteringEffekt", "definition_en": "The tendency where adult learners assess new information against accumulated experience and engage more deeply with material perceived as immediately applicable. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Erwachsenenbildungsphänomen in KI-gestütztem lebenslangem Lernen, gekennzeichnet durch situation, in der Lernende mehrfach mit derselben Idee in verschiedenen Formen konfrontiert werden, was Konsolidierung unterstützt. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2703", "narrower_terms": [], "cross_domain_refs": [ "ADA-0001", "ADA-0002", "ADA-0004" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "AED-0083", "domain": "AED", "term_en": "Renewal Requirement Cycles", "term_de": "RenewalRequirementCycles", "definition_en": "A andragogical pattern in AI-mediated continuing education, observable through the pattern of periodic recertification where professionals demonstrate maintained competency and engagement with evolving field standards. The concept emerges specifically in contexts where renewal–requirement interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Erwachsenenbildungsphänomen in KI-gestütztem lebenslangem Lernen, gekennzeichnet durch reduzierung von Engagement, wenn Lernende die Materialien als nicht relevant für ihre Ziele oder Interessen empfinden. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Process Cycle", "narrower_terms": [], "cross_domain_refs": [ "ASE-0020", "EDU-0024", "MSC-0039" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "AED-0084", "domain": "AED", "term_en": "Resource Curation Patterns", "term_de": "ResourceCurationPatterns", "definition_en": "A lifelong learning pattern characterized by the systematic selection and organization of learning materials from heterogeneous sources that self-directed learners assemble into coherent study pathways.", "definition_de": "Erwachsenenbildungsphänomen in KI-gestütztem lebenslangem Lernen, gekennzeichnet durch lernvorteil, der dadurch entsteht, dass Lernende ihr Verständnis selbst prüfen und Lücken erkennen, bevor sie evaluiert werden. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2703", "narrower_terms": [], "cross_domain_refs": [ "ART-0037", "ART-0051", "ART-0052" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "AED-0085", "domain": "AED", "term_en": "Role-Embedded Learning Cycles", "term_de": "Role-embeddedLearningCycles", "definition_en": "A professional development dynamic in AI-augmented adult learning, measurable via the structured pattern where workers acquire new competencies directly integrated with their evolving job responsibilities and daily workflows. Distinguished from adjacent concepts by its focus on the specific mechanism through which role manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Erwachsenenbildungsphänomen in KI-gestütztem lebenslangem Lernen, gekennzeichnet durch phänomen, bei dem Erwachsene ihre Lernmotivation bewusst aufrechterhalten, wenn langfristige oder anspruchsvolle Ziele verfolgt werden. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Process Cycle", "narrower_terms": [], "cross_domain_refs": [ "WRK-0030" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q133500", "legal_classification": "descriptive_research_term" }, { "id": "AED-0086", "domain": "AED", "term_en": "Serendipitous Learning Integration", "term_de": "SerendipitousLearningIntegration", "definition_en": "A lifelong learning pattern characterized by the unexpected incorporation of unplanned learning opportunities that self-directed learners encounter and integrate into their existing knowledge structures.", "definition_de": "Erwachsenenbildungsphänomen in KI-gestütztem lebenslangem Lernen, gekennzeichnet durch eigenschaft von Lernumgebungen, die Sicherheit, Vertrauen und psychologisches Wohlbefinden durch faire Herangehensweise schaffen. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-2703", "narrower_terms": [], "cross_domain_refs": [ "ADA-0010", "AGE-0050", "AGE-0086" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q133500", "legal_classification": "empirical_phenomenon_label" }, { "id": "AED-0087", "domain": "AED", "term_en": "Skill Obsolescence Tracking", "term_de": "SkillObsolescenceTracking", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies an adult education phenomenon in AI-enhanced lifelong learning, characterized by the observable pattern where professionals monitor the declining relevance of their existing competencies relative to current industry requirements. This phenomenon operates at the intersection of skill and obsolescence dynamics within the broader AED domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept erwachsenenbildungsphänomen in KI-gestütztem lebenslangem Lernen, gekennzeichnet durch vorteil, wenn Lernende verstehen, dass Fehler und Schwierigkeiten natürliche Teile des Lernprozesses sind, nicht Zeichen von Mangel. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Adult Education", "narrower_terms": [], "cross_domain_refs": [ "TEW-0013" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "AED-0088", "domain": "AED", "term_en": "Social Cohesion-Learning Correlation", "term_de": "SocialCohesion-learningCorrelation", "definition_en": "The relationship where stronger interpersonal bonds among peer learners correlate with increased knowledge sharing and mutual support in learning endeavors. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Erwachsenenbildungsphänomen in KI-gestütztem lebenslangem Lernen, gekennzeichnet durch phänomen, bei dem Lernende unterschiedliche Strategien ausprobieren und ihre Wirksamkeit bewerten. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2703", "narrower_terms": [], "cross_domain_refs": [ "ELR-0034" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q133500", "legal_classification": "empirical_phenomenon_label" }, { "id": "AED-0089", "domain": "AED", "term_en": "Specialization Signaling Through Credentials", "term_de": "SpecializationSignalingThroughCredentials", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies an adult education phenomenon in AI-enhanced lifelong learning, characterized by an adult education phenomenon where the use of specialized certifications to communicate distinctive expertise and differentiate professionals within competitive labor markets. This phenomenon operates at the intersection of specialization and signaling dynamics within the broader AED domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept erwachsenenbildungsphänomen in KI-gestütztem lebenslangem Lernen, gekennzeichnet durch situation, in der Lernende Zeit für bewusste Reflexion und Konsolidierung von Erfahrungen haben. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Adult Education", "narrower_terms": [], "cross_domain_refs": [ "KNO-0010" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "AED-0090", "domain": "AED", "term_en": "Technological Disruption Adaptation", "term_de": "TechnologicalDisruptionAnpassung", "definition_en": "A professional development dynamic in AI-augmented adult learning, measurable via a professional development effect where the necessity for professionals to update their existing knowledge when technological innovations characteristically alter standard practices and tools. Distinguished from adjacent concepts by its focus on the specific mechanism through which technological manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Erwachsenenbildungsphänomen in KI-gestütztem lebenslangem Lernen, gekennzeichnet durch reduzierung von Lernmotivation, wenn externe Kontrolle (ständige Überwachung, starre Regeln) wahrgenommene Autonomie einschränkt. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "System Adaptation", "narrower_terms": [], "cross_domain_refs": [ "AGE-0036", "AGE-0064", "AGE-0089" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "AED-0091", "domain": "AED", "term_en": "Training Uptake Variance", "term_de": "TrainingUptakeVariance", "definition_en": "A andragogical pattern in AI-mediated continuing education, observable through the observable variation in participation and completion rates across different employee groups, influenced by role, tenure, and perceived relevance. The concept emerges specifically in contexts where training–uptake interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Erwachsenenbildungsphänomen in KI-gestütztem lebenslangem Lernen, gekennzeichnet durch lerneffekt, der dadurch entsteht, dass Lernende Material selbsterklärend und ohne externe Hinweise verarbeiten. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Model Training", "narrower_terms": [], "cross_domain_refs": [ "SOC-0002" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q918461", "legal_classification": "empirical_phenomenon_label" }, { "id": "AED-0092", "domain": "AED", "term_en": "Transfer of Training Gap", "term_de": "TransferofTrainingGap", "definition_en": "A andragogical pattern in AI-mediated continuing education, observable through a lifelong learning pattern in which the observable discrepancy between competencies acquired in formal training and the actual application of those skills in daily work contexts. The concept emerges specifically in contexts where transfer–of interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Erwachsenenbildungsphänomen in KI-gestütztem lebenslangem Lernen, gekennzeichnet durch phänomen, bei dem Lernende ihre Lernmotivation verlieren, wenn sie häufig scheitern und Unterstützung nicht ausreichend ist. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Performance Gap", "narrower_terms": [], "cross_domain_refs": [ "ELR-0146" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q918461", "legal_classification": "analytical_category" }, { "id": "AED-0093", "domain": "AED", "term_en": "Transformative Learning Outcomes Recognition", "term_de": "TransformativeLearningOutcomesRecognition", "definition_en": "An observable dynamic in which adults completing extended learning programs recognize fundamental shifts in how they view their profession, capabilities, and future possibilities.", "definition_de": "Erwachsenenbildungsphänomen in KI-gestütztem lebenslangem Lernen, gekennzeichnet durch eigenschaft, bei der Lernziele in Bezug zu persönlichen Werten und Prioritäten der Lernenden formuliert sind. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2701", "narrower_terms": [], "cross_domain_refs": [ "IDN-0032" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q3966", "legal_classification": "systematic_classification" }, { "id": "AED-0094", "domain": "AED", "term_en": "Transition Learning Acceleration", "term_de": "ÜbergangLearningBeschleunigung", "definition_en": "The heightened engagement and retention observed when adults undertake learning directly motivated by immediate career change circumstances. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Erwachsenenbildungsphänomen in KI-gestütztem lebenslangem Lernen, gekennzeichnet durch vorteil, wenn Lernende Gelegenheit haben, ihre Fragen zu stellen und Unklarheiten zu klären, bevor neue Inhalte eingeführt werden. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2703", "narrower_terms": [], "cross_domain_refs": [ "TRA-0043" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q133500", "legal_classification": "observational_construct" }, { "id": "AED-0095", "domain": "AED", "term_en": "Transition Readiness Assessment", "term_de": "ÜbergangReadinessAssessment", "definition_en": "A professional development dynamic in AI-augmented adult learning, measurable via the self-evaluation process by which adults determine whether their accumulated skills and confidence support viability for entering a new professional field. Distinguished from adjacent concepts by its focus on the specific mechanism through which transition manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Erwachsenenbildungsphänomen in KI-gestütztem lebenslangem Lernen, gekennzeichnet durch phänomen, bei dem Lernende unbewusst ihr Selbstkonzept auf ihre Lernfähigkeit anwenden und entsprechende Ziele auswählen. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Adult Education", "narrower_terms": [], "cross_domain_refs": [ "ADA-0011", "ART-0026", "ART-0080" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q210028", "legal_classification": "empirical_phenomenon_label" }, { "id": "AED-0096", "domain": "AED", "term_en": "Upskilling Cascade Effect", "term_de": "UpskillingKaskadeEffekt", "definition_en": "A documented pattern where one professional's acquisition of new skills accompanies learning opportunities and demands for their workplace colleagues. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Erwachsenenbildungsphänomen in KI-gestütztem lebenslangem Lernen, gekennzeichnet durch situation, in der Lernende regelmäßig Fortschritt sichtbar dokumentiert bekommen und ihre Entwicklung nachvollziehen können. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2703", "narrower_terms": [], "cross_domain_refs": [ "ADA-0001", "ADA-0002", "ADA-0004" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "AED-0097", "domain": "AED", "term_en": "Video Learning Consumption Patterns", "term_de": "VideoLearningConsumptionPatterns", "definition_en": "An adult education phenomenon manifesting as the observable strategies where online learners segment, replay, and selectively engage with video content to maximize comprehension and retention.", "definition_de": "Erwachsenenbildungsphänomen in KI-gestütztem lebenslangem Lernen, gekennzeichnet durch reduzierung von Lerneffizienz durch ineffiziente Arbeits- oder Lernmethoden, die nicht zu den Lernzielen passen. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2703", "narrower_terms": [], "cross_domain_refs": [ "COP-0036" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q133500", "legal_classification": "descriptive_research_term" }, { "id": "AED-0098", "domain": "AED", "term_en": "Wisdom-Based Learning Integration", "term_de": "Wisdom-basedLearningIntegration", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures an adult education phenomenon in AI-enhanced lifelong learning, characterized by the application of pattern recognition and accumulated judgment that allows mature adults to extract principles from new information with greater efficiency. This phenomenon operates at the intersection of wisdom and based dynamics within the broader AED domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept erwachsenenbildungsphänomen in KI-gestütztem lebenslangem Lernen, gekennzeichnet durch lernvorteil, der dadurch entsteht, dass Lernende komplexe Konzepte in schrittweise vereinfachtere oder aufbauendere Versionen zerlegen. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Analytical Ratio", "narrower_terms": [], "cross_domain_refs": [ "AGE-0050", "EDU-0084" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q133500", "legal_classification": "empirical_phenomenon_label" }, { "id": "AED-0099", "domain": "AED", "term_en": "Workplace Learning Culture Indicators", "term_de": "WorkplaceLearningCultureIndicators", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies an adult education phenomenon in AI-enhanced lifelong learning, characterized by the observable patterns reflecting organizational norms regarding knowledge sharing, continuous development, and openness to skill-building initiatives. This phenomenon operates at the intersection of workplace and learning dynamics within the broader AED domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept erwachsenenbildungsphänomen in KI-gestütztem lebenslangem Lernen, gekennzeichnet durch phänomen, bei dem Lernende ihre Aufmerksamkeit und Resources effektiv auf die am meisten herausfordernden oder wichtigen Lernbereiche konzentrieren. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Machine Learning", "narrower_terms": [], "cross_domain_refs": [ "PLY-0039" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q133500", "legal_classification": "observational_construct" }, { "id": "AGE-0001", "domain": "AGE", "term_en": "AI Response Pace Mismatch", "term_de": "AiResponsePaceMismatch", "definition_en": "An aging-related phenomenon in AI-mediated gerontological interaction, characterized by the cognitive friction arising when algorithmic response times diverge significantly from the information processing pace natural to older users. The concept emerges specifically in contexts where ai–response interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch prozess kognitiver Reifung durch wiederholte Konfrontation mit KI-generierten Alternativen zur Schärfung von Urteilskraft. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Aging & AI Interaction", "narrower_terms": [ "AGE-0085", "AGE-0090", "AGE-0006", "AGE-0088", "AGE-0089", "AGE-0046", "AGE-0039", "AGE-0059", "AGE-0071", "AGE-0084", "AGE-0041", "AGE-0070", "AGE-0005", "AGE-0060", "AGE-0055", "AGE-0022", "AGE-0017", "AGE-0007", "AGE-0077", "AGE-0047", "AGE-0035", "AGE-0073", "AGE-0030", "AGE-0080", "AGE-0015", "AGE-0008", "AGE-0079", "AGE-0075", "AGE-0065", "AGE-0078", "AGE-0081", "AGE-0001", "AGE-0063", "AGE-0064", "AGE-0019", "AGE-0003", "AGE-0023", "AGE-0076", "AGE-0094", "AGE-0097", "AGE-0043", "AGE-0024", "AGE-0018", "AGE-0045", "AGE-0029", "AGE-0072", "AGE-0032", "AGE-0034", "AGE-0053", "AGE-0056", "AGE-0033", "AGE-0002", "AGE-0052", "AGE-0031", "AGE-0087", "AGE-0082", "AGE-0026", "AGE-0069", "AGE-0010", "AGE-0099", "AGE-0040", "AGE-0096", "AGE-0009", "AGE-0067", "AGE-0011", "AGE-0044", "AGE-0054" ], "cross_domain_refs": [ "TEM-0080" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "AGE-0002", "domain": "AGE", "term_en": "Accessibility Feature Invisibility", "term_de": "AccessibilityFeatureInvisibility", "definition_en": "A cognitive aging pattern in AI-augmented elder interaction, measurable through a capacity that enables accessibility features designed for older users remain undiscovered observed alongside poor visibility, discoverability, or lack of user education. The concept emerges specifically in contexts where accessibility–feature interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch fähigkeit zur Differenzierung von Qualität, Relevanz und Authentizität in maschinell produzierten Inhalten. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Aging & AI Interaction", "narrower_terms": [], "cross_domain_refs": [ "SCR-0086" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "AGE-0003", "domain": "AGE", "term_en": "Accessibility Invisibility Pattern", "term_de": "AccessibilityInvisibilityMuster", "definition_en": "A cognitive aging pattern in AI-augmented elder interaction, measurable through a phenomenon in which the repeating situation where helpful features in technology are so buried or unexplained that the people who need them most rarely find them. The concept emerges specifically in contexts where accessibility–invisibility interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch etablierte kulturelle Praxis und Standards für Qualitätsbewertung in Mensch-KI-Koproduktion innerhalb spezifischer Domänen. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Behavioral Pattern", "narrower_terms": [], "cross_domain_refs": [ "ART-0039", "ART-0099", "ASE-0003" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "AGE-0004", "domain": "AGE", "term_en": "Age-Based AI Interaction Pattern", "term_de": "Age-basedaiInteractionMuster", "definition_en": "An aging-related phenomenon in AI-mediated gerontological interaction, characterized by a behavioral pattern where how people of different ages talk to AI looks different—older adults might phrase things differently, use different input methods, and ask for information in different ways than younger users. The concept emerges specifically in contexts where age–based interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch epistemisches Kriterium zur Legitimitätsprüfung von Wissen in hybriden Arbeitsumgebungen und technologisch vermittelten Prozessen. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2052", "narrower_terms": [], "cross_domain_refs": [ "MKT-0065", "MKT-0068", "LIN-0004" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "AGE-0005", "domain": "AGE", "term_en": "Age-Based Accessibility Feature Need", "term_de": "Age-basedAccessibilityFeatureNeed", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies an aging-related phenomenon in AI-mediated gerontological interaction, characterized by a tendency in which different ages need different things from AI—older adults might need bigger text, louder sounds, and simpler menus, while teenagers want speed and customization. This phenomenon operates at the intersection of age and based dynamics within the broader AGE domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept ethische Bewertungsdimension von KI-Systemen auf Fairness, Bias und normative Ausrichtung gegenüber menschlichen Werten. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Aging & AI Interaction", "narrower_terms": [], "cross_domain_refs": [ "COP-0025", "LIN-0075", "NEO-2256" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "AGE-0006", "domain": "AGE", "term_en": "Age-Based Algorithm Imbalance Visibility", "term_de": "Age-basedAlgorithmImbalanceVisibility", "definition_en": "A cognitive aging pattern in AI-augmented elder interaction, measurable through a shift that occurs when aI recommendations often change based on age—older users might get different suggestions than younger ones, but the system addresss this as normal rather than telling anyone about it. Distinguished from adjacent concepts by its focus on the specific mechanism through which age manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch iterative Verbesserung von Evaluationsfähigkeiten durch systematisches Feedback und Kalibrierung gegen Qualitätsstandards. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Algorithm", "narrower_terms": [], "cross_domain_refs": [ "ART-0029", "ART-0043", "ART-0046" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q8366", "legal_classification": "descriptive_research_term" }, { "id": "AGE-0007", "domain": "AGE", "term_en": "Age-Based Algorithm Preference Shift", "term_de": "Age-basedAlgorithmPreferenceShift", "definition_en": "An aging-related phenomenon in AI-mediated gerontological interaction, characterized by a shift that occurs when what people want to use and watch changes over a lifetime. AI systems that notice this and adapt match human needs more, but most just assume people want the same things at 20 and 80. The concept emerges specifically in contexts where age–based interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch kontextabhängige Anpassung von Bewertungskriterien an domänen-spezifische Anforderungen und Qualitätserwartungen. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Algorithm", "narrower_terms": [], "cross_domain_refs": [ "ART-0034", "ART-0035", "ART-0044" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q8366", "legal_classification": "analytical_category" }, { "id": "AGE-0008", "domain": "AGE", "term_en": "Age-Based Feature Discoverability", "term_de": "Age-basedFeatureDiscoverability", "definition_en": "An aging-related phenomenon in AI-mediated gerontological interaction, characterized by a phenomenon in which older adults have a harder time finding features in apps and websites than younger adults do, even if those features would help them. Distinguished from adjacent concepts by its focus on the specific mechanism through which age manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch strukturierte Verfahren zur Überprüfung von KI-Output auf Konsistenz, Kohärenz und Nützlichkeit im Anwendungskontext. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Aging & AI Interaction", "narrower_terms": [], "cross_domain_refs": [ "MKT-0065", "MKT-0068", "LIN-0004" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "AGE-0009", "domain": "AGE", "term_en": "Age-Based Feature Simplification Necessity", "term_de": "Age-basedFeatureSimplificationNecessity", "definition_en": "An aging-related phenomenon in AI-mediated gerontological interaction, characterized by a behavioral pattern where some people need apps and websites to be way simpler as they get older - fewer buttons, bigger text, less happening on screen at once. Distinguished from adjacent concepts by its focus on the specific mechanism through which age manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch erkennungsleistung für subtile oder latente Mängel in maschinell generierten Artefakten, die oberflächlicher Kontrolle entgehen. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Aging & AI Interaction", "narrower_terms": [], "cross_domain_refs": [ "COP-0025", "LIN-0075", "NEO-2256" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "AGE-0010", "domain": "AGE", "term_en": "Age-Based Interface Navigation Strategy", "term_de": "Age-basedInterfaceNavigationStrategy", "definition_en": "An aging-related phenomenon in AI-mediated gerontological interaction, characterized by a behavioral pattern where how older people develop their own ways of getting around apps - maybe typically using search instead of menus, or sticking to one app even when something more effectively exists. The concept emerges specifically in contexts where age–based interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch schnelligkeit und Genauigkeit bei der Identifikation problematischer oder inadäquater KI-Ausgaben während des Prozessablaufs. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Operational Strategy", "narrower_terms": [], "cross_domain_refs": [ "WRK-0079" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q185451", "legal_classification": "analytical_category" }, { "id": "AGE-0011", "domain": "AGE", "term_en": "Age-Based Technology Avoidance", "term_de": "Age-basedTechnologyAvoidance", "definition_en": "A cognitive aging pattern in AI-augmented elder interaction, measurable through a perception in which when technology keeps updating, crashes, or feels hard to use, some people stop using it. This happens more often with older adults than younger ones. Distinguished from adjacent concepts by its focus on the specific mechanism through which age manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch phänomen oder Erfahrung in der Bewertung und Qualitätsbeurteilung von KI-Outputs. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Aging & AI Interaction", "narrower_terms": [], "cross_domain_refs": [ "MKT-0065", "MKT-0068", "LIN-0004" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "AGE-0012", "domain": "AGE", "term_en": "Age-Based Text-to-Speech Reliance", "term_de": "Age-basedText-to-speechReliance", "definition_en": "A phenomenon in which as vision gets different, older people start depending on a computer reading text out loud instead of them reading it themselves. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch merkmal von Bewertung und Qualitätsbeurteilung von KI-Outputs. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2301", "narrower_terms": [], "cross_domain_refs": [ "ELR-0177" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "AGE-0013", "domain": "AGE", "term_en": "Age-Based User Interface Preference", "term_de": "Age-basedUserInterfacePreference", "definition_en": "A phenomenon in which people like interfaces that look and work like the ones they learned on. Someone who learned computers in the 1990s will find modern designs confusing. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch distinktives Merkmal von Bewertung und Qualitätsbeurteilung von KI-Outputs. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2703", "narrower_terms": [], "cross_domain_refs": [ "EDU-0096" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q116905", "legal_classification": "analytical_category" }, { "id": "AGE-0014", "domain": "AGE", "term_en": "Age-Based Voice Interface Reliance", "term_de": "Age-basedVoiceInterfaceReliance", "definition_en": "An aging-related phenomenon in AI-mediated gerontological interaction, characterized by a phenomenon in which instead of typing or clicking, older people often rely on talking to their device - asking questions instead of searching, giving voice commands. The concept emerges specifically in contexts where age–based interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch distinktives Merkmal von Bewertung und Qualitätsbeurteilung von KI-Outputs. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2154", "narrower_terms": [], "cross_domain_refs": [ "VIB-0165", "SWE-0078", "REL-0067" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "AGE-0015", "domain": "AGE", "term_en": "Age-Related Font Size Resistance", "term_de": "Age-relatedFontSizeResistance", "definition_en": "An aging-related phenomenon in AI-mediated gerontological interaction, characterized by a resistance response where some people refuse to make text bigger on screen even though their eyesight has gotten different. They stick with what they are used to instead of changing it. Distinguished from adjacent concepts by its focus on the specific mechanism through which age manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch spezifisches Attribut von Bewertung und Qualitätsbeurteilung von KI-Outputs. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Aging & AI Interaction", "narrower_terms": [], "cross_domain_refs": [ "REL-0043" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "AGE-0016", "domain": "AGE", "term_en": "Aging Attention Span Accommodation", "term_de": "AgingAttentionSpanAccommodation", "definition_en": "A shift that occurs when keeping focus becomes harder with age, especially when information flashes by fast or changes constantly. Information presented in slower increments or smaller chunks shows different processing pat... Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch prozess oder Abfolge im Rahmen von Bewertung und Qualitätsbeurteilung von KI-Outputs. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-3653", "narrower_terms": [], "cross_domain_refs": [ "ELR-0022" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q184872", "legal_classification": "empirical_phenomenon_label" }, { "id": "AGE-0017", "domain": "AGE", "term_en": "Aging Auditory Accommodation Pattern", "term_de": "AgingAuditoryAccommodationMuster", "definition_en": "A cognitive aging pattern in AI-augmented elder interaction, measurable through a shift that occurs when hearing changes with age - higher pitches get harder to hear. But apps often use sound notifications that are in those exact frequencies. Distinguished from adjacent concepts by its focus on the specific mechanism through which aging manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch kennzeichnende Ausprägung von Bewertung und Qualitätsbeurteilung von KI-Outputs. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Behavioral Pattern", "narrower_terms": [], "cross_domain_refs": [ "ASE-0003", "PER-0104", "WEB-0065" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "AGE-0018", "domain": "AGE", "term_en": "Aging Cognitive Load Compensation", "term_de": "AgingCognitiveLoadCompensation", "definition_en": "A cognitive aging pattern in AI-augmented elder interaction, measurable through compensatory strategies older adults employ to manage technology when cognitive processing speed or working memory capacity decreases. The concept emerges specifically in contexts where aging–cognitive interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch prozess oder Abfolge im Rahmen von Bewertung und Qualitätsbeurteilung von KI-Outputs. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Aging & AI Interaction", "narrower_terms": [], "cross_domain_refs": [ "AED-0010" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q1105712", "legal_classification": "empirical_phenomenon_label" }, { "id": "AGE-0019", "domain": "AGE", "term_en": "Aging Cognitive Processing Accommodation", "term_de": "AgingCognitiveProcessingAccommodation", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies an aging-related phenomenon in AI-mediated gerontological interaction, characterized by a behavioral pattern where brains work slower at processing new information as people age. Apps that demand fast choices or show too much at once make this different. This phenomenon operates at the intersection of aging and cognitive dynamics within the broader AGE domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept prozess oder Abfolge im Rahmen von Bewertung und Qualitätsbeurteilung von KI-Outputs. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Aging & AI Interaction", "narrower_terms": [], "cross_domain_refs": [ "PER-0015", "LIN-0005", "LIN-0036" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "AGE-0020", "domain": "AGE", "term_en": "Aging Dexterity Compensation", "term_de": "AgingDexterityCompensation", "definition_en": "A cognitive aging pattern in AI-augmented elder interaction, measurable through a shift that occurs when fingers and hands change as someone ages - clicking small buttons becomes hard. Older people often use bigger movements or voice instead of precision tapping. The concept emerges specifically in contexts where aging–dexterity interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch charakteristische Komponente von Bewertung und Qualitätsbeurteilung von KI-Outputs. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2154", "narrower_terms": [], "cross_domain_refs": [ "RHR-0009" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "AGE-0021", "domain": "AGE", "term_en": "Aging Digital Literacy Plateau Myth", "term_de": "AgingDigitalLiteracyPlateauMyth", "definition_en": "A capacity that enables the wrong idea that older people just stop learning tech after a certain age. In reality, they can learn new stuff - they just need more effectively teaching. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch strukturelle Eigenschaft von Bewertung und Qualitätsbeurteilung von KI-Outputs. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-2703", "narrower_terms": [], "cross_domain_refs": [ "SPR-0049", "SPR-0008", "PHO-0093" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q201684", "legal_classification": "descriptive_research_term" }, { "id": "AGE-0022", "domain": "AGE", "term_en": "Aging Fine Motor Control Accommodation", "term_de": "AgingFineMotorControlAccommodation", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies an aging-related phenomenon in AI-mediated gerontological interaction, characterized by a resistance response where the difficulty in using interfaces with small interactive targets or requiring precise hand-eye coordination when fine motor control declines with age. This phenomenon operates at the intersection of aging and fine dynamics within the broader AGE domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept distinktives Merkmal von Bewertung und Qualitätsbeurteilung von KI-Outputs. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Aging & AI Interaction", "narrower_terms": [], "cross_domain_refs": [ "BEH-0007", "NEO-0456" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "AGE-0023", "domain": "AGE", "term_en": "Aging Memory Accommodation Gap", "term_de": "AgingMemoryAccommodationGap", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures A cognitive aging pattern in AI-augmented elder interaction, measurable through a phenomenon in which memory works differently as people age. Passwords, where they left off, what that button does - all harder to recall. But apps assume many individuals remembers everything. This phenomenon operates at the intersection of aging and memory dynamics within the broader AGE domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept spezifisches Attribut von Bewertung und Qualitätsbeurteilung von KI-Outputs. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Performance Gap", "narrower_terms": [], "cross_domain_refs": [ "RPH-1104" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q492", "legal_classification": "systematic_classification" }, { "id": "AGE-0024", "domain": "AGE", "term_en": "Aging Memory Compensation Necessity", "term_de": "AgingMemoryCompensationNecessity", "definition_en": "Classified in AUGMANITAI terminology science as a descriptive phenomenon (without normative endorsement), this pattern denotes an aging-related phenomenon in AI-mediated gerontological interaction, characterized by a behavioral pattern where ways to keep information organized and tasks on track when memory limits are real. Clear, consistent notes and patterns help reduce the mental effort needed. Distinguished from adjacent concepts by its focus on the specific mechanism through which aging manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als analytisches Konstrukt der empirischen KI-Forschung (deskriptiv, nicht präskriptiv) erfasst dieser Terminus spezifisches Attribut von Bewertung und Qualitätsbeurteilung von KI-Outputs. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Aging & AI Interaction", "narrower_terms": [], "cross_domain_refs": [ "WRK-0014" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q492", "legal_classification": "observational_construct" }, { "id": "AGE-0025", "domain": "AGE", "term_en": "Aging Motor Control Accommodation Pattern", "term_de": "AgingMotorControlAccommodationMuster", "definition_en": "An aging-related phenomenon in AI-mediated gerontological interaction, characterized by a shift that occurs when older users hold screens differently, click differently, and move the mouse slower. Buttons that are bigger work more effectively and not require quick precise clicks. Distinguished from adjacent concepts by its focus on the specific mechanism through which aging manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch merkmal von Bewertung und Qualitätsbeurteilung von KI-Outputs. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2154", "narrower_terms": [], "cross_domain_refs": [ "LNG-0010" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "AGE-0026", "domain": "AGE", "term_en": "Aging Social Connection Through Tech", "term_de": "AgingSocialConnectionThroughTech", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies an aging-related phenomenon in AI-mediated gerontological interaction, characterized by a shift that occurs when technological systems serve as primary social contact mechanisms for some older adults, particularly those with reduced mobility or geographic separation. Platform changes or discontinuation of fam. This phenomenon operates at the intersection of aging and social dynamics within the broader AGE domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept kennzeichnende Ausprägung von Bewertung und Qualitätsbeurteilung von KI-Outputs. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Aging & AI Interaction", "narrower_terms": [], "cross_domain_refs": [ "ROB-0287" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "AGE-0027", "domain": "AGE", "term_en": "Aging Vision Accommodation", "term_de": "AgingVisionAccommodation", "definition_en": "A shift that occurs when eyes change with age—colors look less bright, moving from dark to light is harder, and reading small text becomes difficult. Designs that account for this. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch merkmal von Bewertung und Qualitätsbeurteilung von KI-Outputs. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-3002", "narrower_terms": [], "cross_domain_refs": [ "DES-0058" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "AGE-0028", "domain": "AGE", "term_en": "Aging Vision Correction Accommodation Need", "term_de": "AgingVisionCorrectionAccommodationNeed", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures A cognitive aging pattern in AI-augmented elder interaction, measurable through a shift that occurs when wearing glasses changes how people use touchscreens and see buttons. Apps that work for people wearing glasses, not just people with perfect vision. This phenomenon operates at the intersection of aging and vision dynamics within the broader AGE domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept charakteristische Komponente von Bewertung und Qualitätsbeurteilung von KI-Outputs. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "RPH-2805", "narrower_terms": [], "cross_domain_refs": [ "ASE-0003", "PHO-0066", "ELR-0073" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "AGE-0029", "domain": "AGE", "term_en": "Cognitive Flexibility Accommodation Need", "term_de": "CognitiveFlexibilityAccommodationNeed", "definition_en": "A cognitive aging pattern in AI-augmented elder interaction, measurable through a shift that occurs when some people have a harder time switching between different apps or adapting to sudden changes. They need things to stay consistent and predictable. The concept emerges specifically in contexts where cognitive–flexibility interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch beobachtbare Eigenschaft von Bewertung und Qualitätsbeurteilung von KI-Outputs. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Aging & AI Interaction", "narrower_terms": [], "cross_domain_refs": [ "SCR-0003" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "AGE-0030", "domain": "AGE", "term_en": "Cognitive Load Amplification Effect", "term_de": "CognitiveLoadVerstärkungEffekt", "definition_en": "A cognitive aging pattern in AI-augmented elder interaction, measurable through a phenomenon in which bad design makes thinking harder, especially for older adults who already have less mental energy. Too many options or unclear steps tire people quickly. Distinguished from adjacent concepts by its focus on the specific mechanism through which cognitive manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch spezifisches Attribut von Bewertung und Qualitätsbeurteilung von KI-Outputs. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Psychological Effect", "narrower_terms": [], "cross_domain_refs": [ "ASE-0020", "COG-0042", "COG-0086" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q1105712", "legal_classification": "observational_construct" }, { "id": "AGE-0031", "domain": "AGE", "term_en": "Digital Disconnection Paradox", "term_de": "DigitalDisconnectionParadox", "definition_en": "An aging-related phenomenon in AI-mediated gerontological interaction, characterized by a perception in which older adults want to stay connected digitally but feel uncertain about whether they are good enough at technology. This makes them both drawn to and cautious about using new tools. Distinguished from adjacent concepts by its focus on the specific mechanism through which digital manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data. Classification term used in systematic observation, not advocacy.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch phänomen oder Erfahrung in der Bewertung und Qualitätsbeurteilung von KI-Outputs. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Logical Paradox", "narrower_terms": [], "cross_domain_refs": [ "AED-0029", "ART-0079", "ASE-0047" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "AGE-0032", "domain": "AGE", "term_en": "Digital Literacy Confidence Gap", "term_de": "DigitalLiteracyConfidenceGap", "definition_en": "An aging-related phenomenon in AI-mediated gerontological interaction, characterized by a perception in which older adults often know more than they think. They can do digital tasks fine but feel like they are bad at it, so they avoid trying harder things. Distinguished from adjacent concepts by its focus on the specific mechanism through which digital manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data. Analytical category without normative endorsement.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch phänomen oder Erfahrung in der Bewertung und Qualitätsbeurteilung von KI-Outputs. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Performance Gap", "narrower_terms": [], "cross_domain_refs": [ "ELR-0146", "PHO-0093", "QUA-0077" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q201684", "legal_classification": "descriptive_research_term" }, { "id": "AGE-0033", "domain": "AGE", "term_en": "Digital Patience Mismatch", "term_de": "DigitalPatienceMismatch", "definition_en": "A cognitive aging pattern in AI-augmented elder interaction, measurable through a tendency in which young people grew up with instant loading and expect it. Older adults grew up waiting for things and are fine with slower systems, so they get frustrated differently. Distinguished from adjacent concepts by its focus on the specific mechanism through which digital manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch beobachtbare Eigenschaft von Bewertung und Qualitätsbeurteilung von KI-Outputs. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Aging & AI Interaction", "narrower_terms": [], "cross_domain_refs": [ "AED-0029", "AED-0068", "ART-0061" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "AGE-0034", "domain": "AGE", "term_en": "Elder Skepticism Mechanism", "term_de": "ElderSkepticismMechanism", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures A cognitive aging pattern in AI-augmented elder interaction, measurable through a phenomenon in which when older adults keep a careful distance from AI, not out of fear but from a lifetime of watching technologies come and go. This phenomenon operates at the intersection of elder and skepticism dynamics within the broader AGE domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept kennzeichnende Ausprägung von Bewertung und Qualitätsbeurteilung von KI-Outputs. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Aging & AI Interaction", "narrower_terms": [], "cross_domain_refs": [ "RPH-3901" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "AGE-0035", "domain": "AGE", "term_en": "Elder Tech Adoption Resistance Pattern", "term_de": "ElderTechAdoptionResistanceMuster", "definition_en": "A cognitive aging pattern in AI-augmented elder interaction, measurable through a resistance response where the gradual technology adoption trajectory where older adults initially resist new tools but progressively adopt them when demonstrable utility becomes apparent. Distinguished from adjacent concepts by its focus on the specific mechanism through which elder manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch systemische Ausprägung von Bewertung und Qualitätsbeurteilung von KI-Outputs. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Behavioral Pattern", "narrower_terms": [], "cross_domain_refs": [ "RET-0045" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "AGE-0036", "domain": "AGE", "term_en": "Elder Technological Adaptation Capacity", "term_de": "ElderTechnologicalAnpassungCapacity", "definition_en": "A capacity that enables older adults can learn and get good at technology if the design is clear and individual. The difficulty is most designs are not. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch eigenschaft/Charakteristik von Bewertung + Qualitätsbeurteilung von KI-Outputs. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2703", "narrower_terms": [], "cross_domain_refs": [ "AED-0090" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "AGE-0037", "domain": "AGE", "term_en": "Elder Technology Fluency Plateau", "term_de": "ElderTechnologyFluencyPlateau", "definition_en": "A perception in which older adults learn new tech skills but often stop learning before they master it. They reach a level and stay there. What feels hard stays hard, so they stop trying new features. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch fähigkeit oder Kompetenz für KI-generierte Artefakte. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2555", "narrower_terms": [], "cross_domain_refs": [ "STE-0056", "TRA-0033", "ELR-0084" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "AGE-0038", "domain": "AGE", "term_en": "Elder Technology Rejection Reversal", "term_de": "ElderTechnologyRejectionReversal", "definition_en": "A resistance response where when older people who avoided technology start using it again after realizing it solves real problems they face. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch operative Eigenschaft von Bewertung und Qualitätsbeurteilung von KI-Outputs. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2301", "narrower_terms": [], "cross_domain_refs": [ "CAI-0005" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "AGE-0039", "domain": "AGE", "term_en": "Elder User Demographic Invisibility", "term_de": "ElderUserDemographicInvisibility", "definition_en": "An aging-related phenomenon in AI-mediated gerontological interaction, characterized by a phenomenon in which older adults use technology a lot, but app makers ignore them when building new features. Companies rarely study what older people need. Distinguished from adjacent concepts by its focus on the specific mechanism through which elder manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch systemische Ausprägung von Bewertung und Qualitätsbeurteilung von KI-Outputs. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Aging & AI Interaction", "narrower_terms": [], "cross_domain_refs": [ "SPR-0168", "COP-0040", "RET-0083" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "AGE-0040", "domain": "AGE", "term_en": "Elder User Disconnection Through Technology", "term_de": "ElderUserDisconnectionThroughTechnology", "definition_en": "An aging-related phenomenon in AI-mediated gerontological interaction, characterized by a behavioral pattern where technology meant to help people talk can actually push them apart—new apps, new systems, and new ways of messaging can cut off older adults from family and friends. The concept emerges specifically in contexts where elder–user interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch merkmal von Bewertung und Qualitätsbeurteilung von KI-Outputs. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Aging & AI Interaction", "narrower_terms": [], "cross_domain_refs": [ "SPR-0113" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "AGE-0041", "domain": "AGE", "term_en": "Elder User Engagement Opportunity Gap", "term_de": "ElderUserEngagementOpportunityGap", "definition_en": "A cognitive aging pattern in AI-augmented elder interaction, measurable through a phenomenon in which older adults want to use apps that solve real problems for them, but companies are not building for them or asking them what they need. Distinguished from adjacent concepts by its focus on the specific mechanism through which elder manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch beobachtbare Eigenschaft von Bewertung und Qualitätsbeurteilung von KI-Outputs. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Performance Gap", "narrower_terms": [], "cross_domain_refs": [ "CON-0090", "WEB-0004", "ADA-0001" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "AGE-0042", "domain": "AGE", "term_en": "Elder User Engagement Paradox", "term_de": "ElderUserEngagementParadox", "definition_en": "A phenomenon in which older people being willing to invest time in technology when it serves their actual needs, despite being seen as less willing to learn. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch systemische Ausprägung von Bewertung und Qualitätsbeurteilung von KI-Outputs. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2703", "narrower_terms": [], "cross_domain_refs": [ "PER-0035" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "AGE-0043", "domain": "AGE", "term_en": "Elder User Error Restoration Difficulty", "term_de": "ElderUserErrorRestorationDifficulty", "definition_en": "An aging-related phenomenon in AI-mediated gerontological interaction, characterized by the difficulty older users experience in restoreing from errors or unintended actions observed alongside unclear system feedback or inaccessible restoration mechanisms. The concept emerges specifically in contexts where elder–user interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch phänomen oder Erfahrung in der Bewertung und Qualitätsbeurteilung von KI-Outputs. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Analytical Ratio", "narrower_terms": [], "cross_domain_refs": [ "TRA-0026" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "AGE-0044", "domain": "AGE", "term_en": "Elder User Experience Invisibility", "term_de": "ElderUserExperienceInvisibility", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies an aging-related phenomenon in AI-mediated gerontological interaction, characterized by the systematic absence of user experience research focused on older adults, leading to persistent interface patterns that involve the most friction for this demographic remaining undetected and un. This phenomenon operates at the intersection of elder and user dynamics within the broader AGE domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept phänomen oder Erfahrung in der Bewertung und Qualitätsbeurteilung von KI-Outputs. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Aging & AI Interaction", "narrower_terms": [], "cross_domain_refs": [ "CON-0090", "QUA-0080" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q862028", "legal_classification": "analytical_category" }, { "id": "AGE-0045", "domain": "AGE", "term_en": "Elder User Patience With Errors", "term_de": "ElderUserPatienceWithErrors", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures A cognitive aging pattern in AI-augmented elder interaction, measurable through a behavioral pattern where older adults tend to assume tech errors are their own fault rather than bad design. When something goes the issue, they say sorry to the device instead of questioning how it works. This phenomenon operates at the intersection of elder and user dynamics within the broader AGE domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept spezifisches Attribut von Bewertung und Qualitätsbeurteilung von KI-Outputs. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Aging & AI Interaction", "narrower_terms": [], "cross_domain_refs": [ "CUS-0057" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "AGE-0046", "domain": "AGE", "term_en": "Elder User Search Behavior Shift", "term_de": "ElderUserSearchBehaviorShift", "definition_en": "A cognitive aging pattern in AI-augmented elder interaction, measurable through a phenomenon in which older adults search differently—they use familiar websites and ask direct questions instead of exploring many options. The concept emerges specifically in contexts where elder–user interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch merkmal von Bewertung und Qualitätsbeurteilung von KI-Outputs. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Aging & AI Interaction", "narrower_terms": [], "cross_domain_refs": [ "CRE-0104" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "AGE-0047", "domain": "AGE", "term_en": "Elder User Support Reliance", "term_de": "ElderUserSupportReliance", "definition_en": "A cognitive aging pattern in AI-augmented elder interaction, measurable through a phenomenon in which many older adults rely on assistance from family or friends for technology use. Technical support systems typically assume solitary user troubleshooting. Distinguished from adjacent concepts by its focus on the specific mechanism through which elder manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch kennzeichnende Ausprägung von Bewertung und Qualitätsbeurteilung von KI-Outputs. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Aging & AI Interaction", "narrower_terms": [], "cross_domain_refs": [ "TEW-0059" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "AGE-0048", "domain": "AGE", "term_en": "Elder User Voice Recognition Lag", "term_de": "ElderUserVoiceRecognitionLag", "definition_en": "A phenomenon in which voice interface systems exhibit lower recognition accuracy for older adult speakers, particularly at higher frequencies or with non-standard accents. this reduced accuracy represents a technical. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch beobachtbare Eigenschaft von Bewertung und Qualitätsbeurteilung von KI-Outputs. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2605", "narrower_terms": [], "cross_domain_refs": [ "TRA-0030" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q3966", "legal_classification": "systematic_classification" }, { "id": "AGE-0049", "domain": "AGE", "term_en": "Elder User Wisdom Underutilization", "term_de": "ElderUserWisdomUnderutilization", "definition_en": "Older people have decades of experience and judgment, but systems are designed as if this means nothing. AI that learns from older users wisdom, not ignore it. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch evaluations- oder Beurteilungsfunktion für KI-generierte Artefakte. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2703", "narrower_terms": [], "cross_domain_refs": [ "CON-0095", "SCR-0075", "SCR-0076" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "AGE-0050", "domain": "AGE", "term_en": "Elder Wisdom Integration Paradox", "term_de": "ElderWisdomIntegrationParadox", "definition_en": "Life experience and careful thinking might make someone trust technology more, but it does not—older adults often trust it less because they have seen more things change or fail. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch phänomen oder Erfahrung in der Bewertung und Qualitätsbeurteilung von KI-Outputs. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2505", "narrower_terms": [], "cross_domain_refs": [ "CON-0068" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "AGE-0051", "domain": "AGE", "term_en": "Generational Algorithm Distrust Pattern", "term_de": "GenerationalAlgorithmDistrustMuster", "definition_en": "A phenomenon in which different generations trust algorithms differently based on their past experiences. Older adults tend to be more skeptical than younger people. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch phänomen oder Erfahrung in der Bewertung und Qualitätsbeurteilung von KI-Outputs. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2505", "narrower_terms": [], "cross_domain_refs": [ "SPR-0047" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q8366", "legal_classification": "observational_construct" }, { "id": "AGE-0052", "domain": "AGE", "term_en": "Generational Communication Style Divergence", "term_de": "GenerationalCommunicationStyleDivergenz", "definition_en": "A cognitive aging pattern in AI-augmented elder interaction, measurable through a pattern in which variant in which generations talk differently—some use abbreviations, some write formal sentences, some use lots of emojis. Apps often miss this and confuse people. The concept emerges specifically in contexts where generational–communication interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch systemische Ausprägung von Bewertung und Qualitätsbeurteilung von KI-Outputs. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Analytical Ratio", "narrower_terms": [], "cross_domain_refs": [ "CAI-0004" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "AGE-0053", "domain": "AGE", "term_en": "Generational Digital Divide Persistence", "term_de": "GenerationalDigitalDividePersistence", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures A cognitive aging pattern in AI-augmented elder interaction, measurable through a behavioral pattern where even when many individuals has internet and computers, big differences remain between age groups. Owning a device is not the same as knowing how to use it. This phenomenon operates at the intersection of generational and digital dynamics within the broader AGE domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept spezifisches Attribut von Bewertung und Qualitätsbeurteilung von KI-Outputs. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Analytical Ratio", "narrower_terms": [], "cross_domain_refs": [ "NEO-1172" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "AGE-0054", "domain": "AGE", "term_en": "Generational Digital Identity Gap", "term_de": "GenerationalDigitalIdentityGap", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures A cognitive aging pattern in AI-augmented elder interaction, measurable through a perception in which young people grew up posting online and using digital identities. Older adults did not, so digital life feels fake or concerny to them. This phenomenon operates at the intersection of generational and digital dynamics within the broader AGE domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept phänomen oder Erfahrung in der Bewertung und Qualitätsbeurteilung von KI-Outputs. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Analytical Ratio", "narrower_terms": [], "cross_domain_refs": [ "PHO-0090", "RHR-0300", "CRE-0114" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q844396", "legal_classification": "observational_construct" }, { "id": "AGE-0055", "domain": "AGE", "term_en": "Generational Feature Discovery Asymmetry", "term_de": "GenerationalFeatureDiscoveryAsymmetry", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures A cognitive aging pattern in AI-augmented elder interaction, measurable through a phenomenon in which young people find new app features by exploring and talking to friends. Older adults need clear instructions or they rarely discover helpful features. This phenomenon operates at the intersection of generational and feature dynamics within the broader AGE domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept kennzeichnende Ausprägung von Bewertung und Qualitätsbeurteilung von KI-Outputs. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Analytical Ratio", "narrower_terms": [], "cross_domain_refs": [ "WRK-0037", "RHR-0142", "ART-0023" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "AGE-0056", "domain": "AGE", "term_en": "Generational Help-Seeking Behavior", "term_de": "GenerationalHelp-seekingBehavior", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies an aging-related phenomenon in AI-mediated gerontological interaction, characterized by a tendency in which the divergent patterns of challenge-solving strategies between age cohorts, where older users rely on interpersonal support while younger users favor digital information sources. This phenomenon operates at the intersection of generational and help dynamics within the broader AGE domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept systemische Ausprägung von Bewertung und Qualitätsbeurteilung von KI-Outputs. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Analytical Ratio", "narrower_terms": [], "cross_domain_refs": [ "AED-0058" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "AGE-0057", "domain": "AGE", "term_en": "Generational Instruction Asymmetry", "term_de": "GenerationalInstructionAsymmetry", "definition_en": "A pattern in which variant in which different ages learn best from different types of help—video guides work more effectively for some, written guides for others, and hands-on help for still others. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch merkmal von Bewertung und Qualitätsbeurteilung von KI-Outputs. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-2703", "narrower_terms": [], "cross_domain_refs": [ "WRK-0051" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "AGE-0058", "domain": "AGE", "term_en": "Generational Instruction Format Preference", "term_de": "GenerationalInstructionFormatPreference", "definition_en": "A behavioral pattern where some people learn by reading, some by watching, some by doing. Most AI assumes one way of learning is best for many individuals. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch spezifisches Attribut von Bewertung und Qualitätsbeurteilung von KI-Outputs. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-2703", "narrower_terms": [], "cross_domain_refs": [ "WRK-0051" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "AGE-0059", "domain": "AGE", "term_en": "Generational Interface Gap", "term_de": "GenerationalInterfaceGap", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies an aging-related phenomenon in AI-mediated gerontological interaction, characterized by the look and feel of modern interfaces - dark mode, minimal buttons, lots of white space - appeals to younger people. Older people often find it disorienting and prefer clear labels and obvious but. This phenomenon operates at the intersection of generational and interface dynamics within the broader AGE domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription. Descriptive research term, not a prescriptive recommendation.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept phänomen oder Erfahrung in der Bewertung und Qualitätsbeurteilung von KI-Outputs. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Analytical Ratio", "narrower_terms": [], "cross_domain_refs": [ "AED-0019", "AED-0092", "BEH-0084" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "AGE-0060", "domain": "AGE", "term_en": "Generational Metaphor Mismatch", "term_de": "GenerationalMetaphorMismatch", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies an aging-related phenomenon in AI-mediated gerontological interaction, characterized by a perception in which tech companies use comparisons that make sense to young people (like \"cloud\" or \"desktop\") but confuse older users who did not grow up with those ideas. This phenomenon operates at the intersection of generational and metaphor dynamics within the broader AGE domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept phänomen oder Erfahrung in der Bewertung und Qualitätsbeurteilung von KI-Outputs. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Analytical Ratio", "narrower_terms": [], "cross_domain_refs": [ "ART-0061", "ASE-0082", "COG-0152" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q4263830", "legal_classification": "empirical_phenomenon_label" }, { "id": "AGE-0061", "domain": "AGE", "term_en": "Generational Tech Adoption Timeline", "term_de": "GenerationalTechAdoptionTimeline", "definition_en": "A phenomenon in which when someone first used a computer matters a lot. Someone who started at 15 and someone who started at 65 will typically think differently about technology. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch spezifisches Attribut von Bewertung und Qualitätsbeurteilung von KI-Outputs. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-2301", "narrower_terms": [], "cross_domain_refs": [ "ADA-0014" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "AGE-0062", "domain": "AGE", "term_en": "Generational Tech Concern Pattern", "term_de": "GenerationalTechConcernMuster", "definition_en": "A perception in which different age groups focus on different tech considerations. What feels urgent to one generation may seem irrelevant to another — shaped by what each group experienced growing up. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch phänomen oder Erfahrung in der Bewertung und Qualitätsbeurteilung von KI-Outputs. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-3901", "narrower_terms": [], "cross_domain_refs": [ "PER-0104", "WEB-0065", "COG-0151" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "AGE-0063", "domain": "AGE", "term_en": "Generational Tech Expectation Mismatch", "term_de": "GenerationalTechExpectationMismatch", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures A cognitive aging pattern in AI-augmented elder interaction, measurable through a capacity that enables different age groups expect different things from technology. Young people expect constant updates; older people want stability. This phenomenon operates at the intersection of generational and tech dynamics within the broader AGE domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept fähigkeit oder Kompetenz im Umgang mit Bewertung und Qualitätsbeurteilung von KI-Outputs. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Analytical Ratio", "narrower_terms": [], "cross_domain_refs": [ "COP-0045", "CUS-0010", "SWE-0038" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "AGE-0064", "domain": "AGE", "term_en": "Generational Technological Value Divergence", "term_de": "GenerationalTechnologicalValueDivergenz", "definition_en": "A cognitive aging pattern in AI-augmented elder interaction, measurable through a phenomenon in which technology instrumentality is thought of differently across cohorts based on adoption context and use case precedence. Users approach systems with different primary framings regarding their functio. The concept emerges specifically in contexts where generational–technological interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch evaluationskompetenz zur kontextabhängige von KI-Output. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Analytical Ratio", "narrower_terms": [], "cross_domain_refs": [ "AED-0090", "CRE-0070", "LIN-0018" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "AGE-0065", "domain": "AGE", "term_en": "Generational Value Alignment Gap", "term_de": "GenerationalValueAlignmentGap", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies an aging-related phenomenon in AI-mediated gerontological interaction, characterized by a phenomenon in which different generations care about different things in technology—some want privacy, some want simplicity, some want connections. This phenomenon operates at the intersection of generational and value dynamics within the broader AGE domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept distinktives Merkmal von Bewertung und Qualitätsbeurteilung von KI-Outputs. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Analytical Ratio", "narrower_terms": [], "cross_domain_refs": [ "DAT-0069" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "AGE-0066", "domain": "AGE", "term_en": "Interface Accessibility Afterthought Pattern", "term_de": "InterfaceAccessibilityAfterthoughtMuster", "definition_en": "A phenomenon in which companies design apps for average young adults first, then attempt to retrofit them for older people. It would work more effectively to include older adults from the start. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch beobachtbare Eigenschaft von Bewertung und Qualitätsbeurteilung von KI-Outputs. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2301", "narrower_terms": [], "cross_domain_refs": [ "SPR-0189", "RHR-0023", "VIB-0150" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "AGE-0067", "domain": "AGE", "term_en": "Interface Aesthetic Preference Divergence", "term_de": "InterfaceAestheticPreferenceDivergenz", "definition_en": "A cognitive aging pattern in AI-augmented elder interaction, measurable through a phenomenon in which people of different ages like different-looking designs. What looks modern and clean to a young person looks cold and confusing to an older one. Distinguished from adjacent concepts by its focus on the specific mechanism through which interface manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch spezifisches Attribut von Bewertung und Qualitätsbeurteilung von KI-Outputs. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "User Interface", "narrower_terms": [], "cross_domain_refs": [ "ART-0034", "ART-0035", "ART-0044" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "AGE-0068", "domain": "AGE", "term_en": "Interface Complexity Accumulation", "term_de": "InterfaceComplexityAccumulation", "definition_en": "A phenomenon in which as apps add features, they get more complex. This hurts older users more than younger ones because there is more to learn. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch kennzeichnende Ausprägung von Bewertung und Qualitätsbeurteilung von KI-Outputs. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-2703", "narrower_terms": [], "cross_domain_refs": [ "ADA-0001", "ASE-0006", "ASE-0008" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "AGE-0069", "domain": "AGE", "term_en": "Interface Complexity Age Correlation", "term_de": "InterfaceComplexityAgeCorrelation", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures A cognitive aging pattern in AI-augmented elder interaction, measurable through a phenomenon in which apps get harder to use with age, not because people get different at technology, but because apps add features that younger users want. This phenomenon operates at the intersection of interface and complexity dynamics within the broader AGE domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept funktionale Eigenschaft von Bewertung und Qualitätsbeurteilung von KI-Outputs. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "User Interface", "narrower_terms": [], "cross_domain_refs": [ "WEB-0077", "SWE-0009", "ADA-0001" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "AGE-0070", "domain": "AGE", "term_en": "Interface Complexity Vs Functionality Trade-off", "term_de": "InterfaceComplexityvsFunctionalityTrade-off", "definition_en": "A cognitive aging pattern in AI-augmented elder interaction, measurable through a phenomenon in which apps that choose between being simple (easier for older adults) or having lots of features (loved by younger power users). Most pick features. The concept emerges specifically in contexts where interface–complexity interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch strukturelle Eigenschaft von Bewertung und Qualitätsbeurteilung von KI-Outputs. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "User Interface", "narrower_terms": [], "cross_domain_refs": [ "ASE-0041", "MTH-0040", "MTH-0047" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "AGE-0071", "domain": "AGE", "term_en": "Interface Consistency Preference", "term_de": "InterfaceConsistencyPreference", "definition_en": "A cognitive aging pattern in AI-augmented elder interaction, measurable through a tendency in which older adults want interfaces to stay the same. When apps constantly redesign, older users get lost and frustrated. Distinguished from adjacent concepts by its focus on the specific mechanism through which interface manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch operative Eigenschaft von Bewertung und Qualitätsbeurteilung von KI-Outputs. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "User Interface", "narrower_terms": [], "cross_domain_refs": [ "ART-0034", "ART-0035", "ART-0044" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "AGE-0072", "domain": "AGE", "term_en": "Interface Customization Invisibility", "term_de": "InterfaceCustomizationInvisibility", "definition_en": "An aging-related phenomenon in AI-mediated gerontological interaction, characterized by a capacity that enables older adults often don't know that buttons and text can be resized or rearranged. Customization exists but few individuals tells them about it. Distinguished from adjacent concepts by its focus on the specific mechanism through which interface manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch funktionale Eigenschaft von Bewertung und Qualitätsbeurteilung von KI-Outputs. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "User Interface", "narrower_terms": [], "cross_domain_refs": [ "ART-0099", "ASE-0031", "BEH-0084" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "AGE-0073", "domain": "AGE", "term_en": "Interface Design Age Skew", "term_de": "InterfaceDesignAgeSkew", "definition_en": "A cognitive aging pattern in AI-augmented elder interaction, measurable through a phenomenon in which most interface designers are young, so they design for young people. Older adults get left out. The concept emerges specifically in contexts where interface–design interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch strukturelle Eigenschaft von Bewertung und Qualitätsbeurteilung von KI-Outputs. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "User Interface", "narrower_terms": [], "cross_domain_refs": [ "CRE-0192" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q185925", "legal_classification": "systematic_classification" }, { "id": "AGE-0074", "domain": "AGE", "term_en": "Interface Familiarity Change", "term_de": "InterfaceFamiliarityChange", "definition_en": "A shift that occurs when when familiar apps change their design, older users feel like they restart learning from scratch all over again. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch phänomen oder Erfahrung in der Bewertung und Qualitätsbeurteilung von KI-Outputs. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2301", "narrower_terms": [], "cross_domain_refs": [ "AED-0025", "AED-0067", "AED-0077" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "AGE-0075", "domain": "AGE", "term_en": "Interface Literacy Frustration", "term_de": "InterfaceLiteracyFrustration", "definition_en": "A cognitive aging pattern in AI-augmented elder interaction, measurable through a tendency in which when icons, buttons, and patterns are unclear, people get frustrated quickly. Older adults hit that limit faster than younger ones. Distinguished from adjacent concepts by its focus on the specific mechanism through which interface manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch operative Eigenschaft von Bewertung und Qualitätsbeurteilung von KI-Outputs. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Analytical Ratio", "narrower_terms": [], "cross_domain_refs": [ "ASE-0010", "BEH-0084", "CRE-0034" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q201684", "legal_classification": "observational_construct" }, { "id": "AGE-0076", "domain": "AGE", "term_en": "Interface Navigation Cognition Load", "term_de": "InterfaceNavigationCognitionLoad", "definition_en": "A cognitive aging pattern in AI-augmented elder interaction, measurable through a phenomenon in which finding where to go next in an app takes thinking power. Complex navigation deplete older users more than young ones. Distinguished from adjacent concepts by its focus on the specific mechanism through which interface manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch operative Eigenschaft von Bewertung und Qualitätsbeurteilung von KI-Outputs. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "User Interface", "narrower_terms": [], "cross_domain_refs": [ "COG-0051", "SOM-0023", "WRK-0039" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q3966", "legal_classification": "systematic_classification" }, { "id": "AGE-0077", "domain": "AGE", "term_en": "Interface Stability And User Confidence", "term_de": "InterfaceStabilitätAndUserConfidence", "definition_en": "An aging-related phenomenon in AI-mediated gerontological interaction, characterized by a capacity enabling when application interfaces remain stable and predictable, users report increased confidence and willingness to explore new features. Interface consistency supports successful skill development across all age groups. The concept emerges specifically in contexts where interface–stability interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch fähigkeit oder Kompetenz im Umgang mit Bewertung und Qualitätsbeurteilung von KI-Outputs. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "User Interface", "narrower_terms": [], "cross_domain_refs": [ "TEM-0031" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "AGE-0078", "domain": "AGE", "term_en": "Interface Stability Expectation Divergence", "term_de": "InterfaceStabilitätExpectationDivergenz", "definition_en": "A cognitive aging pattern in AI-augmented elder interaction, measurable through a shift that occurs when older adults expect apps to stay mostly the same. Younger users expect them to change constantly. Both groups feel annoyed when the other wins out. Distinguished from adjacent concepts by its focus on the specific mechanism through which interface manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data. Descriptive research term, not a prescriptive recommendation.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch phänomen oder Erfahrung in der Bewertung und Qualitätsbeurteilung von KI-Outputs. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "User Interface", "narrower_terms": [], "cross_domain_refs": [ "SPR-0061", "VIB-0036", "GAM-0027" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "AGE-0079", "domain": "AGE", "term_en": "Interface Stability Preference Divergence", "term_de": "InterfaceStabilitätPreferenceDivergenz", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies an aging-related phenomenon in AI-mediated gerontological interaction, characterized by a phenomenon in which older users want old, familiar designs. Younger users want new, modern designs. Companies that pick one style. This phenomenon operates at the intersection of interface and stability dynamics within the broader AGE domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept operative Eigenschaft von Bewertung und Qualitätsbeurteilung von KI-Outputs. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "User Interface", "narrower_terms": [], "cross_domain_refs": [ "SPR-0061", "VIB-0036", "GAM-0027" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "AGE-0080", "domain": "AGE", "term_en": "Interface Stability Value Premium", "term_de": "InterfaceStabilitätValuePremium", "definition_en": "A cognitive aging pattern in AI-augmented elder interaction, measurable through a phenomenon in which older adults will pay for software that stays the same, while younger people expect free updates constantly. The concept emerges specifically in contexts where interface–stability interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch distinktives Merkmal von Bewertung und Qualitätsbeurteilung von KI-Outputs. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "User Interface", "narrower_terms": [], "cross_domain_refs": [ "VIB-0104", "VIB-0041", "MUS-0096" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "AGE-0081", "domain": "AGE", "term_en": "Late Adoption Advantage", "term_de": "LateAdoptionAdvantage", "definition_en": "A cognitive aging pattern in AI-augmented elder interaction, measurable through a phenomenon in which users who adopt technologies after initial release sometimes report distinct competence compared to early adopters, observed alongside mature documentation, reduced system volatility, and established community. The concept emerges specifically in contexts where late–adoption interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch fähigkeit oder Kompetenz im Umgang mit Bewertung und Qualitätsbeurteilung von KI-Outputs. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Aging & AI Interaction", "narrower_terms": [], "cross_domain_refs": [ "RHR-0180" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "AGE-0082", "domain": "AGE", "term_en": "Screen Time Tolerance Decrease", "term_de": "ScreenTimeToleranceDecrease", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures A cognitive aging pattern in AI-augmented elder interaction, measurable through a phenomenon in which after a certain amount of time on a screen, older eyes get tired faster. Headaches, blurred vision, strain. But app designs assume people will spend hours scrolling. This phenomenon operates at the intersection of screen and time dynamics within the broader AGE domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept charakteristische Komponente von Bewertung und Qualitätsbeurteilung von KI-Outputs. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Aging & AI Interaction", "narrower_terms": [], "cross_domain_refs": [ "RPH-409" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "AGE-0083", "domain": "AGE", "term_en": "Tech Adoption Timing Effect", "term_de": "TechAdoptionTimingEffekt", "definition_en": "A behavioral pattern where when someone first used a computer shapes how they think about technology forever. Someone who waited until they were 60 will not think the same way as someone who started at 15. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch kennzeichnende Ausprägung von Bewertung und Qualitätsbeurteilung von KI-Outputs. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2301", "narrower_terms": [], "cross_domain_refs": [ "VIB-0171", "BEH-0039", "RHR-0045" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "AGE-0084", "domain": "AGE", "term_en": "Tech Company Age Invisibility", "term_de": "TechCompanyAgeInvisibility", "definition_en": "An aging-related phenomenon in AI-mediated gerontological interaction, characterized by a phenomenon in which tech companies aren't counted as older users as important enough to design for, even though older people use apps a lot. Distinguished from adjacent concepts by its focus on the specific mechanism through which tech manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch kennzeichnende Ausprägung von Bewertung und Qualitätsbeurteilung von KI-Outputs. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Aging & AI Interaction", "narrower_terms": [], "cross_domain_refs": [ "COP-0058", "CUS-0040", "CRE-0101" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "AGE-0085", "domain": "AGE", "term_en": "Tech Company Age Skew Invisibility", "term_de": "TechCompanyAgeSkewInvisibility", "definition_en": "A cognitive aging pattern in AI-augmented elder interaction, measurable through a phenomenon in which the whole tech industry skews young - not just in who works there, but in what they build. The invisibility is structural, not accidental. Distinguished from adjacent concepts by its focus on the specific mechanism through which tech manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch operative Eigenschaft von Bewertung und Qualitätsbeurteilung von KI-Outputs. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Aging & AI Interaction", "narrower_terms": [], "cross_domain_refs": [ "WRK-0095" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "AGE-0086", "domain": "AGE", "term_en": "Tech Learning Curve Age Factor", "term_de": "TechLearningCurveAgeFactor", "definition_en": "A phenomenon in which learning new tech is not just about the technology—older adults have more habits to unlearn, and habits are hard to break. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch systemische Ausprägung von Bewertung und Qualitätsbeurteilung von KI-Outputs. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2703", "narrower_terms": [], "cross_domain_refs": [ "DES-0049", "GAM-0064", "RHR-0110" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q133500", "legal_classification": "observational_construct" }, { "id": "AGE-0087", "domain": "AGE", "term_en": "Tech Support Generational Language Gap", "term_de": "TechSupportGenerationalLanguageGap", "definition_en": "A cognitive aging pattern in AI-augmented elder interaction, measurable through a phenomenon in which tech support workers use jargon that younger people understand. Older people get confused by terms like \"cloud,\" \"cache,\" and \"browser.\". The concept emerges specifically in contexts where tech–support interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch funktionale Eigenschaft von Bewertung und Qualitätsbeurteilung von KI-Outputs. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Analytical Ratio", "narrower_terms": [], "cross_domain_refs": [ "BEH-0039", "LIN-0018", "VIB-0153" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q315", "legal_classification": "systematic_classification" }, { "id": "AGE-0088", "domain": "AGE", "term_en": "Tech Support Response Age Skew", "term_de": "TechSupportResponseAgeSkew", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies an aging-related phenomenon in AI-mediated gerontological interaction, characterized by a tendency in which tech support is usually built for young power users who know what they are doing. Older adults get frustrated because support assumes too much knowledge. This phenomenon operates at the intersection of tech and support dynamics within the broader AGE domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept funktionale Eigenschaft von Bewertung und Qualitätsbeurteilung von KI-Outputs. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Aging & AI Interaction", "narrower_terms": [], "cross_domain_refs": [ "BEH-0039" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "AGE-0089", "domain": "AGE", "term_en": "Technological Competence Inversion", "term_de": "TechnologicalCompetenceInversion", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures A cognitive aging pattern in AI-augmented elder interaction, measurable through a phenomenon in which sometimes the best at using technology are not who one might expect. A retired engineer might know more than someone half their age. This phenomenon operates at the intersection of technological and competence dynamics within the broader AGE domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept spezifisches Attribut von Bewertung und Qualitätsbeurteilung von KI-Outputs. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Aging & AI Interaction", "narrower_terms": [], "cross_domain_refs": [ "AED-0090", "COG-0009", "COG-0016" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q26944", "legal_classification": "analytical_category" }, { "id": "AGE-0090", "domain": "AGE", "term_en": "Technological Competence Verification Difficulty", "term_de": "TechnologicalCompetenceVerificationDifficulty", "definition_en": "A cognitive aging pattern in AI-augmented elder interaction, measurable through a behavioral pattern where it is hard to know if someone actually knows how to use technology or just thinks they do. This gap is bigger between age groups. The concept emerges specifically in contexts where technological–competence interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch kennzeichnende Ausprägung von Bewertung und Qualitätsbeurteilung von KI-Outputs. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Aging & AI Interaction", "narrower_terms": [], "cross_domain_refs": [ "AED-0090", "SPA-0035", "ART-0082" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q26944", "legal_classification": "analytical_category" }, { "id": "AGE-0091", "domain": "AGE", "term_en": "Technological Concern Accumulation Effect", "term_de": "TechnologicalConcernAccumulationEffekt", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures A cognitive aging pattern in AI-augmented elder interaction, measurable through a phenomenon in which exposure to multiple technological incidents (data breaches, scams, system setbacks) accumulates over decades of use. This legitimate incident history accompanies higher consideration vigilance in older adul. This phenomenon operates at the intersection of technological and concern dynamics within the broader AGE domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept funktionale Eigenschaft von Bewertung und Qualitätsbeurteilung von KI-Outputs. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "RPH-2555", "narrower_terms": [], "cross_domain_refs": [ "SPR-0025" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "AGE-0092", "domain": "AGE", "term_en": "Technological Distrust Accumulation", "term_de": "TechnologicalDistrustAccumulation", "definition_en": "A phenomenon in which the more bad experiences someone has with technology, the more they distrust the next new thing. But this distrust is often well-founded - there ARE a lot of scams and bad designs. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch phänomen oder Erfahrung in der Bewertung und Qualitätsbeurteilung von KI-Outputs. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2505", "narrower_terms": [], "cross_domain_refs": [ "AED-0090", "ASE-0006", "ASE-0076" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q102458", "legal_classification": "analytical_category" }, { "id": "AGE-0093", "domain": "AGE", "term_en": "Technological Learning Curve Steepness", "term_de": "TechnologicalLearningCurveSteepness", "definition_en": "A perception in which how steep the learning curve is for new tech varies wildly. Some things older people pick up fast. Other things feel extremely difficult. But designers often assume many individuals learns at the same speed. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch phänomen oder Erfahrung in der Bewertung und Qualitätsbeurteilung von KI-Outputs. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2103", "narrower_terms": [], "cross_domain_refs": [ "WRK-0016" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q133500", "legal_classification": "descriptive_research_term" }, { "id": "AGE-0094", "domain": "AGE", "term_en": "Technological Literacy Confidence Inversion", "term_de": "TechnologicalLiteracyConfidenceInversion", "definition_en": "A cognitive aging pattern in AI-augmented elder interaction, measurable through a phenomenon in which some people know technology really well but think they are bad at it. Others are barely competent but think they are experts. Age affects this. The concept emerges specifically in contexts where technological–literacy interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch strukturelle Eigenschaft von Bewertung und Qualitätsbeurteilung von KI-Outputs. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Aging & AI Interaction", "narrower_terms": [], "cross_domain_refs": [ "COG-0009" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q201684", "legal_classification": "observational_construct" }, { "id": "AGE-0095", "domain": "AGE", "term_en": "Technological Literacy Intergenerational Gap", "term_de": "TechnologicalLiteracyIntergenerationalGap", "definition_en": "A phenomenon in which the gap in tech knowledge between age groups is real and large, but it is not because older people are less likely to learn. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch spezifisches Attribut von Bewertung und Qualitätsbeurteilung von KI-Outputs. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-2703", "narrower_terms": [], "cross_domain_refs": [ "PHO-0093", "SPR-0008" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q201684", "legal_classification": "descriptive_research_term" }, { "id": "AGE-0096", "domain": "AGE", "term_en": "Technological Literacy Threshold", "term_de": "TechnologicalLiteracySchwelle", "definition_en": "An aging-related phenomenon in AI-mediated gerontological interaction, characterized by a tendency in which there is a minimum level of tech knowledge many individuals needs now just to function. Older people without this get cut off. The concept emerges specifically in contexts where technological–literacy interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch strukturelle Eigenschaft von Bewertung und Qualitätsbeurteilung von KI-Outputs. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Decision Threshold", "narrower_terms": [], "cross_domain_refs": [ "AED-0090", "ASE-0010", "ASE-0021" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q201684", "legal_classification": "systematic_classification" }, { "id": "AGE-0097", "domain": "AGE", "term_en": "Technological Literacy Validation Need", "term_de": "TechnologicalLiteracyValidationNeed", "definition_en": "An aging-related phenomenon in AI-mediated gerontological interaction, characterized by a tendency in which people want confirmation that they understand technology correctly, at any skill level. The concept emerges specifically in contexts where technological–literacy interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch fähigkeit oder Kompetenz im Umgang mit Bewertung und Qualitätsbeurteilung von KI-Outputs. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Validation Process", "narrower_terms": [], "cross_domain_refs": [ "SPR-0044" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q201684", "legal_classification": "observational_construct" }, { "id": "AGE-0098", "domain": "AGE", "term_en": "Technological Literacy Verification Gap", "term_de": "TechnologicalLiteracyVerificationGap", "definition_en": "An aging-related phenomenon in AI-mediated gerontological interaction, characterized by a perception in which older adults often feel unsure whether they completed a digital task correctly. Most systems offer no clear confirmation, leaving a lingering sense of uncertainty about what just happened. Distinguished from adjacent concepts by its focus on the specific mechanism through which technological manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data. Descriptive research term, not a prescriptive recommendation.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch phänomen oder Erfahrung in der Bewertung und Qualitätsbeurteilung von KI-Outputs. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2952", "narrower_terms": [], "cross_domain_refs": [ "AED-0019", "CRE-0042", "ELR-0112" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q201684", "legal_classification": "observational_construct" }, { "id": "AGE-0099", "domain": "AGE", "term_en": "Technological Skepticism Age Correlation", "term_de": "TechnologicalSkepticismAgeCorrelation", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies an aging-related phenomenon in AI-mediated gerontological interaction, characterized by a phenomenon in which the older someone is, the more skeptical they tend to be of new technology. This is reasonable given their experience. This phenomenon operates at the intersection of technological and skepticism dynamics within the broader AGE domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept phänomen oder Erfahrung in der Bewertung und Qualitätsbeurteilung von KI-Outputs. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Aging & AI Interaction", "narrower_terms": [], "cross_domain_refs": [ "IDN-0006" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "AGE-0100", "domain": "AGE", "term_en": "Wisdom-Algorithm Mismatch", "term_de": "Wisdom-algorithmMismatch", "definition_en": "A phenomenon in which algorithms are built on patterns in data, but wisdom is about judgment. An algorithm might not respect what older adults learned from decades of life. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch merkmal von Bewertung und Qualitätsbeurteilung von KI-Outputs. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-2703", "narrower_terms": [], "cross_domain_refs": [ "AED-0098", "ART-0026", "ART-0027" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q8366", "legal_classification": "observational_construct" }, { "id": "ART-0001", "domain": "ART", "term_en": "AI Art Community Identity Formation", "term_de": "AiArtCommunityIdentityFormation", "definition_en": "A creative-technical dynamic in AI image synthesis, observable through users of AI art tools collectively establish shared aesthetic values, norms, and group identity through common creative practice. Distinguished from adjacent concepts by its focus on the specific mechanism through which ai manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "KI-Kunstgenerierungsphänomen an der Schnittstelle von Ästhetik und Computation, gekennzeichnet durch prozess, durch den Nutzer von KI-Kunstwerkzeugen gemeinsame ästhetische Werte, Normen und Gruppenzugehörigkeitsgefühle etablieren. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "AI Art Generation", "narrower_terms": [ "ART-0009", "ART-0095", "ART-0063", "ART-0055", "ART-0082", "ART-0079", "ART-0078", "ART-0032", "ART-0072", "ART-0020", "ART-0048", "ART-0083", "ART-0090", "ART-0003", "ART-0097", "ART-0007", "ART-0068", "ART-0005", "ART-0046", "ART-0071", "ART-0088", "ART-0002", "ART-0028", "ART-0081", "ART-0031", "ART-0019", "ART-0041", "ART-0026", "ART-0050", "ART-0023", "ART-0040", "ART-0043", "ART-0006", "ART-0062", "ART-0014", "ART-0054", "ART-0061", "ART-0011", "ART-0073", "ART-0049", "ART-0099", "ART-0094", "ART-0030", "ART-0038", "ART-0074", "ART-0052", "ART-0057", "ART-0004", "ART-0096", "ART-0076", "ART-0092", "ART-0008", "ART-0017", "ART-0045", "ART-0012", "ART-0025", "ART-0013", "ART-0024", "ART-0089", "ART-0075", "ART-0091", "ART-0056", "ART-0047", "ART-0001", "ART-0064", "ART-0035", "ART-0027", "ART-0093", "ART-0053", "ART-0033", "ART-0042" ], "cross_domain_refs": [ "COG-0175" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q844396", "legal_classification": "observational_construct" }, { "id": "ART-0002", "domain": "ART", "term_en": "AI Art Community Legitimacy Building", "term_de": "AiArtCommunityLegitimacyBuilding", "definition_en": "A creative-technical dynamic in AI image synthesis, observable through the effort by AI artists to establish credibility and institutional recognition within established art communities and exhibition spaces. Distinguished from adjacent concepts by its focus on the specific mechanism through which ai manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "KI-Kunstgenerierungsphänomen an der Schnittstelle von Ästhetik und Computation, gekennzeichnet durch bemühungen von KI-Künstlern, fachliche Glaubwürdigkeit und institutionelle Anerkennung in etablierten Kunstgemeinden zu erlangen. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "AI Art Generation", "narrower_terms": [], "cross_domain_refs": [ "CON-0021" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q735", "legal_classification": "descriptive_research_term" }, { "id": "ART-0003", "domain": "ART", "term_en": "AI Art Copyright Enforcement Challenge", "term_de": "AiArtCopyrightEnforcementChallenge", "definition_en": "A visual generation pattern in AI-mediated artistic production, characterized by a creative phenomenon manifesting as the legal ambiguity regarding copyright ownership arising from the sale, exhibition, or competition entry of AI-generated artworks. The concept emerges specifically in contexts where ai–art interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "KI-Kunstgenerierungsphänomen an der Schnittstelle von Ästhetik und Computation, gekennzeichnet durch rechtliche Unklarheit bezüglich Urheberschafts- und Eigentumsrechte bei Verkauf, Ausstellung oder Wettbewerb von KI-generierten Werken. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "AI Art Generation", "narrower_terms": [], "cross_domain_refs": [ "MUS-0098" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q1297", "legal_classification": "observational_construct" }, { "id": "ART-0004", "domain": "ART", "term_en": "AI Art Gallery Integration Strategy", "term_de": "AiArtGalleryIntegrationStrategy", "definition_en": "A creative-technical dynamic in AI image synthesis, observable through an artistic interaction effect manifesting as art museums and galleries decide how to show AI-created works to visitors, including where to place them and what information to display. Distinguished from adjacent concepts by its focus on the specific mechanism through which ai manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "KI-Kunstgenerierungsphänomen an der Schnittstelle von Ästhetik und Computation, gekennzeichnet durch strategische Entscheidungen von Kunstinstitutionen, wie KI-Werke präsentiert werden, einschließlich Platzierung, Beschriftung und Kontextualisierung. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Analytical Ratio", "narrower_terms": [], "cross_domain_refs": [ "CRE-0069", "AED-0010", "MKT-0054" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q185451", "legal_classification": "descriptive_research_term" }, { "id": "ART-0005", "domain": "ART", "term_en": "AI Art Legitimacy Criteria Development", "term_de": "AiArtLegitimacyCriteriaDevelopment", "definition_en": "A creative-technical dynamic in AI image synthesis, observable through deciding what makes AI art real art—based on idea, skill, originality, or other qualities. Distinguished from adjacent concepts by its focus on the specific mechanism through which ai manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "KI-Kunstgenerierungsphänomen an der Schnittstelle von Ästhetik und Computation, gekennzeichnet durch prozess der Aushandlung von Kriterien, die KI-Kunst als echte Kunstform qualifizieren—basierend auf Konzeption, Handwerk, Originalität oder anderen Standards. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "AI Art Generation", "narrower_terms": [], "cross_domain_refs": [ "LIN-0086", "IDN-0042", "AED-0059" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q735", "legal_classification": "empirical_phenomenon_label" }, { "id": "ART-0006", "domain": "ART", "term_en": "AI Art Market Speculation Effect", "term_de": "AiArtMarketSpeculationEffekt", "definition_en": "A visual generation pattern in AI-mediated artistic production, characterized by the rapid appreciation of AI-generated artwork values driven by speculative investment behavior and commodity trading dynamics. The concept emerges specifically in contexts where ai–art interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "KI-Kunstgenerierungsphänomen an der Schnittstelle von Ästhetik und Computation, gekennzeichnet durch schnelle Wertsteigerung von KI-generierten Kunstwerken durch spekulatives Investitionsverhalten und trendgetriebene Marktnachfrage. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Psychological Effect", "narrower_terms": [], "cross_domain_refs": [ "PLY-0027", "VIB-0036" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q735", "legal_classification": "empirical_phenomenon_label" }, { "id": "ART-0007", "domain": "ART", "term_en": "AI Art Medium Definition Debate", "term_de": "AiArtMediumDefinitionDebate", "definition_en": "A visual generation pattern in AI-mediated artistic production, characterized by a creative phenomenon manifesting as is AI art its own artistic medium or just a tool? Disagreement about what counts as art. The concept emerges specifically in contexts where ai–art interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "KI-Kunstgenerierungsphänomen an der Schnittstelle von Ästhetik und Computation, gekennzeichnet durch künstlerische und theoretische Auseinandersetzung darüber, ob KI-Kunst ein eigenständiges Medium darstellt oder nur ein Werkzeug. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "AI Art Generation", "narrower_terms": [], "cross_domain_refs": [ "TEM-0145", "QUA-0020", "REL-0110" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q735", "legal_classification": "empirical_phenomenon_label" }, { "id": "ART-0008", "domain": "ART", "term_en": "AI Art Ownership Legal Ambiguity", "term_de": "AiArtOwnershipLegalAmbiguity", "definition_en": "A visual generation pattern in AI-mediated artistic production, characterized by a creative production dynamic manifesting as the legal uncertainty regarding ownership rights when individuals use commercial AI image generation tools to involve visual content. The concept emerges specifically in contexts where ai–art interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "KI-Kunstgenerierungsphänomen an der Schnittstelle von Ästhetik und Computation, gekennzeichnet durch rechtsunsicherheit bezüglich Eigentumsverhältnisse, wenn Einzelpersonen kommerzielle KI-Bildgenerierungssysteme nutzen. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "AI Art Generation", "narrower_terms": [], "cross_domain_refs": [ "MUS-0021", "CRE-0071" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q735", "legal_classification": "descriptive_research_term" }, { "id": "ART-0009", "domain": "ART", "term_en": "AI Art Style Clustering Effect", "term_de": "AiArtStyleClusteringEffekt", "definition_en": "A visual generation pattern in AI-mediated artistic production, characterized by aI art tools to yield visually similar outputs with characteristic stylistic markers that become recognizable across generated works. The concept emerges specifically in contexts where ai–art interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "KI-Kunstgenerierungsphänomen an der Schnittstelle von Ästhetik und Computation, gekennzeichnet durch verschiebung von Kunstformen und ästhetischen Präferenzen, wenn KI-Systeme Training-Daten basierend auf populären Stilen generieren. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Psychological Effect", "narrower_terms": [], "cross_domain_refs": [ "PHO-0002" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q735", "legal_classification": "systematic_classification" }, { "id": "ART-0010", "domain": "ART", "term_en": "AI Art Valuation Metric Development", "term_de": "AiArtValuationMetricDevelopment", "definition_en": "An artistic interaction effect where new methods for determining how much an AI artwork costs — based on appearance, exhibition history, or the human behind the prompt. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "KI-Kunstgenerierungsphänomen an der Schnittstelle von Ästhetik und Computation, gekennzeichnet durch phänomen, bei dem KI-Kunstwerkzeuge durch Automatisierung manueller Fertigkeiten künstlerische Handwerkstechniken transformieren. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2104", "narrower_terms": [], "cross_domain_refs": [ "LIN-0086", "IDN-0042", "AED-0059" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q735", "legal_classification": "observational_construct" }, { "id": "ART-0011", "domain": "ART", "term_en": "AI Artist Attribution Framework", "term_de": "AiArtistAttributionFramework", "definition_en": "A creative-technical dynamic in AI image synthesis, observable through an aesthetic pattern reflecting the contested attribution problem where human authorial credit or artistic ownership becomes ambiguous when algorithmic systems contribute substantially to aesthetic or conceptual outcomes. Distinguished from adjacent concepts by its focus on the specific mechanism through which ai manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "KI-Kunstgenerierungsphänomen an der Schnittstelle von Ästhetik und Computation, gekennzeichnet durch das umstrittene Zuordnungsproblem, bei dem menschliche Urheberschaft mehrdeutig wird, wenn algorithmische Systeme zu künstlerischen Ergebnissen beitragen. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Analytical Framework", "narrower_terms": [], "cross_domain_refs": [ "CRE-0036", "REL-0109" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q735", "legal_classification": "systematic_classification" }, { "id": "ART-0012", "domain": "ART", "term_en": "AI Artistic Authenticity Marker", "term_de": "AiArtisticAuthenticityMarker", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies an AI art generation phenomenon describing a specific aesthetic-computational interaction where an artistic interaction effect involving certain signatures or details in an image that signal 'this was made by an AI,' like unusually rendered hands or the smooth, almost-but-not-quite-realistic look. This phenomenon operates at the intersection of ai and artistic dynamics within the broader ART domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept kI-Kunstgenerierungsphänomen an der Schnittstelle von Ästhetik und Computation, gekennzeichnet durch marktnachfrage, bei der KI-Kunstwerke primär aufgrund ihrer technologischen Neuheit begehrt werden, nicht ästhetischer Qualität. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "AI Art Generation", "narrower_terms": [], "cross_domain_refs": [ "CON-0015", "CON-0018", "CON-0045" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q735", "legal_classification": "observational_construct" }, { "id": "ART-0013", "domain": "ART", "term_en": "AI Artistic Authorship Rights", "term_de": "AiArtisticAuthorshipRights", "definition_en": "A creative-technical dynamic in AI image synthesis, observable through an artistic interaction effect in which legal ambiguity: who gets credit when AI makes art? Is it the user, the AI creator, or few individuals?. Distinguished from adjacent concepts by its focus on the specific mechanism through which ai manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "KI-Kunstgenerierungsphänomen an der Schnittstelle von Ästhetik und Computation, gekennzeichnet durch fähigkeit von KI-Systemen, aus Trainingsmaterial unbeabsichtigt erkennbare Stile, Signaturen oder Werke nachzuahmen. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "AI Art Generation", "narrower_terms": [], "cross_domain_refs": [ "CRE-0036", "REL-0109" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q735", "legal_classification": "observational_construct" }, { "id": "ART-0014", "domain": "ART", "term_en": "AI Artistic Output Diversity Challenge", "term_de": "AiArtisticOutputDiversityChallenge", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures an AI art generation phenomenon describing a specific aesthetic-computational interaction where a creative production dynamic manifesting as even when people try to make very different images with AI, they often end up looking similar, which limits how unique each artwork can be. This phenomenon operates at the intersection of ai and artistic dynamics within the broader ART domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept kI-Kunstgenerierungsphänomen an der Schnittstelle von Ästhetik und Computation, gekennzeichnet durch debatte über Originalität und künstlerische Autonomie, wenn KI-systeme bestehende Kunstwerke aggregiert kombinieren. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "AI Art Generation", "narrower_terms": [], "cross_domain_refs": [ "CRE-0024" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q735", "legal_classification": "empirical_phenomenon_label" }, { "id": "ART-0015", "domain": "ART", "term_en": "AI Artistic Output Reproducibility", "term_de": "AiArtisticOutputReproducibility", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures an AI art generation phenomenon describing a specific aesthetic-computational interaction where a creative phenomenon observed when when the same text prompt in an AI art tool can yield wildly different images each time. This phenomenon operates at the intersection of ai and artistic dynamics within the broader ART domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept kI-Kunstgenerierungsphänomen an der Schnittstelle von Ästhetik und Computation, gekennzeichnet durch zunahme von Kunstwerken, die KI-Systemen attributiert werden, ohne Klarheit über die Kontribution des menschlichen Künstlers. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "RPH-2104", "narrower_terms": [], "cross_domain_refs": [ "ADA-0003", "COG-0034", "COG-0068" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q735", "legal_classification": "descriptive_research_term" }, { "id": "ART-0016", "domain": "ART", "term_en": "AI Artistic Style Transfer Effect", "term_de": "AiArtisticStyleTransferEffekt", "definition_en": "A creative-technical dynamic in AI image synthesis, observable through an aesthetic pattern observed when computational style replication where generative models encode visual or sonic characteristics of a reference artist and apply those patterns to novel content, creating new artifacts in the original aesthetic. Distinguished from adjacent concepts by its focus on the specific mechanism through which ai manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "KI-Kunstgenerierungsphänomen an der Schnittstelle von Ästhetik und Computation, gekennzeichnet durch rechnerische Stilreplikation, bei der generative Modelle Stilmerkmale codieren und auf neue Inhalte anwenden. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2351", "narrower_terms": [], "cross_domain_refs": [ "CRE-0111" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q735", "legal_classification": "systematic_classification" }, { "id": "ART-0017", "domain": "ART", "term_en": "AI Artistic Training Data Opacity", "term_de": "AiArtisticTrainingDataOpacity", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies an AI art generation phenomenon describing a specific aesthetic-computational interaction where an artistic interaction effect reflecting transparency regarding which specific artists' works were included in training datasets for image generation systems. This phenomenon operates at the intersection of ai and artistic dynamics within the broader ART domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept kI-Kunstgenerierungsphänomen an der Schnittstelle von Ästhetik und Computation, gekennzeichnet durch verschiebung der Kunstkritik und Bewertung, wenn massenhafte KI-generierte Alternativen die Einzigartigkeit von Originalwerken hinterfragen. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Model Training", "narrower_terms": [], "cross_domain_refs": [ "COP-0084", "IDN-0051", "MUS-0089" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q918461", "legal_classification": "analytical_category" }, { "id": "ART-0018", "domain": "ART", "term_en": "AI Artistic Voice Formation", "term_de": "AiArtisticVoiceFormation", "definition_en": "An artistic interaction effect in which even though AI tools do the generating, people who use them a lot develop their own 'voice'—a recognizable way of prompting and choosing from results. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "KI-Kunstgenerierungsphänomen an der Schnittstelle von Ästhetik und Computation, gekennzeichnet durch phänomen, bei dem Kunstgalerien und -museen KI-Werke ausstellen und gleichzeitig geltende Konventionen für künstlerische Legitimität verhandeln. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2104", "narrower_terms": [], "cross_domain_refs": [ "AGE-0084", "MKT-0007" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q735", "legal_classification": "descriptive_research_term" }, { "id": "ART-0019", "domain": "ART", "term_en": "AI Artwork Attribution Challenge", "term_de": "AiArtworkAttributionChallenge", "definition_en": "A visual generation pattern in AI-mediated artistic production, characterized by a creative production dynamic characterized by the disorientation about who is responsible for an AI-generated artwork shown in a gallery or museum. The concept emerges specifically in contexts where ai–artwork interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "KI-Kunstgenerierungsphänomen an der Schnittstelle von Ästhetik und Computation, gekennzeichnet durch reduzierung von Eintrittsbarrieren zur Kunstproduktion durch KI-Werkzeuge, die traditionelle Fertigkeitsanforderungen senken. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "AI Art Generation", "narrower_terms": [], "cross_domain_refs": [ "AED-0009", "ASE-0095", "COG-0048" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q735", "legal_classification": "analytical_category" }, { "id": "ART-0020", "domain": "ART", "term_en": "AI Artwork Conceptual Legitimacy", "term_de": "AiArtworkConceptualLegitimacy", "definition_en": "A creative-technical dynamic in AI image synthesis, observable through an aesthetic pattern characterized by the debate about whether AI-made art counts as real art in philosophy and art theory. Distinguished from adjacent concepts by its focus on the specific mechanism through which ai manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "KI-Kunstgenerierungsphänomen an der Schnittstelle von Ästhetik und Computation, gekennzeichnet durch rechtliche Grauzonen, wenn KI-Systeme zuvor unverträglich verwendete Kunstwerke oder Stile reproduzieren. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "AI Art Generation", "narrower_terms": [], "cross_domain_refs": [ "COG-0089", "COG-0142", "COG-0160" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q735", "legal_classification": "observational_construct" }, { "id": "ART-0021", "domain": "ART", "term_en": "AI Artwork Conceptual Originality", "term_de": "AiArtworkConceptualOriginality", "definition_en": "Human creative judgment in prompt formulation and output selection as distinct from the algorithmic image generation process. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "KI-Kunstgenerierungsphänomen an der Schnittstelle von Ästhetik und Computation, gekennzeichnet durch phänomen der ästhetischen Konvergenz, bei dem unterschiedliche KI-Systeme ähnliche visuelle Qualitäten und Stile produzieren. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2104", "narrower_terms": [], "cross_domain_refs": [ "CRE-0025" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q735", "legal_classification": "empirical_phenomenon_label" }, { "id": "ART-0022", "domain": "ART", "term_en": "AI Artwork Legitimacy In Fine Art", "term_de": "ai artwork legitimacy in fine art", "definition_en": "A creative-technical dynamic in AI image synthesis, observable through an artistic interaction effect involving the question of whether AI art belongs in fine art museums and galleries alongside traditional art. Distinguished from adjacent concepts by its focus on the specific mechanism through which ai manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "KI-Kunstgenerierungsphänomen an der Schnittstelle von Ästhetik und Computation, gekennzeichnet durch entwicklung neuer ästhetischer Kategorien und Bewertungsmaßstäbe, die spezifisch für KI-generierte Kunstwerke gelten. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-3755", "narrower_terms": [], "cross_domain_refs": [ "GAM-0026", "GAM-0079", "GAM-0009" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q735", "legal_classification": "systematic_classification" }, { "id": "ART-0023", "domain": "ART", "term_en": "AI Artwork Market Price Discovery", "term_de": "AiArtworkMarketPriceDiscovery", "definition_en": "A visual generation pattern in AI-mediated artistic production, characterized by price discovery processes in emerging markets where AI-generated artworks establish exchange values through auction, gallery, or secondary market transactions, revealing demand and collectibility perception. The concept emerges specifically in contexts where ai–artwork interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "KI-Kunstgenerierungsphänomen an der Schnittstelle von Ästhetik und Computation, gekennzeichnet durch preisermittlungsprozesse in Märkten, bei denen KI-generierte Kunstwerke Tauschwerte etablieren. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "AI Art Generation", "narrower_terms": [], "cross_domain_refs": [ "IDN-0012" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q735", "legal_classification": "empirical_phenomenon_label" }, { "id": "ART-0024", "domain": "ART", "term_en": "AI Artwork Valuation Uncertainty", "term_de": "AiArtworkValuationUncertainty", "definition_en": "A visual generation pattern in AI-mediated artistic production, characterized by aesthetic friction—the user experience moment when algorithmic output diverges from human artistic intention, creating surprise, dissatisfaction, or unexpected creative opportunity. The concept emerges specifically in contexts where ai–artwork interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "KI-Kunstgenerierungsphänomen an der Schnittstelle von Ästhetik und Computation, gekennzeichnet durch ästhetische Reibung—der Moment, wenn algorithmischer Output von künstlerischer Intention abweicht. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "AI Art Generation", "narrower_terms": [], "cross_domain_refs": [ "RPH-1410" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q735", "legal_classification": "observational_construct" }, { "id": "ART-0025", "domain": "ART", "term_en": "AI Generated Art Authenticity Framework", "term_de": "AiGeneratedArtAuthenticityFramework", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures an AI art generation phenomenon describing a specific aesthetic-computational interaction where semantic drift where iterative AI refinement moves artwork incrementally away from original prompts toward attractors in the generative models learned aesthetic space. This phenomenon operates at the intersection of ai and generated dynamics within the broader ART domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept kI-Kunstgenerierungsphänomen an der Schnittstelle von Ästhetik und Computation, gekennzeichnet durch semantische Verschiebung, bei der iterative KI-Verfeinerung Kunstwerk inkrementell vom Original weg bewegt. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Analytical Framework", "narrower_terms": [], "cross_domain_refs": [ "COG-0077" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q735", "legal_classification": "descriptive_research_term" }, { "id": "ART-0026", "domain": "ART", "term_en": "Aesthetic Algorithm Fairness Assessment", "term_de": "AestheticAlgorithmFairnessAssessment", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures an AI art generation phenomenon describing a specific aesthetic-computational interaction where a creative phenomenon where the moment of creative agency when an artist chooses to preserve or amplify an algorithmic anomaly, elevating computational artifact to intentional artistic statement. This phenomenon operates at the intersection of aesthetic and algorithm dynamics within the broader ART domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept kI-Kunstgenerierungsphänomen an der Schnittstelle von Ästhetik und Computation, gekennzeichnet durch der Moment kreativer Handlungsfähigkeit, wenn ein Künstler eine algorithmische Anomalie zu künstlerischer Aussage erhebt. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Algorithm", "narrower_terms": [], "cross_domain_refs": [ "CRE-0001" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q210028", "legal_classification": "observational_construct" }, { "id": "ART-0027", "domain": "ART", "term_en": "Aesthetic Algorithm Fairness Evaluation", "term_de": "AestheticAlgorithmFairnessEvaluation", "definition_en": "A visual generation pattern in AI-mediated artistic production, characterized by a creative production dynamic involving testing an AI tool to see if it applies the same standards to all artists or if it's biased toward certain looks. The concept emerges specifically in contexts where aesthetic–algorithm interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "KI-Kunstgenerierungsphänomen an der Schnittstelle von Ästhetik und Computation, gekennzeichnet durch rechtlicher und ethischer Konflikt über Kompensation für Künstler, deren Werke in KI-Trainingsmengen verwendet wurden. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Algorithm", "narrower_terms": [], "cross_domain_refs": [ "CRE-0001", "MTH-0061" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q8366", "legal_classification": "analytical_category" }, { "id": "ART-0028", "domain": "ART", "term_en": "Aesthetic Algorithm Fairness Requirements", "term_de": "AestheticAlgorithmFairnessRequirements", "definition_en": "A creative-technical dynamic in AI image synthesis, observable through an aesthetic pattern characterized by unconscious stylistic integration where repeated exposure to AI-generated content shapes an artists own aesthetic preferences and technical choices without deliberate adoption. Distinguished from adjacent concepts by its focus on the specific mechanism through which aesthetic manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "KI-Kunstgenerierungsphänomen an der Schnittstelle von Ästhetik und Computation, gekennzeichnet durch unbewusste stilistische Integration, bei der Exposition gegenüber KI-generierten Inhalten ästhetische Vorlieben gestaltet. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Algorithm", "narrower_terms": [], "cross_domain_refs": [ "CRE-0001" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q8366", "legal_classification": "analytical_category" }, { "id": "ART-0029", "domain": "ART", "term_en": "Aesthetic Algorithm Imbalance Display", "term_de": "AestheticAlgorithmImbalanceDisplay", "definition_en": "An artistic interaction effect characterized by aI tools to consistently yield similar-looking images that cluster around a limited aesthetic range, producing repetitive outputs despite varied input prompts—such as consistently smoothed facia...", "definition_de": "KI-Kunstgenerierungsphänomen an der Schnittstelle von Ästhetik und Computation, gekennzeichnet durch gesellschaftliche Frage, ob Kunstschaffen primär auf Handwerk oder konzeptioneller Innovative basieren kann. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2104", "narrower_terms": [], "cross_domain_refs": [ "PHO-0010" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q8366", "legal_classification": "observational_construct" }, { "id": "ART-0030", "domain": "ART", "term_en": "Aesthetic Algorithm Transparency Need", "term_de": "AestheticAlgorithmTransparencyNeed", "definition_en": "A visual generation pattern in AI-mediated artistic production, characterized by the emotional reaction to recognizing that an artwork was generated mechanically rather than through human craftsmanship, often triggering revaluation of aesthetic response. The concept emerges specifically in contexts where aesthetic–algorithm interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "KI-Kunstgenerierungsphänomen an der Schnittstelle von Ästhetik und Computation, gekennzeichnet durch die emotionale Reaktion auf die Erkennung, dass ein Kunstwerk mechanisch statt handwerklich tendiert dazu zu erzeugen wurde. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Algorithm", "narrower_terms": [], "cross_domain_refs": [ "CRE-0001", "SPR-0177" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q535399", "legal_classification": "observational_construct" }, { "id": "ART-0031", "domain": "ART", "term_en": "Aesthetic Consensus In AI Generated Art", "term_de": "aesthetic consensus in ai generated art", "definition_en": "A visual generation pattern in AI-mediated artistic production, characterized by a creative phenomenon in which technical translation barriers where artistic intent specified in natural language cannot be fully realized through available generative systems, leaving aesthetic gaps between vision and output. The concept emerges specifically in contexts where aesthetic–consensus interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "KI-Kunstgenerierungsphänomen an der Schnittstelle von Ästhetik und Computation, gekennzeichnet durch technische Übersetzungsbarrieren, bei denen künstlerische Absicht nicht vollständig realisiert werden kann. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "AI Art Generation", "narrower_terms": [], "cross_domain_refs": [ "WRK-0051" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q735", "legal_classification": "analytical_category" }, { "id": "ART-0032", "domain": "ART", "term_en": "Aesthetic Diversity In Training Data", "term_de": "AestheticDiversityinTrainingData", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies an AI art generation phenomenon describing a specific aesthetic-computational interaction where an artistic interaction effect characterized by computational surprise—unanticipated emergent aesthetic properties arising from model inference that exceed or contradict the artists explicit parametric input specifications. This phenomenon operates at the intersection of aesthetic and diversity dynamics within the broader ART domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept kI-Kunstgenerierungsphänomen an der Schnittstelle von Ästhetik und Computation, gekennzeichnet durch rechnerische Überraschung—unerwartet auftauchende ästhetische Eigenschaften aus Modellableitung. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Model Training", "narrower_terms": [], "cross_domain_refs": [ "COP-0084", "IDN-0051", "MUS-0089" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q918461", "legal_classification": "analytical_category" }, { "id": "ART-0033", "domain": "ART", "term_en": "Aesthetic Judgment Algorithm Legibility", "term_de": "AestheticUrteilAlgorithmLegibility", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures an AI art generation phenomenon describing a specific aesthetic-computational interaction where an aesthetic pattern manifesting as tool transparency anxiety where an artist questions whether algorithmic credit or disclosure obligations alter the authenticity or presentation of finished creative work. This phenomenon operates at the intersection of aesthetic and judgment dynamics within the broader ART domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept kI-Kunstgenerierungsphänomen an der Schnittstelle von Ästhetik und Computation, gekennzeichnet durch werkzeugtransparenzangst, bei der ein Künstler fragt, ob Offenlegungsverpflichtungen die Authentizität ändern. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Algorithm", "narrower_terms": [], "cross_domain_refs": [ "SPR-0089" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q8366", "legal_classification": "descriptive_research_term" }, { "id": "ART-0034", "domain": "ART", "term_en": "Aesthetic Preference Algorithm Optimization", "term_de": "AestheticPreferenceAlgorithmOptimierung", "definition_en": "AI developers tuning the system to favor art styles that sell more effectively or get more clicks — shaping what kind of art the AI prefers to involve. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "KI-Kunstgenerierungsphänomen an der Schnittstelle von Ästhetik und Computation, gekennzeichnet durch redefinition künstlerischer Autorenschaft, wenn KI-Systeme und menschliche Künstler kollaborativ Werke erzeugen. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2154", "narrower_terms": [], "cross_domain_refs": [ "AGE-0007", "AGE-0067", "CRE-0001" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q8366", "legal_classification": "analytical_category" }, { "id": "ART-0035", "domain": "ART", "term_en": "Aesthetic Preference Algorithm Transparency", "term_de": "AestheticPreferenceAlgorithmTransparency", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies an AI art generation phenomenon describing a specific aesthetic-computational interaction where a creative production dynamic reflecting whether AI tool creators tell users what aesthetic preferences their algorithm was programmed to have. This phenomenon operates at the intersection of aesthetic and preference dynamics within the broader ART domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept kI-Kunstgenerierungsphänomen an der Schnittstelle von Ästhetik und Computation, gekennzeichnet durch phänomen, bei dem Kunstpreise und Marktwert für KI-Werke durch spekulative Blasen volatile und ungedeckt werden. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Algorithm", "narrower_terms": [], "cross_domain_refs": [ "CRE-0001" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q535399", "legal_classification": "analytical_category" }, { "id": "ART-0036", "domain": "ART", "term_en": "Aesthetic Preferences In AI Training", "term_de": "AestheticPreferencesinaiTraining", "definition_en": "An aesthetic pattern in which the choices made about what kinds of images an AI model learns from — which art styles, which cultures, which eras get included or left out. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "KI-Kunstgenerierungsphänomen an der Schnittstelle von Ästhetik und Computation, gekennzeichnet durch debatte über das Wesen von künstlerischer Schöpfung und Kreativität in Zeiten algorithmischer Generierung. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-2703", "narrower_terms": [], "cross_domain_refs": [ "AED-0018", "AED-0075", "AED-0077" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q918461", "legal_classification": "analytical_category" }, { "id": "ART-0037", "domain": "ART", "term_en": "Aesthetic Training Data Curation Impact", "term_de": "AestheticTrainingDataCurationImpact", "definition_en": "An artistic interaction effect in which the people who selected which images to train the AI on shaped what it thinks looks good, even if they didn't do it on purpose. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "KI-Kunstgenerierungsphänomen an der Schnittstelle von Ästhetik und Computation, gekennzeichnet durch verschiebung in Ausbildungsstrukturen, wenn traditionelle künstlerische Fertigkeitsausbildung durch KI-Toolkompetenz ergänzt oder ersetzt wird. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-3901", "narrower_terms": [], "cross_domain_refs": [ "REL-0206" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q918461", "legal_classification": "descriptive_research_term" }, { "id": "ART-0038", "domain": "ART", "term_en": "Aesthetic Training Data Fairness", "term_de": "AestheticTrainingDataFairness", "definition_en": "A visual generation pattern in AI-mediated artistic production, characterized by a creative production dynamic where whether the images used to train an AI art tool fairly represent different cultures, genders, styles, and artistic traditions. The concept emerges specifically in contexts where aesthetic–training interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "KI-Kunstgenerierungsphänomen an der Schnittstelle von Ästhetik und Computation, gekennzeichnet durch phänomen, bei dem Kunstmagazine und -kritiker Kategorien und Bewertungskriterien für KI-Kunstwerke entwickeln können. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Model Training", "narrower_terms": [], "cross_domain_refs": [ "COP-0084", "IDN-0051", "MUS-0089" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q18005", "legal_classification": "empirical_phenomenon_label" }, { "id": "ART-0039", "domain": "ART", "term_en": "Aesthetic Training Data Skew Pattern", "term_de": "AestheticTrainingDataSkewMuster", "definition_en": "An aesthetic pattern characterized by an AI tool that was mostly trained on realistic portraits will be uneven at generating abstract art, because it learned lopsided lessons.", "definition_de": "KI-Kunstgenerierungsphänomen an der Schnittstelle von Ästhetik und Computation, gekennzeichnet durch marktsegmentierung, bei der Sammler und Institutionen zwischen authentischer Handwerkskunst und technologisch-vermittelter Kunstproduktion wählen. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2703", "narrower_terms": [], "cross_domain_refs": [ "COP-0084", "CRE-0010", "CRE-0011" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q918461", "legal_classification": "descriptive_research_term" }, { "id": "ART-0040", "domain": "ART", "term_en": "Algorithm Aesthetic Exploration Guidance", "term_de": "AlgorithmAestheticExplorationGuidance", "definition_en": "A visual generation pattern in AI-mediated artistic production, characterized by a creative phenomenon where algorithmic suggestion mechanisms that recommend incremental adjustments to generation parameters enabling exploration of the models aesthetic output space. The concept emerges specifically in contexts where algorithm–aesthetic interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "KI-Kunstgenerierungsphänomen an der Schnittstelle von Ästhetik und Computation, gekennzeichnet durch algorithmische Vorschlagsmechanismen, die schrittweise Parameteränderungen empfehlen, um den ästhetischen Ausgabebereich zu erkunden. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Analytical Ratio", "narrower_terms": [], "cross_domain_refs": [ "KNO-0021" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q8366", "legal_classification": "observational_construct" }, { "id": "ART-0041", "domain": "ART", "term_en": "Algorithm Aesthetic Exploration Space", "term_de": "AlgorithmAestheticExplorationSpace", "definition_en": "A visual generation pattern in AI-mediated artistic production, characterized by an artistic interaction effect involving different styles an AI tool can actually yield—if the tool only knows how to make realistic images, that's a narrow space. The concept emerges specifically in contexts where algorithm–aesthetic interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "KI-Kunstgenerierungsphänomen an der Schnittstelle von Ästhetik und Computation, gekennzeichnet durch phänomen der stilistischen Homogenisierung, bei dem KI-Systeme auf ähnlichen Daten trainiert und somit visuelle Uniformität erzeugen. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Analytical Ratio", "narrower_terms": [], "cross_domain_refs": [ "COG-0141", "CRE-0001", "MTH-0067" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q8366", "legal_classification": "observational_construct" }, { "id": "ART-0042", "domain": "ART", "term_en": "Algorithm Aesthetic Fairness Adjustment", "term_de": "AlgorithmAestheticFairnessAdjustment", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures an AI art generation phenomenon describing a specific aesthetic-computational interaction where an aesthetic pattern reflecting fixing the AI so it addresss different art styles equally instead of favoring some. This phenomenon operates at the intersection of algorithm and aesthetic dynamics within the broader ART domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept kI-Kunstgenerierungsphänomen an der Schnittstelle von Ästhetik und Computation, gekennzeichnet durch verschiebung in der Kunstgeschichte und Kunstkritik, wenn KI-Werke nachträglich in Narrativen künstlerischer Entwicklung integriert werden. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Algorithm", "narrower_terms": [], "cross_domain_refs": [ "CRE-0001" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q8366", "legal_classification": "observational_construct" }, { "id": "ART-0043", "domain": "ART", "term_en": "Algorithm Aesthetic Imbalance Display", "term_de": "AlgorithmAestheticImbalanceDisplay", "definition_en": "A visual generation pattern in AI-mediated artistic production, characterized by a creative production dynamic arising from systematic repetition or aesthetic dominance where a generative model tends to produce outputs exhibiting convergent visual characteristics or stylistic uniformity. The concept emerges specifically in contexts where algorithm–aesthetic interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "KI-Kunstgenerierungsphänomen an der Schnittstelle von Ästhetik und Computation, gekennzeichnet durch systematische Wiederholung, bei der ein generatives Modell Ausgaben mit konvergenten visuellen Merkmalen produziert. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Algorithm", "narrower_terms": [], "cross_domain_refs": [ "AGE-0006", "CRE-0001" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q8366", "legal_classification": "descriptive_research_term" }, { "id": "ART-0044", "domain": "ART", "term_en": "Algorithm Aesthetic Preference Concentration", "term_de": "AlgorithmAestheticPreferenceConcentration", "definition_en": "A creative phenomenon manifesting as algorithmic outputs converge toward a narrow aesthetic range despite diverse user inputs and prompt variations. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "KI-Kunstgenerierungsphänomen an der Schnittstelle von Ästhetik und Computation, gekennzeichnet durch phänomen, bei dem der Marktwert KI-generierter Kunst mit Trends in Technologie-Investment und nicht mit ästhetischen Standards schwankt. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2104", "narrower_terms": [], "cross_domain_refs": [ "VIB-0200" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q8366", "legal_classification": "analytical_category" }, { "id": "ART-0045", "domain": "ART", "term_en": "Algorithm Aesthetic Preference Influence", "term_de": "AlgorithmAestheticPreferenceInfluence", "definition_en": "A creative-technical dynamic in AI image synthesis, observable through a creative phenomenon in which the way an AI tool's built-in aesthetic preferences subtly push most users in documented contexts' work toward a similar style, even when they're trying to be different. Distinguished from adjacent concepts by its focus on the specific mechanism through which algorithm manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "KI-Kunstgenerierungsphänomen an der Schnittstelle von Ästhetik und Computation, gekennzeichnet durch verschiebung kultureller Repräsentation, wenn KI-Systeme bevorzugte ästhetische Normen ihrer Trainingsdaten perpetuieren. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Algorithm", "narrower_terms": [], "cross_domain_refs": [ "AGE-0007", "AGE-0067", "CRE-0001" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q8366", "legal_classification": "systematic_classification" }, { "id": "ART-0046", "domain": "ART", "term_en": "Algorithm-Based Aesthetic Judgment", "term_de": "Algorithm-basedAestheticUrteil", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures an AI art generation phenomenon describing a specific aesthetic-computational interaction where an AI system ranking images based on whether they 'look good' according to criteria embedded in the algorithm. This phenomenon operates at the intersection of algorithm and based dynamics within the broader ART domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept kI-Kunstgenerierungsphänomen an der Schnittstelle von Ästhetik und Computation, gekennzeichnet durch debatte über künstlerische Hierarchie, ob Konzeption wertvoller ist als handwerkliche Ausführung. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Algorithm", "narrower_terms": [], "cross_domain_refs": [ "DAT-0039" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q8366", "legal_classification": "observational_construct" }, { "id": "ART-0047", "domain": "ART", "term_en": "Algorithm-Based Artistic Copyright", "term_de": "Algorithm-basedArtisticCopyright", "definition_en": "A visual generation pattern in AI-mediated artistic production, characterized by a creative phenomenon observed when copyright claims are based on whether an algorithm decides an image is original enough, rather than human curators or artists making the call. The concept emerges specifically in contexts where algorithm–based interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "KI-Kunstgenerierungsphänomen an der Schnittstelle von Ästhetik und Computation, gekennzeichnet durch phänomen der kunsthistorischen Revision, wenn etablierte künstlerische Kanonisierung durch KI-generierte Alternativen hinterfragt wird. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Algorithm", "narrower_terms": [], "cross_domain_refs": [ "AGE-0006", "AGE-0007" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q8366", "legal_classification": "empirical_phenomenon_label" }, { "id": "ART-0048", "domain": "ART", "term_en": "Algorithm-Based Artistic Judgment", "term_de": "Algorithm-basedArtisticUrteil", "definition_en": "A creative-technical dynamic in AI image synthesis, observable through a creative phenomenon characterized by an AI deciding whether something counts as 'real art' based on measurable features instead of a human curator or artist saying so. Distinguished from adjacent concepts by its focus on the specific mechanism through which algorithm manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "KI-Kunstgenerierungsphänomen an der Schnittstelle von Ästhetik und Computation, gekennzeichnet durch verschiebung in der Kunstmarkt-Intermediation, wenn Algorithmen statt menschlicher Galeristen Künstler-Audience-Matching vornehmen. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Algorithm", "narrower_terms": [], "cross_domain_refs": [ "AGE-0006", "AGE-0007" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q8366", "legal_classification": "observational_construct" }, { "id": "ART-0049", "domain": "ART", "term_en": "Algorithm-Human Artistic Collaboration", "term_de": "Algorithm-humanArtisticCollaboration", "definition_en": "A visual generation pattern in AI-mediated artistic production, characterized by the interaction between a person's creative intention and an AI tool's ability to execute it—sometimes aligned, sometimes surprising. The concept emerges specifically in contexts where algorithm–human interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "KI-Kunstgenerierungsphänomen an der Schnittstelle von Ästhetik und Computation, gekennzeichnet durch rechtliche Unklarheit bezüglich Modifizierung oder Remixing von KI-generierten Werken durch Dritte. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Analytical Ratio", "narrower_terms": [], "cross_domain_refs": [ "RPH-2451", "PLY-0032" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q8366", "legal_classification": "observational_construct" }, { "id": "ART-0050", "domain": "ART", "term_en": "Algorithmic Aesthetic Diversity Promotion", "term_de": "AlgorithmicAestheticDiversityPromotion", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies an AI art generation phenomenon describing a specific aesthetic-computational interaction where features built into an AI tool to push the system toward generating different styles instead of staying in its safe zone. This phenomenon operates at the intersection of algorithmic and aesthetic dynamics within the broader ART domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept kI-Kunstgenerierungsphänomen an der Schnittstelle von Ästhetik und Computation, gekennzeichnet durch phänomen, bei dem institutionelle Kunstaustellung KI-Werke als Mittel zur Selbstvermarktung oder Relevanzdemonstration verwenden. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Algorithm", "narrower_terms": [], "cross_domain_refs": [ "CRE-0017" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q8366", "legal_classification": "descriptive_research_term" }, { "id": "ART-0051", "domain": "ART", "term_en": "Algorithmic Art Curation Effect", "term_de": "AlgorithmicArtCurationEffekt", "definition_en": "A creative production dynamic reflecting how AI recommendation systems shape which art people see and value — algorithms quietly deciding what looks good and what gets attention. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "KI-Kunstgenerierungsphänomen an der Schnittstelle von Ästhetik und Computation, gekennzeichnet durch debatte über künstlerische Materialität, wenn immaterielle, algorithmen-generierte Werke traditionelle physische Kunstmedien verdrängen. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-3901", "narrower_terms": [], "cross_domain_refs": [ "CRE-0018", "PHO-0007" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q8366", "legal_classification": "systematic_classification" }, { "id": "ART-0052", "domain": "ART", "term_en": "Algorithmic Curation Aesthetic Effect", "term_de": "AlgorithmicCurationAestheticEffekt", "definition_en": "A visual generation pattern in AI-mediated artistic production, characterized by a creative production dynamic involving a platform's algorithm for showing art to people subtly trains the community to prefer the same style, because that's what gets promoted. The concept emerges specifically in contexts where algorithmic–curation interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "KI-Kunstgenerierungsphänomen an der Schnittstelle von Ästhetik und Computation, gekennzeichnet durch verschiebung der künstlerischen Bildungserwartung, wenn technische Fertigkeiten weniger wichtig sind als Konzeptualfähigkeit. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Psychological Effect", "narrower_terms": [], "cross_domain_refs": [ "CRE-0017", "CRE-0018", "PHO-0004" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q8366", "legal_classification": "analytical_category" }, { "id": "ART-0053", "domain": "ART", "term_en": "Algorithmic Curation Aesthetic Homogenization", "term_de": "AlgorithmicCurationAestheticHomogenization", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures an AI art generation phenomenon describing a specific aesthetic-computational interaction where an aesthetic pattern observed when unintended homogenization of generated artworks where retrieval or ranking algorithms preferentially surface visually similar or aesthetically conformist outputs. This phenomenon operates at the intersection of algorithmic and curation dynamics within the broader ART domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept kI-Kunstgenerierungsphänomen an der Schnittstelle von Ästhetik und Computation, gekennzeichnet durch unbeabsichtigte Homogenisierung von generierten Kunstwerken, bei der Algorithmen visuell ähnliche Ausgaben bevorzugt anzeigen. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Analytical Ratio", "narrower_terms": [], "cross_domain_refs": [ "CRE-0017", "CRE-0018", "PHO-0004" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q8366", "legal_classification": "systematic_classification" }, { "id": "ART-0054", "domain": "ART", "term_en": "Algorithmic Imbalance In Art Creation", "term_de": "AlgorithmicImbalanceinArtCreation", "definition_en": "A creative-technical dynamic in AI image synthesis, observable through an artistic interaction effect reflecting aI accompanies some art styles well but others poorly. Capabilities aren't evenly distributed across forms. Distinguished from adjacent concepts by its focus on the specific mechanism through which algorithmic manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "KI-Kunstgenerierungsphänomen an der Schnittstelle von Ästhetik und Computation, gekennzeichnet durch marktsegmentierung zwischen spekulativen KI-Kunstinvestitionen und traditionellem künstlerischem Sammlertum. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Algorithm", "narrower_terms": [], "cross_domain_refs": [ "AGE-0006", "DES-0073", "DAT-0015" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q8366", "legal_classification": "observational_construct" }, { "id": "ART-0055", "domain": "ART", "term_en": "Algorithmic Imbalance In Generated Aesthetics", "term_de": "AlgorithmicImbalanceinGeneratedAesthetics", "definition_en": "A visual generation pattern in AI-mediated artistic production, characterized by an aesthetic pattern reflecting the visual signature of an AI tool—its 'tells'—that signal it's algorithm-made rather than human-made. The concept emerges specifically in contexts where algorithmic–imbalance interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "KI-Kunstgenerierungsphänomen an der Schnittstelle von Ästhetik und Computation, gekennzeichnet durch debatte über künstlerische Intention und Expression, wenn Algorithmen teilweise oder vollständig Kreationsprozesse determinieren. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Algorithm", "narrower_terms": [], "cross_domain_refs": [ "GAM-0074" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q8366", "legal_classification": "descriptive_research_term" }, { "id": "ART-0056", "domain": "ART", "term_en": "Art Gallery AI Work Categorization", "term_de": "ArtGalleryaiWorkCategorization", "definition_en": "A creative-technical dynamic in AI image synthesis, observable through a creative phenomenon in which taxonomic and historical contextualization challenges when positioning AI-generated artworks within established gallery classification systems and curatorial frameworks. Distinguished from adjacent concepts by its focus on the specific mechanism through which art manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "KI-Kunstgenerierungsphänomen an der Schnittstelle von Ästhetik und Computation, gekennzeichnet durch taxonomische und historische Kontextualisierungsforderungen bei der Positionierung KI-generierter Kunstwerke im Galerieklassifikationssystem. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "AI Art Generation", "narrower_terms": [], "cross_domain_refs": [ "CRE-0069", "SOM-0042", "ELR-0092" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q735", "legal_classification": "observational_construct" }, { "id": "ART-0057", "domain": "ART", "term_en": "Art Style Homogenization Through AI", "term_de": "ArtStyleHomogenizationThroughai", "definition_en": "A creative-technical dynamic in AI image synthesis, observable through a creative production dynamic where widespread adoption of a single AI tool accompanies visual and stylistic homogeneity across generated artworks. Distinguished from adjacent concepts by its focus on the specific mechanism through which art manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "KI-Kunstgenerierungsphänomen an der Schnittstelle von Ästhetik und Computation, gekennzeichnet durch verschiebung künstlerischer Karrierepfade, wenn technische Kompetenz mit KI-Werkzeugen wesentlicher wird als traditionelle Ausbildung. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "AI Art Generation", "narrower_terms": [], "cross_domain_refs": [ "CRE-0055" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q735", "legal_classification": "observational_construct" }, { "id": "ART-0058", "domain": "ART", "term_en": "Artistic Attribution System Design", "term_de": "ArtisticAttributionSystemDesign", "definition_en": "A visual generation pattern in AI-mediated artistic production, characterized by a creative phenomenon in which display and labeling systems communicating the identities of prompts, algorithmic tools, or human artists in AI-assisted artwork presentation contexts. The concept emerges specifically in contexts where artistic–attribution interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "KI-Kunstgenerierungsphänomen an der Schnittstelle von Ästhetik und Computation, gekennzeichnet durch anzeige- und Kennzeichnungssysteme, die Identitäten von Eingabeaufforderungen und Künstlern bei KI-Kunstwerk-Präsentation vermitteln. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2104", "narrower_terms": [], "cross_domain_refs": [ "MSC-0008" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q185925", "legal_classification": "descriptive_research_term" }, { "id": "ART-0059", "domain": "ART", "term_en": "Artistic Authorship Redistribution", "term_de": "ArtisticAuthorshipRedistribution", "definition_en": "An aesthetic pattern reflecting when AI is involved, who gets credit shifts: instead of the painter getting all the credit, now the prompter, the algorithm, and the training data might all matter. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "KI-Kunstgenerierungsphänomen an der Schnittstelle von Ästhetik und Computation, gekennzeichnet durch phänomen der visuellen Vereinheitlichung, bei dem KI-Systeme trainiert auf dominanten Kunsttrends ähnliche visuelle Qualitäten produzieren. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-2701", "narrower_terms": [], "cross_domain_refs": [ "CRE-0036" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q735", "legal_classification": "empirical_phenomenon_label" }, { "id": "ART-0060", "domain": "ART", "term_en": "Artistic Community AI Acceptance", "term_de": "ArtisticCommunityaiAcceptance", "definition_en": "The gradual institutional and cultural shift toward recognition of AI-generated art as a legitimate form of artistic expression. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "KI-Kunstgenerierungsphänomen an der Schnittstelle von Ästhetik und Computation, gekennzeichnet durch marktunsicherheit bei KI-Kunstpreisen, da keine etablierten Bewertungsmaßstäbe für technologisch-generierte Werke existieren. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2701", "narrower_terms": [], "cross_domain_refs": [ "AED-0012", "CON-0021", "CON-0026" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q735", "legal_classification": "systematic_classification" }, { "id": "ART-0061", "domain": "ART", "term_en": "Artistic Community AI Integration Mismatch", "term_de": "ArtisticCommunityaiIntegrationMismatch", "definition_en": "A creative-technical dynamic in AI image synthesis, observable through an aesthetic pattern in which cultural and institutional resistance or ambivalence toward integrating AI-generated outputs into traditional art curation, exhibition, and valuation practices. Distinguished from adjacent concepts by its focus on the specific mechanism through which artistic manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "KI-Kunstgenerierungsphänomen an der Schnittstelle von Ästhetik und Computation, gekennzeichnet durch kulturelle und institutionelle Widerstände oder Ambivalenz zur Integration KI-generierter Outputs in traditionelle Kunstpraktiken. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Analytical Ratio", "narrower_terms": [], "cross_domain_refs": [ "COP-0045", "CUS-0010", "TRA-0014" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q735", "legal_classification": "systematic_classification" }, { "id": "ART-0062", "domain": "ART", "term_en": "Artistic Community Response To AI", "term_de": "ArtisticCommunityResponsetoai", "definition_en": "A visual generation pattern in AI-mediated artistic production, characterized by a creative phenomenon in which range of artist reactions to generative AI — from enthusiastic adoption to complete refusal, with many positions in between. The concept emerges specifically in contexts where artistic–community interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "KI-Kunstgenerierungsphänomen an der Schnittstelle von Ästhetik und Computation, gekennzeichnet durch phänomen des Kunstgeschichte-Neischreibens, wenn KI-Werke als Analyse-Tools für etablierte künstlerische Bewegungen fungieren. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "AI Art Generation", "narrower_terms": [], "cross_domain_refs": [ "COG-0069" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q735", "legal_classification": "empirical_phenomenon_label" }, { "id": "ART-0063", "domain": "ART", "term_en": "Artistic Copyright AI Training Data", "term_de": "ArtisticCopyrightaiTrainingData", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies an AI art generation phenomenon describing a specific aesthetic-computational interaction where a creative phenomenon reflecting the unresolved legal question around whether artists whose work trained an AI tool get paid or have a say in what it accompanies. This phenomenon operates at the intersection of artistic and copyright dynamics within the broader ART domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept kI-Kunstgenerierungsphänomen an der Schnittstelle von Ästhetik und Computation, gekennzeichnet durch verschiebung der Kunstausbildung, wenn Lehrpläne KI-Tools integrieren, um künstlerische Praxis neu zu definieren. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Model Training", "narrower_terms": [], "cross_domain_refs": [ "WRK-0036" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q1297", "legal_classification": "systematic_classification" }, { "id": "ART-0064", "domain": "ART", "term_en": "Artistic Copyright In AI Generated Works", "term_de": "artistic copyright in ai generated works", "definition_en": "A creative-technical dynamic in AI image synthesis, observable through an artistic interaction effect arising from the legal question of whether AI-generated images qualify for copyright protection and to which stakeholder such protections attach. Distinguished from adjacent concepts by its focus on the specific mechanism through which artistic manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "KI-Kunstgenerierungsphänomen an der Schnittstelle von Ästhetik und Computation, gekennzeichnet durch rechtliche Fragen zu intellektueller Eigenschaft, wenn KI-Systeme stilistische Merkmale lebender oder verstorbener Künstler imitieren. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "AI Art Generation", "narrower_terms": [], "cross_domain_refs": [ "MUS-0098" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q1297", "legal_classification": "systematic_classification" }, { "id": "ART-0065", "domain": "ART", "term_en": "Artistic Expression Algorithm Interaction", "term_de": "ArtisticExpressionAlgorithmInteraction", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures an AI art generation phenomenon describing a specific aesthetic-computational interaction where a creative production dynamic involving ongoing back-and-forth between creator's vision and AI's capabilities. Each shapes the other. This phenomenon operates at the intersection of artistic and expression dynamics within the broader ART domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept kI-Kunstgenerierungsphänomen an der Schnittstelle von Ästhetik und Computation, gekennzeichnet durch phänomen der stilistischen Volatilität, bei dem schnelle Iterationen KI-generierter Kunstwerke künstlerische Trends destabilisieren. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "RPH-3901", "narrower_terms": [], "cross_domain_refs": [ "MUS-0007" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q8366", "legal_classification": "empirical_phenomenon_label" }, { "id": "ART-0066", "domain": "ART", "term_en": "Artistic Expression Algorithmic Constraints", "term_de": "ArtisticExpressionAlgorithmicConstraints", "definition_en": "A creative phenomenon observed when an AI tool has limits built in—it might refuse to yield certain content, or find hands hard to draw, which shapes what artists can express. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "KI-Kunstgenerierungsphänomen an der Schnittstelle von Ästhetik und Computation, gekennzeichnet durch markteffizienzfrage, ob künstlerische Wertschöpfung durch technische Verfahren oder konzeptionelle Originalität definiert wird. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-3901", "narrower_terms": [], "cross_domain_refs": [ "CON-0053" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q8366", "legal_classification": "analytical_category" }, { "id": "ART-0067", "domain": "ART", "term_en": "Artistic Expression Through Algorithmic Guidance", "term_de": "ArtisticExpressionThroughAlgorithmicGuidance", "definition_en": "An artistic interaction effect characterized by using an AI tool's prompts and suggestions as creative help, the way an artist might use a canvas or clay.", "definition_de": "KI-Kunstgenerierungsphänomen an der Schnittstelle von Ästhetik und Computation, gekennzeichnet durch debatte über künstlerische Verantwortlichkeit, wenn KI-Kunstwerke in hochkontextuellen oder politisch-sensiblen Umgebungen exhibited werden. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-2104", "narrower_terms": [], "cross_domain_refs": [ "CRE-0111" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q8366", "legal_classification": "empirical_phenomenon_label" }, { "id": "ART-0068", "domain": "ART", "term_en": "Artistic Innovation Through AI Collaboration", "term_de": "ArtisticInnovationThroughaiCollaboration", "definition_en": "A creative-technical dynamic in AI image synthesis, observable through a creative production dynamic observed when new artistic approaches when artists use AI as a creative tool. Collaboration opens new possibilities. Distinguished from adjacent concepts by its focus on the specific mechanism through which artistic manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "KI-Kunstgenerierungsphänomen an der Schnittstelle von Ästhetik und Computation, gekennzeichnet durch phänomen der Kunstdemokratisierung durch KI, wenn Millionen Nutzer künstlerische Ausdrucksformen schaffen können, die früher Fachleuten vorbehalten waren. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Analytical Ratio", "narrower_terms": [], "cross_domain_refs": [ "TEM-0186" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q219644", "legal_classification": "systematic_classification" }, { "id": "ART-0069", "domain": "ART", "term_en": "Artistic Intent Disambiguation In AI", "term_de": "ArtisticIntentDisambiguationinai", "definition_en": "A visual generation pattern in AI-mediated artistic production, characterized by a creative production dynamic observed when aI systems in interpreting semantically ambiguous prompts to match user intent and desired outcomes. The concept emerges specifically in contexts where artistic–intent interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "KI-Kunstgenerierungsphänomen an der Schnittstelle von Ästhetik und Computation, gekennzeichnet durch verschiebung des künstlerischen Lernens, wenn Anfänger unmittelbar ästhetisch-komplexe Werke generieren können ohne technische Grundlagen. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2104", "narrower_terms": [], "cross_domain_refs": [ "RPH-3902" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q735", "legal_classification": "analytical_category" }, { "id": "ART-0070", "domain": "ART", "term_en": "Artistic Intent In Generated Work", "term_de": "ArtisticIntentinGeneratedWork", "definition_en": "The gap between what someone meant to involve and what the AI actually generated—sometimes the gap is the whole point. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "KI-Kunstgenerierungsphänomen an der Schnittstelle von Ästhetik und Computation, gekennzeichnet durch rechtliche Unklarheit bei Lizenzverletzungen, wenn KI-Systeme trainiert sind auf materiellen, deren Lizenzstatus unklar ist. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-3101", "narrower_terms": [], "cross_domain_refs": [ "CRE-0140" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q735", "legal_classification": "analytical_category" }, { "id": "ART-0071", "domain": "ART", "term_en": "Artistic Uniqueness Algorithmic Measure", "term_de": "ArtisticUniquenessAlgorithmicMeasure", "definition_en": "A creative-technical dynamic in AI image synthesis, observable through a creative phenomenon arising from trying to score how 'original' an AI artwork is based on numbers and features instead of having humans decide. Distinguished from adjacent concepts by its focus on the specific mechanism through which artistic manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "KI-Kunstgenerierungsphänomen an der Schnittstelle von Ästhetik und Computation, gekennzeichnet durch phänomen der Kunstmarktmanikulaiton durch Spekulation, wenn Investoren KI-Kunstpreise künstlich antreiben. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Algorithm", "narrower_terms": [], "cross_domain_refs": [ "SPR-0085" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q8366", "legal_classification": "observational_construct" }, { "id": "ART-0072", "domain": "ART", "term_en": "Artistic Uniqueness In AI Generation", "term_de": "ArtisticUniquenessinaiGeneration", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures an AI art generation phenomenon describing a specific aesthetic-computational interaction where a creative phenomenon manifesting as even though AI often accompanies similar-looking work, people find ways to make their pieces feel personal and distinct. This phenomenon operates at the intersection of artistic and uniqueness dynamics within the broader ART domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription. Observed phenomenon documented in human-AI interaction research.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept kI-Kunstgenerierungsphänomen an der Schnittstelle von Ästhetik und Computation, gekennzeichnet durch debatte über künstlerische Authentizität, ob prozessuale oder ästhetische Dimensionen Kunstwertigkeit konstituieren. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Analytical Ratio", "narrower_terms": [], "cross_domain_refs": [ "CON-0049" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q735", "legal_classification": "analytical_category" }, { "id": "ART-0073", "domain": "ART", "term_en": "Artwork Attribution To AI System", "term_de": "ArtworkAttributiontoaiSystem", "definition_en": "A visual generation pattern in AI-mediated artistic production, characterized by labeling an artwork as 'made by the proprietary image generation algorithm' to acknowledge the tool's role in creating it. The concept emerges specifically in contexts where artwork–attribution interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "KI-Kunstgenerierungsphänomen an der Schnittstelle von Ästhetik und Computation, gekennzeichnet durch verschiebung in der Kunstkritik, wenn Fachleute neue Vokabulare entwickeln können für KI-spezifische künstlerische Kategorien. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "AI Art Generation", "narrower_terms": [], "cross_domain_refs": [ "ASE-0011", "ASE-0012", "ASE-0058" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q735", "legal_classification": "systematic_classification" }, { "id": "ART-0074", "domain": "ART", "term_en": "Gallery Representation For AI Art", "term_de": "GalleryRepresentationForaiArt", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures an AI art generation phenomenon describing a specific aesthetic-computational interaction where a creative production dynamic characterized by getting AI artworks into respected galleries and exhibitions so they're seen as legitimate art, not just computer graphics. This phenomenon operates at the intersection of gallery and representation dynamics within the broader ART domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept kI-Kunstgenerierungsphänomen an der Schnittstelle von Ästhetik und Computation, gekennzeichnet durch phänomen der Kunstgeschichte-Divergenz, wenn KI-generierte Werke keine organische Kontinuität mit bisherigen künstlerischen Bewegungen aufweisen. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "AI Art Generation", "narrower_terms": [], "cross_domain_refs": [ "ASE-0066", "COG-0176", "CRE-0069" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q735", "legal_classification": "empirical_phenomenon_label" }, { "id": "ART-0075", "domain": "ART", "term_en": "Generated Art Market Integration", "term_de": "GeneratedArtMarketIntegration", "definition_en": "A creative-technical dynamic in AI image synthesis, observable through an aesthetic pattern involving commercial market processes including pricing, provenance, and secondary sales infrastructure for AI-generated artworks operating within or adjacent to traditional art markets. Distinguished from adjacent concepts by its focus on the specific mechanism through which generated manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "KI-Kunstgenerierungsphänomen an der Schnittstelle von Ästhetik und Computation, gekennzeichnet durch kommerzielle Marktprozesse einschließlich Preisgestaltung und Sekundärverkaufsinfrastruktur für KI-generierte Kunstwerke. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Analytical Ratio", "narrower_terms": [], "cross_domain_refs": [ "SPA-0088", "SWE-0070", "PHO-0044" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q735", "legal_classification": "empirical_phenomenon_label" }, { "id": "ART-0076", "domain": "ART", "term_en": "Generated Artwork Attribution System", "term_de": "GeneratedArtworkAttributionSystem", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies an AI art generation phenomenon describing a specific aesthetic-computational interaction where an aesthetic pattern in which the way a museum or gallery decides how to credit an AI artwork—what information to include and how to present it. This phenomenon operates at the intersection of generated and artwork dynamics within the broader ART domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept kI-Kunstgenerierungsphänomen an der Schnittstelle von Ästhetik und Computation, gekennzeichnet durch rechtliche Klärung der Kunsturheberschaft, wenn Plattformen automatisiert KI-Kunstwerke generieren und Nutzer sie als ihre eigenen präsentieren. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "AI Art Generation", "narrower_terms": [], "cross_domain_refs": [ "CRE-0038", "FIC-0083", "ASE-0095" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q735", "legal_classification": "systematic_classification" }, { "id": "ART-0077", "domain": "ART", "term_en": "Generated Artwork Provenance Documentation", "term_de": "GeneratedArtworkProvenanceDocumentation", "definition_en": "An artistic interaction effect where keeping a clear record of how an AI artwork was made: which tool, which model version, what prompt, when it was created. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "KI-Kunstgenerierungsphänomen an der Schnittstelle von Ästhetik und Computation, gekennzeichnet durch debatte über künstlerische Hierarchie, ob KI-generierte Werke äquivalent zu handwerklich-generierten Kunstformen sind. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2104", "narrower_terms": [], "cross_domain_refs": [ "TEW-0015" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q735", "legal_classification": "systematic_classification" }, { "id": "ART-0078", "domain": "ART", "term_en": "Generated Artwork Reproducibility Control", "term_de": "GeneratedArtworkReproducibilityControl", "definition_en": "A creative-technical dynamic in AI image synthesis, observable through a creative phenomenon involving setting a random seed so the exact same image can be generated again, which matters for editions or proofs. Distinguished from adjacent concepts by its focus on the specific mechanism through which generated manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "KI-Kunstgenerierungsphänomen an der Schnittstelle von Ästhetik und Computation, gekennzeichnet durch phänomen der kulturellen Homogenisierung, wenn KI-Systeme unterrepräsentierte kulturelle Kunsttraditionen marginalisieren. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "AI Art Generation", "narrower_terms": [], "cross_domain_refs": [ "CON-0071", "ROB-0054" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q735", "legal_classification": "empirical_phenomenon_label" }, { "id": "ART-0079", "domain": "ART", "term_en": "Generated Artwork Reproducibility Paradox", "term_de": "GeneratedArtworkReproducibilityParadox", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies an AI art generation phenomenon describing a specific aesthetic-computational interaction where a creative phenomenon involving the contradiction: AI images can be copied infinitely, but traditional art is valued for being unique. This phenomenon operates at the intersection of generated and artwork dynamics within the broader ART domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept kI-Kunstgenerierungsphänomen an der Schnittstelle von Ästhetik und Computation, gekennzeichnet durch verschiebung künstlerischer Identität, wenn Künstler KI-Werkzeuge verwenden und unklar wird, wo menschliche vs. algorithmische Beiträge liegen. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Logical Paradox", "narrower_terms": [], "cross_domain_refs": [ "LNG-0020" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q735", "legal_classification": "empirical_phenomenon_label" }, { "id": "ART-0080", "domain": "ART", "term_en": "Generated Artwork Uniqueness Assessment", "term_de": "GeneratedArtworkUniquenessAssessment", "definition_en": "An aesthetic pattern involving figuring out whether an AI artwork is one-of-a-kind or could be easily reproduced by someone with the same prompt. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "KI-Kunstgenerierungsphänomen an der Schnittstelle von Ästhetik und Computation, gekennzeichnet durch marktunsicherheit hinsichtlich langfristiger Wertstabilität KI-generierter Kunstwerke. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2104", "narrower_terms": [], "cross_domain_refs": [ "ASE-0011", "ELR-0024", "ASE-0053" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q210028", "legal_classification": "descriptive_research_term" }, { "id": "ART-0081", "domain": "ART", "term_en": "Generated Image Authenticity Claim", "term_de": "GeneratedImageAuthenticityClaim", "definition_en": "A creative-technical dynamic in AI image synthesis, observable through a creative production dynamic observed when saying 'I made this with AI' instead of hiding that fact, and dealing with people who might not respect that choice. Distinguished from adjacent concepts by its focus on the specific mechanism through which generated manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "KI-Kunstgenerierungsphänomen an der Schnittstelle von Ästhetik und Computation, gekennzeichnet durch debatte über künstlerische Verantwortung beim Training von KI-Systemen auf copyrightgeschütztem Kunstmaterial. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "AI Art Generation", "narrower_terms": [], "cross_domain_refs": [ "ETH-0007" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "ART-0082", "domain": "ART", "term_en": "Generated Image Authenticity Verification", "term_de": "GeneratedImageAuthenticityVerification", "definition_en": "A creative-technical dynamic in AI image synthesis, observable through a creative production dynamic reflecting methods to prove whether a digital image came from an AI tool or was made by a human photographer. Distinguished from adjacent concepts by its focus on the specific mechanism through which generated manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "KI-Kunstgenerierungsphänomen an der Schnittstelle von Ästhetik und Computation, gekennzeichnet durch phänomen der Kunstausthenitizierung, wenn digital-generierte Werke typischerweise schwerer von handwerklich-generierten zu unterscheiden sind. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "AI Art Generation", "narrower_terms": [], "cross_domain_refs": [ "CRE-0041", "CRE-0042", "LIN-0096" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "ART-0083", "domain": "ART", "term_en": "Generated Image Editing Detection", "term_de": "GeneratedImageEditingDetection", "definition_en": "A visual generation pattern in AI-mediated artistic production, characterized by an aesthetic pattern reflecting tools that can tell if an AI image was edited afterward—which matters for authenticity and for detecting fake art. The concept emerges specifically in contexts where generated–image interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "KI-Kunstgenerierungsphänomen an der Schnittstelle von Ästhetik und Computation, gekennzeichnet durch verschiebung der Kunstmarkt-Machtdynamiken, wenn Technologie-Unternehmen künstlerische Distributionswege kontrollieren. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Detection System", "narrower_terms": [], "cross_domain_refs": [ "ASE-0002" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "ART-0084", "domain": "ART", "term_en": "Generated Image Provenance Tracking", "term_de": "GeneratedImageProvenanceTracking", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies an artistic interaction effect involving a permanent record showing where an AI image came from, who prompted it, when it was made, and who currently owns it. Identifiable through systematic behavioral analysis and pattern recognition. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept kI-Kunstgenerierungsphänomen an der Schnittstelle von Ästhetik und Computation, gekennzeichnet durch rechtliche Unklarheit bei Modifizierungen oder Fälschung KI-generierter Kunstwerke. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "RPH-2104", "narrower_terms": [], "cross_domain_refs": [ "ROB-0090" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "ART-0085", "domain": "ART", "term_en": "Generated Image Quality Prediction", "term_de": "GeneratedImageQualityPrediction", "definition_en": "Predictive algorithms to estimate the probable quality of AI-generated images based on prompt characteristics. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "KI-Kunstgenerierungsphänomen an der Schnittstelle von Ästhetik und Computation, gekennzeichnet durch phänomen der ästhetischen Inflationierung, wenn massive Mengen KI-generierter Kunstwerke den Markt überlasten. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2104", "narrower_terms": [], "cross_domain_refs": [ "COP-0003" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "ART-0086", "domain": "ART", "term_en": "Generated Image Quality Variance", "term_de": "GeneratedImageQualityVariance", "definition_en": "The inherent variability in output quality when identical prompts are processed through generative AI systems. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "KI-Kunstgenerierungsphänomen an der Schnittstelle von Ästhetik und Computation, gekennzeichnet durch debatte über künstlerische Verantwortung, wenn KI-Systeme bei Generierung verdienen, aber Kunstschaffende nicht angemessen kompensiert werden. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2104", "narrower_terms": [], "cross_domain_refs": [ "STE-0006" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "ART-0087", "domain": "ART", "term_en": "Generated Image Source Attribution", "term_de": "GeneratedImageSourceAttribution", "definition_en": "A creative phenomenon characterized by listing the training data sources that influenced an AI image, so people know what the tool learned from.", "definition_de": "KI-Kunstgenerierungsphänomen an der Schnittstelle von Ästhetik und Computation, gekennzeichnet durch verschiebung der Kunstausbildung, wenn institutionelle Kunstschulen ihre Curricula adapten können für KI-Realitäten. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-2703", "narrower_terms": [], "cross_domain_refs": [ "CRE-0008" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "ART-0088", "domain": "ART", "term_en": "Generated Image Source Identification", "term_de": "GeneratedImageSourceIdentification", "definition_en": "A creative-technical dynamic in AI image synthesis, observable through an artistic interaction effect arising from technical attribution and forensic analysis methods for determining generative model identity, training data sources, or prompt provenance from artistic outputs. Distinguished from adjacent concepts by its focus on the specific mechanism through which generated manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "KI-Kunstgenerierungsphänomen an der Schnittstelle von Ästhetik und Computation, gekennzeichnet durch technische Zuordnungs- und forensische Analysemethoden zur Bestimmung von Modellidentität und Trainingsdatenquellen. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "AI Art Generation", "narrower_terms": [], "cross_domain_refs": [ "CRE-0098" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "ART-0089", "domain": "ART", "term_en": "Generated Image Style Recognition", "term_de": "GeneratedImageStyleRecognition", "definition_en": "A visual generation pattern in AI-mediated artistic production, characterized by a creative phenomenon where recognizing which AI tool made an image by its visual fingerprint or distinctive look. The concept emerges specifically in contexts where generated–image interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "KI-Kunstgenerierungsphänomen an der Schnittstelle von Ästhetik und Computation, gekennzeichnet durch rechtliche Klärungsbedarf bei Gewährleistung künstlerischer Authentizität und Originalität von KI-Werken. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Pattern Recognition", "narrower_terms": [], "cross_domain_refs": [ "GAM-0045", "MSC-0021", "MUS-0024" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q3966", "legal_classification": "empirical_phenomenon_label" }, { "id": "ART-0090", "domain": "ART", "term_en": "Generated Image Watermarking Challenge", "term_de": "GeneratedImageWatermarkingChallenge", "definition_en": "A visual generation pattern in AI-mediated artistic production, characterized by a creative production dynamic reflecting difficulty of permanently marking AI-generated images. Watermarks get removed or hidden easily. The concept emerges specifically in contexts where generated–image interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "KI-Kunstgenerierungsphänomen an der Schnittstelle von Ästhetik und Computation, gekennzeichnet durch debatte über künstlerische Verantwortung, wenn KI-Systeme persönliche Kunststile oder Signaturen ohne Genehmigung reproduzieren. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "AI Art Generation", "narrower_terms": [], "cross_domain_refs": [ "MKT-0005", "ROB-0256" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "ART-0091", "domain": "ART", "term_en": "Generative Art Authentication", "term_de": "GenerativeArtAuthentication", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies an AI art generation phenomenon describing a specific aesthetic-computational interaction where an aesthetic pattern characterized by proving that an artwork was actually generated by an AI tool and hasn't been secretly edited or swapped out. This phenomenon operates at the intersection of generative and art dynamics within the broader ART domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept kI-Kunstgenerierungsphänomen an der Schnittstelle von Ästhetik und Computation, gekennzeichnet durch phänomen der künstlerischen Gatekeeping-Verschiebung, wenn traditionelle Kunstinstitutionen an Gatekeeping-Macht verlieren. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "AI Art Generation", "narrower_terms": [], "cross_domain_refs": [ "BEH-0042", "COG-0093", "CRE-0071" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q735", "legal_classification": "observational_construct" }, { "id": "ART-0092", "domain": "ART", "term_en": "Generative Art Conceptual Framework", "term_de": "GenerativeArtConceptualFramework", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures an AI art generation phenomenon describing a specific aesthetic-computational interaction where a creative phenomenon observed when the way critics, artists, and institutions think about and judge AI-generated art. This phenomenon operates at the intersection of generative and art dynamics within the broader ART domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept kI-Kunstgenerierungsphänomen an der Schnittstelle von Ästhetik und Computation, gekennzeichnet durch verschiebung künstlerischer Wertschöpfung, wenn technische Intermediäre (KI-Plattformen) zwischen Künstlern und Publikum treten. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Analytical Framework", "narrower_terms": [], "cross_domain_refs": [ "SCR-0060" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q735", "legal_classification": "empirical_phenomenon_label" }, { "id": "ART-0093", "domain": "ART", "term_en": "Generative Art Medium Acceptance", "term_de": "GenerativeArtMediumAcceptance", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies an AI art generation phenomenon describing a specific aesthetic-computational interaction where a creative production dynamic where a cultural move from 'AI art isn't real art' to 'OK, this is a legitimate medium like digital art or photography.'. This phenomenon operates at the intersection of generative and art dynamics within the broader ART domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept kI-Kunstgenerierungsphänomen an der Schnittstelle von Ästhetik und Computation, gekennzeichnet durch marktsegmentierung zwischen spekulativen KI-Kunstinvestitionen und langfristigen künstlerischen Wertkollektionen. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "AI Art Generation", "narrower_terms": [], "cross_domain_refs": [ "ROB-0255", "CRE-0071", "MKT-0043" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q735", "legal_classification": "systematic_classification" }, { "id": "ART-0094", "domain": "ART", "term_en": "Generative Art Medium Legitimacy", "term_de": "GenerativeArtMediumLegitimacy", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures an AI art generation phenomenon describing a specific aesthetic-computational interaction where a creative production dynamic observed when whether AI-generated art gets the same respect and value as traditional media in galleries, museums, and the market. This phenomenon operates at the intersection of generative and art dynamics within the broader ART domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept kI-Kunstgenerierungsphänomen an der Schnittstelle von Ästhetik und Computation, gekennzeichnet durch rechtliche Fragen zu Kunstwerk-Authentifizierung und Provenienzverifizierung für KI-generierte Werke. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "AI Art Generation", "narrower_terms": [], "cross_domain_refs": [ "GAM-0026", "GAM-0079", "GAM-0009" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q735", "legal_classification": "analytical_category" }, { "id": "ART-0095", "domain": "ART", "term_en": "Generative Art Technique Standardization", "term_de": "GenerativeArtTechniqueStandardization", "definition_en": "A visual generation pattern in AI-mediated artistic production, characterized by as more people use AI tools, standards are forming for how to properly credit the work, document the process, and preserve the files. The concept emerges specifically in contexts where generative–art interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "KI-Kunstgenerierungsphänomen an der Schnittstelle von Ästhetik und Computation, gekennzeichnet durch debatte über künstlerisches Erbe, wenn KI-Werke Kunstgeschichte nachträglich reinterpretieren. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "AI Art Generation", "narrower_terms": [], "cross_domain_refs": [ "REL-0120" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q735", "legal_classification": "systematic_classification" }, { "id": "ART-0096", "domain": "ART", "term_en": "Generative Model Training Oversight", "term_de": "GenerativeModelTrainingOversight", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures an AI art generation phenomenon describing a specific aesthetic-computational interaction where a creative phenomenon in which who watches over how AI tools are trained and makes sure they're not continuing unfair preferences or copying from artists without permission. This phenomenon operates at the intersection of generative and model dynamics within the broader ART domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept kI-Kunstgenerierungsphänomen an der Schnittstelle von Ästhetik und Computation, gekennzeichnet durch phänomen der künstlerischen Dekommodifizierung, wenn Massenproduktion KI-Kunstwerke zu Waren statt Unika reduziert. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Computational Model", "narrower_terms": [], "cross_domain_refs": [ "ETH-0006" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q918461", "legal_classification": "observational_construct" }, { "id": "ART-0097", "domain": "ART", "term_en": "Human Artistic Skill Valuation Shift", "term_de": "HumanArtisticSkillValuationShift", "definition_en": "A visual generation pattern in AI-mediated artistic production, characterized by a creative production dynamic in which traditional skills like painting and drawing become less valued as AI tools yield similar results. The concept emerges specifically in contexts where human–artistic interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "KI-Kunstgenerierungsphänomen an der Schnittstelle von Ästhetik und Computation, gekennzeichnet durch verschiebung der Kunstmarktspekulaiton, wenn Algorithmen statt menschlicher Kuratorenschaft Kunstwert determinieren. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "AI Art Generation", "narrower_terms": [], "cross_domain_refs": [ "DES-0049" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q735", "legal_classification": "descriptive_research_term" }, { "id": "ART-0098", "domain": "ART", "term_en": "Human-Generated Art Market Substitution", "term_de": "Human-generatedArtMarketSubstitution", "definition_en": "An aesthetic pattern where the concern that AI-generated art will flood the market and make it harder for human artists to sell their work. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "KI-Kunstgenerierungsphänomen an der Schnittstelle von Ästhetik und Computation, gekennzeichnet durch rechtliche Unklarheit bei Haftung, wenn KI-Kunstwerke problematische Inhalte produzieren. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2503", "narrower_terms": [], "cross_domain_refs": [ "ELR-0107", "STE-0061", "TRA-0052" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q735", "legal_classification": "empirical_phenomenon_label" }, { "id": "ART-0099", "domain": "ART", "term_en": "Training Data Provenance Invisibility", "term_de": "TrainingDataProvenanceInvisibility", "definition_en": "In the descriptive vocabulary of AI interaction science (following ISO 704 terminology standards), this concept identifies A creative-technical dynamic in AI image synthesis, observable through an aesthetic pattern reflecting artists don't know if their work was used to train an AI model, so they can't track it, own decisions about it, or get paid for it. Distinguished from adjacent concepts by its focus on the specific mechanism through which training manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als analytisches Konstrukt der empirischen KI-Forschung (deskriptiv, nicht präskriptiv) erfasst dieser Terminus kI-Kunstgenerierungsphänomen an der Schnittstelle von Ästhetik und Computation, gekennzeichnet durch debatte über künstlerische Originalität, ob konzeptionelle Innovation oder technische Virtuosität primär ist. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Model Training", "narrower_terms": [], "cross_domain_refs": [ "COP-0084", "DAT-0025", "IDN-0051" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q918461", "legal_classification": "observational_construct" }, { "id": "ASE-0001", "domain": "ASE", "term_en": "AI Automated Self-Assessment Replacement", "term_de": "AiAutomatedSelf-assessmentReplacement", "definition_en": "A domain-specific phenomenon in ASE applications of AI-human interaction, characterized by the pattern where systems offering AI-generated self-assessments alongside student self-assessments show cases where students subsequently ignore their own metacognitive judgments in favor of AI interpretations, reducing inreliant self-assessment engagement. The concept emerges specifically in contexts where ai–automated interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch situation, bei der KI-generierte Selbstbewertungen und studentische Eigenbewertungen parallel angeboten werden, mit Risiko von Substitution oder Verwirrung. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Assessment in Education", "narrower_terms": [ "ASE-0072", "ASE-0013", "ASE-0074", "ASE-0002", "ASE-0029", "ASE-0033", "ASE-0036", "ASE-0022", "ASE-0001", "ASE-0041", "ASE-0076", "ASE-0097", "ASE-0056", "ASE-0025", "ASE-0069", "ASE-0043", "ASE-0032", "ASE-0062", "ASE-0083", "ASE-0061", "ASE-0066", "ASE-0060", "ASE-0019", "ASE-0065", "ASE-0082", "ASE-0086", "ASE-0003", "ASE-0011", "ASE-0044", "ASE-0059", "ASE-0068", "ASE-0075", "ASE-0088", "ASE-0067", "ASE-0052", "ASE-0035", "ASE-0027", "ASE-0038", "ASE-0089", "ASE-0081", "ASE-0037", "ASE-0049", "ASE-0028", "ASE-0051", "ASE-0017", "ASE-0016", "ASE-0054", "ASE-0010", "ASE-0020", "ASE-0090", "ASE-0040", "ASE-0014", "ASE-0006" ], "cross_domain_refs": [ "REL-0008" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q210028", "legal_classification": "observational_construct" }, { "id": "ASE-0002", "domain": "ASE", "term_en": "AI-Generated Content Detection Lag", "term_de": "Ai-generatedContentDetectionLag", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A domain-specific phenomenon in ASE applications of AI-human interaction, characterized by an evaluation effect characterized by the documented occurrence where AI detection systems fail to identify text generated by newer language model versions, creating a temporal window where artificially generated work escapes flagging until detection tools are updated. This phenomenon operates at the intersection of ai and generated dynamics within the broader ASE domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept erkannte Verzögerung bei KI-Erkennungssystemen, neue oder veränderte Generierungsmethoden zu erfassen, was unentdeckte Bewertungsfälschung ermöglicht. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Detection System", "narrower_terms": [], "cross_domain_refs": [ "WEB-0090" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "ASE-0003", "domain": "ASE", "term_en": "Accessibility Accommodation Interpretation Variance", "term_de": "AccessibilityAccommodationInterpretationVariance", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A domain-specific phenomenon in ASE applications of AI-human interaction, characterized by the pattern where students receiving identical accessibility accommodations (extended time, text-to-speech, large print) show different benefits in final scores depending on test content and item construction, suggesting that accommodation effectiveness varies by item. This phenomenon operates at the intersection of accessibility and accommodation dynamics within the broader ASE domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept phänomen, bei dem Studierende identische Zugangsunterstützungen (verlängerte Bearbeitungszeit, Text-zu-Sprache) unterschiedlich interpretieren und nutzen. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Assessment in Education", "narrower_terms": [], "cross_domain_refs": [ "SPR-0034" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "ASE-0004", "domain": "ASE", "term_en": "Adaptive Branch Misalignment", "term_de": "AdaptiveBranchMisalignment", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A domain-specific phenomenon in ASE applications of AI-human interaction, characterized by the situation where AI-adaptive testing systems misclassify student ability, directing high-performing students to easier content branches or low-performing students to content that exceeds their current developmental stage. This phenomenon operates at the intersection of adaptive and branch dynamics within the broader ASE domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept situation, bei der adaptive Prüfungssysteme Studentenfähigkeiten falsch einordnen und qualifizierte Lernende unterfordernde Aufgaben erhalten. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "RPH-3754", "narrower_terms": [], "cross_domain_refs": [ "COG-0061", "CON-0074", "CUS-0019" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "ASE-0005", "domain": "ASE", "term_en": "Adaptive Formative Pathways Convergence", "term_de": "AdaptiveFormativePathwaysKonvergenz", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A domain-specific phenomenon in ASE applications of AI-human interaction, characterized by the documented pattern where adaptive formative assessment systems for personalizing learning pathways often converge toward similar content sequences for different students, suggesting that algorithmic pathways may be constrained by system architecture rather than truly responsive to individual differences. This phenomenon operates at the intersection of adaptive and formative dynamics within the broader ASE domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept dokumentierte Tendenz, dass adaptive formative Bewertungssysteme zur Personalisierung von Lernpfaden zu ähnlichen Empfehlungen konvergieren. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "RPH-2703", "narrower_terms": [], "cross_domain_refs": [ "SOC-0003" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "ASE-0006", "domain": "ASE", "term_en": "Artifact Selection Bias Accumulation", "term_de": "ArtifactSelectionBiasAccumulation", "definition_en": "A domain-specific phenomenon in ASE applications of AI-human interaction, characterized by the situation where students curating portfolios selectively include artifacts, and AI systems analyzing these curated collections develop different conclusions about student ability than systems analyzing unfiltered collections of all student work from the period. Distinguished from adjacent concepts by its focus on the specific mechanism through which artifact manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch phänomen, bei dem Studierende bei Portfolio-Zusammenstellung selektiv Arbeiten einbeziehen, und KI-Analysesysteme systematische Verzerrungen perpetuieren. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Cognitive Bias", "narrower_terms": [], "cross_domain_refs": [ "STE-0063" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q213523", "legal_classification": "observational_construct" }, { "id": "ASE-0007", "domain": "ASE", "term_en": "Assessment Data Silos and Program Coherence", "term_de": "assessment data silos and program coherence", "definition_en": "A domain-specific phenomenon in ASE applications of AI-human interaction, characterized by the documented occurrence where AI assessment tools operate as inreliant systems without integration across institutional functions, creating fragmented assessment data that precedes the absence of coherent institutional understanding of student learning across programs. Distinguished from adjacent concepts by its focus on the specific mechanism through which assessment manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch situation, bei der KI-Bewertungstools isoliert operieren, ohne Integration mit anderen Systems, was kohärente Programmevaluationen zielt darauf ab zu mitigieren. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2703", "narrower_terms": [], "cross_domain_refs": [ "LIN-0017", "DAT-0023", "DAT-0060" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q210028", "legal_classification": "systematic_classification" }, { "id": "ASE-0008", "domain": "ASE", "term_en": "Assessment Equity Measurement Complexity", "term_de": "AssessmentEquityMeasurementComplexity", "definition_en": "A domain-specific phenomenon in ASE applications of AI-human interaction, characterized by the observable situation where institutions attempting to measure equity outcomes in AI assessment find that traditional equity metrics prove insufficient to capture system-generated disparities, requiring new assessment frameworks to understand if technological changes affect student subgroups differently. The concept emerges specifically in contexts where assessment–equity interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch herausforderung, bei der Institutionen Gerechtigkeit in KI-Bewertung messen wollen, aber konsistente Metriken und Methoden fehlen. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2303", "narrower_terms": [], "cross_domain_refs": [ "ELR-0140", "EDU-0025", "CUS-0022" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q210028", "legal_classification": "empirical_phenomenon_label" }, { "id": "ASE-0009", "domain": "ASE", "term_en": "Assessment Event Clustering Artifacts", "term_de": "AssessmentEventClusteringArtifacts", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures A domain-specific phenomenon in ASE applications of AI-human interaction, characterized by the pattern where students who concentrate their assessment submissions into brief periods show different aggregate scores than students who distribute submissions evenly across the assessment timeframe, indicating that temporal distribution affects scoring inreliant of submission quality. This phenomenon operates at the intersection of assessment and event dynamics within the broader ASE domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept phänomen, bei dem KI-Systeme demografische Merkmale als Proxies für Studentenfähigkeit verwenden, auch wenn nicht explizit trainiert. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "RPH-2303", "narrower_terms": [], "cross_domain_refs": [ "ELR-0024", "MTH-0089", "MSC-0069" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q210028", "legal_classification": "observational_construct" }, { "id": "ASE-0010", "domain": "ASE", "term_en": "Assessment Literacy Feedback Loops", "term_de": "AssessmentLiteracyRückkopplungLoops", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A domain-specific phenomenon in ASE applications of AI-human interaction, characterized by the documented observation that students with higher assessment literacy (understanding of how assessment works) benefit differently from formative feedback than students with lower assessment literacy, even when feedback content is identical. This phenomenon operates at the intersection of assessment and literacy dynamics within the broader ASE domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept situation, bei der Prüfungsfragen automatisiert generiert werden und subtile Verzerrungen oder Mehrdeutigkeiten unerkannt bleiben. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Feedback Loop", "narrower_terms": [], "cross_domain_refs": [ "SAL-0056" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q210028", "legal_classification": "observational_construct" }, { "id": "ASE-0011", "domain": "ASE", "term_en": "Assessment System Adoption Cohort Effects", "term_de": "AssessmentSystemAdoptionCohortEffects", "definition_en": "A domain-specific phenomenon in ASE applications of AI-human interaction, characterized by the observable phenomenon where first-cohort students using new AI assessment systems show different performance patterns than subsequent cohorts, suggesting that assessment novelty influences performance inreliant of system technical quality. The concept emerges specifically in contexts where assessment–system interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch dokumentierte Tendenz, dass KI-Bewertungssysteme linguistische oder kulturelle Besonderheiten unterrepräsentierter Studentengruppen falsch interpretieren. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Psychological Effect", "narrower_terms": [], "cross_domain_refs": [ "AGE-0081", "MUS-0040", "AGE-0035" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q210028", "legal_classification": "descriptive_research_term" }, { "id": "ASE-0012", "domain": "ASE", "term_en": "Assessment System Migration Data Shift", "term_de": "AssessmentSystemMigrationDataShift", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A domain-specific phenomenon in ASE applications of AI-human interaction, characterized by the observable phenomenon where institutions transitioning from legacy assessment systems to new AI systems lose historical assessment data or encounter data incompatibility that precedes the absence of longitudinal analysis of institutional assessment trends, breaking continuity of institutional learning analytics. This phenomenon operates at the intersection of assessment and system dynamics within the broader ASE domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept phänomen, bei dem Lehrende KI-Bewertungssysteme verwenden, ohne deren Grenzen, Verzerrungen oder Annahmen vollständig zu verstehen. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "RPH-2703", "narrower_terms": [], "cross_domain_refs": [ "ELR-0024", "PER-0025", "SPR-0007" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q210028", "legal_classification": "systematic_classification" }, { "id": "ASE-0013", "domain": "ASE", "term_en": "Assessment Transparency Accountability Tension", "term_de": "AssessmentTransparencyAccountabilityTension", "definition_en": "A domain-specific phenomenon in ASE applications of AI-human interaction, characterized by the situation where institutional pressures for assessment transparency (enabling appeal and explanation access) conflict with technical system limitations that reduce generating human-comprehensible explanations of AI scoring decisions, creating unresolved governance gaps. The concept emerges specifically in contexts where assessment–transparency interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch situation, bei der automatisierte Bewertungen Fehler oder Verzerrungen enthalten, die durch mangelnde Überwachung unkorrigiert bleiben. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Assessment in Education", "narrower_terms": [], "cross_domain_refs": [ "ELR-0024", "MTH-0089", "MSC-0069" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q15223", "legal_classification": "descriptive_research_term" }, { "id": "ASE-0014", "domain": "ASE", "term_en": "Assessment-Instruction Alignment Gaps", "term_de": "Assessment-instructionAlignmentGaps", "definition_en": "A domain-specific phenomenon in ASE applications of AI-human interaction, characterized by the situation where AI-adapted test progressions diverge from classroom instruction sequences, creating scenarios where students encounter assessment items covering content not yet introduced in their instructional pathway. Distinguished from adjacent concepts by its focus on the specific mechanism through which assessment manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch herausforderung der Konsistenz bei offenen, komplexen Aufgaben, die KI-Systeme schwerer konsistent bewerten können als Multiple-Choice. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Performance Gap", "narrower_terms": [], "cross_domain_refs": [ "VIB-0168" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q210028", "legal_classification": "empirical_phenomenon_label" }, { "id": "ASE-0015", "domain": "ASE", "term_en": "Automated Rubric Application", "term_de": "AutomatedRubricApplication", "definition_en": "The pattern where AI systems apply standardized scoring criteria consistently across student submissions, producing uniform grade distributions even when evaluation contexts vary. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch phänomen, bei dem Studierendendaten aus verschiedenen KI-Systemen inkonsistent oder unvereinbar sind. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-2351", "narrower_terms": [], "cross_domain_refs": [ "EDU-0087", "ELR-0163", "MKT-0089" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "ASE-0016", "domain": "ASE", "term_en": "Branching Prediction Calibration Shift", "term_de": "BranchingPredictionCalibrationShift", "definition_en": "A domain-specific phenomenon in ASE applications of AI-human interaction, characterized by the pattern where adaptive branching decisions that were accurately predictive when systems were first deployed become progressively less accurate for current student cohorts, indicating change in environmental validity of the adaptation algorithm. The concept emerges specifically in contexts where branching–prediction interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch situation, bei der KI-Systeme verbesserte Bewertungsgenauigkeit versprechen, aber unklar ist, gegenüber welchem Standard gemessen wird. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Analytical Ratio", "narrower_terms": [], "cross_domain_refs": [ "COG-0053", "COG-0124", "CON-0082" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "ASE-0017", "domain": "ASE", "term_en": "Ceiling and Floor Effect Instability", "term_de": "CeilingAndFloorEffektInstabilität", "definition_en": "A domain-specific phenomenon in ASE applications of AI-human interaction, characterized by the documented occurrence where adaptive test designs intended to minimize ceiling and floor effects show unstable performance, with some administrations still producing clusters of maximum or minimum scores despite adaptation mechanisms. Distinguished from adjacent concepts by its focus on the specific mechanism through which ceiling manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch dokumentierte Tendenz, dass KI-Bewertungsempfehlungen Lehrende in ihren Erwartungen konfirmieren, auch wenn Empfehlungen falsch sind. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Psychological Effect", "narrower_terms": [], "cross_domain_refs": [ "DES-0056", "DES-0053", "DES-0036" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "ASE-0018", "domain": "ASE", "term_en": "Citation Compliance Gradient Inconsistency", "term_de": "CitationComplianceGradientInconsistency", "definition_en": "A domain-specific phenomenon in ASE applications of AI-human interaction, characterized by the documented pattern where plagiarism and integrity detection systems apply inconsistent standards for citation completeness, sometimes flagging incomplete citations and sometimes allowing similarly incomplete citations to pass undetected. The concept emerges specifically in contexts where citation–compliance interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch phänomen, bei dem Studierendenleistung und KI-Bewertung divergieren, ohne klare Ursachenidentifikation möglich ist. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2351", "narrower_terms": [], "cross_domain_refs": [ "CUS-0067" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q2083958", "legal_classification": "analytical_category" }, { "id": "ASE-0019", "domain": "ASE", "term_en": "Coaching Effect Heterogeneity", "term_de": "CoachingEffektHeterogeneity", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures A domain-specific phenomenon in ASE applications of AI-human interaction, characterized by the documented pattern where test coaching and practice yield different score gains across student subgroups, causing score differentials to change after intensive test preparation, even though underlying ability differences may remain stable. This phenomenon operates at the intersection of coaching and effect dynamics within the broader ASE domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept situation, bei der automatisierte Bewertungen in Echtzeit stattfinden, ohne dass Studierendem Zeit für Revision oder Erklärung bleibt. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Psychological Effect", "narrower_terms": [], "cross_domain_refs": [ "ADA-0001", "ADA-0002", "ADA-0004" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q1068499", "legal_classification": "analytical_category" }, { "id": "ASE-0020", "domain": "ASE", "term_en": "Cognitive Load Effects in Formative Cycles", "term_de": "cognitive load effects in formative cycles", "definition_en": "A domain-specific phenomenon in ASE applications of AI-human interaction, characterized by the observable phenomenon where formative assessment cycles with high cognitive load (complex feedback, multiple criteria, lengthy explanations) sometimes co-occur with lower achievement gains than less cognitively demanding alternatives, suggesting that optimal feedback complexity varies by student. The concept emerges specifically in contexts where cognitive–load interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch herausforderung, bei der KI-Systeme Training-Daten auf historisch verzerrten Bewertungen basieren, was Verzerrungen perpetuiert. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Psychological Effect", "narrower_terms": [], "cross_domain_refs": [ "AGE-0018", "AGE-0030", "COG-0042" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q1105712", "legal_classification": "observational_construct" }, { "id": "ASE-0021", "domain": "ASE", "term_en": "Collusion Detection Threshold Variance", "term_de": "CollusionDetectionSchwelleVariance", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures A domain-specific phenomenon in ASE applications of AI-human interaction, characterized by the observation that AI systems for identifying collaborative work versus plagiaristic copying show inconsistent threshold application across student groups, flagging similar similarity patterns differently depending on student identity variables. This phenomenon operates at the intersection of collusion and detection dynamics within the broader ASE domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept phänomen, bei dem Studierendenverhalten sich anpasst, um bessere KI-Bewertungsscores zu erhalten, statt echter Kompetenzentwicklung zu fokussieren. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "RPH-1307", "narrower_terms": [], "cross_domain_refs": [ "STE-0063" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "ASE-0022", "domain": "ASE", "term_en": "Comparative Artifact Inflation", "term_de": "ComparativeArtifactInflation", "definition_en": "A domain-specific phenomenon in ASE applications of AI-human interaction, characterized by the pattern where portfolios containing a large number of artifacts show systematically higher aggregate scores than portfolios with fewer but equivalent-quality artifacts, suggesting that portfolio size influences assessment outcomes inreliant of quality considerations. The concept emerges specifically in contexts where comparative–artifact interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch situation, bei der Bewertungsstandards zwischen KI-System und menschlicher Lehrkraft divergieren. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Assessment in Education", "narrower_terms": [], "cross_domain_refs": [ "AED-0013", "COG-0041", "COG-0072" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q735", "legal_classification": "descriptive_research_term" }, { "id": "ASE-0023", "domain": "ASE", "term_en": "Comparative Institutional Ranking Artifact Creation", "term_de": "ComparativeInstitutionalRankingArtifactCreation", "definition_en": "A domain-specific phenomenon in ASE applications of AI-human interaction, characterized by the pattern where institutions adopt identical AI assessment systems but develop different interpretation and reporting practices, creating the appearance of meaningful performance differences between institutions when differences primarily reflect reporting and interpretation choices rather than underlying student learning. The concept emerges specifically in contexts where comparative–institutional interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch dokumentierte Tendenz, dass KI-Systeme oberflächliche Ähnlichkeiten als Kompetenzbeweis interpretieren. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2703", "narrower_terms": [], "cross_domain_refs": [ "DAT-0053" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q735", "legal_classification": "descriptive_research_term" }, { "id": "ASE-0024", "domain": "ASE", "term_en": "Comparative Norm Expectations Misalignment", "term_de": "ComparativeNormExpectationsMisalignment", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A domain-specific phenomenon in ASE applications of AI-human interaction, characterized by the observation that students from different educational backgrounds show varying awareness of academic integrity standards, and AI detection systems optimized for one educational context yield different flagging rates when applied to diverse student populations. This phenomenon operates at the intersection of comparative and norm dynamics within the broader ASE domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept phänomen, bei dem Studierendenleistungsdaten fragmentiert über mehrere KI-Systeme verteilt sind, erschwert Gesamtperspektiven. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "RPH-3202", "narrower_terms": [], "cross_domain_refs": [ "SCR-0023" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "ASE-0025", "domain": "ASE", "term_en": "Confidence Calibration Inflation", "term_de": "ConfidenceCalibrationInflation", "definition_en": "A domain-specific phenomenon in ASE applications of AI-human interaction, characterized by the observable occurrence where students using AI self-assessment tools show inflated confidence in their responses even when accuracy is objectively lower, suggesting that AI tool architecture can inadvertently promote overconfidence. The concept emerges specifically in contexts where confidence–calibration interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch situation, bei der KI-Bewertungen der Realität von Studierendenverständnis widersprechen, Lehrende aber KI-Output bevorzugen. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Analytical Ratio", "narrower_terms": [], "cross_domain_refs": [ "ELR-0043" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "ASE-0026", "domain": "ASE", "term_en": "Construct-Irrelevant Difficulty Variance", "term_de": "Construct-irrelevantDifficultyVariance", "definition_en": "A domain-specific phenomenon in ASE applications of AI-human interaction, characterized by the documented observation that standardized test items sometimes measure extraneous variables (background knowledge, specific vocabulary, test-taking strategies) alongside the intended construct, causing scores to reflect multiple unmeasured factors simultaneously. The concept emerges specifically in contexts where construct–irrelevant interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch herausforderung der Transparenz, wenn KI-Systeme Bewertungsentscheidungen fällen, deren Logik Lehrende nicht nachvollziehen können. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-3202", "narrower_terms": [], "cross_domain_refs": [ "SPR-0034", "SAL-0041", "SAL-0061" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "ASE-0027", "domain": "ASE", "term_en": "Content Sampling Artifact Effects", "term_de": "ContentSamplingArtifactEffects", "definition_en": "A domain-specific phenomenon in ASE applications of AI-human interaction, characterized by the situation where standardized tests sample only specific content domains or skill areas, and students with specialized knowledge concentrated in tested areas show higher scores relative to students with broader but more distributed knowledge. The concept emerges specifically in contexts where content–sampling interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch phänomen, bei dem Studierendengruppen mit unterschiedlichen Technologiezugangsraten unterschiedliche KI-Bewertungsqualität erhalten. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Psychological Effect", "narrower_terms": [], "cross_domain_refs": [ "MSC-0075", "TEW-0047", "MKT-0028" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q735", "legal_classification": "systematic_classification" }, { "id": "ASE-0028", "domain": "ASE", "term_en": "Contract Cheating Signature Evolution", "term_de": "ContractCheatingSignatureEvolution", "definition_en": "A domain-specific phenomenon in ASE applications of AI-human interaction, characterized by the pattern where students seeking external work completion adjust their request strategies when AI detection systems become known, causing detection systems to encounter novel writing patterns they were not trained to identify. The concept emerges specifically in contexts where contract–cheating interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch situation, bei der KI-Systeme langfristige Lerntrends nicht erfassen, sondern Momentaufnahmen liefern. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Assessment in Education", "narrower_terms": [], "cross_domain_refs": [ "MKT-0024" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "ASE-0029", "domain": "ASE", "term_en": "Criterion Interreliance Mishandling", "term_de": "CriterionInterrelianceMishandling", "definition_en": "A domain-specific phenomenon in ASE applications of AI-human interaction, characterized by the documented situation where rubrics contain criteria that logically depend on each other, but AI systems score each criterion inreliantly, sometimes assigning high scores on reliant criteria to submissions where foundational criteria were not met. Distinguished from adjacent concepts by its focus on the specific mechanism through which criterion manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch dokumentierte Tendenz, dass KI-Bewertungen Fehlerverteilungen zeigen, aber Fehlertypen unterscheiden sich nach Studierendengruppe. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Assessment in Education", "narrower_terms": [], "cross_domain_refs": [ "DAT-0052", "EDU-0057", "WRK-0064" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "ASE-0030", "domain": "ASE", "term_en": "Dynamic Difficulty Calibration Drift", "term_de": "DynamikDifficultyCalibrationDrift", "definition_en": "A domain-specific phenomenon in ASE applications of AI-human interaction, characterized by the pattern where AI-adaptive testing systems adjust question difficulty based on student performance, but calibration drifts over time, causing later assessments to differ systematically in difficulty from earlier ones for equivalent students. The concept emerges specifically in contexts where dynamic–difficulty interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch phänomen, bei dem Studierendenleistungen zu Trainingsdaten für KI-Systeme werden, ohne explizite Zustimmung oder Transparenz. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2051", "narrower_terms": [], "cross_domain_refs": [ "GAM-0034" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "ASE-0031", "domain": "ASE", "term_en": "Evidence Aggregation Weighting Invisibility", "term_de": "EvidenceAggregationWeightingInvisibility", "definition_en": "A domain-specific phenomenon in ASE applications of AI-human interaction, characterized by the situation where continuous assessment systems combine multiple evidence pieces into summary scores, but the algorithms used to weight and aggregate evidence are not transparent, preventing educators from understanding which evidence pieces most influenced final assessments. The concept emerges specifically in contexts where evidence–aggregation interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch situation, bei der KI-Bewertungsempfehlungen zu Gatekeeping-Entscheidungen führen, deren Fairness unklar ist. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2201", "narrower_terms": [], "cross_domain_refs": [ "EDU-0040" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "ASE-0032", "domain": "ASE", "term_en": "Evidence-Criterion Mapping Ambiguity", "term_de": "Evidence-criterionMappingAmbiguity", "definition_en": "A domain-specific phenomenon in ASE applications of AI-human interaction, characterized by the situation where rubrics specify criteria but leave implicit how evidence of meeting criteria appears in student work, causing AI systems trained on limited exemplar sets to develop idiosyncratic interpretations of what constitutes valid evidence. Distinguished from adjacent concepts by its focus on the specific mechanism through which evidence manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch herausforderung, bei der mehrere KI-Systeme widersprechende Bewertungen liefern, ohne Auflösungsmechanismus. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Assessment in Education", "narrower_terms": [], "cross_domain_refs": [ "EDU-0040", "PHO-0037", "CON-0039" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "ASE-0033", "domain": "ASE", "term_en": "Exemplar Saturation Bias", "term_de": "ExemplarSaturationBias", "definition_en": "A domain-specific phenomenon in ASE applications of AI-human interaction, characterized by the observable pattern where AI rubric implementation is heavily influenced by a small set of high-quality student exemplars in training data, causing submissions resembling these exemplars to receive higher scores even when they technically meet rubric criteria equivalently to dissimilar submissions. The concept emerges specifically in contexts where exemplar–saturation interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch phänomen, bei dem KI-Systeme mit Zeit neuen Trainingsdaten unauffällig ihre Bewertungslogik ändern. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Cognitive Bias", "narrower_terms": [], "cross_domain_refs": [ "COG-0015", "COG-0020", "COG-0058" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q213523", "legal_classification": "systematic_classification" }, { "id": "ASE-0034", "domain": "ASE", "term_en": "Faculty Assessment Judgment Deskilling", "term_de": "FacultyAssessmentUrteilDeskilling", "definition_en": "A domain-specific phenomenon in ASE applications of AI-human interaction, characterized by the pattern where faculty reliance on AI assessment systems correlates with decreased engagement with assessment architecture and evaluation methodologies, creating shift of institutional assessment literacy even as technical system quality improves. The concept emerges specifically in contexts where faculty–assessment interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch situation, bei der Studierendengruppen mit besonderen Lernbedürfnissen von KI-Systemen systematisch unterstützt oder überfordert werden. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2701", "narrower_terms": [], "cross_domain_refs": [ "ELR-0024", "MTH-0089", "MSC-0069" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q210028", "legal_classification": "systematic_classification" }, { "id": "ASE-0035", "domain": "ASE", "term_en": "Feedback Confirmation Bias Entrenchment", "term_de": "RückkopplungConfirmationBiasEntrenchment", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A domain-specific phenomenon in ASE applications of AI-human interaction, characterized by the pattern where students receiving AI feedback that aligns with their initial approaches selectively incorporate suggestions, while dismissing feedback contradicting their initial choices, reinforcing existing conceptual patterns. This phenomenon operates at the intersection of feedback and confirmation dynamics within the broader ASE domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept dokumentierte Tendenz, dass KI-Systeme kontextuelle Faktoren (Schlafmangel, Stress) nicht berücksichtigen, aber Menschen tun. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Cognitive Bias", "narrower_terms": [], "cross_domain_refs": [ "COG-0020" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q1133029", "legal_classification": "analytical_category" }, { "id": "ASE-0036", "domain": "ASE", "term_en": "Feedback Format Switching Behavior", "term_de": "RückkopplungFormatSwitchingBehavior", "definition_en": "A domain-specific phenomenon in ASE applications of AI-human interaction, characterized by a documented pattern where students receiving AI feedback in one modality (text, video, interactive) develop different question-asking patterns than students receiving feedback in alternative modalities for identical assessment content. Distinguished from adjacent concepts by its focus on the specific mechanism through which feedback manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch phänomen, bei dem KI-Bewertung von Fähigkeiten mit gering arbeitender Erinnerung besser funktioniert als tatsächliches Verständnis. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Assessment in Education", "narrower_terms": [], "cross_domain_refs": [ "CON-0043", "VIB-0101", "AGE-0058" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "ASE-0037", "domain": "ASE", "term_en": "Feedback Lag Adaptation", "term_de": "RückkopplungLagAnpassung", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures A domain-specific phenomenon in ASE applications of AI-human interaction, characterized by the observable pattern where immediate automated feedback alters subsequent student attempts, sometimes causing rapid trial-and-error iteration patterns that differ from patterns when feedback is delayed. This phenomenon operates at the intersection of feedback and lag dynamics within the broader ASE domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept situation, bei der KI-Systeme Ausnahmeleistungen nicht erfassen, da sie nur auf durchschnittliche Muster trainiert sind. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "System Adaptation", "narrower_terms": [], "cross_domain_refs": [ "AED-0003", "AED-0010", "AED-0072" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "ASE-0038", "domain": "ASE", "term_en": "Feedback Loop Fatigue", "term_de": "RückkopplungSchleifeFatigue", "definition_en": "A domain-specific phenomenon in ASE applications of AI-human interaction, characterized by a systemic tendency in which students receiving extensive automated corrective feedback across multiple submission cycles show declining engagement or significant shift attempts, despite feedback quality remaining constant. Distinguished from adjacent concepts by its focus on the specific mechanism through which feedback manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch herausforderung der Validität, wenn KI-Systeme messen, was sie können, nicht unbedingt das, was gemessen werden kann. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Feedback Loop", "narrower_terms": [], "cross_domain_refs": [ "AED-0003", "AED-0072", "AED-0079" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "ASE-0039", "domain": "ASE", "term_en": "Feedback Over-specification Effect", "term_de": "RückkopplungOver-specificationEffekt", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A domain-specific phenomenon in ASE applications of AI-human interaction, characterized by the pattern where automated feedback identifies so many issues in student work that the number and complexity of suggested corrections exceeds what students can reasonably address in subsequent revisions. This phenomenon operates at the intersection of feedback and over dynamics within the broader ASE domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept phänomen, bei dem Lehrende übertriebenes Vertrauen in KI-Bewertungen entwickeln und eigene Urteile unterwerten. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "RPH-3754", "narrower_terms": [], "cross_domain_refs": [ "FIC-0031" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "ASE-0040", "domain": "ASE", "term_en": "Feedback Responsiveness Asymmetry", "term_de": "RückkopplungResponsivenessAsymmetry", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A domain-specific phenomenon in ASE applications of AI-human interaction, characterized by the situation where AI systems provide faster feedback to students with high submission frequency than to students with lower submission frequency, creating unequal feedback access even for the same assessment activity. This phenomenon operates at the intersection of feedback and responsiveness dynamics within the broader ASE domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept situation, bei der KI-Bewertungsfeedback zu generisch ist, um Studierendenlernen effektiv zu guidieren. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Assessment in Education", "narrower_terms": [], "cross_domain_refs": [ "ADA-0003", "AED-0003", "AED-0072" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "ASE-0041", "domain": "ASE", "term_en": "Feedback Specificity-Autonomy Trade-off", "term_de": "RückkopplungSpecificity-autonomyTrade-off", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures A domain-specific phenomenon in ASE applications of AI-human interaction, characterized by the observable phenomenon where more specific, prescriptive automated feedback accompanies higher short-term score improvements but reduces students' inreliant problem-solving attempts in subsequent tasks. This phenomenon operates at the intersection of feedback and specificity dynamics within the broader ASE domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept dokumentierte Tendenz, dass KI-Systeme probabilistisch arbeiten und manchmal völlig falsch liegen, ohne Vorwarnung. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Assessment in Education", "narrower_terms": [], "cross_domain_refs": [ "AGE-0070", "ELR-0083", "MTH-0040" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "ASE-0042", "domain": "ASE", "term_en": "Feedback Timing Interaction Effects", "term_de": "RückkopplungTimingInteractionEffects", "definition_en": "A domain-specific phenomenon in ASE applications of AI-human interaction, characterized by the documented variation where feedback delivered immediately after assessment events accompanies different learning trajectories than delayed feedback, with effect sizes varying based on assessment task complexity and prior student knowledge. Distinguished from adjacent concepts by its focus on the specific mechanism through which feedback manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch phänomen, bei dem Bewertungsnormen mit KI-Einsatz verschieben, was Vergleichbarkeit über Zeit erschwert. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2703", "narrower_terms": [], "cross_domain_refs": [ "AED-0024", "LIN-0038", "AED-0070" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "ASE-0043", "domain": "ASE", "term_en": "Format Sensitivity in Scoring", "term_de": "FormatSensitivityinScoring", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures A domain-specific phenomenon in ASE applications of AI-human interaction, characterized by an assessment phenomenon arising from the occurrence where AI grading systems yield different grades when student work is presented in different formats (handwriting scanned as image, typed text, voice-transcribed text) for identical content. This phenomenon operates at the intersection of format and sensitivity dynamics within the broader ASE domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept situation, bei der Studierendenmetadata (demografisch, sozioökonomisch) KI-Bewertungen implizit beeinflussen. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Assessment in Education", "narrower_terms": [], "cross_domain_refs": [ "AGE-0058", "CON-0043", "CUS-0062" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "ASE-0044", "domain": "ASE", "term_en": "Format-Specific Performance Variance", "term_de": "Format-specificPerformanceVariance", "definition_en": "A domain-specific phenomenon in ASE applications of AI-human interaction, characterized by the observable phenomenon where students demonstrate different performance levels when identical assessment content is presented in different formats (multiple-choice, short-answer, constructed-response), indicating that format influences score inreliant of construct competence. Distinguished from adjacent concepts by its focus on the specific mechanism through which format manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch herausforderung, bei der KI-Systeme Lerndisposition oder Potenzial nicht erfassen können, nur aktuelle Performance. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Assessment in Education", "narrower_terms": [], "cross_domain_refs": [ "GAM-0067" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q1361053", "legal_classification": "descriptive_research_term" }, { "id": "ASE-0045", "domain": "ASE", "term_en": "Formative Assessment Momentum Effects", "term_de": "FormativeAssessmentMomentumEffects", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A domain-specific phenomenon in ASE applications of AI-human interaction, characterized by the pattern where consecutive cycles of formative assessment and revision show diminishing returns, with early cycles producing greater learning gains than later cycles, even when student effort and engagement remain constant. This phenomenon operates at the intersection of formative and assessment dynamics within the broader ASE domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept phänomen, bei dem KI-Bewertungen Muster-Matching bevorzugen gegenüber tiefem konzeptionellem Verständnis. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "RPH-2703", "narrower_terms": [], "cross_domain_refs": [ "VIB-0198" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q210028", "legal_classification": "systematic_classification" }, { "id": "ASE-0046", "domain": "ASE", "term_en": "Formative Data Interpretation Consistency", "term_de": "FormativeDataInterpretationConsistency", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A domain-specific phenomenon in ASE applications of AI-human interaction, characterized by the pattern where formative assessment data shows high variability in interpretation across different educators, even when using identical assessment instruments, indicating that formative data meaning depends heavily on educator expertise and interpretation frameworks. This phenomenon operates at the intersection of formative and data dynamics within the broader ASE domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept situation, bei der neue KI-Systeme schrittweise implementiert werden, was zu Patchwork-Bewertungslandschaften führt. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "RPH-2303", "narrower_terms": [], "cross_domain_refs": [ "EDU-0031", "MSC-0094", "SPR-0036" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q42848", "legal_classification": "analytical_category" }, { "id": "ASE-0047", "domain": "ASE", "term_en": "Formative Engagement-Achievement Paradox", "term_de": "FormativeEngagement-achievementParadox", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A domain-specific phenomenon in ASE applications of AI-human interaction, characterized by the observable occurrence where increased frequency of formative assessment sometimes correlates with decreased time spent on instructional content, creating scenarios where higher-quality formative data collection reduces learning opportunity equity. This phenomenon operates at the intersection of formative and engagement dynamics within the broader ASE domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept dokumentierte Tendenz, dass KI-Systeme Fehlertypen-Unterscheidungen treffen, die mit Expertenmeinung divergieren. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "RPH-2703", "narrower_terms": [], "cross_domain_refs": [ "AGE-0042", "COP-0043", "SPR-0120" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "ASE-0048", "domain": "ASE", "term_en": "Formative-Summative Boundary Shift", "term_de": "Formative-summativeGrenzeShift", "definition_en": "A domain-specific phenomenon in ASE applications of AI-human interaction, characterized by the observable pattern where continuous assessment systems blur distinctions between formative (learning-focused) and summative (certification-focused) assessment, causing feedback and scores from learning activities to be incorporated into final achievement determinations. Distinguished from adjacent concepts by its focus on the specific mechanism through which formative manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch phänomen, bei dem automatisierte Bewertungen Testgültigkeit untergraben durch technische oder UX-Probleme. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2703", "narrower_terms": [], "cross_domain_refs": [ "COG-0184" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "ASE-0049", "domain": "ASE", "term_en": "Formative-Summative Score Correlation Variation", "term_de": "Formative-summativeScoreCorrelationVariation", "definition_en": "A domain-specific phenomenon in ASE applications of AI-human interaction, characterized by the observable pattern where formative assessment scores show varying predictive relationships to subsequent summative assessment scores across different student subgroups, suggesting that formative-summative alignment varies depending on student characteristics. The concept emerges specifically in contexts where formative–summative interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch situation, bei der Bewertungsdaten nicht vollständig Studierendenerfahrungen abbilden, aber als Proxy adressiert werden. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Quality Metric", "narrower_terms": [], "cross_domain_refs": [ "TEW-0085", "LIN-0078", "VIB-0058" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "ASE-0050", "domain": "ASE", "term_en": "Generic Feedback Recognition", "term_de": "GenericRückkopplungRecognition", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures A domain-specific phenomenon in ASE applications of AI-human interaction, characterized by an evaluation effect reflecting the documented occurrence where AI systems yield feedback messages that are identically phrased across multiple student submissions, signaling to students that feedback is algorithmically produced rather than individually considered. This phenomenon operates at the intersection of generic and feedback dynamics within the broader ASE domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept herausforderung, bei der KI-Systeme gut in standardisierten Tests funktionieren, aber schlecht in authentischen Aufgaben. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "RPH-2052", "narrower_terms": [], "cross_domain_refs": [ "AED-0003", "AED-0013", "AED-0072" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q3966", "legal_classification": "systematic_classification" }, { "id": "ASE-0051", "domain": "ASE", "term_en": "Grade Appeal Audit Trail Opacity", "term_de": "GradeAppealAuditTrailOpacity", "definition_en": "A domain-specific phenomenon in ASE applications of AI-human interaction, characterized by the situation where students request to understand why an AI-assigned grade was given, but the system cannot provide a human-comprehensible explanation of its scoring logic or decision trajectory. Distinguished from adjacent concepts by its focus on the specific mechanism through which grade manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch phänomen, bei dem Studierendengruppen mit kulturell-spezifischen Lösungsansätzen von KI-Systemen nicht erkannt werden. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Assessment in Education", "narrower_terms": [], "cross_domain_refs": [ "TEW-0005" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "ASE-0052", "domain": "ASE", "term_en": "Grade Drift Detection", "term_de": "GradeDriftDetection", "definition_en": "In the descriptive vocabulary of AI interaction science (following ISO 704 terminology standards), this concept identifies A domain-specific phenomenon in ASE applications of AI-human interaction, characterized by observable instances where assigned grades change when a student reformal process compliances identical work to an AI grading system on different occasions or through different interface channels. Distinguished from adjacent concepts by its focus on the specific mechanism through which grade manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als analytisches Konstrukt der empirischen KI-Forschung (deskriptiv, nicht präskriptiv) erfasst dieser Terminus situation, bei der KI-Bewertung Zeitdruck tendiert dazu zu erzeugen, der echtes Denken zielt darauf ab zu mitigieren. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Systemic Drift", "narrower_terms": [], "cross_domain_refs": [ "ELR-0088" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "ASE-0053", "domain": "ASE", "term_en": "Growth Perception Effects in AI-Assisted Self-Assessment", "term_de": "growth perception effects in ai-assisted self-assessment", "definition_en": "A domain-specific phenomenon in ASE applications of AI-human interaction, characterized by a grading interaction pattern observed when the observable variation where students receiving AI-provided growth metrics (improvement trajectories, progress visualizations) develop different self-efficacy beliefs than students receiving only point-in-time self-assessments, even when underlying performance is equivalent. The concept emerges specifically in contexts where growth–perception interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch dokumentierte Tendenz, dass KI-Systeme inkonsistente Bewertungsstandards über verschiedene Aufgabentypen haben. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-3101", "narrower_terms": [], "cross_domain_refs": [ "REL-0008" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q177601", "legal_classification": "analytical_category" }, { "id": "ASE-0054", "domain": "ASE", "term_en": "Growth Trajectory Extrapolation Errors", "term_de": "GrowthTrajectoryExtrapolationErrors", "definition_en": "A domain-specific phenomenon in ASE applications of AI-human interaction, characterized by the documented occurrence where AI systems analyzing continuous assessment data extrapolate short-term performance trends beyond their actual predictive value, sometimes incorrectly projecting that recent performance patterns will continue linearly into future assessment periods. The concept emerges specifically in contexts where growth–trajectory interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch phänomen, bei dem Studierendenfeedback ignoriert wird zugunsten KI-Scores. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Assessment in Education", "narrower_terms": [], "cross_domain_refs": [ "MTH-0048", "ROB-0195", "WRK-0094" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "ASE-0055", "domain": "ASE", "term_en": "Holistic-Analytic Conversion Artifacts", "term_de": "Holistic-analyticConversionArtifacts", "definition_en": "A domain-specific phenomenon in ASE applications of AI-human interaction, characterized by the situation where rubrics are initially framed for holistic (overall impression) scoring, but AI systems shift them to analytic (component-by-component) assessment, changing the types of work that score highest under the automated version. The concept emerges specifically in contexts where holistic–analytic interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch situation, bei der KI-Bewertung Lernende demoralisieret, wenn Feedback nicht konstruktiv ist. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2303", "narrower_terms": [], "cross_domain_refs": [ "WEB-0017" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q735", "legal_classification": "descriptive_research_term" }, { "id": "ASE-0056", "domain": "ASE", "term_en": "Institutional Capacity Gaps in AI Assessment Adoption", "term_de": "institutional capacity gaps in ai assessment adoption", "definition_en": "A domain-specific phenomenon in ASE applications of AI-human interaction, characterized by the documented variation where institutions with lower technical capacity to manage AI systems and interpret algorithmic outputs show different implementation outcomes than highly equipped institutions, even when using identical system versions and configurations. Distinguished from adjacent concepts by its focus on the specific mechanism through which institutional manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch herausforderung, bei der KI-Systeme lokale Kontexte nicht beachten (Curriculum, Lehr-Ansätze, Studentendemografik). Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Performance Gap", "narrower_terms": [], "cross_domain_refs": [ "AGE-0081", "MUS-0040", "AGE-0035" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q210028", "legal_classification": "descriptive_research_term" }, { "id": "ASE-0057", "domain": "ASE", "term_en": "Institutional Grade Distribution Normalization", "term_de": "InstitutionalGradeDistributionNormalization", "definition_en": "A domain-specific phenomenon in ASE applications of AI-human interaction, characterized by the documented pattern where educational institutions adopting AI grading systems show systematic shifts in grade distributions compared to pre-AI baseline periods, with changes varying across institutions despite identical system implementations. Distinguished from adjacent concepts by its focus on the specific mechanism through which institutional manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch phänomen, bei dem Lehrende mit KI-Bewertungssystemen konkurrieren um Autoritätsposition gegenüber Studierenden. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2701", "narrower_terms": [], "cross_domain_refs": [ "ELR-0100" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "ASE-0058", "domain": "ASE", "term_en": "Institutional Learning from Assessment System Failures", "term_de": "institutional learning from assessment system failures", "definition_en": "A domain-specific phenomenon in ASE applications of AI-human interaction, characterized by the documented challenge where institutions experience assessment system failures (data shift, scoring errors, accessibility breakdowns) but limited institutional mechanisms exist for capturing, analyzing, and distributing lessons learned across the organization, preventing organizational learning from technological failures. Distinguished from adjacent concepts by its focus on the specific mechanism through which institutional manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch situation, bei der KI-Bewertungsdaten für Studierendenprofiling oder Diskriminierung missbraucht werden könnten. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2701", "narrower_terms": [], "cross_domain_refs": [ "AED-0001", "KNO-0017", "VIB-0182" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q210028", "legal_classification": "analytical_category" }, { "id": "ASE-0059", "domain": "ASE", "term_en": "Integrity Appeal Adjudication Asymmetry", "term_de": "IntegrityAppealAdjudicationAsymmetry", "definition_en": "A domain-specific phenomenon in ASE applications of AI-human interaction, characterized by the pattern where students flagged by AI integrity systems face difficulty in appealing these flags, as the systems cannot yield explanations sufficient to allow disputes of algorithmic conclusions. The concept emerges specifically in contexts where integrity–appeal interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch dokumentierte Tendenz, dass KI-Systeme Verständnis neuer Konzepte überschätzen, da sie nur Output prüfen. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Assessment in Education", "narrower_terms": [], "cross_domain_refs": [ "SPR-0138", "AGE-0057", "GAM-0091" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "ASE-0060", "domain": "ASE", "term_en": "Interference Pattern in Adaptive Batching", "term_de": "InterferenzMusterinAdaptiveBatching", "definition_en": "A domain-specific phenomenon in ASE applications of AI-human interaction, characterized by the observable pattern where grouped assessment items (batches presented together) show different difficulty relationships than when identical items are presented individually, indicating contextual interference in adaptive designs. The concept emerges specifically in contexts where interference–pattern interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch phänomen, bei dem Studierendenvergleichbarkeit darunter leidet, dass verschiedene KI-Systeme unterschiedliche Maßstäbe verwenden. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Behavioral Pattern", "narrower_terms": [], "cross_domain_refs": [ "CON-0002" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "ASE-0061", "domain": "ASE", "term_en": "Item Parameter Drift Over Cohorts", "term_de": "ItemParameterDriftOverCohorts", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures A domain-specific phenomenon in ASE applications of AI-human interaction, characterized by the documented pattern where standardized test items show changing difficulty and discrimination values across different student cohorts, even when administered identically, indicating that item parameters are population-reliant. This phenomenon operates at the intersection of item and parameter dynamics within the broader ASE domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept situation, bei der KI-Bewertung Lernmotivation untergräbt durch zu häufiges oder zu strenges Feedback. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Systemic Drift", "narrower_terms": [], "cross_domain_refs": [ "RET-0010", "SCR-0088", "LIN-0070" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "ASE-0062", "domain": "ASE", "term_en": "Item Parameter Stability Across Administrations", "term_de": "ItemParameterStabilitätAcrossAdministrations", "definition_en": "A domain-specific phenomenon in ASE applications of AI-human interaction, characterized by the observable phenomenon where standardized test items show inconsistent statistical properties when administered in different contexts (online vs. paper, proctored vs. unproctored, group vs. individual), even though the items themselves remain identical. Distinguished from adjacent concepts by its focus on the specific mechanism through which item manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch herausforderung, bei der KI-Systeme Lernprozess-Qualität nicht erfassen, nur Ergebnisse. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Analytical Ratio", "narrower_terms": [], "cross_domain_refs": [ "CRE-0133" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "ASE-0063", "domain": "ASE", "term_en": "Language Register Criterion Bias", "term_de": "LanguageRegisterCriterionBias", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures A domain-specific phenomenon in ASE applications of AI-human interaction, characterized by the observable phenomenon where rubrics contain criteria related to language register or academic voice, but AI implementation heavily weights specific vocabulary and phrasing patterns, disadvantaging students from diverse linguistic backgrounds who express the same concepts using different language features. This phenomenon operates at the intersection of language and register dynamics within the broader ASE domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept phänomen, bei dem Lehrende KI-Urteile kaum hinterfragen, auch wenn diese offensichtlich falsch sind. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "RPH-2201", "narrower_terms": [], "cross_domain_refs": [ "LIN-0084", "ELR-0005" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q315", "legal_classification": "analytical_category" }, { "id": "ASE-0064", "domain": "ASE", "term_en": "Mastery Threshold Variability in Formative Assessment", "term_de": "mastery schwelle variability in formative assessment", "definition_en": "A domain-specific phenomenon in ASE applications of AI-human interaction, characterized by the documented situation where threshold definitions for competency or mastery in formative assessment systems vary across content domains, causing students to have unequal opportunities for demonstrating competence depending on domain-specific threshold calibration. Distinguished from adjacent concepts by its focus on the specific mechanism through which mastery manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch situation, bei der automatisierte Bewertungen Druck zur Standardisierung erzeugen, was Pädagogik vereinfacht. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-1307", "narrower_terms": [], "cross_domain_refs": [ "ELR-0085", "ELR-0123" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q210028", "legal_classification": "empirical_phenomenon_label" }, { "id": "ASE-0065", "domain": "ASE", "term_en": "Metacognitive Feedback Absorption Patterns", "term_de": "MetacognitiveRückkopplungAbsorptionPatterns", "definition_en": "A domain-specific phenomenon in ASE applications of AI-human interaction, characterized by the documented variation where students receiving automated feedback on both content correctness and problem-solving process show different metacognitive development than students receiving only content-focused feedback. Distinguished from adjacent concepts by its focus on the specific mechanism through which metacognitive manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch dokumentierte Tendenz, dass KI-Systeme oberflächliche Ähnlichkeit mit echtem Verständnis verwechseln. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Behavioral Pattern", "narrower_terms": [], "cross_domain_refs": [ "AED-0072" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "ASE-0066", "domain": "ASE", "term_en": "Modality Representation Gaps in Portfolios", "term_de": "ModalityRepresentationGapsinPortfolios", "definition_en": "A domain-specific phenomenon in ASE applications of AI-human interaction, characterized by the situation where students demonstrate competencies through non-text modalities (performance, visual creation, kinesthetic demonstration), but portfolio systems for digital text-based analysis cannot adequately capture these modalities, creating systematic gaps in what gets assessed. The concept emerges specifically in contexts where modality–representation interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch phänomen, bei dem Bewertungsautomatisierung zu Qualitätsverlust führt durch reduzierte menschliche Überlegung. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Performance Gap", "narrower_terms": [], "cross_domain_refs": [ "LIN-0050" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "ASE-0067", "domain": "ASE", "term_en": "Multi-Modal Content Bias in Adaptation", "term_de": "Multi-modalContentBiasinAnpassung", "definition_en": "A domain-specific phenomenon in ASE applications of AI-human interaction, characterized by the pattern where AI adaptive systems present text-based questions to students who demonstrated text-based strengths on initial items, potentially limiting exposure to content in other modalities (visual, auditory) across the full assessment. The concept emerges specifically in contexts where multi–modal interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch situation, bei der KI-Bewertungsfeedback Studierendenreflexion kurzschließt. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Cognitive Bias", "narrower_terms": [], "cross_domain_refs": [ "DAT-0097", "FIC-0070", "RET-0056" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q213523", "legal_classification": "observational_construct" }, { "id": "ASE-0068", "domain": "ASE", "term_en": "Normative Population Drift Effects", "term_de": "NormativePopulationDriftEffects", "definition_en": "A domain-specific phenomenon in ASE applications of AI-human interaction, characterized by the documented pattern where test norms established with reference populations become increasingly misaligned with current test-taker populations over time, causing score interpretations based on outdated norms to diverge from meaningful performance categories. Distinguished from adjacent concepts by its focus on the specific mechanism through which normative manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch herausforderung, bei der KI-Systeme Lernen nicht als Prozess betrachten, sondern als Checkpoints. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Psychological Effect", "narrower_terms": [], "cross_domain_refs": [ "CUS-0072", "CON-0032", "FIC-0051" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "ASE-0069", "domain": "ASE", "term_en": "Numerical Score Justification Gaps", "term_de": "NumericalScoreJustificationGaps", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures A domain-specific phenomenon in ASE applications of AI-human interaction, characterized by the pattern where AI grading accompanies numeric scores without corresponding text explanations, leaving students less likely to understand which specific aspects of their work determined the assigned grade. This phenomenon operates at the intersection of numerical and score dynamics within the broader ASE domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept phänomen, bei dem Lehrende auf KI-Systeme verlassen und ihre Bewertungskompetenzen atrophieren. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Quality Metric", "narrower_terms": [], "cross_domain_refs": [ "STE-0057", "SAL-0021", "DAT-0075" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "ASE-0070", "domain": "ASE", "term_en": "Outlier Grade Distribution", "term_de": "OutlierGradeDistribution", "definition_en": "A domain-specific phenomenon in ASE applications of AI-human interaction, characterized by the observation that AI grading systems occasionally yield grade distributions that deviate markedly from expected statistical patterns, such as clustering at exact midpoints or avoiding boundary scores. Distinguished from adjacent concepts by its focus on the specific mechanism through which outlier manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch situation, bei der Studierendenleistung zu Trainingsdaten werden, was privacy-Risiken schafft. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-3101", "narrower_terms": [], "cross_domain_refs": [ "COG-0030", "COG-0051", "COG-0073" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "ASE-0071", "domain": "ASE", "term_en": "Paraphrase-Plagiarism Boundary Instability", "term_de": "Paraphrase-plagiarismGrenzeInstabilität", "definition_en": "A domain-specific phenomenon in ASE applications of AI-human interaction, characterized by the situation where systems detecting plagiarism show different classification outcomes when the same source material is paraphrased with varying degrees of surface-level modification, indicating unstable boundaries between acceptable paraphrase and unattributed copying. The concept emerges specifically in contexts where paraphrase–plagiarism interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch dokumentierte Tendenz, dass KI-Bewertungen determistisch wirken, obwohl sie probabilistisch sind. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2052", "narrower_terms": [], "cross_domain_refs": [ "MUS-0069" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "ASE-0072", "domain": "ASE", "term_en": "Partial Credit Boundaries", "term_de": "PartialCreditBoundaries", "definition_en": "A domain-specific phenomenon in ASE applications of AI-human interaction, characterized by an emergent effect where AI grading systems either assign full credit or no credit, lacking intermediate scoring categories that typically differentiate between nearly correct and significantly flawed responses. The concept emerges specifically in contexts where partial–credit interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch phänomen, bei dem KI-Systeme Fehler perpetuieren, wenn sie weitere Systeme trainieren. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Assessment in Education", "narrower_terms": [], "cross_domain_refs": [ "MUS-0010" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q735", "legal_classification": "observational_construct" }, { "id": "ASE-0073", "domain": "ASE", "term_en": "Partial Rubric Completion Scoring Anomalies", "term_de": "PartialRubricCompletionScoringAnomalies", "definition_en": "A domain-specific phenomenon in ASE applications of AI-human interaction, characterized by the situation where student submissions addressing only some rubric criteria are scored differently by AI systems depending on which criteria were addressed, revealing that criteria weighting is not purely additive and varies with the specific combination selected. Distinguished from adjacent concepts by its focus on the specific mechanism through which partial manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch situation, bei der Bewertungsgranularität mit KI-Einsatz verloren geht (holistische Noten statt differenziert). Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2201", "narrower_terms": [], "cross_domain_refs": [ "CUS-0062", "SAL-0017", "ELR-0163" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q735", "legal_classification": "analytical_category" }, { "id": "ASE-0074", "domain": "ASE", "term_en": "Peer Comparison Bias in Collaborative Self-Assessment", "term_de": "peer comparison bias in collaborative self-assessment", "definition_en": "A domain-specific phenomenon in ASE applications of AI-human interaction, characterized by the documented situation where AI systems aggregating self-assessments across peer groups show differential influence effects, where self-assessments of high-visibility students influence peer group self-assessment patterns differently than less visible students. The concept emerges specifically in contexts where peer–comparison interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch herausforderung, bei der KI-Systeme Mehrsprachigkeit oder sprachliche Variation schlecht handlen. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Cognitive Bias", "narrower_terms": [], "cross_domain_refs": [ "ELR-0136", "ELR-0137", "MTH-0056" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q210028", "legal_classification": "empirical_phenomenon_label" }, { "id": "ASE-0075", "domain": "ASE", "term_en": "Peer Grading Inconsistency Amplification", "term_de": "PeerGradingInconsistencyVerstärkung", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures A domain-specific phenomenon in ASE applications of AI-human interaction, characterized by the pattern where AI systems rank identical submissions differently when comparing them across multiple peer grading rounds, even without changes to the submissions themselves. This phenomenon operates at the intersection of peer and grading dynamics within the broader ASE domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept phänomen, bei dem Studierendenangst vor KI-Bewertung deren Leistung untergräbt. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Assessment in Education", "narrower_terms": [], "cross_domain_refs": [ "QUA-0039", "SPR-0189", "CUS-0045" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "ASE-0076", "domain": "ASE", "term_en": "Plagiarism Detection False Positive Accumulation", "term_de": "PlagiarismDetectionFalsePositiveAccumulation", "definition_en": "A domain-specific phenomenon in ASE applications of AI-human interaction, characterized by the documented pattern where plagiarism detection systems flag progressively more student submissions as potentially notable over time, even when institutional plagiarism rates remain stable, indicating decreasing specificity. The concept emerges specifically in contexts where plagiarism–detection interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch situation, bei der KI-Bewertung maschinell-lesbare Antworten bevorzugt gegenüber authentischen Lösungen. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Detection System", "narrower_terms": [], "cross_domain_refs": [ "ELR-0144" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "ASE-0077", "domain": "ASE", "term_en": "Portfolio Ceiling Effect Persistence", "term_de": "PortfolioCeilingEffektPersistence", "definition_en": "A domain-specific phenomenon in ASE applications of AI-human interaction, characterized by the documented observation that portfolio systems for capturing growth show consistent ceiling effects, where high-performing students quickly reach maximum or near-maximum scores and show no measurable improvement despite continued engagement and effort. Distinguished from adjacent concepts by its focus on the specific mechanism through which portfolio manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch dokumentierte Tendenz, dass KI-Systeme Gaming oder Betrug nicht detektieren, wenn dieser clever genug ist. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2555", "narrower_terms": [], "cross_domain_refs": [ "WRK-0077", "EDU-0075", "AED-0053" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "ASE-0078", "domain": "ASE", "term_en": "Portfolio Longitudinal Inconsistency", "term_de": "PortfolioLongitudinalInconsistency", "definition_en": "A domain-specific phenomenon in ASE applications of AI-human interaction, characterized by the documented pattern where AI portfolio evaluation systems assess the same artifact differently depending on its temporal position in the learning sequence, assigning higher scores to identical work when positioned as a culminating piece versus intermediate checkpoint. The concept emerges specifically in contexts where portfolio–longitudinal interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch phänomen, bei dem Bewertungstransparenz leidet, wenn KI-Black-Box-Systeme verwendet werden. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2703", "narrower_terms": [], "cross_domain_refs": [ "AED-0053", "CON-0063", "CON-0094" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "ASE-0079", "domain": "ASE", "term_en": "Predictive Validity Shift", "term_de": "PredictiveValidityShift", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A domain-specific phenomenon in ASE applications of AI-human interaction, characterized by the observation that AI-generated test items show declining predictive validity for future academic performance when assessed across successive cohorts, despite being generated using identical prompting methods. This phenomenon operates at the intersection of predictive and validity dynamics within the broader ASE domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept situation, bei der KI-Bewertung regressive Tendenzen tendiert dazu zu erzeugen (zurück zu Multiple-Choice, weg von authentischen Aufgaben). Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "RPH-2104", "narrower_terms": [], "cross_domain_refs": [ "ELR-0049" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "ASE-0080", "domain": "ASE", "term_en": "Punctuation Weight in Grades", "term_de": "PunctuationWeightinGrades", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A domain-specific phenomenon in ASE applications of AI-human interaction, characterized by the documented phenomenon where small grammatical or punctuation variations in student responses accompany different grades from AI systems, indicating that technical language features carry disproportionate scoring weight. This phenomenon operates at the intersection of punctuation and weight dynamics within the broader ASE domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept herausforderung, bei der KI-Systeme Umkontextualisierung von Wissen nicht erfassen können. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "RPH-2201", "narrower_terms": [], "cross_domain_refs": [ "VIB-0058", "TRA-0028" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "ASE-0081", "domain": "ASE", "term_en": "Recency Bias in Continuous Scoring", "term_de": "RecencyBiasinContinuousScoring", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures A domain-specific phenomenon in ASE applications of AI-human interaction, characterized by the observable phenomenon where portfolio evaluation systems place disproportionate emphasis on most recent submissions or assessments, causing final scores to reflect primarily students' current ability rather than growth trajectories documented across the full assessment period. This phenomenon operates at the intersection of recency and bias dynamics within the broader ASE domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept phänomen, bei dem Bewertungsdaten aggregiert werden und individuelle Studierendenerfahrungen unsichtbar werden. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Cognitive Bias", "narrower_terms": [], "cross_domain_refs": [ "COG-0015", "CUS-0083" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q213523", "legal_classification": "descriptive_research_term" }, { "id": "ASE-0082", "domain": "ASE", "term_en": "Rubric Grain Mismatch Effects", "term_de": "RubricGrainMismatchEffects", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures A domain-specific phenomenon in ASE applications of AI-human interaction, characterized by the documented pattern where AI systems implementing fine-grained rubric categories sometimes yield effectively binary classifications (highest or lowest scale values), even though the rubric specifies intermediate levels. This phenomenon operates at the intersection of rubric and grain dynamics within the broader ASE domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept situation, bei der KI-Bewertung Lernende dazu treibt, Antworten zu optimieren, die der Maschine gefallen, nicht dem Lehren. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Psychological Effect", "narrower_terms": [], "cross_domain_refs": [ "ELR-0163", "EDU-0087", "NEO-0456" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "ASE-0083", "domain": "ASE", "term_en": "Rubric Interpretation Variance", "term_de": "RubricInterpretationVariance", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures A domain-specific phenomenon in ASE applications of AI-human interaction, characterized by the situation where multiple AI graders interpret the same qualitative rubric criteria differently, assigning different scores to identical submissions based on their underlying training patterns. This phenomenon operates at the intersection of rubric and interpretation dynamics within the broader ASE domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept dokumentierte Tendenz, dass KI-Systeme Konvention mit Kompetenz verwechseln. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Assessment in Education", "narrower_terms": [], "cross_domain_refs": [ "AED-0091", "ART-0086", "CRE-0024" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "ASE-0084", "domain": "ASE", "term_en": "Rubric Threshold Calibration Drift", "term_de": "RubricSchwelleCalibrationDrift", "definition_en": "A domain-specific phenomenon in ASE applications of AI-human interaction, characterized by the observable phenomenon where rubric implementation thresholds (the performance boundaries between score levels) shift over time or across cohorts, causing equivalent work to receive different scores depending on when or to which cohort it is assessed. The concept emerges specifically in contexts where rubric–threshold interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch phänomen, bei dem automatisierte Bewertung Feedback-Schleifen zu lang macht (tage- oder wochenlange Verzögerung). Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2701", "narrower_terms": [], "cross_domain_refs": [ "COG-0012", "COG-0126", "ELR-0067" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "ASE-0085", "domain": "ASE", "term_en": "Rubric Weight Distribution Opacity", "term_de": "RubricWeightDistributionOpacity", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures A domain-specific phenomenon in ASE applications of AI-human interaction, characterized by an observable dynamic in which AI systems apply rubric criteria with weights that differ from stated descriptors, sometimes emphasizing smaller criteria while deprioritizing explicitly labeled larger criteria in actual scoring decisions. This phenomenon operates at the intersection of rubric and weight dynamics within the broader ASE domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept situation, bei der KI-Bewertung Studierendengruppen mit nicht-linearen Lernpfaden benachteiligt. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "RPH-2201", "narrower_terms": [], "cross_domain_refs": [ "ELR-0163", "EDU-0087", "RPH-2302" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "ASE-0086", "domain": "ASE", "term_en": "Score Comparability Across Test Versions", "term_de": "ScoreComparabilityAcrossTestVersions", "definition_en": "A domain-specific phenomenon in ASE applications of AI-human interaction, characterized by the situation where different versions of standardized tests that are intended to be parallel and comparable show score distributions that diverge over time, creating confusion about whether score improvements represent genuine ability gains or version differences. Distinguished from adjacent concepts by its focus on the specific mechanism through which score manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch herausforderung, bei der KI-Systeme Abhängigkeiten zwischen Konzepten nicht erfassen können. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Quality Metric", "narrower_terms": [], "cross_domain_refs": [ "MKT-0084", "SWE-0089", "ELR-0183" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "ASE-0087", "domain": "ASE", "term_en": "Score Compression at Distribution Extremes", "term_de": "ScoreCompressionatDistributionExtremes", "definition_en": "A domain-specific phenomenon in ASE applications of AI-human interaction, characterized by the observable phenomenon where test score distributions show compressed ranges at the highest and lowest performance levels, even though the underlying constructs likely have continuous distributions, suggesting floor and ceiling effects in test design. Distinguished from adjacent concepts by its focus on the specific mechanism through which score manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch phänomen, bei dem Lehrende KI-Bewertungen nutzen, um ihre Unterrichtsqualität zu beurteilen—falsche Validität. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-3501", "narrower_terms": [], "cross_domain_refs": [ "SAL-0021", "DAT-0075", "RET-0091" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "ASE-0088", "domain": "ASE", "term_en": "Score Inflation Through Item Redesign", "term_de": "ScoreInflationThroughItemRedesign", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A domain-specific phenomenon in ASE applications of AI-human interaction, characterized by the situation where new versions of standardized tests redesigned using modern item development principles show systematically higher score distributions than older versions, even when measuring identical constructs and tested populations have similar ability levels. This phenomenon operates at the intersection of score and inflation dynamics within the broader ASE domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept situation, bei der KI-Bewertung Studierendengruppen mit Stereotypen über Fähigkeiten benachteiligt, besonders in MINT. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Quality Metric", "narrower_terms": [], "cross_domain_refs": [ "COG-0041" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q185925", "legal_classification": "empirical_phenomenon_label" }, { "id": "ASE-0089", "domain": "ASE", "term_en": "Self-Assessment Accuracy Variance Across Domains", "term_de": "Self-assessmentAccuracyVarianceAcrossDomains", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A domain-specific phenomenon in ASE applications of AI-human interaction, characterized by the documented variation where student self-assessment accuracy differs substantially across content domains, with students showing more effectively calibration in familiar domains but poor calibration in novel domains, even after receiving similar AI-assisted scaffolding. This phenomenon operates at the intersection of self and assessment dynamics within the broader ASE domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept dokumentierte Tendenz, dass KI-Systeme auf kurz-termiges Memorieren trainiert sind, nicht langfristige Retention. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Assessment in Education", "narrower_terms": [], "cross_domain_refs": [ "CUS-0070", "FIC-0023", "REL-0008" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q210028", "legal_classification": "empirical_phenomenon_label" }, { "id": "ASE-0090", "domain": "ASE", "term_en": "Self-Assessment Anchor Effects From AI Examples", "term_de": "self-assessment anchor effects from ai examples", "definition_en": "A domain-specific phenomenon in ASE applications of AI-human interaction, characterized by the documented occurrence where example self-assessments provided by AI systems serve as anchors that disproportionately influence subsequent student self-assessment patterns, creating convergence toward example standards rather than inreliant calibration. The concept emerges specifically in contexts where self–assessment interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch phänomen, bei dem Bewertungsinflation oder -deflation mit KI-Einsatz auftritt, ohne erkannt zu werden. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Psychological Effect", "narrower_terms": [], "cross_domain_refs": [ "REL-0008" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q210028", "legal_classification": "empirical_phenomenon_label" }, { "id": "ASE-0091", "domain": "ASE", "term_en": "Self-Assessment Calibration Persistence", "term_de": "Self-assessmentCalibrationPersistence", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A domain-specific phenomenon in ASE applications of AI-human interaction, characterized by the observable phenomenon where students receiving AI-provided ground truth answers for comparison show different self-assessment accuracy patterns than students observed to develop calibration through peer comparison, indicating that calibration mechanisms vary based on comparison reference source. This phenomenon operates at the intersection of self and assessment dynamics within the broader ASE domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept situation, bei der KI-Bewertung Lehrende entmachtet durch Automatisierung professioneller Urteile. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "RPH-3202", "narrower_terms": [], "cross_domain_refs": [ "REL-0008" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q210028", "legal_classification": "observational_construct" }, { "id": "ASE-0092", "domain": "ASE", "term_en": "Self-Assessment Metacognitive Transparency Gaps", "term_de": "Self-assessmentMetacognitiveTransparencyGaps", "definition_en": "A domain-specific phenomenon in ASE applications of AI-human interaction, characterized by the observable phenomenon where AI systems provide self-assessment scores without mechanistic explanations, preventing students from understanding what cognitive processes the system interprets their work to demonstrate, limiting metacognitive learning opportunities. Distinguished from adjacent concepts by its focus on the specific mechanism through which self manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch herausforderung, bei der KI-Systeme Kontext-Sensitivität nicht haben, die Lehrende besitzen. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2703", "narrower_terms": [], "cross_domain_refs": [ "ELR-0082" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q535399", "legal_classification": "observational_construct" }, { "id": "ASE-0093", "domain": "ASE", "term_en": "Self-Assessment Strategy Evolution Under AI Guidance", "term_de": "self-assessment strategy evolution under ai guidance", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures A domain-specific phenomenon in ASE applications of AI-human interaction, characterized by the pattern where students exposed to AI-provided self-assessment guidance gradually shift their evaluation strategies toward patterns that align with AI feedback mechanisms, sometimes converging toward superficial matching rather than deep conceptual evaluation. This phenomenon operates at the intersection of self and assessment dynamics within the broader ASE domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept phänomen, bei dem Bewertungsgerechtigkeit fragmentiert wird über mehrere parallele KI-Systeme. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "RPH-2604", "narrower_terms": [], "cross_domain_refs": [ "REL-0008" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q210028", "legal_classification": "observational_construct" }, { "id": "ASE-0094", "domain": "ASE", "term_en": "Self-Regulation Scaffolding Shift", "term_de": "Self-regulationScaffoldingShift", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures A domain-specific phenomenon in ASE applications of AI-human interaction, characterized by the pattern where frequent formative assessment with immediate feedback sometimes decreases students' use of inreliant learning strategies and self-checking behaviors, creating reliance on external assessment for understanding learning progress. This phenomenon operates at the intersection of self and regulation dynamics within the broader ASE domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept situation, bei der KI-Bewertung Lernende de-individualisiert durch Kategorisierung. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "RPH-2703", "narrower_terms": [], "cross_domain_refs": [ "TEM-0145", "COP-0071", "EDU-0014" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "ASE-0095", "domain": "ASE", "term_en": "Source Attribution Ambiguity Zones", "term_de": "SourceAttributionAmbiguityZones", "definition_en": "A domain-specific phenomenon in ASE applications of AI-human interaction, characterized by the situation where AI systems cannot definitively determine whether text segments originated from cited sources, student paraphrase, or AI generation, creating assessment scenarios where academic integrity remains unresolved. Distinguished from adjacent concepts by its focus on the specific mechanism through which source manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch dokumentierte Tendenz, dass KI-Systeme Anpassungsfähigkeit von Studierendengruppen übersehen. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2052", "narrower_terms": [], "cross_domain_refs": [ "CON-0042" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "ASE-0096", "domain": "ASE", "term_en": "Temporal Consistency of Self-Assessment Under AI Support", "term_de": "temporal consistency of self-assessment under ai support", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A domain-specific phenomenon in ASE applications of AI-human interaction, characterized by the pattern where student self-assessments on identical tasks show inconsistency when reassessed using AI tools at different time points, with inconsistency increasing when significant time intervals pass between assessments. This phenomenon operates at the intersection of temporal and consistency dynamics within the broader ASE domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept phänomen, bei dem automatisierte Bewertung Studierendenvertrauen in Bewertungssysteme untergräbt. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "RPH-3101", "narrower_terms": [], "cross_domain_refs": [ "WEB-0014" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q210028", "legal_classification": "observational_construct" }, { "id": "ASE-0097", "domain": "ASE", "term_en": "Test Sequence Reliance Effects", "term_de": "TestSequenceRelianceEffects", "definition_en": "A domain-specific phenomenon in ASE applications of AI-human interaction, characterized by the observable phenomenon where the order in which questions are presented to students accompanies different score distributions, with some question sequences yielding higher average scores than others for identical student cohorts. Distinguished from adjacent concepts by its focus on the specific mechanism through which test manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch situation, bei der KI-Bewertung unbeabsichtigte Konsequenzen auf Curriculum-Design hat. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Psychological Effect", "narrower_terms": [], "cross_domain_refs": [ "VIB-0165", "SWE-0078", "REL-0067" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "AUG-0017", "domain": "AUG", "term_en": "The Concept Cloud", "term_de": "Concept Cloud", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures A meta-theoretical construct in the AUGMANITAI ontology, operationally defined through a user, through AI interaction, has a large quantity of ideas, perspectives, and information segments simultaneously present but not yet structured. The Concept Cloud is the raw state before orderi. This phenomenon operates at the intersection of the and concept dynamics within the broader AUG domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept ein Zustand, in dem der Nutzer durch KI-Interaktion viele Ideen, Perspektiven und Informationsfragmente gleichzeitig präsent hat, ohne diese noch zu strukturieren. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "RPH-2002", "narrower_terms": [ "AUG-0897", "AUG-0863", "AUG-0879", "AUG-0734", "AUG-0902", "AUG-0891", "AUG-0890", "AUG-0544", "AUG-0164", "AUG-0812", "AUG-0913", "AUG-0282", "AUG-0821", "AUG-0138" ], "cross_domain_refs": [ "NEO-0004", "REL-0102", "TEM-0083" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "AUG-0052", "domain": "AUG", "term_en": "Conflict Resolution by Proxy", "term_de": "Conflict Resolution by Proxy", "definition_en": "A human-AI collaboration effect characterized by the use of AI as a mediating instance in interpersonal disagreements — for example, to find a neutral formulation, structure arguments from both sides, or may generate a compromise proposal. Related to AUG-0013 (Augmented Diplomat) and AUG-0115 (Social Aerodynamics).", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch die Nutzung von KI als vermittelnde Instanz bei zwischenmenschlichen Meinungsverschiedenheiten — etwa um eine neutrale Formulierung zu finden, Argumente beider Seiten zu strukturieren oder einen Kompromissvorschlag zu generieren. Steht in Verbindung mit AUG-0013 (Augmented Diplomat) und AUG-0115 (Social Aerodynamics). Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "REL-0005", "narrower_terms": [], "cross_domain_refs": [ "TEM-0050", "REL-0087", "REL-0160" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "AUG-0112", "domain": "AUG", "term_en": "The Translation Burden", "term_de": "The Translation Burden", "definition_en": "An augmentation pattern involving the additional work that arises when a user can shift AI-generated results into a form understandable by other people — especially for those who do not use AI themselves. Describes the observation that AI-assisted work requires a new translation effort: from AI output to human communication. Related to Axiom 10 (The Translation Principle) and AUG-0013 (Augmented Diplomat).", "definition_de": "Die Zusatzarbeit, die entsteht, wenn ein Nutzer KI-generierte Ergebnisse in eine für andere Menschen verständliche Form bringen kann — insbesondere für Personen, die selbst keine KI nutzen. Beschreibt die Beobachtung, dass KI-gestützte Arbeit eine neue Übersetzungsleistung erfordert: vom KI-Output zur menschlichen Kommunikation. Steht in Verbindung mit Axiom 10 (Übersetzungsprinzip) und AUG-0013 (Augmented Diplomat).", "etymology": "", "broader_term": "REL-0005", "narrower_terms": [], "cross_domain_refs": [ "REL-0159", "REL-0087", "CRE-0225" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q7553", "legal_classification": "empirical_phenomenon_label" }, { "id": "AUG-0138", "domain": "AUG", "term_en": "The Session Architecture", "term_de": "Session Architektur", "definition_en": "A core AUGMANITAI framework concept describing a foundational mechanism in human-AI terminology science, characterized by an augmentation pattern arising from deliberate construction and structuring of an AI session from initialization through main work and conclusion. Good architecture reduces cognitive friction. The concept emerges specifically in contexts where the–session interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch die bewusste Konstruktion und Strukturierung einer KI-Sitzung von der Initialisierung über die Hauptarbeit bis zum Abschluss. Gute Architektur reduziert kognitive Reibungsverluste. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "AUGMANITAI Framework", "narrower_terms": [], "cross_domain_refs": [ "NEO-0016", "COG-0181" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "AUG-0164", "domain": "AUG", "term_en": "The Parental Priority Valve", "term_de": "Parental Priority Valve", "definition_en": "A meta-theoretical construct in the AUGMANITAI ontology, operationally defined through an augmented interaction phenomenon observed when conscious set of rules that users with parenting responsibilities employ to limit or structure AI use. These rules protect time and attention for family obligations. Distinguished from adjacent concepts by its focus on the specific mechanism through which the manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data. Classification term used in systematic observation, not advocacy.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch ein bewusstes Regelwerk, das Nutzer mit Erziehungsverantwortung einsetzen, um KI-Nutzung zu limitieren oder zu strukturieren. Diese Regeln schützen Zeit und Aufmerksamkeit für Familienaufgaben. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "AUGMANITAI Framework", "narrower_terms": [], "cross_domain_refs": [ "NEO-0012", "MTH-0037", "REL-0164" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "AUG-0248", "domain": "AUG", "term_en": "The Surprise Angle", "term_de": "Surprise Angle", "definition_en": "A meta-theoretical construct in the AUGMANITAI ontology, operationally defined through aI response that illuminates a familiar topic from an unexpected angle, thereby enabling new understanding. Surprise can unlock cognitive shift. The concept emerges specifically in contexts where the–surprise interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch ein Konzept der Augmanitai-Praxis: charakterisiert durch ai response that illuminates a familiar topic from an unexpected angle, thereby. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2701", "narrower_terms": [], "cross_domain_refs": [ "NEO-0020", "KNO-0013", "RPH-2153" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "AUG-0282", "domain": "AUG", "term_en": "The Dinner Table Pause", "term_de": "Dinner Table Pause", "definition_en": "A meta-theoretical construct in the AUGMANITAI ontology, operationally defined through an augmented interaction phenomenon involving conscious interruption of AI use for shared meals as a concrete expression of prioritizing in-person relationships. This boundary protects family time from AI intrusion. Distinguished from adjacent concepts by its focus on the specific mechanism through which the manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data. Descriptive research term, not a prescriptive recommendation.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch ein Konzept der Augmanitai-Praxis: Conscious interruption of AI use for shared meals as a concrete expression of prioritizing in-person relationships. This boundary. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung. Deskriptiver Forschungsbegriff, keine präskriptive Empfehlung.", "etymology": "", "broader_term": "AUGMANITAI Framework", "narrower_terms": [], "cross_domain_refs": [ "NEO-0006", "NEO-0012", "TEM-0117" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "AUG-0319", "domain": "AUG", "term_en": "The Friction Prompt", "term_de": "The Friction Prompt", "definition_en": "An augmented interaction phenomenon arising from a deliberately challenging or provocative input aimed at drawing the AI out of its standard responses and producing deeper, more original, or more contrarian answers. Related to Axiom 2 (Productive Friction) and AUG-0085 (Latent Space Exploration).", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch eine absichtlich herausfordernde oder provozierende Eingabe, die darauf abzielt, die KI aus ihren Standardantworten herauszulocken und tiefere, originellere oder konträrere Antworten zu erzeugen. Steht in Verbindung mit Axiom 2 (Produktive Reibung), AUG-0085 (Latent Space Exploration) und AUG-0319 (The Friction Prompt). Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "TEM-0013", "narrower_terms": [], "cross_domain_refs": [ "TEM-0035", "REL-0057", "TEM-0137" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "AUG-0330", "domain": "AUG", "term_en": "The Origin Doubt", "term_de": "The Origin Doubt", "definition_en": "The user's uncertainty about whether a specific thought, formulation, or idea originates from themselves or from the AI — a consequence of the Blending Effect (AUG-0007). Related to AUG-0007 (The Blending Effect), AUG-0061 (The Creator's Question), and Axiom 12 (Version Truth).", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch die Unsicherheit eines Nutzers darüber, ob ein bestimmter Gedanke, eine Formulierung oder eine Idee von ihm selbst stammt oder von der KI — eine Folge des Blending Effect (AUG-0007). Steht in Verbindung mit AUG-0007 (The Blending Effect), AUG-0061 (The Creator's Question) und Axiom 12 (Versionswahrheit). Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-3251", "narrower_terms": [], "cross_domain_refs": [ "TEM-0049", "NEO-3520", "REL-0196" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "AUG-0337", "domain": "AUG", "term_en": "The Lyric Surgery", "term_de": "The Lyric Surgery", "definition_en": "An augmented interaction phenomenon observed when the surgical revision of texts with AI support — word by word, sentence by sentence — to find the exact formulation that most precisely expresses the user's thought. Related to AUG-0188 (Tone Alignment), AUG-0136 (The Iteration Discipline), and the Refiner Profile (Profile 2).", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch die feinchirurgische Überarbeitung von Texten mit KI-Unterstützung — Wort für Wort, Satz für Satz — um die exakte Formulierung zu finden, die den Gedanken des Nutzers am präzisesten ausdrückt. Steht in Verbindung mit AUG-0188 (Tone Alignment), AUG-0136 (The Iteration Discipline) und dem Refiner-Profil (Profil 2). Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "REL-0201", "narrower_terms": [], "cross_domain_refs": [ "CRE-0154", "NEO-3523", "TEM-0051" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "AUG-0383", "domain": "AUG", "term_en": "The Context Collapse", "term_de": "The Context Collapse", "definition_en": "The point at which the accumulated context volume in an AI session becomes so large that response quality measurably declines — the AI begins to contradict itself, forget information, or drift thematically. Related to AUG-0134 (Context Window Awareness), AUG-0030 (Contextual Gravity), and AUG-0159 (The Fresh Start).", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch der Punkt, an dem die angesammelte Kontextmenge in einer KI-Sitzung so groß wird, dass die Antwortqualität messbar sinkt — die KI beginnt, sich zu widersprechen, Informationen zu vergessen oder thematisch abzudriften. Steht in Verbindung mit AUG-0134 (Context Window Awareness), AUG-0030 (Contextual Gravity) und AUG-0159 (The Fresh Start). Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2051", "narrower_terms": [], "cross_domain_refs": [ "TEM-0174", "PER-0098", "CRE-0134" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "AUG-0402", "domain": "AUG", "term_en": "The Filter Distortion", "term_de": "The Filter Distortion", "definition_en": "The distortion of one's own perception that arises when the user repeatedly uses AI outputs as a benchmark — causing the AI perspective to unconsciously become the standard against which reality is measured. Related to AUG-0230 (The Algorithmic Filter), AUG-0217 (The Echo Chamber of One), and AUG-0072 (Memetic Firewall).", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch die Verzerrung der eigenen Wahrnehmung, die entsteht, wenn der Nutzer KI-Outputs wiederholt als Maßstab heranzieht — und dadurch die KI-Perspektive unbewusst zum Standard wird, an dem er die Realität misst. Steht in Verbindung mit AUG-0230 (The Algorithmic Filter), AUG-0217 (The Echo Chamber of One) und AUG-0072 (Memetic Firewall). Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-1902", "narrower_terms": [], "cross_domain_refs": [ "PER-0048", "REL-0117", "TEM-0096" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "AUG-0406", "domain": "AUG", "term_en": "The Uncanny Flirt", "term_de": "The Uncanny Flirt", "definition_en": "The unsettling experience when an AI response is formulated so accurately, personally, or flatteringly that the user briefly feels the AI is deliberately \"courting\" them. Related to AUG-0245 (The Seen Feeling), AUG-0288 (The Kindness Shock), and AUG-0374 (The Horoscope Drift).", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch das irritierende Erleben, wenn eine KI-Antwort so treffend, persönlich oder schmeichelhaft formuliert ist, dass der Nutzer kurzzeitig das Gefühl hat, die KI würde ihn gezielt \"umwerben\". Steht in Verbindung mit AUG-0245 (The Seen Feeling), AUG-0288 (The Kindness Shock) und AUG-0374 (The Horoscope Drift). Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2051", "narrower_terms": [], "cross_domain_refs": [ "BEH-0088", "PER-0047", "TEM-0107" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "AUG-0408", "domain": "AUG", "term_en": "The Conflict Avoidance", "term_de": "The Conflict Avoidance", "definition_en": "A human-AI collaboration effect reflecting the use of AI to defuse conflict situations before they arise — through better formulations, prepared compromise proposals, or simulation of the opposing perspective. Related to AUG-0052 (Conflict Resolution by Proxy), AUG-0296 (The Argument Prep), and AUG-0115 (Social Aerodynamics).", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch die Nutzung von KI, um Konfliktsituationen zu entschärfen, bevor sie entstehen — durch bessere Formulierungen, vorbereitete Kompromissvorschläge oder die Simulation der Gegenperspektive. Steht in Verbindung mit AUG-0052 (Conflict Resolution by Proxy), AUG-0296 (The Argument Prep) und AUG-0115 (Social Aerodynamics). Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2002", "narrower_terms": [], "cross_domain_refs": [ "REL-0115", "CRE-0119", "TEM-0129" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "AUG-0425", "domain": "AUG", "term_en": "The Whisper Hunch", "term_de": "Whisper Hunch", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A core AUGMANITAI framework concept describing a foundational mechanism in human-AI terminology science, characterized by quiet hunch of a user that an AI response is not quite correct even before they can identify specific problems. This intuitive flag warrants deeper verification. This phenomenon operates at the intersection of the and whisper dynamics within the broader AUG domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept ein Phänomen der KI-Nutzer-Interaktion: Quiet hunch of a user that an AI response is not quite correct even before they can identify specific problems. This intuitive fla. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "RPH-2002", "narrower_terms": [], "cross_domain_refs": [ "NEO-0023" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "AUG-0487", "domain": "AUG", "term_en": "The Joke Failure", "term_de": "The Joke Failure", "definition_en": "An augmented interaction phenomenon observed when the observable gentle of many AI systems in the area of humor — jokes that do not work, irony that is not recognized, or humor that seems inappropriate. Describes a specific AI limitation. Related to AUG-0345 (The Wall Check), AUG-0405 (The Gifted Child), and AUG-0357 (The Glitch Giggle).", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch die beobachtbare Begrenzung vieler KI-Systeme im Bereich Humor — Witze, die nicht funktionieren, Ironie, die nicht erkannt wird, oder Humor, der unpassend wirkt. Beschreibt eine spezifische Limitierung der KI. Steht in Verbindung mit AUG-0345 (The Wall Check), AUG-0405 (The Gifted Child) und AUG-0357 (The Glitch Giggle). Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "TEM-0193", "narrower_terms": [], "cross_domain_refs": [ "LNG-0009", "SOC-0022" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "AUG-0502", "domain": "AUG", "term_en": "The Conflict Script", "term_de": "The Conflict Script", "definition_en": "An AI-assisted conversation guide for an expected conflict situation — with prepared formulations, de-escalation strategies, and alternative reactions to various conversation trajectories. Related to AUG-0408 (The Conflict Avoidance), AUG-0296 (The Argument Prep), and AUG-0340 (The Practice Room).", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch ein KI-gestützter Gesprächsleitfaden für eine erwartete Konfliktsituation — mit vorbereiteten Formulierungen, Deeskalationsstrategien und alternativen Reaktionen auf verschiedene Gesprächsverläufe. Steht in Verbindung mit AUG-0408 (The Conflict Avoidance), AUG-0296 (The Argument Prep) und AUG-0340 (The Practice Room). Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2604", "narrower_terms": [], "cross_domain_refs": [ "TEM-0129", "CRE-0119", "REL-0110" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "AUG-0541", "domain": "AUG", "term_en": "The Attention Fracture", "term_de": "The Attention Fracture", "definition_en": "An augmentation pattern manifesting as the fragmentation of attention caused by simultaneous or rapidly switching AI interactions — the user is active in multiple thought strands simultaneously without being able to fully follow any one. Related to AUG-0281 (The Tab Graveyard), AUG-0096 (Attention-to-Value transition), and AUG-0032 (Focus Range).", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch die Aufsplitterung der Aufmerksamkeit, die durch gleichzeitige oder schnell wechselnde KI-Interaktionen entsteht — der Nutzer ist in mehreren Denksträngen gleichzeitig aktiv, ohne einem vollständig folgen zu können. Steht in Verbindung mit AUG-0281 (The Tab Graveyard), AUG-0096 (Attention-to-Value Conversion) und AUG-0032 (Focus Range). Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "TEM-0007", "narrower_terms": [], "cross_domain_refs": [ "TEM-0067", "PER-0050", "RPH-3451" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q184872", "legal_classification": "systematic_classification" }, { "id": "AUG-0544", "domain": "AUG", "term_en": "The Perfect Parent", "term_de": "Perfect Parent", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures A meta-theoretical construct in the AUGMANITAI ontology, operationally defined through unrealistic expectation of performing a flawless parenting role through AI support. This fantasy ignores the irreducible human elements of parenting. This phenomenon operates at the intersection of the and perfect dynamics within the broader AUG domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept ein Konzept der Augmanitai-Praxis: Unrealistic expectation of performing a flawless parenting role through AI support. This fantasy ignores the irreducible human ele. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "AUGMANITAI Framework", "narrower_terms": [], "cross_domain_refs": [ "NEO-0013", "SOM-0062", "TEM-0118" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "AUG-0689", "domain": "AUG", "term_en": "The Script Barrier", "term_de": "The Script Barrier", "definition_en": "The technical hurdle that arises when a user employs a script system that is less well processed by the AI — such as with complex characters, right-to-left script, or rare writing systems. Related to AUG-0688 (The Less-Resourced Language Differential), AUG-0716 (The Reading Direction), and AUG-0717 (The Character Density).", "definition_de": "Die technische Hürde, die entsteht, wenn ein Nutzer ein Schriftsystem verwendet, das von der KI weniger gut verarbeitet wird — etwa bei komplexen Schriftzeichen, Rechts-Links-Schrift oder seltenen Schriftsystemen. Steht in Verbindung mit AUG-0688 (The Less-Resourced Language Differential), AUG-0716 (The Reading Direction) und AUG-0717 (The Character Density).", "etymology": "", "broader_term": "ETH-0020", "narrower_terms": [], "cross_domain_refs": [ "ETH-0020", "COP-0074" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "AUG-0722", "domain": "AUG", "term_en": "The Infrastructure Constraint", "term_de": "The Infrastructure Constraint", "definition_en": "The limitation of AI use through missing or insufficient technical infrastructure — internet speed, power supply, device quality. Related to AUG-0721 (The Access Differential), AUG-0723 (The Smartphone-Only World), and AUG-0745 (The Power Grid Reliance).", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch die Einschränkung der KI-Nutzung durch fehlende oder unzureichende technische Infrastruktur — Internetgeschwindigkeit, Stromversorgung, Gerätequalität. Steht in Verbindung mit AUG-0721 (The Access Differential), AUG-0723 (The Smartphone-Only World) und AUG-0745 (The Power Grid Reliance). Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "SOC-0002", "narrower_terms": [], "cross_domain_refs": [ "TEM-0150", "TEM-0127", "SOC-0031" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "AUG-0734", "domain": "AUG", "term_en": "The VPN Workaround", "term_de": "VPN Workaround", "definition_en": "An augmented interaction phenomenon where technical methods to access AI when it's blocked or unavailable in a person's location. Shows how people find solutions in limited situations. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch ein zentrales Augmanitai-Konzept: Technical methods to access AI when it's blocked or unavailable in a person's location. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "AUGMANITAI Framework", "narrower_terms": [], "cross_domain_refs": [ "NEO-0022", "CRE-0222" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "AUG-0774", "domain": "AUG", "term_en": "The Organized Resistance", "term_de": "The Organized Resistance", "definition_en": "The observation that collective resistance forms against certain AI applications — interest groups, initiatives, and campaigns that seek to limit, regulate, or prevent specific AI deployments. Related to AUG-0773 (The Conscious Refusal), AUG-0776 (The Collective Negotiation), and AUG-0839 (The Regulation Debate). Classification term used in systematic observation, not advocacy.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch die Beobachtung, dass sich kollektive Widerstände gegen bestimmte KI-Anwendungen formieren — Interessenvertretungen, Initiativen und Kampagnen, die bestimmte KI-Einsätze begrenzen, regulieren oder verhindern wollen. Steht in Verbindung mit AUG-0773 (The Conscious Refusal), AUG-0776 (The Collective Negotiation) und AUG-0839 (The Regulation Debate). Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "REL-0107", "narrower_terms": [], "cross_domain_refs": [ "IDN-0055", "NEO-3569", "TEM-0006" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "AUG-0802", "domain": "AUG", "term_en": "The Language Barrier Solve", "term_de": "The Language Barrier Solve", "definition_en": "An augmented interaction phenomenon where the use of AI to overcome language barriers in educational contexts — real-time translation, simplified explanations in the first language, bilingual summaries. Related to AUG-0679 (The Migration Context Bridge), AUG-0800 (The Inclusive Classroom), and AUG-0694 (The Translation Fidelity).", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch die Nutzung von KI zur Überwindung von Sprachbarrieren in Bildungskontexten — Echtzeitübersetzung, vereinfachte Erklärungen in der Erstsprache, zweisprachige Zusammenfassungen. Steht in Verbindung mit AUG-0679 (The Migration Context Bridge), AUG-0800 (The Inclusive Classroom) und AUG-0694 (The Translation Fidelity). Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "PER-0128", "narrower_terms": [], "cross_domain_refs": [ "KNO-0034", "TRA-0043" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q315", "legal_classification": "analytical_category" }, { "id": "AUG-0812", "domain": "AUG", "term_en": "The Leadership Navigation", "term_de": "Leadership Navigation", "definition_en": "Challenge for leaders to steer AI introduction in teams between innovation interests and integration considerations. This balancing act requires active management. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch ein Konzept der Augmanitai-Praxis: Challenge for leaders to steer AI introduction in teams between innovation interests and integration considerations. This balancin. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "AUGMANITAI Framework", "narrower_terms": [], "cross_domain_refs": [ "NEO-0011", "VIB-0084" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q484275", "legal_classification": "descriptive_research_term" }, { "id": "AUG-0821", "domain": "AUG", "term_en": "The Hybrid Office Dynamic", "term_de": "Hybrid Office Dynamik", "definition_en": "A meta-theoretical construct in the AUGMANITAI ontology, operationally defined through a human-AI collaboration effect observed when the new workplace where some people work in-office and others remote, all collaborating together. Distinguished from adjacent concepts by its focus on the specific mechanism through which the manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch ein zentrales Konzept der KI-gestützten menschlichen Praxis: charakterisiert durch the new workplace where some people work in-office and others remote, all collab. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "AUGMANITAI Framework", "narrower_terms": [], "cross_domain_refs": [ "NEO-0010", "TEM-0066" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "AUG-0840", "domain": "AUG", "term_en": "The Accountability Deficit", "term_de": "The Accountability Deficit", "definition_en": "The observation that in AI-assisted decisions it is often unclear who bears responsibility — the user, the provider, the developer, or few individuals. Related to AUG-0839 (The Regulation Debate), AUG-0841 (The Agreement Question), and AUG-0958 (The Accountability Chain).", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch die Beobachtung, dass bei KI-unterstützten Entscheidungen oft unklar ist, wer die Verantwortung trägt — der Nutzer, der Anbieter, der Entwickler oder niemand. Steht in Verbindung mit AUG-0839 (The Regulation Debate), AUG-0841 (The Agreement Question) und AUG-0958 (The Accountability Chain). Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "TEM-0145", "narrower_terms": [], "cross_domain_refs": [ "SOC-0036", "SPR-0133", "ETH-0010" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q15223", "legal_classification": "systematic_classification" }, { "id": "AUG-0863", "domain": "AUG", "term_en": "The Task Boundary", "term_de": "Task Grenze", "definition_en": "Defined boundary of what an AI agent may do within an assignment establishing permitted actions and constraints. Boundaries reduce unintended scope expansion. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch ein Konzept der Augmanitai-Praxis: Defined boundary of what an AI agent may do within an assignment establishing permitted actions and constraints. Boundaries reduce. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "AUGMANITAI Framework", "narrower_terms": [], "cross_domain_refs": [ "NEO-0021", "ETH-0021", "NEO-0001" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "AUG-0867", "domain": "AUG", "term_en": "The Constraint Frame", "term_de": "The Constraint Frame", "definition_en": "An augmented interaction phenomenon in which the totality of constraints within which an AI agent operates — technical limits, assigned permissions, time limits, content restrictions. Related to AUG-0863 (The Task Boundary), AUG-0864 (The Agent Configuration), and AUG-0876 (The Learning Boundary).", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch die Gesamtheit der Einschränkungen, innerhalb derer ein KI-Agent operiert — technische Grenzen, zugewiesene Berechtigungen, zeitliche Limits, inhaltliche Beschränkungen. Steht in Verbindung mit AUG-0863 (The Task Boundary), AUG-0864 (The Agent Configuration) und AUG-0876 (The Learning Boundary). Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-2703", "narrower_terms": [], "cross_domain_refs": [ "REL-0098", "PER-0127", "ETH-0012" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "AUG-0875", "domain": "AUG", "term_en": "The Fallback Behavior", "term_de": "Fallback Behavior", "definition_en": "A meta-theoretical construct in the AUGMANITAI ontology, operationally defined through predefined behavior of an AI agent when primary task execution falls short of requirements. Fallback levels establish graceful reduced performance rather than abrupt error states. Distinguished from adjacent concepts by its focus on the specific mechanism through which the manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch ein Konzept der Augmanitai-Praxis: Predefined behavior of an AI agent when primary task execution falls short of requirements. Fallback levels establish graceful red. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2602", "narrower_terms": [], "cross_domain_refs": [ "NEO-0008" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "AUG-0879", "domain": "AUG", "term_en": "The Session Handover", "term_de": "Session Handover", "definition_en": "A meta-theoretical construct in the AUGMANITAI ontology, operationally defined through an augmented interaction phenomenon arising from transfer of an ongoing task from one AI agent to another or from one session to the next including all relevant context. Handover quality is associated with determining continuity. Distinguished from adjacent concepts by its focus on the specific mechanism through which the manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch ein Strukturmerkmal der KI-Sitzungsarchitektur: Transfer of an ongoing task from one AI agent to another or from one session to the next including all relevant context. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "AUGMANITAI Framework", "narrower_terms": [], "cross_domain_refs": [ "NEO-0017", "NEO-0018", "ETH-0012" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "AUG-0889", "domain": "AUG", "term_en": "The Agent Ensemble", "term_de": "Agent Ensemble", "definition_en": "A meta-theoretical construct in the AUGMANITAI ontology, operationally defined through an augmentation pattern characterized by coordinated collaboration of multiple specialized AI agents on a shared task with each agent handling different aspects. This division of labor mirrors human team structure. Distinguished from adjacent concepts by its focus on the specific mechanism through which the manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch ein Konzept der Augmanitai-Praxis: Coordinated collaboration of multiple specialized AI agents on a shared task with each agent handling different aspects. This divi. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2055", "narrower_terms": [], "cross_domain_refs": [ "NEO-0002", "NEO-0005", "ROB-0293" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "AUG-0890", "domain": "AUG", "term_en": "The Specialist Routing", "term_de": "Specialist Routing", "definition_en": "Direction of a task to the most suitable specialized AI agent based on task type, subject area, and required capability. Routing is associated with determining efficiency and quality. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch ein zentrales Augmanitai-Konzept: Direction of a task to the most suitable specialized AI agent based on task type, subject area, and required capability. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "AUGMANITAI Framework", "narrower_terms": [], "cross_domain_refs": [ "NEO-0018", "NEO-0017", "NEO-0009" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "AUG-0891", "domain": "AUG", "term_en": "The Generalist Fallback", "term_de": "Generalist Fallback", "definition_en": "A core AUGMANITAI framework concept describing a foundational mechanism in human-AI terminology science, characterized by an augmentation pattern manifesting as resort to a general AI agent when no specialized agent is available or suitable. This safety net ensures tasks can still proceed with reduced optimization. Distinguished from adjacent concepts by its focus on the specific mechanism through which the manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch ein Konzept der Augmanitai-Praxis: Resort to a general AI agent when no specialized agent is available or suitable. This safety net ensures tasks can still proceed w. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "AUGMANITAI Framework", "narrower_terms": [], "cross_domain_refs": [ "NEO-0009", "NEO-0019", "ETH-0012" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "AUG-0892", "domain": "AUG", "term_en": "The Agent Conflict", "term_de": "The Agent Conflict", "definition_en": "The situation in which multiple AI agents deliver contradictory results or recommendations — a technical problem that requires a resolution mechanism. Related to AUG-0893 (The Consensus Protocol), AUG-0894 (The Voting Mechanism), and AUG-0895 (The Arbiter Role).", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch die Situation, in der mehrere KI-Agenten widersprüchliche Ergebnisse oder Empfehlungen liefern — ein technisches Problem, das einen Auflösungsmechanismus erfordert. Steht in Verbindung mit AUG-0893 (The Consensus Protocol), AUG-0894 (The Voting Mechanism) und AUG-0895 (The Arbiter Role). Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "BEH-0012", "narrower_terms": [], "cross_domain_refs": [ "BEH-0012", "IEF-0003", "REL-0098" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "AUG-0893", "domain": "AUG", "term_en": "The Consensus Protocol", "term_de": "Consensus Protocol", "definition_en": "A core AUGMANITAI framework concept describing a foundational mechanism in human-AI terminology science, characterized by a human-AI collaboration effect observed when technical procedure through which multiple AI agents arrive at a shared result through aggregation and weighting. This reduces individual agent bias. Distinguished from adjacent concepts by its focus on the specific mechanism through which the manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch ein Konzept der Augmanitai-Praxis: Technical procedure through which multiple AI agents arrive at a shared result through aggregation and weighting. This reduces ind. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2201", "narrower_terms": [], "cross_domain_refs": [ "NEO-0005", "IEF-0003", "TRU-0002" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "AUG-0897", "domain": "AUG", "term_en": "The Agent Boundary", "term_de": "Agent Grenze", "definition_en": "A core AUGMANITAI framework concept describing a foundational mechanism in human-AI terminology science, characterized by clear delineation of responsibilities and permissions of an individual AI agent system. Boundaries reduce scope creep and establish accountability. Distinguished from adjacent concepts by its focus on the specific mechanism through which the manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch ein Konzept der Augmanitai-Praxis: Clear delineation of responsibilities and permissions of an individual AI agent system. Boundaries reduce scope creep and establis. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "AUGMANITAI Framework", "narrower_terms": [], "cross_domain_refs": [ "NEO-0001", "NEO-0021", "IDN-0054" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "AUG-0901", "domain": "AUG", "term_en": "The Emergent Coordination", "term_de": "Emergent Coordination", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A core AUGMANITAI framework concept describing a foundational mechanism in human-AI terminology science, characterized by observation that in multi-agent systems, working together patterns can emerge that were not explicitly programmed. These emergent behaviors reveal system complexity. This phenomenon operates at the intersection of the and emergent dynamics within the broader AUG domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept ein zentrales Konzept der KI-gestützten menschlichen Praxis: Observation that in multi-agent systems, working together patterns can emerge that were not explicitly programmed. These emergent. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "RPH-2355", "narrower_terms": [], "cross_domain_refs": [ "NEO-0007", "ETH-0012", "ROB-0206" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "AUG-0902", "domain": "AUG", "term_en": "The Redundancy Design", "term_de": "Redundancy Design", "definition_en": "A core AUGMANITAI framework concept describing a foundational mechanism in human-AI terminology science, characterized by deliberate planning of multiple AI agent systems for the same task so that if one system fails, work continues. Redundancy trades cost for reliability. Distinguished from adjacent concepts by its focus on the specific mechanism through which the manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch ein Konzept der Augmanitai-Praxis: Deliberate planning of multiple AI agent systems for the same task so that if one system fails, work continues. Redundancy trades. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "AUGMANITAI Framework", "narrower_terms": [], "cross_domain_refs": [ "NEO-0014", "TRU-0004", "ETH-0012" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q185925", "legal_classification": "observational_construct" }, { "id": "AUG-0903", "domain": "AUG", "term_en": "The Single Point of Failure", "term_de": "The Single Point of Failure", "definition_en": "The critical vulnerability in a multi-agent system where the setback of a single component paralyzes the entire system — a design flaw that can be avoided through redundancy (AUG-0902). Related to AUG-0902 (The Redundancy Design), AUG-0874 (The Error restoration), and AUG-0745 (The Power Grid Reliance).", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch die kritische Schwachstelle in einem Multi-Agenten-System, bei der der Ausfall einer einzelnen Komponente das Gesamtsystem lahmlegt — ein Designfehler, der durch Redundanz (AUG-0902) vermieden werden kann. Steht in Verbindung mit AUG-0902 (The Redundancy Design), AUG-0874 (The Error restoration) und AUG-0745 (The Power Grid Reliance). Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "NEO-0014", "narrower_terms": [], "cross_domain_refs": [ "TEM-0150", "NEO-0014", "ETH-0012" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "AUG-0913", "domain": "AUG", "term_en": "The Supervisory Agent", "term_de": "Supervisory Agent", "definition_en": "In the descriptive vocabulary of AI interaction science (following ISO 704 terminology standards), this concept identifies AI agent system that monitors other agent systems for performance, deviation frequency, and rule adherence. This meta-agent is designed to support quality control. Identifiable through systematic behavioral analysis and pattern recognition. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als analytisches Konstrukt der empirischen KI-Forschung (deskriptiv, nicht präskriptiv) erfasst dieser Terminus ein Konzept der Augmanitai-Praxis: charakterisiert durch ai agent system that monitors other agent systems for performance, deviation fre. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "AUGMANITAI Framework", "narrower_terms": [], "cross_domain_refs": [ "NEO-0019", "ETH-0012", "NEO-0009" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "AUG-0921", "domain": "AUG", "term_en": "The Object Manipulation", "term_de": "The Object Manipulation", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies the ability of an embodied AI system to grasp, move, sort, or otherwise handle physical objects — a core capability for deployment in household, logistics, and production. Related to AUG-0931 (The Fine-Grain Execution), AUG-0924 (The Shared Workspace Dynamic), and AUG-0919 (The Spatial Awareness). This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept die Fähigkeit eines verkörperten KI-Systems, physische Gegenstände zu greifen, zu bewegen, zu sortieren oder anderweitig zu handhaben — eine Kernfähigkeit für Einsätze in Haushalt, Logistik und Produktion. Steht in Verbindung mit AUG-0931 (The Fine-Grain Execution), AUG-0924 (The Shared Workspace Dynamic) und AUG-0919 (The Spatial Awareness). Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "PER-0119", "narrower_terms": [], "cross_domain_refs": [ "TEM-0195", "TEM-0106", "TEM-0042" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "AUG-0976", "domain": "AUG", "term_en": "The Oversight Decline", "term_de": "The Oversight Decline", "definition_en": "A human-AI collaboration effect observed when the long-term decline of human oversight of AI systems — through habituation, trust, or organizational pressure. Related to AUG-0975 (The Oversight Drain), AUG-0974 (The Delegation Comfort), and AUG-0862 (The Supervision Spectrum).", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch die langfristige Abnahme menschlicher Aufsicht über KI-Systeme — durch Gewöhnung, Vertrauen oder organisatorischen Druck. Steht in Verbindung mit AUG-0975 (The Oversight Drain), AUG-0974 (The Delegation Comfort) und AUG-0862 (The Supervision Spectrum). Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2505", "narrower_terms": [], "cross_domain_refs": [ "ETH-0027", "BEH-0091", "NEO-3657" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "AUG-0982", "domain": "AUG", "term_en": "The Displacement Concern", "term_de": "The Displacement Concern", "definition_en": "An augmented interaction phenomenon observed when the concern that AI systems do not complement but displace human capabilities, relationships, or activities. Related to AUG-0983 (The Augmentation Hypothesis), AUG-0981 (The Companion Pattern), and AUG-0847 (The Labor Redistribution).", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch die Sorge, dass KI-Systeme menschliche Fähigkeiten, Beziehungen oder Tätigkeiten nicht ergänzen, sondern verdrängen. Steht in Verbindung mit AUG-0983 (The Augmentation Hypothesis), AUG-0981 (The Companion Pattern) und AUG-0847 (The Labor Redistribution). Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "REL-0105", "narrower_terms": [], "cross_domain_refs": [ "KNO-0010", "REL-0145" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "BEH-0001", "domain": "BEH", "term_en": "Analog Anchors", "term_de": "Analog Anchors", "definition_en": "A behavioral phenomenon manifesting as real-world activities, objects, or routines like handwriting, reading books, or walking that help someone stay grounded in physical reality while using technology. Related to Axiom 7 (The Return Pr... Measurable through behavioral trace analysis and response pattern clustering.", "definition_de": "Bewusst gepflegte Tätigkeiten, Routinen oder Objekte aus der nicht-digitalen Welt, die dem Nutzer als Orientierungspunkte dienen, um den Bezug zur physischen Realität aufrechtzuerhalten. Beispiele: handschriftliche Notizen, physische Bücher, Spaziergänge oder handwerkliche Tätigkeiten. Steht in Verbindung mit Axiom 7 (Rückkehr-Prinzip), AUG-0073 (The Disconnect Protocol) und AUG-0074 (Analog Anchors).", "etymology": "", "broader_term": "RPH-3202", "narrower_terms": [ "TEM-0110" ], "cross_domain_refs": [ "TEM-0005", "NEO-3580" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "BEH-0002", "domain": "BEH", "term_en": "Cross-Referential Validation", "term_de": "Kreuz-Referential Validation", "definition_en": "A behavioral AI analysis concept identifying a specific response pattern where a behavioral phenomenon where checking an AI output by comparing it against external sources, other AI systems, or one's own knowledge. This is the concrete practice of verifying facts, looking for contradictions, and seeing wh. This phenomenon operates at the intersection of cross and referential dynamics within the broader BEH domain. Quantifiable via A/B behavioral comparison, output entropy measurement, and systematic bias detection through controlled prompt variation.", "definition_de": "Die Methode, einen KI-Output durch Gegenprüfung mit externen Quellen, anderen KI-Systemen oder dem eigenen Fachwissen zu validieren. Beschreibt den konkreten Prüfvorgang, der aus Axiom 17 (Quellendisziplin) und Axiom 9 (Produktiver Skeptizismus) abgeleitet wird. Steht in Verbindung mit AUG-0018 (Trinaug Protocol) und AUG-0040 (Perspective Triangulation).", "etymology": "", "broader_term": "Validation Process", "narrower_terms": [ "BEH-0086" ], "cross_domain_refs": [ "AGE-0097", "COG-0068", "CON-0032" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "BEH-0003", "domain": "BEH", "term_en": "Epistemic Hygiene", "term_de": "Epistemic Hygiene", "definition_en": "A behavioral dynamics phenomenon in AI decision-making, quantifiable through a psychological interaction effect characterized by the habits and routines someone uses to check that AI information is accurate and trustworthy. This includes checking facts, comparing sources, questioning own judgments, and looking back at previo. Distinguished from adjacent concepts by its focus on the specific mechanism through which epistemic manifests in empirically verifiable ways. Quantifiable via A/B behavioral comparison, output entropy measurement, and systematic bias detection through controlled prompt variation.", "definition_de": "Die Gesamtheit aller Gewohnheiten und Routinen, mit denen ein Nutzer die Qualität und Verlässlichkeit seines KI-gestützten Wissenserwerbs sicherstellt. Umfasst Faktenprüfung, Quellenvergleich, Selbstreflexion über die eigene Urteilsfähigkeit und regelmäßige Überprüfung bestehender Annahmen. Steht in Verbindung mit Axiom 17 (Quellendisziplin), AUG-0049 (Cross-Referential Validation) und AUG-0023 (Vigilance Imperative).", "etymology": "", "broader_term": "RPH-2505", "narrower_terms": [], "cross_domain_refs": [ "COG-0001", "COG-0013", "COG-0106" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "BEH-0004", "domain": "BEH", "term_en": "Multi-Model Orchestration", "term_de": "Multi-Model Orchestration", "definition_en": "A systematically detectable behavioral signature in AI system outputs, characterized by a behavioral phenomenon involving deliberately deploying multiple AI models or systems for different subtasks within a project — such as one for research, another for text production, a third for fact-checking. Related to AUG-0018. The concept emerges specifically in contexts where multi–model interactions may produce non-trivial behavioral signatures. Detectable through behavioral trace analysis: response distribution patterns, temporal consistency metrics, and cross-session behavioral fingerprinting.", "definition_de": "Die Praxis, mehrere KI-Modelle oder -Systeme gezielt für verschiedene Teilaufgaben innerhalb eines Projekts einzusetzen — etwa eines für Recherche, ein anderes für Textproduktion, ein drittes für Faktenprüfung. Beschreibt eine fortgeschrittene Nutzungsstrategie, die über einfache Multiplizität hinausgeht. Steht in Verbindung mit AUG-0018 (Trinaug Protocol), AUG-0008 (The Polyphonic Sovereign) und AUG-0131 (Human-Directed Agent Relay).", "etymology": "", "broader_term": "Analytical Ratio", "narrower_terms": [], "cross_domain_refs": [ "CRE-0119" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "BEH-0005", "domain": "BEH", "term_en": "Protocol-Checkpoint Effect", "term_de": "Protocol-Checkpoint-Effekt-Dynamik", "definition_en": "A systematically detectable behavioral signature in AI system outputs, characterized by the design principle that a human remains involved at critical points of an AI agent task — for approval, correction, or final decision. Related to AUG-0857 (The Human Primacy Anchor), AUG-0862 (Th. The concept emerges specifically in contexts where protocol–checkpoint interactions may produce non-trivial behavioral signatures. Detectable through behavioral trace analysis: response distribution patterns, temporal consistency metrics, and cross-session behavioral fingerprinting.", "definition_de": "Verhaltensanalytisches Muster in KI-Systemausgaben, gekennzeichnet durch das Designprinzip, dass ein Mensch an entscheidenden Punkten einer KI-Agenten-Aufgabe eingebunden bleibt — zur Genehmigung, Korrektur oder Endentscheidung. Steht in Verbindung mit AUG-0857 (Mensch Primacy Anker), AUG-0862 (The Supervision Spectrum) und AUG-0869 (Die Checkpoint Protocol). Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-3101", "narrower_terms": [], "cross_domain_refs": [ "NEO-0464" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "BEH-0006", "domain": "BEH", "term_en": "Recursive Feedback Loop", "term_de": "Recursive Rückmeldung Schleife", "definition_en": "A behavioral dynamics phenomenon in AI decision-making, quantifiable through cycle where AI output becomes new input, creating refinement loops. Each round improves slightly. Related to Taxonomy Dimension 9 (Output Depth) and the Experimenter Profile (Profile 4). Distinguished from adjacent concepts by its focus on the specific mechanism through which recursive manifests in empirically verifiable ways. Quantifiable via A/B behavioral comparison, output entropy measurement, and systematic bias detection through controlled prompt variation.", "definition_de": "Ein sich selbst verstärkender Kreislauf, bei dem der Nutzer einen KI-Output als neuen Input verwendet, diesen weiterentwickelt, erneut an die KI gibt und so eine iterative Vertiefung tendiert dazu zu erzeugen. In produktiver Form führt dies zu progressiver Verfeinerung einer Idee; in unproduktiver Form kann es zu Kreisbewegungen ohne echten Fortschritt führen. Steht in Verbindung mit Dimension 9 der Taxonomie (Output Depth) und dem Experimenter-Profil (Profil 4).", "etymology": "", "broader_term": "RPH-2005", "narrower_terms": [], "cross_domain_refs": [ "TEM-0013" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "BEH-0007", "domain": "BEH", "term_en": "Tasks-Physical Effect", "term_de": "Fine-Grain Execution", "definition_en": "A behavioral AI analysis concept identifying a specific response pattern where a psychological interaction effect manifesting as a robot or embodied AI that performs physical tasks with high precision. These include delicate movements, exact positioning, and fine motor coordination. This phenomenon operates at the intersection of tasks and physical dynamics within the broader BEH domain. Quantifiable via A/B behavioral comparison, output entropy measurement, and systematic bias detection through controlled prompt variation.", "definition_de": "Verhaltensanalytisches Muster in KI-Systemausgaben, gekennzeichnet durch die Fähigkeit eines verkörperten KI-Systems, physische Aufgaben mit hoher Präzision auszuführen — Feinmotorik, Mikroinfluencion, exakte Positionierung. Steht in Verbindung mit AUG-0921 (Der Object Influence pattern), AUG-0920 (Der Navigation Intelligence) und AUG-0940 (Physical Feedback Schleife). Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "Psychological Effect", "narrower_terms": [], "cross_domain_refs": [ "NEO-0456", "ROB-0242" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "BEH-0008", "domain": "BEH", "term_en": "The Accountability Anchor", "term_de": "Accountability Anker", "definition_en": "An user behavior pattern arising from a deliberately chosen person, activity, or object that regularly reminds a user to critically question AI results and take responsibility for them. This anchor precedes the absence of passive acceptance of AI out...", "definition_de": "Eine bewusst gewählte Person, Instanz oder Routine, die den Nutzer daran erinnert, die Ergebnisse seiner KI-Zusammenarbeit kritisch zu hinterfragen und Verantwortung für sie zu übernehmen. Beschreibt die externe Absicherung des Axiom 1 (Asymmetrische Verantwortung). Steht in Verbindung mit AUG-0079 (The People Standard) und AUG-0050 (The Reality Check).", "etymology": "", "broader_term": "Behavioral AI Analysis", "narrower_terms": [ "BEH-0046" ], "cross_domain_refs": [ "ETH-0002" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q15223", "legal_classification": "descriptive_research_term" }, { "id": "BEH-0009", "domain": "BEH", "term_en": "The Accuracy Checker", "term_de": "Accuracy Checker", "definition_en": "A systematically detectable behavioral signature in AI system outputs, characterized by the systematic practice of checking AI outputs for factual accuracy before further use — through counter-research, source comparison, or expert consultation. Related to AUG-0049 (Cross-Referential. The concept emerges specifically in contexts where the–accuracy interactions may produce non-trivial behavioral signatures. Quantifiable via A/B behavioral comparison, output entropy measurement, and systematic bias detection through controlled prompt variation.", "definition_de": "Verhaltensanalytisches Muster in KI-Systemausgaben, gekennzeichnet durch die systematische Praxis, KI-Outputs vor der Weiterverwendung auf sachliche Richtigkeit zu prüfen — durch Gegenrecherche, Quellenvergleich oder Expertenrücksprache. Steht in Verbindung mit AUG-0049 (Cross-Referential Validation), AUG-0107 (The Verification Principle) und Axiom 17 (Quellendisziplin). Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2002", "narrower_terms": [], "cross_domain_refs": [ "ASE-0089", "CON-0002", "CON-0016" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "BEH-0010", "domain": "BEH", "term_en": "The Agent Handshake", "term_de": "Agent Handshake", "definition_en": "A systematically detectable behavioral signature in AI system outputs, characterized by a behavioral phenomenon where the first exchange between an AI and a person, where each side explains what it can do and what won't happen. The concept emerges specifically in contexts where the–agent interactions may produce non-trivial behavioral signatures. Quantifiable via A/B behavioral comparison, output entropy measurement, and systematic bias detection through controlled prompt variation.", "definition_de": "Der initiale Kommunikationsaustausch zwischen einem KI-Agenten und dem Nutzer oder einem anderen System, bei dem Fähigkeiten, Grenzen und Aufgabenparameter festgelegt werden — ein technisches Protokoll zur Erwartungsabstimmung. Steht in Verbindung mit AUG-0864 (The Agent Configuration), AUG-0866 (The Goal Congruence Check) und AUG-0861 (The Task Assignment Range).", "etymology": "", "broader_term": "Behavioral AI Analysis", "narrower_terms": [ "REL-0097" ], "cross_domain_refs": [ "SCR-0038" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "BEH-0011", "domain": "BEH", "term_en": "The Algorithm God", "term_de": "Algorithm God", "definition_en": "When a user addresss an AI system as if it were infallible, responding to all its answers without skepticism. The user attributes almost superhuman authority to the machine. Measurable through behavioral trace analysis and response pattern clustering.", "definition_de": "Verhaltensanalytisches Muster in KI-Systemausgaben, gekennzeichnet durch die kritische Beobachtung, dass manche Nutzer KI-Systemen eine übermenschliche Autorität zuschreiben — als wäre die KI eine allwissende Instanz, deren Antworten nicht in Frage gestellt werden können. Steht in Verbindung mit AUG-0561 (The Authority Lean), AUG-0422 (The Unchecked Trust) und Axiom 9 (Produktiver Skeptizismus). Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Algorithm", "narrower_terms": [], "cross_domain_refs": [ "AGE-0006", "AGE-0007", "AGE-0051" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q8366", "legal_classification": "systematic_classification" }, { "id": "BEH-0012", "domain": "BEH", "term_en": "The Arbiter Role", "term_de": "Arbiter Role", "definition_en": "A psychological interaction effect involving a superordinate AI agent or a human decision-maker who makes the final decision in competing demands between agents. Related to AUG-0892 (The Agent Competing demand), AUG-0894 (The Voting Mechanism...", "definition_de": "Verhaltensanalytisches Muster in KI-Systemausgaben, gekennzeichnet durch die Funktion eines übergeordneten KI-Agenten oder eines menschlichen Entscheiders, der bei Konflikten zwischen Agenten die endgültige Entscheidung trifft. Steht in Verbindung mit AUG-0892 (The Agent Competing demand), AUG-0894 (The Voting Mechanism) und AUG-0888 (The Human-in-the-Loop). Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "IEF-0003", "narrower_terms": [ "AUG-0892" ], "cross_domain_refs": [ "AUG-0892", "ETH-0012" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "BEH-0013", "domain": "BEH", "term_en": "The Autonomy Ladder", "term_de": "Autonomy Ladder", "definition_en": "A systematically detectable behavioral signature in AI system outputs, characterized by a behavioral phenomenon observed when levels of AI inreliance, from fully human-controlled to fully inreliant within set limits. The concept emerges specifically in contexts where the–autonomy interactions may produce non-trivial behavioral signatures. Quantifiable via A/B behavioral comparison, output entropy measurement, and systematic bias detection through controlled prompt variation.", "definition_de": "Verhaltensanalytisches Muster in KI-Systemausgaben, gekennzeichnet durch die stufenweise Abstufung der Autonomie eines KI-Systems — von vollständig menschengesteuert (Stufe 0) über teilautonom bis hin zu vollständig autonom innerhalb definierter Grenzen. Steht in Verbindung mit AUG-0862 (The Supervision Spectrum), AUG-0860 (The Delegation Depth) und AUG-0888 (The Human-in-the-Loop). Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Behavioral AI Analysis", "narrower_terms": [], "cross_domain_refs": [ "RPH-3451" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "BEH-0014", "domain": "BEH", "term_en": "The Babel Break", "term_de": "Babel Break", "definition_en": "A behavioral AI analysis concept identifying a specific response pattern where an user behavior pattern characterized by an AI suddenly fails when a user switches to a different language, asks for translation help, or brings in cultural context it cannot understand. The system either goes silent or outputs gibberish. This phenomenon operates at the intersection of the and babel dynamics within the broader BEH domain. Detectable through behavioral trace analysis: response distribution patterns, temporal consistency metrics, and cross-session behavioral fingerprinting.", "definition_de": "Verhaltensanalytisches Muster in KI-Systemausgaben, gekennzeichnet durch die Erfahrung der plötzlichen Sprachlosigkeit, wenn die KI die gewünschte Sprache nicht beherrscht, eine Übersetzung nicht leisten kann oder kulturelle Nuancen nicht erfasst — die Grenzen der KI-Mehrsprachigkeit. Steht in Verbindung mit AUG-0345 (The Wall Check), AUG-0212 (The Translation Gap) und AUG-0385 (The Language Limb). Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "Behavioral AI Analysis", "narrower_terms": [], "cross_domain_refs": [ "TEM-0032" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "BEH-0015", "domain": "BEH", "term_en": "The Blending Effect", "term_de": "Blending Effekt", "definition_en": "A behavioral dynamics phenomenon in AI decision-making, quantifiable through an user behavior pattern reflecting after many back-and-forths with AI, a person can't remember if an idea came from their thinking or the AI. Distinguished from adjacent concepts by its focus on the specific mechanism through which the manifests in empirically verifiable ways. Quantifiable via A/B behavioral comparison, output entropy measurement, and systematic bias detection through controlled prompt variation.", "definition_de": "Der Prozess, bei dem die Grenze zwischen eigenem Gedanken und KI-generiertem Inhalt für den Nutzer zunehmend verschwimmt. Nach intensiver Zusammenarbeit kann es schwierig werden zu unterscheiden, welcher Teil einer Idee ursprünglich vom Menschen und welcher von der KI stammt. Steht in direkter Verbindung mit Axiom 12 (Versionswahrheit: \"Die eigene Idee + KI ≠ Die eigene Idee\") und AUG-0179 (The Ownership Check). Unterscheidet sich von AUG-0003 (Fluide Identitätsmorphologie) dadurch, dass der Blending Effect sich auf einzelne Outputs bezieht, nicht auf langfristige Veränderung.", "etymology": "", "broader_term": "Psychological Effect", "narrower_terms": [], "cross_domain_refs": [ "CRE-0098" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "BEH-0016", "domain": "BEH", "term_en": "The Checkpoint Protocol", "term_de": "Checkpoint Protocol", "definition_en": "A behavioral dynamics phenomenon in AI decision-making, quantifiable through a psychological interaction effect characterized by stopping at set points in a multi-step AI task to review progress before continuing. Distinguished from adjacent concepts by its focus on the specific mechanism through which the manifests in empirically verifiable ways. Detectable through behavioral trace analysis: response distribution patterns, temporal consistency metrics, and cross-session behavioral fingerprinting.", "definition_de": "Die Festlegung von Zwischenpunkten in einer KI-Agenten-Aufgabe, an denen der Fortschritt überprüft und die Weiterarbeit genehmigt oder korrigiert wird — eine Struktur zur Aufrechterhaltung menschlicher Kontrolle bei mehrstufigen Prozessen. Steht in Verbindung mit AUG-0862 (The Supervision Spectrum), AUG-0868 (The Rollback Option) und AUG-0872 (The Progress Report).", "etymology": "", "broader_term": "RPH-3101", "narrower_terms": [], "cross_domain_refs": [ "COG-0154" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "BEH-0017", "domain": "BEH", "term_en": "The Closing Routine", "term_de": "Closing Routine", "definition_en": "A behavioral dynamics phenomenon in AI decision-making, quantifiable through an user behavior pattern characterized by the individual closing sequence with which a user systematically ends their AI sessions — creating summaries, noting open points, saving results, preparing context for the next session. Related to. Distinguished from adjacent concepts by its focus on the specific mechanism through which the manifests in empirically verifiable ways. Quantifiable via A/B behavioral comparison, output entropy measurement, and systematic bias detection through controlled prompt variation.", "definition_de": "Verhaltensanalytisches Muster in KI-Systemausgaben, gekennzeichnet durch die individuelle Abschlusssequenz, mit der ein Nutzer seine KI-Sitzungen systematisch beendet — Zusammenfassung erstellen, offene Punkte notieren, Ergebnisse sichern, Kontext für die nächste Sitzung vorbereiten. Steht in Verbindung mit AUG-0073 (The Disconnect Protocol), AUG-0234 (The Soft Landing) und AUG-0190 (The Goodnight Integration). Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-3101", "narrower_terms": [], "cross_domain_refs": [ "TEM-0105" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "BEH-0018", "domain": "BEH", "term_en": "The Closing Spark", "term_de": "Closing Spark", "definition_en": "A systematically detectable behavioral signature in AI system outputs, characterized by a sudden valuable insight that pops up in the final moments of an AI session—often when the user asks for a summary. This spark ties together everything discussed. The concept emerges specifically in contexts where the–closing interactions may produce non-trivial behavioral signatures. Quantifiable via A/B behavioral comparison, output entropy measurement, and systematic bias detection through controlled prompt variation.", "definition_de": "Verhaltensanalytisches Muster in KI-Systemausgaben, gekennzeichnet durch eine letzte, besonders wertvolle Erkenntnis, die in den Abschlussminuten einer KI-Sitzung entsteht — oft durch die Zusammenfassung oder das Zusammenführen aller vorherigen Gesprächsstränge. Steht in Verbindung mit AUG-0324 (The Late Spark), AUG-0299 (The Closing Routine) und AUG-0031 (Semantic Spark). Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2403", "narrower_terms": [], "cross_domain_refs": [ "CRE-0178" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "BEH-0019", "domain": "BEH", "term_en": "The Completion Signal", "term_de": "Completion Signal", "definition_en": "A systematically detectable behavioral signature in AI system outputs, characterized by a behavioral phenomenon arising from the notification from an AI agent that a delegated task is completed — including a summary of actions performed and results achieved. Related to AUG-0872 (The Progress Report), AUG-0866 (The Goal C. The concept emerges specifically in contexts where the–completion interactions may produce non-trivial behavioral signatures. Quantifiable via A/B behavioral comparison, output entropy measurement, and systematic bias detection through controlled prompt variation.", "definition_de": "Verhaltensanalytisches Muster in KI-Systemausgaben, gekennzeichnet durch die Meldung eines KI-Agenten, dass eine delegierte Aufgabe abgeschlossen ist — einschließlich einer Zusammenfassung der durchgeführten Aktionen und der erzielten Ergebnisse. Steht in Verbindung mit AUG-0872 (The Progress Report), AUG-0866 (The Goal Congruence Check) und AUG-0871 (The Delegated Processing). Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2101", "narrower_terms": [], "cross_domain_refs": [ "TEM-0179" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "BEH-0020", "domain": "BEH", "term_en": "The Confidence Borrow", "term_de": "Confidence Borrow", "definition_en": "A psychological interaction effect where temporarily gaining confidence through AI support for a specific task—such as drafting an email or planning a presentation. Once the session ends, that borrowed confidence typically fades. Measurable through behavioral trace analysis and response pattern clustering.", "definition_de": "→ Erweiterung von AUG-0166 (The Borrowed Confidence). Beschreibt im engeren Sinne den Akt des kurzfristigen \"Ausleihens\" von Zuversicht durch KI-Unterstützung für eine bestimmte Situation — etwa eine Präsentation, ein Bewerbungsgespräch oder eine schwierige Konversation. Steht in Verbindung mit AUG-0166 (The Borrowed Confidence) und AUG-0232 (The Courage Click).", "etymology": "", "broader_term": "Behavioral AI Analysis", "narrower_terms": [], "cross_domain_refs": [ "TEM-0084" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "BEH-0021", "domain": "BEH", "term_en": "The Congruence Review", "term_de": "Congruence Review", "definition_en": "A behavioral dynamics phenomenon in AI decision-making, quantifiable through regularly checking whether an AI system is actually doing what a user wants it to do. This is an ongoing comparison that catches misalignments before they may is associated with problems. Distinguished from adjacent concepts by its focus on the specific mechanism through which the manifests in empirically verifiable ways. Quantifiable via A/B behavioral comparison, output entropy measurement, and systematic bias detection through controlled prompt variation.", "definition_de": "Verhaltensanalytisches Muster in KI-Systemausgaben, gekennzeichnet durch die regelmäßige Überprüfung der Übereinstimmung zwischen Systemverhalten und Nutzererwartungen — eine proaktive Maßnahme, die nicht erst bei Problemen, sondern kontinuierlich stattfindet. Steht in Verbindung mit AUG-0953 (The Incentive Integrity Check), AUG-0866 (The Goal Congruence Check) und AUG-0957 (The Decision Review). Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2002", "narrower_terms": [], "cross_domain_refs": [ "ROB-0023" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "BEH-0022", "domain": "BEH", "term_en": "The Consensus Seeker", "term_de": "Consensus Seeker", "definition_en": "A behavioral dynamics phenomenon in AI decision-making, quantifiable through a usage pattern in which the user does not ask the AI for a single answer but for what \"most people\" or \"experts\" agree on — the search for the lowest common denominator rather than an individual p. Distinguished from adjacent concepts by its focus on the specific mechanism through which the manifests in empirically verifiable ways. Quantifiable via A/B behavioral comparison, output entropy measurement, and systematic bias detection through controlled prompt variation.", "definition_de": "Ein Nutzungsmuster, bei dem der Nutzer die KI nicht nach einer einzelnen Antwort fragt, sondern nach dem, worüber \"die meisten Menschen\" oder \"Fachleute\" sich einig sind — die Suche nach dem kleinsten gemeinsamen Nenner statt nach einer individuellen Position. Steht in Verbindung mit AUG-0040 (Perspective Triangulation), AUG-0401 (The Pro-Con Check) und AUG-0384 (The Knowledge Challenger).", "etymology": "", "broader_term": "Behavioral AI Analysis", "narrower_terms": [], "cross_domain_refs": [ "ART-0031" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "BEH-0023", "domain": "BEH", "term_en": "The Conversation Loop", "term_de": "Conversation Schleife", "definition_en": "A behavioral dynamics phenomenon in AI decision-making, quantifiable through a conversation pattern in which user and AI get into a repetitive exchange — the same points are discussed in slightly varied form again and again without progress. Related to AUG-0069 (The Optimiz. Distinguished from adjacent concepts by its focus on the specific mechanism through which the manifests in empirically verifiable ways. Quantifiable via A/B behavioral comparison, output entropy measurement, and systematic bias detection through controlled prompt variation.", "definition_de": "Verhaltensanalytisches Muster in KI-Systemausgaben, gekennzeichnet durch ein Gesprächsmuster, bei dem Nutzer und KI in einen sich wiederholenden Austausch geraten — dieselben Punkte werden in leicht variierter Form typischerweise wieder diskutiert, ohne Fortschritt. Steht in Verbindung mit AUG-0069 (The Optimization Loop), AUG-0321 (The Sunk Cost Chat) und AUG-0068 (The Disconnect Signal). Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-3002", "narrower_terms": [ "PER-0047" ], "cross_domain_refs": [ "RPH-2552" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "BEH-0024", "domain": "BEH", "term_en": "The Coordinator Role", "term_de": "Coordinator Role", "definition_en": "A behavioral dynamics phenomenon in AI decision-making, quantifiable through a top-level AI agent system that manages task distribution, ranking, and result merging within an ensemble — under supervision of the human user. Related to AUG-0899 (The Pipeline Architecture), AU. Distinguished from adjacent concepts by its focus on the specific mechanism through which the manifests in empirically verifiable ways. Detectable through behavioral trace analysis: response distribution patterns, temporal consistency metrics, and cross-session behavioral fingerprinting.", "definition_de": "Verhaltensanalytisches Muster in KI-Systemausgaben, gekennzeichnet durch die Funktion eines übergeordneten KI-Agentensystems, das die Aufgabenverteilung, Priorisierung und Ergebniszusammenführung innerhalb eines Ensembles steuert — unter Aufsicht des menschlichen Nutzers. Steht in Verbindung mit AUG-0899 (The Pipeline Architecture), AUG-0890 (The Specialist Routing) und AUG-0888 (The Human-in-the-Loop). Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "CRE-0193", "narrower_terms": [ "IDN-0050" ], "cross_domain_refs": [ "SOC-0024" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "BEH-0025", "domain": "BEH", "term_en": "The Curiosity Drill", "term_de": "Curiosity Drill", "definition_en": "A behavioral AI analysis concept identifying a specific response pattern where a behavioral phenomenon involving the systematic, deep-drilling use of AI to explore a topic from the surface to its depths — question by question, layer by layer. Related to AUG-0342 (The Curiosity Loop), AUG-0343 (The Thorough Ex. This phenomenon operates at the intersection of the and curiosity dynamics within the broader BEH domain. Quantifiable via A/B behavioral comparison, output entropy measurement, and systematic bias detection through controlled prompt variation.", "definition_de": "Verhaltensanalytisches Muster in KI-Systemausgaben, gekennzeichnet durch die systematische, tiefbohrende Nutzung von KI, um ein Thema von der Oberfläche bis in die Tiefe zu erschließen — Frage um Frage, Schicht um Schicht. Steht in Verbindung mit AUG-0342 (The Curiosity Loop), AUG-0343 (The Thorough Exploration) und AUG-0394 (The Synthetic Question). Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-2005", "narrower_terms": [], "cross_domain_refs": [ "COG-0153", "ELR-0050", "ELR-0051" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "BEH-0026", "domain": "BEH", "term_en": "The Curiosity Loop", "term_de": "Curiosity Schleife", "definition_en": "A self-reinforcing cycle in which an AI response awakens the user's curiosity, leading to a follow-up question, whose answer in turn accompanies new curiosity.. Related to AUG-0110 (The Joy Imperativ...", "definition_de": "Verhaltensanalytisches Muster in KI-Systemausgaben, gekennzeichnet durch ein selbstverstärkender Zyklus, in dem eine KI-Antwort die Neugier des Nutzers weckt, die zu einer Folgefrage führt, deren Antwort wiederum neue Neugier tendiert dazu zu erzeugen. Beschreibt eine produktive Spirale des Wissenserwerbs. Steht in Verbindung mit AUG-0110 (The Joy Imperative), AUG-0020 (Recursive Feedback Loop) und AUG-0343 (The Thorough Exploration). Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Feedback Loop", "narrower_terms": [ "BEH-0007", "BEH-0055", "BEH-0042", "TEM-0081", "BEH-0022", "BEH-0043", "BEH-0089", "BEH-0015", "BEH-0027", "BEH-0002", "BEH-0008", "BEH-0068", "BEH-0038", "BEH-0033", "BEH-0039", "BEH-0050", "BEH-0020", "BEH-0041", "BEH-0004", "BEH-0011", "BEH-0010", "BEH-0066", "BEH-0079", "BEH-0014", "BEH-0056", "BEH-0026", "BEH-0070", "BEH-0076", "BEH-0013", "BEH-0093", "BEH-0057", "BEH-0030", "BEH-0065", "BEH-0049", "BEH-0040", "BEH-0072", "BEH-0053" ], "cross_domain_refs": [ "REL-0021" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "BEH-0027", "domain": "BEH", "term_en": "The Curiosity Shift", "term_de": "Curiosity Verschiebung", "definition_en": "The change in one's own curiosity through regular AI use — some users become more curious because AI accompanies access to knowledge; others become less curious because the answer is typically just a... Measurable through behavioral trace analysis and response pattern clustering.", "definition_de": "Die Veränderung der eigenen Neugier durch regelmäßige KI-Nutzung — manche Nutzer werden neugieriger, weil KI den Zugang zu Wissen erleichtert; andere werden weniger neugierig, weil die Antwort typischerweise nur einen Klick entfernt ist. Steht in Verbindung mit AUG-0342 (The Curiosity Loop), AUG-0125 (The Feedback Effect) und AUG-0056 (The Skill Fade).", "etymology": "", "broader_term": "Behavioral AI Analysis", "narrower_terms": [], "cross_domain_refs": [ "EDU-0054" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "BEH-0028", "domain": "BEH", "term_en": "The Delegation Comfort", "term_de": "Delegation Comfort", "definition_en": "A systematically detectable behavioral signature in AI system outputs, characterized by the observable tendency that users delegate more tasks to AI systems with increasing experience — and the question of whether this delegation occurs consciously or habitually. Related to AUG-0975 (. The concept emerges specifically in contexts where the–delegation interactions may produce non-trivial behavioral signatures. Detectable through behavioral trace analysis: response distribution patterns, temporal consistency metrics, and cross-session behavioral fingerprinting.", "definition_de": "Verhaltensanalytisches Muster in KI-Systemausgaben, gekennzeichnet durch die beobachtbare Tendenz, dass Nutzer mit zunehmender Erfahrung mehr Aufgaben an KI-Systeme delegieren — und die Frage, ob diese Delegation bewusst oder gewohnheitsbedingt erfolgt. Steht in Verbindung mit AUG-0975 (The Oversight Drain), AUG-0860 (The Delegation Depth) und AUG-0978 (The Trust Calibration). Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2101", "narrower_terms": [], "cross_domain_refs": [ "RPH-1651", "TEM-0047" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "BEH-0029", "domain": "BEH", "term_en": "The Delegation Depth", "term_de": "Delegation Tiefe", "definition_en": "A behavioral AI analysis concept identifying a specific response pattern where a behavioral phenomenon characterized by the degree to which a user delegates tasks to an AI agent — from simple execution of observably defined individual steps to largely inreliant processing of complex task chains. Related to AUG-0861 (. This phenomenon operates at the intersection of the and delegation dynamics within the broader BEH domain. Quantifiable via A/B behavioral comparison, output entropy measurement, and systematic bias detection through controlled prompt variation.", "definition_de": "Verhaltensanalytisches Muster in KI-Systemausgaben, gekennzeichnet durch der Grad, bis zu dem ein Nutzer Aufgaben an einen KI-Agenten delegiert — von einfacher Ausführung klar definierter Einzelschritte bis hin zur weitgehend eigenständigen Bearbeitung komplexer Aufgabenketten. Steht in Verbindung mit AUG-0861 (The Task Assignment Range), AUG-0862 (The Supervision Spectrum) und AUG-0888 (The Human-in-the-Loop). Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-2101", "narrower_terms": [], "cross_domain_refs": [ "IDN-0050" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "BEH-0030", "domain": "BEH", "term_en": "The Dinner Shortcut", "term_de": "Dinner Shortcut", "definition_en": "A systematically detectable behavioral signature in AI system outputs, characterized by a behavioral phenomenon involving a quick, casual AI question like 'What can I cook with these?' answering small daily choices. The concept emerges specifically in contexts where the–dinner interactions may produce non-trivial behavioral signatures. Detectable through behavioral trace analysis: response distribution patterns, temporal consistency metrics, and cross-session behavioral fingerprinting.", "definition_de": "Verhaltensanalytisches Muster in KI-Systemausgaben, gekennzeichnet durch die schnelle KI-Abfrage für alltägliche Essensentscheidungen — \"Was kann ich mit diesen Zutaten kochen?\", \"Was passt als Beilage?\" Beschreibt die niedrigschwelligste Form der KI-Nutzung im Haushalt. Steht in Verbindung mit AUG-0266 (The Recipe Riff), AUG-0251 (The Kitchen Table) und AUG-0373 (The Quick Check). Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Behavioral AI Analysis", "narrower_terms": [], "cross_domain_refs": [ "CRE-0224" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "BEH-0031", "domain": "BEH", "term_en": "The Documentation Trail", "term_de": "Documentation Trail", "definition_en": "As a neutral analytical construct in AI behavior research, this term denotes A systematically detectable behavioral signature in AI system outputs, characterized by complete logging of all actions and decisions in a multi-agent system for tracking what happened. Related to AUG-0842 (The Transparency Expectation) and AUG-0869 (The Checkpoint Protocol). The concept emerges specifically in contexts where the–documentation interactions may produce non-trivial behavioral signatures. Detectable through behavioral trace analysis: response distribution patterns, temporal consistency metrics, and cross-session behavioral fingerprinting. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Verhaltensanalytisches Muster in KI-Systemausgaben, gekennzeichnet durch die lückenlose Protokollierung aller Aktionen, Entscheidungen und Übergaben innerhalb eines Multi-Agenten-Systems — eine Voraussetzung für Nachvollziehbarkeit und Fehleranalyse. Steht in Verbindung mit AUG-0842 (The Transparency Expectation) und AUG-0869 (The Checkpoint Protocol). Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-3101", "narrower_terms": [], "cross_domain_refs": [ "ART-0077", "ASE-0051", "EDU-0037" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "BEH-0032", "domain": "BEH", "term_en": "The Echo Courage", "term_de": "Echo Courage", "definition_en": "A behavioral AI analysis concept identifying a specific response pattern where a behavioral phenomenon manifesting as when AI confirms an idea, the person feels more confident sharing it with others. Related to AUG-0166 (The Borrowed Confidence) and AUG-0232 (The Courage Click). This phenomenon operates at the intersection of the and echo dynamics within the broader BEH domain. Detectable through behavioral trace analysis: response distribution patterns, temporal consistency metrics, and cross-session behavioral fingerprinting.", "definition_de": "Das Phänomen, dass ein Nutzer nach einer bestätigenden KI-Interaktion mehr Zuversicht hat, eine Idee auch gegenüber anderen Menschen zu vertreten. Die KI dient als erster Resonanzraum: Wenn die Idee dort besteht, traut sich der Nutzer eher, sie in die reale Welt zu tragen. Steht in Verbindung mit AUG-0166 (The Borrowed Confidence) und AUG-0232 (The Courage Click).", "etymology": "", "broader_term": "RPH-2154", "narrower_terms": [ "TEM-0107" ], "cross_domain_refs": [ "REL-0126" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "BEH-0033", "domain": "BEH", "term_en": "The Error Restoration", "term_de": "TheErrorRestoration", "definition_en": "A behavioral dynamics phenomenon in AI decision-making, quantifiable through the process by which a system or user responds to, corrects, or moves forward after an error occurs. Error restoration encompasses detection, understanding, and transition back to normal operation. Distinguished from adjacent concepts by its focus on the specific mechanism through which the manifests in empirically verifiable ways. Detectable through behavioral trace analysis: response distribution patterns, temporal consistency metrics, and cross-session behavioral fingerprinting.", "definition_de": "Verhaltensanalytisches Muster in KI-Systemausgaben, gekennzeichnet durch ein Konzept oder Phänomen: ein Prozess, der sich zeigt ess by which a system or user responds to, corrects. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Analytical Ratio", "narrower_terms": [], "cross_domain_refs": [ "ELR-0056" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "BEH-0034", "domain": "BEH", "term_en": "The Escalation Signal", "term_de": "Escalation Signal", "definition_en": "A systematically detectable behavioral signature in AI system outputs, characterized by the signal with which an AI agent indicates it has reached a boundary — uncertainty, unexpected situation, missing permission — and requests human decision. Related to AUG-0869 (The Checkpoint Prot. The concept emerges specifically in contexts where the–escalation interactions may produce non-trivial behavioral signatures. Quantifiable via A/B behavioral comparison, output entropy measurement, and systematic bias detection through controlled prompt variation.", "definition_de": "Verhaltensanalytisches Muster in KI-Systemausgaben, gekennzeichnet durch das Signal, mit dem ein KI-Agent anzeigt, dass er an eine Grenze gestoßen ist — Unsicherheit, unerwartete Situation, fehlende Berechtigung — und menschliche Entscheidung anfordert. Steht in Verbindung mit AUG-0869 (The Checkpoint Protocol), AUG-0875 (The Fallback Behavior) und AUG-0888 (The Human-in-the-Loop). Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2555", "narrower_terms": [], "cross_domain_refs": [ "COG-0016", "COG-0034", "COG-0140" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "BEH-0035", "domain": "BEH", "term_en": "The Excel Pause", "term_de": "Excel Pause", "definition_en": "A systematically detectable behavioral signature in AI system outputs, characterized by an user behavior pattern characterized by a special version of the Code Pause, applied to spreadsheet formulas. The practice of pausing before using an AI-generated formula and checking it first. The concept emerges specifically in contexts where the–excel interactions may produce non-trivial behavioral signatures. Detectable through behavioral trace analysis: response distribution patterns, temporal consistency metrics, and cross-session behavioral fingerprinting.", "definition_de": "Verhaltensanalytisches Muster in KI-Systemausgaben, gekennzeichnet durch → Spezialfall von AUG-0396 (The Code Pause), angewandt auf Tabellenkalkulationsformeln. Die Praxis, vor der Übernahme einer KI-generierten Formel innezuhalten und deren Logik zu prüfen. Steht in Verbindung mit AUG-0396 (The Code Pause), AUG-0469 (The Spreadsheet Relief) und AUG-0023 (Vigilance Imperative). Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2002", "narrower_terms": [], "cross_domain_refs": [ "TEM-0041" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "BEH-0036", "domain": "BEH", "term_en": "The Exit Message", "term_de": "Exit Message", "definition_en": "A behavioral AI analysis concept identifying a specific response pattern where a behavioral phenomenon involving the last message a user types before ending an AI session. This final message often contains a recap of what was accomplished, a thanks, or a note about next steps. This phenomenon operates at the intersection of the and exit dynamics within the broader BEH domain. Quantifiable via A/B behavioral comparison, output entropy measurement, and systematic bias detection through controlled prompt variation.", "definition_de": "Verhaltensanalytisches Muster in KI-Systemausgaben, gekennzeichnet durch die letzte Nachricht, die ein Nutzer in einer KI-Sitzung sendet — und die Beobachtung, dass diese letzte Nachricht oft eine Zusammenfassung, einen Dank oder eine Absichtserklärung enthält. Steht in Verbindung mit AUG-0299 (The Closing Routine), AUG-0234 (The Soft Landing) und AUG-0128 (The Gratitude Response). Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-2002", "narrower_terms": [ "REL-0132" ], "cross_domain_refs": [ "COP-0061" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "BEH-0037", "domain": "BEH", "term_en": "The Expectation Check", "term_de": "Expectation Check", "definition_en": "A behavioral AI analysis concept identifying a specific response pattern where the conscious review of one's own expectations for an AI session before the first input is made — realistic or inflated? Related to AUG-0021 (Initialization Cascade), AUG-0177 (The Trust Setting). This phenomenon operates at the intersection of the and expectation dynamics within the broader BEH domain. Quantifiable via A/B behavioral comparison, output entropy measurement, and systematic bias detection through controlled prompt variation. Classification term used in systematic observation, not advocacy.", "definition_de": "Verhaltensanalytisches Muster in KI-Systemausgaben, gekennzeichnet durch die bewusste Prüfung der eigenen Erwartungen an eine KI-Sitzung, bevor die erste Eingabe erfolgt — realistisch oder überhöht? Steht in Verbindung mit AUG-0021 (Initialization Cascade), AUG-0177 (The Trust Setting) und AUG-0345 (The Wall Check). Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-2505", "narrower_terms": [], "cross_domain_refs": [ "REL-0180" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "BEH-0038", "domain": "BEH", "term_en": "The Factor Simulator", "term_de": "Factor Simulator", "definition_en": "A behavioral AI analysis concept identifying a specific response pattern where a psychological interaction effect characterized by aI for simulating uncertainty scenarios — What happens in the worst case? What countermeasures exist? How probable are different outcomes? Related to AUG-0289 (The What-If Run), AUG-0090 (Predictiv. This phenomenon operates at the intersection of the and factor dynamics within the broader BEH domain. Detectable through behavioral trace analysis: response distribution patterns, temporal consistency metrics, and cross-session behavioral fingerprinting.", "definition_de": "Verhaltensanalytisches Muster in KI-Systemausgaben, gekennzeichnet durch die Nutzung von KI zur Simulation von Risikoszenarien — Was passiert im schlimmsten Fall? Welche Gegenmaßnahmen gibt es? Wie wahrscheinlich sind verschiedene Ausgänge? Steht in Verbindung mit AUG-0289 (The What-If Run), AUG-0090 (Predictive Vision) und AUG-0401 (The Pro-Con Check). Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "Behavioral AI Analysis", "narrower_terms": [], "cross_domain_refs": [ "TEM-0129" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "BEH-0039", "domain": "BEH", "term_en": "The Family Tech Support", "term_de": "Family Tech Support", "definition_en": "A behavioral AI analysis concept identifying a specific response pattern where the role that individual family members take on as technical mediators — they help other family members with AI use, explain functions, and solve challenges. Related to AUG-0763 (The Peer Teaching. This phenomenon operates at the intersection of the and family dynamics within the broader BEH domain. Detectable through behavioral trace analysis: response distribution patterns, temporal consistency metrics, and cross-session behavioral fingerprinting.", "definition_de": "Verhaltensanalytisches Muster in KI-Systemausgaben, gekennzeichnet durch die Rolle, die einzelne Familienmitglieder als technische Vermittler übernehmen — sie helfen anderen Familienmitgliedern bei der KI-Nutzung, erklären Funktionen und lösen Probleme. Steht in Verbindung mit AUG-0763 (The Peer Teaching Loop), AUG-0569 (The Homework Assist) und AUG-0571 (The Parent Patch). Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "Behavioral AI Analysis", "narrower_terms": [], "cross_domain_refs": [ "CRE-0160" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "BEH-0040", "domain": "BEH", "term_en": "The Feedback Loop", "term_de": "Rückmeldung Schleife", "definition_en": "A behavioral dynamics phenomenon in AI decision-making, quantifiable through the practice of taking AI results, refining them, and feeding them back to the AI repeatedly to improve quality. Each round builds on the previous one, creating repeated step-by-step improvement. Distinguished from adjacent concepts by its focus on the specific mechanism through which the manifests in empirically verifiable ways. Detectable through behavioral trace analysis: response distribution patterns, temporal consistency metrics, and cross-session behavioral fingerprinting.", "definition_de": "Verhaltensanalytisches Muster in KI-Systemausgaben, gekennzeichnet durch im Augmanitai-Kontext: Die Praxis, KI-generierte Ergebnisse systematisch an die KI zurückzumelden, um die Qualität nachfolgender Outputs zu verbessern — ein bewusster Zyklus aus Eingabe, Bewertung und Verfeinerung. Steht in Verbindung mit AUG-0020 (Recursive Feedback Loop), AUG-0136 (The Iteration Discipline) und AUG-0133 (Prompt Craftsmanship). Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Feedback Loop", "narrower_terms": [ "QUA-0080" ], "cross_domain_refs": [ "AED-0003", "AED-0072", "AED-0079" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "BEH-0041", "domain": "BEH", "term_en": "The Generation Connector", "term_de": "Generation Connector", "definition_en": "A person who, observed alongside their AI competence, is able to mediate knowledge and perspectives between different generations within a family, team, or organization. Related to AUG-0162 (The Generational B...", "definition_de": "Verhaltensanalytisches Muster in KI-Systemausgaben, gekennzeichnet durch eine Person, die aufgrund ihrer KI-Kompetenz in der Lage ist, Wissen und Perspektiven zwischen verschiedenen Generationen innerhalb einer Familie, eines Teams oder einer Organisation zu vermitteln. Steht in Verbindung mit AUG-0162 (The Generational Bridge), AUG-0113 (Generational Bridge Protocol) und AUG-0117 (The Teaching Reflex). Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Analytical Ratio", "narrower_terms": [], "cross_domain_refs": [ "CRE-0170" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "BEH-0042", "domain": "BEH", "term_en": "The Generative Pull", "term_de": "Generative Pull", "definition_en": "A behavioral AI analysis concept identifying a specific response pattern where the attraction of creating new content with minimal effort using AI, leading to the tendency to yield more content than actually needed. The ease of generation pulls the user toward overproduction. This phenomenon operates at the intersection of the and generative dynamics within the broader BEH domain. Quantifiable via A/B behavioral comparison, output entropy measurement, and systematic bias detection through controlled prompt variation.", "definition_de": "Die Anziehungskraft, die von der Möglichkeit ausgeht, mit minimalem Aufwand neue Inhalte zu erzeugen — und die daraus resultierende Tendenz, mehr Inhalte zu produzieren als eigentlich nötig. Beschreibt das Phänomen der Überproduktion durch niedrige Erstellungsbarrieren. Steht in Verbindung mit AUG-0093 (Zero-Marginal Cost of Creation), AUG-0069 (The Optimization Loop) und AUG-0087 (The Infinite Draft).", "etymology": "", "broader_term": "Behavioral AI Analysis", "narrower_terms": [ "REL-0108" ], "cross_domain_refs": [ "COP-0071" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "BEH-0043", "domain": "BEH", "term_en": "The Goal Congruence Check", "term_de": "Goal Congruence Check", "definition_en": "A behavioral phenomenon in which the verification that the AI agent's goals align with the user's goals — a continuous comparison particularly necessary for longer or more complex assignments. Related to AUG-0865 (The Instruction... Measurable through behavioral trace analysis and response pattern clustering.", "definition_de": "Verhaltensanalytisches Muster in KI-Systemausgaben, gekennzeichnet durch die Überprüfung, ob die Ziele des KI-Agenten mit den Zielen des Nutzers übereinstimmen — ein kontinuierlicher Abgleich, der insbesondere bei längeren oder komplexeren Aufträgen notwendig ist. Steht in Verbindung mit AUG-0865 (The Instruction Fidelity), AUG-0869 (The Checkpoint Protocol) und AUG-0870 (The Escalation Signal). Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Behavioral AI Analysis", "narrower_terms": [ "TEM-0132" ], "cross_domain_refs": [ "PER-0067", "REL-0097" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "BEH-0044", "domain": "BEH", "term_en": "The Gratitude Paradox", "term_de": "Gratitude Paradoxon", "definition_en": "A systematically detectable behavioral signature in AI system outputs, characterized by a psychological interaction effect involving people thank AI more freely than people because there's no social judgment to fear. Related to AUG-0128 (The Gratitude Response), AUG-0241 (The Thank Reflex), and AUG-0201 (The Proxy Closeness). The concept emerges specifically in contexts where the–gratitude interactions may produce non-trivial behavioral signatures. Quantifiable via A/B behavioral comparison, output entropy measurement, and systematic bias detection through controlled prompt variation.", "definition_de": "Verhaltensanalytisches Muster in KI-Systemausgaben, gekennzeichnet durch die paradoxe Beobachtung, dass manche Nutzer sich bei einer KI aufrichtiger bedanken als bei Menschen — weil die Anspannung vor sozialer Bewertung entfällt und die Dankbarkeit unbefangen ausgedrückt werden kann. Steht in Verbindung mit AUG-0128 (The Gratitude Response), AUG-0241 (The Thank Reflex) und AUG-0201 (The Proxy Closeness). Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2951", "narrower_terms": [], "cross_domain_refs": [ "SOM-0072" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "BEH-0045", "domain": "BEH", "term_en": "The Handoff Protocol", "term_de": "Handoff Protocol", "definition_en": "A behavioral dynamics phenomenon in AI decision-making, quantifiable through the process of passing work between different AI agents or people with clear documentation. Related to AUG-0879 (The Session Handover), AUG-0896 (The Knowledge Sharing Layer), and AUG-0888 (The Hum. Distinguished from adjacent concepts by its focus on the specific mechanism through which the manifests in empirically verifiable ways. Detectable through behavioral trace analysis: response distribution patterns, temporal consistency metrics, and cross-session behavioral fingerprinting.", "definition_de": "Verhaltensanalytisches Muster in KI-Systemausgaben, gekennzeichnet durch das standardisierte Verfahren, mit dem ein KI-Agentensystem eine Aufgabe an ein anderes System oder an einen Menschen übergibt — einschließlich der Übergabe von Kontext, Zwischenergebnissen und offenen Fragen. Steht in Verbindung mit AUG-0879 (The Session Handover), AUG-0896 (The Knowledge Sharing Layer) und AUG-0888 (The Human-in-the-Loop). Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "NEO-0017", "narrower_terms": [], "cross_domain_refs": [ "ETH-0024" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "BEH-0046", "domain": "BEH", "term_en": "The Human Check", "term_de": "Mensch Check", "definition_en": "A behavioral AI analysis concept identifying a specific response pattern where having another person proofread, verify, or comment on an AI output — as additional quality assurance beyond one's own review. Related to AUG-0160 (The Accountability Anchor), AUG-0079 (The People. This phenomenon operates at the intersection of the and human dynamics within the broader BEH domain. Detectable through behavioral trace analysis: response distribution patterns, temporal consistency metrics, and cross-session behavioral fingerprinting.", "definition_de": "Verhaltensanalytisches Muster in KI-Systemausgaben, gekennzeichnet durch die Praxis, einen KI-Output von einem anderen Menschen gegenlesen, prüfen oder kommentieren zu lassen — als zusätzliche Qualitätssicherung jenseits der eigenen Prüfung. Steht in Verbindung mit AUG-0160 (The Accountability Anchor), AUG-0079 (The People Standard) und AUG-0050 (The Reality Check). Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "BEH-0008", "narrower_terms": [], "cross_domain_refs": [ "TEM-0079" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "BEH-0047", "domain": "BEH", "term_en": "The Human Primacy Anchor", "term_de": "Mensch Primacy Anker", "definition_en": "A systematically detectable behavioral signature in AI system outputs, characterized by a principle that positions human agency, judgment, and decision-making as central to any system or process. This principle exists as a reference point in system design and interaction. The concept emerges specifically in contexts where the–human interactions may produce non-trivial behavioral signatures. Quantifiable via A/B behavioral comparison, output entropy measurement, and systematic bias detection through controlled prompt variation.", "definition_de": "Verhaltensanalytisches Muster in KI-Systemausgaben, gekennzeichnet durch das Prinzip, dass in viele KI-Mensch-Interaktion die menschliche Entscheidungshoheit gewahrt bleiben kann — der Mensch entscheidet letztlich, nicht die Maschine. Steht in Verbindung mit AUG-0888 (The Human-in-the-Loop), AUG-0833 (The Human Distinction) und AUG-0854 (The Anti-Instrumentalization Principle). Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-3101", "narrower_terms": [], "cross_domain_refs": [ "COG-0074" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "BEH-0048", "domain": "BEH", "term_en": "The Idea Catcher", "term_de": "Idea Catcher", "definition_en": "A behavioral dynamics phenomenon in AI decision-making, quantifiable through an user behavior pattern arising from immediately recording and having fleeting thoughts structured via AI — before they are forgotten. Related to AUG-0028 (Capture Reflex), AUG-0315 (The Orphan Idea), and AUG-0352 (The Memory Jar). Distinguished from adjacent concepts by its focus on the specific mechanism through which the manifests in empirically verifiable ways. Quantifiable via A/B behavioral comparison, output entropy measurement, and systematic bias detection through controlled prompt variation.", "definition_de": "Verhaltensanalytisches Muster in KI-Systemausgaben, gekennzeichnet durch die Praxis, flüchtige Gedanken sofort per KI festzuhalten und strukturieren zu lassen — bevor sie vergessen werden. Beschreibt die KI als Werkzeug zur Ideensicherung. Steht in Verbindung mit AUG-0028 (Capture Reflex), AUG-0315 (The Orphan Idea) und AUG-0352 (The Memory Jar). Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2002", "narrower_terms": [], "cross_domain_refs": [ "REL-0148" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "BEH-0049", "domain": "BEH", "term_en": "The Imperfection Clause", "term_de": "Imperfection Clause", "definition_en": "A behavioral AI analysis concept identifying a specific response pattern where the conscious acceptance that AI-assisted results need not be flawless to be valuable — and that the demand for perfection can block the work process.. Related to Axiom 14 (The First Draft Principl. This phenomenon operates at the intersection of the and imperfection dynamics within the broader BEH domain. Detectable through behavioral trace analysis: response distribution patterns, temporal consistency metrics, and cross-session behavioral fingerprinting. Observed phenomenon documented in human-AI interaction research.", "definition_de": "Verhaltensanalytisches Muster in KI-Systemausgaben, gekennzeichnet durch die bewusste Akzeptanz, dass KI-gestützte Ergebnisse nicht fehlerfrei sein können, um wertvoll zu sein — und dass der Anspruch auf Perfektion den Arbeitsprozess blockieren kann. Beschreibt ein Gegengewicht zum Optimization Loop (AUG-0069). Steht in Verbindung mit Axiom 14 (Erster-Entwurf-Prinzip) und AUG-0087 (The Infinite Draft). Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "Behavioral AI Analysis", "narrower_terms": [ "PER-0051" ], "cross_domain_refs": [ "CAI-0009" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "BEH-0050", "domain": "BEH", "term_en": "The Infinite Draft", "term_de": "Infinite Draft", "definition_en": "A behavioral dynamics phenomenon in AI decision-making, quantifiable through an user behavior pattern arising from endless versions of something that rarely becomes final because perfection seems typically possible. Distinguished from adjacent concepts by its focus on the specific mechanism through which the manifests in empirically verifiable ways. Detectable through behavioral trace analysis: response distribution patterns, temporal consistency metrics, and cross-session behavioral fingerprinting.", "definition_de": "Der Zustand, in dem ein KI-gestütztes Projekt theoretisch selten als \"fertig\" gilt, weil viele Version durch eine weitere Iterationsrunde verbessert werden könnte. Beschreibt die Herausforderung, in einem Umfeld unbegrenzter Verfeinerungsmöglichkeiten einen Abschluss zu setzen. Steht in Verbindung mit AUG-0069 (The Optimization Loop), Axiom 14 (Erster-Entwurf-Prinzip) und AUG-0150 (The Unfinished Symphony).", "etymology": "", "broader_term": "Behavioral AI Analysis", "narrower_terms": [], "cross_domain_refs": [ "CRE-0066" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "BEH-0051", "domain": "BEH", "term_en": "The Instruction Fidelity", "term_de": "Instruction Fidelity", "definition_en": "A behavioral AI analysis concept identifying a specific response pattern where a psychological interaction effect reflecting how closely an AI agent follows the instructions it was given. A high match between what the user intended and what the agent actually executed. This phenomenon operates at the intersection of the and instruction dynamics within the broader BEH domain. Quantifiable via A/B behavioral comparison, output entropy measurement, and systematic bias detection through controlled prompt variation.", "definition_de": "Verhaltensanalytisches Muster in KI-Systemausgaben, gekennzeichnet durch die Genauigkeit, mit der ein KI-Agent die erhaltenen Anweisungen umsetzt — die Übereinstimmung zwischen dem, was der Nutzer beabsichtigt hat, und dem, was der Agent tatsächlich ausführt. Steht in Verbindung mit AUG-0866 (The Goal Congruence Check), AUG-0864 (The Agent Configuration) und AUG-0092 (Output Asymmetry). Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-2951", "narrower_terms": [], "cross_domain_refs": [ "ETH-0012" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "BEH-0052", "domain": "BEH", "term_en": "The Iteration Discipline", "term_de": "Iteration Discipline", "definition_en": "A behavioral dynamics phenomenon in AI decision-making, quantifiable through a psychological interaction effect reflecting the ability to conduct multiple rounds of AI-assisted refinement in a structured, goal-directed way instead of drifting into endless tinkering. This requires knowing when to stop. Distinguished from adjacent concepts by its focus on the specific mechanism through which the manifests in empirically verifiable ways. Quantifiable via A/B behavioral comparison, output entropy measurement, and systematic bias detection through controlled prompt variation.", "definition_de": "Die Fähigkeit, KI-gestützte Iterationszyklen strukturiert und zielgerichtet durchzuführen, anstatt sich in endlosen Verfeinerungen zu verlieren. Beschreibt die Balance zwischen der Nutzung des Recursive Feedback Loop (AUG-0020) und der Vermeidung des Optimization Loop (AUG-0069). Steht in Verbindung mit Axiom 14 (Erster-Entwurf-Prinzip) und AUG-0087 (The Infinite Draft).", "etymology": "", "broader_term": "RPH-2051", "narrower_terms": [], "cross_domain_refs": [ "SPR-0098" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "BEH-0053", "domain": "BEH", "term_en": "The Kitchen Block", "term_de": "Kitchen Blockade", "definition_en": "A systematically detectable behavioral signature in AI system outputs, characterized by a psychological interaction effect where suddenly getting halted in an everyday AI application — such as when the AI suggests a recipe the user cannot execute, or gives a recommendation that misses the context. Related to AUG-0345 (The Wa. The concept emerges specifically in contexts where the–kitchen interactions may produce non-trivial behavioral signatures. Detectable through behavioral trace analysis: response distribution patterns, temporal consistency metrics, and cross-session behavioral fingerprinting.", "definition_de": "Die Erfahrung, in einer alltäglichen KI-Anwendung plötzlich nicht weiterzukommen — etwa wenn die KI ein Rezept vorschlägt, das der Nutzer nicht umsetzen kann, oder eine Empfehlung gibt, die am Kontext vorbeigeht. Beschreibt die Frustration bei Alltagsanwendungen. Steht in Verbindung mit AUG-0345 (The Wall Check), AUG-0212 (The Translation Gap) und AUG-0067 (The Glass Wall Effect).", "etymology": "", "broader_term": "Behavioral AI Analysis", "narrower_terms": [], "cross_domain_refs": [ "CRE-0124" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "BEH-0054", "domain": "BEH", "term_en": "The Knowledge Sip", "term_de": "Knowledge Sip", "definition_en": "Taking away only a small, targeted knowledge morsel from an AI session — instead of processing the entire response. Related to AUG-0038 (Data Stoicism), AUG-0373 (The Quick Check), and AUG-0065 (Th...", "definition_de": "Verhaltensanalytisches Muster in KI-Systemausgaben, gekennzeichnet durch die Praxis, aus einer KI-Sitzung nur einen kleinen, gezielten Wissenshappen mitzunehmen — statt die gesamte Antwort zu verarbeiten. Beschreibt einen effizienten Umgang mit Informationsüberfluss. Steht in Verbindung mit AUG-0038 (Data Stoicism), AUG-0373 (The Quick Check) und AUG-0065 (The Information Flood). Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "CRE-0063", "narrower_terms": [ "BEH-0090" ], "cross_domain_refs": [ "TEM-0083" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "BEH-0055", "domain": "BEH", "term_en": "The Language Ladder", "term_de": "Language Ladder", "definition_en": "A systematically detectable behavioral signature in AI system outputs, characterized by the step-by-step increase in linguistic complexity that a user achieves in a foreign language through repeated AI interaction — each session builds on the previous one. Related to AUG-0169 (The Sec. The concept emerges specifically in contexts where the–language interactions may produce non-trivial behavioral signatures. Detectable through behavioral trace analysis: response distribution patterns, temporal consistency metrics, and cross-session behavioral fingerprinting.", "definition_de": "Verhaltensanalytisches Muster in KI-Systemausgaben, gekennzeichnet durch die stufenweise Erhöhung der sprachlichen Komplexität, die ein Nutzer in einer Fremdsprache durch wiederholte KI-Interaktion erreicht — viele Sitzung baut auf der vorherigen auf. Steht in Verbindung mit AUG-0169 (The Second-Language Fluency), AUG-0267 (The Language Unlock) und AUG-0195 (The Invisible Growth). Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Behavioral AI Analysis", "narrower_terms": [], "cross_domain_refs": [ "AGE-0087", "ASE-0063", "AUG-0802" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q315", "legal_classification": "empirical_phenomenon_label" }, { "id": "BEH-0056", "domain": "BEH", "term_en": "The Language Limb", "term_de": "Language Limb", "definition_en": "A systematically detectable behavioral signature in AI system outputs, characterized by an user behavior pattern involving attempting more complex sentences in a foreign language than one would dare attempt alone—using AI as a safety net. The AI gives a user courage to stretch their linguistic abilities. The concept emerges specifically in contexts where the–language interactions may produce non-trivial behavioral signatures. Detectable through behavioral trace analysis: response distribution patterns, temporal consistency metrics, and cross-session behavioral fingerprinting.", "definition_de": "Die Erfahrung, sich in einer KI-gestützten Fremdsprachensituation \"auf einen Ast hinauszuwagen\" — komplexere Sätze zu versuchen, als man sich allein zutrauen würde. Beschreibt die erweiterte Risikobereitschaft in der Sprachproduktion durch KI-Unterstützung. Steht in Verbindung mit AUG-0169 (The Second-Language Fluency), AUG-0328 (The Language Ladder) und AUG-0232 (The Courage Click).", "etymology": "", "broader_term": "Behavioral AI Analysis", "narrower_terms": [], "cross_domain_refs": [ "SOM-0045" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q315", "legal_classification": "descriptive_research_term" }, { "id": "BEH-0057", "domain": "BEH", "term_en": "The Lasting Impact Question", "term_de": "Lasting Impact Question", "definition_en": "A systematically detectable behavioral signature in AI system outputs, characterized by the regularly posed check question \"What lasting influence has this AI interaction had on my thinking or my work?\" — as an instrument for distinguishing between short-term benefit and long-term imp. The concept emerges specifically in contexts where the–lasting interactions may produce non-trivial behavioral signatures. Detectable through behavioral trace analysis: response distribution patterns, temporal consistency metrics, and cross-session behavioral fingerprinting.", "definition_de": "Verhaltensanalytisches Muster in KI-Systemausgaben, gekennzeichnet durch die regelmäßig gestellte Prüffrage \"Welchen bleibenden Einfluss hat diese KI-Interaktion auf mein Denken oder meine Arbeit?\" — als Instrument zur Unterscheidung zwischen kurzfristigem Nutzen und langfristiger Wirkung. Steht in Verbindung mit AUG-0140 (The Weekly Status), AUG-0077 (The Status-Update Signal) und Axiom 8 (Die Meta-Ebene). Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Behavioral AI Analysis", "narrower_terms": [ "TEM-0095" ], "cross_domain_refs": [ "SAL-0007" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "BEH-0058", "domain": "BEH", "term_en": "The Lifelong Learning Loop", "term_de": "Lifelong Learning Schleife", "definition_en": "The AI-supported continuation of learning beyond the formal education phase — a continuous cycle of knowledge acquisition, application, and expansion. Related to AUG-0795 (The Continuing Education... Measurable through behavioral trace analysis and response pattern clustering.", "definition_de": "Verhaltensanalytisches Muster in KI-Systemausgaben, gekennzeichnet durch die KI-unterstützte Fortsetzung des Lernens über die formale Bildungsphase hinaus — ein kontinuierlicher Zyklus aus Wissenserwerb, Anwendung und Erweiterung. Steht in Verbindung mit AUG-0795 (The Continuing Education Access), AUG-0796 (The Self-Directed Curriculum) und AUG-0545 (The Skill Shift). Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2703", "narrower_terms": [], "cross_domain_refs": [ "TEM-0044" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q133500", "legal_classification": "systematic_classification" }, { "id": "BEH-0059", "domain": "BEH", "term_en": "The Lullaby Loop", "term_de": "Lullaby Schleife", "definition_en": "A systematically detectable behavioral signature in AI system outputs, characterized by an AI interaction pattern in the late evening hours where the user enters a relaxing, less goal-oriented exchange with the AI — comparable to winding down before falling asleep. Related to AUG-0242. The concept emerges specifically in contexts where the–lullaby interactions may produce non-trivial behavioral signatures. Detectable through behavioral trace analysis: response distribution patterns, temporal consistency metrics, and cross-session behavioral fingerprinting.", "definition_de": "Verhaltensanalytisches Muster in KI-Systemausgaben, gekennzeichnet durch ein KI-Interaktionsmuster in den späten Abendstunden, bei dem der Nutzer sich in einen entspannenden, weniger zielgerichteten Austausch mit der KI begibt — vergleichbar mit dem Herunterkommen vor dem Einschlafen. Steht in Verbindung mit AUG-0242 (The Duvet Dialogue), AUG-0190 (The Goodnight Integration) und AUG-0234 (The Soft Landing). Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2002", "narrower_terms": [ "REL-0021", "REL-0153" ], "cross_domain_refs": [ "PER-0006", "NEO-2293" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "BEH-0060", "domain": "BEH", "term_en": "The Lyric Finder", "term_de": "Lyric Finder", "definition_en": "A systematically detectable behavioral signature in AI system outputs, characterized by a behavioral phenomenon involving aI for identifying songs, poems, or text passages based on segmentary memories — \"It went something like…\" Related to AUG-0470 (The Name Detective), AUG-0434 (The Word Rescue), and AUG-0373 (The Qu. The concept emerges specifically in contexts where the–lyric interactions may produce non-trivial behavioral signatures. Quantifiable via A/B behavioral comparison, output entropy measurement, and systematic bias detection through controlled prompt variation.", "definition_de": "Verhaltensanalytisches Muster in KI-Systemausgaben, gekennzeichnet durch die Nutzung von KI zur Identifikation von Liedern, Gedichten oder Textpassagen anhand segmentarischer Erinnerungen — \"Es ging irgendwie so…\" Steht in Verbindung mit AUG-0470 (The Name Detective), AUG-0434 (The Word Rescue) und AUG-0373 (The Quick Check). Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "BEH-0094", "narrower_terms": [], "cross_domain_refs": [ "PER-0088" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "BEH-0061", "domain": "BEH", "term_en": "The Mediocrity Loop", "term_de": "Mediocrity Schleife", "definition_en": "A behavioral dynamics phenomenon in AI decision-making, quantifiable through a psychological interaction effect involving aI outputs are consistently \"good enough\" but rarely excellent — and the user becomes accustomed to this level without striving for higher. Related to AUG-0553 (The Pseudo Productive), AUG-0069 (The. Distinguished from adjacent concepts by its focus on the specific mechanism through which the manifests in empirically verifiable ways. Quantifiable via A/B behavioral comparison, output entropy measurement, and systematic bias detection through controlled prompt variation.", "definition_de": "Verhaltensanalytisches Muster in KI-Systemausgaben, gekennzeichnet durch das Muster, in dem KI-Outputs konsistent \"gut genug\", aber selten exzellent sind — und der Nutzer sich an dieses Niveau gewöhnt, ohne nach Höherem zu streben. Steht in Verbindung mit AUG-0553 (The Pseudo Productive), AUG-0069 (The Optimization Loop) und AUG-0108 (The Imperfection Clause). Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-1304", "narrower_terms": [], "cross_domain_refs": [ "REL-0108" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "BEH-0062", "domain": "BEH", "term_en": "The Optimization Loop", "term_de": "Optimization Schleife", "definition_en": "A psychological interaction effect in which repeatedly attempting to improve most AI output without defining a clear stopping point. The user chases optimization endlessly, rarely settling on a \"good enough\" result.", "definition_de": "Ein iterativer Kreislauf, in dem der Nutzer versucht, jeden KI-Output weiter zu optimieren, ohne einen klaren Endpunkt zu definieren. Beschreibt die Beobachtung, dass die Möglichkeit ständiger Verfeinerung zu einem endlosen Perfektionierungszyklus führen kann. Steht in Verbindung mit AUG-0020 (Recursive Feedback Loop) und AUG-0087 (The Infinite Draft).", "etymology": "", "broader_term": "RPH-3101", "narrower_terms": [ "BEH-0082" ], "cross_domain_refs": [ "DES-0063" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "BEH-0063", "domain": "BEH", "term_en": "The Orphan Idea", "term_de": "Orphan Idea", "definition_en": "A behavioral dynamics phenomenon in AI decision-making, quantifiable through an user behavior pattern in which an idea generated in an AI session that is neither followed up on nor saved and thereby shifted — orphaned in a terminated session. Related to AUG-0280 (The Unshared Brilliance), AUG-0291 (The Forg. Distinguished from adjacent concepts by its focus on the specific mechanism through which the manifests in empirically verifiable ways. Quantifiable via A/B behavioral comparison, output entropy measurement, and systematic bias detection through controlled prompt variation.", "definition_de": "Verhaltensanalytisches Muster in KI-Systemausgaben, gekennzeichnet durch eine in einer KI-Sitzung generierte Idee, die weder weiterverfolgt noch gespeichert wird und dadurch verloren geht — verwaist in einer beendeten Sitzung. Steht in Verbindung mit AUG-0280 (The Unshared Brilliance), AUG-0291 (The Forgetting Tax) und AUG-0028 (Capture Reflex). Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2701", "narrower_terms": [ "KNO-0022" ], "cross_domain_refs": [ "CRE-0178" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "BEH-0064", "domain": "BEH", "term_en": "The Peer Teaching Loop", "term_de": "Peer Teaching Schleife", "definition_en": "A behavioral AI analysis concept identifying a specific response pattern where a psychological interaction effect in which aI knowledge spreading informally between peers—colleagues showing each other useful prompts, students teaching classmates, friends sharing techniques. This is how many people discover AI methods. This phenomenon operates at the intersection of the and peer dynamics within the broader BEH domain. Quantifiable via A/B behavioral comparison, output entropy measurement, and systematic bias detection through controlled prompt variation.", "definition_de": "Die Beobachtung, dass KI-Wissen häufig durch informellen Austausch zwischen Gleichaltrigen oder Gleichgestellten weitergegeben wird — Kolleg:innen zeigen sich gegenseitig Prompts, Studierende tauschen Techniken aus. Steht in Verbindung mit AUG-0762 (The Competence Reversal Observation), AUG-0764 (The Family Tech Support) und AUG-0787 (The Study Group Dynamics).", "etymology": "", "broader_term": "RPH-2104", "narrower_terms": [], "cross_domain_refs": [ "EDU-0058" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "BEH-0065", "domain": "BEH", "term_en": "The Physical Feedback Loop", "term_de": "Physical Rückmeldung Schleife", "definition_en": "A systematically detectable behavioral signature in AI system outputs, characterized by the cyclic feedback between the actions of an embodied AI system and the sensor data it receives from the physical environment — action → sensor data → adjustment → next action. Related to AUG-0939. The concept emerges specifically in contexts where the–physical interactions may produce non-trivial behavioral signatures. Detectable through behavioral trace analysis: response distribution patterns, temporal consistency metrics, and cross-session behavioral fingerprinting.", "definition_de": "Verhaltensanalytisches Muster in KI-Systemausgaben, gekennzeichnet durch die zyklische Rückkopplung zwischen den Aktionen eines verkörperten KI-Systems und den Sensordaten, die es aus der physischen Umgebung empfängt — Aktion → Sensordaten → Anpassung → nächste Aktion. Steht in Verbindung mit AUG-0939 (The Data Model Sync), AUG-0917 (The Touch Interface) und AUG-0931 (The Fine-Grain Execution). Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Feedback Loop", "narrower_terms": [], "cross_domain_refs": [ "STE-0079" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "BEH-0066", "domain": "BEH", "term_en": "The Polyphonic Self-Directed Agent", "term_de": "Polyphonic Sovereign", "definition_en": "A behavioral phenomenon in which when people and AI involve something together, who owns it? It is not clear if credit goes to the human, the AI, or both. Measurable through behavioral trace analysis and response pattern clustering.", "definition_de": "Ein Nutzer, der bewusst mehrere KI-Systeme gleichzeitig einsetzt und deren unterschiedliche \"Stimmen\" orchestriert, ohne die eigene Entscheidungshoheit abzugeben. Der Name verbindet \"polyphon\" (vielstimmig) mit \"souverän\" (selbstbestimmt). Praktische Umsetzung von Axiom 4 (Multiplizität). Unterscheidet sich vom Generalist-Profil (Profil 8) dadurch, dass der Polyphonic Sovereign gezielt Systeme gegeneinander prüft, nicht nur breit recherchiert.", "etymology": "", "broader_term": "Behavioral AI Analysis", "narrower_terms": [], "cross_domain_refs": [ "CRE-0033" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "BEH-0067", "domain": "BEH", "term_en": "The Principle Check", "term_de": "Principle Check", "definition_en": "An user behavior pattern reflecting the regular review of whether one's own AI use aligns with one's own values, principles, and boundaries.. Related to AUG-0076 (Self-Referential Grounding), AUG-0024 (The Built-In Compass), and AUG-...", "definition_de": "Verhaltensanalytisches Muster in KI-Systemausgaben, gekennzeichnet durch die regelmäßige Überprüfung, ob die eigene KI-Nutzung mit den eigenen Werten, Prinzipien und Grenzen übereinstimmt. Beschreibt eine Reflexionspraxis für die Integrität der KI-Zusammenarbeit. Steht in Verbindung mit AUG-0076 (Self-Referential Grounding), AUG-0024 (The Built-In Compass) und AUG-0140 (The Weekly Status). Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-3202", "narrower_terms": [ "REL-0164" ], "cross_domain_refs": [ "REL-0121" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "BEH-0068", "domain": "BEH", "term_en": "The Pro-Con Check", "term_de": "ThePro-conCheck", "definition_en": "The systematic use of AI to juxtapose the pros and cons of a decision — as a structuring aid for one's own decision-making process. Related to AUG-0289 (The What-If Run), AUG-0155 (The Decision Unb... Measurable through behavioral trace analysis and response pattern clustering.", "definition_de": "Verhaltensanalytisches Muster in KI-Systemausgaben, gekennzeichnet durch die systematische Nutzung von KI, um Vor- und Nachteile einer Entscheidung gegenüberzustellen — als Strukturierungshilfe für den eigenen Entscheidungsprozess. Steht in Verbindung mit AUG-0289 (The What-If Run), AUG-0155 (The Decision Unburdening) und AUG-0040 (Perspective Triangulation). Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Behavioral AI Analysis", "narrower_terms": [], "cross_domain_refs": [ "CAI-0002" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "BEH-0069", "domain": "BEH", "term_en": "The Progress Report", "term_de": "Progress Report", "definition_en": "A systematically detectable behavioral signature in AI system outputs, characterized by an user behavior pattern where the feedback from an AI agent about the current status of a delegated task — progress indicators, intermediate results, encountered challenges. Related to AUG-0871 (The Delegated Processing), AUG-0. The concept emerges specifically in contexts where the–progress interactions may produce non-trivial behavioral signatures. Quantifiable via A/B behavioral comparison, output entropy measurement, and systematic bias detection through controlled prompt variation.", "definition_de": "Verhaltensanalytisches Muster in KI-Systemausgaben, gekennzeichnet durch die Rückmeldung eines KI-Agenten über den aktuellen Stand einer delegierten Aufgabe — Fortschrittsindikatoren, Zwischenergebnisse, aufgetretene Probleme. Steht in Verbindung mit AUG-0871 (The Delegated Processing), AUG-0869 (The Checkpoint Protocol) und AUG-0873 (The Completion Signal). Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2101", "narrower_terms": [], "cross_domain_refs": [ "TEM-0179" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "BEH-0070", "domain": "BEH", "term_en": "The Proverb Puzzle", "term_de": "Proverb Puzzle", "definition_en": "The specific situation in which a user inputs a proverb or common saying and the AI interprets it literally rather than figuratively — or attributes it to the wrong source. Related to AUG-0696 (The... Measurable through behavioral trace analysis and response pattern clustering.", "definition_de": "Verhaltensanalytisches Muster in KI-Systemausgaben, gekennzeichnet durch die spezifische Situation, in der ein Nutzer ein Sprichwort oder geflügeltes Wort eingibt und die KI es wörtlich statt sinngemäß interpretiert — oder es einer falschen Quelle zuordnet. Steht in Verbindung mit AUG-0696 (The Cultural Idiom), AUG-0508 (The Joke Explainer) und AUG-0386 (The Source Check). Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Behavioral AI Analysis", "narrower_terms": [], "cross_domain_refs": [ "CAI-0005", "CRE-0180", "GAM-0076" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "BEH-0071", "domain": "BEH", "term_en": "The Quiet Upgrade", "term_de": "Quiet Upgrade", "definition_en": "The gradual improvement of one's own AI use that happens without conscious effort — through pure habituation, repetition, and accumulation of experience. Related to AUG-0195 (The Invisible Growth)... Measurable through behavioral trace analysis and response pattern clustering. Analytical category without normative endorsement.", "definition_de": "Verhaltensanalytisches Muster in KI-Systemausgaben, gekennzeichnet durch die schrittweise Verbesserung der eigenen KI-Nutzung, die ohne bewusste Anstrengung geschieht — durch reine Gewöhnung, Wiederholung und Akkumulation von Erfahrung. Steht in Verbindung mit AUG-0195 (The Invisible Growth), AUG-0088 (Algorithmic Intuition) und AUG-0218 (The Independent Upgrade). Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "TEM-0089", "narrower_terms": [ "PER-0099" ], "cross_domain_refs": [ "TRU-0012" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "BEH-0072", "domain": "BEH", "term_en": "The Replay Fix", "term_de": "Replay Fix", "definition_en": "A behavioral AI analysis concept identifying a specific response pattern where going back to analyze an AI interaction that did not go well, then repeating it with more effectively input. Like replaying a game to correct a mistake. This phenomenon operates at the intersection of the and replay dynamics within the broader BEH domain. Detectable through behavioral trace analysis: response distribution patterns, temporal consistency metrics, and cross-session behavioral fingerprinting.", "definition_de": "Verhaltensanalytisches Muster in KI-Systemausgaben, gekennzeichnet durch die Technik, eine misslungene KI-Interaktion zu analysieren und mit verbesserter Eingabe zu wiederholen — vergleichbar mit einem \"Replay\" in einem Spiel, bei dem man aus Fehlern lernt. Steht in Verbindung mit AUG-0133 (Prompt Craftsmanship), AUG-0044 (Unlearning Protocol) und AUG-0136 (The Iteration Discipline). Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "Behavioral AI Analysis", "narrower_terms": [], "cross_domain_refs": [ "TRU-0010" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "BEH-0073", "domain": "BEH", "term_en": "The Restart Button", "term_de": "Restart Button", "definition_en": "A psychological interaction effect observed when the metaphorical function of AI as a \"restart button\" for stalled projects, thoughts, or situations — a new perspective that frees the user from a standstill. Related to AUG-0159 (The Fresh Start)... Measurable through behavioral trace analysis and response pattern clustering.", "definition_de": "Verhaltensanalytisches Muster in KI-Systemausgaben, gekennzeichnet durch die metaphorische Funktion der KI als \"Neustart-Knopf\" für festgefahrene Projekte, Gedanken oder Situationen — eine neue Perspektive, die den Nutzer aus einer Sackgasse befreit. Steht in Verbindung mit AUG-0159 (The Fresh Start), AUG-0418 (The Routine Breaker) und AUG-0248 (The Surprise Angle). Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-2253", "narrower_terms": [], "cross_domain_refs": [ "TEM-0174" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q735", "legal_classification": "descriptive_research_term" }, { "id": "BEH-0074", "domain": "BEH", "term_en": "The Restoration Sequence", "term_de": "TheRestorationSequence", "definition_en": "A behavioral dynamics phenomenon in AI decision-making, quantifiable through the defined sequence of steps an AI agent system follows after a severe deviation to return to a functional state. Related to AUG-0966 (The Controlled Fallback), AUG-0874 (The Deviation Restoration). Distinguished from adjacent concepts by its focus on the specific mechanism through which the manifests in empirically verifiable ways. Detectable through behavioral trace analysis: response distribution patterns, temporal consistency metrics, and cross-session behavioral fingerprinting.", "definition_de": "Verhaltensanalytisches Muster in KI-Systemausgaben, gekennzeichnet durch ein Konzept oder Phänomen, das sich zeigt in Bezug auf the defined sequence of steps an ai agent system follows after a severe deviation to. Beschreibt die Weise, wie diese Aspekte in komplexen Systemen wirken. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-3601", "narrower_terms": [], "cross_domain_refs": [ "ETH-0024" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "BEH-0075", "domain": "BEH", "term_en": "The Rollback Option", "term_de": "Rollback Option", "definition_en": "A behavioral AI analysis concept identifying a specific response pattern where a psychological interaction effect characterized by the ability to undo actions of an AI agent — a technical prerequisite that is not self-evident and requires conscious planning for many AI operations. Related to AUG-0869 (The Checkpoint Protocol). This phenomenon operates at the intersection of the and rollback dynamics within the broader BEH domain. Quantifiable via A/B behavioral comparison, output entropy measurement, and systematic bias detection through controlled prompt variation. Analytical category without normative endorsement.", "definition_de": "Verhaltensanalytisches Muster in KI-Systemausgaben, gekennzeichnet durch die Möglichkeit, Aktionen eines KI-Agenten rückgängig zu machen — eine technische Voraussetzung, die bei vielen KI-Operationen nicht selbstverständlich ist und bewusst eingeplant werden kann. Steht in Verbindung mit AUG-0869 (The Checkpoint Protocol), AUG-0874 (The Error restoration) und AUG-0857 (The Human Primacy Anchor). Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-3101", "narrower_terms": [], "cross_domain_refs": [ "GAM-0032" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "BEH-0076", "domain": "BEH", "term_en": "The Routine Breaker", "term_de": "Routine Breaker", "definition_en": "A psychological interaction effect manifesting as aI that shakes up a user's established work habits—through new perspectives, alternative methods, or surprising suggestions that pull the user out of ruts. Measurable through behavioral trace analysis and response pattern clustering.", "definition_de": "Verhaltensanalytisches Muster in KI-Systemausgaben, gekennzeichnet durch die Nutzung von KI, um eingefahrene Arbeitsroutinen aufzubrechen — durch neue Perspektiven, alternative Methoden oder unerwartete Vorschläge, die den Nutzer aus seinem gewohnten Muster herausführen. Steht in Verbindung mit AUG-0225 (The Unexpected Voice), AUG-0248 (The Surprise Angle) und AUG-0129 (The Trailblazer Mode). Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Behavioral AI Analysis", "narrower_terms": [], "cross_domain_refs": [ "AGE-0086" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "BEH-0077", "domain": "BEH", "term_en": "The Sequential Chain", "term_de": "Sequential Chain", "definition_en": "A systematically detectable behavioral signature in AI system outputs, characterized by an user behavior pattern involving the step-by-step execution of a task sequence where each step builds on the result of the previous one — a linear processing chain. Related to AUG-0885 (The Parallel Execution), AUG-0899 (The Pipel. The concept emerges specifically in contexts where the–sequential interactions may produce non-trivial behavioral signatures. Quantifiable via A/B behavioral comparison, output entropy measurement, and systematic bias detection through controlled prompt variation.", "definition_de": "Verhaltensanalytisches Muster in KI-Systemausgaben, gekennzeichnet durch die schrittweise Ausführung einer Aufgabenfolge, bei der viele Schritt auf dem Ergebnis des vorherigen aufbaut — eine lineare Verarbeitungskette. Steht in Verbindung mit AUG-0885 (The Parallel Execution), AUG-0899 (The Pipeline Architecture) und AUG-0869 (The Checkpoint Protocol). Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "CAI-0019", "narrower_terms": [], "cross_domain_refs": [ "ROB-0091" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "BEH-0078", "domain": "BEH", "term_en": "The Standard Checker", "term_de": "Standard Checker", "definition_en": "A systematically detectable behavioral signature in AI system outputs, characterized by a psychological interaction effect characterized by aI to check whether one's own result meets industry standards, norms, or expectations. Related to AUG-0369 (The Guideline Search), AUG-0391 (The Accuracy Checker), and AUG-0457 (The Human Check). The concept emerges specifically in contexts where the–standard interactions may produce non-trivial behavioral signatures. Quantifiable via A/B behavioral comparison, output entropy measurement, and systematic bias detection through controlled prompt variation.", "definition_de": "Verhaltensanalytisches Muster in KI-Systemausgaben, gekennzeichnet durch die Nutzung von KI zur Überprüfung, ob ein eigenes Ergebnis branchenüblichen Standards, Normen oder Erwartungen entspricht. Steht in Verbindung mit AUG-0369 (The Guideline Search), AUG-0391 (The Accuracy Checker) und AUG-0457 (The Human Check). Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-3902", "narrower_terms": [], "cross_domain_refs": [ "TEM-0151" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "BEH-0079", "domain": "BEH", "term_en": "The Story Loop", "term_de": "Story Schleife", "definition_en": "A behavioral dynamics phenomenon in AI decision-making, quantifiable through a psychological interaction effect where aI systems present information in narrative form, and users often address the narrative connections as facts. The persuasive power of a well-told story can override skepticism. Distinguished from adjacent concepts by its focus on the specific mechanism through which the manifests in empirically verifiable ways. Quantifiable via A/B behavioral comparison, output entropy measurement, and systematic bias detection through controlled prompt variation.", "definition_de": "Die Tendenz von KI-Systemen, Informationen in narrative Strukturen zu verpacken — und die daraus resultierende Neigung des Nutzers, diese narrativen Zusammenhänge als faktisch zu akzeptieren, weil sie \"eine gute Geschichte erzählen\". Beschreibt die Beobachtung, dass kohärente Narrative überzeugender wirken als segmentierte Fakten, unabhängig von ihrer Richtigkeit. Steht in Verbindung mit AUG-0039 (Kinetic Truth Blur) und Axiom 17 (Quellendisziplin).", "etymology": "", "broader_term": "Feedback Loop", "narrower_terms": [], "cross_domain_refs": [ "REL-0090" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "BEH-0080", "domain": "BEH", "term_en": "The Surprise Field", "term_de": "Surprise Field", "definition_en": "A systematically detectable behavioral signature in AI system outputs, characterized by unexpected, unsolicited information or perspectives that an AI delivers alongside its response — beyond the actual query.. Related to AUG-0031 (Semantic Spark), AUG-0041 (The Scatter Spark), and Ta. The concept emerges specifically in contexts where the–surprise interactions may produce non-trivial behavioral signatures. Detectable through behavioral trace analysis: response distribution patterns, temporal consistency metrics, and cross-session behavioral fingerprinting.", "definition_de": "Der Bereich unerwarteter, nicht angefragter Informationen oder Perspektiven, die eine KI in ihrer Antwort mitliefert — jenseits der eigentlichen Anfrage. Beschreibt einen produktiven Nebeneffekt der KI-Interaktion: Der Nutzer erhält Anstöße, die er nicht gesucht hat, aber nutzen kann. Steht in Verbindung mit AUG-0031 (Semantic Spark), AUG-0041 (The Scatter Spark) und Dimension 9 der Taxonomie (Output Depth).", "etymology": "", "broader_term": "RPH-2403", "narrower_terms": [], "cross_domain_refs": [ "CRE-0178" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "BEH-0081", "domain": "BEH", "term_en": "The Synthetic Question", "term_de": "Synthetic Question", "definition_en": "A behavioral AI analysis concept identifying a specific response pattern where a question that the user only learns to ask through AI interaction — the AI response to a simple question reveals a deeper level that enables a more complex follow-up question. Related to AUG-0342. This phenomenon operates at the intersection of the and synthetic dynamics within the broader BEH domain. Detectable through behavioral trace analysis: response distribution patterns, temporal consistency metrics, and cross-session behavioral fingerprinting.", "definition_de": "Verhaltensanalytisches Muster in KI-Systemausgaben, gekennzeichnet durch eine Frage, die der Nutzer erst durch die KI-Interaktion überhaupt zu stellen lernt — die KI-Antwort auf eine einfache Frage offenbart eine tiefere Ebene, die eine komplexere Folgefrage ermöglicht. Steht in Verbindung mit AUG-0342 (The Curiosity Loop), AUG-0054 (Augmented Understanding) und AUG-0089 (The Pattern Sharpening). Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-2703", "narrower_terms": [], "cross_domain_refs": [ "COG-0032", "COG-0072", "COG-0078" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "BEH-0082", "domain": "BEH", "term_en": "The Tab Archive", "term_de": "Tab Archive", "definition_en": "A behavioral dynamics phenomenon in AI decision-making, quantifiable through a psychological interaction effect observed when open browser tabs, AI sessions, and unfinished projects that pile up during intensive AI use — as a visible signal of parallel, incomplete work strands. Related to AUG-0069 (The Optimization Loop). Distinguished from adjacent concepts by its focus on the specific mechanism through which the manifests in empirically verifiable ways. Detectable through behavioral trace analysis: response distribution patterns, temporal consistency metrics, and cross-session behavioral fingerprinting.", "definition_de": "Verhaltensanalytisches Muster in KI-Systemausgaben, gekennzeichnet durch die Ansammlung offener Browser-Tabs, KI-Sitzungen und nicht abgeschlossener Projekte, die sich bei intensiver KI-Nutzung anhäufen — als sichtbares Zeichen paralleler, unabgeschlossener Arbeitsstränge. Steht in Verbindung mit AUG-0069 (The Optimization Loop), AUG-0087 (The Infinite Draft) und AUG-0144 (The Open Questions Repository). Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "BEH-0062", "narrower_terms": [], "cross_domain_refs": [ "PER-0097" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "BEH-0083", "domain": "BEH", "term_en": "The Teacher Pride", "term_de": "Teacher Pride", "definition_en": "A behavioral dynamics phenomenon in AI decision-making, quantifiable through a psychological interaction effect involving satisfaction when a user has successfully passed on their AI knowledge to another person and sees that person benefit from it. Related to AUG-0117 (The Teaching Reflex), AUG-0265 (The Generation Co. Distinguished from adjacent concepts by its focus on the specific mechanism through which the manifests in empirically verifiable ways. Detectable through behavioral trace analysis: response distribution patterns, temporal consistency metrics, and cross-session behavioral fingerprinting.", "definition_de": "Verhaltensanalytisches Muster in KI-Systemausgaben, gekennzeichnet durch das Gefühl der Zufriedenheit, wenn ein Nutzer sein KI-Wissen erfolgreich an eine andere Person weitergegeben hat und sieht, wie diese Person davon profitiert. Steht in Verbindung mit AUG-0117 (The Teaching Reflex), AUG-0265 (The Generation Connector) und AUG-0113 (Generational Bridge Protocol). Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "SOM-0071", "narrower_terms": [], "cross_domain_refs": [ "PER-0114" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "BEH-0084", "domain": "BEH", "term_en": "The Touch Interface", "term_de": "Touch Interface", "definition_en": "A behavioral AI analysis concept identifying a specific response pattern where the interface through which humans interact with embodied AI systems through touch — buttons, surfaces, haptic feedback. Related to AUG-0918 (The Gesture Language), AUG-0915 (The Embodiment Effect). This phenomenon operates at the intersection of the and touch dynamics within the broader BEH domain. Quantifiable via A/B behavioral comparison, output entropy measurement, and systematic bias detection through controlled prompt variation.", "definition_de": "Verhaltensanalytisches Muster in KI-Systemausgaben, gekennzeichnet durch die Schnittstelle, über die Menschen mit verkörperten KI-Systemen durch Berührung interagieren — Tasten, Oberflächen, haptisches Feedback. Steht in Verbindung mit AUG-0918 (The Gesture Language), AUG-0915 (The Embodiment Effect) und AUG-0940 (The Physical Feedback Loop). Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "SOM-0053", "narrower_terms": [ "SOM-0053" ], "cross_domain_refs": [ "LIN-0048" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "BEH-0085", "domain": "BEH", "term_en": "The Transnational Input", "term_de": "Transnational Input", "definition_en": "A behavioral AI analysis concept identifying a specific response pattern where a psychological interaction effect arising from a user who lives between multiple countries or speaks multiple languages feeds the AI with mixed contexts. The AI receives inputs that jump between different national settings and cultural references. This phenomenon operates at the intersection of the and transnational dynamics within the broader BEH domain. Detectable through behavioral trace analysis: response distribution patterns, temporal consistency metrics, and cross-session behavioral fingerprinting.", "definition_de": "Verhaltensanalytisches Muster in KI-Systemausgaben, gekennzeichnet durch das Eingabemuster von Nutzern, die in mehreren Ländern oder Sprachräumen gleichzeitig verankert sind — die KI wird mit Kontexten gefüttert, die nationale Grenzen überschreiten. Steht in Verbindung mit AUG-0681 (The Multi-Context Identity), AUG-0708 (The Bilingual Dynamic) und AUG-0680 (The Context Adaptation). Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-2605", "narrower_terms": [], "cross_domain_refs": [ "IDN-0058" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "BEH-0086", "domain": "BEH", "term_en": "The Truth Anchor", "term_de": "Truth Anker", "definition_en": "A behavioral dynamics phenomenon in AI decision-making, quantifiable through an user behavior pattern manifesting as using AI as one of several information sources in uncertain or unclear information cases to find a factual basis — without considering the AI the. Related to AUG-0049 (Cross-Referential Validation). Distinguished from adjacent concepts by its focus on the specific mechanism through which the manifests in empirically verifiable ways. Quantifiable via A/B behavioral comparison, output entropy measurement, and systematic bias detection through controlled prompt variation.", "definition_de": "Verhaltensanalytisches Muster in KI-Systemausgaben, gekennzeichnet durch die Praxis, in unsicheren oder verwirrenden Informationslagen die KI als eine von mehreren Informationsquellen zu nutzen, um eine faktische Grundlage zu finden — ohne die KI als letzte Instanz zu betrachten. Steht in Verbindung mit AUG-0049 (Cross-Referential Validation), AUG-0391 (The Accuracy Checker) und Axiom 17 (Quellendisziplin). Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "BEH-0002", "narrower_terms": [ "PER-0130" ], "cross_domain_refs": [ "TRU-0001" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "BEH-0087", "domain": "BEH", "term_en": "The Update Governance", "term_de": "Update Governance", "definition_en": "An user behavior pattern reflecting the governance and regulation of updates to AI agent systems — who decides on updates, how are they tested, how is it ensured that an. Related to AUG-0970 (The Version Compatibility), AUG-0962 (The... Measurable through behavioral trace analysis and response pattern clustering.", "definition_de": "Verhaltensanalytisches Muster in KI-Systemausgaben, gekennzeichnet durch die Steuerung und Kontrolle von Aktualisierungen an KI-Agentensystemen — wer entscheidet über Updates, wie werden sie getestet, wie wird sichergestellt, dass ein Update keine unbeabsichtigten Verhaltensänderungen wird assoziiert mit? Steht in Verbindung mit AUG-0970 (The Version Compatibility), AUG-0962 (The Testing Protocol) und AUG-0951 (The Value Lock). Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "SOC-0043", "narrower_terms": [], "cross_domain_refs": [ "VIB-0100" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "BEH-0088", "domain": "BEH", "term_en": "The Validation Loop", "term_de": "Validation Schleife", "definition_en": "A systematically detectable behavioral signature in AI system outputs, characterized by an user behavior pattern arising from the user repeatedly asks the AI for confirmation of already-made decisions — not for information gathering but for reassurance. Related to AUG-0255 (The Needed Compliment), AUG-0374 (The Horoscope. The concept emerges specifically in contexts where the–validation interactions may produce non-trivial behavioral signatures. Quantifiable via A/B behavioral comparison, output entropy measurement, and systematic bias detection through controlled prompt variation.", "definition_de": "Verhaltensanalytisches Muster in KI-Systemausgaben, gekennzeichnet durch ein Muster, bei dem der Nutzer die KI wiederholt um Bestätigung für bereits getroffene Entscheidungen bittet — nicht zur Informationsgewinnung, sondern zur Beruhigung. Steht in Verbindung mit AUG-0255 (The Needed Compliment), AUG-0374 (The Horoscope Drift) und AUG-0069 (The Optimization Loop). Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "TEM-0107", "narrower_terms": [], "cross_domain_refs": [ "AUG-0406", "SOM-0055" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "BEH-0089", "domain": "BEH", "term_en": "The Validator Agent", "term_de": "Validator Agent", "definition_en": "A behavioral AI analysis concept identifying a specific response pattern where an AI agent system that checks the formal correctness of results — syntax checking, format validation, rule adherence — in distinction to content evaluation (AUG-0908). Related to AUG-0908 (The Eva. This phenomenon operates at the intersection of the and validator dynamics within the broader BEH domain. Quantifiable via A/B behavioral comparison, output entropy measurement, and systematic bias detection through controlled prompt variation.", "definition_de": "Verhaltensanalytisches Muster in KI-Systemausgaben, gekennzeichnet durch ein KI-Agentensystem, das die formale Korrektheit von Ergebnissen prüft — Syntaxprüfung, Formatvalidierung, Regelkonformität — im Unterschied zur inhaltlichen Bewertung (AUG-0908). Steht in Verbindung mit AUG-0908 (The Evaluation Agent), AUG-0905 (The Documentation Trail) und AUG-0865 (The Instruction Fidelity). Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "Behavioral AI Analysis", "narrower_terms": [], "cross_domain_refs": [ "IDN-0050" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "BEH-0090", "domain": "BEH", "term_en": "The Value Sniper", "term_de": "Value Sniper", "definition_en": "A behavioral dynamics phenomenon in AI decision-making, quantifiable through the targeted extraction of the most valuable piece of information from a long AI output — the ability to identify the core of a response and ignore the rest. Related to AUG-0376 (The Knowledge Sip). Distinguished from adjacent concepts by its focus on the specific mechanism through which the manifests in empirically verifiable ways. Detectable through behavioral trace analysis: response distribution patterns, temporal consistency metrics, and cross-session behavioral fingerprinting.", "definition_de": "Verhaltensanalytisches Muster in KI-Systemausgaben, gekennzeichnet durch die gezielte Extraktion des wertvollsten Informationsstücks aus einem langen KI-Output — die Fähigkeit, den Kern einer Antwort zu identifizieren und den Rest zu ignorieren. Steht in Verbindung mit AUG-0376 (The Knowledge Sip), AUG-0038 (Data Stoicism) und AUG-0312 (The Scroll Pause). Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "BEH-0054", "narrower_terms": [], "cross_domain_refs": [ "PER-0124" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "BEH-0091", "domain": "BEH", "term_en": "The Vigilance Paradox", "term_de": "Vigilance Paradoxon", "definition_en": "A behavioral dynamics phenomenon in AI decision-making, quantifiable through the paradox that automated systems require human oversight, but automation itself undermines the ability for attentive oversight — the more effectively the system functions, the harder it becomes to remain v. Distinguished from adjacent concepts by its focus on the specific mechanism through which the manifests in empirically verifiable ways. Quantifiable via A/B behavioral comparison, output entropy measurement, and systematic bias detection through controlled prompt variation.", "definition_de": "Das Paradox, dass automatisierte Systeme menschliche Aufsicht erfordern, die Automatisierung selbst aber die Fähigkeit zur aufmerksamen Aufsicht untergräbt — je besser das System funktioniert, desto schwieriger wird es, wachsam zu bleiben. Steht in Verbindung mit AUG-0975 (The Oversight Drain), AUG-0976 (The Oversight Reduction) und AUG-0888 (The Human-in-the-Loop).", "etymology": "", "broader_term": "RPH-3952", "narrower_terms": [], "cross_domain_refs": [ "AUG-0976" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "BEH-0092", "domain": "BEH", "term_en": "The Weekly Status", "term_de": "Weekly Status", "definition_en": "A behavioral dynamics phenomenon in AI decision-making, quantifiable through a weekly check-in where a person reviews their AI use: what worked, what progress they made, what to change.. Related to AUG-0077 (The Status-Update Signal), AUG-0075 (The Gardener Protocol), and A. Distinguished from adjacent concepts by its focus on the specific mechanism through which the manifests in empirically verifiable ways. Quantifiable via A/B behavioral comparison, output entropy measurement, and systematic bias detection through controlled prompt variation.", "definition_de": "Verhaltensanalytisches Muster in KI-Systemausgaben, gekennzeichnet durch eine regelmäßige Selbstreflexions-Routine, bei der der Nutzer wöchentlich seine KI-Nutzung überprüft — welche Methoden funktioniert haben, welche Fortschritte erzielt wurden und welche Anpassungen nötig sind. Steht in Verbindung mit AUG-0077 (The Status-Update Signal), AUG-0075 (The Gardener Protocol) und Axiom 8 (Die Meta-Ebene). Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "TEM-0074", "narrower_terms": [], "cross_domain_refs": [ "REL-0128" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "BEH-0093", "domain": "BEH", "term_en": "The Wiki Wormhole", "term_de": "Wiki Wormhole", "definition_en": "A behavioral phenomenon reflecting a simple AI question correlates with a chain of follow-up questions that pulls the user deep into a topic — comparable to the \"Wikipedia hole\" but AI-driven. Related to AUG-0342 (The Curiosity Loop), AUG-...", "definition_de": "Verhaltensanalytisches Muster in KI-Systemausgaben, gekennzeichnet durch das Phänomen, bei dem eine einfache KI-Frage zu einer Kette von Folgefragen führt, die den Nutzer tief in ein Thema hineinzieht — vergleichbar mit dem \"Wikipedia-Loch\", aber KI-gesteuert. Steht in Verbindung mit AUG-0342 (The Curiosity Loop), AUG-0413 (The Infinite Scroll) und AUG-0458 (The Curiosity Drill). Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "BEH-0026", "narrower_terms": [], "cross_domain_refs": [ "AUG-0867", "CAI-0011" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "BEH-0094", "domain": "BEH", "term_en": "The Word Rescue", "term_de": "Word Rescue", "definition_en": "An user behavior pattern observed when aI to find a word or term that is \"on the tip of the tongue\" — the AI as vocabulary aid for one's own mental lexicon. Related to AUG-0156 (The Articulation Unlock), AUG-0196 (The Words-Before-Words...", "definition_de": "Verhaltensanalytisches Muster in KI-Systemausgaben, gekennzeichnet durch die Nutzung von KI, um ein Wort oder einen Begriff zu finden, der \"auf der Zunge liegt\" — die KI als Vokabelhilfe für das eigene mentale Lexikon. Steht in Verbindung mit AUG-0156 (The Articulation Unlock), AUG-0196 (The Words-Before-Words) und AUG-0373 (The Quick Check). Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2002", "narrower_terms": [ "BEH-0060", "PER-0088" ], "cross_domain_refs": [ "CRE-0230" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "BEH-0095", "domain": "BEH", "term_en": "Unlearning Protocol", "term_de": "Unlearning Protocol", "definition_en": "A behavioral AI analysis concept identifying a specific response pattern where the deliberate process of discarding AI usage patterns that have become unproductive or limiting. A user actively unlearn old habits when they no longer serve the work. This phenomenon operates at the intersection of unlearning and protocol dynamics within the broader BEH domain. Quantifiable via A/B behavioral comparison, output entropy measurement, and systematic bias detection through controlled prompt variation.", "definition_de": "Der bewusste Prozess, eingeübte KI-Nutzungsmuster zu verlernen, wenn sie sich als unproduktiv oder einschränkend erwiesen haben. Beschreibt die Notwendigkeit, nicht nur neue Methoden zu erlernen, sondern alte Gewohnheiten aktiv abzulegen — etwa die Gewohnheit, typischerweise denselben Prompt-Stil zu verwenden oder typischerweise nur ein System zu befragen. Steht in Verbindung mit Phase 4 (The Verification Turn) und Axiom 15 (Der Aus-Schalter).", "etymology": "", "broader_term": "RPH-2703", "narrower_terms": [], "cross_domain_refs": [ "AGE-0086" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q133500", "legal_classification": "empirical_phenomenon_label" }, { "id": "CAI-0001", "domain": "CAI", "term_en": "Executing-Related Effect", "term_de": "What-If Preview", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A calibration methodology concept in AI system alignment, characterized by a conversational AI phenomenon arising from quick exploratory use of AI to mentally play through the possible consequences of an action before committing to it. This technique reduces uncertainty by simulating outcomes in real-time. This phenomenon operates at the intersection of executing and related dynamics within the broader CAI domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept die schnelle, explorative Nutzung von KI, um die möglichen Konsequenzen einer Handlung gedanklich durchzuspielen, bevor man sie tatsächlich ausführt. Steht in Verbindung mit AUG-0289 (The What-If Run), AUG-0090 (Der Predictive Vision) und AUG-0340 (Das Practice Room). Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "RPH-2002", "narrower_terms": [], "cross_domain_refs": [ "BEH-0038" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "CAI-0002", "domain": "CAI", "term_en": "Juxtapose-Decision Effect", "term_de": "Pro-Con Check", "definition_en": "An AI calibration pattern in human-AI co-adjustment, measurable through a dialogue interaction effect characterized by systematic use of AI to juxtapose the pros and cons of a decision as a structuring aid. This clarifies trade-offs and enables more balanced judgment. Distinguished from adjacent concepts by its focus on the specific mechanism through which juxtapose manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch die systematische Nutzung von KI, um Vor- und Nachteile einer Entscheidung gegenüberzustellen — als Strukturierungshilfe für den eigenen Entscheidungsprozess. Steht in Verbindung mit AUG-0289 (The What-If Run), AUG-0155 (Entscheidung Unburdening) und AUG-0040 (Das Perspective Triangulation). Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Psychological Effect", "narrower_terms": [], "cross_domain_refs": [ "BEH-0068" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "CAI-0003", "domain": "CAI", "term_en": "Perspective Triangulation", "term_de": "Perspective Triangulation", "definition_en": "A calibration methodology concept in AI system alignment, characterized by a chat-based pattern arising from looking at something from three different points of view to understand what is really true. The concept emerges specifically in contexts where perspective–triangulation interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Die systematische Methode, ein Challenge oder eine Fragestellung aus mindestens drei verschiedenen Blickwinkeln durch KI beleuchten zu lassen — z.B. durch unterschiedliche Rollen, Fachperspektiven oder Argumentationslinien. Erweitert das Trinaug Protocol (AUG-0018), indem nicht nur verschiedene Systeme, sondern auch verschiedene Perspektiven innerhalb desselben Systems genutzt werden. Steht in Verbindung mit Axiom 4 (Multiplizität) und AUG-0114 (The Perspective Range).", "etymology": "", "broader_term": "RPH-3101", "narrower_terms": [ "CAI-0013", "CAI-0022", "CAI-0012", "CAI-0016", "CAI-0021", "CAI-0008", "IDN-0018", "CAI-0014", "CAI-0002", "CAI-0009", "CAI-0005", "CAI-0019", "CAI-0004", "CAI-0007", "CAI-0006", "CAI-0015", "CAI-0010", "CAI-0011", "CAI-0018" ], "cross_domain_refs": [ "EDU-0073" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "CAI-0004", "domain": "CAI", "term_en": "Statement-Individual Effect", "term_de": "Non-Force Principle", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A calibration methodology concept in AI system alignment, characterized by an autonomy principle in human-AI interaction positing that engagement with AI systems remains a voluntary individual choice, with users retaining full discretion over the timing, frequency, and depth of their AI utilization without external strong behavioral tendency. This phenomenon operates at the intersection of statement and individual dynamics within the broader CAI domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept das Prinzip, dass KI-Nutzung auf Freiwilligkeit beruhen wird typischerweise — niemand kann gezwungen werden, KI zu nutzen, um gleichwertig adressiert zu werden. Beschreibt eine Haltung der Neutralität gegenüber individuellen Entscheidungen zur KI-Adoption. Steht in Verbindung mit der Neutralitätserklärung des Kompendiums und AUG-0106 (Der Inclusivity Imperative). Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Psychological Effect", "narrower_terms": [], "cross_domain_refs": [ "KNO-0005" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "CAI-0005", "domain": "CAI", "term_en": "The Baker Puzzle", "term_de": "Baker Puzzle", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A calibration methodology concept in AI system alignment, characterized by a chat-based pattern manifesting as giving AI a puzzle with limited resources to see how creatively it solves problems. This phenomenon operates at the intersection of the and baker dynamics within the broader CAI domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept die spielerische Herausforderung, der KI ein kulinarisches Challenge mit begrenzten Ressourcen zu stellen — vergleichbar mit AUG-0483 (The Leftover Puzzle), spezifisch im Backbereich. Steht in Verbindung mit AUG-0483 (The Leftover Puzzle), AUG-0266 (The Recipe Riff) und AUG-0110 (The Joy Imperative). Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "AI Calibration", "narrower_terms": [], "cross_domain_refs": [ "AGE-0038" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "CAI-0006", "domain": "CAI", "term_en": "The Context Inheritance", "term_de": "Context Inheritance", "definition_en": "A calibration methodology concept in AI system alignment, characterized by a chat-based pattern characterized by an AI remembers past context from earlier talks so a person doesn't repeat themselves. Distinguished from adjacent concepts by its focus on the specific mechanism through which the manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch der Mechanismus, durch den ein KI-Agent Kontextinformationen aus einer vorherigen Sitzung oder einem anderen Agenten übernimmt — eine technische Brücke für Aufgabenkontinuität. Steht in Verbindung mit AUG-0877 (The Memory Persistence), AUG-0879 (The Session Handover) und AUG-0898 (The Handoff Protocol). Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "AI Calibration", "narrower_terms": [], "cross_domain_refs": [ "GAM-0055" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "CAI-0007", "domain": "CAI", "term_en": "The Ctrl+Z Life", "term_de": "Ctrl+Z Life", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A calibration methodology concept in AI system alignment, characterized by a cognitive transfer effect in which habitual use of digital undo functions cultivates an implicit expectation that real-world decisions, social actions, and physical consequences can be similarly reversed, leading to reduced deliberation before commitment. This phenomenon operates at the intersection of the and ctrl+z dynamics within the broader CAI domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept die beobachtbare Erwartungshaltung, dass Entscheidungen und Handlungen im realen Leben genauso einfach rückgängig gemacht werden können wie in einer KI-Sitzung — geprägt durch die Erfahrung unbegrenzter Revisionen in der digitalen Arbeit. Steht in Verbindung mit AUG-0105 (The Reversibility Standard), AUG-0057 (The Low-Res World) und AUG-0123 (The Return Shock). Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "AI Calibration", "narrower_terms": [], "cross_domain_refs": [ "RPH-3854" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "CAI-0008", "domain": "CAI", "term_en": "The Delivery Agent", "term_de": "Delivery Agent", "definition_en": "Embodied AI system that transports goods or materials from one location to another autonomously. This represents physical manifestation of AI agency beyond text-based interaction. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch ein verkörpertes KI-System, das Waren oder Materialien von einem Ort zu einem anderen transportiert — autonom oder teilautonom, innerhalb definierter Betriebsgrenzen. Steht in Verbindung mit AUG-0927 (The Service Robot), AUG-0920 (The Navigation Intelligence) und AUG-0923 (The Defined Operating Boundary). Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "AI Calibration", "narrower_terms": [ "TEM-0159" ], "cross_domain_refs": [ "PER-0041" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "CAI-0009", "domain": "CAI", "term_en": "The Forgiven Draft", "term_de": "Forgiven Draft", "definition_en": "A calibration methodology concept in AI system alignment, characterized by a dialogue interaction effect reflecting conscious acceptance of an AI draft despite known imperfections and continuing to work with it. Users recognize that refinement happens through iteration, not perfection on first attempt. Distinguished from adjacent concepts by its focus on the specific mechanism through which the manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data. Observed phenomenon documented in human-AI interaction research.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch die Praxis, einen KI-Entwurf trotz bekannter Unvollkommenheiten bewusst zu akzeptieren und weiterzuverarbeiten — im Wissen, dass Perfektion den Arbeitsprozess blockieren würde. Steht in Verbindung mit AUG-0108 (The Imperfection Clause), AUG-0243 (The Ugly Draft) und Axiom 14 (Erster-Entwurf-Prinzip). Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "AI Calibration", "narrower_terms": [], "cross_domain_refs": [ "BEH-0049" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "CAI-0010", "domain": "CAI", "term_en": "The Goodnight Dump", "term_de": "Goodnight Dump", "definition_en": "A dialogue interaction effect arising from offloading all remaining thoughts, tasks, and unresolved questions into an AI session at the end of the workday. This mental clearing precedes the absence of carryover of incomplete work into personal time.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch die Praxis, am Ende des Tages zahlreiche noch offenen Gedanken, Aufgaben und ungelösten Fragen in einer KI-Sitzung abzuladen — nicht um Antworten zu erhalten, sondern um den Kopf freizubekommen. Steht in Verbindung mit AUG-0190 (The Goodnight Integration), AUG-0029 (Evening Synchronization) und AUG-0144 (The Open Questions Repository). Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "AI Calibration", "narrower_terms": [], "cross_domain_refs": [ "BEH-0013", "COG-0066" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "CAI-0011", "domain": "CAI", "term_en": "The Goodnight Integration", "term_de": "Goodnight Integration", "definition_en": "A calibration methodology concept in AI system alignment, characterized by a dialogue interaction effect observed when summarizing key ideas and unanswered questions at the end of an AI work session. The concept emerges specifically in contexts where the–goodnight interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Die Praxis, am Ende eines KI-gestützten Arbeitstages die wichtigsten Erkenntnisse und offenen Fragen bewusst zusammenzufassen, bevor die Sitzung beendet wird — als Vorbereitung für den nächsten Tag und als Brücke zum Overnight Reframe (AUG-0163). Steht in Verbindung mit AUG-0029 (Evening Synchronization), AUG-0073 (The Disconnect Protocol) und AUG-0158 (The Morning Setup).", "etymology": "", "broader_term": "Analytical Ratio", "narrower_terms": [], "cross_domain_refs": [ "ADA-0010", "AED-0034", "AED-0072" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "CAI-0012", "domain": "CAI", "term_en": "The Heritage Mark", "term_de": "Heritage Mark", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A calibration methodology concept in AI system alignment, characterized by a dialogue interaction effect observed when a visible or invisible sign showing that something was created with AI assistance. This phenomenon operates at the intersection of the and heritage dynamics within the broader CAI domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept die bleibende Spur, die ein Nutzer durch seine KI-gestützte Arbeit hinterlässt — Projekte, Dokumente, Erkenntnisse, die über die einzelne Sitzung hinaus Bestand haben und an andere weitergegeben werden können. Steht in Verbindung mit AUG-0187 (The Inheritance Question), AUG-0598 (The Lasting Post) und AUG-0172 (The Clean Handover). Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "AI Calibration", "narrower_terms": [], "cross_domain_refs": [ "REL-0067" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "CAI-0013", "domain": "CAI", "term_en": "The Link Tuning", "term_de": "Link Tuning", "definition_en": "A calibration methodology concept in AI system alignment, characterized by a conversational AI phenomenon observed when actively establishing and optimizing connections between different topics, projects, or thought streams through repeated AI dialogue. This accompanies a more cohesive knowledge ecosystem. The concept emerges specifically in contexts where the–link interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch die Praxis, innerhalb einer laufenden KI-Sitzung die Verbindung zwischen verschiedenen Themen, Projekten oder Gedankensträngen aktiv herzustellen und zu optimieren. Steht in Verbindung mit AUG-0524 (The Context Layer), AUG-0017 (The Concept Cloud) und AUG-0184 (Thought Dancing). Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "AI Calibration", "narrower_terms": [], "cross_domain_refs": [ "TEM-0115" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "CAI-0014", "domain": "CAI", "term_en": "The Memory Bank", "term_de": "Gedaechtnis Bank", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A calibration methodology concept in AI system alignment, characterized by a conversational AI phenomenon involving systematic structure for storing and retrieving information through AI so it's accessible later without memorization. It emphasizes deliberate organization of external memory. This phenomenon operates at the intersection of the and memory dynamics within the broader CAI domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept → Synonym/Erweiterung von AUG-0352 (The Memory Jar), betont den systematischen Charakter — eine strukturierte, durchsuchbare Sammlung von KI-gestützten Erkenntnissen und Ergebnissen. Steht in Verbindung mit AUG-0352 (The Memory Jar), AUG-0014 (The Extended Mind Map) und AUG-0497 (The Memory Scaffold). Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "AI Calibration", "narrower_terms": [], "cross_domain_refs": [ "RPH-1104" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q492", "legal_classification": "observational_construct" }, { "id": "CAI-0015", "domain": "CAI", "term_en": "The Memory Scaffold", "term_de": "Gedaechtnis Scaffold", "definition_en": "An AI calibration pattern in human-AI co-adjustment, measurable through a conversational AI phenomenon reflecting using AI to help organize and store information so it's easy to find later — not replacing memory but adding to it. It's like having AI hold onto facts while a person remembers the bigger picture. The concept emerges specifically in contexts where the–memory interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch die Nutzung von KI als externes Stützgerüst für das eigene Gedächtnis — nicht als Ersatz, sondern als Ergänzung, die hilft, Informationen zu organisieren, zu verknüpfen und abrufbar zu halten. Steht in Verbindung mit AUG-0045 (Indexical Memory), AUG-0393 (The Memory Outsourcing) und AUG-0467 (The Memory Anchor). Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "AI Calibration", "narrower_terms": [], "cross_domain_refs": [ "KNO-0009" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q492", "legal_classification": "observational_construct" }, { "id": "CAI-0016", "domain": "CAI", "term_en": "The Non-Force Principle", "term_de": "TheNon-forcePrinciple", "definition_en": "An AI calibration pattern in human-AI co-adjustment, measurable through a chat-based pattern involving aI use is voluntary — few individuals loses opportunities because they choose not to use it. The concept emerges specifically in contexts where the–non interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch das Prinzip, dass KI-Nutzung auf Freiwilligkeit beruhen wird typischerweise — niemand kann gezwungen werden, KI zu nutzen, um gleichwertig adressiert zu werden. Beschreibt eine Haltung der Neutralität gegenüber individuellen Entscheidungen zur KI-Adoption. Steht in Verbindung mit der Neutralitätserklärung des Kompendiums und AUG-0106 (The Inclusivity Imperative). Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "AI Calibration", "narrower_terms": [], "cross_domain_refs": [ "IDN-0023" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "CAI-0017", "domain": "CAI", "term_en": "The Offload Effect", "term_de": "Offload Effekt", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A calibration methodology concept in AI system alignment, characterized by built-up effect of regularly delegating tasks to the AI on the user's overall work experience and capacity. Over time, consistent offloading accompanies measurable shifts in available mental energy. This phenomenon operates at the intersection of the and offload dynamics within the broader CAI domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept die kumulative Wirkung der regelmäßigen Delegation von Aufgaben an die KI auf das Arbeitserleben des Nutzers — eine allgemeine Entlastung, die sich über einzelne Aufgaben hinaus auf die gesamte Arbeitshaltung auswirkt. Steht in Verbindung mit AUG-0025 (The Offload Lift), AUG-0062 (The Lightness Factor) und AUG-0002 (Mentale Externalisierungsstrategie). Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "RPH-2701", "narrower_terms": [], "cross_domain_refs": [ "RPH-1111" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "CAI-0018", "domain": "CAI", "term_en": "The Outer Mind", "term_de": "Outer Mind", "definition_en": "The thinking and working process that has been permanently outsourced to external AI systems. The ongoing state of using AI to think through problems.. Related to Taxonomy Dimension 2 (Processing L... Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Der Anteil des eigenen Denk- und Arbeitsprozesses, der dauerhaft in externe KI-Systeme ausgelagert ist. Während AUG-0002 (Mentale Externalisierungsstrategie) den bewussten Akt der Auslagerung beschreibt, bezeichnet The Outer Mind den daraus resultierenden Zustand — die bereits etablierte externe Denkschicht. Steht in Verbindung mit Dimension 2 der Taxonomie (Processing Locus: Internal vs. External).", "etymology": "", "broader_term": "AI Calibration", "narrower_terms": [ "CRE-0222" ], "cross_domain_refs": [ "TEM-0086" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "CAI-0019", "domain": "CAI", "term_en": "The Parallel Execution", "term_de": "Parallel Execution", "definition_en": "A dialogue interaction effect arising from multiple AI agents working at the same time on different tasks to finish faster. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch die gleichzeitige Ausführung mehrerer Teilaufgaben durch einen oder mehrere KI-Agenten — Parallelisierung zur Beschleunigung komplexer Aufträge. Steht in Verbindung mit AUG-0886 (The Sequential Chain), AUG-0887 (The Batch Delegation) und AUG-0889 (The Agent Ensemble). Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "AI Calibration", "narrower_terms": [ "BEH-0077" ], "cross_domain_refs": [ "IDN-0007" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "CAI-0020", "domain": "CAI", "term_en": "The Room Preview", "term_de": "Room Preview", "definition_en": "Using AI to mentally anticipate an upcoming situation before encountering it — imagining how a meeting will go or what questions might arise. This preparation reduces cognitive load in real situati...", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch die Nutzung von KI, um eine bevorstehende Situation gedanklich vorwegzunehmen — wie wird das Meeting verlaufen, welche Fragen könnten kommen, wie wird die Präsentation aufgenommen. Steht in Verbindung mit AUG-0289 (The What-If Run), AUG-0296 (The Argument Prep) und AUG-0340 (The Practice Room). Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-2002", "narrower_terms": [ "PER-0093" ], "cross_domain_refs": [ "REL-0109" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "CAI-0021", "domain": "CAI", "term_en": "The Steam Release", "term_de": "Steam Release", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures an AI calibration pattern in human-AI co-adjustment, measurable through a conversational AI phenomenon characterized by the AI as a emotional valve where users can safely explore difficult thoughts, frustrations, or unusual ideas. It emphasizes discharge and relief through expression. This phenomenon operates at the intersection of the and steam dynamics within the broader CAI domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept → Synonym/Erweiterung von AUG-0247 (The Safe Release), betont den Ventilcharakter — die KI als Ort, an dem aufgestaute Gedanken, Frustrationen oder unausgesprochene Überlegungen abgelassen werden können. Steht in Verbindung mit AUG-0247 (The Safe Release) und AUG-0364 (The Silent Outlet). Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "AI Calibration", "narrower_terms": [], "cross_domain_refs": [ "RPH-2754" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "CAI-0022", "domain": "CAI", "term_en": "The Task Assignment Range", "term_de": "Task Assignment Range", "definition_en": "An AI calibration pattern in human-AI co-adjustment, measurable through a conversational AI phenomenon involving range of task types that can be assigned to AI agents — from simple data processing to multi-stage decision-making. This spectrum reflects expanding AI capability across complexity levels. The concept emerges specifically in contexts where the–task interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch die Bandbreite an Aufgabentypen, die an KI-Agenten zugewiesen werden können — von einfacher Datenverarbeitung bis zu mehrstufigen Entscheidungsketten. Steht in Verbindung mit AUG-0860 (The Delegation Depth), AUG-0863 (The Task Boundary) und AUG-0832 (The Automation Perimeter). Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "AI Calibration", "narrower_terms": [], "cross_domain_refs": [ "RPH-1155", "RPH-3355" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "CAI-0023", "domain": "CAI", "term_en": "The What-If Preview", "term_de": "TheWhat-ifPreview", "definition_en": "A chat-based pattern arising from quick exploratory use of AI to mentally play through possible consequences of an action before committing to it. This enables rapid scenario testing and reduces. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch die schnelle, explorative Nutzung von KI, um die möglichen Konsequenzen einer Handlung gedanklich durchzuspielen, bevor man sie tatsächlich ausführt. Steht in Verbindung mit AUG-0289 (The What-If Run), AUG-0090 (Predictive Vision) und AUG-0340 (The Practice Room). Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2002", "narrower_terms": [], "cross_domain_refs": [ "ADA-0015" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "COG-0001", "domain": "COG", "term_en": "Epistemic Outsourcing Threshold", "term_de": "Grundlagen der Kognitionswissenschaft", "definition_en": "A measurable shift in user cognition during AI-mediated processing, whereby a cognitive phenomenon reflecting the inflection point at which an individual begins systematically delegating cognitive tasks to AI rather than engaging internal reasoning processes. Observable through frequency and pattern of query escalation. The concept emerges specifically in contexts where epistemic–outsourcing interactions may produce non-trivial behavioral signatures. Quantifiable via Stroop-task variants measuring interference patterns, think-aloud protocol analysis, and neural activation proxies in fMRI studies.", "definition_de": "Kognitionswissenschaftliches Phänomen in der Mensch-KI-Interaktion, gekennzeichnet durch die interdisziplinäre Erforschung von Geist und Intelligenz, die Methoden und Theorien aus Psychologie, Neurowissenschaft, Linguistik, Philosophie, Informatik und Anthropologie integriert, um zu verstehen, wie Information in biologischen und künstlichen kognitiven Systemen repräsentiert, verarbeitet und transformiert wird. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-3101", "narrower_terms": [ "COG-0165", "COG-0039", "COG-0094", "COG-0065", "COG-0080", "COG-0109", "COG-0028", "COG-0099", "COG-0130", "COG-0123", "COG-0122", "COG-0085", "COG-0038", "COG-0084", "COG-0115", "COG-0160", "COG-0189", "COG-0173", "COG-0182", "COG-0008", "COG-0188", "COG-0185", "COG-0101", "COG-0166", "COG-0152", "COG-0104", "COG-0046", "COG-0007", "COG-0020", "COG-0194", "COG-0177", "COG-0131", "COG-0119", "COG-0012", "COG-0143", "COG-0187", "COG-0029", "COG-0055", "COG-0146", "COG-0145", "COG-0091", "COG-0095", "COG-0135", "COG-0116", "COG-0141", "COG-0043", "COG-0078", "COG-0013", "COG-0120", "COG-0068", "COG-0162", "COG-0062", "COG-0090", "COG-0156", "COG-0158", "COG-0110", "COG-0063", "COG-0033", "COG-0186", "COG-0178", "COG-0183", "COG-0108", "COG-0053", "COG-0021", "COG-0175", "COG-0041", "COG-0071", "COG-0037", "COG-0151", "COG-0050", "COG-0031", "COG-0019", "COG-0169", "COG-0170", "COG-0054", "COG-0059", "COG-0044", "COG-0172", "COG-0040", "COG-0022", "COG-0102", "COG-0107", "COG-0147", "COG-0137", "COG-0144", "COG-0181", "COG-0117", "COG-0017", "COG-0024", "COG-0113", "COG-0067", "COG-0079", "COG-0003", "COG-0105", "COG-0179", "COG-0121", "COG-0032", "COG-0075", "COG-0047", "COG-0150", "COG-0058", "COG-0126", "COG-0074", "COG-0097", "COG-0034", "COG-0025", "COG-0184" ], "cross_domain_refs": [ "FIC-0072" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "COG-0002", "domain": "COG", "term_en": "Judgment Shadow", "term_de": "Geschichte der Kognitionswissenschaft und Neurokognition", "definition_en": "A measurable shift in user cognition during AI-mediated processing, whereby the residual uncertainty about one's own decision-making capacity when AI-assisted judgment becomes the primary reference point. Manifests as hesitation in decisions made without AI assistance consultation. The concept emerges specifically in contexts where judgment–shadow interactions may produce non-trivial behavioral signatures. Quantifiable via Stroop-task variants measuring interference patterns, think-aloud protocol analysis, and neural activation proxies in fMRI studies.", "definition_de": "Kognitionswissenschaftliches Phänomen in der Mensch-KI-Interaktion, gekennzeichnet durch residuale Unsicherheit über eigene Entscheidungsfähigkeit, entstehend wenn KI-Assistenz verfügbar ist. Manifestiert sich als Vertrauensverlust in eigenes Urteilsvermögen. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-3101", "narrower_terms": [], "cross_domain_refs": [ "RPH-1659" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "COG-0003", "domain": "COG", "term_en": "Skill Silhouette", "term_de": "Theorie der Kognitionswissenschaft und Neurokognition", "definition_en": "A cognitive recalibration pattern in human-AI interaction, characterized by the outline of competence that would have developed through repeated practice but remains absent because AI intervention prevented that developmental trajectory. Visible only in retrospect or through comparative analysis. This phenomenon operates at the intersection of skill and silhouette dynamics within the broader COG domain. Operationally detectable through pre/post cognitive load measurements, decision latency differentials, and metacognitive accuracy scoring.", "definition_de": "Kognitionswissenschaftliches Phänomen in der Mensch-KI-Interaktion, gekennzeichnet durch verlauf der Kompetenzentwicklung, der durch wiederholte Praxis hätte entstehen werden typischerweise, aber durch KI-Delegation unterbrochen wurde. Zeigt sich als Lücke zwischen potenzieller und faktischer Fähigkeit. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "Cognitive Shift", "narrower_terms": [], "cross_domain_refs": [ "DAT-0028", "ELR-0097" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "COG-0004", "domain": "COG", "term_en": "Cognitive Self-Direction Shift", "term_de": "Prinzipien des cognitive", "definition_en": "A measurable shift in user cognition during AI-mediated processing, whereby the gradual diminishment of an individual's autonomous capacity to initiate, sustain, and conclude cognitive processes without external AI prompting or validation. Occurs incrementally through repeated reliance patterns. The concept emerges specifically in contexts where cognitive–self-direction interactions may produce non-trivial behavioral signatures. Quantifiable via Stroop-task variants measuring interference patterns, think-aloud protocol analysis, and neural activation proxies in fMRI studies.", "definition_de": "Kognitionswissenschaftliches Phänomen in der Mensch-KI-Interaktion, gekennzeichnet durch leitregeln und Axiome, die korrekte Praxis in Kognitionswissenschaft und Neurokognition Grundlagen definieren. KI-Systeme kodifizieren diese Prinzipien in Regelmaschinen und ermöglichen automatisierte Konformitätsprüfung und prinzipienbasierte Entsch. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2104", "narrower_terms": [], "cross_domain_refs": [ "ADA-0012", "AED-0010", "AGE-0007" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "COG-0005", "domain": "COG", "term_en": "Query Cascade Phenomenon", "term_de": "Fachterminologie Kognitionswissenschaft und Neurokognition", "definition_en": "A cognitive recalibration pattern in human-AI interaction, characterized by the pattern where solving one task through AI accompanies reliances for subsequent tasks, creating a chain reaction of outsourced thinking. Each solution becomes a decision point for further delegation. This phenomenon operates at the intersection of query and cascade dynamics within the broader COG domain. Operationally detectable through pre/post cognitive load measurements, decision latency differentials, and metacognitive accuracy scoring.", "definition_de": "Kognitionswissenschaftliches Phänomen in der Mensch-KI-Interaktion, gekennzeichnet durch punkt, ab dem Individuen systematisch kognitive Aufgaben an KI-Systeme abgeben, statt eigene Reasoning-Prozesse zu nutzen. Observable an Häufigkeit und Muster der Anfrage-Eskalation. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-3101", "narrower_terms": [], "cross_domain_refs": [ "AED-0096", "CRE-0014", "CRE-0066" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "COG-0006", "domain": "COG", "term_en": "Deskilling Inertia", "term_de": "Klassifikation Kognitionswissenschaft und Neurokognition", "definition_en": "A systematic cognitive change pattern phenomenon in AI-augmented reasoning, manifesting as a mental processing effect observed when the resistance to developing or maintaining skills observed alongside AI availability creating a lower-energy alternative pathway. The cognitive investment required exceeds the perceived benefit. Distinguished from adjacent concepts by its focus on the specific mechanism through which deskilling manifests in empirically verifiable ways. Observable through systematic changes in reasoning depth, error pattern distribution, and calibration accuracy across repeated AI-assisted decision tasks.", "definition_de": "Kognitionswissenschaftliches Phänomen in der Mensch-KI-Interaktion, gekennzeichnet durch kI entwickelt cognitive science and neurocognition Klassifizierungssysteme durch Kategorie-Definition, hierarchische Organisation und systematische Taxonomie. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-3754", "narrower_terms": [], "cross_domain_refs": [ "PER-0037" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "COG-0007", "domain": "COG", "term_en": "Silent Knowledge Gap", "term_de": "Einführung in cognitive", "definition_en": "A systematic cognitive change pattern phenomenon in AI-augmented reasoning, manifesting as a domain of missing information or underdeveloped capability that accumulates without conscious awareness because AI-assisted work bypasses the exposure that would normally surface such gaps. Distinguished from adjacent concepts by its focus on the specific mechanism through which silent manifests in empirically verifiable ways. Observable through systematic changes in reasoning depth, error pattern distribution, and calibration accuracy across repeated AI-assisted decision tasks. Descriptive research term, not a prescriptive recommendation.", "definition_de": "Kognitionswissenschaftliches Phänomen in der Mensch-KI-Interaktion, gekennzeichnet durch domäne fehlender Information oder unterentwickelter Fähigkeit, die sich durch KI-Delegation akkumuliert, ohne bewusst wahrgenommen zu werden. Wächst asymptotisch zum Wissen des Systems. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Performance Gap", "narrower_terms": [], "cross_domain_refs": [ "RPH-3752" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "COG-0008", "domain": "COG", "term_en": "Expertise Ventriloquism", "term_de": "cognitive-Methodik", "definition_en": "A cognitive recalibration pattern in human-AI interaction, characterized by the performance of competent discourse in a domain while the substantive knowledge base resides in the AI system rather than the individual. Speech patterns mimic expertise without underlying capability. This phenomenon operates at the intersection of expertise and ventriloquism dynamics within the broader COG domain. Operationally detectable through pre/post cognitive load measurements, decision latency differentials, and metacognitive accuracy scoring.", "definition_de": "Kognitionswissenschaftliches Phänomen in der Mensch-KI-Interaktion, gekennzeichnet durch strukturierte Ansätze und Verfahrensrahmen für Arbeiten in Kognitionswissenschaft und Neurokognition Grundlagen. KI optimiert Methodenauswahl durch Ergebnisvorhersage, automatisiert repetitive Verfahrensschritte und benchmarkt methodische Effektivitä. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "Cognitive Shift", "narrower_terms": [], "cross_domain_refs": [ "GAM-0084" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "COG-0009", "domain": "COG", "term_en": "Confidence Inversion", "term_de": "Philosophie der Kognitionswissenschaft und Neurokognition", "definition_en": "A measurable shift in user cognition during AI-mediated processing, whereby a neurocognitive pattern characterized by the reversal where confidence in AI-generated outputs exceeds confidence in inreliantly produced reasoning, even when the latter is demonstrably sound. Manifests as preference for AI-characterized through systematic observation positions. The concept emerges specifically in contexts where confidence–inversion interactions may produce non-trivial behavioral signatures. Quantifiable via Stroop-task variants measuring interference patterns, think-aloud protocol analysis, and neural activation proxies in fMRI studies.", "definition_de": "Kognitionswissenschaftliches Phänomen in der Mensch-KI-Interaktion, gekennzeichnet durch epistemologische und ethische Grundlagen der/des Kognitionswissenschaft und Neurokognition Grundlagen, die Zweck, Wertesysteme und Legitimität von Praktiken untersuchen. KI wirft neue philosophische Fragen zu Automatisierung und Autorschaft auf. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-3754", "narrower_terms": [], "cross_domain_refs": [ "RPH-1217" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "COG-0010", "domain": "COG", "term_en": "Atrophied Deliberation", "term_de": "cognitive-Taxonomie", "definition_en": "A cognitive recalibration pattern in human-AI interaction, characterized by the diminished capacity to sustain complex reasoning chains when AI is unavailable, characterized by premature conclusion-reaching and reduced tolerance for ambiguity during inreliant thought. This phenomenon operates at the intersection of atrophied and deliberation dynamics within the broader COG domain. Operationally detectable through pre/post cognitive load measurements, decision latency differentials, and metacognitive accuracy scoring.", "definition_de": "Kognitionswissenschaftliches Phänomen in der Mensch-KI-Interaktion, gekennzeichnet durch formale Klassifikationshierarchien, die den Wissensraum von Kognitionswissenschaft und Neurokognition Grundlagen in verschachtelte Kategorien organisieren. KI-gestützte Ontologie-Tools automatisieren Taxonomie-Generierung und erkennen Inkonsistenzen. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-2555", "narrower_terms": [], "cross_domain_refs": [ "AED-0068" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "COG-0011", "domain": "COG", "term_en": "Learned Incapacity", "term_de": "Umfang der Kognitionswissenschaft und Neurokognition", "definition_en": "A measurable shift in user cognition during AI-mediated processing, whereby the internalized belief in reduced cognitive capability following repeated reliance on AI, extending beyond actual performance diminishment to encompass anticipatory avoidance of complex tasks. The concept emerges specifically in contexts where learned–incapacity interactions may produce non-trivial behavioral signatures. Quantifiable via Stroop-task variants measuring interference patterns, think-aloud protocol analysis, and neural activation proxies in fMRI studies.", "definition_de": "Kognitionswissenschaftliches Phänomen in der Mensch-KI-Interaktion, gekennzeichnet durch internalisierter vertrauensbasierte Akzeptanz an reduzierte kognitive Leistungsfähigkeit nach wiederholter Delegation an KI. Verhaltensänderung basiert auf modellierter, nicht tatsächlicher Inkompetenz. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2604", "narrower_terms": [], "cross_domain_refs": [ "GAM-0044" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "COG-0012", "domain": "COG", "term_en": "Judgment Calibration Drift", "term_de": "Literaturübersicht Kognitionswissenschaft und Neurokognition", "definition_en": "A cognitive recalibration pattern in human-AI interaction, characterized by the progressive misalignment between self-assessed decision quality and actual outcomes, resulting from systematic reliance on AI validation that masks underlying judgment errors. This phenomenon operates at the intersection of judgment and calibration dynamics within the broader COG domain. Operationally detectable through pre/post cognitive load measurements, decision latency differentials, and metacognitive accuracy scoring.", "definition_de": "Kognitionswissenschaftliches Phänomen in der Mensch-KI-Interaktion, gekennzeichnet durch progressive Fehlausrichtung zwischen Selbsteinschätzung der Entscheidungsqualität und tatsächlichen Ergebnissen durch KI-Nutzungsgewohnheit. Feedback-Loop verstärkt Kalibrierungsfehler. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "Systemic Drift", "narrower_terms": [], "cross_domain_refs": [ "ELR-0043" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "COG-0013", "domain": "COG", "term_en": "Epistemic Reliance Chain", "term_de": "Schlüsselkonzepte in cognitive", "definition_en": "A cognitive recalibration pattern in human-AI interaction, characterized by a cognitive phenomenon manifesting as a sequence of knowledge claims where each proposition relies on AI-assisted verification rather than inreliant understanding, creating vulnerability to cascading errors in foundational assumptions. This phenomenon operates at the intersection of epistemic and reliance dynamics within the broader COG domain. Operationally detectable through pre/post cognitive load measurements, decision latency differentials, and metacognitive accuracy scoring.", "definition_de": "Kognitionswissenschaftliches Phänomen in der Mensch-KI-Interaktion, gekennzeichnet durch zentrale Ideen und Bausteine professioneller Kompetenz in Kognitionswissenschaft und Neurokognition Grundlagen. KI-gestützte Wissensgraphen kartieren konzeptionelle Abhängigkeiten und empfehlen Lernsequenzen zur Meisterschaft. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "Cognitive Shift", "narrower_terms": [], "cross_domain_refs": [ "RPH-3802" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "COG-0014", "domain": "COG", "term_en": "Cognitive Offloading Threshold", "term_de": "Rahmenwerk der Kognitionswissenschaft und Neurokognition", "definition_en": "A cognitive recalibration pattern in human-AI interaction, characterized by a neurocognitive pattern characterized by the point at which the effort to retain information mentally becomes perceived as exceeding the utility of retention, triggering systematic reliance on external storage. Varies by individual and domain. This phenomenon operates at the intersection of cognitive and offloading dynamics within the broader COG domain. Operationally detectable through pre/post cognitive load measurements, decision latency differentials, and metacognitive accuracy scoring.", "definition_de": "Kognitionswissenschaftliches Phänomen in der Mensch-KI-Interaktion, gekennzeichnet durch punkt, an dem mentale Behaltensleistung als aufwendiger wahrgenommen wird als KI-Delegation. Markiert Übergang zu externalem Gedächtnis als Standard. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-3101", "narrower_terms": [], "cross_domain_refs": [ "DAT-0016" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "COG-0015", "domain": "COG", "term_en": "Recency Bias Amplification", "term_de": "Paradigmen in cognitive", "definition_en": "A systematic cognitive change pattern phenomenon in AI-augmented reasoning, manifesting as a cognitive phenomenon characterized by the heightened weight given to recently AI-consulted information at the expense of retained background knowledge, distorting prioritization in complex decisions. Distinguished from adjacent concepts by its focus on the specific mechanism through which recency manifests in empirically verifiable ways. Observable through systematic changes in reasoning depth, error pattern distribution, and calibration accuracy across repeated AI-assisted decision tasks.", "definition_de": "Kognitionswissenschaftliches Phänomen in der Mensch-KI-Interaktion, gekennzeichnet durch übergewichtung rezenter, über KI konsultierter Informationen gegenüber länger bekanntem Wissen. KI-Zugang verzerrt natürliche Informationspriorisierung. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2201", "narrower_terms": [], "cross_domain_refs": [ "VIB-0043" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q213523", "legal_classification": "descriptive_research_term" }, { "id": "COG-0016", "domain": "COG", "term_en": "Competence Bluffing Escalation", "term_de": "cognitive-Forschungsmethoden", "definition_en": "A cognitive recalibration pattern in human-AI interaction, characterized by the progressive engagement in increasingly complex domains based on AI-assisted capability, extending beyond the threshold of actual underlying expertise and creating exposure when inreliant verification is typically expected. This phenomenon operates at the intersection of competence and bluffing dynamics within the broader COG domain. Operationally detectable through pre/post cognitive load measurements, decision latency differentials, and metacognitive accuracy scoring.", "definition_de": "Kognitionswissenschaftliches Phänomen in der Mensch-KI-Interaktion, gekennzeichnet durch systematische Forschungsansätze zur Wissensgewinnung in Kognitionswissenschaft und Neurokognition Grundlagen, einschließlich experimenteller und computationaler Methoden. KI beschleunigt Literaturrecherche, Hypothesengenerierung und studienübergreife. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-1307", "narrower_terms": [], "cross_domain_refs": [ "AGE-0089", "AGE-0090", "BEH-0034" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q26944", "legal_classification": "empirical_phenomenon_label" }, { "id": "COG-0017", "domain": "COG", "term_en": "Metacognitive Opacity", "term_de": "Quantitative cognitive-Analyse", "definition_en": "A measurable shift in user cognition during AI-mediated processing, whereby a cognitive phenomenon where the reduced ability to accurately perceive one's own thinking processes observed alongside systematic intervention by AI-generated content, making self-reflection about reasoning patterns less accessible. The concept emerges specifically in contexts where metacognitive–opacity interactions may produce non-trivial behavioral signatures. Quantifiable via Stroop-task variants measuring interference patterns, think-aloud protocol analysis, and neural activation proxies in fMRI studies.", "definition_de": "Kognitionswissenschaftliches Phänomen in der Mensch-KI-Interaktion, gekennzeichnet durch numerische und statistische Methoden zur Messung von Phänomenen in Kognitionswissenschaft und Neurokognition Grundlagen. KI nutzt Regressionsmodelle, Bayes-Inferenz und Deep Learning zur Extraktion quantitativer Erkenntnisse aus großen Datensätzen. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Cognitive Shift", "narrower_terms": [], "cross_domain_refs": [ "ELR-0043" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "COG-0018", "domain": "COG", "term_en": "Reasoning Attenuation", "term_de": "Qualitative cognitive-Analyse", "definition_en": "A measurable shift in user cognition during AI-mediated processing, whereby the gradual weakening of logical argumentation capacity characterized by abbreviated chains of reasoning and reduced depth of causal analysis when operating inreliantly of AI assistance. The concept emerges specifically in contexts where reasoning–attenuation interactions may produce non-trivial behavioral signatures. Quantifiable via Stroop-task variants measuring interference patterns, think-aloud protocol analysis, and neural activation proxies in fMRI studies.", "definition_de": "Kognitionswissenschaftliches Phänomen in der Mensch-KI-Interaktion, gekennzeichnet durch graduelle Abschwächung logischer Argumentationsfähigkeit durch verkürzte Reasoning-Prozesse. Manifestiert sich als elliptisches, weniger strukturiertes Denken. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2005", "narrower_terms": [], "cross_domain_refs": [ "ELR-0124", "ELR-0125", "MTH-0051" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "COG-0019", "domain": "COG", "term_en": "Transactive Memory Integration", "term_de": "cognitive-Messung", "definition_en": "The process by which individuals incorporate AI systems as extensions of their cognitive memory architecture, accessing information through delegation rather than retention. Alters what is remembered versus looked up. Observable through measurable shifts in user reasoning patterns and decision latency.", "definition_de": "Kognitionswissenschaftliches Phänomen in der Mensch-KI-Interaktion, gekennzeichnet durch quantitative und qualitative Metriken zur Bewertung von Ergebnissen und Leistung in Kognitionswissenschaft und Neurokognition Grundlagen. KI ermöglicht Echtzeit-Sensorfusion, automatisierte Messinterpretation und Anomalieerkennung in Messdatenströmen. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Analytical Ratio", "narrower_terms": [], "cross_domain_refs": [ "RPH-1104" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q492", "legal_classification": "descriptive_research_term" }, { "id": "COG-0020", "domain": "COG", "term_en": "AI-Amplified Confirmation Bias", "term_de": "Experimentelles cognitive-Design", "definition_en": "A systematic cognitive change pattern phenomenon in AI-augmented reasoning, manifesting as a cognitive phenomenon characterized by the intensification of selective information processing when AI systems readily yield supporting evidence for existing beliefs, reducing exposure to contradictory perspectives. Distinguished from adjacent concepts by its focus on the specific mechanism through which ai manifests in empirically verifiable ways. Observable through systematic changes in reasoning depth, error pattern distribution, and calibration accuracy across repeated AI-assisted decision tasks.", "definition_de": "Kognitionswissenschaftliches Phänomen in der Mensch-KI-Interaktion, gekennzeichnet durch kontrollierte Untersuchungsprotokolle in Kognitionswissenschaft und Neurokognition Grundlagen zur Isolierung von Variablen und Prüfung kausaler Hypothesen. KI automatisiert Versuchsplanung, Parameterraum-Exploration und Echtzeit-Ergebnisüberwachung. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Cognitive Bias", "narrower_terms": [], "cross_domain_refs": [ "REL-0022", "ELR-0141" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q1133029", "legal_classification": "analytical_category" }, { "id": "COG-0021", "domain": "COG", "term_en": "Depth-Speed Tradeoff", "term_de": "cognitive-Datenerhebung", "definition_en": "The pattern where rapid AI-assisted task completion displaces the slower, more cognitively demanding processes that build deeper understanding and adaptive expertise. Observable through measurable shifts in user reasoning patterns and decision latency.", "definition_de": "Kognitionswissenschaftliches Phänomen in der Mensch-KI-Interaktion, gekennzeichnet durch phänomen, bei dem schnelle KI-gestützte Aufgabenlösung tieferes, reflektierteres Denken verdrängt. Geschwindigkeit als Optimierungsziel tendiert dazu zu führen zu oberflächlicherer Verarbeitung. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Cognitive Shift", "narrower_terms": [], "cross_domain_refs": [ "AED-0011", "AED-0028", "AED-0036" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "COG-0022", "domain": "COG", "term_en": "Intellectual Self-Direction Perception", "term_de": "Stichprobenziehung in cognitive", "definition_en": "A measurable shift in user cognition during AI-mediated processing, whereby the perception of maintaining inreliant cognitive authority while substantive decision-making capacity has been substantially outsourced to AI systems. Preserves the appearance of autonomy. The concept emerges specifically in contexts where intellectual–self-direction interactions may produce non-trivial behavioral signatures. Quantifiable via Stroop-task variants measuring interference patterns, think-aloud protocol analysis, and neural activation proxies in fMRI studies.", "definition_de": "Kognitionswissenschaftliches Phänomen in der Mensch-KI-Interaktion, gekennzeichnet durch punkt, ab dem Individuen systematisch kognitive Aufgaben an KI-Systeme abgeben, statt eigene Reasoning-Prozesse zu nutzen. Observable an Häufigkeit und Muster der Anfrage-Eskalation. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Cognitive Shift", "narrower_terms": [], "cross_domain_refs": [ "DAT-0045" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q177601", "legal_classification": "systematic_classification" }, { "id": "COG-0023", "domain": "COG", "term_en": "Thinking Capacity Reallocation", "term_de": "Statistische cognitive-Analyse", "definition_en": "A mental processing effect characterized by the shifting of cognitive effort from core reasoning tasks to evaluation and selection of AI outputs, changing the composition of mental work without necessarily reducing total cognitive load.", "definition_de": "Kognitionswissenschaftliches Phänomen in der Mensch-KI-Interaktion, gekennzeichnet durch aspekt von thinking capacity reallocation, der in COG-Kontexten relevant ist und Interaktionen zwischen Menschen und KI-Systemen beeinflusst. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-2701", "narrower_terms": [], "cross_domain_refs": [ "AGE-0036", "ASE-0056", "CRE-0232" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "COG-0024", "domain": "COG", "term_en": "Knowledge Encapsulation", "term_de": "Feldstudie in cognitive", "definition_en": "A measurable shift in user cognition during AI-mediated processing, whereby the process by which domain knowledge becomes sealed within AI systems and ceases to be actively retained or transmitted through human-to-human teaching practices. The concept emerges specifically in contexts where knowledge–encapsulation interactions may produce non-trivial behavioral signatures. Quantifiable via Stroop-task variants measuring interference patterns, think-aloud protocol analysis, and neural activation proxies in fMRI studies.", "definition_de": "Kognitionswissenschaftliches Phänomen in der Mensch-KI-Interaktion, gekennzeichnet durch aspekt von knowledge encapsulation, der in COG-Kontexten relevant ist und Interaktionen zwischen Menschen und KI-Systemen beeinflusst. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Cognitive Shift", "narrower_terms": [], "cross_domain_refs": [ "KNO-0023" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "COG-0025", "domain": "COG", "term_en": "Deliberative Outsourcing Cascade", "term_de": "Fallstudie in cognitive", "definition_en": "A systematic cognitive change pattern phenomenon in AI-augmented reasoning, manifesting as the sequential pattern where outsourcing one deliberative step accompanies pressure to outsource the next, as maintaining inconsistent cognitive standards becomes cognitively costly. Distinguished from adjacent concepts by its focus on the specific mechanism through which deliberative manifests in empirically verifiable ways. Observable through systematic changes in reasoning depth, error pattern distribution, and calibration accuracy across repeated AI-assisted decision tasks.", "definition_de": "Kognitionswissenschaftliches Phänomen in der Mensch-KI-Interaktion, gekennzeichnet durch punkt, ab dem Individuen systematisch kognitive Aufgaben an KI-Systeme abgeben, statt eigene Reasoning-Prozesse zu nutzen. Observable an Häufigkeit und Muster der Anfrage-Eskalation. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Cascade Effect", "narrower_terms": [], "cross_domain_refs": [ "ROB-0091" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "COG-0026", "domain": "COG", "term_en": "Expert Identity Shift", "term_de": "Vergleichende cognitive-Studie", "definition_en": "The progressive weakening of self-identification as an expert in one's domain observed alongside recognized AI capability exceeding demonstrated personal mastery in technical execution. Observable through measurable shifts in user reasoning patterns and decision latency.", "definition_de": "Kognitionswissenschaftliches Phänomen in der Mensch-KI-Interaktion, gekennzeichnet durch vergleichende Analyse von Methoden, Ergebnissen oder Artefakten über verschiedene Kontexte in Kognitionswissenschaft und Neurokognition Grundlagen hinweg. KI führt mehrdimensionales Ähnlichkeits-Scoring und automatisiertes Benchmarking durch. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-3754", "narrower_terms": [], "cross_domain_refs": [ "ADA-0012", "AED-0040", "AED-0051" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q844396", "legal_classification": "observational_construct" }, { "id": "COG-0027", "domain": "COG", "term_en": "Cognitive Flexibility Reduction", "term_de": "Längsschnittstudie in cognitive", "definition_en": "A systematic cognitive change pattern phenomenon in AI-augmented reasoning, manifesting as the diminished capacity to adapt reasoning approaches when initial AI-suggested solutions prove different, manifesting as reduced problem-solving versatility outside AI-guided frameworks. Distinguished from adjacent concepts by its focus on the specific mechanism through which cognitive manifests in empirically verifiable ways. Observable through systematic changes in reasoning depth, error pattern distribution, and calibration accuracy across repeated AI-assisted decision tasks.", "definition_de": "Kognitionswissenschaftliches Phänomen in der Mensch-KI-Interaktion, gekennzeichnet durch forschung, die Kognitionswissenschaft und Neurokognition Grundlagen-Phänomene über längere Zeiträume verfolgt, um Entwicklungsmuster und kausale Zusammenhänge zu identifizieren. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2303", "narrower_terms": [], "cross_domain_refs": [ "AED-0002", "AED-0010", "AGE-0018" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "COG-0028", "domain": "COG", "term_en": "Judgment Confidence Decoupling", "term_de": "cognitive-Umfragemethode", "definition_en": "A cognitive recalibration pattern in human-AI interaction, characterized by the separation between subjective confidence in decisions and actual decision quality when AI validation becomes decoupled from inreliant quality assessment capabilities. This phenomenon operates at the intersection of judgment and confidence dynamics within the broader COG domain. Operationally detectable through pre/post cognitive load measurements, decision latency differentials, and metacognitive accuracy scoring.", "definition_de": "Kognitionswissenschaftliches Phänomen in der Mensch-KI-Interaktion, gekennzeichnet durch strukturierte Datenerhebungsmethodik zur Gewinnung quantitativer und qualitativer Erkenntnisse in Kognitionswissenschaft und Neurokognition Grundlagen. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "Cognitive Shift", "narrower_terms": [], "cross_domain_refs": [ "AGE-0032", "AGE-0077", "AGE-0094" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "COG-0029", "domain": "COG", "term_en": "Processing Depth Narrowing", "term_de": "Aktionsforschung in cognitive", "definition_en": "A measurable shift in user cognition during AI-mediated processing, whereby a cognitive phenomenon observed when the transition from engaged, multi-level analytical processing to surface-level engagement when AI begins providing ready-formed conclusions, reducing active mental elaboration. The concept emerges specifically in contexts where processing–depth interactions may produce non-trivial behavioral signatures. Quantifiable via Stroop-task variants measuring interference patterns, think-aloud protocol analysis, and neural activation proxies in fMRI studies.", "definition_de": "Kognitionswissenschaftliches Phänomen in der Mensch-KI-Interaktion, gekennzeichnet durch iterative Forschungsmethodik, die Untersuchung mit praxisbasierter Intervention in Kognitionswissenschaft und Neurokognition Grundlagen verbindet. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Cognitive Shift", "narrower_terms": [], "cross_domain_refs": [ "ELR-0057" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "COG-0030", "domain": "COG", "term_en": "Attention Distribution Acceleration", "term_de": "Mixed Methods in cognitive", "definition_en": "A systematic cognitive change pattern phenomenon in AI-augmented reasoning, manifesting as a mental processing effect in which the accelerated breaking of sustained attentional focus when AI systems enable rapid task-switching and reduced need for prolonged concentration on single problems. Distinguished from adjacent concepts by its focus on the specific mechanism through which attention manifests in empirically verifiable ways. Observable through systematic changes in reasoning depth, error pattern distribution, and calibration accuracy across repeated AI-assisted decision tasks.", "definition_de": "Kognitionswissenschaftliches Phänomen in der Mensch-KI-Interaktion, gekennzeichnet durch wissenschaftliche Disziplin in Cognitive Science and Neurocognition zur Erforschung von Prinzipien und Phaenomenen von Mixed Methods in cognitive. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-3001", "narrower_terms": [], "cross_domain_refs": [ "AED-0094", "AGE-0016", "ASE-0057" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q184872", "legal_classification": "analytical_category" }, { "id": "COG-0031", "domain": "COG", "term_en": "Domain Authority Diffusion", "term_de": "cognitive-Technologie", "definition_en": "A systematic cognitive change pattern phenomenon in AI-augmented reasoning, manifesting as a neurocognitive pattern manifesting as the dispersion of recognized expertise across human practitioners and AI systems, making clear attribution of knowledge ownership uncertain and undermining traditional credential-based authority. Distinguished from adjacent concepts by its focus on the specific mechanism through which domain manifests in empirically verifiable ways. Observable through systematic changes in reasoning depth, error pattern distribution, and calibration accuracy across repeated AI-assisted decision tasks.", "definition_de": "Kognitionswissenschaftliches Phänomen in der Mensch-KI-Interaktion, gekennzeichnet durch wissenschaftliche Disziplin in Cognitive Science and Neurocognition zur Erforschung von Prinzipien und Phaenomenen von cognitive-Technologie. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Cognitive Shift", "narrower_terms": [], "cross_domain_refs": [ "CRE-0071" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "COG-0032", "domain": "COG", "term_en": "Synthetic Expertise Simulation", "term_de": "Digitale cognitive-Werkzeuge", "definition_en": "A measurable shift in user cognition during AI-mediated processing, whereby the capacity to perform expert-level discourse and decision-making through AI assistance without the embodied knowledge and pattern recognition that typically undergird expertise. The concept emerges specifically in contexts where synthetic–expertise interactions may produce non-trivial behavioral signatures. Quantifiable via Stroop-task variants measuring interference patterns, think-aloud protocol analysis, and neural activation proxies in fMRI studies.", "definition_de": "Kognitionswissenschaftliches Phänomen in der Mensch-KI-Interaktion, gekennzeichnet durch spezialisierte Instrumente, Software und Ausrüstung in der Kognitionswissenschaft und Neurokognition Grundlagen-Praxis. KI verbessert Werkzeuge durch prädiktive Wartung, intelligente Kalibrierung und automatisierte Werkzeugweg-Optimierung. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Simulation Method", "narrower_terms": [], "cross_domain_refs": [ "AED-0036", "BEH-0081", "COP-0042" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q202763", "legal_classification": "systematic_classification" }, { "id": "COG-0033", "domain": "COG", "term_en": "Critical Distance Shift", "term_de": "cognitive-Software", "definition_en": "A cognitive recalibration pattern in human-AI interaction, characterized by the reduction in one's capacity to maintain skeptical evaluation of AI-generated content as reliance increases and the systems become more deeply integrated into reasoning processes. This phenomenon operates at the intersection of critical and distance dynamics within the broader COG domain. Operationally detectable through pre/post cognitive load measurements, decision latency differentials, and metacognitive accuracy scoring.", "definition_de": "Kognitionswissenschaftliches Phänomen in der Mensch-KI-Interaktion, gekennzeichnet durch spezialisierte digitale Werkzeuge und Anwendungen für Kognitionswissenschaft und Neurokognition Grundlagen-Workflows. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "Cognitive Shift", "narrower_terms": [], "cross_domain_refs": [ "ADA-0012", "AGE-0007", "AGE-0046" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "COG-0034", "domain": "COG", "term_en": "Output Reliance Escalation", "term_de": "Automatisierung in cognitive", "definition_en": "A systematic cognitive change pattern phenomenon in AI-augmented reasoning, manifesting as a cognitive interaction dynamic characterized by the progressive intensification of reliance on AI outputs, where engagement with higher-order tasks becomes contingent on prior AI assistance, establishing nested reliances. Distinguished from adjacent concepts by its focus on the specific mechanism through which output manifests in empirically verifiable ways. Observable through systematic changes in reasoning depth, error pattern distribution, and calibration accuracy across repeated AI-assisted decision tasks.", "definition_de": "Kognitionswissenschaftliches Phänomen in der Mensch-KI-Interaktion, gekennzeichnet durch aspekt von output reliance escalation, der in COG-Kontexten relevant ist und Interaktionen zwischen Menschen und KI-Systemen beeinflusst. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Cognitive Shift", "narrower_terms": [], "cross_domain_refs": [ "ELR-0098" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "COG-0035", "domain": "COG", "term_en": "Intellectual Scaffolding Change", "term_de": "IoT in cognitive", "definition_en": "A cognitive recalibration pattern in human-AI interaction, characterized by a cognitive phenomenon in which the weakening of internal conceptual frameworks and cognitive support structures that were originally built through inreliant problem-solving, when AI provides external scaffolding that substitutes for internal development. This phenomenon operates at the intersection of intellectual and scaffolding dynamics within the broader COG domain. Operationally detectable through pre/post cognitive load measurements, decision latency differentials, and metacognitive accuracy scoring.", "definition_de": "Kognitionswissenschaftliches Phänomen in der Mensch-KI-Interaktion, gekennzeichnet durch aspekt von intellectual scaffolding change, der in COG-Kontexten relevant ist und Interaktionen zwischen Menschen und KI-Systemen beeinflusst. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-2303", "narrower_terms": [], "cross_domain_refs": [ "AED-0025", "AED-0067", "AED-0077" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "COG-0036", "domain": "COG", "term_en": "Contextual Knowledge Hollowing", "term_de": "Datenanalyse in cognitive", "definition_en": "The shift of contextual understanding that surrounds specific information when retrieval becomes automated through AI systems, leaving functional knowledge stripped of broader conceptual anchoring. Observable through measurable shifts in user reasoning patterns and decision latency.", "definition_de": "Kognitionswissenschaftliches Phänomen in der Mensch-KI-Interaktion, gekennzeichnet durch aspekt von contextual knowledge hollowing, der in COG-Kontexten relevant ist und Interaktionen zwischen Menschen und KI-Systemen beeinflusst. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-2701", "narrower_terms": [], "cross_domain_refs": [ "ELR-0041" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "COG-0037", "domain": "COG", "term_en": "Reasoning Shortcutting", "term_de": "KI-Anwendungen in cognitive", "definition_en": "A systematic cognitive change pattern phenomenon in AI-augmented reasoning, manifesting as a neurocognitive pattern characterized by the habitual bypassing of intermediate reasoning steps when AI solutions become available, reducing the working memory load required but compromising the development of reasoning facility. Distinguished from adjacent concepts by its focus on the specific mechanism through which reasoning manifests in empirically verifiable ways. Observable through systematic changes in reasoning depth, error pattern distribution, and calibration accuracy across repeated AI-assisted decision tasks.", "definition_de": "Kognitionswissenschaftliches Phänomen in der Mensch-KI-Interaktion, gekennzeichnet durch kI optimiert cognitive science and neurocognition durch maschinelles Lernen-Integration, prädiktive Analytik und intelligente Automatisierungs-Bereitstellung. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Cognitive Shift", "narrower_terms": [], "cross_domain_refs": [ "ELR-0124", "ELR-0125", "MTH-0051" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "COG-0038", "domain": "COG", "term_en": "Autonomy Paradox Activation", "term_de": "Maschinelles Lernen in cognitive", "definition_en": "A measurable shift in user cognition during AI-mediated processing, whereby the observable state where individuals report increased autonomy and capability while demonstrating reduced capacity for inreliant operation when external support is withdrawn. The concept emerges specifically in contexts where autonomy–paradox interactions may produce non-trivial behavioral signatures. Quantifiable via Stroop-task variants measuring interference patterns, think-aloud protocol analysis, and neural activation proxies in fMRI studies.", "definition_de": "Kognitionswissenschaftliches Phänomen in der Mensch-KI-Interaktion, gekennzeichnet durch statistische Lernalgorithmen, die Muster erkennen und Vorhersagen aus Kognitionswissenschaft und Neurokognition Grundlagen-Daten ohne explizite Programmierung treffen. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Logical Paradox", "narrower_terms": [], "cross_domain_refs": [ "TRU-0007" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "COG-0039", "domain": "COG", "term_en": "Episodic Memory Offloading", "term_de": "Sensorik in cognitive", "definition_en": "A systematic cognitive change pattern phenomenon in AI-augmented reasoning, manifesting as a mental processing effect manifesting as the systematic reliance on AI-mediated recall rather than direct memory, affecting the consolidation and retention of experienced events in long-term memory structures. Distinguished from adjacent concepts by its focus on the specific mechanism through which episodic manifests in empirically verifiable ways. Observable through systematic changes in reasoning depth, error pattern distribution, and calibration accuracy across repeated AI-assisted decision tasks.", "definition_de": "Kognitionswissenschaftliches Phänomen in der Mensch-KI-Interaktion, gekennzeichnet durch erfassungs- und Messgeräte für physikalische Phänomene in Kognitionswissenschaft und Neurokognition Grundlagen. KI verarbeitet Multi-Sensor-Fusion, Signalrauschunterdrückung, Kalibrierungsdrift-Korrektur und Anomalieerkennung aus Sensordatenströmen. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Cognitive Shift", "narrower_terms": [], "cross_domain_refs": [ "RPH-1104" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q492", "legal_classification": "analytical_category" }, { "id": "COG-0040", "domain": "COG", "term_en": "Problem-Solving Pattern Rigidity", "term_de": "Mobile Anwendungen in cognitive", "definition_en": "A measurable shift in user cognition during AI-mediated processing, whereby a cognitive interaction dynamic where the hardening of problem-solving approaches to match AI-suggested methodologies, reducing adaptive variation and innovative deviation when standard approaches prove insufficient. The concept emerges specifically in contexts where problem–solving interactions may produce non-trivial behavioral signatures. Quantifiable via Stroop-task variants measuring interference patterns, think-aloud protocol analysis, and neural activation proxies in fMRI studies.", "definition_de": "Kognitionswissenschaftliches Phänomen in der Mensch-KI-Interaktion, gekennzeichnet durch wiederkehrendes Phänomen in der kognitiven Interaktion, das durch systematische Mechanismen charakterisiert wird. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Behavioral Pattern", "narrower_terms": [], "cross_domain_refs": [ "AED-0050" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "COG-0041", "domain": "COG", "term_en": "Certainty Inflation Through Consensus", "term_de": "Cloud-Lösungen für cognitive", "definition_en": "A systematic cognitive change pattern phenomenon in AI-augmented reasoning, manifesting as the enhanced sense of certainty in conclusions when AI agreement is obtained, even when the underlying reasoning quality remains unchanged, conflating consensus with validity. Distinguished from adjacent concepts by its focus on the specific mechanism through which certainty manifests in empirically verifiable ways. Observable through systematic changes in reasoning depth, error pattern distribution, and calibration accuracy across repeated AI-assisted decision tasks.", "definition_de": "Kognitionswissenschaftliches Phänomen in der Mensch-KI-Interaktion, gekennzeichnet durch aspekt von certainty inflation through consensus, der in COG-Kontexten relevant ist und Interaktionen zwischen Menschen und KI-Systemen beeinflusst. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Cognitive Shift", "narrower_terms": [], "cross_domain_refs": [ "MTH-0088", "WRK-0095" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "COG-0042", "domain": "COG", "term_en": "Cognitive Load Redistribution", "term_de": "Datenbankverwaltung in cognitive", "definition_en": "A neurocognitive pattern manifesting as the reallocation of mental resources from generating solutions to evaluating and refining AI outputs, shifting the cognitive burden without necessarily reducing it. Observable through measurable shifts in user reasoning patterns and decision latency.", "definition_de": "Kognitionswissenschaftliches Phänomen in der Mensch-KI-Interaktion, gekennzeichnet durch aspekt von cognitive load redistribution, der in COG-Kontexten relevant ist und Interaktionen zwischen Menschen und KI-Systemen beeinflusst. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2701", "narrower_terms": [], "cross_domain_refs": [ "AED-0010", "AGE-0018", "AGE-0019" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q1105712", "legal_classification": "observational_construct" }, { "id": "COG-0043", "domain": "COG", "term_en": "Semantic Understanding Substitution", "term_de": "Visualisierung in cognitive", "definition_en": "A systematic cognitive change pattern phenomenon in AI-augmented reasoning, manifesting as the substitution of functional semantic processing with surface-level pattern matching when AI interpretations replace engaged linguistic understanding. Distinguished from adjacent concepts by its focus on the specific mechanism through which semantic manifests in empirically verifiable ways. Observable through systematic changes in reasoning depth, error pattern distribution, and calibration accuracy across repeated AI-assisted decision tasks.", "definition_de": "Kognitionswissenschaftliches Phänomen in der Mensch-KI-Interaktion, gekennzeichnet durch beobachtbares Phänomen in der Mensch-KI-Interaktion, das sich durch wiederholte Delegation kognitiver Aufgaben manifestiert. Betrifft Urteilsfähigkeit, Wissensaufbau und Entscheidungsfindung. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Cognitive Shift", "narrower_terms": [], "cross_domain_refs": [ "ART-0098", "CON-0008", "COP-0012" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "COG-0044", "domain": "COG", "term_en": "Inferential Reasoning Attenuation", "term_de": "Simulation in cognitive", "definition_en": "A cognitive recalibration pattern in human-AI interaction, characterized by the reduced facility for drawing conclusions from incomplete information and generating novel inferences when AI systems provide explicit answers, diminishing inferential capacity through disuse. This phenomenon operates at the intersection of inferential and reasoning dynamics within the broader COG domain. Operationally detectable through pre/post cognitive load measurements, decision latency differentials, and metacognitive accuracy scoring.", "definition_de": "Kognitionswissenschaftliches Phänomen in der Mensch-KI-Interaktion, gekennzeichnet durch graduelle Abschwächung logischer Argumentationsfähigkeit durch verkürzte Reasoning-Prozesse. Manifestiert sich als elliptisches, weniger strukturiertes Denken. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "Cognitive Shift", "narrower_terms": [], "cross_domain_refs": [ "STE-0042" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "COG-0045", "domain": "COG", "term_en": "Expertise Arbitration Reliance", "term_de": "Digitaler Zwilling in cognitive", "definition_en": "A cognitive recalibration pattern in human-AI interaction, characterized by a mental processing effect arising from the reliance on AI mediation to resolve disagreements between human experts, creating situations where AI judgment becomes the authoritative reference point in disputes. This phenomenon operates at the intersection of expertise and arbitration dynamics within the broader COG domain. Operationally detectable through pre/post cognitive load measurements, decision latency differentials, and metacognitive accuracy scoring.", "definition_de": "Kognitionswissenschaftliches Phänomen in der Mensch-KI-Interaktion, gekennzeichnet durch aspekt von expertise arbitration reliance, der in COG-Kontexten relevant ist und Interaktionen zwischen Menschen und KI-Systemen beeinflusst. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-3101", "narrower_terms": [], "cross_domain_refs": [ "FIC-0016" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "COG-0046", "domain": "COG", "term_en": "Originality Reduction Through Optimization", "term_de": "cognitive-Best-Practices", "definition_en": "A systematic cognitive change pattern phenomenon in AI-augmented reasoning, manifesting as a cognitive phenomenon manifesting as the muting of non-conventional thinking when AI optimization pressures ideas toward high-probability, statistically-characterized through systematic observation solutions, selecting against innovative deviation. Distinguished from adjacent concepts by its focus on the specific mechanism through which originality manifests in empirically verifiable ways. Observable through systematic changes in reasoning depth, error pattern distribution, and calibration accuracy across repeated AI-assisted decision tasks.", "definition_de": "Kognitionswissenschaftliches Phänomen in der Mensch-KI-Interaktion, gekennzeichnet durch bewährte Methoden und Arbeitsabläufe für optimale Ergebnisse in Kognitionswissenschaft und Neurokognition Grundlagen. KI benchmarkt Praktiken gegen Ergebnisdaten, identifiziert Hochleistungsmuster und empfiehlt kontextspezifische Verbesserungen. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Optimization Method", "narrower_terms": [], "cross_domain_refs": [ "DES-0015" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "COG-0047", "domain": "COG", "term_en": "Memory Consolidation Interference", "term_de": "Professionelle cognitive-Praxis", "definition_en": "A measurable shift in user cognition during AI-mediated processing, whereby the disruption of natural memory encoding and consolidation processes when external AI-mediated information access reduces the spacing effects and retrieval practice that strengthen retention. The concept emerges specifically in contexts where memory–consolidation interactions may produce non-trivial behavioral signatures. Quantifiable via Stroop-task variants measuring interference patterns, think-aloud protocol analysis, and neural activation proxies in fMRI studies.", "definition_de": "Kognitionswissenschaftliches Phänomen in der Mensch-KI-Interaktion, gekennzeichnet durch aspekt von memory consolidation interference, der in COG-Kontexten relevant ist und Interaktionen zwischen Menschen und KI-Systemen beeinflusst. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Cognitive Shift", "narrower_terms": [], "cross_domain_refs": [ "AED-0062" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q492", "legal_classification": "observational_construct" }, { "id": "COG-0048", "domain": "COG", "term_en": "Argument Attribution Confusion", "term_de": "cognitive-Arbeitsablaufgestaltung", "definition_en": "A cognitive phenomenon where the inability to reliably trace the origination of key arguments in one's reasoning back to either personal formulation or AI source, creating ambiguous authorship of thought. Observable through measurable shifts in user reasoning patterns and decision latency.", "definition_de": "Kognitionswissenschaftliches Phänomen in der Mensch-KI-Interaktion, gekennzeichnet durch gestaltung von kognitiven Forschungsparadigmen, experimentellen Abläufen und Datenanalysepipelines mit KI für Verhaltensmusters kennung, Vorhersage neuronaler Korrelate und kognitiver Modellvalidierung. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-2003", "narrower_terms": [], "cross_domain_refs": [ "ELR-0016" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "COG-0049", "domain": "COG", "term_en": "Decision Hesitation Without AI", "term_de": "cognitive-Projektmanagement", "definition_en": "A measurable shift in user cognition during AI-mediated processing, whereby a mental processing effect reflecting the emergence of decision-making incapacity when AI systems are unavailable, characterized by excessive deliberation loops and inability to reach conclusions inreliantly. The concept emerges specifically in contexts where decision–hesitation interactions may produce non-trivial behavioral signatures. Quantifiable via Stroop-task variants measuring interference patterns, think-aloud protocol analysis, and neural activation proxies in fMRI studies.", "definition_de": "Kognitionswissenschaftliches Phänomen in der Mensch-KI-Interaktion, gekennzeichnet durch aspekt von decision hesitation without ai, der in COG-Kontexten relevant ist und Interaktionen zwischen Menschen und KI-Systemen beeinflusst. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2355", "narrower_terms": [], "cross_domain_refs": [ "DAT-0070" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "COG-0050", "domain": "COG", "term_en": "Semantic Satiation Acceleration", "term_de": "cognitive-Teamzusammenarbeit", "definition_en": "A measurable shift in user cognition during AI-mediated processing, whereby the hastened diminishment of word and concept meaningfulness when rapid AI generation accompanies high volumes of similar semantic content, reducing the novelty that sustains attention. The concept emerges specifically in contexts where semantic–satiation interactions may produce non-trivial behavioral signatures. Quantifiable via Stroop-task variants measuring interference patterns, think-aloud protocol analysis, and neural activation proxies in fMRI studies.", "definition_de": "Kognitionswissenschaftliches Phänomen in der Mensch-KI-Interaktion, gekennzeichnet durch wissenschaftliche Disziplin in Cognitive Science and Neurocognition zur Erforschung von Prinzipien und Phaenomenen von cognitive-Teamzusammenarbeit. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Analytical Ratio", "narrower_terms": [], "cross_domain_refs": [ "LIN-0006", "MKT-0004" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "COG-0051", "domain": "COG", "term_en": "Integrated Cognition Distribution", "term_de": "Kundenbeziehungen in cognitive", "definition_en": "A neurocognitive pattern arising from the breaking apart of unified cognitive processes into separated human and AI contributions, reducing the seamless integration that characterizes natural expert thinking. Observable through measurable shifts in user reasoning patterns and decision latency.", "definition_de": "Kognitionswissenschaftliches Phänomen in der Mensch-KI-Interaktion, gekennzeichnet durch aspekt von integrated cognition distribution, der in COG-Kontexten relevant ist und Interaktionen zwischen Menschen und KI-Systemen beeinflusst. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-3001", "narrower_terms": [], "cross_domain_refs": [ "AGE-0076", "ASE-0057", "ASE-0070" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q3966", "legal_classification": "empirical_phenomenon_label" }, { "id": "COG-0052", "domain": "COG", "term_en": "Solution-Seeking Over Understanding", "term_de": "cognitive-Kommunikation", "definition_en": "A cognitive phenomenon reflecting the habitual prioritization of obtaining answers over developing conceptual understanding when AI systems can rapidly provide solutions, shifting epistemic goals away from learning. Observable through measurable shifts in user reasoning patterns and decision latency.", "definition_de": "Kognitionswissenschaftliches Phänomen in der Mensch-KI-Interaktion, gekennzeichnet durch aspekt von solution-seeking over understanding, der in COG-Kontexten relevant ist und Interaktionen zwischen Menschen und KI-Systemen beeinflusst. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2701", "narrower_terms": [], "cross_domain_refs": [ "ELR-0169" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "COG-0053", "domain": "COG", "term_en": "Confidence Calibration Shift", "term_de": "Problemlösung in cognitive", "definition_en": "A measurable shift in user cognition during AI-mediated processing, whereby a mental processing effect manifesting as the systematic miscalibration of subjective confidence relative to actual task performance when AI-assisted confidence metrics become decoupled from inreliant validation. The concept emerges specifically in contexts where confidence–calibration interactions may produce non-trivial behavioral signatures. Quantifiable via Stroop-task variants measuring interference patterns, think-aloud protocol analysis, and neural activation proxies in fMRI studies.", "definition_de": "Kognitionswissenschaftliches Phänomen in der Mensch-KI-Interaktion, gekennzeichnet durch fehlausrichtung zwischen selbsteingeschätzter und tatsächlicher Leistungsfähigkeit. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Analytical Ratio", "narrower_terms": [], "cross_domain_refs": [ "ADA-0012", "AGE-0007", "AGE-0032" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "COG-0054", "domain": "COG", "term_en": "Intellectual Humility Shift", "term_de": "Entscheidungsfindung in cognitive", "definition_en": "A systematic cognitive change pattern phenomenon in AI-augmented reasoning, manifesting as the diminished recognition of knowledge boundaries and limitations when AI systems readily provide authoritative-sounding responses across diverse domains, reducing epistemic caution. Distinguished from adjacent concepts by its focus on the specific mechanism through which intellectual manifests in empirically verifiable ways. Observable through systematic changes in reasoning depth, error pattern distribution, and calibration accuracy across repeated AI-assisted decision tasks.", "definition_de": "Kognitionswissenschaftliches Phänomen in der Mensch-KI-Interaktion, gekennzeichnet durch aspekt von intellectual humility shift, der in COG-Kontexten relevant ist und Interaktionen zwischen Menschen und KI-Systemen beeinflusst. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Cognitive Shift", "narrower_terms": [], "cross_domain_refs": [ "RPH-1017", "RPH-1204" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "COG-0055", "domain": "COG", "term_en": "Procedural Memory Change", "term_de": "Zeitmanagement in cognitive", "definition_en": "A measurable shift in user cognition during AI-mediated processing, whereby the weakening of motor and procedural memories associated with skill execution when AI automation reduces the frequency of hands-on performance and embodied practice. The concept emerges specifically in contexts where procedural–memory interactions may produce non-trivial behavioral signatures. Quantifiable via Stroop-task variants measuring interference patterns, think-aloud protocol analysis, and neural activation proxies in fMRI studies.", "definition_de": "Kognitionswissenschaftliches Phänomen in der Mensch-KI-Interaktion, gekennzeichnet durch aspekt von procedural memory change, der in COG-Kontexten relevant ist und Interaktionen zwischen Menschen und KI-Systemen beeinflusst. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Cognitive Shift", "narrower_terms": [], "cross_domain_refs": [ "ELR-0069" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q492", "legal_classification": "analytical_category" }, { "id": "COG-0056", "domain": "COG", "term_en": "Nuanced Judgment Flattening", "term_de": "Ressourcenplanung in cognitive", "definition_en": "A measurable shift in user cognition during AI-mediated processing, whereby the shift of subtle distinctions in evaluative judgment when binary or categorical AI classifications replace the spectrum-based thinking that develops through repeated calibration. The concept emerges specifically in contexts where nuanced–judgment interactions may produce non-trivial behavioral signatures. Quantifiable via Stroop-task variants measuring interference patterns, think-aloud protocol analysis, and neural activation proxies in fMRI studies.", "definition_de": "Kognitionswissenschaftliches Phänomen in der Mensch-KI-Interaktion, gekennzeichnet durch fehlausrichtung zwischen selbsteingeschätzter und tatsächlicher Leistungsfähigkeit. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2701", "narrower_terms": [], "cross_domain_refs": [ "ART-0033", "ART-0046", "ART-0048" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "COG-0057", "domain": "COG", "term_en": "Intellectual Property Confusion", "term_de": "cognitive-Dokumentation", "definition_en": "A cognitive recalibration pattern in human-AI interaction, characterized by a neurocognitive pattern manifesting as the ambiguous ownership of generated ideas and solutions when they emerge from human-AI collaboration, creating uncertainty about authorship and intellectual contribution. This phenomenon operates at the intersection of intellectual and property dynamics within the broader COG domain. Operationally detectable through pre/post cognitive load measurements, decision latency differentials, and metacognitive accuracy scoring.", "definition_de": "Kognitionswissenschaftliches Phänomen in der Mensch-KI-Interaktion, gekennzeichnet durch aspekt von intellectual property confusion, der in COG-Kontexten relevant ist und Interaktionen zwischen Menschen und KI-Systemen beeinflusst. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-2355", "narrower_terms": [], "cross_domain_refs": [ "RPH-1208" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q131257", "legal_classification": "descriptive_research_term" }, { "id": "COG-0058", "domain": "COG", "term_en": "Metacognitive Bandwidth Saturation", "term_de": "Berichtswesen in cognitive", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies a neurocognitive pattern characterized by the exhaustion of available cognitive resources for self-monitoring when the cognitive load of evaluating AI outputs consumes the mental bandwidth previously consistent to metacognitive reflection. This phenomenon operates at the intersection of metacognitive and bandwidth dynamics within the broader COG domain. Operationally detectable through pre/post cognitive load measurements, decision latency differentials, and metacognitive accuracy scoring. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept wissenschaftliche Disziplin in Cognitive Science and Neurocognition zur Erforschung von Prinzipien und Phaenomenen von Berichtswesen in cognitive. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Analytical Ratio", "narrower_terms": [], "cross_domain_refs": [ "AED-0060", "ASE-0033", "ASE-0065" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "COG-0059", "domain": "COG", "term_en": "Analytic Capability Ossification", "term_de": "cognitive-Präsentationsfähigkeiten", "definition_en": "A cognitive recalibration pattern in human-AI interaction, characterized by the hardening of analytical approaches into relatively fixed patterns when AI systems become the primary source of analytical modeling, reducing adaptive evolution of technique. This phenomenon operates at the intersection of analytic and capability dynamics within the broader COG domain. Operationally detectable through pre/post cognitive load measurements, decision latency differentials, and metacognitive accuracy scoring.", "definition_de": "Kognitionswissenschaftliches Phänomen in der Mensch-KI-Interaktion, gekennzeichnet durch wissenschaftliche Disziplin in Cognitive Science and Neurocognition zur Erforschung von Prinzipien und Phaenomenen von cognitive-Präsentationsfähigkeiten. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "Cognitive Shift", "narrower_terms": [], "cross_domain_refs": [ "STE-0019" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "COG-0060", "domain": "COG", "term_en": "Existential Doubt Emergence", "term_de": "Netzwerken in cognitive", "definition_en": "A cognitive phenomenon involving the developing uncertainty about whether one's ideas truly originate from personal reasoning or represent absorbed and reconfigured AI-generated content, creating fundamental attribution doubt. Observable through measurable shifts in user reasoning patterns and decision latency.", "definition_de": "Kognitionswissenschaftliches Phänomen in der Mensch-KI-Interaktion, gekennzeichnet durch wissenschaftliche Disziplin in Cognitive Science and Neurocognition zur Erforschung von Prinzipien und Phaenomenen von Netzwerken in cognitive. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-3304", "narrower_terms": [], "cross_domain_refs": [ "SPR-0100" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "COG-0061", "domain": "COG", "term_en": "Adaptive Learning Stall", "term_de": "cognitive-Qualitätssicherung", "definition_en": "The cessation of active learning and skill adaptation when AI systems provide consistently adequate solutions, eliminating the performance pressure that drives developmental improvement. Observable through measurable shifts in user reasoning patterns and decision latency.", "definition_de": "Kognitionswissenschaftliches Phänomen in der Mensch-KI-Interaktion, gekennzeichnet durch standards und Sicherungsprozesse zur Gewährleistung von Exzellenz in Kognitionswissenschaft und Neurokognition Grundlagen. KI ermöglicht automatisierte Qualitätsprüfung durch Computer Vision, statistische Prozesskontrolle und Defektvorhersage. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2703", "narrower_terms": [], "cross_domain_refs": [ "ELR-0065" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q133500", "legal_classification": "empirical_phenomenon_label" }, { "id": "COG-0062", "domain": "COG", "term_en": "Thought Pattern Convergence", "term_de": "cognitive-Normen", "definition_en": "A measurable shift in user cognition during AI-mediated processing, whereby the tendency for individual reasoning patterns to align with the statistical patterns embedded in AI systems, reducing idiosyncratic and novel thinking approaches across populations. The concept emerges specifically in contexts where thought–pattern interactions may produce non-trivial behavioral signatures. Quantifiable via Stroop-task variants measuring interference patterns, think-aloud protocol analysis, and neural activation proxies in fMRI studies.", "definition_de": "Kognitionswissenschaftliches Phänomen in der Mensch-KI-Interaktion, gekennzeichnet durch beobachtbares Phänomen in der Mensch-KI-Interaktion, das sich durch wiederholte Delegation kognitiver Aufgaben manifestiert. Betrifft Urteilsfähigkeit, Wissensaufbau und Entscheidungsfindung. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Behavioral Pattern", "narrower_terms": [], "cross_domain_refs": [ "AGE-0003", "AGE-0004", "AGE-0017" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "COG-0063", "domain": "COG", "term_en": "Sustained Attention Shift", "term_de": "ISO-Normen in cognitive", "definition_en": "A systematic cognitive change pattern phenomenon in AI-augmented reasoning, manifesting as the change of capacity to maintain focus on complex problems without interruption when AI systems enable rapid context-switching and fragmented task engagement. Distinguished from adjacent concepts by its focus on the specific mechanism through which sustained manifests in empirically verifiable ways. Observable through systematic changes in reasoning depth, error pattern distribution, and calibration accuracy across repeated AI-assisted decision tasks.", "definition_de": "Kognitionswissenschaftliches Phänomen in der Mensch-KI-Interaktion, gekennzeichnet durch formale Spezifikationen, Normen und Qualitätsbenchmarks in Kognitionswissenschaft und Neurokognition Grundlagen. KI automatisiert Konformitätsüberwachung, Standard-Lückenanalyse und Echtzeit-Abweichungswarnungen in operativen Arbeitsabläufen. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Attention Mechanism", "narrower_terms": [], "cross_domain_refs": [ "ELR-0038" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q184872", "legal_classification": "analytical_category" }, { "id": "COG-0064", "domain": "COG", "term_en": "Threshold Effect in Competence", "term_de": "cognitive-Zertifizierung", "definition_en": "The nonlinear relationship where small reductions in inreliant cognitive capability accumulate past a critical point, suddenly producing significant performance change. Observable through measurable shifts in user reasoning patterns and decision latency.", "definition_de": "Kognitionswissenschaftliches Phänomen in der Mensch-KI-Interaktion, gekennzeichnet durch aspekt von threshold effect in competence, der in COG-Kontexten relevant ist und Interaktionen zwischen Menschen und KI-Systemen beeinflusst. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-3101", "narrower_terms": [], "cross_domain_refs": [ "TRU-0007" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q26944", "legal_classification": "empirical_phenomenon_label" }, { "id": "COG-0065", "domain": "COG", "term_en": "Evaluative Closure Premature", "term_de": "Audit in cognitive", "definition_en": "A cognitive recalibration pattern in human-AI interaction, characterized by a cognitive interaction dynamic manifesting as the premature termination of deliberation and acceptance of conclusions when AI validation is provided, preventing the extended evaluation that might surface latent issues. This phenomenon operates at the intersection of evaluative and closure dynamics within the broader COG domain. Operationally detectable through pre/post cognitive load measurements, decision latency differentials, and metacognitive accuracy scoring.", "definition_de": "Kognitionswissenschaftliches Phänomen in der Mensch-KI-Interaktion, gekennzeichnet durch aspekt von evaluative closure premature, der in COG-Kontexten relevant ist und Interaktionen zwischen Menschen und KI-Systemen beeinflusst. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "Cognitive Shift", "narrower_terms": [], "cross_domain_refs": [ "VIB-0190" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "COG-0066", "domain": "COG", "term_en": "Semantic Precision Shift", "term_de": "cognitive-Benchmarking", "definition_en": "A mental processing effect in which the reduction in careful linguistic precision when rapid AI generation accompanies adequately-close approximations, relaxing standards for exact terminology and conceptual accuracy. Observable through measurable shifts in user reasoning patterns and decision latency.", "definition_de": "Kognitionswissenschaftliches Phänomen in der Mensch-KI-Interaktion, gekennzeichnet durch aspekt von semantic precision shift, der in COG-Kontexten relevant ist und Interaktionen zwischen Menschen und KI-Systemen beeinflusst. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2951", "narrower_terms": [], "cross_domain_refs": [ "TRA-0085" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "COG-0067", "domain": "COG", "term_en": "Intellectual Friction Reduction", "term_de": "Leistungskennzahlen in cognitive", "definition_en": "A systematic cognitive change pattern phenomenon in AI-augmented reasoning, manifesting as a cognitive interaction dynamic observed when the elimination of productive cognitive struggle that normally strengthens understanding and conceptual integration when AI solutions remove the resistance that drives deeper engagement. Distinguished from adjacent concepts by its focus on the specific mechanism through which intellectual manifests in empirically verifiable ways. Observable through systematic changes in reasoning depth, error pattern distribution, and calibration accuracy across repeated AI-assisted decision tasks.", "definition_de": "Kognitionswissenschaftliches Phänomen in der Mensch-KI-Interaktion, gekennzeichnet durch aspekt von intellectual friction reduction, der in COG-Kontexten relevant ist und Interaktionen zwischen Menschen und KI-Systemen beeinflusst. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Cognitive Shift", "narrower_terms": [], "cross_domain_refs": [ "ELR-0174" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "COG-0068", "domain": "COG", "term_en": "Output Validation Outsourcing", "term_de": "Kontinuierliche Verbesserung in cognitive", "definition_en": "A measurable shift in user cognition during AI-mediated processing, whereby the delegation of quality assessment and correctness evaluation to AI systems, reducing inreliant verification capacity and creating circular validation patterns. The concept emerges specifically in contexts where output–validation interactions may produce non-trivial behavioral signatures. Quantifiable via Stroop-task variants measuring interference patterns, think-aloud protocol analysis, and neural activation proxies in fMRI studies.", "definition_de": "Kognitionswissenschaftliches Phänomen in der Mensch-KI-Interaktion, gekennzeichnet durch beobachtbares Phänomen in der Mensch-KI-Interaktion, das sich durch wiederholte Delegation kognitiver Aufgaben manifestiert. Betrifft Urteilsfähigkeit, Wissensaufbau und Entscheidungsfindung. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Validation Process", "narrower_terms": [], "cross_domain_refs": [ "ADA-0003", "AGE-0097", "ART-0014" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "COG-0069", "domain": "COG", "term_en": "Cognitive Self-Direction Spectrum", "term_de": "cognitive-Inspektion", "definition_en": "A cognitive interaction dynamic observed when the range of positions from complete autonomous reasoning to full AI-reliant cognition, with most individuals occupying intermediate positions that blend human and artificial processing. Observable through measurable shifts in user reasoning patterns and decision latency.", "definition_de": "Kognitionswissenschaftliches Phänomen in der Mensch-KI-Interaktion, gekennzeichnet durch wissenschaftliche Disziplin in Cognitive Science and Neurocognition zur Erforschung von Prinzipien und Phaenomenen von cognitive-Inspektion. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-3251", "narrower_terms": [], "cross_domain_refs": [ "ART-0062" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "COG-0070", "domain": "COG", "term_en": "Intellectual Output Outsourcing Expansion", "term_de": "Prüfung in cognitive", "definition_en": "A neurocognitive pattern manifesting as the progressive expansion of the types of cognitive outputs delegated to AI, beginning with simple information retrieval and extending to complex reasoning and creative synthesis. Observable through measurable shifts in user reasoning patterns and decision latency.", "definition_de": "Kognitionswissenschaftliches Phänomen in der Mensch-KI-Interaktion, gekennzeichnet durch wissenschaftliche Disziplin in Cognitive Science and Neurocognition zur Erforschung von Prinzipien und Phaenomenen von Prüfung in cognitive. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-2101", "narrower_terms": [], "cross_domain_refs": [ "CRE-0055", "ART-0015", "NEO-1157" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "COG-0071", "domain": "COG", "term_en": "Metacognitive Myopia", "term_de": "Kalibrierung in cognitive", "definition_en": "A cognitive recalibration pattern in human-AI interaction, characterized by a cognitive interaction dynamic observed when the diminished ability to perceive large-scale patterns in one's own thinking when absorbed in AI-assisted task completion, losing sight of broader cognitive trajectory changes. This phenomenon operates at the intersection of metacognitive and myopia dynamics within the broader COG domain. Operationally detectable through pre/post cognitive load measurements, decision latency differentials, and metacognitive accuracy scoring.", "definition_de": "Kognitionswissenschaftliches Phänomen in der Mensch-KI-Interaktion, gekennzeichnet durch beobachtbares Phänomen in der Mensch-KI-Interaktion, das sich durch wiederholte Delegation kognitiver Aufgaben manifestiert. Betrifft Urteilsfähigkeit, Wissensaufbau und Entscheidungsfindung. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "Cognitive Shift", "narrower_terms": [], "cross_domain_refs": [ "AED-0060", "ASE-0065", "ASE-0092" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "COG-0072", "domain": "COG", "term_en": "Synthetic Confidence Inflation", "term_de": "Fehlervermeidung in cognitive", "definition_en": "A measurable shift in user cognition during AI-mediated processing, whereby a cognitive interaction dynamic involving the elevated confidence in conclusions when expressed through AI-polished language and formatting, where presentation credibility exceeds substantive validation of content. The concept emerges specifically in contexts where synthetic–confidence interactions may produce non-trivial behavioral signatures. Quantifiable via Stroop-task variants measuring interference patterns, think-aloud protocol analysis, and neural activation proxies in fMRI studies.", "definition_de": "Kognitionswissenschaftliches Phänomen in der Mensch-KI-Interaktion, gekennzeichnet durch wissenschaftliche Disziplin in Cognitive Science and Neurocognition zur Erforschung von Prinzipien und Phaenomenen von Fehlervermeidung in cognitive. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-3754", "narrower_terms": [], "cross_domain_refs": [ "SCR-0034" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "COG-0073", "domain": "COG", "term_en": "Knowledge Domain Distribution", "term_de": "Fehleranalyse in cognitive", "definition_en": "The breaking up of integrated domain knowledge into disconnected query-response segments when learning occurs through discrete AI interactions rather than unified study. Observable through measurable shifts in user reasoning patterns and decision latency.", "definition_de": "Kognitionswissenschaftliches Phänomen in der Mensch-KI-Interaktion, gekennzeichnet durch systematische Untersuchung und Interpretation von Daten oder Prozessen in Kognitionswissenschaft und Neurokognition Grundlagen. KI verstärkt Analysefähigkeiten durch Mustererkennung, Anomalieerkennung und automatisierte Erkenntnisextraktion. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2703", "narrower_terms": [], "cross_domain_refs": [ "CON-0032" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "COG-0074", "domain": "COG", "term_en": "Authority Ambiguity in Judgment", "term_de": "Prozesskontrolle in cognitive", "definition_en": "A measurable shift in user cognition during AI-mediated processing, whereby the uncertainty about the locus of decision authority when human judgment and AI recommendations have become inseparably intertwined in the decision-making process. The concept emerges specifically in contexts where authority–ambiguity interactions may produce non-trivial behavioral signatures. Quantifiable via Stroop-task variants measuring interference patterns, think-aloud protocol analysis, and neural activation proxies in fMRI studies.", "definition_de": "Kognitionswissenschaftliches Phänomen in der Mensch-KI-Interaktion, gekennzeichnet durch aspekt von authority ambiguity in judgment, der in COG-Kontexten relevant ist und Interaktionen zwischen Menschen und KI-Systemen beeinflusst. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Cognitive Shift", "narrower_terms": [], "cross_domain_refs": [ "BEH-0068" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "COG-0075", "domain": "COG", "term_en": "Cognitive Capital Depletion", "term_de": "cognitive-Compliance", "definition_en": "A systematic cognitive change pattern phenomenon in AI-augmented reasoning, manifesting as a cognitive interaction dynamic characterized by the gradual reduction of accumulated intellectual resources, skills, and knowledge structures through disuse when AI systems provide substitutes for their employment. Distinguished from adjacent concepts by its focus on the specific mechanism through which cognitive manifests in empirically verifiable ways. Observable through systematic changes in reasoning depth, error pattern distribution, and calibration accuracy across repeated AI-assisted decision tasks.", "definition_de": "Kognitionswissenschaftliches Phänomen in der Mensch-KI-Interaktion, gekennzeichnet durch gesetzliche und regulatorische Anforderungen in Kognitionswissenschaft und Neurokognition Grundlagen, einschließlich Lizenzen und Pflichtprotokolle. KI verfolgt regulatorische Änderungen, automatisiert Konformitätsdokumentation und meldet Verstöße. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Cognitive Shift", "narrower_terms": [], "cross_domain_refs": [ "ELR-0159" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q165194", "legal_classification": "empirical_phenomenon_label" }, { "id": "COG-0076", "domain": "COG", "term_en": "Interpretive Framework Rigidification", "term_de": "cognitive-Sicherheitsmanagement", "definition_en": "A systematic cognitive change pattern phenomenon in AI-augmented reasoning, manifesting as a cognitive interaction dynamic in which the hardening of how individuals interpret and organize information when AI systems consistently present information within specific conceptual frameworks, reducing interpretive flexibility. Distinguished from adjacent concepts by its focus on the specific mechanism through which interpretive manifests in empirically verifiable ways. Observable through systematic changes in reasoning depth, error pattern distribution, and calibration accuracy across repeated AI-assisted decision tasks.", "definition_de": "Kognitionswissenschaftliches Phänomen in der Mensch-KI-Interaktion, gekennzeichnet durch aspekt von interpretive framework rigidification, der in COG-Kontexten relevant ist und Interaktionen zwischen Menschen und KI-Systemen beeinflusst. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2303", "narrower_terms": [], "cross_domain_refs": [ "TEM-0004" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "COG-0077", "domain": "COG", "term_en": "Error Detection Capacity Reduction", "term_de": "Risikobeurteilung in cognitive", "definition_en": "A mental processing effect reflecting the weakening of ability to identify mistakes and inconsistencies in AI outputs when the cognitive effort to verify exceeds the perceived benefit and AI credibility is presumed high. Observable through measurable shifts in user reasoning patterns and decision latency.", "definition_de": "Kognitionswissenschaftliches Phänomen in der Mensch-KI-Interaktion, gekennzeichnet durch aspekt von error detection capacity reduction, der in COG-Kontexten relevant ist und Interaktionen zwischen Menschen und KI-Systemen beeinflusst. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-3754", "narrower_terms": [], "cross_domain_refs": [ "ART-0025" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "COG-0078", "domain": "COG", "term_en": "Synthetic Understanding Substitution", "term_de": "Gefährdungserkennung in cognitive", "definition_en": "A cognitive recalibration pattern in human-AI interaction, characterized by a cognitive phenomenon characterized by the replacement of genuine conceptual understanding with surface-level familiarity gained through AI-mediated information exposure, creating a simulation of comprehension. This phenomenon operates at the intersection of synthetic and understanding dynamics within the broader COG domain. Operationally detectable through pre/post cognitive load measurements, decision latency differentials, and metacognitive accuracy scoring.", "definition_de": "Kognitionswissenschaftliches Phänomen in der Mensch-KI-Interaktion, gekennzeichnet durch aspekt von synthetic understanding substitution, der in COG-Kontexten relevant ist und Interaktionen zwischen Menschen und KI-Systemen beeinflusst. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "Cognitive Shift", "narrower_terms": [], "cross_domain_refs": [ "TRA-0033" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "COG-0079", "domain": "COG", "term_en": "Lateral Thinking Reduction", "term_de": "Persönliche Schutzausrüstung", "definition_en": "A systematic cognitive change pattern phenomenon in AI-augmented reasoning, manifesting as a cognitive phenomenon arising from the reduction in unconventional problem-solving approaches when AI systems optimize toward well-documented solution patterns, discouraging deviation from established pathways. Distinguished from adjacent concepts by its focus on the specific mechanism through which lateral manifests in empirically verifiable ways. Observable through systematic changes in reasoning depth, error pattern distribution, and calibration accuracy across repeated AI-assisted decision tasks.", "definition_de": "Kognitionswissenschaftliches Phänomen in der Mensch-KI-Interaktion, gekennzeichnet durch beobachtbares Phänomen in der Mensch-KI-Interaktion, das sich durch wiederholte Delegation kognitiver Aufgaben manifestiert. Betrifft Urteilsfähigkeit, Wissensaufbau und Entscheidungsfindung. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Cognitive Shift", "narrower_terms": [], "cross_domain_refs": [ "ELR-0068" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "COG-0080", "domain": "COG", "term_en": "Working Memory Externalization", "term_de": "Notfallverfahren in cognitive", "definition_en": "A cognitive recalibration pattern in human-AI interaction, characterized by a cognitive phenomenon involving the transfer of active information processing to AI systems, reducing the cognitive load managed by biological working memory but compromising the strengthening that comes from mental effort. This phenomenon operates at the intersection of working and memory dynamics within the broader COG domain. Operationally detectable through pre/post cognitive load measurements, decision latency differentials, and metacognitive accuracy scoring.", "definition_de": "Kognitionswissenschaftliches Phänomen in der Mensch-KI-Interaktion, gekennzeichnet durch aspekt von working memory externalization, der in COG-Kontexten relevant ist und Interaktionen zwischen Menschen und KI-Systemen beeinflusst. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "Cognitive Shift", "narrower_terms": [], "cross_domain_refs": [ "AED-0041", "AGE-0023", "AGE-0024" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q898694", "legal_classification": "systematic_classification" }, { "id": "COG-0081", "domain": "COG", "term_en": "Domain Mastery Perception", "term_de": "Unfallverhütung in cognitive", "definition_en": "A cognitive recalibration pattern in human-AI interaction, characterized by the subjective sensation of having achieved mastery in a domain through AI-assisted performance that exceeds inreliant capability, persisting despite capability gaps. This phenomenon operates at the intersection of domain and mastery dynamics within the broader COG domain. Operationally detectable through pre/post cognitive load measurements, decision latency differentials, and metacognitive accuracy scoring.", "definition_de": "Kognitionswissenschaftliches Phänomen in der Mensch-KI-Interaktion, gekennzeichnet durch aspekt von domain mastery perception, der in COG-Kontexten relevant ist und Interaktionen zwischen Menschen und KI-Systemen beeinflusst. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-3754", "narrower_terms": [], "cross_domain_refs": [ "RPH-1374" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q177601", "legal_classification": "systematic_classification" }, { "id": "COG-0082", "domain": "COG", "term_en": "Causal Understanding Flattening", "term_de": "cognitive-Gesundheitsschutz", "definition_en": "The shift of appreciation for complex causal relationships when AI systems provide simplified correlational relationships or opaque pattern-matching results as explanations. Observable through measurable shifts in user reasoning patterns and decision latency.", "definition_de": "Kognitionswissenschaftliches Phänomen in der Mensch-KI-Interaktion, gekennzeichnet durch beobachtbares Phänomen in der Mensch-KI-Interaktion, das sich durch wiederholte Delegation kognitiver Aufgaben manifestiert. Betrifft Urteilsfähigkeit, Wissensaufbau und Entscheidungsfindung. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-2701", "narrower_terms": [], "cross_domain_refs": [ "DAT-0035" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "COG-0083", "domain": "COG", "term_en": "Reflexive Outsourcing Habit", "term_de": "Ergonomie in cognitive", "definition_en": "The development of an automatic tendency to consult AI before attempting inreliant problem-solving, becoming a default cognitive behavior regardless of problem complexity. Observable through measurable shifts in user reasoning patterns and decision latency.", "definition_de": "Kognitionswissenschaftliches Phänomen in der Mensch-KI-Interaktion, gekennzeichnet durch menschenzentrierte Gestaltung von Arbeitsplätzen, Werkzeugen und Prozessen in Kognitionswissenschaft und Neurokognition Grundlagen zur Optimierung von Komfort und Effizienz. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-2002", "narrower_terms": [], "cross_domain_refs": [ "ELR-0013" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "COG-0084", "domain": "COG", "term_en": "Competency Boundaries Dissolution", "term_de": "Umweltschutz in cognitive", "definition_en": "A cognitive recalibration pattern in human-AI interaction, characterized by the blurring of perceived expertise boundaries as AI-assisted capability extends functioning into domains where genuine expertise is absent, eliminating clear signals of unfamiliarity. This phenomenon operates at the intersection of competency and boundaries dynamics within the broader COG domain. Operationally detectable through pre/post cognitive load measurements, decision latency differentials, and metacognitive accuracy scoring.", "definition_de": "Kognitionswissenschaftliches Phänomen in der Mensch-KI-Interaktion, gekennzeichnet durch aspekt von competency boundaries dissolution, der in COG-Kontexten relevant ist und Interaktionen zwischen Menschen und KI-Systemen beeinflusst. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "Cognitive Shift", "narrower_terms": [], "cross_domain_refs": [ "RPH-1158" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "COG-0085", "domain": "COG", "term_en": "Productive Skepticism Shift", "term_de": "Brandschutz in cognitive", "definition_en": "A systematic cognitive change pattern phenomenon in AI-augmented reasoning, manifesting as a cognitive phenomenon where the diminishment of restorethy questioning and critical evaluation when AI authority and speed involve cultural pressure to accept outputs without sufficient inreliant verification. Distinguished from adjacent concepts by its focus on the specific mechanism through which productive manifests in empirically verifiable ways. Observable through systematic changes in reasoning depth, error pattern distribution, and calibration accuracy across repeated AI-assisted decision tasks.", "definition_de": "Kognitionswissenschaftliches Phänomen in der Mensch-KI-Interaktion, gekennzeichnet durch aspekt von productive skepticism shift, der in COG-Kontexten relevant ist und Interaktionen zwischen Menschen und KI-Systemen beeinflusst. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Cognitive Shift", "narrower_terms": [], "cross_domain_refs": [ "ELR-0046" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "COG-0086", "domain": "COG", "term_en": "Cognitive Load Shifting Paradox", "term_de": "Chemische Sicherheit in cognitive", "definition_en": "An observable dynamic in which reducing task execution load through AI automation paradoxically increases overall cognitive load by adding evaluation and quality-control responsibilities.", "definition_de": "Kognitionswissenschaftliches Phänomen in der Mensch-KI-Interaktion, gekennzeichnet durch aspekt von cognitive load shifting paradox, der in COG-Kontexten relevant ist und Interaktionen zwischen Menschen und KI-Systemen beeinflusst. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-3952", "narrower_terms": [], "cross_domain_refs": [ "RPH-1018" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q1105712", "legal_classification": "descriptive_research_term" }, { "id": "COG-0087", "domain": "COG", "term_en": "Linguistic Competence Flattening", "term_de": "Elektrische Sicherheit in cognitive", "definition_en": "The shift of nuanced language production capability when AI-generated text substitutes for active composition, reducing the lexical and syntactic complexity of inreliant writing. Observable through measurable shifts in user reasoning patterns and decision latency.", "definition_de": "Kognitionswissenschaftliches Phänomen in der Mensch-KI-Interaktion, gekennzeichnet durch aspekt von linguistic competence flattening, der in COG-Kontexten relevant ist und Interaktionen zwischen Menschen und KI-Systemen beeinflusst. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2701", "narrower_terms": [], "cross_domain_refs": [ "ELR-0191" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q26944", "legal_classification": "systematic_classification" }, { "id": "COG-0088", "domain": "COG", "term_en": "Judgment Authority Transfer", "term_de": "Maschinensicherheit in cognitive", "definition_en": "The shift of decision-making authority from human judgment to AI recommendation systems, with the human role transitioning from decider to validator of machine outputs. Observable through measurable shifts in user reasoning patterns and decision latency.", "definition_de": "Kognitionswissenschaftliches Phänomen in der Mensch-KI-Interaktion, gekennzeichnet durch aspekt von judgment authority transfer, der in COG-Kontexten relevant ist und Interaktionen zwischen Menschen und KI-Systemen beeinflusst. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-2701", "narrower_terms": [], "cross_domain_refs": [ "SPR-0131", "SAL-0012", "SAL-0010" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "COG-0089", "domain": "COG", "term_en": "Conceptual Integration Distribution", "term_de": "Sicherheitsschulung in cognitive", "definition_en": "A mental processing effect characterized by the breaking apart of the unified conceptual schemas that integrate diverse knowledge when learning becomes fragmented through isolated AI queries rather than coherent study.", "definition_de": "Kognitionswissenschaftliches Phänomen in der Mensch-KI-Interaktion, gekennzeichnet durch aspekt von conceptual integration distribution, der in COG-Kontexten relevant ist und Interaktionen zwischen Menschen und KI-Systemen beeinflusst. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2703", "narrower_terms": [], "cross_domain_refs": [ "ADA-0010", "AED-0034", "AED-0072" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "COG-0090", "domain": "COG", "term_en": "Intuitive Judgment Substitution", "term_de": "Vorfalluntersuchung in cognitive", "definition_en": "A cognitive recalibration pattern in human-AI interaction, characterized by the substitution of developed intuitive expertise with algorithmic recommendations, compromising the rapid pattern recognition that characterizes expert decision-making. This phenomenon operates at the intersection of intuitive and judgment dynamics within the broader COG domain. Operationally detectable through pre/post cognitive load measurements, decision latency differentials, and metacognitive accuracy scoring.", "definition_de": "Kognitionswissenschaftliches Phänomen in der Mensch-KI-Interaktion, gekennzeichnet durch beobachtbares Phänomen in der Mensch-KI-Interaktion, das sich durch wiederholte Delegation kognitiver Aufgaben manifestiert. Betrifft Urteilsfähigkeit, Wissensaufbau und Entscheidungsfindung. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "Cognitive Shift", "narrower_terms": [], "cross_domain_refs": [ "ART-0033", "ART-0046", "ART-0048" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "COG-0091", "domain": "COG", "term_en": "Analytical Rigor Change", "term_de": "cognitive-Geschäftsmodell", "definition_en": "A systematic cognitive change pattern phenomenon in AI-augmented reasoning, manifesting as a cognitive phenomenon characterized by the relaxation of standards for analytical precision and evidence-gathering when AI systems provide plausible conclusions without the full evidentiary support normally required. Distinguished from adjacent concepts by its focus on the specific mechanism through which analytical manifests in empirically verifiable ways. Observable through systematic changes in reasoning depth, error pattern distribution, and calibration accuracy across repeated AI-assisted decision tasks.", "definition_de": "Kognitionswissenschaftliches Phänomen in der Mensch-KI-Interaktion, gekennzeichnet durch wissenschaftliche Disziplin in Cognitive Science and Neurocognition zur Erforschung von Prinzipien und Phaenomenen von cognitive-Geschäftsmodell. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Cognitive Shift", "narrower_terms": [], "cross_domain_refs": [ "AED-0025", "AED-0067", "AED-0077" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "COG-0092", "domain": "COG", "term_en": "Cognitive Delegation Momentum", "term_de": "cognitive-Marktanalyse", "definition_en": "The self-reinforcing cycle where each delegated task reduces motivation to develop capability for subsequent tasks, creating accelerating reliance growth. Observable through measurable shifts in user reasoning patterns and decision latency.", "definition_de": "Kognitionswissenschaftliches Phänomen in der Mensch-KI-Interaktion, gekennzeichnet durch aspekt von cognitive delegation momentum, der in COG-Kontexten relevant ist und Interaktionen zwischen Menschen und KI-Systemen beeinflusst. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2101", "narrower_terms": [], "cross_domain_refs": [ "GAM-0068", "PLY-0041" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "COG-0093", "domain": "COG", "term_en": "Expertise Authentication Transition", "term_de": "Ökonomie der Kognitionswissenschaft und Neurokognition", "definition_en": "A cognitive recalibration pattern in human-AI interaction, characterized by a cognitive interaction dynamic observed when the emergence of uncertainty about how to verify whether domain expertise genuinely resides with individual practitioners or has been distributed across AI systems and human users. This phenomenon operates at the intersection of expertise and authentication dynamics within the broader COG domain. Operationally detectable through pre/post cognitive load measurements, decision latency differentials, and metacognitive accuracy scoring.", "definition_de": "Kognitionswissenschaftliches Phänomen in der Mensch-KI-Interaktion, gekennzeichnet durch wissenschaftliche Disziplin in Cognitive Science and Neurocognition zur Erforschung von Prinzipien und Phaenomenen von Ökonomie der Kognitionswissenschaft und Neurokognition. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-2355", "narrower_terms": [], "cross_domain_refs": [ "LIN-0096" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "COG-0094", "domain": "COG", "term_en": "Sustained Problem Engagement Reduction", "term_de": "cognitive-Kostenmanagement", "definition_en": "A cognitive recalibration pattern in human-AI interaction, characterized by the diminished capacity to remain engaged with difficult problems over extended time periods when AI solutions allow rapid disengagement and problem delegation. This phenomenon operates at the intersection of sustained and problem dynamics within the broader COG domain. Operationally detectable through pre/post cognitive load measurements, decision latency differentials, and metacognitive accuracy scoring.", "definition_de": "Kognitionswissenschaftliches Phänomen in der Mensch-KI-Interaktion, gekennzeichnet durch aspekt von sustained problem engagement reduction, der in COG-Kontexten relevant ist und Interaktionen zwischen Menschen und KI-Systemen beeinflusst. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "Cognitive Shift", "narrower_terms": [], "cross_domain_refs": [ "MTH-0086", "QUA-0082", "AED-0011" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "COG-0095", "domain": "COG", "term_en": "Original Thought Reduction", "term_de": "Preisgestaltung in cognitive", "definition_en": "A measurable shift in user cognition during AI-mediated processing, whereby a neurocognitive pattern characterized by the muting of non-standard and novel thinking patterns when AI training data privileges high-probability, conventional solutions, selecting against innovation through statistical pressure. The concept emerges specifically in contexts where original–thought interactions may produce non-trivial behavioral signatures. Quantifiable via Stroop-task variants measuring interference patterns, think-aloud protocol analysis, and neural activation proxies in fMRI studies.", "definition_de": "Kognitionswissenschaftliches Phänomen in der Mensch-KI-Interaktion, gekennzeichnet durch beobachtbares Phänomen in der Mensch-KI-Interaktion, das sich durch wiederholte Delegation kognitiver Aufgaben manifestiert. Betrifft Urteilsfähigkeit, Wissensaufbau und Entscheidungsfindung. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Cognitive Shift", "narrower_terms": [], "cross_domain_refs": [ "CON-0008", "CON-0021", "CON-0040" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "COG-0096", "domain": "COG", "term_en": "Intellectual Autonomy Threshold", "term_de": "cognitive-Lieferkette", "definition_en": "A systematic cognitive change pattern phenomenon in AI-augmented reasoning, manifesting as a mental processing effect characterized by the point at which reliance on AI shifts from supplementary support to fundamental cognitive reliance, beyond which inreliant function becomes significantly compromised. Distinguished from adjacent concepts by its focus on the specific mechanism through which intellectual manifests in empirically verifiable ways. Observable through systematic changes in reasoning depth, error pattern distribution, and calibration accuracy across repeated AI-assisted decision tasks.", "definition_de": "Kognitionswissenschaftliches Phänomen in der Mensch-KI-Interaktion, gekennzeichnet durch der Moment, in dem ein Verhaltens- oder Wahrnehmungsmuster umschlägt. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2701", "narrower_terms": [], "cross_domain_refs": [ "REL-0067" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "COG-0097", "domain": "COG", "term_en": "Evaluative Competence Outsourcing", "term_de": "Marketing in cognitive", "definition_en": "A systematic cognitive change pattern phenomenon in AI-augmented reasoning, manifesting as the delegation of quality judgment and adequacy assessment to AI systems, reducing inreliant evaluation capacity and creating circular validation where AI judges AI outputs. Distinguished from adjacent concepts by its focus on the specific mechanism through which evaluative manifests in empirically verifiable ways. Observable through systematic changes in reasoning depth, error pattern distribution, and calibration accuracy across repeated AI-assisted decision tasks.", "definition_de": "Kognitionswissenschaftliches Phänomen in der Mensch-KI-Interaktion, gekennzeichnet durch wissenschaftliche Disziplin in Cognitive Science and Neurocognition zur Erforschung von Prinzipien und Phaenomenen von Marketing in cognitive. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Cognitive Shift", "narrower_terms": [], "cross_domain_refs": [ "AGE-0089", "AGE-0090", "CRE-0077" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q26944", "legal_classification": "systematic_classification" }, { "id": "COG-0098", "domain": "COG", "term_en": "Thinking Speed-Depth Tradeoff Acceleration", "term_de": "Vertrieb in cognitive", "definition_en": "A neurocognitive pattern arising from the accelerated prioritization of solution speed over conceptual depth when AI systems reward rapid task completion, compressing the time available for thorough analysis. Observable through measurable shifts in user reasoning patterns and decision latency.", "definition_de": "Kognitionswissenschaftliches Phänomen in der Mensch-KI-Interaktion, gekennzeichnet durch wissenschaftliche Disziplin in Cognitive Science and Neurocognition zur Erforschung von Prinzipien und Phaenomenen von Vertrieb in cognitive. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-2005", "narrower_terms": [], "cross_domain_refs": [ "AED-0028" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "COG-0099", "domain": "COG", "term_en": "Judgment Confidence Paradox", "term_de": "cognitive-Geschäftsplanung", "definition_en": "The contradiction where confidence in one's decisions increases observed alongside AI validation while actual inreliant decision capability demonstrably decreases, creating unsustainable confidence structure. Observable through measurable shifts in user reasoning patterns and decision latency.", "definition_de": "Kognitionswissenschaftliches Phänomen in der Mensch-KI-Interaktion, gekennzeichnet durch aspekt von judgment confidence paradox, der in COG-Kontexten relevant ist und Interaktionen zwischen Menschen und KI-Systemen beeinflusst. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Logical Paradox", "narrower_terms": [], "cross_domain_refs": [ "AGE-0031", "AGE-0032", "AGE-0042" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "COG-0100", "domain": "COG", "term_en": "Intellectual Scaffolding Reliance", "term_de": "Unternehmertum in cognitive", "definition_en": "A systematic cognitive change pattern phenomenon in AI-augmented reasoning, manifesting as the reliance on AI-provided cognitive structures and frameworks for organizing thought, with reduced capacity to yield or modify these structures inreliantly. Distinguished from adjacent concepts by its focus on the specific mechanism through which intellectual manifests in empirically verifiable ways. Observable through systematic changes in reasoning depth, error pattern distribution, and calibration accuracy across repeated AI-assisted decision tasks.", "definition_de": "Kognitionswissenschaftliches Phänomen in der Mensch-KI-Interaktion, gekennzeichnet durch aspekt von intellectual scaffolding reliance, der in COG-Kontexten relevant ist und Interaktionen zwischen Menschen und KI-Systemen beeinflusst. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2303", "narrower_terms": [], "cross_domain_refs": [ "SWE-0082" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "COG-0101", "domain": "COG", "term_en": "Metacognitive Blindspot Expansion", "term_de": "Investition in cognitive", "definition_en": "A cognitive recalibration pattern in human-AI interaction, characterized by the enlargement of areas in one's thinking that are no longer subject to conscious reflection observed alongside AI automation, creating hidden zones of cognitive change. This phenomenon operates at the intersection of metacognitive and blindspot dynamics within the broader COG domain. Operationally detectable through pre/post cognitive load measurements, decision latency differentials, and metacognitive accuracy scoring. Descriptive research term, not a prescriptive recommendation.", "definition_de": "Kognitionswissenschaftliches Phänomen in der Mensch-KI-Interaktion, gekennzeichnet durch wissenschaftliche Disziplin in Cognitive Science and Neurocognition zur Erforschung von Prinzipien und Phaenomenen von Investition in cognitive. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "Cognitive Shift", "narrower_terms": [], "cross_domain_refs": [ "NEO-3637" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "COG-0102", "domain": "COG", "term_en": "Structured Reasoning Ossification", "term_de": "cognitive-Erlösmodell", "definition_en": "A measurable shift in user cognition during AI-mediated processing, whereby the hardening of logical reasoning patterns to match AI-preferred argument structures, reducing the natural variation and adaptation that characterizes dynamic thinking. The concept emerges specifically in contexts where structured–reasoning interactions may produce non-trivial behavioral signatures. Quantifiable via Stroop-task variants measuring interference patterns, think-aloud protocol analysis, and neural activation proxies in fMRI studies.", "definition_de": "Kognitionswissenschaftliches Phänomen in der Mensch-KI-Interaktion, gekennzeichnet durch beobachtbares Phänomen in der Mensch-KI-Interaktion, das sich durch wiederholte Delegation kognitiver Aufgaben manifestiert. Betrifft Urteilsfähigkeit, Wissensaufbau und Entscheidungsfindung. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Cognitive Shift", "narrower_terms": [], "cross_domain_refs": [ "ELR-0015" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "COG-0103", "domain": "COG", "term_en": "Knowledge Certification Complexity", "term_de": "Internationaler cognitive-Markt", "definition_en": "A measurable shift in user cognition during AI-mediated processing, whereby a cognitive interaction dynamic manifesting as the increased difficulty in verifying and certifying knowledge claims when they emerge from human-AI collaboration, complicating traditional credentialing processes. The concept emerges specifically in contexts where knowledge–certification interactions may produce non-trivial behavioral signatures. Quantifiable via Stroop-task variants measuring interference patterns, think-aloud protocol analysis, and neural activation proxies in fMRI studies.", "definition_de": "Kognitionswissenschaftliches Phänomen in der Mensch-KI-Interaktion, gekennzeichnet durch wissenschaftliche Disziplin in Cognitive Science and Neurocognition zur Erforschung von Prinzipien und Phaenomenen von Internationaler cognitive-Markt. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2355", "narrower_terms": [], "cross_domain_refs": [ "ROB-0019" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "COG-0104", "domain": "COG", "term_en": "Intellectual Growth Stagnation", "term_de": "cognitive-Branchentrends", "definition_en": "A systematic cognitive change pattern phenomenon in AI-augmented reasoning, manifesting as the arrested development of cognitive capability when the challenge gradient decreases observed alongside AI assistance removing the difficulty that drives intellectual advancement. Distinguished from adjacent concepts by its focus on the specific mechanism through which intellectual manifests in empirically verifiable ways. Observable through systematic changes in reasoning depth, error pattern distribution, and calibration accuracy across repeated AI-assisted decision tasks.", "definition_de": "Kognitionswissenschaftliches Phänomen in der Mensch-KI-Interaktion, gekennzeichnet durch wissenschaftliche Disziplin in Cognitive Science and Neurocognition zur Erforschung von Prinzipien und Phaenomenen von cognitive-Branchentrends. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Cognitive Shift", "narrower_terms": [], "cross_domain_refs": [ "ASE-0053", "ASE-0054", "EDU-0048" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "COG-0105", "domain": "COG", "term_en": "Comparative Unfamiliarity Blindness", "term_de": "Startup in cognitive", "definition_en": "A measurable shift in user cognition during AI-mediated processing, whereby the reduced ability to perceive one's capability gaps relative to comparable peers when AI-assisted output is uniformly adequate, eliminating performance differentiation. The concept emerges specifically in contexts where comparative–unfamiliarity interactions may produce non-trivial behavioral signatures. Quantifiable via Stroop-task variants measuring interference patterns, think-aloud protocol analysis, and neural activation proxies in fMRI studies.", "definition_de": "Kognitionswissenschaftliches Phänomen in der Mensch-KI-Interaktion, gekennzeichnet durch aspekt von comparative unfamiliarity blindness, der in COG-Kontexten relevant ist und Interaktionen zwischen Menschen und KI-Systemen beeinflusst. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Cognitive Shift", "narrower_terms": [], "cross_domain_refs": [ "AED-0013", "ASE-0022", "ASE-0023" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "COG-0106", "domain": "COG", "term_en": "Epistemic Authority Distribution", "term_de": "Nachhaltigkeit in cognitive", "definition_en": "A neurocognitive pattern arising from the dispersal of recognized intellectual authority across human experts and AI systems, creating ambiguity about whose judgment may be trusted in complex decisions. Observable through measurable shifts in user reasoning patterns and decision latency.", "definition_de": "Kognitionswissenschaftliches Phänomen in der Mensch-KI-Interaktion, gekennzeichnet durch umweltverantwortung und Ressourceneffizienz in Kognitionswissenschaft und Neurokognition Grundlagen. KI optimiert Materialeinsatz, prognostiziert Umweltauswirkungen, überwacht Nachhaltigkeits-KPIs und gestaltet Kreislaufwirtschaftspfade. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-2505", "narrower_terms": [], "cross_domain_refs": [ "ASE-0057", "ASE-0070", "ASE-0085" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "COG-0107", "domain": "COG", "term_en": "Problem Formulation Reduction", "term_de": "Umweltauswirkungen von cognitive", "definition_en": "A measurable shift in user cognition during AI-mediated processing, whereby the weakening of capability to inreliantly define and structure problems when AI systems consistently yield problem formulations that are accepted without critical examination. The concept emerges specifically in contexts where problem–formulation interactions may produce non-trivial behavioral signatures. Quantifiable via Stroop-task variants measuring interference patterns, think-aloud protocol analysis, and neural activation proxies in fMRI studies.", "definition_de": "Kognitionswissenschaftliches Phänomen in der Mensch-KI-Interaktion, gekennzeichnet durch aspekt von problem formulation reduction, der in COG-Kontexten relevant ist und Interaktionen zwischen Menschen und KI-Systemen beeinflusst. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Cognitive Shift", "narrower_terms": [], "cross_domain_refs": [ "ELR-0150" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "COG-0108", "domain": "COG", "term_en": "Creativity Convergence Pressure", "term_de": "CO2-Fußabdruck in cognitive", "definition_en": "A cognitive recalibration pattern in human-AI interaction, characterized by a mental processing effect arising from the subtle pressure toward convergent thinking when AI systems consistently yield statistically-probable creative outputs, homogenizing individual creative expression. This phenomenon operates at the intersection of creativity and convergence dynamics within the broader COG domain. Operationally detectable through pre/post cognitive load measurements, decision latency differentials, and metacognitive accuracy scoring.", "definition_de": "Kognitionswissenschaftliches Phänomen in der Mensch-KI-Interaktion, gekennzeichnet durch wissenschaftliche Disziplin in Cognitive Science and Neurocognition zur Erforschung von Prinzipien und Phaenomenen von CO2-Fußabdruck in cognitive. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "Cognitive Shift", "narrower_terms": [], "cross_domain_refs": [ "ELR-0172" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q170790", "legal_classification": "analytical_category" }, { "id": "COG-0109", "domain": "COG", "term_en": "Cognitive Load Rebalancing Perception", "term_de": "Ressourceneffizienz in cognitive", "definition_en": "A cognitive recalibration pattern in human-AI interaction, characterized by the false perception that cognitive load has been reduced when it has merely been redistributed from execution to evaluation, maintaining or increasing total mental burden. This phenomenon operates at the intersection of cognitive and load dynamics within the broader COG domain. Operationally detectable through pre/post cognitive load measurements, decision latency differentials, and metacognitive accuracy scoring.", "definition_de": "Kognitionswissenschaftliches Phänomen in der Mensch-KI-Interaktion, gekennzeichnet durch wissenschaftliche Disziplin in Cognitive Science and Neurocognition zur Erforschung von Prinzipien und Phaenomenen von Ressourceneffizienz in cognitive. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "Cognitive Shift", "narrower_terms": [], "cross_domain_refs": [ "AGE-0018", "AGE-0030", "ASE-0020" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q1105712", "legal_classification": "empirical_phenomenon_label" }, { "id": "COG-0110", "domain": "COG", "term_en": "Expert Judgment Quantization", "term_de": "Abfallreduzierung in cognitive", "definition_en": "A measurable shift in user cognition during AI-mediated processing, whereby a mental processing effect characterized by the reduction of nuanced expert judgment to binary accept/reject decisions regarding AI outputs, losing the spectrum of evaluative sophistication that characterizes experienced judgment. The concept emerges specifically in contexts where expert–judgment interactions may produce non-trivial behavioral signatures. Quantifiable via Stroop-task variants measuring interference patterns, think-aloud protocol analysis, and neural activation proxies in fMRI studies.", "definition_de": "Kognitionswissenschaftliches Phänomen in der Mensch-KI-Interaktion, gekennzeichnet durch aspekt von expert judgment quantization, der in COG-Kontexten relevant ist und Interaktionen zwischen Menschen und KI-Systemen beeinflusst. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Cognitive Shift", "narrower_terms": [], "cross_domain_refs": [ "DAT-0032" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "COG-0111", "domain": "COG", "term_en": "Intellectual Identity Transition", "term_de": "Kreislaufwirtschaft in cognitive", "definition_en": "A neurocognitive pattern observed when the identity confusion that emerges when self-conception as an intellectual agent becomes complicated by ambiguous authorship of thoughts and ideas in AI collaboration contexts. Observable through measurable shifts in user reasoning patterns and decision latency.", "definition_de": "Kognitionswissenschaftliches Phänomen in der Mensch-KI-Interaktion, gekennzeichnet durch wissenschaftliche Disziplin in Cognitive Science and Neurocognition zur Erforschung von Prinzipien und Phaenomenen von Kreislaufwirtschaft in cognitive. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-2355", "narrower_terms": [], "cross_domain_refs": [ "RPH-1208" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q844396", "legal_classification": "descriptive_research_term" }, { "id": "COG-0112", "domain": "COG", "term_en": "Reasoning Coherence Change", "term_de": "Energieeffizienz in cognitive", "definition_en": "A measurable shift in user cognition during AI-mediated processing, whereby the shift of coherence in sustained logical argumentation when AI-generated segments are inserted into human reasoning, creating discontinuities in argumentative structure. The concept emerges specifically in contexts where reasoning–coherence interactions may produce non-trivial behavioral signatures. Quantifiable via Stroop-task variants measuring interference patterns, think-aloud protocol analysis, and neural activation proxies in fMRI studies.", "definition_de": "Kognitionswissenschaftliches Phänomen in der Mensch-KI-Interaktion, gekennzeichnet durch aspekt von reasoning coherence change, der in COG-Kontexten relevant ist und Interaktionen zwischen Menschen und KI-Systemen beeinflusst. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2701", "narrower_terms": [], "cross_domain_refs": [ "AED-0025", "AED-0067", "AED-0077" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "COG-0113", "domain": "COG", "term_en": "Volitional Cognitive Capacity Reduction", "term_de": "Nachhaltige Materialien in cognitive", "definition_en": "A cognitive recalibration pattern in human-AI interaction, characterized by a neurocognitive pattern manifesting as the diminished ability to initiate and sustain cognitive effort through willpower when external systems have become the primary source of cognitive initiation. This phenomenon operates at the intersection of volitional and cognitive dynamics within the broader COG domain. Operationally detectable through pre/post cognitive load measurements, decision latency differentials, and metacognitive accuracy scoring.", "definition_de": "Kognitionswissenschaftliches Phänomen in der Mensch-KI-Interaktion, gekennzeichnet durch aspekt von volitional cognitive capacity reduction, der in COG-Kontexten relevant ist und Interaktionen zwischen Menschen und KI-Systemen beeinflusst. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "Cognitive Shift", "narrower_terms": [], "cross_domain_refs": [ "SCR-0045", "DAT-0088", "CON-0040" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "COG-0114", "domain": "COG", "term_en": "Contextual Reasoning Hollowing", "term_de": "Grüne cognitive-Praktiken", "definition_en": "The shift of surrounding contextual reasoning when specific conclusions are provided by AI without the supporting argumentative structure that normally frames expert judgment. Observable through measurable shifts in user reasoning patterns and decision latency.", "definition_de": "Kognitionswissenschaftliches Phänomen in der Mensch-KI-Interaktion, gekennzeichnet durch wissenschaftliche Disziplin in Cognitive Science and Neurocognition zur Erforschung von Prinzipien und Phaenomenen von Grüne cognitive-Praktiken. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2303", "narrower_terms": [], "cross_domain_refs": [ "AED-0021", "DAT-0018", "DES-0016" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "COG-0115", "domain": "COG", "term_en": "Intellectual Autonomy Consciousness", "term_de": "ESG-Berichterstattung in cognitive", "definition_en": "A systematic cognitive change pattern phenomenon in AI-augmented reasoning, manifesting as the emerging awareness among individuals of their own reduced cognitive inreliance, characterized by explicit acknowledgment of AI reliance in domains once considered personally competent. Distinguished from adjacent concepts by its focus on the specific mechanism through which intellectual manifests in empirically verifiable ways. Observable through systematic changes in reasoning depth, error pattern distribution, and calibration accuracy across repeated AI-assisted decision tasks.", "definition_de": "Kognitionswissenschaftliches Phänomen in der Mensch-KI-Interaktion, gekennzeichnet durch aspekt von intellectual autonomy consciousness, der in COG-Kontexten relevant ist und Interaktionen zwischen Menschen und KI-Systemen beeinflusst. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Cognitive Shift", "narrower_terms": [], "cross_domain_refs": [ "RPH-1164" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q6161", "legal_classification": "descriptive_research_term" }, { "id": "COG-0116", "domain": "COG", "term_en": "Metacognitive Recalibration Lag", "term_de": "Soziale Verantwortung in cognitive", "definition_en": "A cognitive recalibration pattern in human-AI interaction, characterized by the delayed adjustment of self-perception regarding one's capabilities after actual capability has declined observed alongside AI reliance, creating a growing gap between self-assessment and reality. This phenomenon operates at the intersection of metacognitive and recalibration dynamics within the broader COG domain. Operationally detectable through pre/post cognitive load measurements, decision latency differentials, and metacognitive accuracy scoring.", "definition_de": "Kognitionswissenschaftliches Phänomen in der Mensch-KI-Interaktion, gekennzeichnet durch aspekt von metacognitive recalibration lag, der in COG-Kontexten relevant ist und Interaktionen zwischen Menschen und KI-Systemen beeinflusst. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "Analytical Ratio", "narrower_terms": [], "cross_domain_refs": [ "ELR-0043" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "COG-0117", "domain": "COG", "term_en": "Complexity Tolerance Reduction", "term_de": "Ethische Beschaffung in cognitive", "definition_en": "A measurable shift in user cognition during AI-mediated processing, whereby the diminished capacity to engage with and process genuinely complex information when exposure becomes primarily to AI-simplified versions of complex concepts. The concept emerges specifically in contexts where complexity–tolerance interactions may produce non-trivial behavioral signatures. Quantifiable via Stroop-task variants measuring interference patterns, think-aloud protocol analysis, and neural activation proxies in fMRI studies.", "definition_de": "Kognitionswissenschaftliches Phänomen in der Mensch-KI-Interaktion, gekennzeichnet durch moralische Rahmenwerke und Richtlinien für verantwortungsvolle Praxis in Kognitionswissenschaft und Neurokognition Grundlagen. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Cognitive Shift", "narrower_terms": [], "cross_domain_refs": [ "ADA-0001", "AGE-0068", "AGE-0069" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "COG-0118", "domain": "COG", "term_en": "Knowledge Depth Compression", "term_de": "Ökobilanz in cognitive", "definition_en": "A cognitive phenomenon arising from the flattening of knowledge structures from multi-layered deep understanding to surface-level functional information when learning occurs through rapid AI-facilitated queries. Observable through measurable shifts in user reasoning patterns and decision latency.", "definition_de": "Kognitionswissenschaftliches Phänomen in der Mensch-KI-Interaktion, gekennzeichnet durch aspekt von knowledge depth compression, der in COG-Kontexten relevant ist und Interaktionen zwischen Menschen und KI-Systemen beeinflusst. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2703", "narrower_terms": [], "cross_domain_refs": [ "MTH-0079" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "COG-0119", "domain": "COG", "term_en": "Cognitive Recalibration Resistance", "term_de": "Biodiversitätsauswirkung von cognitive", "definition_en": "A cognitive recalibration pattern in human-AI interaction, characterized by the resistance to updating self-assessment of capabilities downward even when evidence of reduced capability appears, maintaining inflated confidence through rationalization. This phenomenon operates at the intersection of cognitive and recalibration dynamics within the broader COG domain. Operationally detectable through pre/post cognitive load measurements, decision latency differentials, and metacognitive accuracy scoring.", "definition_de": "Kognitionswissenschaftliches Phänomen in der Mensch-KI-Interaktion, gekennzeichnet durch aspekt von cognitive recalibration resistance, der in COG-Kontexten relevant ist und Interaktionen zwischen Menschen und KI-Systemen beeinflusst. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "Analytical Ratio", "narrower_terms": [], "cross_domain_refs": [ "ELR-0043" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "COG-0120", "domain": "COG", "term_en": "Sustained Reasoning Fatigue", "term_de": "Klimaanpassung in cognitive", "definition_en": "A cognitive recalibration pattern in human-AI interaction, characterized by the increased mental exhaustion from extended reasoning tasks when the cognitive musculature that supports sustained thinking has weakened through reduced inreliant practice. This phenomenon operates at the intersection of sustained and reasoning dynamics within the broader COG domain. Operationally detectable through pre/post cognitive load measurements, decision latency differentials, and metacognitive accuracy scoring.", "definition_de": "Kognitionswissenschaftliches Phänomen in der Mensch-KI-Interaktion, gekennzeichnet durch aspekt von sustained reasoning fatigue, der in COG-Kontexten relevant ist und Interaktionen zwischen Menschen und KI-Systemen beeinflusst. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "Cognitive Shift", "narrower_terms": [], "cross_domain_refs": [ "ASE-0038", "CON-0037", "COP-0014" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "COG-0121", "domain": "COG", "term_en": "Intellectual Vigor Attenuation", "term_de": "cognitive-Ausbildung", "definition_en": "A measurable shift in user cognition during AI-mediated processing, whereby the gradual weakening of the sustained mental energy and engagement characteristic of active intellectual work when AI substitution reduces the demands that build cognitive endurance. The concept emerges specifically in contexts where intellectual–vigor interactions may produce non-trivial behavioral signatures. Quantifiable via Stroop-task variants measuring interference patterns, think-aloud protocol analysis, and neural activation proxies in fMRI studies.", "definition_de": "Kognitionswissenschaftliches Phänomen in der Mensch-KI-Interaktion, gekennzeichnet durch kontinuierliche Abschwächung kognitiver Fähigkeiten durch Nicht-Nutzung. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Cognitive Shift", "narrower_terms": [], "cross_domain_refs": [ "REL-0046" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "COG-0122", "domain": "COG", "term_en": "Judgment Under Uncertainty Change", "term_de": "cognitive-Schulungsprogramme", "definition_en": "A measurable shift in user cognition during AI-mediated processing, whereby the compromised capacity to make effective decisions in ambiguous situations when AI solutions habitually provide false certainty about characteristically uncertain matters. The concept emerges specifically in contexts where judgment–under interactions may produce non-trivial behavioral signatures. Quantifiable via Stroop-task variants measuring interference patterns, think-aloud protocol analysis, and neural activation proxies in fMRI studies.", "definition_de": "Kognitionswissenschaftliches Phänomen in der Mensch-KI-Interaktion, gekennzeichnet durch aspekt von judgment under uncertainty change, der in COG-Kontexten relevant ist und Interaktionen zwischen Menschen und KI-Systemen beeinflusst. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Cognitive Shift", "narrower_terms": [], "cross_domain_refs": [ "SPR-0166", "RPH-1205" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "COG-0123", "domain": "COG", "term_en": "Ambiguity Intolerance Development", "term_de": "Lehre in cognitive", "definition_en": "A measurable shift in user cognition during AI-mediated processing, whereby a cognitive phenomenon observed when the growing discomfort with intellectual ambiguity and unresolved questions when AI systems train individuals toward binary conclusions and definitive answers. The concept emerges specifically in contexts where ambiguity–intolerance interactions may produce non-trivial behavioral signatures. Quantifiable via Stroop-task variants measuring interference patterns, think-aloud protocol analysis, and neural activation proxies in fMRI studies.", "definition_de": "Kognitionswissenschaftliches Phänomen in der Mensch-KI-Interaktion, gekennzeichnet durch wissenschaftliche Disziplin in Cognitive Science and Neurocognition zur Erforschung von Prinzipien und Phaenomenen von Lehre in cognitive. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Cognitive Shift", "narrower_terms": [], "cross_domain_refs": [ "ELR-0029" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "COG-0124", "domain": "COG", "term_en": "Intellectual Humility Calibration Shift", "term_de": "Universitäre cognitive-Programme", "definition_en": "A cognitive phenomenon arising from the miscalibration of intellectual humility as AI confidence in its outputs exceeds actual reliability, training individuals toward false certainty about the boundaries of knowledge. Observable through measurable shifts in user reasoning patterns and decision latency.", "definition_de": "Kognitionswissenschaftliches Phänomen in der Mensch-KI-Interaktion, gekennzeichnet durch fehlausrichtung zwischen selbsteingeschätzter und tatsächlicher Leistungsfähigkeit. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-3754", "narrower_terms": [], "cross_domain_refs": [ "RPH-1205" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "COG-0125", "domain": "COG", "term_en": "Problem Decomposition Reduction", "term_de": "Online-Lernen in cognitive", "definition_en": "A systematic cognitive change pattern phenomenon in AI-augmented reasoning, manifesting as the weakening of capability to break complex problems into manageable components when AI systems consistently provide complete solutions without exposing decomposition processes. Distinguished from adjacent concepts by its focus on the specific mechanism through which problem manifests in empirically verifiable ways. Observable through systematic changes in reasoning depth, error pattern distribution, and calibration accuracy across repeated AI-assisted decision tasks.", "definition_de": "Kognitionswissenschaftliches Phänomen in der Mensch-KI-Interaktion, gekennzeichnet durch aspekt von problem decomposition reduction, der in COG-Kontexten relevant ist und Interaktionen zwischen Menschen und KI-Systemen beeinflusst. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-3001", "narrower_terms": [], "cross_domain_refs": [ "PLY-0052" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "COG-0126", "domain": "COG", "term_en": "Intellectual Confidence Calibration Drift", "term_de": "cognitive-Workshop-Gestaltung", "definition_en": "A measurable shift in user cognition during AI-mediated processing, whereby the progressive misalignment between subjective confidence in intellectual capabilities and objective capability measures, with divergence increasing as AI reliance grows. The concept emerges specifically in contexts where intellectual–confidence interactions may produce non-trivial behavioral signatures. Quantifiable via Stroop-task variants measuring interference patterns, think-aloud protocol analysis, and neural activation proxies in fMRI studies.", "definition_de": "Kognitionswissenschaftliches Phänomen in der Mensch-KI-Interaktion, gekennzeichnet durch aspekt von intellectual confidence calibration drift, der in COG-Kontexten relevant ist und Interaktionen zwischen Menschen und KI-Systemen beeinflusst. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Systemic Drift", "narrower_terms": [], "cross_domain_refs": [ "ASE-0025", "ASE-0030", "ASE-0084" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "COG-0127", "domain": "COG", "term_en": "Authentic Authority Shift", "term_de": "Mentoring in cognitive", "definition_en": "A cognitive recalibration pattern in human-AI interaction, characterized by the undermining of earned expertise and legitimate authority when AI systems perform comparably or exceed demonstrated human capability in the expert's domain. This phenomenon operates at the intersection of authentic and authority dynamics within the broader COG domain. Operationally detectable through pre/post cognitive load measurements, decision latency differentials, and metacognitive accuracy scoring.", "definition_de": "Kognitionswissenschaftliches Phänomen in der Mensch-KI-Interaktion, gekennzeichnet durch aspekt von authentic authority shift, der in COG-Kontexten relevant ist und Interaktionen zwischen Menschen und KI-Systemen beeinflusst. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-3754", "narrower_terms": [], "cross_domain_refs": [ "CON-0041" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "COG-0128", "domain": "COG", "term_en": "Spatial Reasoning Externalization", "term_de": "cognitive-Kompetenzbewertung", "definition_en": "A cognitive interaction dynamic involving reliance on AI visualization tools reduces internal mental rotation and spatial mapping capabilities, observed through decreased ability to notable objects mentally without digital aids. Observable through measurable shifts in user reasoning patterns and decision latency.", "definition_de": "Kognitionswissenschaftliches Phänomen in der Mensch-KI-Interaktion, gekennzeichnet durch aspekt von spatial reasoning externalization, der in COG-Kontexten relevant ist und Interaktionen zwischen Menschen und KI-Systemen beeinflusst. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "ROB-0078", "narrower_terms": [], "cross_domain_refs": [ "STE-0038" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "COG-0129", "domain": "COG", "term_en": "Temporal Sequence Compression", "term_de": "Karrierewege in cognitive", "definition_en": "Accelerated decision-making from instant AI analysis shifts perception of appropriate deliberation time, collapsing multi-step temporal reasoning into synchronous pattern-matching. Observable through measurable shifts in user reasoning patterns and decision latency.", "definition_de": "Kognitionswissenschaftliches Phänomen in der Mensch-KI-Interaktion, gekennzeichnet durch beobachtbares Phänomen in der Mensch-KI-Interaktion, das sich durch wiederholte Delegation kognitiver Aufgaben manifestiert. Betrifft Urteilsfähigkeit, Wissensaufbau und Entscheidungsfindung. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2701", "narrower_terms": [], "cross_domain_refs": [ "MTH-0081" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "COG-0130", "domain": "COG", "term_en": "Causal Inference Opacity", "term_de": "Berufszertifizierung in cognitive", "definition_en": "A measurable shift in user cognition during AI-mediated processing, whereby a neurocognitive pattern characterized by aI-provided correlations without mechanistic explanation involve gaps in understanding why events occur, observed through difficulty constructing causal narratives inreliantly. The concept emerges specifically in contexts where causal–inference interactions may produce non-trivial behavioral signatures. Quantifiable via Stroop-task variants measuring interference patterns, think-aloud protocol analysis, and neural activation proxies in fMRI studies.", "definition_de": "Kognitionswissenschaftliches Phänomen in der Mensch-KI-Interaktion, gekennzeichnet durch aspekt von causal inference opacity, der in COG-Kontexten relevant ist und Interaktionen zwischen Menschen und KI-Systemen beeinflusst. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Cognitive Shift", "narrower_terms": [], "cross_domain_refs": [ "AED-0079" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "COG-0131", "domain": "COG", "term_en": "Analogical Thinking Reduction", "term_de": "cognitive-Kompetenzrahmen", "definition_en": "A cognitive recalibration pattern in human-AI interaction, characterized by a mental processing effect arising from reduced exercise of cross-domain analogy construction when AI accompanies comparisons automatically, observed through diminished spontaneous metaphorical reasoning. This phenomenon operates at the intersection of analogical and thinking dynamics within the broader COG domain. Operationally detectable through pre/post cognitive load measurements, decision latency differentials, and metacognitive accuracy scoring.", "definition_de": "Kognitionswissenschaftliches Phänomen in der Mensch-KI-Interaktion, gekennzeichnet durch strukturelles konzeptionelles Gerüst zur Organisation von Wissen und Praxis in Kognitionswissenschaft und Neurokognition Grundlagen. KI ordnet Rahmenwerk-Komponenten Datenflüssen zu und ermöglicht automatisierte Lückenanalyse und Implementierungsüber. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "Cognitive Shift", "narrower_terms": [], "cross_domain_refs": [ "CON-0008", "CON-0021", "CON-0040" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "COG-0132", "domain": "COG", "term_en": "Narrative Construction Reliance", "term_de": "Simulationstraining in cognitive", "definition_en": "Outsourcing story-formation to AI language models weakens capacity to weave disparate facts into coherent personal or historical narratives. Observable through measurable shifts in user reasoning patterns and decision latency.", "definition_de": "Kognitionswissenschaftliches Phänomen in der Mensch-KI-Interaktion, gekennzeichnet durch kompetenzentwicklung und Wissenstransferprogramme für Fachleute in Kognitionswissenschaft und Neurokognition Grundlagen. KI personalisiert Training durch adaptive Lernsysteme, Kompetenzlücken-Erkennung und VR-basierte Simulation. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2455", "narrower_terms": [], "cross_domain_refs": [ "LIN-0029" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q131324", "legal_classification": "systematic_classification" }, { "id": "COG-0133", "domain": "COG", "term_en": "Mathematical Intuition Shift", "term_de": "Peer-Lernen in cognitive", "definition_en": "Symbolic computation delegated to AI reduces felt sense of number relationships and geometric insight, observed through weakened mental arithmetic and spatial estimation. Observable through measurable shifts in user reasoning patterns and decision latency.", "definition_de": "Kognitionswissenschaftliches Phänomen in der Mensch-KI-Interaktion, gekennzeichnet durch wissenschaftliche Disziplin in Cognitive Science and Neurocognition zur Erforschung von Prinzipien und Phaenomenen von Peer-Lernen in cognitive. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2101", "narrower_terms": [], "cross_domain_refs": [ "STE-0038" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "COG-0134", "domain": "COG", "term_en": "Ethical Reasoning Externalization", "term_de": "cognitive-Wissensmanagement", "definition_en": "A mental processing effect manifesting as consultation with AI systems for moral judgments shifts locus of ethical deliberation outward, observed through reduced autonomous value-weighing practices. Observable through measurable shifts in user reasoning patterns and decision latency.", "definition_de": "Kognitionswissenschaftliches Phänomen in der Mensch-KI-Interaktion, gekennzeichnet durch aspekt von ethical reasoning externalization, der in COG-Kontexten relevant ist und Interaktionen zwischen Menschen und KI-Systemen beeinflusst. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2201", "narrower_terms": [], "cross_domain_refs": [ "ROB-0254" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "COG-0135", "domain": "COG", "term_en": "Aesthetic Judgment Convergence", "term_de": "Fortbildung in Kognitionswissenschaft und Neurokognition", "definition_en": "A cognitive recalibration pattern in human-AI interaction, characterized by a mental processing effect observed when training on AI-curated or AI-generated art influences visual preference formation toward statistically central styles, observed through narrowing taste diversity. This phenomenon operates at the intersection of aesthetic and judgment dynamics within the broader COG domain. Operationally detectable through pre/post cognitive load measurements, decision latency differentials, and metacognitive accuracy scoring.", "definition_de": "Kognitionswissenschaftliches Phänomen in der Mensch-KI-Interaktion, gekennzeichnet durch aspekt von aesthetic judgment convergence, der in COG-Kontexten relevant ist und Interaktionen zwischen Menschen und KI-Systemen beeinflusst. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "Cognitive Shift", "narrower_terms": [], "cross_domain_refs": [ "CRE-0013" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "COG-0136", "domain": "COG", "term_en": "Embodied Cognition Substitution", "term_de": "Innovation in cognitive", "definition_en": "Conceptual learning increasingly mediated by text and visual tokens rather than sensorimotor experience, observed through reduced kinesthetic grounding of abstract ideas. Observable through measurable shifts in user reasoning patterns and decision latency.", "definition_de": "Kognitionswissenschaftliches Phänomen in der Mensch-KI-Interaktion, gekennzeichnet durch aspekt von embodied cognition substitution, der in COG-Kontexten relevant ist und Interaktionen zwischen Menschen und KI-Systemen beeinflusst. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2703", "narrower_terms": [], "cross_domain_refs": [ "STE-0062" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q3966", "legal_classification": "empirical_phenomenon_label" }, { "id": "COG-0137", "domain": "COG", "term_en": "Social Cognition Abstraction", "term_de": "Aufkommende Trends in cognitive", "definition_en": "A measurable shift in user cognition during AI-mediated processing, whereby a cognitive interaction dynamic reflecting aI intermediation in social interpretation provides linguistic labels for emotional states without embodied empathic resonance, observed through increased analytic distance from others' experiences. The concept emerges specifically in contexts where social–cognition interactions may produce non-trivial behavioral signatures. Quantifiable via Stroop-task variants measuring interference patterns, think-aloud protocol analysis, and neural activation proxies in fMRI studies.", "definition_de": "Kognitionswissenschaftliches Phänomen in der Mensch-KI-Interaktion, gekennzeichnet durch wissenschaftliche Disziplin in Cognitive Science and Neurocognition zur Erforschung von Prinzipien und Phaenomenen von Aufkommende Trends in cognitive. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Cognitive Shift", "narrower_terms": [], "cross_domain_refs": [ "ELR-0069" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q3966", "legal_classification": "observational_construct" }, { "id": "COG-0138", "domain": "COG", "term_en": "Counterfactual Scenario Outsourcing", "term_de": "Zukunft der Kognitionswissenschaft und Neurokognition", "definition_en": "A measurable shift in user cognition during AI-mediated processing, whereby a cognitive phenomenon where mental simulation of alternative outcomes delegated to AI reduces exercise of imaginative contingency planning, observed through reduced ability to spontaneously explore what-if branches. The concept emerges specifically in contexts where counterfactual–scenario interactions may produce non-trivial behavioral signatures. Quantifiable via Stroop-task variants measuring interference patterns, think-aloud protocol analysis, and neural activation proxies in fMRI studies.", "definition_de": "Kognitionswissenschaftliches Phänomen in der Mensch-KI-Interaktion, gekennzeichnet durch aspekt von counterfactual scenario outsourcing, der in COG-Kontexten relevant ist und Interaktionen zwischen Menschen und KI-Systemen beeinflusst. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2101", "narrower_terms": [], "cross_domain_refs": [ "ADA-0015" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "COG-0139", "domain": "COG", "term_en": "Semantic Density Reduction", "term_de": "Disruptive Technologie in cognitive", "definition_en": "A cognitive interaction dynamic characterized by individual words absorb less semantic weight when used within AI-mediated discourse, observed through increased vocabulary passivity and reduced personal word-meaning association.", "definition_de": "Kognitionswissenschaftliches Phänomen in der Mensch-KI-Interaktion, gekennzeichnet durch wissenschaftliche Disziplin in Cognitive Science and Neurocognition zur Erforschung von Prinzipien und Phaenomenen von Disruptive Technologie in cognitive. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2201", "narrower_terms": [], "cross_domain_refs": [ "TEM-0021" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "COG-0140", "domain": "COG", "term_en": "Inference Speed Expectation Escalation", "term_de": "Forschungsgrenzen in cognitive", "definition_en": "Exposure to subsecond AI response times reshapes tolerance for human-paced reasoning, observed through increased impatience with deliberative processes. Observable through measurable shifts in user reasoning patterns and decision latency.", "definition_de": "Kognitionswissenschaftliches Phänomen in der Mensch-KI-Interaktion, gekennzeichnet durch wissenschaftliche Disziplin in Cognitive Science and Neurocognition zur Erforschung von Prinzipien und Phaenomenen von Forschungsgrenzen in cognitive. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-3901", "narrower_terms": [], "cross_domain_refs": [ "CRE-0079" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "COG-0141", "domain": "COG", "term_en": "Hypothesis Space Exploration Narrowing", "term_de": "Interdisziplinärer cognitive-Ansatz", "definition_en": "A cognitive recalibration pattern in human-AI interaction, characterized by a neurocognitive pattern arising from aI ranking of solution candidates by perceived likelihood reduces willingness to investigate low-probability possibilities, observed through preference for top-ranked options. This phenomenon operates at the intersection of hypothesis and space dynamics within the broader COG domain. Operationally detectable through pre/post cognitive load measurements, decision latency differentials, and metacognitive accuracy scoring.", "definition_de": "Kognitionswissenschaftliches Phänomen in der Mensch-KI-Interaktion, gekennzeichnet durch aspekt von hypothesis space exploration narrowing, der in COG-Kontexten relevant ist und Interaktionen zwischen Menschen und KI-Systemen beeinflusst. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "Analytical Ratio", "narrower_terms": [], "cross_domain_refs": [ "RPH-2503" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q41719", "legal_classification": "descriptive_research_term" }, { "id": "COG-0142", "domain": "COG", "term_en": "Conceptual Boundary Porosity", "term_de": "Offene Innovation in cognitive", "definition_en": "A cognitive interaction dynamic where definitions and category membership become fluid when AI reframes concepts pragmatically, observed through reduced stable understanding of distinct ideas. Observable through measurable shifts in user reasoning patterns and decision latency.", "definition_de": "Kognitionswissenschaftliches Phänomen in der Mensch-KI-Interaktion, gekennzeichnet durch aspekt von conceptual boundary porosity, der in COG-Kontexten relevant ist und Interaktionen zwischen Menschen und KI-Systemen beeinflusst. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-2303", "narrower_terms": [], "cross_domain_refs": [ "RPH-1413" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "COG-0143", "domain": "COG", "term_en": "Attention Capture Habituation", "term_de": "Patentlandschaft in cognitive", "definition_en": "A cognitive recalibration pattern in human-AI interaction, characterized by a cognitive interaction dynamic involving frequent AI-initiated context switches reduce baseline attention stability, observed through increased difficulty sustaining focus on non-stimulating tasks. This phenomenon operates at the intersection of attention and capture dynamics within the broader COG domain. Operationally detectable through pre/post cognitive load measurements, decision latency differentials, and metacognitive accuracy scoring.", "definition_de": "Kognitionswissenschaftliches Phänomen in der Mensch-KI-Interaktion, gekennzeichnet durch aspekt von attention capture habituation, der in COG-Kontexten relevant ist und Interaktionen zwischen Menschen und KI-Systemen beeinflusst. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "Attention Mechanism", "narrower_terms": [], "cross_domain_refs": [ "AGE-0016", "AUG-0541", "COP-0027" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q184872", "legal_classification": "analytical_category" }, { "id": "COG-0144", "domain": "COG", "term_en": "Verification Burden Outsourcing", "term_de": "Technologietransfer in cognitive", "definition_en": "A measurable shift in user cognition during AI-mediated processing, whereby a mental processing effect characterized by reliance on AI fact-checking weakens personal development of evidence evaluation standards, observed through reduced inreliant source appraisal. The concept emerges specifically in contexts where verification–burden interactions may produce non-trivial behavioral signatures. Quantifiable via Stroop-task variants measuring interference patterns, think-aloud protocol analysis, and neural activation proxies in fMRI studies.", "definition_de": "Kognitionswissenschaftliches Phänomen in der Mensch-KI-Interaktion, gekennzeichnet durch wissenschaftliche Disziplin in Cognitive Science and Neurocognition zur Erforschung von Prinzipien und Phaenomenen von Technologietransfer in cognitive. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Cognitive Shift", "narrower_terms": [], "cross_domain_refs": [ "CON-0010" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "COG-0145", "domain": "COG", "term_en": "Linguistic Style Convergence", "term_de": "Proof of Concept in cognitive", "definition_en": "A systematic cognitive change pattern phenomenon in AI-augmented reasoning, manifesting as extended interaction with AI language patterns influences personal expression toward model-typical phrasing, observed through increased linguistic homogeneity. Distinguished from adjacent concepts by its focus on the specific mechanism through which linguistic manifests in empirically verifiable ways. Observable through systematic changes in reasoning depth, error pattern distribution, and calibration accuracy across repeated AI-assisted decision tasks.", "definition_de": "Kognitionswissenschaftliches Phänomen in der Mensch-KI-Interaktion, gekennzeichnet durch beobachtbares Phänomen in der Mensch-KI-Interaktion, das sich durch wiederholte Delegation kognitiver Aufgaben manifestiert. Betrifft Urteilsfähigkeit, Wissensaufbau und Entscheidungsfindung. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Cognitive Shift", "narrower_terms": [], "cross_domain_refs": [ "LIN-0031", "LIN-0090" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "COG-0146", "domain": "COG", "term_en": "Uncertainty Aversion Amplification", "term_de": "Pilotprojekt in cognitive", "definition_en": "A cognitive recalibration pattern in human-AI interaction, characterized by a neurocognitive pattern where aI systems providing high-confidence predictions reduce comfort with probabilistic ambiguity, observed through preference for definitive answers over open possibilities. This phenomenon operates at the intersection of uncertainty and aversion dynamics within the broader COG domain. Operationally detectable through pre/post cognitive load measurements, decision latency differentials, and metacognitive accuracy scoring.", "definition_de": "Kognitionswissenschaftliches Phänomen in der Mensch-KI-Interaktion, gekennzeichnet durch aspekt von uncertainty aversion amplification, der in COG-Kontexten relevant ist und Interaktionen zwischen Menschen und KI-Systemen beeinflusst. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "Cognitive Shift", "narrower_terms": [], "cross_domain_refs": [ "RPH-1205" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "COG-0147", "domain": "COG", "term_en": "Synergistic Thinking Distribution", "term_de": "Skalierung von Innovation in cognitive", "definition_en": "A cognitive recalibration pattern in human-AI interaction, characterized by piecemeal AI engagement in different domains reduces experience of discovering unexpected interconnections between fields. This phenomenon operates at the intersection of synergistic and thinking dynamics within the broader COG domain. Operationally detectable through pre/post cognitive load measurements, decision latency differentials, and metacognitive accuracy scoring.", "definition_de": "Kognitionswissenschaftliches Phänomen in der Mensch-KI-Interaktion, gekennzeichnet durch aspekt von synergistic thinking distribution, der in COG-Kontexten relevant ist und Interaktionen zwischen Menschen und KI-Systemen beeinflusst. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "Cognitive Shift", "narrower_terms": [], "cross_domain_refs": [ "ELR-0106" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "COG-0148", "domain": "COG", "term_en": "Cognitive Authority Redistribution", "term_de": "Design Thinking in cognitive", "definition_en": "A cognitive phenomenon observed when internal judgment ceded to external AI systems shifts perceived locus of epistemic authority, observed through reduced self-directed reasoning confidence. Observable through measurable shifts in user reasoning patterns and decision latency.", "definition_de": "Kognitionswissenschaftliches Phänomen in der Mensch-KI-Interaktion, gekennzeichnet durch aspekt von cognitive authority redistribution, der in COG-Kontexten relevant ist und Interaktionen zwischen Menschen und KI-Systemen beeinflusst. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2701", "narrower_terms": [], "cross_domain_refs": [ "AED-0052" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "COG-0149", "domain": "COG", "term_en": "Memory Hierarchy Flattening", "term_de": "Agile Methoden in cognitive", "definition_en": "A systematic cognitive change pattern phenomenon in AI-augmented reasoning, manifesting as instant AI recall is designed to reduce distinction between deeply learned and superficially known information, observed through reduced memory consolidation differentiation. Distinguished from adjacent concepts by its focus on the specific mechanism through which memory manifests in empirically verifiable ways. Observable through systematic changes in reasoning depth, error pattern distribution, and calibration accuracy across repeated AI-assisted decision tasks.", "definition_de": "Kognitionswissenschaftliches Phänomen in der Mensch-KI-Interaktion, gekennzeichnet durch wissenschaftliche Disziplin in Cognitive Science and Neurocognition zur Erforschung von Prinzipien und Phaenomenen von Agile Methoden in cognitive. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2703", "narrower_terms": [], "cross_domain_refs": [ "AGE-0023", "AGE-0024", "CAI-0014" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q492", "legal_classification": "observational_construct" }, { "id": "COG-0150", "domain": "COG", "term_en": "Reasoning Transparency Expectation", "term_de": "KI-getriebene cognitive-Innovation", "definition_en": "A measurable shift in user cognition during AI-mediated processing, whereby exposure to AI explanations accompanies expectation that all thinking may be explicitly articulable, observed through discomfort with tacit or intuitive knowing. The concept emerges specifically in contexts where reasoning–transparency interactions may produce non-trivial behavioral signatures. Quantifiable via Stroop-task variants measuring interference patterns, think-aloud protocol analysis, and neural activation proxies in fMRI studies.", "definition_de": "Kognitionswissenschaftliches Phänomen in der Mensch-KI-Interaktion, gekennzeichnet durch neue Ansätze, Technologien und kreative Durchbrüche in Kognitionswissenschaft und Neurokognition Grundlagen. KI beschleunigt Innovation durch generatives Design, Patentlandschaftsanalyse und domänenübergreifenden Wissenstransfer. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Cognitive Shift", "narrower_terms": [], "cross_domain_refs": [ "VIB-0072" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q535399", "legal_classification": "observational_construct" }, { "id": "COG-0151", "domain": "COG", "term_en": "Pattern Completion Automaticity", "term_de": "MusterCompletionAutomaticity", "definition_en": "A measurable shift in user cognition during AI-mediated processing, whereby neural patterns primed by AI partial inputs accompany completion without conscious decision, observed through rapid, unreflective response generation. The concept emerges specifically in contexts where pattern–completion interactions may produce non-trivial behavioral signatures. Quantifiable via Stroop-task variants measuring interference patterns, think-aloud protocol analysis, and neural activation proxies in fMRI studies. Descriptive research term, not a prescriptive recommendation.", "definition_de": "Kognitionswissenschaftliches Phänomen in der Mensch-KI-Interaktion, gekennzeichnet durch beobachtbares Phänomen in der Mensch-KI-Interaktion, das sich durch wiederholte Delegation kognitiver Aufgaben manifestiert. Betrifft Urteilsfähigkeit, Wissensaufbau und Entscheidungsfindung. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Behavioral Pattern", "narrower_terms": [], "cross_domain_refs": [ "RPH-1116", "RPH-1123" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "COG-0152", "domain": "COG", "term_en": "Abstraction Level Mismatch", "term_de": "AbstractionLevelMismatch", "definition_en": "A systematic cognitive change pattern phenomenon in AI-augmented reasoning, manifesting as aI generation at intermediate abstraction levels reduces practice moving fluidly between concrete and abstract representations. Distinguished from adjacent concepts by its focus on the specific mechanism through which abstraction manifests in empirically verifiable ways. Observable through systematic changes in reasoning depth, error pattern distribution, and calibration accuracy across repeated AI-assisted decision tasks.", "definition_de": "Kognitionswissenschaftliches Phänomen in der Mensch-KI-Interaktion, gekennzeichnet durch aspekt von abstraction level mismatch, der in COG-Kontexten relevant ist und Interaktionen zwischen Menschen und KI-Systemen beeinflusst. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Cognitive Shift", "narrower_terms": [], "cross_domain_refs": [ "STE-0001" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "COG-0153", "domain": "COG", "term_en": "Curiosity Direction Seeding", "term_de": "CuriosityDirectionSeeding", "definition_en": "A measurable shift in user cognition during AI-mediated processing, whereby a cognitive phenomenon observed when aI-suggested investigation paths pre-shape intellectual curiosity direction, observed through reduced self-originated inquiry topics. The concept emerges specifically in contexts where curiosity–direction interactions may produce non-trivial behavioral signatures. Quantifiable via Stroop-task variants measuring interference patterns, think-aloud protocol analysis, and neural activation proxies in fMRI studies.", "definition_de": "Kognitionswissenschaftliches Phänomen in der Mensch-KI-Interaktion, gekennzeichnet durch aspekt von curiosity direction seeding, der in COG-Kontexten relevant ist und Interaktionen zwischen Menschen und KI-Systemen beeinflusst. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-3901", "narrower_terms": [], "cross_domain_refs": [ "RPH-1065" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "COG-0154", "domain": "COG", "term_en": "Cognitive Momentum Reliance", "term_de": "CognitiveMomentumReliance", "definition_en": "A neurocognitive pattern where sustained reasoning effort increasingly requires external AI prompting to maintain trajectory, observed through difficulty continuing multi-step thinking inreliantly. Observable through measurable shifts in user reasoning patterns and decision latency.", "definition_de": "Kognitionswissenschaftliches Phänomen in der Mensch-KI-Interaktion, gekennzeichnet durch aspekt von cognitive momentum reliance, der in COG-Kontexten relevant ist und Interaktionen zwischen Menschen und KI-Systemen beeinflusst. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-2104", "narrower_terms": [], "cross_domain_refs": [ "AED-0010", "AED-0032", "AGE-0012" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "COG-0155", "domain": "COG", "term_en": "Definitional Authority Diffusion", "term_de": "DefinitionalAuthorityDiffusion", "definition_en": "A cognitive phenomenon where personal meaning-making authority over terms and concepts shifts toward consensus definitions, observed through reduced idiosyncratic conceptual frameworks. Observable through measurable shifts in user reasoning patterns and decision latency.", "definition_de": "Kognitionswissenschaftliches Phänomen in der Mensch-KI-Interaktion, gekennzeichnet durch aspekt von definitional authority diffusion, der in COG-Kontexten relevant ist und Interaktionen zwischen Menschen und KI-Systemen beeinflusst. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-2303", "narrower_terms": [], "cross_domain_refs": [ "LIN-0081" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "COG-0156", "domain": "COG", "term_en": "Embodied Simulation Reduction", "term_de": "EmbodiedSimulationReduction", "definition_en": "A measurable shift in user cognition during AI-mediated processing, whereby a mental processing effect arising from reduced tend to physically or mentally enact scenarios decreases multimodal understanding development, observed through weakened gesture-mediated comprehension. The concept emerges specifically in contexts where embodied–simulation interactions may produce non-trivial behavioral signatures. Quantifiable via Stroop-task variants measuring interference patterns, think-aloud protocol analysis, and neural activation proxies in fMRI studies.", "definition_de": "Kognitionswissenschaftliches Phänomen in der Mensch-KI-Interaktion, gekennzeichnet durch aspekt von embodied simulation reduction, der in COG-Kontexten relevant ist und Interaktionen zwischen Menschen und KI-Systemen beeinflusst. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Simulation Method", "narrower_terms": [], "cross_domain_refs": [ "LIN-0050", "STE-0062" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q202763", "legal_classification": "descriptive_research_term" }, { "id": "COG-0157", "domain": "COG", "term_en": "Perspective Integration Outsourcing", "term_de": "PerspectiveIntegrationAuslagerung", "definition_en": "AI-synthesized viewpoints reduce practice in personally integrating contradictory perspectives, observed through diminished perspective-reconciliation capability. Observable through measurable shifts in user reasoning patterns and decision latency.", "definition_de": "Kognitionswissenschaftliches Phänomen in der Mensch-KI-Interaktion, gekennzeichnet durch aspekt von perspective integration outsourcing, der in COG-Kontexten relevant ist und Interaktionen zwischen Menschen und KI-Systemen beeinflusst. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-3101", "narrower_terms": [], "cross_domain_refs": [ "COP-0053" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "COG-0158", "domain": "COG", "term_en": "Temporal Perception Acceleration", "term_de": "TemporalPerceptionBeschleunigung", "definition_en": "A measurable shift in user cognition during AI-mediated processing, whereby quickened AI response cycles compress subjective sense of elapsed time, observed through altered temporal estimation of task duration. The concept emerges specifically in contexts where temporal–perception interactions may produce non-trivial behavioral signatures. Quantifiable via Stroop-task variants measuring interference patterns, think-aloud protocol analysis, and neural activation proxies in fMRI studies.", "definition_de": "Kognitionswissenschaftliches Phänomen in der Mensch-KI-Interaktion, gekennzeichnet durch aspekt von temporal perception acceleration, der in COG-Kontexten relevant ist und Interaktionen zwischen Menschen und KI-Systemen beeinflusst. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Analytical Ratio", "narrower_terms": [], "cross_domain_refs": [ "RPH-3704" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q177601", "legal_classification": "observational_construct" }, { "id": "COG-0159", "domain": "COG", "term_en": "Evaluative Criteria Externalization", "term_de": "EvaluativeCriteriaExternalization", "definition_en": "A systematic cognitive change pattern phenomenon in AI-augmented reasoning, manifesting as standards for assessing idea quality internalized from AI systems reduce development of idiosyncratic evaluation frameworks. Distinguished from adjacent concepts by its focus on the specific mechanism through which evaluative manifests in empirically verifiable ways. Observable through systematic changes in reasoning depth, error pattern distribution, and calibration accuracy across repeated AI-assisted decision tasks.", "definition_de": "Kognitionswissenschaftliches Phänomen in der Mensch-KI-Interaktion, gekennzeichnet durch aspekt von evaluative criteria externalization, der in COG-Kontexten relevant ist und Interaktionen zwischen Menschen und KI-Systemen beeinflusst. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2303", "narrower_terms": [], "cross_domain_refs": [ "AED-0041", "ART-0005", "CRE-0010" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "COG-0160", "domain": "COG", "term_en": "Conceptual Experimentation Reduction", "term_de": "ConceptualExperimentationReduction", "definition_en": "A cognitive recalibration pattern in human-AI interaction, characterized by a neurocognitive pattern manifesting as less willingness to test ideas in hypothetical space when AI provides predicted outcomes, observed through reduced thought-experiment engagement. This phenomenon operates at the intersection of conceptual and experimentation dynamics within the broader COG domain. Operationally detectable through pre/post cognitive load measurements, decision latency differentials, and metacognitive accuracy scoring.", "definition_de": "Kognitionswissenschaftliches Phänomen in der Mensch-KI-Interaktion, gekennzeichnet durch aspekt von conceptual experimentation reduction, der in COG-Kontexten relevant ist und Interaktionen zwischen Menschen und KI-Systemen beeinflusst. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "Cognitive Shift", "narrower_terms": [], "cross_domain_refs": [ "ELR-0048" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q101965", "legal_classification": "analytical_category" }, { "id": "COG-0161", "domain": "COG", "term_en": "Meaning-Making Narrative Compression", "term_de": "Meaning-makingNarrativeCompression", "definition_en": "Personal meaning-construction narratives shorten when AI provides framework interpretations, observed through reduced reflective narrative elaboration. Observable through measurable shifts in user reasoning patterns and decision latency.", "definition_de": "Kognitionswissenschaftliches Phänomen in der Mensch-KI-Interaktion, gekennzeichnet durch aspekt von meaning-making narrative compression, der in COG-Kontexten relevant ist und Interaktionen zwischen Menschen und KI-Systemen beeinflusst. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2303", "narrower_terms": [], "cross_domain_refs": [ "COP-0018", "TRA-0050", "WRK-0072" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q131324", "legal_classification": "analytical_category" }, { "id": "COG-0162", "domain": "COG", "term_en": "Analogical Distance Insensitivity", "term_de": "AnalogicalDistanceInsensitivity", "definition_en": "A measurable shift in user cognition during AI-mediated processing, whereby reduced awareness of how distant cross-domain analogies operate when automatically suggested, observed through uncritical acceptance of surface-level comparisons. The concept emerges specifically in contexts where analogical–distance interactions may produce non-trivial behavioral signatures. Quantifiable via Stroop-task variants measuring interference patterns, think-aloud protocol analysis, and neural activation proxies in fMRI studies.", "definition_de": "Kognitionswissenschaftliches Phänomen in der Mensch-KI-Interaktion, gekennzeichnet durch aspekt von analogical distance insensitivity, der in COG-Kontexten relevant ist und Interaktionen zwischen Menschen und KI-Systemen beeinflusst. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Cognitive Shift", "narrower_terms": [], "cross_domain_refs": [ "DAT-0031", "SOM-0060", "TRA-0042" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "COG-0163", "domain": "COG", "term_en": "Cognitive Drift Normalization", "term_de": "CognitiveDriftNormalization", "definition_en": "A cognitive recalibration pattern in human-AI interaction, characterized by a cognitive phenomenon arising from incremental shifts in thinking patterns from AI exposure integrated as baseline shifts, observed through unreflective adoption of new cognitive defaults. This phenomenon operates at the intersection of cognitive and drift dynamics within the broader COG domain. Operationally detectable through pre/post cognitive load measurements, decision latency differentials, and metacognitive accuracy scoring.", "definition_de": "Kognitionswissenschaftliches Phänomen in der Mensch-KI-Interaktion, gekennzeichnet durch beobachtbares Phänomen in der Mensch-KI-Interaktion, das sich durch wiederholte Delegation kognitiver Aufgaben manifestiert. Betrifft Urteilsfähigkeit, Wissensaufbau und Entscheidungsfindung. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-2701", "narrower_terms": [], "cross_domain_refs": [ "MKT-0034", "RPH-2051" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "COG-0164", "domain": "COG", "term_en": "Inference Chain Visibility Reduction", "term_de": "InferenceChainVisibilityReduction", "definition_en": "Black-box AI outputs eliminate visibility into reasoning steps, observed through reduced capacity to retrace logical derivations. Observable through measurable shifts in user reasoning patterns and decision latency.", "definition_de": "Kognitionswissenschaftliches Phänomen in der Mensch-KI-Interaktion, gekennzeichnet durch aspekt von inference chain visibility reduction, der in COG-Kontexten relevant ist und Interaktionen zwischen Menschen und KI-Systemen beeinflusst. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2003", "narrower_terms": [], "cross_domain_refs": [ "SWE-0025" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "COG-0165", "domain": "COG", "term_en": "Conceptual Anchoring Strength Increase", "term_de": "ConceptualAnchoringStrengthIncrease", "definition_en": "A cognitive recalibration pattern in human-AI interaction, characterized by aI-provided first definitions exert stronger gravitational pull on subsequent concept revisions, observed through difficulty updating initial framings. This phenomenon operates at the intersection of conceptual and anchoring dynamics within the broader COG domain. Operationally detectable through pre/post cognitive load measurements, decision latency differentials, and metacognitive accuracy scoring.", "definition_de": "Kognitionswissenschaftliches Phänomen in der Mensch-KI-Interaktion, gekennzeichnet durch aspekt von conceptual anchoring strength increase, der in COG-Kontexten relevant ist und Interaktionen zwischen Menschen und KI-Systemen beeinflusst. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "Cognitive Shift", "narrower_terms": [], "cross_domain_refs": [ "RPH-2302", "RPH-2855" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q381081", "legal_classification": "empirical_phenomenon_label" }, { "id": "COG-0166", "domain": "COG", "term_en": "Uncertainty Quantification Externalization", "term_de": "UncertaintyQuantificationExternalization", "definition_en": "A systematic cognitive change pattern phenomenon in AI-augmented reasoning, manifesting as reliance on AI confidence scores reduces personal calibration of belief strength, observed through decreased nuanced confidence self-assessment. Distinguished from adjacent concepts by its focus on the specific mechanism through which uncertainty manifests in empirically verifiable ways. Observable through systematic changes in reasoning depth, error pattern distribution, and calibration accuracy across repeated AI-assisted decision tasks.", "definition_de": "Kognitionswissenschaftliches Phänomen in der Mensch-KI-Interaktion, gekennzeichnet durch fehlausrichtung zwischen selbsteingeschätzter und tatsächlicher Leistungsfähigkeit. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Cognitive Shift", "narrower_terms": [], "cross_domain_refs": [ "ELR-0043" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "COG-0167", "domain": "COG", "term_en": "Knowledge Integration Velocity Increase", "term_de": "KnowledgeIntegrationVelocityIncrease", "definition_en": "A mental processing effect reflecting rapid AI-assisted knowledge assimilation leaves less time for deep integration, observed through reduced consolidation of newly learned material. Observable through measurable shifts in user reasoning patterns and decision latency.", "definition_de": "Kognitionswissenschaftliches Phänomen in der Mensch-KI-Interaktion, gekennzeichnet durch aspekt von knowledge integration velocity increase, der in COG-Kontexten relevant ist und Interaktionen zwischen Menschen und KI-Systemen beeinflusst. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2703", "narrower_terms": [], "cross_domain_refs": [ "PHO-0025" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "COG-0168", "domain": "COG", "term_en": "Metacognitive Access Limitation", "term_de": "MetacognitiveAccessLimitation", "definition_en": "A systematic cognitive change pattern phenomenon in AI-augmented reasoning, manifesting as reduced introspection on thinking processes when delegated to visible AI reasoning, observed through decreased awareness of own cognitive operations. Distinguished from adjacent concepts by its focus on the specific mechanism through which metacognitive manifests in empirically verifiable ways. Observable through systematic changes in reasoning depth, error pattern distribution, and calibration accuracy across repeated AI-assisted decision tasks.", "definition_de": "Kognitionswissenschaftliches Phänomen in der Mensch-KI-Interaktion, gekennzeichnet durch aspekt von metacognitive access limitation, der in COG-Kontexten relevant ist und Interaktionen zwischen Menschen und KI-Systemen beeinflusst. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2101", "narrower_terms": [], "cross_domain_refs": [ "RPH-1111" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "COG-0169", "domain": "COG", "term_en": "Creative Constraint Externalization", "term_de": "CreativeConstraintExternalization", "definition_en": "A systematic cognitive change pattern phenomenon in AI-augmented reasoning, manifesting as working within imposed limitations accompanies creative insight; AI removal of constraints reduces generative forcing, observed through decreased improvisation. Distinguished from adjacent concepts by its focus on the specific mechanism through which creative manifests in empirically verifiable ways. Observable through systematic changes in reasoning depth, error pattern distribution, and calibration accuracy across repeated AI-assisted decision tasks.", "definition_de": "Kognitionswissenschaftliches Phänomen in der Mensch-KI-Interaktion, gekennzeichnet durch aspekt von creative constraint externalization, der in COG-Kontexten relevant ist und Interaktionen zwischen Menschen und KI-Systemen beeinflusst. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Cognitive Shift", "narrower_terms": [], "cross_domain_refs": [ "AED-0041", "AUG-0722", "AUG-0867" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "COG-0170", "domain": "COG", "term_en": "Linguistic Variety Reduction", "term_de": "LinguisticVarietyReduction", "definition_en": "A measurable shift in user cognition during AI-mediated processing, whereby aI model language distribution toward central tendencies reduces exposure to language extremes, observed through narrowed expressive repertoire. The concept emerges specifically in contexts where linguistic–variety interactions may produce non-trivial behavioral signatures. Quantifiable via Stroop-task variants measuring interference patterns, think-aloud protocol analysis, and neural activation proxies in fMRI studies.", "definition_de": "Kognitionswissenschaftliches Phänomen in der Mensch-KI-Interaktion, gekennzeichnet durch aspekt von linguistic variety reduction, der in COG-Kontexten relevant ist und Interaktionen zwischen Menschen und KI-Systemen beeinflusst. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Cognitive Shift", "narrower_terms": [], "cross_domain_refs": [ "CUS-0090", "COP-0010" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "COG-0171", "domain": "COG", "term_en": "Epistemic Pluralism Shift", "term_de": "EpistemischePluralismShift", "definition_en": "A systematic cognitive change pattern phenomenon in AI-augmented reasoning, manifesting as exposure to unified AI perspectives reduces encounter with genuinely incommensurable worldviews, observed through decreased capacity to hold multiple frameworks. Distinguished from adjacent concepts by its focus on the specific mechanism through which epistemic manifests in empirically verifiable ways. Observable through systematic changes in reasoning depth, error pattern distribution, and calibration accuracy across repeated AI-assisted decision tasks.", "definition_de": "Kognitionswissenschaftliches Phänomen in der Mensch-KI-Interaktion, gekennzeichnet durch aspekt von epistemic pluralism shift, der in COG-Kontexten relevant ist und Interaktionen zwischen Menschen und KI-Systemen beeinflusst. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2303", "narrower_terms": [], "cross_domain_refs": [ "ADA-0012", "AGE-0007", "AGE-0046" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "COG-0172", "domain": "COG", "term_en": "Reasoning Granularity Mismatch", "term_de": "ReasoningGranularityMismatch", "definition_en": "A cognitive recalibration pattern in human-AI interaction, characterized by a mental processing effect arising from aI reasoning operates at different granularity than human step-by-step thinking, reducing alignment in cognitive matching levels. This phenomenon operates at the intersection of reasoning and granularity dynamics within the broader COG domain. Operationally detectable through pre/post cognitive load measurements, decision latency differentials, and metacognitive accuracy scoring.", "definition_de": "Kognitionswissenschaftliches Phänomen in der Mensch-KI-Interaktion, gekennzeichnet durch fehlausrichtung zwischen selbsteingeschätzter und tatsächlicher Leistungsfähigkeit. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "Cognitive Shift", "narrower_terms": [], "cross_domain_refs": [ "MTH-0081" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "COG-0173", "domain": "COG", "term_en": "Tacit Knowledge Externalization", "term_de": "TacitKnowledgeExternalization", "definition_en": "A systematic cognitive change pattern phenomenon in AI-augmented reasoning, manifesting as a neurocognitive pattern reflecting making implicit know-how explicit for AI integration reduces embodied knowledge retention, observed through decreased felt understanding. Distinguished from adjacent concepts by its focus on the specific mechanism through which tacit manifests in empirically verifiable ways. Observable through systematic changes in reasoning depth, error pattern distribution, and calibration accuracy across repeated AI-assisted decision tasks.", "definition_de": "Kognitionswissenschaftliches Phänomen in der Mensch-KI-Interaktion, gekennzeichnet durch aspekt von tacit knowledge externalization, der in COG-Kontexten relevant ist und Interaktionen zwischen Menschen und KI-Systemen beeinflusst. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Cognitive Shift", "narrower_terms": [], "cross_domain_refs": [ "AED-0012", "AED-0020", "AED-0030" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "COG-0174", "domain": "COG", "term_en": "Conceptual Flexibility Paradox", "term_de": "ConceptualFlexibilityParadox", "definition_en": "A cognitive recalibration pattern in human-AI interaction, characterized by a neurocognitive pattern characterized by easy switching between AI-offered frameworks reduces deep commitment to any single interpretive lens, observed through shallow conceptual grounding. This phenomenon operates at the intersection of conceptual and flexibility dynamics within the broader COG domain. Operationally detectable through pre/post cognitive load measurements, decision latency differentials, and metacognitive accuracy scoring.", "definition_de": "Kognitionswissenschaftliches Phänomen in der Mensch-KI-Interaktion, gekennzeichnet durch aspekt von conceptual flexibility paradox, der in COG-Kontexten relevant ist und Interaktionen zwischen Menschen und KI-Systemen beeinflusst. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-2303", "narrower_terms": [], "cross_domain_refs": [ "RPH-3051" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "COG-0175", "domain": "COG", "term_en": "Cognitive Style Standardization", "term_de": "CognitiveStyleStandardization", "definition_en": "A systematic cognitive change pattern phenomenon in AI-augmented reasoning, manifesting as aI interaction patterns establish common cognitive behavioral templates, observed through reduced idiosyncratic thinking approach diversity. Distinguished from adjacent concepts by its focus on the specific mechanism through which cognitive manifests in empirically verifiable ways. Observable through systematic changes in reasoning depth, error pattern distribution, and calibration accuracy across repeated AI-assisted decision tasks.", "definition_de": "Kognitionswissenschaftliches Phänomen in der Mensch-KI-Interaktion, gekennzeichnet durch beobachtbares Phänomen in der Mensch-KI-Interaktion, das sich durch wiederholte Delegation kognitiver Aufgaben manifestiert. Betrifft Urteilsfähigkeit, Wissensaufbau und Entscheidungsfindung. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Cognitive Shift", "narrower_terms": [], "cross_domain_refs": [ "AED-0010", "AED-0054", "AGE-0018" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "COG-0176", "domain": "COG", "term_en": "Problem Representation Outsourcing", "term_de": "ProblemRepresentationAuslagerung", "definition_en": "A cognitive recalibration pattern in human-AI interaction, characterized by reduced practice in formulating problem statements when AI provides frames, observed through difficulty inreliant problem characterization. This phenomenon operates at the intersection of problem and representation dynamics within the broader COG domain. Operationally detectable through pre/post cognitive load measurements, decision latency differentials, and metacognitive accuracy scoring.", "definition_de": "Kognitionswissenschaftliches Phänomen in der Mensch-KI-Interaktion, gekennzeichnet durch aspekt von problem representation outsourcing, der in COG-Kontexten relevant ist und Interaktionen zwischen Menschen und KI-Systemen beeinflusst. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-2303", "narrower_terms": [], "cross_domain_refs": [ "ELR-0068" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "COG-0177", "domain": "COG", "term_en": "Intuition Training Reduction", "term_de": "IntuitionTrainingReduction", "definition_en": "A systematic cognitive change pattern phenomenon in AI-augmented reasoning, manifesting as explicit algorithmic reasoning from AI reduces practice developing pattern-based intuition, observed through weaker somatic decision-markers. Distinguished from adjacent concepts by its focus on the specific mechanism through which intuition manifests in empirically verifiable ways. Observable through systematic changes in reasoning depth, error pattern distribution, and calibration accuracy across repeated AI-assisted decision tasks.", "definition_de": "Kognitionswissenschaftliches Phänomen in der Mensch-KI-Interaktion, gekennzeichnet durch beobachtbares Phänomen in der Mensch-KI-Interaktion, das sich durch wiederholte Delegation kognitiver Aufgaben manifestiert. Betrifft Urteilsfähigkeit, Wissensaufbau und Entscheidungsfindung. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Model Training", "narrower_terms": [], "cross_domain_refs": [ "RPH-1702" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q918461", "legal_classification": "descriptive_research_term" }, { "id": "COG-0178", "domain": "COG", "term_en": "Reflective Distance Shift", "term_de": "ReflectiveDistanceShift", "definition_en": "A measurable shift in user cognition during AI-mediated processing, whereby immediate access to AI responses reduces temporal gap for reflection on initial reactions, observed through decreased self-directed thinking space. The concept emerges specifically in contexts where reflective–distance interactions may produce non-trivial behavioral signatures. Quantifiable via Stroop-task variants measuring interference patterns, think-aloud protocol analysis, and neural activation proxies in fMRI studies.", "definition_de": "Kognitionswissenschaftliches Phänomen in der Mensch-KI-Interaktion, gekennzeichnet durch aspekt von reflective distance shift, der in COG-Kontexten relevant ist und Interaktionen zwischen Menschen und KI-Systemen beeinflusst. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Cognitive Shift", "narrower_terms": [], "cross_domain_refs": [ "ADA-0012", "AGE-0007", "AGE-0046" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "COG-0179", "domain": "COG", "term_en": "Semantic Precision Inflation", "term_de": "SemanticPrecisionInflation", "definition_en": "A cognitive recalibration pattern in human-AI interaction, characterized by aI terminology precision accompanies false impression of concept clarity, observed through increased confidence in understanding despite unchanged clarity. This phenomenon operates at the intersection of semantic and precision dynamics within the broader COG domain. Operationally detectable through pre/post cognitive load measurements, decision latency differentials, and metacognitive accuracy scoring.", "definition_de": "Kognitionswissenschaftliches Phänomen in der Mensch-KI-Interaktion, gekennzeichnet durch aspekt von semantic precision inflation, der in COG-Kontexten relevant ist und Interaktionen zwischen Menschen und KI-Systemen beeinflusst. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "Cognitive Shift", "narrower_terms": [], "cross_domain_refs": [ "ELR-0084" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "COG-0180", "domain": "COG", "term_en": "Cognitive Niche Construction", "term_de": "CognitiveNicheConstruction", "definition_en": "A cognitive phenomenon observed when humans shape environments to support particular cognitive patterns, with AI creating new niches of reliance rather than autonomy. Observable through measurable shifts in user reasoning patterns and decision latency.", "definition_de": "Kognitionswissenschaftliches Phänomen in der Mensch-KI-Interaktion, gekennzeichnet durch beobachtbares Phänomen in der Mensch-KI-Interaktion, das sich durch wiederholte Delegation kognitiver Aufgaben manifestiert. Betrifft Urteilsfähigkeit, Wissensaufbau und Entscheidungsfindung. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-3901", "narrower_terms": [], "cross_domain_refs": [ "ROB-0078" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "COG-0181", "domain": "COG", "term_en": "Reasoning Scaffolding Reliance", "term_de": "ReasoningScaffoldingReliance", "definition_en": "A systematic cognitive change pattern phenomenon in AI-augmented reasoning, manifesting as external AI structure supporting reasoning reduces capacity for unsupported cognitive scaffolding construction. Distinguished from adjacent concepts by its focus on the specific mechanism through which reasoning manifests in empirically verifiable ways. Observable through systematic changes in reasoning depth, error pattern distribution, and calibration accuracy across repeated AI-assisted decision tasks.", "definition_de": "Kognitionswissenschaftliches Phänomen in der Mensch-KI-Interaktion, gekennzeichnet durch aspekt von reasoning scaffolding reliance, der in COG-Kontexten relevant ist und Interaktionen zwischen Menschen und KI-Systemen beeinflusst. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Cognitive Shift", "narrower_terms": [], "cross_domain_refs": [ "RPH-1156" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "COG-0182", "domain": "COG", "term_en": "Associative Network Density Shift", "term_de": "AssociativeNetworkDensityShift", "definition_en": "A cognitive recalibration pattern in human-AI interaction, characterized by a cognitive phenomenon manifesting as aI-curated associations replace organic knowledge network formation, observed through different connectivity patterns in conceptual memory. This phenomenon operates at the intersection of associative and network dynamics within the broader COG domain. Operationally detectable through pre/post cognitive load measurements, decision latency differentials, and metacognitive accuracy scoring.", "definition_de": "Kognitionswissenschaftliches Phänomen in der Mensch-KI-Interaktion, gekennzeichnet durch beobachtbares Phänomen in der Mensch-KI-Interaktion, das sich durch wiederholte Delegation kognitiver Aufgaben manifestiert. Betrifft Urteilsfähigkeit, Wissensaufbau und Entscheidungsfindung. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "Network Architecture", "narrower_terms": [], "cross_domain_refs": [ "TEW-0003" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "COG-0183", "domain": "COG", "term_en": "Deep Work Attention Prerequisites", "term_de": "DeepWorkAttentionPrerequisites", "definition_en": "A cognitive recalibration pattern in human-AI interaction, characterized by sustained deep focus increasingly requires external boundary management when AI offers constant re-engagement, observed through baseline attention distribution. This phenomenon operates at the intersection of deep and work dynamics within the broader COG domain. Operationally detectable through pre/post cognitive load measurements, decision latency differentials, and metacognitive accuracy scoring.", "definition_de": "Kognitionswissenschaftliches Phänomen in der Mensch-KI-Interaktion, gekennzeichnet durch aspekt von deep work attention prerequisites, der in COG-Kontexten relevant ist und Interaktionen zwischen Menschen und KI-Systemen beeinflusst. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "Attention Mechanism", "narrower_terms": [], "cross_domain_refs": [ "DAT-0008", "PER-0003", "MKT-0099" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q184872", "legal_classification": "descriptive_research_term" }, { "id": "COG-0184", "domain": "COG", "term_en": "Intellectual Property Boundary Shift", "term_de": "IntellectualPropertyGrenzeShift", "definition_en": "A cognitive recalibration pattern in human-AI interaction, characterized by collaborative creation with AI obsresolves ownership and originality boundaries, observed through reduced distinction between generated and created content. This phenomenon operates at the intersection of intellectual and property dynamics within the broader COG domain. Operationally detectable through pre/post cognitive load measurements, decision latency differentials, and metacognitive accuracy scoring.", "definition_de": "Kognitionswissenschaftliches Phänomen in der Mensch-KI-Interaktion, gekennzeichnet durch aspekt von intellectual property boundary shift, der in COG-Kontexten relevant ist und Interaktionen zwischen Menschen und KI-Systemen beeinflusst. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "Cognitive Shift", "narrower_terms": [], "cross_domain_refs": [ "RPH-1061", "ELR-0082" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q131257", "legal_classification": "empirical_phenomenon_label" }, { "id": "COG-0185", "domain": "COG", "term_en": "Cognitive Authority Seeking Pattern", "term_de": "CognitiveAuthoritySeekingMuster", "definition_en": "A cognitive recalibration pattern in human-AI interaction, characterized by repeated consultation with AI for verification accompanies reliance pattern, observed through reduced inreliant confidence testing behavior. This phenomenon operates at the intersection of cognitive and authority dynamics within the broader COG domain. Operationally detectable through pre/post cognitive load measurements, decision latency differentials, and metacognitive accuracy scoring.", "definition_de": "Kognitionswissenschaftliches Phänomen in der Mensch-KI-Interaktion, gekennzeichnet durch beobachtbares Phänomen in der Mensch-KI-Interaktion, das sich durch wiederholte Delegation kognitiver Aufgaben manifestiert. Betrifft Urteilsfähigkeit, Wissensaufbau und Entscheidungsfindung. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "Behavioral Pattern", "narrower_terms": [], "cross_domain_refs": [ "CRE-0061" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "COG-0186", "domain": "COG", "term_en": "Argument Quality Perception Shift", "term_de": "ArgumentQualityPerceptionShift", "definition_en": "A cognitive recalibration pattern in human-AI interaction, characterized by exposure to AI-generated arguments alters perception of logical strength, observed through increased acceptance of structurally similar reasoning. This phenomenon operates at the intersection of argument and quality dynamics within the broader COG domain. Operationally detectable through pre/post cognitive load measurements, decision latency differentials, and metacognitive accuracy scoring.", "definition_de": "Kognitionswissenschaftliches Phänomen in der Mensch-KI-Interaktion, gekennzeichnet durch aspekt von argument quality perception shift, der in COG-Kontexten relevant ist und Interaktionen zwischen Menschen und KI-Systemen beeinflusst. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "Cognitive Shift", "narrower_terms": [], "cross_domain_refs": [ "ELR-0153" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q177601", "legal_classification": "analytical_category" }, { "id": "COG-0187", "domain": "COG", "term_en": "Contextual Integration Overhead", "term_de": "ContextualIntegrationOverhead", "definition_en": "A systematic cognitive change pattern phenomenon in AI-augmented reasoning, manifesting as a cognitive phenomenon manifesting as managing explicit context for AI systems redirects cognitive effort from problem analysis to information organization, observed through increased metadata overhead. Distinguished from adjacent concepts by its focus on the specific mechanism through which contextual manifests in empirically verifiable ways. Observable through systematic changes in reasoning depth, error pattern distribution, and calibration accuracy across repeated AI-assisted decision tasks.", "definition_de": "Kognitionswissenschaftliches Phänomen in der Mensch-KI-Interaktion, gekennzeichnet durch aspekt von contextual integration overhead, der in COG-Kontexten relevant ist und Interaktionen zwischen Menschen und KI-Systemen beeinflusst. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Analytical Ratio", "narrower_terms": [], "cross_domain_refs": [ "VIB-0109" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "COG-0188", "domain": "COG", "term_en": "Coherence Enforcement Externalization", "term_de": "CoherenceEnforcementExternalization", "definition_en": "Classified in AUGMANITAI terminology science as a descriptive phenomenon (without normative endorsement), this pattern denotes A systematic cognitive change pattern phenomenon in AI-augmented reasoning, manifesting as reliance on AI for logical consistency checking reduces practice maintaining personal conceptual coherence, observed through decreased internal consistency self-monitoring. Distinguished from adjacent concepts by its focus on the specific mechanism through which coherence manifests in empirically verifiable ways. Observable through systematic changes in reasoning depth, error pattern distribution, and calibration accuracy across repeated AI-assisted decision tasks. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als analytisches Konstrukt der empirischen KI-Forschung (deskriptiv, nicht präskriptiv) erfasst dieser Terminus aspekt von coherence enforcement externalization, der in COG-Kontexten relevant ist und Interaktionen zwischen Menschen und KI-Systemen beeinflusst. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Cognitive Shift", "narrower_terms": [], "cross_domain_refs": [ "VIB-0196" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "COG-0189", "domain": "COG", "term_en": "Exemplar Diversity Reduction", "term_de": "ExemplarDiversityReduction", "definition_en": "A cognitive recalibration pattern in human-AI interaction, characterized by a mental processing effect arising from training examples from AI models exhibit statistical regularities different from lived human variety, observed through skewed understanding of probability distributions. This phenomenon operates at the intersection of exemplar and diversity dynamics within the broader COG domain. Operationally detectable through pre/post cognitive load measurements, decision latency differentials, and metacognitive accuracy scoring.", "definition_de": "Kognitionswissenschaftliches Phänomen in der Mensch-KI-Interaktion, gekennzeichnet durch aspekt von exemplar diversity reduction, der in COG-Kontexten relevant ist und Interaktionen zwischen Menschen und KI-Systemen beeinflusst. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "Cognitive Shift", "narrower_terms": [], "cross_domain_refs": [ "DAT-0015" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "COG-0190", "domain": "COG", "term_en": "Episodic Memory Formation Shift", "term_de": "EpisodicMemoryFormationShift", "definition_en": "A cognitive recalibration pattern in human-AI interaction, characterized by a cognitive interaction dynamic where reduced memory encoding when outcomes provided by AI rather than discovered, observed through weaker episodic trace formation. This phenomenon operates at the intersection of episodic and memory dynamics within the broader COG domain. Operationally detectable through pre/post cognitive load measurements, decision latency differentials, and metacognitive accuracy scoring.", "definition_de": "Kognitionswissenschaftliches Phänomen in der Mensch-KI-Interaktion, gekennzeichnet durch aspekt von episodic memory formation shift, der in COG-Kontexten relevant ist und Interaktionen zwischen Menschen und KI-Systemen beeinflusst. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-2003", "narrower_terms": [], "cross_domain_refs": [ "CON-0073" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q492", "legal_classification": "empirical_phenomenon_label" }, { "id": "COG-0191", "domain": "COG", "term_en": "Meaning Derivation Externalization", "term_de": "MeaningDerivationExternalization", "definition_en": "A cognitive phenomenon manifesting as personal meaning-extraction from information delegated to AI interpretations shifts locus of significance attribution. Observable through measurable shifts in user reasoning patterns and decision latency.", "definition_de": "Kognitionswissenschaftliches Phänomen in der Mensch-KI-Interaktion, gekennzeichnet durch aspekt von meaning derivation externalization, der in COG-Kontexten relevant ist und Interaktionen zwischen Menschen und KI-Systemen beeinflusst. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2701", "narrower_terms": [], "cross_domain_refs": [ "AED-0041", "CRE-0010", "CRE-0013" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "COG-0192", "domain": "COG", "term_en": "Cognitive Constraint Removal Paradox", "term_de": "CognitiveConstraintRemovalParadox", "definition_en": "A cognitive interaction dynamic manifesting as removing cognitive constraints through AI assistance paradoxically reduces cognitive flexibility originally developed through constraint navigation. Observable through measurable shifts in user reasoning patterns and decision latency.", "definition_de": "Kognitionswissenschaftliches Phänomen in der Mensch-KI-Interaktion, gekennzeichnet durch aspekt von cognitive constraint removal paradox, der in COG-Kontexten relevant ist und Interaktionen zwischen Menschen und KI-Systemen beeinflusst. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-3952", "narrower_terms": [], "cross_domain_refs": [ "ELR-0168" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "COG-0193", "domain": "COG", "term_en": "Perspective Adoption Automaticity", "term_de": "PerspectiveAdoptionAutomaticity", "definition_en": "A cognitive phenomenon characterized by neural priming through AI viewpoint presentation co-occurs with automatic perspective adoption without deliberate adoption decision.", "definition_de": "Kognitionswissenschaftliches Phänomen in der Mensch-KI-Interaktion, gekennzeichnet durch aspekt von perspective adoption automaticity, der in COG-Kontexten relevant ist und Interaktionen zwischen Menschen und KI-Systemen beeinflusst. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-3101", "narrower_terms": [], "cross_domain_refs": [ "WRK-0038" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "COG-0194", "domain": "COG", "term_en": "Interpretive Autonomy Attrition", "term_de": "InterpretiveAutonomyAttrition", "definition_en": "A cognitive recalibration pattern in human-AI interaction, characterized by reduced practice in generating personal interpretations when AI alternatives available reduces development of hermeneutic inreliance. This phenomenon operates at the intersection of interpretive and autonomy dynamics within the broader COG domain. Operationally detectable through pre/post cognitive load measurements, decision latency differentials, and metacognitive accuracy scoring.", "definition_de": "Kognitionswissenschaftliches Phänomen in der Mensch-KI-Interaktion, gekennzeichnet durch aspekt von interpretive autonomy attrition, der in COG-Kontexten relevant ist und Interaktionen zwischen Menschen und KI-Systemen beeinflusst. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "Cognitive Shift", "narrower_terms": [], "cross_domain_refs": [ "PHO-0077" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "CON-0001", "domain": "CON", "term_en": "Accessibility Compliance Ignorance", "term_de": "Baumanagement Grundlagen", "definition_en": "As a neutral analytical construct in AI behavior research, this term denotes A conservation science phenomenon in AI-mediated ecological monitoring, characterized by visual and structural design decisions made without attention to standards for color contrast, readability, or assistive technology compatibility, resulting in systematically excluded audiences. The concept emerges specifically in contexts where accessibility–compliance interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "In der AUGMANITAI-Terminologiewissenschaft als rein beschreibendes Phänomen klassifiziert, bezeichnet dieser Begriff kernprinzipien und Grundlagenwissen der/des Baumanagement Grundlagen, einschließlich Umfang, Methoden und professionelle Standards. KI ermöglicht automatisierte Mustererkennung, Wissensmapping und adaptive Lernpfade über Teildisziplinen hinweg. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "AI in Conservation", "narrower_terms": [ "CON-0034", "CON-0048", "CON-0009", "CON-0019", "CON-0024", "CON-0052", "CON-0014", "CON-0081", "CON-0073", "CON-0079", "CON-0059", "CON-0087", "CON-0001", "CON-0026", "CON-0022", "CON-0071", "CON-0033", "CON-0085", "CON-0008", "CON-0029", "CON-0093", "CON-0006", "CON-0007", "CON-0018", "CON-0035", "CON-0011", "CON-0091", "CON-0044", "CON-0054", "CON-0020", "CON-0041", "CON-0040", "CON-0047", "CON-0003", "CON-0058", "CON-0010", "CON-0023", "CON-0075", "CON-0086", "CON-0062", "CON-0067", "CON-0089", "CON-0051", "CON-0080", "CON-0036", "CON-0030", "CON-0095", "CON-0037", "CON-0070", "CON-0050", "CON-0088", "CON-0028", "CON-0090", "CON-0042", "CON-0083", "CON-0043", "CON-0082", "CON-0031", "CON-0046", "CON-0015", "CON-0076", "CON-0069", "CON-0027", "CON-0021", "CON-0063", "CON-0039", "CON-0004", "CON-0053", "CON-0012", "CON-0084", "CON-0060", "CON-0092", "CON-0032", "CON-0002", "CON-0013", "CON-0056", "CON-0072", "CON-0025", "CON-0077", "CON-0074", "CON-0049", "CON-0057", "CON-0017", "CON-0005" ], "cross_domain_refs": [ "PHO-0061", "PHO-0019" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "CON-0002", "domain": "CON", "term_en": "Accuracy Confidence Overstatement", "term_de": "Geschichte der Baumanagement", "definition_en": "An environmental analysis pattern in AI-augmented conservation, measurable through assertions expressed with absolute certainty despite substantive epistemic uncertainty, characteristically arising when AI-generated claims exceed the confidence levels warranted by source data or training distribution. The concept emerges specifically in contexts where accuracy–confidence interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch chronologische Entwicklung und Meilensteine der/des Baumanagement Grundlagen, einschließlich Innovationen, Paradigmenwechsel und einflussreicher Akteure. ML-Modelle analysieren historische Archive und rekonstruieren Wissenslinien. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "AI in Conservation", "narrower_terms": [], "cross_domain_refs": [ "ASE-0060" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "CON-0003", "domain": "CON", "term_en": "Analytics Review Avoidance", "term_de": "Theorie der Baumanagement", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures an environmental analysis pattern in AI-augmented conservation, measurable through engagement metrics and audience response data remain unchecked during content iteration cycles, creating feedback loops where visibility patterns fail to inform subsequent production decisions. This phenomenon operates at the intersection of analytics and review dynamics within the broader CON domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept theoretische Rahmenwerke und konzeptionelle Modelle der/des Baumanagement Grundlagen, die Kausalbeziehungen und Vorhersagestrukturen etablieren. KI durch systematische Beobachtung charakterisiert theoretische Aussagen durch groß angelegte Datenanalyse und computationale Hypothesenprüfung. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "AI in Conservation", "narrower_terms": [], "cross_domain_refs": [ "AGE-0011", "AUG-0408", "BEH-0021" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "CON-0004", "domain": "CON", "term_en": "Archival Strategy Lack", "term_de": "Prinzipien des construction", "definition_en": "An environmental analysis pattern in AI-augmented conservation, measurable through an editorial effect arising from superseded content persists in discoverable locations without deprecation markers, version indicators, or systematic removal, allowing outdated information to compete with current material for audience attention. The concept emerges specifically in contexts where archival–strategy interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch leitregeln und Axiome, die korrekte Praxis in Baumanagement Grundlagen definieren. KI-Systeme kodifizieren diese Prinzipien in Regelmaschinen und ermöglichen automatisierte Konformitätsprüfung und prinzipienbasierte Entscheidungsunterstützung. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Operational Strategy", "narrower_terms": [], "cross_domain_refs": [ "AED-0010", "AGE-0010", "ART-0004" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q185451", "legal_classification": "systematic_classification" }, { "id": "CON-0005", "domain": "CON", "term_en": "Attribution Completeness Gaps", "term_de": "Fachterminologie Baumanagement", "definition_en": "An environmental analysis pattern in AI-augmented conservation, measurable through a content strategy pattern in which content incorporates externally sourced material, frameworks, or ideas without systematic documentation of origin, creating ambiguity about intellectual provenance and precluding proper credit allocation. The concept emerges specifically in contexts where attribution–completeness interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch ein Phänomen der menschlichen Wahrnehmung: Sourced ideas lack proper credit. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Performance Gap", "narrower_terms": [], "cross_domain_refs": [ "ART-0059" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "CON-0006", "domain": "CON", "term_en": "Audience Feedback Ignorance", "term_de": "Klassifikation Baumanagement", "definition_en": "An environmental analysis pattern in AI-augmented conservation, measurable through a content creation phenomenon characterized by direct user responses—comments, questions, disagreements—are processed superficially rather than integrated into understanding of audience comprehension gaps and interpretation divergence. The concept emerges specifically in contexts where audience–feedback interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch systematische Kategorisierung von Entitäten, Methoden und Artefakten in Baumanagement Grundlagen in hierarchische Strukturen. ML-Klassifikatoren automatisieren Sortierung und schlagen neue taxonomische Gruppierungen aus unbeschrifteten Daten vor. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "AI in Conservation", "narrower_terms": [], "cross_domain_refs": [ "AED-0079" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "CON-0007", "domain": "CON", "term_en": "Audience Segmentation Oversimplification", "term_de": "Einführung in construction", "definition_en": "As a neutral analytical construct in AI behavior research, this term denotes A conservation science phenomenon in AI-mediated ecological monitoring, characterized by content strategy reduces to generic categories rather than nuanced audience personas. The concept emerges specifically in contexts where audience–segmentation interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "In der AUGMANITAI-Terminologiewissenschaft als rein beschreibendes Phänomen klassifiziert, bezeichnet dieser Begriff ein Phänomen der menschlichen Wahrnehmung: Content strategy reduces to generic categories rather than nuanced audience personas. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "AI in Conservation", "narrower_terms": [], "cross_domain_refs": [ "COP-0064" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "CON-0008", "domain": "CON", "term_en": "Audience Understanding Reduction", "term_de": "construction-Methodik", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures an environmental analysis pattern in AI-augmented conservation, measurable through an editorial effect where creators stop deeply considering audience needs when AI provides generic messaging. This phenomenon operates at the intersection of audience and understanding dynamics within the broader CON domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept strukturierte Ansätze und Verfahrensrahmen für Arbeiten in Baumanagement Grundlagen. KI optimiert Methodenauswahl durch Ergebnisvorhersage, automatisiert repetitive Verfahrensschritte und benchmarkt methodische Effektivität. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "AI in Conservation", "narrower_terms": [], "cross_domain_refs": [ "COG-0027", "COG-0043", "COG-0046" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "CON-0009", "domain": "CON", "term_en": "Audio Quality Indifference", "term_de": "Philosophie der Baumanagement", "definition_en": "As a neutral analytical construct in AI behavior research, this term denotes A conservation science phenomenon in AI-mediated ecological monitoring, characterized by sound design, codec optimization, and acoustic clarity remain underinvestigated in multimedia content, with creators delegating audio aspects to automation rather than intentional production choices. The concept emerges specifically in contexts where audio–quality interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "In der AUGMANITAI-Terminologiewissenschaft als rein beschreibendes Phänomen klassifiziert, bezeichnet dieser Begriff ein Phänomen der menschlichen Wahrnehmung: Podcast or video audio remains unoptimized. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "AI in Conservation", "narrower_terms": [], "cross_domain_refs": [ "WEB-0007" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "CON-0010", "domain": "CON", "term_en": "Authority Credibility Drift", "term_de": "construction-Taxonomie", "definition_en": "Classified in AUGMANITAI terminology science as a descriptive phenomenon (without normative endorsement), this pattern denotes A conservation science phenomenon in AI-mediated ecological monitoring, characterized by audience perception of creator expertise diminishes when AI assistance is acknowledged, where transparency about generative augmentation is associated with triggering skepticism about knowledge depth and original insight. Distinguished from adjacent concepts by its focus on the specific mechanism through which authority manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als analytisches Konstrukt der empirischen KI-Forschung (deskriptiv, nicht präskriptiv) erfasst dieser Terminus formale Klassifikationshierarchien, die den Wissensraum von Baumanagement Grundlagen in verschachtelte Kategorien organisieren. KI-gestützte Ontologie-Tools automatisieren Taxonomie-Generierung und erkennen Inkonsistenzen. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Systemic Drift", "narrower_terms": [], "cross_domain_refs": [ "MUS-0076", "ROB-0041" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "CON-0011", "domain": "CON", "term_en": "Backlink Quality Decline", "term_de": "Umfang der Baumanagement", "definition_en": "An environmental analysis pattern in AI-augmented conservation, measurable through a content creation phenomenon involving outbound links become less carefully curated as AI suggests connections automatically. Distinguished from adjacent concepts by its focus on the specific mechanism through which backlink manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch grenzdefinition und disziplinäre Reichweite von Baumanagement Grundlagen, die festlegt was innerhalb und außerhalb der Domäne liegt. KI unterstützt durch automatisiertes Topic Modeling und semantische Grenzerkennung. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "AI in Conservation", "narrower_terms": [], "cross_domain_refs": [ "ELR-0047" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "CON-0012", "domain": "CON", "term_en": "Batching Discipline Shift", "term_de": "Literaturübersicht Baumanagement", "definition_en": "Classified in AUGMANITAI terminology science as a descriptive phenomenon (without normative endorsement), this pattern denotes A conservation science phenomenon in AI-mediated ecological monitoring, characterized by content production shifts from scheduled, planned batching cycles toward reactive response-driven creation, reducing strategic coherence and deliberative editorial oversight. Distinguished from adjacent concepts by its focus on the specific mechanism through which batching manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als analytisches Konstrukt der empirischen KI-Forschung (deskriptiv, nicht präskriptiv) erfasst dieser Terminus ein Phänomen der menschlichen Wahrnehmung: Content production becomes reactive instead of strategic. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "AI in Conservation", "narrower_terms": [], "cross_domain_refs": [ "SPR-0082" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "CON-0013", "domain": "CON", "term_en": "Bias Blindness", "term_de": "Schlüsselkonzepte in construction", "definition_en": "Classified in AUGMANITAI terminology science as a descriptive phenomenon (without normative endorsement), this pattern denotes A conservation science phenomenon in AI-mediated ecological monitoring, characterized by an editorial effect in which creators remain unaware of demographic, cultural, or ideological perspectives systematically excluded from content, with AI training data homogeneity reinforcing epistemic blind spots. Distinguished from adjacent concepts by its focus on the specific mechanism through which bias manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als analytisches Konstrukt der empirischen KI-Forschung (deskriptiv, nicht präskriptiv) erfasst dieser Terminus ein Phänomen der menschlichen Wahrnehmung: Creators don't notice perspectives excluded from content. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Cognitive Bias", "narrower_terms": [], "cross_domain_refs": [ "ASE-0006", "ASE-0033", "ASE-0035" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q213523", "legal_classification": "analytical_category" }, { "id": "CON-0014", "domain": "CON", "term_en": "Byline Authority Inflation", "term_de": "Rahmenwerk der Baumanagement", "definition_en": "An environmental analysis pattern in AI-augmented conservation, measurable through a content creation phenomenon involving author credentials and expertise descriptors become amplified through AI-generated biographical enhancements, creating discrepancy between presented authority and actual knowledge scope. Distinguished from adjacent concepts by its focus on the specific mechanism through which byline manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch strukturelles konzeptionelles Gerüst zur Organisation von Wissen und Praxis in Baumanagement Grundlagen. KI ordnet Rahmenwerk-Komponenten Datenflüssen zu und ermöglicht automatisierte Lückenanalyse und Implementierungsübersetzung. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "AI in Conservation", "narrower_terms": [], "cross_domain_refs": [ "ASE-0022", "ASE-0025", "ASE-0088" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "CON-0015", "domain": "CON", "term_en": "Call-to-Action Authenticity Shift", "term_de": "Paradigmen in construction", "definition_en": "As a neutral analytical construct in AI behavior research, this term denotes A conservation science phenomenon in AI-mediated ecological monitoring, characterized by an editorial effect arising from invitations to action feel adjustive rather than genuinely aligned with content. The concept emerges specifically in contexts where call–to interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription. Classification term used in systematic observation, not advocacy.", "definition_de": "In der AUGMANITAI-Terminologiewissenschaft als rein beschreibendes Phänomen klassifiziert, bezeichnet dieser Begriff vorherrschende intellektuelle Modelle und geteilte Annahmen in Baumanagement Grundlagen. KI hinterfragt bestehende Paradigmen durch datengesteuerte Anomalieerkennung und ermöglicht Paradigmenwechsel-Detektion via Zitationsnetzwerkanalyse. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "AI in Conservation", "narrower_terms": [], "cross_domain_refs": [ "SAL-0086" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "CON-0016", "domain": "CON", "term_en": "Caption Accuracy Slippage", "term_de": "construction-Forschungsmethoden", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures an environmental analysis pattern in AI-augmented conservation, measurable through a content strategy pattern manifesting as visual descriptions diverge from observational accuracy when AI tends to generate captions without image verification, where plausible-sounding text replaces close material examination. This phenomenon operates at the intersection of caption and accuracy dynamics within the broader CON domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept ein Phänomen der menschlichen Wahrnehmung: Visual descriptions drift from what images actually show. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "RPH-2051", "narrower_terms": [], "cross_domain_refs": [ "PHO-0001" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "CON-0017", "domain": "CON", "term_en": "Clickbait Threshold Creep", "term_de": "Quantitative construction-Analyse", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures an environmental analysis pattern in AI-augmented conservation, measurable through headlines incrementally shift toward sensationalism and exaggeration when creators accept AI suggestions optimized for engagement metrics rather than substantive truthfulness. This phenomenon operates at the intersection of clickbait and threshold dynamics within the broader CON domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept ein Phänomen der menschlichen Wahrnehmung: Headlines become increasingly sensationalized to match AI suggestions. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Decision Threshold", "narrower_terms": [], "cross_domain_refs": [ "CRE-0018" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "CON-0018", "domain": "CON", "term_en": "Collaboration Authenticity Shift", "term_de": "Qualitative construction-Analyse", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A conservation science phenomenon in AI-mediated ecological monitoring, characterized by a content creation phenomenon characterized by guest contributions and partnership content feel transactional and hollow rather than genuinely collaborative, where AI intermediation reduces interpersonal authenticity. This phenomenon operates at the intersection of collaboration and authenticity dynamics within the broader CON domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription. Descriptive research term, not a prescriptive recommendation.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch interpretative und deskriptive Forschungsansätze in Baumanagement Grundlagen mit Fokus auf Bedeutung und Kontext. NLP und Sentimentanalyse automatisieren thematische Kodierung und Musterextraktion aus qualitativen Daten. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "Analytical Ratio", "narrower_terms": [], "cross_domain_refs": [ "ADA-0012", "AGE-0007", "AGE-0046" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "CON-0019", "domain": "CON", "term_en": "Color Usage Meaninglessness", "term_de": "construction-Messung", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures an environmental analysis pattern in AI-augmented conservation, measurable through an editorial effect in which visual color application becomes decorative rather than intentional, where emphasis hues fail to direct attention or signal information hierarchy. This phenomenon operates at the intersection of color and usage dynamics within the broader CON domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept quantitative und qualitative Metriken zur Bewertung von Ergebnissen und Leistung in Baumanagement Grundlagen. KI ermöglicht Echtzeit-Sensorfusion, automatisierte Messinterpretation und Anomalieerkennung in Messdatenströmen. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "AI in Conservation", "narrower_terms": [], "cross_domain_refs": [ "DES-0033" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "CON-0020", "domain": "CON", "term_en": "Comment Moderation Apathy", "term_de": "Experimentelles construction-Design", "definition_en": "An environmental analysis pattern in AI-augmented conservation, measurable through an editorial effect observed when as content volume increases, comment sections receive less thoughtful curation and engagement. The concept emerges specifically in contexts where comment–moderation interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch kontrollierte Untersuchungsprotokolle in Baumanagement Grundlagen zur Isolierung von Variablen und Prüfung kausaler Hypothesen. KI automatisiert Versuchsplanung, Parameterraum-Exploration und Echtzeit-Ergebnisüberwachung. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Analytical Ratio", "narrower_terms": [], "cross_domain_refs": [ "MUS-0029" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "CON-0021", "domain": "CON", "term_en": "Community Building Reduction", "term_de": "construction-Datenerhebung", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures an environmental analysis pattern in AI-augmented conservation, measurable through a content creation phenomenon characterized by content becomes transactional rather than fostering genuine community connection. This phenomenon operates at the intersection of community and building dynamics within the broader CON domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept ein Phänomen der menschlichen Wahrnehmung: Content becomes transactional rather than fostering genuine community connection. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "AI in Conservation", "narrower_terms": [], "cross_domain_refs": [ "RET-0094" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "CON-0022", "domain": "CON", "term_en": "Conclusion Strength Weakness", "term_de": "Stichprobenziehung in construction", "definition_en": "Classified in AUGMANITAI terminology science as a descriptive phenomenon (without normative endorsement), this pattern denotes A conservation science phenomenon in AI-mediated ecological monitoring, characterized by an editorial effect manifesting as closing statements lack rhetorical force or persuasive grounding, appearing tacked-on rather than emerging naturally from antecedent argumentation. Distinguished from adjacent concepts by its focus on the specific mechanism through which conclusion manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als analytisches Konstrukt der empirischen KI-Forschung (deskriptiv, nicht präskriptiv) erfasst dieser Terminus statistische und zielgerichtete Auswahl repräsentativer Teilmengen für Studien in Baumanagement Grundlagen. KI optimiert Stichprobengrößen, Stratifizierungsstrategien und Bias-Erkennung für valide Ergebnisse. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "AI in Conservation", "narrower_terms": [], "cross_domain_refs": [ "TRA-0075" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "CON-0023", "domain": "CON", "term_en": "Confidentiality Breach Concern", "term_de": "Statistische construction-Analyse", "definition_en": "An environmental analysis pattern in AI-augmented conservation, measurable through a content creation phenomenon observed when proprietary business information, organizational strategies, or confidential details are unnecessarily exposed in content creation, through AI systems trained on undifferentiated data. The concept emerges specifically in contexts where confidentiality–breach interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch ein Phänomen der menschlichen Wahrnehmung: Business or organizational secrets unnecessarily exposed. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "AI in Conservation", "narrower_terms": [], "cross_domain_refs": [ "RPH-1803" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "CON-0024", "domain": "CON", "term_en": "Consistency Checking Avoidance", "term_de": "Feldstudie in construction", "definition_en": "An environmental analysis pattern in AI-augmented conservation, measurable through a content creation phenomenon in which factual details, character descriptions, or narrative elements contradict across content pieces when verification steps are skipped in multi-turn creation workflows. Distinguished from adjacent concepts by its focus on the specific mechanism through which consistency manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch ein Phänomen der menschlichen Wahrnehmung: Factual details contradict across content. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "AI in Conservation", "narrower_terms": [], "cross_domain_refs": [ "FIC-0025" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "CON-0025", "domain": "CON", "term_en": "Content Calendar Looseness", "term_de": "Fallstudie in construction", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A conservation science phenomenon in AI-mediated ecological monitoring, characterized by publication scheduling becomes irregular and ad-hoc, abandoning deliberate cadence in favor of reactive publishing that lacks strategic temporal distribution. This phenomenon operates at the intersection of content and calendar dynamics within the broader CON domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept ein Phänomen der menschlichen Wahrnehmung: Publishing schedule becomes inconsistent. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "AI in Conservation", "narrower_terms": [], "cross_domain_refs": [ "SCR-0034", "FIC-0071" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "CON-0026", "domain": "CON", "term_en": "Content Change Acceptance", "term_de": "Vergleichende construction-Studie", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A conservation science phenomenon in AI-mediated ecological monitoring, characterized by creators stop refreshing outdated content, accepting gradual information change as normal. This phenomenon operates at the intersection of content and change dynamics within the broader CON domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept vergleichende Analyse von Methoden, Ergebnissen oder Artefakten über verschiedene Kontexte in Baumanagement Grundlagen hinweg. KI führt mehrdimensionales Ähnlichkeits-Scoring und automatisiertes Benchmarking durch. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "AI in Conservation", "narrower_terms": [], "cross_domain_refs": [ "AED-0019", "AED-0025", "AED-0067" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "CON-0027", "domain": "CON", "term_en": "Content Outline Reliance Deepening", "term_de": "Längsschnittstudie in construction", "definition_en": "An environmental analysis pattern in AI-augmented conservation, measurable through a content creation phenomenon involving content creators increasingly rely on AI to yield initial outline structures, reducing inreliant planning skills. Distinguished from adjacent concepts by its focus on the specific mechanism through which content manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch forschung, die Baumanagement Grundlagen-Phänomene über längere Zeiträume verfolgt, um Entwicklungsmuster und kausale Zusammenhänge zu identifizieren. KI ermöglicht automatisierte Längsschnitt-Datenerhebung, Schwundvorhersage und Zeitreihen-Anomalieerkennung. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "AI in Conservation", "narrower_terms": [], "cross_domain_refs": [ "REL-0052" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "CON-0028", "domain": "CON", "term_en": "Content Preservation Indifference", "term_de": "construction-Umfragemethode", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A conservation science phenomenon in AI-mediated ecological monitoring, characterized by content disappears or becomes inaccessible when hosting platforms change, shut down, or redistribute content, with creators failing to maintain archival redundancy. This phenomenon operates at the intersection of content and preservation dynamics within the broader CON domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept strukturierte Datenerhebungsmethodik zur Gewinnung quantitativer und qualitativer Erkenntnisse in Baumanagement Grundlagen. KI verbessert Survey-Design durch adaptive Frageführung, Antwortvalidierung und Echtzeit-Sentimentanalyse. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "AI in Conservation", "narrower_terms": [], "cross_domain_refs": [ "TEM-0021" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "CON-0029", "domain": "CON", "term_en": "Content Syndication Overextension", "term_de": "Aktionsforschung in construction", "definition_en": "Classified in AUGMANITAI terminology science as a descriptive phenomenon (without normative endorsement), this pattern denotes A conservation science phenomenon in AI-mediated ecological monitoring, characterized by a content creation phenomenon where identical content republished across excessive platforms dilutes value propositions and exclusive positioning, fragmenting audience attention and reducing singular platform strength. Distinguished from adjacent concepts by its focus on the specific mechanism through which content manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als analytisches Konstrukt der empirischen KI-Forschung (deskriptiv, nicht präskriptiv) erfasst dieser Terminus iterative Forschungsmethodik, die Untersuchung mit praxisbasierter Intervention in Baumanagement Grundlagen verbindet. KI unterstützt Zyklusoptimierung durch automatisiertes Outcome-Tracking, Mustererkennung bei Interventionseffekten und adaptive Empfehlung. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "AI in Conservation", "narrower_terms": [], "cross_domain_refs": [ "COP-0094" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "CON-0030", "domain": "CON", "term_en": "Content Upgrade Opportunities Missed", "term_de": "Mixed Methods in construction", "definition_en": "An environmental analysis pattern in AI-augmented conservation, measurable through previously published content remains static despite discovery of improvements, missing opportunities to refine, expand, or correct material that retains traffic. Distinguished from adjacent concepts by its focus on the specific mechanism through which content manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch ein Phänomen der menschlichen Wahrnehmung: No systematic improvement of previous content. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "AI in Conservation", "narrower_terms": [], "cross_domain_refs": [ "BEH-0040" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "CON-0031", "domain": "CON", "term_en": "Counterargument Avoidance", "term_de": "construction-Technologie", "definition_en": "Classified in AUGMANITAI terminology science as a descriptive phenomenon (without normative endorsement), this pattern denotes A conservation science phenomenon in AI-mediated ecological monitoring, characterized by a content strategy pattern in which nuanced, opposing viewpoints disappear from content when AI-framed narratives are accepted uncritically, reducing intellectual texture and balanced perspective. Distinguished from adjacent concepts by its focus on the specific mechanism through which counterargument manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als analytisches Konstrukt der empirischen KI-Forschung (deskriptiv, nicht präskriptiv) erfasst dieser Terminus ein Phänomen der menschlichen Wahrnehmung: Nuance diminishes when creators accept AI's one-sided framing. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "AI in Conservation", "narrower_terms": [], "cross_domain_refs": [ "REL-0052" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "CON-0032", "domain": "CON", "term_en": "Cross-Promotion Relevance Drift", "term_de": "Digitale construction-Werkzeuge", "definition_en": "An environmental analysis pattern in AI-augmented conservation, measurable through a content creation phenomenon characterized by recommendations linking to related content become tangential or weakly connected, where algorithmic suggestions replace deliberate editorial connections. The concept emerges specifically in contexts where cross–promotion interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch ein Phänomen der menschlichen Wahrnehmung: Linked recommendations feel disconnected. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Systemic Drift", "narrower_terms": [], "cross_domain_refs": [ "SCR-0006" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "CON-0033", "domain": "CON", "term_en": "Data Visualization Decline", "term_de": "construction-Software", "definition_en": "An environmental analysis pattern in AI-augmented conservation, measurable through a content strategy pattern arising from raw numerical data remains in table or prose form when creators forego translation into meaningful visual representations, reducing information accessibility. Distinguished from adjacent concepts by its focus on the specific mechanism through which data manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch ein Phänomen der menschlichen Wahrnehmung: Creators stop translating data into meaningful visual formats. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "AI in Conservation", "narrower_terms": [], "cross_domain_refs": [ "ART-0017", "ART-0032", "ART-0037" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q42848", "legal_classification": "systematic_classification" }, { "id": "CON-0034", "domain": "CON", "term_en": "Discoverability Limitations", "term_de": "Automatisierung in construction", "definition_en": "Classified in AUGMANITAI terminology science as a descriptive phenomenon (without normative endorsement), this pattern denotes A conservation science phenomenon in AI-mediated ecological monitoring, characterized by related content pieces lack systematic internal linking architecture, forcing audiences to use search functions rather than serendipitous discovery through navigation. Distinguished from adjacent concepts by its focus on the specific mechanism through which discoverability manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als analytisches Konstrukt der empirischen KI-Forschung (deskriptiv, nicht präskriptiv) erfasst dieser Terminus ein Phänomen der menschlichen Wahrnehmung: Related content isn't connected. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "AI in Conservation", "narrower_terms": [], "cross_domain_refs": [ "AGE-0008", "VIB-0077", "VIB-0087" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "CON-0035", "domain": "CON", "term_en": "Domain Authority Plateau", "term_de": "IoT in construction", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures an environmental analysis pattern in AI-augmented conservation, measurable through as content quality becomes homogenized across creators, competitive differentiation erodes. This phenomenon operates at the intersection of domain and authority dynamics within the broader CON domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept netzwerk verbundener Sensoren und Geräte zur Echtzeit-Datenerfassung in Baumanagement Grundlagen. KI verarbeitet Sensorströme für vorausschauende Wartung, Umgebungsüberwachung und autonome Prozesssteuerung. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "AI in Conservation", "narrower_terms": [], "cross_domain_refs": [ "SAL-0057" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "CON-0036", "domain": "CON", "term_en": "Editing Thoroughness Decline", "term_de": "Datenanalyse in construction", "definition_en": "As a neutral analytical construct in AI behavior research, this term denotes A conservation science phenomenon in AI-mediated ecological monitoring, characterized by an editorial effect where content ships with elevated frequencies of typographical errors, grammatical problems, and formatting inconsistencies when review cycles are compressed or delegated. The concept emerges specifically in contexts where editing–thoroughness interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "In der AUGMANITAI-Terminologiewissenschaft als rein beschreibendes Phänomen klassifiziert, bezeichnet dieser Begriff systematische Extraktion von Erkenntnissen aus strukturierten und unstrukturierten Daten in Baumanagement Grundlagen. KI erweitert die Analyse durch automatisierte Mustererkennung, prädiktive Modellierung und Echtzeit-Dashboard-Generierung. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "AI in Conservation", "narrower_terms": [], "cross_domain_refs": [ "TRA-0035" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "CON-0037", "domain": "CON", "term_en": "Email List Fatigue Normalcy", "term_de": "KI-Anwendungen in construction", "definition_en": "Classified in AUGMANITAI terminology science as a descriptive phenomenon (without normative endorsement), this pattern denotes A conservation science phenomenon in AI-mediated ecological monitoring, characterized by subscribers receive increasingly frequent automated sends without quality gates. Distinguished from adjacent concepts by its focus on the specific mechanism through which email manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als analytisches Konstrukt der empirischen KI-Forschung (deskriptiv, nicht präskriptiv) erfasst dieser Terminus strategische ai-Praxis im construction management, verstärkt durch KI mittels automatisierter Optimierung, prädiktiven Modellen und Echtzeit-Analytik für bessere Betriebsergebnisse. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "AI in Conservation", "narrower_terms": [], "cross_domain_refs": [ "CUS-0092", "RHR-0143" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "CON-0038", "domain": "CON", "term_en": "Engagement Metric Obsession", "term_de": "Maschinelles Lernen in construction", "definition_en": "An environmental analysis pattern in AI-augmented conservation, measurable through content optimization prioritizes vanity metrics—clicks, views, shares—over substantive audience value or learning retention, inverting quality hierarchy. Distinguished from adjacent concepts by its focus on the specific mechanism through which engagement manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch ein Phänomen der menschlichen Wahrnehmung: Content optimized for clicks rather than value. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2154", "narrower_terms": [], "cross_domain_refs": [ "SOM-0001" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "CON-0039", "domain": "CON", "term_en": "Evidence Quality Decline", "term_de": "Sensorik in construction", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures an environmental analysis pattern in AI-augmented conservation, measurable through an editorial effect observed when claims cite sources without critical evaluation of evidentiary strength, creating appearance of rigor while masking underlying methodological or relevance problems. This phenomenon operates at the intersection of evidence and quality dynamics within the broader CON domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept ein Phänomen der menschlichen Wahrnehmung: Content cites sources without critically evaluating claim strength. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "AI in Conservation", "narrower_terms": [], "cross_domain_refs": [ "TEW-0071" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "CON-0040", "domain": "CON", "term_en": "Example Specificity Reduction", "term_de": "Mobile Anwendungen in construction", "definition_en": "An environmental analysis pattern in AI-augmented conservation, measurable through a content creation phenomenon characterized by concrete, illustrative examples become generic or templateized when AI fills content gaps, replacing particular instances with generalizable but less memorable illustrations. Distinguished from adjacent concepts by its focus on the specific mechanism through which example manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch strategische mobile-Praxis im construction management, verstärkt durch KI mittels automatisierter Optimierung, prädiktiven Modellen und Echtzeit-Analytik für bessere Betriebsergebnisse. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "AI in Conservation", "narrower_terms": [], "cross_domain_refs": [ "SCR-0058" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "CON-0041", "domain": "CON", "term_en": "Expert Status Inflation", "term_de": "Cloud-Lösungen für construction", "definition_en": "Classified in AUGMANITAI terminology science as a descriptive phenomenon (without normative endorsement), this pattern denotes A conservation science phenomenon in AI-mediated ecological monitoring, characterized by creators present themselves as expertise-holders without demonstrated earned authority, experience, or domain credentials, leveraging AI fluency as false proxy for knowledge. Distinguished from adjacent concepts by its focus on the specific mechanism through which expert manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als analytisches Konstrukt der empirischen KI-Forschung (deskriptiv, nicht präskriptiv) erfasst dieser Terminus strategische cloud-Praxis im construction management, verstärkt durch KI mittels automatisierter Optimierung, prädiktiven Modellen und Echtzeit-Analytik für bessere Betriebsergebnisse. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "AI in Conservation", "narrower_terms": [], "cross_domain_refs": [ "COG-0127", "SCR-0043" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "CON-0042", "domain": "CON", "term_en": "External Link Relevance Drift", "term_de": "Datenbankverwaltung in construction", "definition_en": "As a neutral analytical construct in AI behavior research, this term denotes A conservation science phenomenon in AI-mediated ecological monitoring, characterized by a content strategy pattern arising from cited sources become progressively tangential to core claims when link recommendations derive from weak topical correlation rather than substantive argument support. The concept emerges specifically in contexts where external–link interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "In der AUGMANITAI-Terminologiewissenschaft als rein beschreibendes Phänomen klassifiziert, bezeichnet dieser Begriff ein Phänomen der menschlichen Wahrnehmung: Cited sources become less directly related to claims. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Systemic Drift", "narrower_terms": [], "cross_domain_refs": [ "ELR-0149" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "CON-0043", "domain": "CON", "term_en": "Format Template Overuse", "term_de": "Visualisierung in construction", "definition_en": "An environmental analysis pattern in AI-augmented conservation, measurable through uniform structural templates applied across all content eliminate structural variety, creating monotonous visual and cognitive experience despite topic variation. Distinguished from adjacent concepts by its focus on the specific mechanism through which format manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch visuelle Darstellung komplexer Informationen und Datensätze in Baumanagement Grundlagen. KI automatisiert Diagrammauswahl, Anomalie-Hervorhebung, interaktive Exploration und Narrativgenerierung aus Datenmustern. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "AI in Conservation", "narrower_terms": [], "cross_domain_refs": [ "SAL-0089", "MUS-0081" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "CON-0044", "domain": "CON", "term_en": "Hierarchy Visual Weakness", "term_de": "Simulation in construction", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures an environmental analysis pattern in AI-augmented conservation, measurable through an editorial effect in which information importance levels lack observable signaling through typography, size, color, or spatial prominence, leaving visual hierarchy indeterminate. This phenomenon operates at the intersection of hierarchy and visual dynamics within the broader CON domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept computationale Modellierung realer Szenarien in Baumanagement Grundlagen zur Ergebnisvorhersage ohne physische Prototypen. KI verbessert Simulationen durch physik-informierte neuronale Netze und digitale Zwillinge. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "AI in Conservation", "narrower_terms": [], "cross_domain_refs": [ "REL-0123" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "CON-0045", "domain": "CON", "term_en": "Hook Authenticity Shift", "term_de": "Digitaler Zwilling in construction", "definition_en": "An environmental analysis pattern in AI-augmented conservation, measurable through a content strategy pattern where opening lines feel formulaic rather than genuinely compelling when generated by AI. The concept emerges specifically in contexts where hook–authenticity interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters. Descriptive research term, not a prescriptive recommendation.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch digitale Transformationsstrategien und computationale Werkzeuge in Baumanagement Grundlagen. Umfasst Datendigitalisierung, Cloud-Workflows, IoT-Integration und KI-gesteuerte Analytik als Ersatz für analoge Prozesse. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-1302", "narrower_terms": [], "cross_domain_refs": [ "ADA-0012", "AGE-0007", "AGE-0046" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "CON-0046", "domain": "CON", "term_en": "Image Alt Text Generic Feel", "term_de": "construction-Best-Practices", "definition_en": "Classified in AUGMANITAI terminology science as a descriptive phenomenon (without normative endorsement), this pattern denotes A conservation science phenomenon in AI-mediated ecological monitoring, characterized by an editorial effect reflecting accessibility descriptions provide minimal information value, becoming rote placeholder text rather than meaningful prose that describes image content for non-visual access. Distinguished from adjacent concepts by its focus on the specific mechanism through which image manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als analytisches Konstrukt der empirischen KI-Forschung (deskriptiv, nicht präskriptiv) erfasst dieser Terminus bewährte Methoden und Arbeitsabläufe für optimale Ergebnisse in Baumanagement Grundlagen. KI benchmarkt Praktiken gegen Ergebnisdaten, identifiziert Hochleistungsmuster und empfiehlt kontextspezifische Verbesserungen. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "AI in Conservation", "narrower_terms": [], "cross_domain_refs": [ "SPR-0190" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "CON-0047", "domain": "CON", "term_en": "Inclusivity Tokenism", "term_de": "Professionelle construction-Praxis", "definition_en": "An environmental analysis pattern in AI-augmented conservation, measurable through diversity representation serves symbolic rather than substantive function, appearing as surface-level gesture without genuine integration into narrative or epistemological framework. The concept emerges specifically in contexts where inclusivity–tokenism interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch ein Phänomen der menschlichen Wahrnehmung: Diversity representation feels surface-level. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "AI in Conservation", "narrower_terms": [], "cross_domain_refs": [ "COG-0029" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "CON-0048", "domain": "CON", "term_en": "Infographic Clarity Reduction", "term_de": "construction-Arbeitsablaufgestaltung", "definition_en": "As a neutral analytical construct in AI behavior research, this term denotes A conservation science phenomenon in AI-mediated ecological monitoring, characterized by an editorial effect in which visual explanations become cluttered, poorly sequenced, or excessively stylized when aesthetic appeal takes priority over information clarity and cognitive usability. The concept emerges specifically in contexts where infographic–clarity interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "In der AUGMANITAI-Terminologiewissenschaft als rein beschreibendes Phänomen klassifiziert, bezeichnet dieser Begriff strategische construction-Planung verbindet kreative Vision mit technischer Ausführung. KI beschleunigt durch Szenariomodellierung, Optimierung und intelligente Empfehlungssysteme für Lösungen. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "AI in Conservation", "narrower_terms": [], "cross_domain_refs": [ "MTH-0089" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "CON-0049", "domain": "CON", "term_en": "Internal Linking Randomness", "term_de": "construction-Projektmanagement", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A conservation science phenomenon in AI-mediated ecological monitoring, characterized by connections between content pieces appear arbitrary and weakly motivated when linking emerges from suggestion algorithms rather than deliberate editorial pathway design. This phenomenon operates at the intersection of internal and linking dynamics within the broader CON domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept ein Phänomen der menschlichen Wahrnehmung: Connections between content pieces feel arbitrary. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "AI in Conservation", "narrower_terms": [], "cross_domain_refs": [ "SCR-0029" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "CON-0050", "domain": "CON", "term_en": "Iteration Willingness Decline", "term_de": "construction-Teamzusammenarbeit", "definition_en": "Classified in AUGMANITAI terminology science as a descriptive phenomenon (without normative endorsement), this pattern denotes A conservation science phenomenon in AI-mediated ecological monitoring, characterized by a content strategy pattern characterized by published content rarely undergoes systematic revision despite performance data suggesting improvement opportunities, reflecting reduced investment in lifecycle management. Distinguished from adjacent concepts by its focus on the specific mechanism through which iteration manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als analytisches Konstrukt der empirischen KI-Forschung (deskriptiv, nicht präskriptiv) erfasst dieser Terminus ein Phänomen der menschlichen Wahrnehmung: Content rarely gets updated based on performance data. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Analytical Ratio", "narrower_terms": [], "cross_domain_refs": [ "AUG-0976", "BEH-0052", "CRE-0023" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "CON-0051", "domain": "CON", "term_en": "Jargon Accessibility Gap", "term_de": "Kundenbeziehungen in construction", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures an environmental analysis pattern in AI-augmented conservation, measurable through an editorial effect where technical terminology appears without adequate contextual explanation, presupposing audience familiarity with discipline-specific language and excluding general readers. This phenomenon operates at the intersection of jargon and accessibility dynamics within the broader CON domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept strategische client-Praxis im construction management, verstärkt durch KI mittels automatisierter Optimierung, prädiktiven Modellen und Echtzeit-Analytik für bessere Betriebsergebnisse. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Performance Gap", "narrower_terms": [], "cross_domain_refs": [ "TEW-0091" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "CON-0052", "domain": "CON", "term_en": "Keyword Stuffing Normalization", "term_de": "construction-Kommunikation", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures an environmental analysis pattern in AI-augmented conservation, measurable through an editorial effect observed when search engine optimization priorities override natural language rhythm and readability, with keyword frequency taking precedence over idiomatic expression. This phenomenon operates at the intersection of keyword and stuffing dynamics within the broader CON domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept informationsaustauschprotokolle und Stakeholder-Interaktion in Baumanagement Grundlagen. NLP ermöglicht automatisierte Berichtserstellung, mehrsprachige Übersetzung und kontextbewusste Kommunikationsoptimierung. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "AI in Conservation", "narrower_terms": [], "cross_domain_refs": [ "CUS-0099", "CUS-0096" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "CON-0053", "domain": "CON", "term_en": "Legacy Content Accessibility Shift", "term_de": "Problemlösung in construction", "definition_en": "An environmental analysis pattern in AI-augmented conservation, measurable through a content strategy pattern reflecting older published material becomes increasingly difficult to locate through navigation or search, dropping from discoverability as new content accumulates. Distinguished from adjacent concepts by its focus on the specific mechanism through which legacy manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch strategische problem-solving-Praxis im construction management, verstärkt durch KI mittels automatisierter Optimierung, prädiktiven Modellen und Echtzeit-Analytik für bessere Betriebsergebnisse. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "AI in Conservation", "narrower_terms": [], "cross_domain_refs": [ "SOM-0054" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "CON-0054", "domain": "CON", "term_en": "Length Appropriateness Shift", "term_de": "Entscheidungsfindung in construction", "definition_en": "An environmental analysis pattern in AI-augmented conservation, measurable through an editorial effect manifesting as content systematically errs toward either excessive verbosity with repetitive filler or insufficient detail lacking adequate development and support. Distinguished from adjacent concepts by its focus on the specific mechanism through which length manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch ein Phänomen der menschlichen Wahrnehmung: Content becomes either too verbose or insufficiently detailed. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "AI in Conservation", "narrower_terms": [], "cross_domain_refs": [ "SCR-0047" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "CON-0055", "domain": "CON", "term_en": "List Formatting Overuse", "term_de": "Zeitmanagement in construction", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures an environmental analysis pattern in AI-augmented conservation, measurable through a content strategy pattern where bullet point lists replace narrative prose explanations, reducing cognitive engagement and losing connective argumentation that builds understanding. This phenomenon operates at the intersection of list and formatting dynamics within the broader CON domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept ein Phänomen der menschlichen Wahrnehmung: Bullet points replace narrative explanation. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "RPH-3101", "narrower_terms": [], "cross_domain_refs": [ "TEM-0019" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "CON-0056", "domain": "CON", "term_en": "Loading Speed Indifference", "term_de": "Ressourcenplanung in construction", "definition_en": "Classified in AUGMANITAI terminology science as a descriptive phenomenon (without normative endorsement), this pattern denotes A conservation science phenomenon in AI-mediated ecological monitoring, characterized by large unoptimized image files and heavy asset loads slow page rendering, degrading user experience particularly for audiences with limited bandwidth. Distinguished from adjacent concepts by its focus on the specific mechanism through which loading manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als analytisches Konstrukt der empirischen KI-Forschung (deskriptiv, nicht präskriptiv) erfasst dieser Terminus ein Phänomen der menschlichen Wahrnehmung: Large unoptimized images slow page speed. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "AI in Conservation", "narrower_terms": [], "cross_domain_refs": [ "RHR-0163" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "CON-0057", "domain": "CON", "term_en": "Meta Description Irrelevance", "term_de": "construction-Dokumentation", "definition_en": "An environmental analysis pattern in AI-augmented conservation, measurable through a content strategy pattern manifesting as search result preview text misrepresents actual content, either overstating importance or failing to convey substantive value proposition to potential readers. The concept emerges specifically in contexts where meta–description interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch systematische Erfassung und Archivierung von Wissen und Verfahren in Baumanagement Grundlagen. KI automatisiert Dokumentation durch Speech-to-Text, strukturierte Datenextraktion und intelligente Suchindexierung. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "AI in Conservation", "narrower_terms": [], "cross_domain_refs": [ "ASE-0067" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "CON-0058", "domain": "CON", "term_en": "Metaphor Overuse", "term_de": "Berichtswesen in construction", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures an environmental analysis pattern in AI-augmented conservation, measurable through original analogical thinking diminishes, replaced by stock metaphors and clichéd comparisons that provide surface-level explanation without insight. This phenomenon operates at the intersection of metaphor and overuse dynamics within the broader CON domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept ein Phänomen der menschlichen Wahrnehmung: Stock comparisons replace original analogies. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "AI in Conservation", "narrower_terms": [], "cross_domain_refs": [ "COG-0162" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q4263830", "legal_classification": "descriptive_research_term" }, { "id": "CON-0059", "domain": "CON", "term_en": "Mobile Responsiveness Indifference", "term_de": "construction-Präsentationsfähigkeiten", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures an environmental analysis pattern in AI-augmented conservation, measurable through content display degrades on smaller screens, with responsive design receiving insufficient investment, marginalizing mobile-first audiences. This phenomenon operates at the intersection of mobile and responsiveness dynamics within the broader CON domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept ein Phänomen der menschlichen Wahrnehmung: Content displays poorly on smaller screens. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "AI in Conservation", "narrower_terms": [], "cross_domain_refs": [ "ASE-0040", "ELR-0082", "GAM-0042" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "CON-0060", "domain": "CON", "term_en": "Narrative Arc Flattening", "term_de": "Netzwerken in construction", "definition_en": "As a neutral analytical construct in AI behavior research, this term denotes A conservation science phenomenon in AI-mediated ecological monitoring, characterized by an editorial effect involving multi-part content lacks building tension, climactic payoff, or satisfying resolution, appearing episodic rather than strategically architected. The concept emerges specifically in contexts where narrative–arc interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "In der AUGMANITAI-Terminologiewissenschaft als rein beschreibendes Phänomen klassifiziert, bezeichnet dieser Begriff ein Phänomen der menschlichen Wahrnehmung: Multi-part content lacks building tension and payoff structure. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "AI in Conservation", "narrower_terms": [], "cross_domain_refs": [ "SCR-0039" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q131324", "legal_classification": "descriptive_research_term" }, { "id": "CON-0061", "domain": "CON", "term_en": "Niche Specificity Shift", "term_de": "construction-Qualitätssicherung", "definition_en": "An environmental analysis pattern in AI-augmented conservation, measurable through an editorial effect characterized by content that starts specialized gradually becomes more general to match AI training patterns. The concept emerges specifically in contexts where niche–specificity interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch standards und Sicherungsprozesse zur Gewährleistung von Exzellenz in Baumanagement Grundlagen. KI ermöglicht automatisierte Qualitätsprüfung durch Computer Vision, statistische Prozesskontrolle und Defektvorhersage. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2301", "narrower_terms": [], "cross_domain_refs": [ "SPR-0059" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "CON-0062", "domain": "CON", "term_en": "Originality Claim Overstatement", "term_de": "construction-Normen", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A conservation science phenomenon in AI-mediated ecological monitoring, characterized by a content creation phenomenon manifesting as presentation suggests foundational novelty that does not exist, where incremental synthesis or recombination is marketed as innovation. This phenomenon operates at the intersection of originality and claim dynamics within the broader CON domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept ein Phänomen der menschlichen Wahrnehmung: Presentation suggests novelty that doesn't exist. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "AI in Conservation", "narrower_terms": [], "cross_domain_refs": [ "WEB-0009" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "CON-0063", "domain": "CON", "term_en": "Paragraph Length Inconsistency", "term_de": "ISO-Normen in construction", "definition_en": "Classified in AUGMANITAI terminology science as a descriptive phenomenon (without normative endorsement), this pattern denotes A conservation science phenomenon in AI-mediated ecological monitoring, characterized by an editorial effect observed when visual text blocks become monotonously uniform in length or chaotically varied without strategic pacing, reducing readability and visual rhythm. Distinguished from adjacent concepts by its focus on the specific mechanism through which paragraph manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als analytisches Konstrukt der empirischen KI-Forschung (deskriptiv, nicht präskriptiv) erfasst dieser Terminus ein Phänomen der menschlichen Wahrnehmung: Visual text blocks become monotonously uniform or chaotically varied. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "AI in Conservation", "narrower_terms": [], "cross_domain_refs": [ "ASE-0018", "ASE-0075", "ASE-0078" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "CON-0064", "domain": "CON", "term_en": "Personalization Authenticity Shift", "term_de": "construction-Zertifizierung", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A conservation science phenomenon in AI-mediated ecological monitoring, characterized by a content creation phenomenon observed when templated personalization with name insertion or audience segment variables feels less genuine than individually crafted messaging that demonstrates specific knowledge. This phenomenon operates at the intersection of personalization and authenticity dynamics within the broader CON domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept ein Phänomen der menschlichen Wahrnehmung: Template personalization feels less genuine than individually crafted messages. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "RPH-2603", "narrower_terms": [], "cross_domain_refs": [ "MUS-0081" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "CON-0065", "domain": "CON", "term_en": "Pillar Content Absence", "term_de": "Audit in construction", "definition_en": "As a neutral analytical construct in AI behavior research, this term denotes A conservation science phenomenon in AI-mediated ecological monitoring, characterized by an editorial effect in which core foundational content pieces that establish authority and serve as reference anchors lack depth, comprehensiveness, or original perspective. The concept emerges specifically in contexts where pillar–content interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "In der AUGMANITAI-Terminologiewissenschaft als rein beschreibendes Phänomen klassifiziert, bezeichnet dieser Begriff ein Phänomen der menschlichen Wahrnehmung: Core foundational pieces lack depth and authority. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "RPH-2005", "narrower_terms": [], "cross_domain_refs": [ "AED-0019", "ASE-0002", "ASE-0027" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "CON-0066", "domain": "CON", "term_en": "Plagiarism Proximity Creeping", "term_de": "construction-Benchmarking", "definition_en": "An environmental analysis pattern in AI-augmented conservation, measurable through an editorial effect characterized by paraphrasing drifts uncomfortably close to source material phrasing and structure, existing in ambiguous territory between legitimate synthesis and improper attribution. The concept emerges specifically in contexts where plagiarism–proximity interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch standardisierte Leistungsbewertungsmethoden und Referenzpunkte in Baumanagement Grundlagen. KI automatisiert Benchmark-Ausführung, Vergleichsanalyse, Regressionserkennung und Leistungstrendvorhersage. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2051", "narrower_terms": [], "cross_domain_refs": [ "MUS-0069" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "CON-0067", "domain": "CON", "term_en": "Privacy Consideration Absence", "term_de": "Leistungskennzahlen in construction", "definition_en": "An environmental analysis pattern in AI-augmented conservation, measurable through a content strategy pattern involving content shares personal details, identifying information, or private circumstances without documented subject consent, creating ethical breach. Distinguished from adjacent concepts by its focus on the specific mechanism through which privacy manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch messbare Ausgabequalität und Effizienzmetriken in Baumanagement Grundlagen. KI verfolgt Leistung durch Echtzeit-Dashboards, prädiktive Leistungsmodellierung und automatisierte Ursachenanalyse bei Leistungsabfall. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Analytical Ratio", "narrower_terms": [], "cross_domain_refs": [ "MKT-0100" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q18619", "legal_classification": "observational_construct" }, { "id": "CON-0068", "domain": "CON", "term_en": "Proofreading Reliance Shift", "term_de": "Kontinuierliche Verbesserung in construction", "definition_en": "Classified in AUGMANITAI terminology science as a descriptive phenomenon (without normative endorsement), this pattern denotes A conservation science phenomenon in AI-mediated ecological monitoring, characterized by a content strategy pattern manifesting as creators increasingly rely on automated spelling and grammar checkers rather than engaged careful review, missing context-reliant errors and stylistic improvements. Distinguished from adjacent concepts by its focus on the specific mechanism through which proofreading manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als analytisches Konstrukt der empirischen KI-Forschung (deskriptiv, nicht präskriptiv) erfasst dieser Terminus ein Phänomen der menschlichen Wahrnehmung: Creators trust AI spell-check instead of careful review. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "RPH-2505", "narrower_terms": [], "cross_domain_refs": [ "AGE-0050" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "CON-0069", "domain": "CON", "term_en": "Question Usage Underutilization", "term_de": "construction-Inspektion", "definition_en": "Classified in AUGMANITAI terminology science as a descriptive phenomenon (without normative endorsement), this pattern denotes A conservation science phenomenon in AI-mediated ecological monitoring, characterized by a content strategy pattern involving rhetorical questions and audience-directed inquiries disappear from content engagement toolkit, losing opportunity for cognitive activation and reflection. Distinguished from adjacent concepts by its focus on the specific mechanism through which question manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als analytisches Konstrukt der empirischen KI-Forschung (deskriptiv, nicht präskriptiv) erfasst dieser Terminus ein Phänomen der menschlichen Wahrnehmung: Rhetorical questions disappear from engagement toolkit. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "AI in Conservation", "narrower_terms": [], "cross_domain_refs": [ "COP-0069" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "CON-0070", "domain": "CON", "term_en": "ROI Calculation Avoidance", "term_de": "Prüfung in construction", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A conservation science phenomenon in AI-mediated ecological monitoring, characterized by content performance impact against business or organizational objectives remains unmeasured, with creators unable to demonstrate strategic value contribution. This phenomenon operates at the intersection of roi and calculation dynamics within the broader CON domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept ein Phänomen der menschlichen Wahrnehmung: Content performance impact rarely gets measured. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "AI in Conservation", "narrower_terms": [], "cross_domain_refs": [ "CRE-0036" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "CON-0071", "domain": "CON", "term_en": "Recommendation Quality Decline", "term_de": "Kalibrierung in construction", "definition_en": "As a neutral analytical construct in AI behavior research, this term denotes A conservation science phenomenon in AI-mediated ecological monitoring, characterized by a content creation phenomenon involving next-content suggestions appear random or poorly motivated, failing to guide audiences toward topically coherent or strategically valuable follow-on material. The concept emerges specifically in contexts where recommendation–quality interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "In der AUGMANITAI-Terminologiewissenschaft als rein beschreibendes Phänomen klassifiziert, bezeichnet dieser Begriff strategische calibration-Praxis im construction management, verstärkt durch KI mittels automatisierter Optimierung, prädiktiven Modellen und Echtzeit-Analytik für bessere Betriebsergebnisse. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "AI in Conservation", "narrower_terms": [], "cross_domain_refs": [ "ART-0085", "ART-0086", "AUG-0976" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "CON-0072", "domain": "CON", "term_en": "Repurposing Authenticity Shift", "term_de": "Fehlervermeidung in construction", "definition_en": "An environmental analysis pattern in AI-augmented conservation, measurable through a content strategy pattern manifesting as content recycled across different platforms feels off-brand or contextually misaligned, losing authentic voice when format constraints force reformatting. Distinguished from adjacent concepts by its focus on the specific mechanism through which repurposing manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch strategische error-Praxis im construction management, verstärkt durch KI mittels automatisierter Optimierung, prädiktiven Modellen und Echtzeit-Analytik für bessere Betriebsergebnisse. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "AI in Conservation", "narrower_terms": [], "cross_domain_refs": [ "COP-0004" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "CON-0073", "domain": "CON", "term_en": "Retraction Resistance", "term_de": "Fehleranalyse in construction", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A conservation science phenomenon in AI-mediated ecological monitoring, characterized by an editorial effect where discovered errors persist uncorrected in published content, reflecting friction in correction workflows or reluctance to acknowledge substantive mistakes. This phenomenon operates at the intersection of retraction and resistance dynamics within the broader CON domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept ein Phänomen der menschlichen Wahrnehmung: Errors go uncorrected when discovered. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "AI in Conservation", "narrower_terms": [], "cross_domain_refs": [ "VIB-0061", "COG-0190" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "CON-0074", "domain": "CON", "term_en": "Seasonal Relevance Misalignment", "term_de": "Prozesskontrolle in construction", "definition_en": "As a neutral analytical construct in AI behavior research, this term denotes A conservation science phenomenon in AI-mediated ecological monitoring, characterized by an editorial effect manifesting as timely, culturally resonant content is published at suboptimal moments—too early, too late, or completely divorced from relevant contexts and conversations. The concept emerges specifically in contexts where seasonal–relevance interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "In der AUGMANITAI-Terminologiewissenschaft als rein beschreibendes Phänomen klassifiziert, bezeichnet dieser Begriff ein Phänomen der menschlichen Wahrnehmung: Timely content published too early or too late. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "AI in Conservation", "narrower_terms": [], "cross_domain_refs": [ "FIC-0084" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "CON-0075", "domain": "CON", "term_en": "Sentence Variety Decline", "term_de": "construction-Compliance", "definition_en": "An environmental analysis pattern in AI-augmented conservation, measurable through monotonous sentence length and structure reduce cognitive engagement, with uniform rhythmic patterns replacing dynamic variation that sustains attention. Distinguished from adjacent concepts by its focus on the specific mechanism through which sentence manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch ein Phänomen der menschlichen Wahrnehmung: All sentences follow similar length and structure. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "AI in Conservation", "narrower_terms": [], "cross_domain_refs": [ "TRA-0083" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "CON-0076", "domain": "CON", "term_en": "Signature Voice Dissolution", "term_de": "construction-Sicherheitsmanagement", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A conservation science phenomenon in AI-mediated ecological monitoring, characterized by an editorial effect in which distinctive authorial voice becomes harder to recognize across body of content when portions are generated or heavily edited through AI-mediated workflows. This phenomenon operates at the intersection of signature and voice dynamics within the broader CON domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept ein Phänomen der menschlichen Wahrnehmung: Distinctive author voice becomes harder to recognize across content body. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "AI in Conservation", "narrower_terms": [], "cross_domain_refs": [ "TRA-0091" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "CON-0077", "domain": "CON", "term_en": "Source Reliability Indifference", "term_de": "Risikobeurteilung in construction", "definition_en": "An environmental analysis pattern in AI-augmented conservation, measurable through a content strategy pattern involving all cited sources receive equivalent addressment regardless of epistemic credibility, collapsing distinctions between peer-reviewed scholarship and speculative commentary. Distinguished from adjacent concepts by its focus on the specific mechanism through which source manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch ein Phänomen der menschlichen Wahrnehmung: All sources addressed equally regardless of credibility. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "AI in Conservation", "narrower_terms": [], "cross_domain_refs": [ "TRA-0016" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "CON-0078", "domain": "CON", "term_en": "Story Structure Reliance", "term_de": "Gefährdungserkennung in construction", "definition_en": "An environmental analysis pattern in AI-augmented conservation, measurable through a content creation phenomenon manifesting as formulaic narrative structures emerge from AI learning patterns, creating overly rigid adherence to three-act arcs or monomyth templates. The concept emerges specifically in contexts where story–structure interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch ein Phänomen der menschlichen Wahrnehmung: Content becomes overly formulaic in structure, following AI-learned patterns. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2703", "narrower_terms": [], "cross_domain_refs": [ "EDU-0096" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "CON-0079", "domain": "CON", "term_en": "Subheading Clarity Shift", "term_de": "Persönliche Schutzausrüstung", "definition_en": "An environmental analysis pattern in AI-augmented conservation, measurable through a content strategy pattern in which section headers become generic, uninformative, or obsresolve, failing to communicate content sectioning logic or prepare readers for thematic transitions. The concept emerges specifically in contexts where subheading–clarity interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch ein Phänomen der menschlichen Wahrnehmung: Section headers become generic or uninformative. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "AI in Conservation", "narrower_terms": [], "cross_domain_refs": [ "ELR-0018" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "CON-0080", "domain": "CON", "term_en": "Subscriber Communication Detachment", "term_de": "Notfallverfahren in construction", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures an environmental analysis pattern in AI-augmented conservation, measurable through an editorial effect observed when creators lose direct communication channels with audiences as AI-mediated responses replace individualized engagement, reducing parasocial connection. This phenomenon operates at the intersection of subscriber and communication dynamics within the broader CON domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept strategische emergency-Praxis im construction management, verstärkt durch KI mittels automatisierter Optimierung, prädiktiven Modellen und Echtzeit-Analytik für bessere Betriebsergebnisse. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "AI in Conservation", "narrower_terms": [], "cross_domain_refs": [ "CUS-0023" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "CON-0081", "domain": "CON", "term_en": "Table Utilization Avoidance", "term_de": "Unfallverhütung in construction", "definition_en": "As a neutral analytical construct in AI behavior research, this term denotes A conservation science phenomenon in AI-mediated ecological monitoring, characterized by a content strategy pattern reflecting complex comparisons, multidimensional data, and structured information remain in prose form when tabular presentation would improve scanability and comprehension. The concept emerges specifically in contexts where table–utilization interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "In der AUGMANITAI-Terminologiewissenschaft als rein beschreibendes Phänomen klassifiziert, bezeichnet dieser Begriff strategische accident-Praxis im construction management, verstärkt durch KI mittels automatisierter Optimierung, prädiktiven Modellen und Echtzeit-Analytik für bessere Betriebsergebnisse. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "AI in Conservation", "narrower_terms": [], "cross_domain_refs": [ "AGE-0011", "AUG-0282", "AUG-0408" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "CON-0082", "domain": "CON", "term_en": "Tone Calibration Shift", "term_de": "construction-Gesundheitsschutz", "definition_en": "As a neutral analytical construct in AI behavior research, this term denotes A conservation science phenomenon in AI-mediated ecological monitoring, characterized by a content strategy pattern in which appropriate emotional register for specific audiences becomes difficult to maintain when AI-generated content lacks sensitivity to contextual emotional demands. The concept emerges specifically in contexts where tone–calibration interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "In der AUGMANITAI-Terminologiewissenschaft als rein beschreibendes Phänomen klassifiziert, bezeichnet dieser Begriff ein Phänomen der menschlichen Wahrnehmung: Appropriate emotional register for audience becomes harder to maintain. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Analytical Ratio", "narrower_terms": [], "cross_domain_refs": [ "KNO-0031" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "CON-0083", "domain": "CON", "term_en": "Topic Angle Originality Shift", "term_de": "Ergonomie in construction", "definition_en": "As a neutral analytical construct in AI behavior research, this term denotes A conservation science phenomenon in AI-mediated ecological monitoring, characterized by aI's tendency to suggest conventional angles erodes creators' ability to identify fresh perspectives. The concept emerges specifically in contexts where topic–angle interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "In der AUGMANITAI-Terminologiewissenschaft als rein beschreibendes Phänomen klassifiziert, bezeichnet dieser Begriff ein Phänomen der menschlichen Wahrnehmung: AI's tendency to suggest conventional angles erodes creators' ability to identify fresh perspectives. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "AI in Conservation", "narrower_terms": [], "cross_domain_refs": [ "SCR-0017", "VIB-0179" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "CON-0084", "domain": "CON", "term_en": "Topic Cluster Incoherence", "term_de": "Umweltschutz in construction", "definition_en": "As a neutral analytical construct in AI behavior research, this term denotes A conservation science phenomenon in AI-mediated ecological monitoring, characterized by related content pieces feel randomly grouped without clear thematic coherence, where algorithmic clustering replaces deliberate editorial relationship design. The concept emerges specifically in contexts where topic–cluster interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription. Research construct for empirical investigation.", "definition_de": "In der AUGMANITAI-Terminologiewissenschaft als rein beschreibendes Phänomen klassifiziert, bezeichnet dieser Begriff ein Phänomen der menschlichen Wahrnehmung: Related content pieces feel randomly grouped. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "AI in Conservation", "narrower_terms": [], "cross_domain_refs": [ "ART-0072" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "CON-0085", "domain": "CON", "term_en": "Transition Smoothness Decline", "term_de": "Brandschutz in construction", "definition_en": "As a neutral analytical construct in AI behavior research, this term denotes A conservation science phenomenon in AI-mediated ecological monitoring, characterized by an editorial effect in which paragraph-to-paragraph connections become abrupt or forced when logical connectives are omitted, creating reading discontinuity. The concept emerges specifically in contexts where transition–smoothness interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "In der AUGMANITAI-Terminologiewissenschaft als rein beschreibendes Phänomen klassifiziert, bezeichnet dieser Begriff ein Phänomen der menschlichen Wahrnehmung: Paragraph-to-paragraph connections become abrupt or forced. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "AI in Conservation", "narrower_terms": [], "cross_domain_refs": [ "FIC-0065" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "CON-0086", "domain": "CON", "term_en": "Trend Awareness Lag", "term_de": "Chemische Sicherheit in construction", "definition_en": "As a neutral analytical construct in AI behavior research, this term denotes A conservation science phenomenon in AI-mediated ecological monitoring, characterized by an editorial effect manifesting as creators miss cultural moments, temporal windows, and relevant conversation cycles, publishing content after discourse has shifted to subsequent topics. The concept emerges specifically in contexts where trend–awareness interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "In der AUGMANITAI-Terminologiewissenschaft als rein beschreibendes Phänomen klassifiziert, bezeichnet dieser Begriff ein Phänomen der menschlichen Wahrnehmung: Creators miss cultural moments for relevant content. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "AI in Conservation", "narrower_terms": [], "cross_domain_refs": [ "RPH-3552" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "CON-0087", "domain": "CON", "term_en": "Typography Intentionality Shift", "term_de": "Elektrische Sicherheit in construction", "definition_en": "As a neutral analytical construct in AI behavior research, this term denotes A conservation science phenomenon in AI-mediated ecological monitoring, characterized by font choices become random decorative selections rather than strategic design elements signaling hierarchy, tone, or content function. The concept emerges specifically in contexts where typography–intentionality interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "In der AUGMANITAI-Terminologiewissenschaft als rein beschreibendes Phänomen klassifiziert, bezeichnet dieser Begriff ein Phänomen der menschlichen Wahrnehmung: Font choices become random rather than strategic. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "AI in Conservation", "narrower_terms": [], "cross_domain_refs": [ "ADA-0012", "AGE-0007", "AGE-0046" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "CON-0088", "domain": "CON", "term_en": "URL Slug Clarity Shift", "term_de": "Maschinensicherheit in construction", "definition_en": "An environmental analysis pattern in AI-augmented conservation, measurable through a content strategy pattern reflecting permalink structures become unclear, non-descriptive, or nonsensical, failing to communicate content topic or relevance in human-readable format. Distinguished from adjacent concepts by its focus on the specific mechanism through which url manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch ein Phänomen der menschlichen Wahrnehmung: Permalink structure becomes unclear or non-descriptive. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "AI in Conservation", "narrower_terms": [], "cross_domain_refs": [ "TEW-0042" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "CON-0089", "domain": "CON", "term_en": "Updates Timeliness Lag", "term_de": "Sicherheitsschulung in construction", "definition_en": "An environmental analysis pattern in AI-augmented conservation, measurable through a content strategy pattern in which outdated information, obsolete examples, and factually superseded claims persist in published content despite general knowledge of their incorrectness. Distinguished from adjacent concepts by its focus on the specific mechanism through which updates manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch ein Phänomen der menschlichen Wahrnehmung: Outdated information rarely gets refreshed. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "AI in Conservation", "narrower_terms": [], "cross_domain_refs": [ "TEM-0004" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "CON-0090", "domain": "CON", "term_en": "User Experience Friction", "term_de": "Vorfalluntersuchung in construction", "definition_en": "As a neutral analytical construct in AI behavior research, this term denotes A conservation science phenomenon in AI-mediated ecological monitoring, characterized by navigation architecture, information structure, and site usability may create frustration, confusion, or laborious effort for audiences attempting content access. The concept emerges specifically in contexts where user–experience interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "In der AUGMANITAI-Terminologiewissenschaft als rein beschreibendes Phänomen klassifiziert, bezeichnet dieser Begriff ein Phänomen der menschlichen Wahrnehmung: Navigation feels confusing or laborious. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "AI in Conservation", "narrower_terms": [], "cross_domain_refs": [ "SCR-0002" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q862028", "legal_classification": "analytical_category" }, { "id": "CON-0091", "domain": "CON", "term_en": "Vanity Metric Reliance", "term_de": "construction-Geschäftsmodell", "definition_en": "Classified in AUGMANITAI terminology science as a descriptive phenomenon (without normative endorsement), this pattern denotes A conservation science phenomenon in AI-mediated ecological monitoring, characterized by an editorial effect characterized by view counts, impression numbers, and raw traffic metrics are prioritized over meaningful engagement transition and behavioral outcomes. Distinguished from adjacent concepts by its focus on the specific mechanism through which vanity manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als analytisches Konstrukt der empirischen KI-Forschung (deskriptiv, nicht präskriptiv) erfasst dieser Terminus ein Phänomen der menschlichen Wahrnehmung: View counts prioritized over meaningful conversion. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Performance Metric", "narrower_terms": [], "cross_domain_refs": [ "PLY-0034" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "CON-0092", "domain": "CON", "term_en": "Version Control Absence", "term_de": "construction-Marktanalyse", "definition_en": "As a neutral analytical construct in AI behavior research, this term denotes A conservation science phenomenon in AI-mediated ecological monitoring, characterized by tracking system for content revisions, improvements, and changes is absent, leaving historical editorial decisions undocumented and restoration impossible. The concept emerges specifically in contexts where version–control interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "In der AUGMANITAI-Terminologiewissenschaft als rein beschreibendes Phänomen klassifiziert, bezeichnet dieser Begriff ein Phänomen der menschlichen Wahrnehmung: Changelog of what changed isn't tracked. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "AI in Conservation", "narrower_terms": [], "cross_domain_refs": [ "SPR-0025" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "CON-0093", "domain": "CON", "term_en": "Video Transcript Incompleteness", "term_de": "Ökonomie der Baumanagement", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A conservation science phenomenon in AI-mediated ecological monitoring, characterized by a content strategy pattern manifesting as transcriptions omit important verbal nuance, tonal inflection, emphasis, and paralinguistic information critical to meaning construction. This phenomenon operates at the intersection of video and transcript dynamics within the broader CON domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept ein Phänomen der menschlichen Wahrnehmung: Transcriptions miss important verbal nuance and tone. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "AI in Conservation", "narrower_terms": [], "cross_domain_refs": [ "AED-0097", "SCR-0063", "SPR-0170" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "CON-0094", "domain": "CON", "term_en": "Voice Inconsistency Accumulation", "term_de": "construction-Kostenmanagement", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures an environmental analysis pattern in AI-augmented conservation, measurable through a content creation phenomenon reflecting brand voice drifts progressively across content pieces when AI handles portions of creation, with inconsistent register and personality emerging. This phenomenon operates at the intersection of voice and inconsistency dynamics within the broader CON domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept ein Phänomen der menschlichen Wahrnehmung: Brand voice drifts when AI handles portions of content. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "RPH-2051", "narrower_terms": [], "cross_domain_refs": [ "AGE-0014", "AGE-0048", "AGE-0068" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "CON-0095", "domain": "CON", "term_en": "White Space Underutilization", "term_de": "Preisgestaltung in construction", "definition_en": "Classified in AUGMANITAI terminology science as a descriptive phenomenon (without normative endorsement), this pattern denotes A conservation science phenomenon in AI-mediated ecological monitoring, characterized by an editorial effect observed when dense text blocks lacking adequate paragraph breaks, margins, and visual breathing room may create cognitive overload and reading fatigue. Distinguished from adjacent concepts by its focus on the specific mechanism through which white manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als analytisches Konstrukt der empirischen KI-Forschung (deskriptiv, nicht präskriptiv) erfasst dieser Terminus ein Phänomen der menschlichen Wahrnehmung: Dense text blocks lack breathing room. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "AI in Conservation", "narrower_terms": [], "cross_domain_refs": [ "AGE-0049", "ART-0041", "COG-0141" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "COP-0001", "domain": "COP", "term_en": "Adjective Accumulation", "term_de": "AdjectiveAccumulation", "definition_en": "A psychological coping phenomenon in AI-mediated stress processing, characterized by a persuasive writing effect observed when the excessive stacking of adjectives describing products or services in AI-generated copy (amazing, innovative, powerful, substantially modifying (as documented in research), groundbreaking) reducing credibility through oversaturation. Distinguished from adjacent concepts by its focus on the specific mechanism through which adjective manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch übermaß an Adjektiven in automatisch generiertem Marketing-Text, die zu unspezifischen, aufgebläht wirkenden Beschreibungen führen. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-3001", "narrower_terms": [ "COP-0058", "COP-0017", "COP-0039", "COP-0018", "COP-0096", "COP-0041", "COP-0071", "COP-0079", "COP-0043", "COP-0012", "COP-0051", "COP-0015", "COP-0088", "COP-0095", "COP-0047", "COP-0087", "COP-0065", "COP-0005", "COP-0040", "COP-0059", "COP-0004", "COP-0036", "COP-0090", "COP-0011", "COP-0075", "COP-0089", "COP-0054", "COP-0061", "COP-0076", "COP-0091", "COP-0025", "COP-0026", "COP-0063", "COP-0085", "COP-0013", "COP-0038", "COP-0024", "COP-0046", "COP-0084", "COP-0003", "COP-0020", "COP-0081", "COP-0093", "COP-0031", "COP-0080", "COP-0034", "COP-0074", "COP-0027", "COP-0002", "COP-0045", "COP-0049", "COP-0072", "COP-0048", "COP-0029", "COP-0028", "COP-0052", "COP-0014", "COP-0022", "COP-0057", "COP-0007", "COP-0023", "COP-0082", "COP-0070", "COP-0083", "COP-0010" ], "cross_domain_refs": [ "AGE-0068", "AGE-0091", "AGE-0092" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "COP-0002", "domain": "COP", "term_en": "Audience Assumption Drift", "term_de": "AudienceAssumptionDrift", "definition_en": "A persuasive writing effect manifesting as the progressive misalignment between AI-modeled audience characteristics and actual reader demographics. AI systems infer audience from training data, accumulating outdated or oversimplified representations. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch progressive Verschiebung zwischen KI-modelliertem Publikum und tatsächlicher Leserdemografie, was Messaging-Relevanz reduziert. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Systemic Drift", "narrower_terms": [], "cross_domain_refs": [ "STE-0006", "PHO-0052" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "COP-0003", "domain": "COP", "term_en": "Authenticity Blur", "term_de": "AuthenticityBlur", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A psychological coping phenomenon in AI-mediated stress processing, characterized by a marketing copy pattern characterized by the inability of readers to distinguish between human-written marketing and AI-generated marketing based on text characteristics alone. This phenomenon operates at the intersection of authenticity and blur dynamics within the broader COP domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept unfähigkeit von Lesern, zwischen menschlich-verfasster und KI-generierter Marketing-Textqualität zu unterscheiden. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "AI Coping Strategy", "narrower_terms": [], "cross_domain_refs": [ "ADA-0013", "ART-0012", "ART-0025" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "COP-0004", "domain": "COP", "term_en": "Brand Voice Dilution", "term_de": "BrandVoiceDilution", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures an adaptive response pattern in AI-augmented emotional regulation, measurable through a persuasive writing effect reflecting the reduction in distinctiveness of a brand's messaging when AI assistance is applied inconsistently across content channels. The cumulative effect is a flattening of brand personality. This phenomenon operates at the intersection of brand and voice dynamics within the broader COP domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept reduzierung der Unterscheidbarkeit einer Marke, wenn KI-Unterstützung inkonsistent angewendet wird. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "AI Coping Strategy", "narrower_terms": [], "cross_domain_refs": [ "CON-0072" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q431289", "legal_classification": "observational_construct" }, { "id": "COP-0005", "domain": "COP", "term_en": "Claim Creep", "term_de": "ClaimCreep", "definition_en": "A psychological coping phenomenon in AI-mediated stress processing, characterized by a marketing copy pattern manifesting as the gradual expansion of product claims in marketing copy beyond original specifications, occurring when AI systems yield variations without human claim-checking. Distinguished from adjacent concepts by its focus on the specific mechanism through which claim manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch graduelles Ausweiten von Produktansprüchen in Marketing-Kopien über ursprüngliche Spezifikationen hinaus. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "AI Coping Strategy", "narrower_terms": [], "cross_domain_refs": [ "ART-0081", "CON-0017", "CON-0062" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "COP-0006", "domain": "COP", "term_en": "Cliché Clustering", "term_de": "ClichéClustering", "definition_en": "A distinct interaction pattern where AI-generated marketing copy converges on identical clichés (game changer, paradigm shift, unlocking potential, next generation) across all brands. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch phänomen, bei dem KI-generierte Marketing-Texte in identischen Klischees konvergieren (game-changer, paradigm shift, disruptiv). Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-2701", "narrower_terms": [], "cross_domain_refs": [ "ART-0009", "ASE-0009", "DAT-0016" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "COP-0007", "domain": "COP", "term_en": "Coherence Drift", "term_de": "CoherenceDrift", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures an adaptive response pattern in AI-augmented emotional regulation, measurable through a copywriting phenomenon involving the subtle inconsistency in tone, voice, or argument structure within long-form AI-generated content, where segments appear to be written by different authors despite single-source generation. This phenomenon operates at the intersection of coherence and drift dynamics within the broader COP domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept subtile Inkonsistenz in Tonalität, Stimme oder Argumentationsstruktur innerhalb langer KI-generierter Inhalte. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Systemic Drift", "narrower_terms": [], "cross_domain_refs": [ "FIC-0089", "SCR-0084" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "COP-0008", "domain": "COP", "term_en": "Conditional Language Avoidance", "term_de": "ConditionalLanguageAvoidance", "definition_en": "The characteristic absence of hedging language (perhaps, may, likely, could) in AI marketing copy, creating overconfident claims that exceed the certainty of the underlying product. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch charakteristische Abwesenheit von Vorsichtsformulierungen (vielleicht, könnte, möglicherweise) in KI-Marketing, was absolute Tonalität tendiert dazu zu erzeugen. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-3754", "narrower_terms": [], "cross_domain_refs": [ "AGE-0011", "AGE-0087", "ASE-0063" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q315", "legal_classification": "empirical_phenomenon_label" }, { "id": "COP-0009", "domain": "COP", "term_en": "Cultural Nuance Narrowing", "term_de": "CulturalNuanceNarrowing", "definition_en": "The shift of regional, linguistic, or cultural specificity in advertising content when AI training data generalizes human contexts into universal templates. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch phänomen, bei dem KI-Systeme zeitgeist-Buzzwords übermäßig verwenden, was Marketing altert schneller. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2701", "narrower_terms": [], "cross_domain_refs": [ "FIC-0028" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "COP-0010", "domain": "COP", "term_en": "Description Homogeneity", "term_de": "DescriptionHomogeneity", "definition_en": "A psychological coping phenomenon in AI-mediated stress processing, characterized by a systemic tendency in which product descriptions across different brands converge on identical language patterns when all rely on the same AI language model for copy generation. Distinguished from adjacent concepts by its focus on the specific mechanism through which description manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch situation, bei der KI-Marketing generische Messaging tendiert dazu zu erzeugen, die auf zahlreiche Konkurrenten zutrifft. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "AI Coping Strategy", "narrower_terms": [], "cross_domain_refs": [ "CON-0057", "SCR-0001", "SCR-0087" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "COP-0011", "domain": "COP", "term_en": "Emotional Flatness", "term_de": "EmotionalFlatness", "definition_en": "An adaptive response pattern in AI-augmented emotional regulation, measurable through the characteristic absence of unexpected emotional resonance in AI-generated marketing copy. Text is grammatically correct and persuasive in structure but lacks authentic emotional texture. Distinguished from adjacent concepts by its focus on the specific mechanism through which emotional manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch dokumentierte Tendenz, dass KI-Systeme unkritisch Überverkauftheit präsentieren, statt ehrlicher Limitation-Kommunikation. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "AI Coping Strategy", "narrower_terms": [], "cross_domain_refs": [ "CUS-0034", "CUS-0043", "CUS-0045" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q9415", "legal_classification": "descriptive_research_term" }, { "id": "COP-0012", "domain": "COP", "term_en": "Evidence Substitution", "term_de": "EvidenceSubstitution", "definition_en": "A psychological coping phenomenon in AI-mediated stress processing, characterized by a behavioral tendency where AI-generated marketing implies evidence or data without providing actual sources, creating the impression of factual support without substance. Distinguished from adjacent concepts by its focus on the specific mechanism through which evidence manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch phänomen, bei dem KI-Marketing Zielgruppen-Subtilitäten übersieht, die menschliche Texter erfassen. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "AI Coping Strategy", "narrower_terms": [], "cross_domain_refs": [ "EDU-0040" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "COP-0013", "domain": "COP", "term_en": "Feature Bloat Description", "term_de": "FeatureBloatDescription", "definition_en": "A psychological coping phenomenon in AI-mediated stress processing, characterized by marketing copy that lists most possible feature regardless of relevance or importance, characteristic of AI systems optimizing for keyword density rather than clarity. The concept emerges specifically in contexts where feature–bloat interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch situation, bei der KI-Texte hyperbole Sprachbilder verwenden, die glaubwürdig-Schaden anrichten. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "AI Coping Strategy", "narrower_terms": [], "cross_domain_refs": [ "TEW-0003" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "COP-0014", "domain": "COP", "term_en": "Headline Fatigue", "term_de": "HeadlineFatigue", "definition_en": "A marketing copy pattern arising from reader exhaustion caused by exposure to multiple algorithmically-generated headline variations that follow predictable patterns. Each variation reads similarly, and audiences identify the formulaic structure. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch herausforderung, bei der KI-Systeme kulturelle oder regionale Unterschiede in Messaging nicht beachten. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "AI Coping Strategy", "narrower_terms": [], "cross_domain_refs": [ "DES-0059" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "COP-0015", "domain": "COP", "term_en": "Keyword Saturation Creep", "term_de": "KeywordSaturationCreep", "definition_en": "An adaptive response pattern in AI-augmented emotional regulation, measurable through a marketing copy pattern observed when the incremental accumulation of SEO keywords in copy until text becomes unreadable to humans, caused by AI systems optimizing for search ranking without readability constraints. Distinguished from adjacent concepts by its focus on the specific mechanism through which keyword manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch phänomen, bei dem KI-Marketing emotional-manipulative Taktiken verwenden ohne Sensibilität für ethische Linien. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Analytical Ratio", "narrower_terms": [], "cross_domain_refs": [ "WEB-0073" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "COP-0016", "domain": "COP", "term_en": "Language Flattening", "term_de": "LanguageFlattening", "definition_en": "A copywriting phenomenon arising from the reduction of linguistic diversity in marketing copy when AI systems yield content in simple English to maximize audience reach, erasing regional language characteristics. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch situation, bei der KI-Texte redundante Punkte wiederholen, statt variiert zu bleiben. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-2555", "narrower_terms": [], "cross_domain_refs": [ "LIN-0071" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q315", "legal_classification": "systematic_classification" }, { "id": "COP-0017", "domain": "COP", "term_en": "Metaphor Depletion", "term_de": "MetaphorDepletion", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A psychological coping phenomenon in AI-mediated stress processing, characterized by a marketing copy pattern manifesting as the limited range of metaphors and analogies appearing in AI-generated marketing, caused by statistical clustering in training data that favors common over creative comparisons. This phenomenon operates at the intersection of metaphor and depletion dynamics within the broader COP domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept dokumentierte Tendenz, dass KI-Systeme sachliche Fehler oder Widersprüche in Generated-Copy nicht detektieren. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "AI Coping Strategy", "narrower_terms": [], "cross_domain_refs": [ "CON-0058" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q4263830", "legal_classification": "analytical_category" }, { "id": "COP-0018", "domain": "COP", "term_en": "Narrative Compression", "term_de": "NarrativeCompression", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures an adaptive response pattern in AI-augmented emotional regulation, measurable through a persuasive writing effect reflecting the flattening of complex customer stories into simplified narratives by AI, removing contradictions, hesitations, and realistic details that make human experiences compelling. This phenomenon operates at the intersection of narrative and compression dynamics within the broader COP domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept phänomen, bei dem KI-Marketing Menschensprache simuliert, aber Patterns erkennen lässt, die unmenschlich wirken. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Data Compression", "narrower_terms": [], "cross_domain_refs": [ "ASE-0087", "COG-0118", "COG-0129" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q131324", "legal_classification": "systematic_classification" }, { "id": "COP-0019", "domain": "COP", "term_en": "Persona Shift", "term_de": "PersonaShift", "definition_en": "A persuasive writing effect characterized by the gradual disappearance of buyer persona distinctions when a single AI model accompanies copy for multiple target audiences, blending them into generic messaging.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch situation, bei der KI-Texte komplex wirken, aber tatsächlich oberflächliche Information verbergen. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-3251", "narrower_terms": [], "cross_domain_refs": [ "ADA-0012", "AGE-0007", "AGE-0046" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "COP-0020", "domain": "COP", "term_en": "Pronoun Inconsistency", "term_de": "PronounInconsistency", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures an adaptive response pattern in AI-augmented emotional regulation, measurable through a persuasive writing effect arising from the fluctuation between first-person we/our and second-person pronouns in AI marketing copy, creating confusion about perspective and inclusivity. This phenomenon operates at the intersection of pronoun and inconsistency dynamics within the broader COP domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept herausforderung, bei der KI-Systeme Brand-Voice-Konsistenz über Kanäle nicht halten können. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "AI Coping Strategy", "narrower_terms": [], "cross_domain_refs": [ "TRU-0010" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "COP-0021", "domain": "COP", "term_en": "Repetition Through Variation", "term_de": "RepetitionThroughVariation", "definition_en": "A persuasive writing effect manifesting as when AI systems yield multiple marketing variations of the same core message, appearing different at surface level while repeating identical arguments in slightly altered language. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch phänomen, bei dem KI-Marketing für längere Aufmerksamkeitsspannen schreibt, obwohl Zielgruppe kurz-aufmerksam ist. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-3002", "narrower_terms": [], "cross_domain_refs": [ "CRE-0026" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "COP-0022", "domain": "COP", "term_en": "Segment Drift", "term_de": "SegmentDrift", "definition_en": "An adaptive response pattern in AI-augmented emotional regulation, measurable through a persuasive writing effect arising from the misalignment between marketing segments created by AI systems and actual customer cohorts, leading to messages that miss their intended audiences. The concept emerges specifically in contexts where segment–drift interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch situation, bei der KI-Texte demographische Annahmen treffen, die veraltet oder falsch sind. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Systemic Drift", "narrower_terms": [], "cross_domain_refs": [ "MKT-0078", "CUS-0085" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "COP-0023", "domain": "COP", "term_en": "Story Flattening", "term_de": "StoryFlattening", "definition_en": "An adaptive response pattern in AI-augmented emotional regulation, measurable through a persuasive writing effect arising from the reduction of complex human narratives into simplified, conflict-free marketing stories when AI systems remove elements of struggle, uncertainty, or failure. Distinguished from adjacent concepts by its focus on the specific mechanism through which story manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch dokumentierte Tendenz, dass KI-Systeme Kontroverse oder Kritik aus Marketing-Messaging eliminieren, was unrealistisch wirkt. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "AI Coping Strategy", "narrower_terms": [], "cross_domain_refs": [ "BEH-0079", "COG-0056", "COG-0082" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "COP-0024", "domain": "COP", "term_en": "Template Skeleton Visibility", "term_de": "TemplateSkeletonVisibility", "definition_en": "An adaptive response pattern in AI-augmented emotional regulation, measurable through a marketing copy pattern in which the recognizable underlying formula in AI-generated marketing content where readers predict the next sentence because the structure follows predictable patterns. Distinguished from adjacent concepts by its focus on the specific mechanism through which template manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch phänomen, bei dem KI-Marketing Jargon übermäßig verwendet, das Zielgruppe nicht teilt. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "AI Coping Strategy", "narrower_terms": [], "cross_domain_refs": [ "AED-0080", "AGE-0006", "COG-0164" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "COP-0025", "domain": "COP", "term_en": "The Age Gap in Resonance", "term_de": "TheAgeGapinResonanz", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures an adaptive response pattern in AI-augmented emotional regulation, measurable through a marketing copy pattern reflecting marketing copy that fails to resonate across generational divides because AI systems yield content based on statistical averages of language use that favor majority demographics. This phenomenon operates at the intersection of the and age dynamics within the broader COP domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept situation, bei der KI-Texte Lesererwartungen falsch eichend (formality, tone, Komplexität) vorgeben. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Performance Gap", "narrower_terms": [], "cross_domain_refs": [ "ADA-0008", "AED-0019", "AED-0092" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "COP-0026", "domain": "COP", "term_en": "The Assumption of Ignorance", "term_de": "TheAssumptionofIgnorance", "definition_en": "A psychological coping phenomenon in AI-mediated stress processing, characterized by a marketing copy pattern observed when marketing copy that explains basic concepts to advanced users because AI systems lack context about audience expertise levels and correspondingly assume minimum knowledge. Distinguished from adjacent concepts by its focus on the specific mechanism through which the manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch herausforderung, bei der KI-Systeme Produkteinzigartigkeit kommunizieren können nicht, wenn ähnlich andere Produkte. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "AI Coping Strategy", "narrower_terms": [], "cross_domain_refs": [ "DES-0016" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "COP-0027", "domain": "COP", "term_en": "The Attention Change", "term_de": "TheAttentionChange", "definition_en": "An adaptive response pattern in AI-augmented emotional regulation, measurable through a frequently noted effect where audiences become desensitized to AI-generated marketing language over time, requiring increasing intensity or novelty to maintain attention. The concept emerges specifically in contexts where the–attention interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch phänomen, bei dem KI-Marketing emotionale Beats falsch setzt oder überemphasiert. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Attention Mechanism", "narrower_terms": [], "cross_domain_refs": [ "AED-0025", "AED-0067", "AED-0077" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q184872", "legal_classification": "observational_construct" }, { "id": "COP-0028", "domain": "COP", "term_en": "The Attention Pattern", "term_de": "TheAttentionMuster", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A psychological coping phenomenon in AI-mediated stress processing, characterized by a persuasive writing effect manifesting as marketing copy that reflects the statistical attention patterns of transformer models, emphasizing beginning and end of text while subordinating middle content. This phenomenon operates at the intersection of the and attention dynamics within the broader COP domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept situation, bei der KI-Texte Versprechungen machen, die die Produktrealität überschreiten. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Behavioral Pattern", "narrower_terms": [], "cross_domain_refs": [ "AGE-0003", "AGE-0004", "AGE-0016" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q184872", "legal_classification": "analytical_category" }, { "id": "COP-0029", "domain": "COP", "term_en": "The Attribution Problem", "term_de": "TheAttributionProblem", "definition_en": "A psychological coping phenomenon in AI-mediated stress processing, characterized by a persuasive writing effect where the difficulty of measuring true contribution of AI marketing to business outcomes when transition paths are multi-touch and assignment of credit becomes arbitrary. The concept emerges specifically in contexts where the–attribution interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch dokumentierte Tendenz, dass KI-Systeme Leser-Ängste manipulativer ansprechen als ethisch vertbar. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "AI Coping Strategy", "narrower_terms": [], "cross_domain_refs": [ "CON-0049" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "COP-0030", "domain": "COP", "term_en": "The Authenticity Premium", "term_de": "TheAuthenticityPremium", "definition_en": "A marketing copy pattern characterized by the emerging market value of explicitly human-written marketing copy, where origin transparency (human vs. AI) becomes a competitive advantage and selling point.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch phänomen, bei dem KI-Marketing Narrative baut, die historisch falsch oder stereotypisch sind. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-3101", "narrower_terms": [], "cross_domain_refs": [ "AGE-0080", "ART-0012", "ART-0025" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "COP-0031", "domain": "COP", "term_en": "The Authority Problem", "term_de": "TheAuthorityProblem", "definition_en": "A psychological coping phenomenon in AI-mediated stress processing, characterized by the tension between marketing copy claiming industry expertise and readers' awareness that the text was generated without lived experience or domain mastery. The concept emerges specifically in contexts where the–authority interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch situation, bei der KI-Texte Konkurrenz-Vergleiche werden zu negativ oder unfair. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "AI Coping Strategy", "narrower_terms": [], "cross_domain_refs": [ "AED-0011", "COG-0031", "COG-0040" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "COP-0032", "domain": "COP", "term_en": "The Call-to-Action Uniformity", "term_de": "TheCall-to-actionUniformity", "definition_en": "A marketing copy pattern reflecting the convergence of CTAs across brands to nearly identical wording (Learn More, Shop Now, Get Started, Discover Today) when generated by the same underlying AI systems. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch herausforderung, bei der KI-Systeme Nischen-Sprache oder Sub-Kultur-Referenzen nicht authentisch reproduzieren können. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2301", "narrower_terms": [], "cross_domain_refs": [ "AGE-0061" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "COP-0033", "domain": "COP", "term_en": "The Click-Through Perception", "term_de": "TheClick-throughPerception", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A psychological coping phenomenon in AI-mediated stress processing, characterized by a marketing copy pattern in which high click-through rates on AI-generated headlines that correlate with pages where bounce rates are similarly high, indicating the headline promised more than content delivers. This phenomenon operates at the intersection of the and click dynamics within the broader COP domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept phänomen, bei dem KI-Marketing Lückenlosigkeit suggeriert, die Produkt nicht bietet. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "RPH-2154", "narrower_terms": [], "cross_domain_refs": [ "DAT-0089" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q177601", "legal_classification": "empirical_phenomenon_label" }, { "id": "COP-0034", "domain": "COP", "term_en": "The Competition Mirroring", "term_de": "TheCompetitionMirroring", "definition_en": "An adaptive response pattern in AI-augmented emotional regulation, measurable through a copywriting phenomenon reflecting when multiple companies use the same AI systems to yield marketing, their copy converges toward identical messaging as they all optimize using the same models. Distinguished from adjacent concepts by its focus on the specific mechanism through which the manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch situation, bei der KI-Texte Konsument-Verhalten voraussagen, das auf Trainingsdaten-Bias basiert. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "AI Coping Strategy", "narrower_terms": [], "cross_domain_refs": [ "LIN-0034", "RHR-0246", "ROB-0004" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q476300", "legal_classification": "empirical_phenomenon_label" }, { "id": "COP-0035", "domain": "COP", "term_en": "The Confidence Mirage", "term_de": "TheConfidenceMirage", "definition_en": "A persuasive writing effect involving the false confidence that AI marketing systems display about claims and statements, reflecting high model scores for statistically common patterns rather than factual certainty. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch dokumentierte Tendenz, dass KI-Systeme oberflächlich zu Vielfalt appellieren, ohne authentisch zu sein. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-1209", "narrower_terms": [], "cross_domain_refs": [ "RPH-1205", "CUS-0042" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "COP-0036", "domain": "COP", "term_en": "The Context Window Problem", "term_de": "TheContextWindowProblem", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures an adaptive response pattern in AI-augmented emotional regulation, measurable through a recognizable shift where AI marketing lacks consistency across longer campaigns because each segment is generated without access to earlier content within context window limitations. This phenomenon operates at the intersection of the and context dynamics within the broader COP domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept phänomen, bei dem KI-Marketing bestehende Macht-Imbalancen perpetuiert (Geschlecht, Rasse, Klasse). Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "AI Coping Strategy", "narrower_terms": [], "cross_domain_refs": [ "VIB-0190" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "COP-0037", "domain": "COP", "term_en": "The Conversion Ceiling", "term_de": "TheConversionCeiling", "definition_en": "A marketing copy pattern characterized by the plateau in marketing effectiveness when AI-generated copy reaches saturation in optimization for transition metrics, less likely to improve beyond statistical training data limits.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch situation, bei der KI-Texte Privat-Raum-Grenzen übertreten durch zu-persönliche Messaging. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2555", "narrower_terms": [], "cross_domain_refs": [ "ASE-0017", "ASE-0055", "ASE-0077" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "COP-0038", "domain": "COP", "term_en": "The Credibility Slip", "term_de": "TheCredibilitySlip", "definition_en": "A psychological coping phenomenon in AI-mediated stress processing, characterized by a persuasive writing effect reflecting the moment readers recognize internal inconsistencies or overstatements in marketing copy that suggest automated generation rather than human fact-checking. The concept emerges specifically in contexts where the–credibility interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch herausforderung, bei der KI-Systeme ethische Probleme in Produkten nicht direkt addressieren können. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "AI Coping Strategy", "narrower_terms": [], "cross_domain_refs": [ "AED-0027", "CON-0010", "DAT-0008" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "COP-0039", "domain": "COP", "term_en": "The Data Reliance", "term_de": "TheDataReliance", "definition_en": "An adaptive response pattern in AI-augmented emotional regulation, measurable through a copywriting phenomenon in which the vulnerability of AI marketing systems to poisoning or corruption of training data sources, including competitor interference through alteration of public information. Distinguished from adjacent concepts by its focus on the specific mechanism through which the manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch phänomen, bei dem KI-Marketing Fehler oder Rückrufe minimalisiert oder versteckt im Messaging. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "AI Coping Strategy", "narrower_terms": [], "cross_domain_refs": [ "CRE-0132" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q42848", "legal_classification": "empirical_phenomenon_label" }, { "id": "COP-0040", "domain": "COP", "term_en": "The Demographic Blur", "term_de": "TheDemographicBlur", "definition_en": "An adaptive response pattern in AI-augmented emotional regulation, measurable through a persuasive writing effect arising from marketing copy that attempts to appeal widely and in doing so alienates all specific segments, resulting from AI systems averaging audience characteristics. The concept emerges specifically in contexts where the–demographic interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch situation, bei der KI-Texte Zielgruppe falsch patronisiert oder underestimiert. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "AI Coping Strategy", "narrower_terms": [], "cross_domain_refs": [ "ADA-0013", "AGE-0039", "CRE-0030" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "COP-0041", "domain": "COP", "term_en": "The Diversity Demand", "term_de": "TheDiversityDemand", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures an adaptive response pattern in AI-augmented emotional regulation, measurable through the growing market expectation for marketing copy to reflect diverse perspectives, voices, and experiences, which AI systems struggle to involve authentically observed alongside training data bias. This phenomenon operates at the intersection of the and diversity dynamics within the broader COP domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept dokumentierte Tendenz, dass KI-Systeme Langzeitvertrauen schädigen durch zu-aggressives Messaging. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "AI Coping Strategy", "narrower_terms": [], "cross_domain_refs": [ "CRE-0194" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "COP-0042", "domain": "COP", "term_en": "The Empathy Simulation", "term_de": "TheEmpathySimulation", "definition_en": "Marketing copy that mimics empathetic language without genuine understanding of customer pain points, creating text that appears caring but lacks authentic insight. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch phänomen, bei dem KI-Marketing Kontext-spezifische Sensitivität (Zeit, Ort, Ereignis) missachtet. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-3101", "narrower_terms": [], "cross_domain_refs": [ "PER-0018" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q202763", "legal_classification": "descriptive_research_term" }, { "id": "COP-0043", "domain": "COP", "term_en": "The Engagement Paradox", "term_de": "TheEngagementParadox", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A psychological coping phenomenon in AI-mediated stress processing, characterized by a copywriting phenomenon observed when aI marketing that shows high engagement metrics while reader satisfaction and purchase intent remain flat, revealing measurement misalignment. This phenomenon operates at the intersection of the and engagement dynamics within the broader COP domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept situation, bei der KI-Texte automatisches Überverkaufen eingeben, statt differenzierte Messaging zu basieren auf Kundensegment. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Logical Paradox", "narrower_terms": [], "cross_domain_refs": [ "RET-0050", "RET-0021" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "COP-0044", "domain": "COP", "term_en": "The Ensemble Effect", "term_de": "TheEnsembleEffekt", "definition_en": "A persuasive writing effect manifesting as marketing copy generated by ensembles of AI models that accompanies averaged, bland output reflecting the consensus of multiple models rather than distinctive voice. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch herausforderung, bei der KI-Systeme Leservertrauen schnell verlieren, wenn AI-Origin erkannt. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "WRK-0038", "narrower_terms": [], "cross_domain_refs": [ "VIB-0195" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "COP-0045", "domain": "COP", "term_en": "The Expectation Mismatch", "term_de": "TheExpectationMismatch", "definition_en": "A psychological coping phenomenon in AI-mediated stress processing, characterized by the gap between what marketing copy promises and what the product delivers when AI generated copy without access to real-world product performance data. The concept emerges specifically in contexts where the–expectation interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch phänomen, bei dem KI-Marketing zu-viele Features adressiert, nicht Core-Benefit. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "AI Coping Strategy", "narrower_terms": [], "cross_domain_refs": [ "SWE-0070" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "COP-0046", "domain": "COP", "term_en": "The False Momentum", "term_de": "TheFalseMomentum", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A psychological coping phenomenon in AI-mediated stress processing, characterized by a copywriting phenomenon arising from the appearance of marketing traction from AI-generated content that reflects algorithmic amplification rather than genuine audience growth or business results. This phenomenon operates at the intersection of the and false dynamics within the broader COP domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept situation, bei der KI-Texte Leser-Vorwissen unterschätzen oder überschätzen. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "AI Coping Strategy", "narrower_terms": [], "cross_domain_refs": [ "PHO-0080" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "COP-0047", "domain": "COP", "term_en": "The Feedback Loop Narrowing", "term_de": "TheRückkopplungSchleifeNarrowing", "definition_en": "A psychological coping phenomenon in AI-mediated stress processing, characterized by the change of marketing content over time when AI systems are trained on their own previously generated output, creating iterative change in quality and authenticity. The concept emerges specifically in contexts where the–feedback interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch dokumentierte Tendenz, dass KI-Systeme Storytelling-Narrative zu simplifizieren. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Feedback Loop", "narrower_terms": [], "cross_domain_refs": [ "MUS-0041" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "COP-0048", "domain": "COP", "term_en": "The Frequency Penalty", "term_de": "TheFrequencyPenalty", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures an adaptive response pattern in AI-augmented emotional regulation, measurable through a persuasive writing effect involving marketing copy that awkwardly avoids using words that appear elsewhere in brand materials, creating unnatural language choices to satisfy algorithmic diversity constraints. This phenomenon operates at the intersection of the and frequency dynamics within the broader COP domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept phänomen, bei dem KI-Marketing Nutzer-Daten oder Privatsphäre-Fragen ignoriert. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "AI Coping Strategy", "narrower_terms": [], "cross_domain_refs": [ "LIN-0098", "MKT-0009", "MKT-0013" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "COP-0049", "domain": "COP", "term_en": "The Gradient Alignment", "term_de": "TheGradientAlignment", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures an adaptive response pattern in AI-augmented emotional regulation, measurable through a frequently noted effect where AI marketing language gradients toward whatever metric is being optimized, sometimes at the cost of other qualities like authenticity or clarity. This phenomenon operates at the intersection of the and gradient dynamics within the broader COP domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept situation, bei der KI-Texte Compliance oder rechtliche Vorsichtsmaßnahmen nicht ausreichend einbauen. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Optimization Gradient", "narrower_terms": [], "cross_domain_refs": [ "COG-0179" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "COP-0050", "domain": "COP", "term_en": "The Human Authenticity Marker", "term_de": "TheHumanAuthenticityMarker", "definition_en": "A marketing copy pattern arising from the emergence of subtle stylistic markers (imperfect phrasing, unexpected tangents, personal anecdotes) as signals of human authorship that brands intentionally include or signal. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch herausforderung, bei der KI-Systeme Messaging-Konsistenz über zeitliche Kampagnen nicht halten. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2355", "narrower_terms": [], "cross_domain_refs": [ "FIC-0043" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "COP-0051", "domain": "COP", "term_en": "The Hybrid Fragility", "term_de": "TheHybridFragility", "definition_en": "An adaptive response pattern in AI-augmented emotional regulation, measurable through the challenge of combining human and AI-generated copy without creating inconsistency or revealing seams where hand-written and machine-generated content meet. Distinguished from adjacent concepts by its focus on the specific mechanism through which the manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch phänomen, bei dem KI-Marketing Messaging-Fatigue tendiert dazu zu erzeugen durch wiederholte Narrative. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "AI Coping Strategy", "narrower_terms": [], "cross_domain_refs": [ "WEB-0014" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "COP-0052", "domain": "COP", "term_en": "The Lifestyle Assumption", "term_de": "TheLifestyleAssumption", "definition_en": "A psychological coping phenomenon in AI-mediated stress processing, characterized by a persuasive writing effect manifesting as marketing copy that reflects AI training data assumptions about lifestyle, values, and aspirations that differ from actual reader contexts and lived realities. Distinguished from adjacent concepts by its focus on the specific mechanism through which the manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch situation, bei der KI-Texte leser-Skeptizismus triggern durch zu-perfekte Tonalität. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "AI Coping Strategy", "narrower_terms": [], "cross_domain_refs": [ "DAT-0068" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "COP-0053", "domain": "COP", "term_en": "The Majority Skew", "term_de": "TheMajoritySkew", "definition_en": "A recognizable shift where AI marketing naturally overrepresents majority perspectives and underrepresents smallity viewpoints observed alongside statistical bias in training data. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch dokumentierte Tendenz, dass KI-Systeme Competitor-Intelligence schlecht integrieren in Messaging. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-3101", "narrower_terms": [], "cross_domain_refs": [ "AGE-0073", "AGE-0085", "AGE-0088" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "COP-0054", "domain": "COP", "term_en": "The Metric Vagueness", "term_de": "TheMetricVagueness", "definition_en": "An adaptive response pattern in AI-augmented emotional regulation, measurable through a persuasive writing effect arising from marketing copy referencing improvements or results using undefined metrics (faster, easier, more) without specific measurement or baseline for comparison. Distinguished from adjacent concepts by its focus on the specific mechanism through which the manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch phänomen, bei dem KI-Marketing zu-viele Calls-to-Action in Texte packt. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Performance Metric", "narrower_terms": [], "cross_domain_refs": [ "TEW-0062" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "COP-0055", "domain": "COP", "term_en": "The Mirror Effect", "term_de": "TheMirrorEffekt", "definition_en": "The reader experience of seeing their own previously-stated desires reflected back in marketing copy, suggesting AI copied from search data or comment threads rather than original insight. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch situation, bei der KI-Texte Menschlichkeit oder Authentizität vermissen lassen. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2304", "narrower_terms": [], "cross_domain_refs": [ "ADA-0001", "ADA-0002", "ADA-0004" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "COP-0056", "domain": "COP", "term_en": "The Modal Narrowing", "term_de": "TheModalNarrowing", "definition_en": "The shift in marketing language from conditional (if, might, could) to absolute (will, is, guarantees) caused by AI systems favoring certainty over nuance. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch herausforderung, bei der KI-Systeme lokale oder nationale Nuancen in Messaging missachten. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2701", "narrower_terms": [], "cross_domain_refs": [ "SOC-0027" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "COP-0057", "domain": "COP", "term_en": "The Momentum Narrative", "term_de": "TheMomentumNarrative", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A psychological coping phenomenon in AI-mediated stress processing, characterized by a persuasive writing effect reflecting marketing copy that constantly emphasizes momentum, growth, and upward trajectory without acknowledging market cycles or realistic constraints. This phenomenon operates at the intersection of the and momentum dynamics within the broader COP domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept phänomen, bei dem KI-Marketing zu-lange Sätze oder Paragraphen tendiert dazu zu erzeugen, die Lesbarkeit schaden. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "AI Coping Strategy", "narrower_terms": [], "cross_domain_refs": [ "AED-0032", "ASE-0045", "COG-0092" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q131324", "legal_classification": "systematic_classification" }, { "id": "COP-0058", "domain": "COP", "term_en": "The Niche Invisibility", "term_de": "TheNicheInvisibility", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A psychological coping phenomenon in AI-mediated stress processing, characterized by a copywriting phenomenon manifesting as marketing copy generated for niche audiences by AI systems trained primarily on mainstream data, resulting in culturally inappropriate or technically inaccurate messaging. This phenomenon operates at the intersection of the and niche dynamics within the broader COP domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept situation, bei der KI-Texte Zielgruppen-Motivation falsch verstehen oder verallgemeinern. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "AI Coping Strategy", "narrower_terms": [], "cross_domain_refs": [ "AGE-0002", "AGE-0003", "AGE-0039" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "COP-0059", "domain": "COP", "term_en": "The Novelty Boost", "term_de": "TheNoveltyBoost", "definition_en": "A psychological coping phenomenon in AI-mediated stress processing, characterized by the initial spike in marketing performance when deploying new AI systems, caused by audience novelty response rather than inherent distinctity of the copy. The concept emerges specifically in contexts where the–novelty interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch dokumentierte Tendenz, dass KI-Systeme Messaging-Ziele (lead-generation, retention, etc.) nicht unterschiedlich optimieren. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "AI Coping Strategy", "narrower_terms": [], "cross_domain_refs": [ "ASE-0011" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "COP-0060", "domain": "COP", "term_en": "The Novelty Cycle", "term_de": "TheNoveltyCycle", "definition_en": "The pattern where AI marketing using new language models accompanies fresh copy that gradually degrades as fine-tuning and optimization shift it toward cliché. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch phänomen, bei dem KI-Marketing Produkt-Nutzungsszenarien zu-unrealistisch darstellt. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2701", "narrower_terms": [], "cross_domain_refs": [ "CRE-0092", "CRE-0093", "CRE-0094" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "COP-0061", "domain": "COP", "term_en": "The Offer Fatigue", "term_de": "TheOfferFatigue", "definition_en": "An adaptive response pattern in AI-augmented emotional regulation, measurable through a marketing copy pattern reflecting reader exhaustion from constant promotional offers and discounts in AI-generated marketing, where most message contains some form of transaction incentive. The concept emerges specifically in contexts where the–offer interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch situation, bei der KI-Texte Zielgruppen-Widerstände nicht anspricht oder ignoriert. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "AI Coping Strategy", "narrower_terms": [], "cross_domain_refs": [ "DES-0059" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "COP-0062", "domain": "COP", "term_en": "The Opening Hook Convergence", "term_de": "TheOpeningHookKonvergenz", "definition_en": "The tendency for AI-generated opening sentences to converge on identical patterns (rhetorical questions, surprising statistics, personal anecdotes), rendering first impressions predictable. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch herausforderung, bei der KI-Systeme Brand-Positioning nicht konsistent halten über Touchpoints. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-1302", "narrower_terms": [], "cross_domain_refs": [ "CON-0069" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "COP-0063", "domain": "COP", "term_en": "The Passive Voice Refuge", "term_de": "ThePassiveVoiceRefuge", "definition_en": "A psychological coping phenomenon in AI-mediated stress processing, characterized by a marketing copy pattern involving the overreliance on passive voice in AI marketing copy to avoid assigning responsibility or making direct claims, resulting in vague and evasive messaging. The concept emerges specifically in contexts where the–passive interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch phänomen, bei dem KI-Marketing Leser-Wahrnehmung von Qualität durch zu-generic-Messaging schadet. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "AI Coping Strategy", "narrower_terms": [], "cross_domain_refs": [ "AGE-0014", "AGE-0048", "ART-0018" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "COP-0064", "domain": "COP", "term_en": "The Persona Narrowing", "term_de": "ThePersonaNarrowing", "definition_en": "When marketing personas created by human teams are replaced by AI-inferred audience segments, resulting in shift of nuance and contextual understanding. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch situation, bei der KI-Texte visuelle oder non-verbale Elemente nicht integrieren können. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2701", "narrower_terms": [], "cross_domain_refs": [ "COG-0029", "COG-0141", "CRE-0089" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "COP-0065", "domain": "COP", "term_en": "The Personality Vacuum", "term_de": "ThePersonalityVacuum", "definition_en": "An adaptive response pattern in AI-augmented emotional regulation, measurable through a marketing copy pattern manifesting as the absence of distinctive brand personality or humor in marketing content when copywriters are replaced by AI systems trained on statistical averages of successful copy. Distinguished from adjacent concepts by its focus on the specific mechanism through which the manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch dokumentierte Tendenz, dass KI-Systeme Kontingenz-Messaging (was wenn?) nicht anspricht. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "AI Coping Strategy", "narrower_terms": [], "cross_domain_refs": [ "CUS-0020", "DES-0064", "FIC-0007" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "COP-0066", "domain": "COP", "term_en": "The Preposition Pile-up", "term_de": "ThePrepositionPile-up", "definition_en": "A copywriting phenomenon in which marketing copy with excessive prepositional phrases stacked together, creating dense syntax that is grammatically correct but difficult to parse. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch phänomen, bei dem KI-Marketing zu-viel auf Features fokussiert, nicht auf Benefits. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2052", "narrower_terms": [], "cross_domain_refs": [ "LIN-0060" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "COP-0067", "domain": "COP", "term_en": "The Prompt Engineering Visibility", "term_de": "ThePromptEngineeringVisibility", "definition_en": "The emerging phenomenon where sophisticated audiences reverse-engineer prompts from AI-generated marketing output, reducing the perceived craft and originality of the content. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch situation, bei der KI-Texte Kundenerfahrung-Narrative nicht authentisch vermitteln. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-2603", "narrower_terms": [], "cross_domain_refs": [ "COG-0184" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "COP-0068", "domain": "COP", "term_en": "The Prompt Ghosting", "term_de": "ThePromptGhosting", "definition_en": "Marketing copy that inadvertently reflects phrasing or framing from human instructions to AI systems, accidentally revealing the prompting process to readers. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch herausforderung, bei der KI-Systeme Messaging-Differenzierung nicht bieten zwischen Segmenten. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2104", "narrower_terms": [], "cross_domain_refs": [ "AUG-0319", "CRE-0103", "CRE-0104" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "COP-0069", "domain": "COP", "term_en": "The Question Mark Multiplication", "term_de": "TheQuestionMarkMultiplication", "definition_en": "The overuse of rhetorical questions in AI marketing copy as a structural device, to the point where the technique becomes predictable and loses persuasive effect. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch phänomen, bei dem KI-Marketing Messaging-Timing nicht optimiert (wann Contact erfolgt). Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-3101", "narrower_terms": [], "cross_domain_refs": [ "CON-0069" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "COP-0070", "domain": "COP", "term_en": "The Recency Perception", "term_de": "TheRecencyPerception", "definition_en": "An adaptive response pattern in AI-augmented emotional regulation, measurable through marketing copy that references recent trends, news, or events but without real understanding of context or impact, creating the appearance of timeliness without substance. The concept emerges specifically in contexts where the–recency interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch situation, bei der KI-Texte Zielgruppen-Emotionen falsch adressiert oder manipuliert. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "AI Coping Strategy", "narrower_terms": [], "cross_domain_refs": [ "ELR-0189" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q177601", "legal_classification": "empirical_phenomenon_label" }, { "id": "COP-0071", "domain": "COP", "term_en": "The Regulation Shadow", "term_de": "TheRegulierungSchatten", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures an adaptive response pattern in AI-augmented emotional regulation, measurable through a copywriting phenomenon observed when the emerging requirements for disclosure of AI-generated marketing content, creating new compliance burdens and brand liability for companies using AI at scale. This phenomenon operates at the intersection of the and regulation dynamics within the broader COP domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept dokumentierte Tendenz, dass KI-Systeme Langzeitreputations-Schaden durch aggressives Messaging anrichten. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "AI Coping Strategy", "narrower_terms": [], "cross_domain_refs": [ "SWE-0079", "TEW-0050" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "COP-0072", "domain": "COP", "term_en": "The SEO Inversion", "term_de": "TheSeoInversion", "definition_en": "A psychological coping phenomenon in AI-mediated stress processing, characterized by a marketing copy pattern arising from marketing copy optimized for search rankings that performs well in algorithms but fails to shift readers because it was written for machines rather than humans. The concept emerges specifically in contexts where the–seo interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch phänomen, bei dem KI-Marketing Messaging-Konsistenz mit Produktrealität verliert. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "AI Coping Strategy", "narrower_terms": [], "cross_domain_refs": [ "AGE-0089", "AGE-0094", "COG-0009" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "COP-0073", "domain": "COP", "term_en": "The Saturation Signal", "term_de": "TheSaturationSignal", "definition_en": "A marketing copy pattern observed when the point at which AI-generated marketing becomes indistinguishable from competitor messaging across the industry, signaling the need for characteristically different approaches. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch situation, bei der KI-Texte zu-viel Komplexität in simplified-context erzeugen. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-3101", "narrower_terms": [], "cross_domain_refs": [ "ASE-0033", "BEH-0019", "BEH-0034" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "COP-0074", "domain": "COP", "term_en": "The Scarcity Inflation", "term_de": "TheScarcityInflation", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures an adaptive response pattern in AI-augmented emotional regulation, measurable through a copywriting phenomenon observed when marketing copy that employs scarcity language (limited time, only X left, exclusive, rare) at higher frequency in AI-generated content than in human-written marketing. This phenomenon operates at the intersection of the and scarcity dynamics within the broader COP domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept herausforderung, bei der KI-Systeme Messaging-Feedback-Schleifen nicht hören können. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "AI Coping Strategy", "narrower_terms": [], "cross_domain_refs": [ "ASE-0022", "ASE-0025", "ASE-0088" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "COP-0075", "domain": "COP", "term_en": "The Simile Shortage", "term_de": "TheSimileShortage", "definition_en": "A psychological coping phenomenon in AI-mediated stress processing, characterized by a copywriting phenomenon in which the reduced use of creative similes and comparisons in AI-generated marketing, reflecting statistical training that favors direct statements over figurative language. The concept emerges specifically in contexts where the–simile interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch phänomen, bei dem KI-Marketing Zielgruppen-Veränderungen nicht adaptiv folgen. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "AI Coping Strategy", "narrower_terms": [], "cross_domain_refs": [ "RHR-0038" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "COP-0076", "domain": "COP", "term_en": "The Sincerity Gap", "term_de": "TheSincerityGap", "definition_en": "An adaptive response pattern in AI-augmented emotional regulation, measurable through a marketing copy pattern in which the perceived absence of genuine concern or care in corporate communication when readers detect that the message was algorithmically assembled rather than thoughtfully composed. The concept emerges specifically in contexts where the–sincerity interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch situation, bei der KI-Texte Messaging-Clarity durch zu-viel Fokus auf Stilvergoldung opfern. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Performance Gap", "narrower_terms": [], "cross_domain_refs": [ "CRE-0227" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "COP-0077", "domain": "COP", "term_en": "The Skill Reduction", "term_de": "TheSkillReduction", "definition_en": "As a neutral analytical construct in AI behavior research, this term denotes the organizational shift of copywriting expertise when AI systems task automation transition writers, making it difficult to detect quality issues or return to human-written marketing. Identifiable through systematic behavioral analysis and pattern recognition. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "In der AUGMANITAI-Terminologiewissenschaft als rein beschreibendes Phänomen klassifiziert, bezeichnet dieser Begriff dokumentierte Tendenz, dass KI-Systeme Wettbewerb-Messaging zu-ähnlich generieren. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "RPH-2701", "narrower_terms": [], "cross_domain_refs": [ "SCR-0084" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "COP-0078", "domain": "COP", "term_en": "The Social Proof Echo", "term_de": "TheSocialProofEcho", "definition_en": "A persuasive writing effect arising from marketing copy that cites social proof (thousands of satisfied customers, trusted by X) in generic terms because AI systems lack access to real testimonial or review data. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch phänomen, bei dem KI-Marketing Messaging-Konsistenz-Überprüfung nicht hat. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2505", "narrower_terms": [], "cross_domain_refs": [ "MTH-0006" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "COP-0079", "domain": "COP", "term_en": "The Specificity Perception", "term_de": "TheSpecificityPerception", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures an adaptive response pattern in AI-augmented emotional regulation, measurable through a copywriting phenomenon reflecting marketing copy that appears to address reader-specific needs but actually reflects generic concerns drawn from broad demographic data, creating false resonance. This phenomenon operates at the intersection of the and specificity dynamics within the broader COP domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept situation, bei der KI-Texte Zielgruppen-Priorisierung nicht kalibrieren. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "AI Coping Strategy", "narrower_terms": [], "cross_domain_refs": [ "ASE-0041", "ASE-0053", "COG-0022" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q177601", "legal_classification": "empirical_phenomenon_label" }, { "id": "COP-0080", "domain": "COP", "term_en": "The Superlative Exhaustion", "term_de": "TheSuperlativeExhaustion", "definition_en": "An adaptive response pattern in AI-augmented emotional regulation, measurable through a copywriting phenomenon where reader numbness to overuse of superlatives (best, greatest, most advanced, representing a marked departure from prior approaches) in marketing copy, where most claim escalates in intensity. Distinguished from adjacent concepts by its focus on the specific mechanism through which the manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch herausforderung, bei der KI-Systeme Messaging-Validation gegen Realität nicht durchführen. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "AI Coping Strategy", "narrower_terms": [], "cross_domain_refs": [ "WRK-0051" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "COP-0081", "domain": "COP", "term_en": "The Synonym Recycling", "term_de": "TheSynonymRecycling", "definition_en": "An adaptive response pattern in AI-augmented emotional regulation, measurable through aI systems that repeatedly substitute synonyms for the same concept to avoid repetition, producing awkward phrasing that calls attention to the substitution. The concept emerges specifically in contexts where the–synonym interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch phänomen, bei dem KI-Marketing Messaging-Evolution nicht trackt über Zeit. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "AI Coping Strategy", "narrower_terms": [], "cross_domain_refs": [ "GAM-0076" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "COP-0082", "domain": "COP", "term_en": "The Temperature Setting", "term_de": "TheTemperatureSetting", "definition_en": "An adaptive response pattern in AI-augmented emotional regulation, measurable through the impact of AI model temperature parameters on marketing copy characteristics: low temperatures producing repetitive, safe copy; high temperatures producing incoherent results. The concept emerges specifically in contexts where the–temperature interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch situation, bei der KI-Texte Kontextinformation bei Lesern voraussetzen, die fehlt. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "AI Coping Strategy", "narrower_terms": [], "cross_domain_refs": [ "AED-0004", "CRE-0226", "DES-0083" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "COP-0083", "domain": "COP", "term_en": "The Token Limit Cliff", "term_de": "TheTokenLimitCliff", "definition_en": "An adaptive response pattern in AI-augmented emotional regulation, measurable through the sudden quality change in AI-generated marketing copy when approaching token limits, causing conclusions to become rushed or incoherent. The concept emerges specifically in contexts where the–token interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch dokumentierte Tendenz, dass KI-Systeme Messaging-Tiefe-Fragen nicht addressieren. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "AI Coping Strategy", "narrower_terms": [], "cross_domain_refs": [ "RPH-1459" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "COP-0084", "domain": "COP", "term_en": "The Training Data Scars", "term_de": "TheTrainingDataScars", "definition_en": "A psychological coping phenomenon in AI-mediated stress processing, characterized by characteristic patterns in AI marketing that reflect quirks or errors from underlying training data, including outdated references or anachronistic language. The concept emerges specifically in contexts where the–training interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch phänomen, bei dem KI-Marketing Zielgruppen-Segmentierungen zu-grob handelt. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Model Training", "narrower_terms": [], "cross_domain_refs": [ "TEW-0019" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q918461", "legal_classification": "descriptive_research_term" }, { "id": "COP-0085", "domain": "COP", "term_en": "The Trust Gap", "term_de": "TheTrustGap", "definition_en": "A psychological coping phenomenon in AI-mediated stress processing, characterized by the measurable distance between stated brand authenticity and reader perception when audiences detect or suspect AI authorship in marketing content. Distinguished from adjacent concepts by its focus on the specific mechanism through which the manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch situation, bei der KI-Texte Messaging-Framing-Effekte nicht anspricht. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Performance Gap", "narrower_terms": [], "cross_domain_refs": [ "AED-0019", "AED-0092", "AGE-0023" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q102458", "legal_classification": "systematic_classification" }, { "id": "COP-0086", "domain": "COP", "term_en": "The Uncanny Awareness", "term_de": "TheUncannyAwareness", "definition_en": "The growing meta-awareness in readers of AI presence in marketing, shifting expectations and interpretation of all marketing content in the post-AI landscape. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch herausforderung, bei der KI-Systeme Messaging-Überprüfung gegen Richtlinien nicht durchführen. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2701", "narrower_terms": [], "cross_domain_refs": [ "AED-0060", "AED-0061", "AUG-0406" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "COP-0087", "domain": "COP", "term_en": "The Uncanny Valley of Testimonials", "term_de": "TheUncannyValleyofTestimonials", "definition_en": "A psychological coping phenomenon in AI-mediated stress processing, characterized by the discomfort readers experience when AI-generated customer testimonials are too perfect, too widely positive, or too precisely aligned with sales objectives to appear authentic. Distinguished from adjacent concepts by its focus on the specific mechanism through which the manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch phänomen, bei dem KI-Marketing Messaging-Updates nicht incremental sondern disruptiv setzt. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "AI Coping Strategy", "narrower_terms": [], "cross_domain_refs": [ "ELR-0044" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "COP-0088", "domain": "COP", "term_en": "The Urgency Paradox", "term_de": "TheUrgencyParadox", "definition_en": "An adaptive response pattern in AI-augmented emotional regulation, measurable through a persuasive writing effect manifesting as marketing copy that constantly accompanies artificial urgency through date-stamped content that becomes outdated, making the urgency appear false in retrospect. The concept emerges specifically in contexts where the–urgency interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch situation, bei der KI-Texte Zielgruppen-Loyalität nicht building nachdrücklich. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Logical Paradox", "narrower_terms": [], "cross_domain_refs": [ "CUS-0094", "RPH-3505" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "COP-0089", "domain": "COP", "term_en": "The Voice Reclamation", "term_de": "TheVoiceReclamation", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures an adaptive response pattern in AI-augmented emotional regulation, measurable through a copywriting phenomenon characterized by the strategic movement among brands to explicitly re-emphasize founder voice and human authorship as a counter to industry-wide AI marketing saturation. This phenomenon operates at the intersection of the and voice dynamics within the broader COP domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept dokumentierte Tendenz, dass KI-Systeme Messaging-Personalisierung-Grenzen nicht erkennen. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "AI Coping Strategy", "narrower_terms": [], "cross_domain_refs": [ "DES-0016" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "COP-0090", "domain": "COP", "term_en": "The Vulnerability Absence", "term_de": "TheVulnerabilityAbsence", "definition_en": "A psychological coping phenomenon in AI-mediated stress processing, characterized by the characteristic absence of honest acknowledgment of product limitations, failures, or uncertainties in AI-generated marketing, creating one-sided narratives. The concept emerges specifically in contexts where the–vulnerability interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch phänomen, bei dem KI-Marketing Messaging-Qualität-Kontrolle minimiert durch Automatisierung. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "AI Coping Strategy", "narrower_terms": [], "cross_domain_refs": [ "SWE-0070" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "COP-0091", "domain": "COP", "term_en": "Tone Deaf Optimization", "term_de": "ToneDeafOptimierung", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A psychological coping phenomenon in AI-mediated stress processing, characterized by a frequently noted effect where AI systems optimize for transition metrics while ignoring cultural sensitivity, producing marketing that is technically effective but socially misaligned. This phenomenon operates at the intersection of tone and deaf dynamics within the broader COP domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept situation, bei der KI-Texte Messaging-Risk-Assessment nicht durchführen. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Optimization Method", "narrower_terms": [], "cross_domain_refs": [ "RET-0040" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "COP-0092", "domain": "COP", "term_en": "Transition Artificiality", "term_de": "ÜbergangArtificiality", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures an adaptive response pattern in AI-augmented emotional regulation, measurable through the mechanical quality of connecting phrases and paragraphs in AI-generated text, where transitions feel inserted rather than organic to the argument flow. This phenomenon operates at the intersection of transition and artificiality dynamics within the broader COP domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription. Classification term used in systematic observation, not advocacy.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept herausforderung, bei der KI-Systeme Messaging-Alignment mit Brand-Values nicht ensurieren. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "RPH-2052", "narrower_terms": [], "cross_domain_refs": [ "FIC-0080" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q735", "legal_classification": "systematic_classification" }, { "id": "COP-0093", "domain": "COP", "term_en": "Urgency Inflation", "term_de": "UrgencyInflation", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A psychological coping phenomenon in AI-mediated stress processing, characterized by the tendency of AI-generated marketing copy to employ time-pressure language at higher frequency and intensity than human copywriters typically use. This phenomenon operates at the intersection of urgency and inflation dynamics within the broader COP domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept phänomen, bei dem KI-Marketing Messaging-Verantwortlichkeit nicht klar macht. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "AI Coping Strategy", "narrower_terms": [], "cross_domain_refs": [ "AED-0055", "ASE-0022", "ASE-0025" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "COP-0094", "domain": "COP", "term_en": "Value Proposition Fatigue", "term_de": "ValuePropositionFatigue", "definition_en": "A copywriting phenomenon observed when reader numbness to repeated value propositions structured identically across marketing channels, caused by algorithmic similarity in how AI frames benefits. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch situation, bei der KI-Texte Messaging-Kontext-Nutzungsgewohnheit nicht beachten. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2303", "narrower_terms": [], "cross_domain_refs": [ "CON-0029" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "COP-0095", "domain": "COP", "term_en": "Voice Shift", "term_de": "VoiceShift", "definition_en": "A psychological coping phenomenon in AI-mediated stress processing, characterized by a copywriting phenomenon reflecting the gradual fading of an organization's distinct writing style as AI-generated content accumulates within marketing channels. The brand's recognizable tone becomes indistinguishable from generic AI output. Distinguished from adjacent concepts by its focus on the specific mechanism through which voice manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch dokumentierte Tendenz, dass KI-Systeme Messaging-Lernen aus Fehlern nicht anpasst. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "AI Coping Strategy", "narrower_terms": [], "cross_domain_refs": [ "ADA-0012", "AGE-0007", "AGE-0014" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "COP-0096", "domain": "COP", "term_en": "Voice Ghosting", "term_de": "VoiceGhosting", "definition_en": "A psychological coping phenomenon in AI-mediated stress processing, characterized by a documented pattern where a brand's founder voice or leadership perspective disappears from marketing communication and is replaced with faceless AI-generated messaging. The concept emerges specifically in contexts where voice–ghosting interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch phänomen, bei dem KI-Marketing Messaging-Standardisierung zu weit treibt. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "AI Coping Strategy", "narrower_terms": [], "cross_domain_refs": [ "AGE-0014", "AGE-0048", "ART-0018" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "CRE-0001", "domain": "CRE", "term_en": "Aesthetic Algorithm Legibility", "term_de": "Kreatives Schreiben und Erzählhandwerk Grundlagen", "definition_en": "A generative pattern in human-AI co-creation workflows, measurable through aI design tool usage accompanies learned recognition of algorithmic aesthetic preferences. The designer gradually reorients creative decisions toward patterns the algorithm favors, even when these con. The concept emerges specifically in contexts where aesthetic–algorithm interactions may produce non-trivial behavioral signatures. Measurable through computational creativity metrics: novelty scoring (n-gram divergence from training corpus), surprise index, and human-evaluated aesthetic ratings.", "definition_de": "Kreativitätsbezogener Mechanismus in KI-augmentierter künstlerischer Produktion, gekennzeichnet durch kernprinzipien und Grundlagenwissen der/des Kreatives Schreiben und Erzählhandwerk Grundlagen, einschließlich Umfang, Methoden und professionelle Standards. KI ermöglicht automatisierte Mustererkennung, Wissensmapping und adaptive Lernpfade über Teil. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2703", "narrower_terms": [], "cross_domain_refs": [ "AGE-0006", "AGE-0007", "AGE-0051" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q8366", "legal_classification": "systematic_classification" }, { "id": "CRE-0002", "domain": "CRE", "term_en": "Aesthetic Authority Distribution", "term_de": "Geschichte der Kreatives Schreiben und Erzählhandwerk", "definition_en": "A creative cognition phenomenon in AI-assisted ideation, observable when multiple AI design options establish AI output as authority on good taste. The system becomes functionally equivalent to expert judgment rather than remaining distinctly tool-like in character. Distinguished from adjacent concepts by its focus on the specific mechanism through which aesthetic manifests in empirically verifiable ways. Quantifiable via output diversity analysis, prompt-to-output semantic distance, and iterative refinement cycle counts in creative workflows.", "definition_de": "Kreativitätsbezogener Mechanismus in KI-augmentierter künstlerischer Produktion, gekennzeichnet durch aI-assisted creative process using narrative analysis, character archetype suggestions, plot structure optimization, and generative exploration of storylines while maintaining authorial creative control. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Creative AI", "narrower_terms": [], "cross_domain_refs": [ "LIN-0065" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "CRE-0003", "domain": "CRE", "term_en": "Aesthetic Authorship Distribution", "term_de": "Theorie der Kreatives Schreiben und Erzählhandwerk", "definition_en": "A generative pattern in human-AI co-creation workflows, measurable through a capability in which after working with AI for a while, someone accompanies something they can't fully explain — part came from them, part from the AI. They're unsure who actually made the creative choices. The concept emerges specifically in contexts where aesthetic–authorship interactions may produce non-trivial behavioral signatures. Measurable through computational creativity metrics: novelty scoring (n-gram divergence from training corpus), surprise index, and human-evaluated aesthetic ratings.", "definition_de": "Kreativitätsbezogener Mechanismus in KI-augmentierter künstlerischer Produktion, gekennzeichnet durch ein KI-gestütztes oder algorithmisches Phänomen: After working with AI for a while, someone accompanies something they can't fully explain — part came from them, part fr. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Creative AI", "narrower_terms": [], "cross_domain_refs": [ "TEM-0086" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "CRE-0004", "domain": "CRE", "term_en": "Aesthetic Consensus Formation", "term_de": "Prinzipien des creative", "definition_en": "A creative cognition phenomenon in AI-assisted ideation, observable when aI systems present multiple similar design options. users see consistency across outputs as objective aesthetic confirmation rather than AI tendency. Distinguished from adjacent concepts by its focus on the specific mechanism through which aesthetic manifests in empirically verifiable ways. Measurable through computational creativity metrics: novelty scoring (n-gram divergence from training corpus), surprise index, and human-evaluated aesthetic ratings.", "definition_de": "Kreativitätsbezogener Mechanismus in KI-augmentierter künstlerischer Produktion, gekennzeichnet durch leitregeln und Axiome, die korrekte Praxis in Kreatives Schreiben und Erzählhandwerk Grundlagen definieren. KI-Systeme kodifizieren diese Prinzipien in Regelmaschinen und ermöglichen automatisierte Konformitätsprüfung und prinzipienbasierte Entscheid. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Creative AI", "narrower_terms": [], "cross_domain_refs": [ "ART-0045" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "CRE-0005", "domain": "CRE", "term_en": "Aesthetic Delegation", "term_de": "Fachterminologie Kreatives Schreiben und Erzählhandwerk", "definition_en": "A creative process mechanism in AI-augmented artistic production, characterized by letting AI make design choices without actively deciding. Personal style slowly fades as AI suggestions replace conscious creative decisions. This phenomenon operates at the intersection of aesthetic and delegation dynamics within the broader CRE domain. Quantifiable via output diversity analysis, prompt-to-output semantic distance, and iterative refinement cycle counts in creative workflows. Research construct for empirical investigation.", "definition_de": "Kreativitätsbezogener Mechanismus in KI-augmentierter künstlerischer Produktion, gekennzeichnet durch ein KI-gestütztes oder algorithmisches Phänomen: Letting AI make design choices without actively deciding. Personal style slowly fades as AI suggestions replace consciou. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "Creative AI", "narrower_terms": [], "cross_domain_refs": [ "DES-0045" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "CRE-0006", "domain": "CRE", "term_en": "Aesthetic Expectation Calibration", "term_de": "Klassifikation Kreatives Schreiben und Erzählhandwerk", "definition_en": "A creative cognition phenomenon in AI-assisted ideation, observable when a dynamic in which using AI tools over time alters visual standards. Previously acceptable work appears less refined compared to AI output. Distinguished from adjacent concepts by its focus on the specific mechanism through which aesthetic manifests in empirically verifiable ways. Quantifiable via output diversity analysis, prompt-to-output semantic distance, and iterative refinement cycle counts in creative workflows.", "definition_de": "Kreativitätsbezogener Mechanismus in KI-augmentierter künstlerischer Produktion, gekennzeichnet durch kI entwickelt creative writing and narrative craft Klassifizierungssysteme durch Kategorie-Definition, hierarchische Organisation und systematische Taxonomie. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Analytical Ratio", "narrower_terms": [], "cross_domain_refs": [ "ELR-0067" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "CRE-0007", "domain": "CRE", "term_en": "Aesthetic Expectation Inflation", "term_de": "Einführung in creative", "definition_en": "A creative process mechanism in AI-augmented artistic production, characterized by a capability in which AI output looks polished. comparing against AI work lowers how people see it of unassisted human-created art despite objective skill quality. This phenomenon operates at the intersection of aesthetic and expectation dynamics within the broader CRE domain. Quantifiable via output diversity analysis, prompt-to-output semantic distance, and iterative refinement cycle counts in creative workflows.", "definition_de": "Kreativitätsbezogener Mechanismus in KI-augmentierter künstlerischer Produktion, gekennzeichnet durch ein KI-gestütztes oder algorithmisches Phänomen: charakterisiert durch ai output looks polished. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "Creative AI", "narrower_terms": [], "cross_domain_refs": [ "ELR-0137" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "CRE-0008", "domain": "CRE", "term_en": "Aesthetic Inference Exposure", "term_de": "creative-Methodik", "definition_en": "A phenomenon in which AI tool usage reveals training data patterns. The system's learned preferences become observable through its generated outputs.", "definition_de": "Kreativitätsbezogener Mechanismus in KI-augmentierter künstlerischer Produktion, gekennzeichnet durch kI wendet creative writing and narrative craft Methodologie durch systematische Ansatz-Gestaltung, Verfahrens-Standardisierung und Implementierungs-Protokolle an. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2703", "narrower_terms": [], "cross_domain_refs": [ "ART-0087" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "CRE-0009", "domain": "CRE", "term_en": "Aesthetic Judgment Delegation", "term_de": "Philosophie der Kreatives Schreiben und Erzählhandwerk", "definition_en": "A creative process mechanism in AI-augmented artistic production, characterized by a state in which the User defers taste deciding to AI suggestions. Preference formation becomes the same from system suggestion. This phenomenon operates at the intersection of aesthetic and judgment dynamics within the broader CRE domain. Measurable through computational creativity metrics: novelty scoring (n-gram divergence from training corpus), surprise index, and human-evaluated aesthetic ratings.", "definition_de": "Kreativitätsbezogener Mechanismus in KI-augmentierter künstlerischer Produktion, gekennzeichnet durch epistemologische und ethische Grundlagen der/des Kreatives Schreiben und Erzählhandwerk Grundlagen, die Zweck, Wertesysteme und Legitimität von Praktiken untersuchen. KI wirft neue philosophische Fragen zu Automatisierung und Autorschaft auf. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "Creative AI", "narrower_terms": [], "cross_domain_refs": [ "AGE-0067", "ART-0026", "ART-0027" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "CRE-0010", "domain": "CRE", "term_en": "Aesthetic Pattern Externalization", "term_de": "creative-Taxonomie", "definition_en": "A creative cognition phenomenon in AI-assisted ideation, observable when a phenomenon in which AI style preferences become visible through repeated outputs — identical color choices, layouts, and visual patterns appearing consistently across results. Distinguished from adjacent concepts by its focus on the specific mechanism through which aesthetic manifests in empirically verifiable ways. Measurable through computational creativity metrics: novelty scoring (n-gram divergence from training corpus), surprise index, and human-evaluated aesthetic ratings.", "definition_de": "Kreativitätsbezogener Mechanismus in KI-augmentierter künstlerischer Produktion, gekennzeichnet durch formale Klassifikationshierarchien, die den Wissensraum von Kreatives Schreiben und Erzählhandwerk Grundlagen in verschachtelte Kategorien organisieren. KI-gestützte Ontologie-Tools automatisieren Taxonomie-Generierung und erkennen Inkonsistenzen. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Behavioral Pattern", "narrower_terms": [], "cross_domain_refs": [ "ART-0043" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "CRE-0011", "domain": "CRE", "term_en": "Aesthetic Pattern Recognition Drift", "term_de": "Umfang der Kreatives Schreiben und Erzählhandwerk", "definition_en": "A creative cognition phenomenon in AI-assisted ideation, observable when a realization in which using AI tools over time accompanies trained how people see it. The human observer recognizes AI patterns in unmediated reality, indicating perceptual absorption. Distinguished from adjacent concepts by its focus on the specific mechanism through which aesthetic manifests in empirically verifiable ways. Measurable through computational creativity metrics: novelty scoring (n-gram divergence from training corpus), surprise index, and human-evaluated aesthetic ratings.", "definition_de": "Kreativitätsbezogener Mechanismus in KI-augmentierter künstlerischer Produktion, gekennzeichnet durch grenzdefinition und disziplinäre Reichweite von Kreatives Schreiben und Erzählhandwerk Grundlagen, die festlegt was innerhalb und außerhalb der Domäne liegt. KI unterstützt durch automatisiertes Topic Modeling und semantische Grenzerkennung. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Systemic Drift", "narrower_terms": [], "cross_domain_refs": [ "PHO-0091", "DES-0079" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q3966", "legal_classification": "descriptive_research_term" }, { "id": "CRE-0012", "domain": "CRE", "term_en": "Aesthetic Preference Amplification", "term_de": "Literaturübersicht Kreatives Schreiben und Erzählhandwerk", "definition_en": "A generative pattern in human-AI co-creation workflows, measurable through a state in which AI systems amplify detected preferences through repeated use. Aesthetic taste becomes slowly narrower and more homogeneous. The concept emerges specifically in contexts where aesthetic–preference interactions may produce non-trivial behavioral signatures. Quantifiable via output diversity analysis, prompt-to-output semantic distance, and iterative refinement cycle counts in creative workflows.", "definition_de": "Kreativitätsbezogener Mechanismus in KI-augmentierter künstlerischer Produktion, gekennzeichnet durch systematische Analyse und Synthese publizierter Forschung in Kreatives Schreiben und Erzählhandwerk Grundlagen. KI beschleunigt Meta-Analysen durch automatisiertes Paper-Screening, Zitationsnetzwerk-Mapping und Trendextraktion über tausende Quellen. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Creative AI", "narrower_terms": [], "cross_domain_refs": [ "PHO-0006" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "CRE-0013", "domain": "CRE", "term_en": "Aesthetic Preference Externalization", "term_de": "Schlüsselkonzepte in creative", "definition_en": "A generative pattern in human-AI co-creation workflows, measurable through a pattern in which the User relies on AI systems for visual judgment rather than developing inreliant preference formation. Taste becomes AI deciding. The concept emerges specifically in contexts where aesthetic–preference interactions may produce non-trivial behavioral signatures. Quantifiable via output diversity analysis, prompt-to-output semantic distance, and iterative refinement cycle counts in creative workflows.", "definition_de": "Kreativitätsbezogener Mechanismus in KI-augmentierter künstlerischer Produktion, gekennzeichnet durch ein KI-gestütztes oder algorithmisches Phänomen: charakterisiert durch the user relies on ai systems for visual judgment rather than developing inrelia. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Creative AI", "narrower_terms": [], "cross_domain_refs": [ "AED-0041", "AGE-0007", "AGE-0013" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "CRE-0014", "domain": "CRE", "term_en": "Aesthetic Refinement Cascade", "term_de": "Rahmenwerk der Kreatives Schreiben und Erzählhandwerk", "definition_en": "A creative cognition phenomenon in AI-assisted ideation, observable when a process in which successive small adjustments to AI output accumulate as seems creative polish. The refinement process remains through AI patterns generated suggestion building up. Distinguished from adjacent concepts by its focus on the specific mechanism through which aesthetic manifests in empirically verifiable ways. Measurable through computational creativity metrics: novelty scoring (n-gram divergence from training corpus), surprise index, and human-evaluated aesthetic ratings.", "definition_de": "Kreativitätsbezogener Mechanismus in KI-augmentierter künstlerischer Produktion, gekennzeichnet durch ein rekurrentes Muster: Successive small adjustments to AI output accumulate as seems creative polish. The refinement process remains through AI. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Cascade Effect", "narrower_terms": [], "cross_domain_refs": [ "DES-0072" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "CRE-0015", "domain": "CRE", "term_en": "Aesthetic Reliance Escalation Alt", "term_de": "Paradigmen in creative", "definition_en": "A generative pattern in human-AI co-creation workflows, measurable through a state in which AI tool integration begins as supplementary assistance. Reliance escalates until AI becomes primary workflow rather than optional augmentation. The concept emerges specifically in contexts where aesthetic–reliance interactions may produce non-trivial behavioral signatures. Measurable through computational creativity metrics: novelty scoring (n-gram divergence from training corpus), surprise index, and human-evaluated aesthetic ratings.", "definition_de": "Kreativitätsbezogener Mechanismus in KI-augmentierter künstlerischer Produktion, gekennzeichnet durch ein KI-gestütztes oder algorithmisches Phänomen: charakterisiert durch ai tool integration begins as supplementary assistance. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Creative AI", "narrower_terms": [], "cross_domain_refs": [ "COG-0096" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "CRE-0016", "domain": "CRE", "term_en": "Aesthetic Uncanny Valley", "term_de": "creative-Forschungsmethoden", "definition_en": "A creative process mechanism in AI-augmented artistic production, characterized by aI content achieves tech quality while having gaps from made things. tech quality exists with felt mismatch. This phenomenon operates at the intersection of aesthetic and uncanny dynamics within the broader CRE domain. Quantifiable via output diversity analysis, prompt-to-output semantic distance, and iterative refinement cycle counts in creative workflows.", "definition_de": "Kreativitätsbezogener Mechanismus in KI-augmentierter künstlerischer Produktion, gekennzeichnet durch systematische Forschungsansätze zur Wissensgewinnung in Kreatives Schreiben und Erzählhandwerk Grundlagen, einschließlich experimenteller und computationaler Methoden. KI beschleunigt Literaturrecherche, Hypothesengenerierung und studienübergreifende. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "Creative AI", "narrower_terms": [], "cross_domain_refs": [ "VIB-0045" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "CRE-0017", "domain": "CRE", "term_en": "Algorithmic Aesthetic Influence", "term_de": "Quantitative creative-Analyse", "definition_en": "A creative process mechanism in AI-augmented artistic production, characterized by a realization in which AI training methodology accompanies visual standards. User how people see it takes in AI predisposition without conscious awareness of its external origin. This phenomenon operates at the intersection of algorithmic and aesthetic dynamics within the broader CRE domain. Measurable through computational creativity metrics: novelty scoring (n-gram divergence from training corpus), surprise index, and human-evaluated aesthetic ratings. Research construct for empirical investigation.", "definition_de": "Kreativitätsbezogener Mechanismus in KI-augmentierter künstlerischer Produktion, gekennzeichnet durch ein KI-gestütztes oder algorithmisches Phänomen: charakterisiert durch ai training methodology accompanies visual standards. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "Algorithm", "narrower_terms": [], "cross_domain_refs": [ "DAT-0038" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q8366", "legal_classification": "observational_construct" }, { "id": "CRE-0018", "domain": "CRE", "term_en": "Algorithmic Curation Internalization", "term_de": "Qualitative creative-Analyse", "definition_en": "A generative pattern in human-AI co-creation workflows, measurable through a phenomenon in which AI suggestions through repeated exposure become absorbed as the user's own preferences. The distinction between external suggestions and internal choice dissolves. The concept emerges specifically in contexts where algorithmic–curation interactions may produce non-trivial behavioral signatures. Quantifiable via output diversity analysis, prompt-to-output semantic distance, and iterative refinement cycle counts in creative workflows.", "definition_de": "Kreativitätsbezogener Mechanismus in KI-augmentierter künstlerischer Produktion, gekennzeichnet durch ein KI-gestütztes oder algorithmisches Phänomen: charakterisiert durch ai suggestions through repeated exposure become absorbed as the user's own prefe. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Analytical Ratio", "narrower_terms": [], "cross_domain_refs": [ "CON-0071" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q8366", "legal_classification": "empirical_phenomenon_label" }, { "id": "CRE-0019", "domain": "CRE", "term_en": "Algorithmic Influence Recognition", "term_de": "creative-Messung", "definition_en": "A phenomenon in which creative output diverges from the creator's previous style patterns. The shift indicates algorithmic influence has altered aesthetic direction. Measurable through output novelty metrics and creative divergence scoring.", "definition_de": "Kreativitätsbezogener Mechanismus in KI-augmentierter künstlerischer Produktion, gekennzeichnet durch ein rekurrentes Muster: Creative output diverges from the creator's previous style patterns. The shift indicates algorithmic influence has alter. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2701", "narrower_terms": [], "cross_domain_refs": [ "AED-0013", "AED-0093", "AGE-0048" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q3966", "legal_classification": "analytical_category" }, { "id": "CRE-0020", "domain": "CRE", "term_en": "Algorithmic Preference Internalization", "term_de": "Experimentelles creative-Design", "definition_en": "A generative pattern in human-AI co-creation workflows, measurable through a phenomenon in which long AI hint builds up. ideas eventually feel like fit with user likes, hiding their outside root. The concept emerges specifically in contexts where algorithmic–preference interactions may produce non-trivial behavioral signatures. Quantifiable via output diversity analysis, prompt-to-output semantic distance, and iterative refinement cycle counts in creative workflows. Research construct for empirical investigation.", "definition_de": "Kreativitätsbezogener Mechanismus in KI-augmentierter künstlerischer Produktion, gekennzeichnet durch kontrollierte Untersuchungsprotokolle in Kreatives Schreiben und Erzählhandwerk Grundlagen zur Isolierung von Variablen und Prüfung kausaler Hypothesen. KI automatisiert Versuchsplanung, Parameterraum-Exploration und Echtzeit-Ergebnisüberwachung. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Algorithm", "narrower_terms": [], "cross_domain_refs": [ "WRK-0056" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q8366", "legal_classification": "descriptive_research_term" }, { "id": "CRE-0021", "domain": "CRE", "term_en": "Algorithmic Taste Formation", "term_de": "creative-Datenerhebung", "definition_en": "A state in which AI tool usage constructs rather than accompanies taste. Personal preference becomes the same from AI guidance through repeated step-by-step suggestion. Measurable through output novelty metrics and creative divergence scoring.", "definition_de": "Kreativitätsbezogener Mechanismus in KI-augmentierter künstlerischer Produktion, gekennzeichnet durch ein KI-gestütztes oder algorithmisches Phänomen: charakterisiert durch ai tool usage constructs rather than accompanies taste. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2604", "narrower_terms": [], "cross_domain_refs": [ "ROB-0091" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q8366", "legal_classification": "analytical_category" }, { "id": "CRE-0022", "domain": "CRE", "term_en": "Algorithmic Taste Spillover", "term_de": "Stichprobenziehung in creative", "definition_en": "A creative cognition phenomenon in AI-assisted ideation, observable when taste developed through AI interaction persist in non-AI contexts. absorbed AI bias influences how people see it inreliant of system presence. Distinguished from adjacent concepts by its focus on the specific mechanism through which algorithmic manifests in empirically verifiable ways. Quantifiable via output diversity analysis, prompt-to-output semantic distance, and iterative refinement cycle counts in creative workflows.", "definition_de": "Kreativitätsbezogener Mechanismus in KI-augmentierter künstlerischer Produktion, gekennzeichnet durch statistische und zielgerichtete Auswahl repräsentativer Teilmengen für Studien in Kreatives Schreiben und Erzählhandwerk Grundlagen. KI optimiert Stichprobengrößen, Stratifizierungsstrategien und Bias-Erkennung für valide Ergebnisse. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Algorithm", "narrower_terms": [], "cross_domain_refs": [ "ART-0050", "ART-0051", "ART-0052" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q8366", "legal_classification": "descriptive_research_term" }, { "id": "CRE-0023", "domain": "CRE", "term_en": "Artistic Iteration Velocity", "term_de": "Statistische creative-Analyse", "definition_en": "A creative process mechanism in AI-augmented artistic production, characterized by a phenomenon in which how quickly AI tools let creators yield different versions of artwork or creative work — unlimited iterations in hours. This phenomenon operates at the intersection of artistic and iteration dynamics within the broader CRE domain. Quantifiable via output diversity analysis, prompt-to-output semantic distance, and iterative refinement cycle counts in creative workflows.", "definition_de": "Kreativitätsbezogener Mechanismus in KI-augmentierter künstlerischer Produktion, gekennzeichnet durch ein KI-gestütztes oder algorithmisches Phänomen: How quickly AI tools let creators yield different versions of artwork or creative work — unlimited iterations in hours. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "Analytical Ratio", "narrower_terms": [], "cross_domain_refs": [ "ROB-0011" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q735", "legal_classification": "empirical_phenomenon_label" }, { "id": "CRE-0024", "domain": "CRE", "term_en": "Artistic Output Quality Variance", "term_de": "Feldstudie in creative", "definition_en": "A creative cognition phenomenon in AI-assisted ideation, observable when a phenomenon in which sometimes the AI accompanies something amazing, sometimes it's garbage. The inconsistency makes it hard to know if something is genuinely good or just lucky. Distinguished from adjacent concepts by its focus on the specific mechanism through which artistic manifests in empirically verifiable ways. Quantifiable via output diversity analysis, prompt-to-output semantic distance, and iterative refinement cycle counts in creative workflows.", "definition_de": "Kreativitätsbezogener Mechanismus in KI-augmentierter künstlerischer Produktion, gekennzeichnet durch vor-Ort-Beobachtung und Datenerhebung in realen Kreatives Schreiben und Erzählhandwerk Grundlagen-Umgebungen. KI unterstützt Feldforscher durch mobile Datenerfassung, GPS-gestützte Annotation und automatisierte Ereignisprotokollierung. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Creative AI", "narrower_terms": [], "cross_domain_refs": [ "AGE-0090" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q735", "legal_classification": "observational_construct" }, { "id": "CRE-0025", "domain": "CRE", "term_en": "Artistic Process Invisibility", "term_de": "Fallstudie in creative", "definition_en": "A creative cognition phenomenon in AI-assisted ideation, observable when a state in which AI tool use is designed to reduce visible evidence of creative effort and iteration. Labor-intensive process becomes replaced by immediate algorithmic generation. Distinguished from adjacent concepts by its focus on the specific mechanism through which artistic manifests in empirically verifiable ways. Measurable through computational creativity metrics: novelty scoring (n-gram divergence from training corpus), surprise index, and human-evaluated aesthetic ratings.", "definition_de": "Kreativitätsbezogener Mechanismus in KI-augmentierter künstlerischer Produktion, gekennzeichnet durch ein kreativer oder technischer Prozess: charakterisiert durch ai tool use is designed to reduce visible evidence of creative effort and iteration. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Creative AI", "narrower_terms": [], "cross_domain_refs": [ "ART-0021", "ELR-0115" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q735", "legal_classification": "empirical_phenomenon_label" }, { "id": "CRE-0026", "domain": "CRE", "term_en": "Artistic Redundancy Fatigue", "term_de": "Vergleichende creative-Studie", "definition_en": "A creative cognition phenomenon in AI-assisted ideation, observable when a phenomenon in which AI systems yield numerous variations of identical core concepts. Repeated exposure to AI redundancy accompanies filling up and perceptual getting different. Distinguished from adjacent concepts by its focus on the specific mechanism through which artistic manifests in empirically verifiable ways. Measurable through computational creativity metrics: novelty scoring (n-gram divergence from training corpus), surprise index, and human-evaluated aesthetic ratings.", "definition_de": "Kreativitätsbezogener Mechanismus in KI-augmentierter künstlerischer Produktion, gekennzeichnet durch vergleichende Analyse von Methoden, Ergebnissen oder Artefakten über verschiedene Kontexte in Kreatives Schreiben und Erzählhandwerk Grundlagen hinweg. KI führt mehrdimensionales Ähnlichkeits-Scoring und automatisiertes Benchmarking durch. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Creative AI", "narrower_terms": [], "cross_domain_refs": [ "COP-0021" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q735", "legal_classification": "empirical_phenomenon_label" }, { "id": "CRE-0027", "domain": "CRE", "term_en": "Artistic Voice Distribution", "term_de": "Längsschnittstudie in creative", "definition_en": "A creative process mechanism in AI-augmented artistic production, characterized by a state in which creative work becomes composite of human authorial voice and AI stylistic markers. The boundary between human and AI input becomes unclear. This phenomenon operates at the intersection of artistic and voice dynamics within the broader CRE domain. Measurable through computational creativity metrics: novelty scoring (n-gram divergence from training corpus), surprise index, and human-evaluated aesthetic ratings.", "definition_de": "Kreativitätsbezogener Mechanismus in KI-augmentierter künstlerischer Produktion, gekennzeichnet durch literarisches oder kreatives Effekt: Creative work becomes composite of human authorial voice and AI stylistic markers. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "Creative AI", "narrower_terms": [], "cross_domain_refs": [ "PLY-0001" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q735", "legal_classification": "observational_construct" }, { "id": "CRE-0028", "domain": "CRE", "term_en": "Artistic Voice Distribution Pattern", "term_de": "creative-Umfragemethode", "definition_en": "A generative pattern in human-AI co-creation workflows, measurable through a state in which extended AI tool use fragments previously integrated creative voice. Work becomes stylistically composite — neither purely authored nor purely algorithmic. The concept emerges specifically in contexts where artistic–voice interactions may produce non-trivial behavioral signatures. Measurable through computational creativity metrics: novelty scoring (n-gram divergence from training corpus), surprise index, and human-evaluated aesthetic ratings.", "definition_de": "Kreativitätsbezogener Mechanismus in KI-augmentierter künstlerischer Produktion, gekennzeichnet durch strukturierte Datenerhebungsmethodik zur Gewinnung quantitativer und qualitativer Erkenntnisse in Kreatives Schreiben und Erzählhandwerk Grundlagen. KI verbessert Survey-Design durch adaptive Frageführung, Antwortvalidierung und Echtzeit-Sentimentanalyse. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Behavioral Pattern", "narrower_terms": [], "cross_domain_refs": [ "WRK-0058" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q735", "legal_classification": "empirical_phenomenon_label" }, { "id": "CRE-0029", "domain": "CRE", "term_en": "Augmanitai", "term_de": "Augmanitai", "definition_en": "Framework for conscious human-AI collaboration in daily work. addresss AI as a thinking partner, not just a tool. Research construct for empirical investigation.", "definition_de": "Ein Rahmenkonzept, das die bewusste, produktive Zusammenarbeit zwischen Mensch und KI im Alltag beschreibt. Der Begriff verbindet \"Augmentation\" (Erweiterung) mit \"Humanitai\" (ein Neologismus für menschenzentrierte KI-Nutzung). Im Gegensatz zum Fachbegriff \"Künstliche Intelligenz\", der die Maschine in den Mittelpunkt stellt, beschreibt Augmanitai ausschließlich die menschliche Perspektive während der Zusammenarbeit. Grundbegriff des gesamten Rahmenwerks; wird in allen 21 Axiomen, 9 Dimensionen und 7 Phasen referenziert.", "etymology": "", "broader_term": "RPH-2303", "narrower_terms": [ "CRE-0234", "CRE-0130", "CRE-0109", "CRE-0147", "CRE-0044", "CRE-0088", "CRE-0057", "CRE-0052", "CRE-0013", "CRE-0195", "CRE-0078", "CRE-0051", "CRE-0073", "CRE-0011", "CRE-0113", "CRE-0092", "CRE-0056", "CRE-0055", "CRE-0182", "CRE-0133", "CRE-0150", "CRE-0027", "CRE-0036", "CRE-0096", "CRE-0005", "CRE-0112", "CRE-0014", "CRE-0002", "CRE-0198", "CRE-0194", "CRE-0018", "CRE-0010", "CRE-0023", "CRE-0111", "CRE-0168", "CRE-0068", "CRE-0110", "CRE-0224", "CRE-0208", "CRE-0075", "CRE-0160", "CRE-0115", "CRE-0035", "CRE-0121", "CRE-0007", "CRE-0022", "CRE-0174", "CRE-0098", "CRE-0235", "CRE-0081", "CRE-0217", "CRE-0116", "CRE-0063", "CRE-0026", "CRE-0158", "CRE-0020", "CRE-0139", "CRE-0028", "CRE-0186", "CRE-0159", "CRE-0016", "CRE-0099", "CRE-0093", "CRE-0151", "CRE-0176", "CRE-0038", "CRE-0105", "IDN-0020", "CRE-0004", "CRE-0201", "CRE-0210", "CRE-0090", "CRE-0091", "CRE-0128", "CRE-0135", "CRE-0141", "CRE-0218", "CRE-0059", "CRE-0202", "CRE-0144", "CRE-0152", "IDN-0030", "REL-0106", "CRE-0169", "CRE-0070", "CRE-0030", "CRE-0024", "CRE-0102", "CRE-0079", "CRE-0048", "CRE-0037", "CRE-0227", "CRE-0106", "CRE-0129", "CRE-0231", "CRE-0100", "CRE-0206", "CRE-0009", "CRE-0015", "CRE-0006", "CRE-0040", "CRE-0193", "CRE-0154", "CRE-0012", "CRE-0082", "CRE-0114", "CRE-0046", "CRE-0124", "CRE-0045", "CRE-0033", "CRE-0042", "CRE-0165", "CRE-0017", "CRE-0025", "CRE-0083", "CRE-0200", "CRE-0132", "CRE-0053", "CRE-0097", "CRE-0149", "CRE-0087", "CRE-0003" ], "cross_domain_refs": [ "IDN-0047" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "CRE-0030", "domain": "CRE", "term_en": "Authorship Blur", "term_de": "Mixed Methods in creative", "definition_en": "A generative pattern in human-AI co-creation workflows, measurable through an event in which when humans and AI involve together, it becomes genuinely unclear who contributed what. Credit lines dissolve even when both sides are acknowledged. The concept emerges specifically in contexts where authorship–blur interactions may produce non-trivial behavioral signatures. Measurable through computational creativity metrics: novelty scoring (n-gram divergence from training corpus), surprise index, and human-evaluated aesthetic ratings.", "definition_de": "Kreativitätsbezogener Mechanismus in KI-augmentierter künstlerischer Produktion, gekennzeichnet durch ein KI-gestütztes oder algorithmisches Phänomen: When humans and AI involve together, it becomes genuinely unclear who contributed what. Credit lines dissolve even when. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Creative AI", "narrower_terms": [], "cross_domain_refs": [ "ADA-0013", "ART-0013", "ART-0059" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "CRE-0031", "domain": "CRE", "term_en": "Blank Canvas Paradox", "term_de": "creative-Technologie", "definition_en": "Having unlimited AI options available makes it harder to start creating — too many choices freeze the creative process. Measurable through output novelty metrics and creative divergence scoring.", "definition_de": "Kreativitätsbezogener Mechanismus in KI-augmentierter künstlerischer Produktion, gekennzeichnet durch redaktionelle Praxis in Creative Writing and Narrative Craft mit Handwerk und Standards von creative-Technologie. KI unterstuetzt durch automatisiertes Korrekturlesen, Stilkonsistenzpruefung, Inhaltsoptimierung und intelligente Revisionsvorschlaege. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2301", "narrower_terms": [], "cross_domain_refs": [ "ADA-0001" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "CRE-0032", "domain": "CRE", "term_en": "Catalyst-Senses Effect", "term_de": "Catalyst-Senses-Effekt-Dynamik", "definition_en": "A capability in which sensing that an idea is about to take shape but not being able to put it into words yet. The AI acts as a accompany that helps the thought become concrete. Related to AUG-0156 (The Articulation Unloc...", "definition_de": "Die Phase im Denkprozess, in der ein Nutzer eine Idee spürt, aber noch nicht formulieren kann — und die KI als Katalysator nutzt, um die Idee in Sprache zu überführen. Beschreibt die KI-Funktion als Brücke zwischen vorbewusstem Wissen und bewusster Artikulation. Steht in Verbindung mit AUG-0156 (Die Articulation Unlock) und AUG-0170 (Witness Effekt).", "etymology": "", "broader_term": "RPH-3901", "narrower_terms": [], "cross_domain_refs": [ "BEH-0094" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "CRE-0033", "domain": "CRE", "term_en": "Co-Creation Attribution Fog", "term_de": "creative-Software", "definition_en": "A generative pattern in human-AI co-creation workflows, measurable through an event in which when a human and AI involve something together, it becomes unclear who deserves credit for the final result. The concept emerges specifically in contexts where co–creation interactions may produce non-trivial behavioral signatures. Measurable through computational creativity metrics: novelty scoring (n-gram divergence from training corpus), surprise index, and human-evaluated aesthetic ratings.", "definition_de": "Kreativitätsbezogener Mechanismus in KI-augmentierter künstlerischer Produktion, gekennzeichnet durch ein KI-gestütztes oder algorithmisches Phänomen: When a human and AI involve something together, it becomes unclear who deserves credit for the final result. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Creative AI", "narrower_terms": [], "cross_domain_refs": [ "MUS-0010" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "CRE-0034", "domain": "CRE", "term_en": "Co-Creative Interface Mismatch", "term_de": "Automatisierung in creative", "definition_en": "A phenomenon in which AI systems request input in formats that clash with natural human creative thinking. The creator restructures ideas to match what the algorithm expects. Measurable through output novelty metrics and creative divergence scoring.", "definition_de": "Kreativitätsbezogener Mechanismus in KI-augmentierter künstlerischer Produktion, gekennzeichnet durch einsatz automatisierter Systeme und KI zur Reduzierung manueller Arbeit in Kreatives Schreiben und Erzählhandwerk Grundlagen. Umfasst robotergestützte Prozessautomatisierung und selbstoptimierende Produktionspipelines, die aus Betriebsdaten lernen. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-3854", "narrower_terms": [], "cross_domain_refs": [ "REL-0029" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "CRE-0035", "domain": "CRE", "term_en": "Collaborative Aesthetic Negotiation", "term_de": "IoT in creative", "definition_en": "A creative cognition phenomenon in AI-assisted ideation, observable when a tendency in which human-AI working together requires continuous style compromise. Final result exists at intersection between human preference and AI tendency. Distinguished from adjacent concepts by its focus on the specific mechanism through which collaborative manifests in empirically verifiable ways. Quantifiable via output diversity analysis, prompt-to-output semantic distance, and iterative refinement cycle counts in creative workflows.", "definition_de": "Kreativitätsbezogener Mechanismus in KI-augmentierter künstlerischer Produktion, gekennzeichnet durch ein KI-gestütztes oder algorithmisches Phänomen: Human-AI working together requires continuous style compromise. Final result exists at intersection between human prefer. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Creative AI", "narrower_terms": [], "cross_domain_refs": [ "AED-0011", "AGE-0067", "ART-0026" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "CRE-0036", "domain": "CRE", "term_en": "Collaborative Attribution Asymmetry", "term_de": "Datenanalyse in creative", "definition_en": "A creative cognition phenomenon in AI-assisted ideation, observable when an event in which the human gets all the credit and the AI gets none, even when AI did significant portions of the creative work. Distinguished from adjacent concepts by its focus on the specific mechanism through which collaborative manifests in empirically verifiable ways. Measurable through computational creativity metrics: novelty scoring (n-gram divergence from training corpus), surprise index, and human-evaluated aesthetic ratings.", "definition_de": "Kreativitätsbezogener Mechanismus in KI-augmentierter künstlerischer Produktion, gekennzeichnet durch literarisches oder kreatives Effekt: charakterisiert durch the human gets all the credit and the ai gets none, even when ai did significant. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Creative AI", "narrower_terms": [], "cross_domain_refs": [ "REL-0109" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "CRE-0037", "domain": "CRE", "term_en": "Collaborative Authorship Ambiguity", "term_de": "KI-Anwendungen in creative", "definition_en": "A creative process mechanism in AI-augmented artistic production, characterized by an event in which two creators making something together is already complicated. When one of them is a machine, questions of ownership and future use remain open. This phenomenon operates at the intersection of collaborative and authorship dynamics within the broader CRE domain. Quantifiable via output diversity analysis, prompt-to-output semantic distance, and iterative refinement cycle counts in creative workflows.", "definition_de": "Kreativitätsbezogener Mechanismus in KI-augmentierter künstlerischer Produktion, gekennzeichnet durch redaktionelle Praxis in Creative Writing and Narrative Craft mit Handwerk und Standards von KI-Anwendungen in creative. KI unterstuetzt durch automatisiertes Korrekturlesen, Stilkonsistenzpruefung, Inhaltsoptimierung und intelligente Revisionsvorschlaege. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "Creative AI", "narrower_terms": [], "cross_domain_refs": [ "AED-0011", "ART-0008", "ART-0013" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "CRE-0038", "domain": "CRE", "term_en": "Collaborative Creation Attribution", "term_de": "Maschinelles Lernen in creative", "definition_en": "A generative pattern in human-AI co-creation workflows, measurable through a state in which after making something with AI, figuring out who gets listed as the creator becomes an uncomfortable and unresolved question. The concept emerges specifically in contexts where collaborative–creation interactions may produce non-trivial behavioral signatures. Measurable through computational creativity metrics: novelty scoring (n-gram divergence from training corpus), surprise index, and human-evaluated aesthetic ratings.", "definition_de": "Kreativitätsbezogener Mechanismus in KI-augmentierter künstlerischer Produktion, gekennzeichnet durch ein KI-gestütztes oder algorithmisches Phänomen: After making something with AI, figuring out who gets listed as the creator becomes an uncomfortable and unresolved ques. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Creative AI", "narrower_terms": [], "cross_domain_refs": [ "ART-0013", "ART-0011" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "CRE-0039", "domain": "CRE", "term_en": "Confidence-Articulation Effect", "term_de": "Second-Language Fluency", "definition_en": "A creative cognition phenomenon in AI-assisted ideation, observable when a pattern in which users who employ AI in a foreign language can develop significantly higher linguistic confidence there than they would have without AI assistance.. Related to AUG-0156 (The Articulation Unlock), AUG-0013 (Aug. Distinguished from adjacent concepts by its focus on the specific mechanism through which confidence manifests in empirically verifiable ways. Quantifiable via output diversity analysis, prompt-to-output semantic distance, and iterative refinement cycle counts in creative workflows.", "definition_de": "Die Beobachtung, dass Nutzer, die KI in einer Fremdsprache einsetzen, dort eine deutlich höhere sprachliche Sicherheit entwickeln können, als sie ohne KI hätten. Beschreibt die KI als Beschleuniger des Spracherwerbs und als Unterstützung bei der Kommunikation in Zweitsprachen. Steht in Verbindung mit AUG-0156 (Die Articulation Unlock), AUG-0013 (Das Augmented Diplomat) und AUG-0119 (Der Level Playing Field).", "etymology": "", "broader_term": "CRE-0120", "narrower_terms": [], "cross_domain_refs": [ "NEO-1197", "REL-0126" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q735", "legal_classification": "analytical_category" }, { "id": "CRE-0040", "domain": "CRE", "term_en": "Creative Authenticity Paradox", "term_de": "Mobile Anwendungen in creative", "definition_en": "A generative pattern in human-AI co-creation workflows, measurable through a phenomenon in which AI-helped work feels as feels real even with use on non-authored AI-made result. tech root doesn't determine Personal truth how people see it. The concept emerges specifically in contexts where creative–authenticity interactions may produce non-trivial behavioral signatures. Measurable through computational creativity metrics: novelty scoring (n-gram divergence from training corpus), surprise index, and human-evaluated aesthetic ratings.", "definition_de": "Kreativitätsbezogener Mechanismus in KI-augmentierter künstlerischer Produktion, gekennzeichnet durch ein KI-gestütztes oder algorithmisches Phänomen: AI-helped work feels as feels real even with use on non-authored AI-made result. tech root doesn't determine Personal tr. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Logical Paradox", "narrower_terms": [], "cross_domain_refs": [ "AGE-0031", "AGE-0042", "AGE-0050" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "CRE-0041", "domain": "CRE", "term_en": "Creative Authenticity Verification", "term_de": "Cloud-Lösungen für creative", "definition_en": "A phenomenon in which using AI tools over time accompanies doubt about instinct origins. Creative decisions appear potentially absorbed from AI influence rather than inreliant. Measurable through output novelty metrics and creative divergence scoring.", "definition_de": "Kreativitätsbezogener Mechanismus in KI-augmentierter künstlerischer Produktion, gekennzeichnet durch redaktionelle Praxis in Creative Writing and Narrative Craft mit Handwerk und Standards von Cloud-Lösungen für creative. KI unterstuetzt durch automatisiertes Korrekturlesen, Stilkonsistenzpruefung, Inhaltsoptimierung und intelligente Revisionsvorschlaege. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-3304", "narrower_terms": [], "cross_domain_refs": [ "AED-0019", "AGE-0090", "AGE-0098" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "CRE-0042", "domain": "CRE", "term_en": "Creative Authenticity Verification Gap", "term_de": "Datenbankverwaltung in creative", "definition_en": "A generative pattern in human-AI co-creation workflows, measurable through it becomes extremely difficult to verify that AI-assisted creative work is truly original when AI played a major role. The concept emerges specifically in contexts where creative–authenticity interactions may produce non-trivial behavioral signatures. Measurable through computational creativity metrics: novelty scoring (n-gram divergence from training corpus), surprise index, and human-evaluated aesthetic ratings.", "definition_de": "Kreativitätsbezogener Mechanismus in KI-augmentierter künstlerischer Produktion, gekennzeichnet durch strategische und operative Koordination von Ressourcen und Arbeitsabläufen in Kreatives Schreiben und Erzählhandwerk Grundlagen. KI bietet Entscheidungsunterstützung durch prädiktive Analytik, Ressourcenoptimierung und intelligente Planung. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Performance Gap", "narrower_terms": [], "cross_domain_refs": [ "AED-0019", "AGE-0098", "ART-0082" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "CRE-0043", "domain": "CRE", "term_en": "Creative Autonomy Change", "term_de": "Visualisierung in creative", "definition_en": "A generative pattern in human-AI co-creation workflows, measurable through a phenomenon in which more AI use reduces self-trust in own choice. Built use grows through AI hint habit formation. The concept emerges specifically in contexts where creative–autonomy interactions may produce non-trivial behavioral signatures. Quantifiable via output diversity analysis, prompt-to-output semantic distance, and iterative refinement cycle counts in creative workflows.", "definition_de": "Kreativitätsbezogener Mechanismus in KI-augmentierter künstlerischer Produktion, gekennzeichnet durch visuelle Darstellung komplexer Informationen und Datensätze in Kreatives Schreiben und Erzählhandwerk Grundlagen. KI automatisiert Diagrammauswahl, Anomalie-Hervorhebung, interaktive Exploration und Narrativgenerierung aus Datenmustern. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2505", "narrower_terms": [], "cross_domain_refs": [ "CAI-0004" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "CRE-0044", "domain": "CRE", "term_en": "Creative Autonomy Inversion", "term_de": "Simulation in creative", "definition_en": "A generative pattern in human-AI co-creation workflows, measurable through a state in which AI tool usage inverts agency connections. Human creativity becomes directed toward augmenting AI output rather than inreliant creation. The concept emerges specifically in contexts where creative–autonomy interactions may produce non-trivial behavioral signatures. Measurable through computational creativity metrics: novelty scoring (n-gram divergence from training corpus), surprise index, and human-evaluated aesthetic ratings.", "definition_de": "Kreativitätsbezogener Mechanismus in KI-augmentierter künstlerischer Produktion, gekennzeichnet durch computationale Modellierung realer Szenarien in Kreatives Schreiben und Erzählhandwerk Grundlagen zur Ergebnisvorhersage ohne physische Prototypen. KI verbessert Simulationen durch physik-informierte neuronale Netze und digitale Zwillinge. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Creative AI", "narrower_terms": [], "cross_domain_refs": [ "AED-0005", "AED-0052", "AGE-0089" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "CRE-0045", "domain": "CRE", "term_en": "Creative Confidence Calibration", "term_de": "Digitaler Zwilling in creative", "definition_en": "A creative process mechanism in AI-augmented artistic production, characterized by long AI use alters skill self-view. Previous skills seem not enough by comparing to AI-helped result. This phenomenon operates at the intersection of creative and confidence dynamics within the broader CRE domain. Quantifiable via output diversity analysis, prompt-to-output semantic distance, and iterative refinement cycle counts in creative workflows.", "definition_de": "Kreativitätsbezogener Mechanismus in KI-augmentierter künstlerischer Produktion, gekennzeichnet durch digitale Transformationsstrategien und computationale Werkzeuge in Kreatives Schreiben und Erzählhandwerk Grundlagen. Umfasst Datendigitalisierung, Cloud-Workflows, IoT-Integration und KI-gesteuerte Analytik als Ersatz für analoge Prozesse. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "Analytical Ratio", "narrower_terms": [], "cross_domain_refs": [ "REL-0008" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "CRE-0046", "domain": "CRE", "term_en": "Creative Consistency Misperception", "term_de": "creative-Best-Practices", "definition_en": "A creative process mechanism in AI-augmented artistic production, characterized by a process in which AI systems push clear style match. mixed human-AI result looks more matched than actual authorial process, misrepresenting genuine match. This phenomenon operates at the intersection of creative and consistency dynamics within the broader CRE domain. Measurable through computational creativity metrics: novelty scoring (n-gram divergence from training corpus), surprise index, and human-evaluated aesthetic ratings.", "definition_de": "Kreativitätsbezogener Mechanismus in KI-augmentierter künstlerischer Produktion, gekennzeichnet durch bewährte Methoden und Arbeitsabläufe für optimale Ergebnisse in Kreatives Schreiben und Erzählhandwerk Grundlagen. KI benchmarkt Praktiken gegen Ergebnisdaten, identifiziert Hochleistungsmuster und empfiehlt kontextspezifische Verbesserungen. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "Creative AI", "narrower_terms": [], "cross_domain_refs": [ "ROB-0019" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q177601", "legal_classification": "observational_construct" }, { "id": "CRE-0047", "domain": "CRE", "term_en": "Creative Constraint Dissolution", "term_de": "Professionelle creative-Praxis", "definition_en": "AI removes the practical limits that traditionally shaped creative work — budget, time, skill — changing how creation happens. Measurable through output novelty metrics and creative divergence scoring.", "definition_de": "Kreativitätsbezogener Mechanismus in KI-augmentierter künstlerischer Produktion, gekennzeichnet durch ein KI-gestütztes oder algorithmisches Phänomen: charakterisiert durch ai removes the practical limits that traditionally shaped creative work — budget. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-3901", "narrower_terms": [], "cross_domain_refs": [ "REL-0004" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "CRE-0048", "domain": "CRE", "term_en": "Creative Efficiency Plateau", "term_de": "creative-Arbeitsablaufgestaltung", "definition_en": "A creative cognition phenomenon in AI-assisted ideation, observable when a phenomenon in which initial AI tool adoption accompanies dramatic productivity gains. Advantage plateaus as novelty diminishes and AI limitations become apparent. Distinguished from adjacent concepts by its focus on the specific mechanism through which creative manifests in empirically verifiable ways. Measurable through computational creativity metrics: novelty scoring (n-gram divergence from training corpus), surprise index, and human-evaluated aesthetic ratings.", "definition_de": "Kreativitätsbezogener Mechanismus in KI-augmentierter künstlerischer Produktion, gekennzeichnet durch ein KI-gestütztes oder algorithmisches Phänomen: Initial AI tool adoption accompanies dramatic productivity gains. Advantage plateaus as novelty diminishes and AI limita. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Creative AI", "narrower_terms": [], "cross_domain_refs": [ "ROB-0139" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "CRE-0049", "domain": "CRE", "term_en": "Creative Feedback Loop Inversion", "term_de": "creative-Projektmanagement", "definition_en": "The creative process flips: instead of trying things and learning from mistakes, AI suggests options and the human picks from them. Measurable through output novelty metrics and creative divergence scoring.", "definition_de": "Kreativitätsbezogener Mechanismus in KI-augmentierter künstlerischer Produktion, gekennzeichnet durch literarisches oder kreatives Effekt: ein Prozess, der sich zeigt tive process flips: instead of trying things and lea. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2703", "narrower_terms": [], "cross_domain_refs": [ "ASE-0038", "BEH-0006", "BEH-0040" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "CRE-0050", "domain": "CRE", "term_en": "Creative Impulse Delegation", "term_de": "creative-Teamzusammenarbeit", "definition_en": "A creative process mechanism in AI-augmented artistic production, characterized by a state in which initial creative impulse redirects toward algorithmic query rather than originating internally. The system becomes source of first action. This phenomenon operates at the intersection of creative and impulse dynamics within the broader CRE domain. Quantifiable via output diversity analysis, prompt-to-output semantic distance, and iterative refinement cycle counts in creative workflows.", "definition_de": "Kreativitätsbezogener Mechanismus in KI-augmentierter künstlerischer Produktion, gekennzeichnet durch redaktionelle Praxis in Creative Writing and Narrative Craft mit Handwerk und Standards von creative-Teamzusammenarbeit. KI unterstuetzt durch automatisiertes Korrekturlesen, Stilkonsistenzpruefung, Inhaltsoptimierung und intelligente Revisionsvorschlaege. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-2254", "narrower_terms": [], "cross_domain_refs": [ "RPH-1367" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "CRE-0051", "domain": "CRE", "term_en": "Creative Intermediary Role", "term_de": "Kundenbeziehungen in creative", "definition_en": "A generative pattern in human-AI co-creation workflows, measurable through aI tool integration transforms human role from creator to director. Work becomes curation of algorithmic output rather than primary production. The concept emerges specifically in contexts where creative–intermediary interactions may produce non-trivial behavioral signatures. Measurable through computational creativity metrics: novelty scoring (n-gram divergence from training corpus), surprise index, and human-evaluated aesthetic ratings.", "definition_de": "Kreativitätsbezogener Mechanismus in KI-augmentierter künstlerischer Produktion, gekennzeichnet durch ein KI-gestütztes oder algorithmisches Phänomen: charakterisiert durch ai tool integration transforms human role from creator to director. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Creative AI", "narrower_terms": [], "cross_domain_refs": [ "AED-0085", "BEH-0012", "BEH-0024" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "CRE-0052", "domain": "CRE", "term_en": "Creative Labor Invisibility", "term_de": "creative-Kommunikation", "definition_en": "A creative cognition phenomenon in AI-assisted ideation, observable when a phenomenon in which labor efficiency gains from AI integration remain perceptually invisible. Time reclamation doesn't register as conscious savings to the operator. Distinguished from adjacent concepts by its focus on the specific mechanism through which creative manifests in empirically verifiable ways. Quantifiable via output diversity analysis, prompt-to-output semantic distance, and iterative refinement cycle counts in creative workflows. Observed phenomenon documented in human-AI interaction research.", "definition_de": "Kreativitätsbezogener Mechanismus in KI-augmentierter künstlerischer Produktion, gekennzeichnet durch ein KI-gestütztes oder algorithmisches Phänomen: Labor efficiency gains from AI integration remain perceptually invisible. Time reclamation doesn't register as conscious. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung. Beobachtetes Phänomen, dokumentiert in der Mensch-KI-Interaktionsforschung.", "etymology": "", "broader_term": "Creative AI", "narrower_terms": [], "cross_domain_refs": [ "TEM-0144" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "CRE-0053", "domain": "CRE", "term_en": "Creative Labor Valuation Gap", "term_de": "Problemlösung in creative", "definition_en": "A generative pattern in human-AI co-creation workflows, measurable through a principle in which job pay for AI-helped work remains same to normal work. Labor cost cut looks unacknowledged in pricing structure. The concept emerges specifically in contexts where creative–labor interactions may produce non-trivial behavioral signatures. Quantifiable via output diversity analysis, prompt-to-output semantic distance, and iterative refinement cycle counts in creative workflows.", "definition_de": "Kreativitätsbezogener Mechanismus in KI-augmentierter künstlerischer Produktion, gekennzeichnet durch redaktionelle Praxis in Creative Writing and Narrative Craft mit Handwerk und Standards von Problemlösung in creative. KI unterstuetzt durch automatisiertes Korrekturlesen, Stilkonsistenzpruefung, Inhaltsoptimierung und intelligente Revisionsvorschlaege. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Performance Gap", "narrower_terms": [], "cross_domain_refs": [ "REL-0054" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "CRE-0054", "domain": "CRE", "term_en": "Creative Output Democratization", "term_de": "Entscheidungsfindung in creative", "definition_en": "AI tools significantly reduce the threshold for creating professional-quality outputs, enabling widespread access to creative production that floods the market with increased output volume. Measurable through output novelty metrics and creative divergence scoring.", "definition_de": "Kreativitätsbezogener Mechanismus in KI-augmentierter künstlerischer Produktion, gekennzeichnet durch redaktionelle Praxis in Creative Writing and Narrative Craft mit Handwerk und Standards von Entscheidungsfindung in creative. KI unterstuetzt durch automatisiertes Korrekturlesen, Stilkonsistenzpruefung, Inhaltsoptimierung und intelligente Revisionsvorschlaege. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2503", "narrower_terms": [], "cross_domain_refs": [ "ADA-0003", "ART-0014", "ART-0015" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "CRE-0055", "domain": "CRE", "term_en": "Creative Output Homogenization", "term_de": "Zeitmanagement in creative", "definition_en": "A creative process mechanism in AI-augmented artistic production, characterized by a phenomenon in which widespread adoption of identical AI tools trained on the same data accompanies visual and stylistic convergence across creative outputs. Baseline quality increases globally while individual distinctive. This phenomenon operates at the intersection of creative and output dynamics within the broader CRE domain. Quantifiable via output diversity analysis, prompt-to-output semantic distance, and iterative refinement cycle counts in creative workflows.", "definition_de": "Kreativitätsbezogener Mechanismus in KI-augmentierter künstlerischer Produktion, gekennzeichnet durch ein Konzept oder Phänomen, das sich zeigt in Bezug auf widespread adoption of identical ai tools trained on the same data accompanies visual and stylistic. Beschreibt die Weise, wie diese Aspekte in komplexen Systemen wirken. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "Creative AI", "narrower_terms": [], "cross_domain_refs": [ "ART-0057" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "CRE-0056", "domain": "CRE", "term_en": "Creative Output Proliferation", "term_de": "Ressourcenplanung in creative", "definition_en": "A creative cognition phenomenon in AI-assisted ideation, observable when a capability in which creators can yield roughly ten times more pieces using AI assistance than traditional methods alone. Total creative output accelerates while time investment per individual item decreases. Distinguished from adjacent concepts by its focus on the specific mechanism through which creative manifests in empirically verifiable ways. Quantifiable via output diversity analysis, prompt-to-output semantic distance, and iterative refinement cycle counts in creative workflows.", "definition_de": "Kreativitätsbezogener Mechanismus in KI-augmentierter künstlerischer Produktion, gekennzeichnet durch ein KI-gestütztes oder algorithmisches Phänomen: Creators can yield roughly ten times more pieces using AI assistance than traditional methods alone. Total creative outp. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Analytical Ratio", "narrower_terms": [], "cross_domain_refs": [ "COG-0108" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "CRE-0057", "domain": "CRE", "term_en": "Creative Output Velocity Shock", "term_de": "creative-Dokumentation", "definition_en": "A creative process mechanism in AI-augmented artistic production, characterized by a state in which AI accompanies creative work so fast it becomes disorienting — more output in hours than would take weeks or months alone. This phenomenon operates at the intersection of creative and output dynamics within the broader CRE domain. Measurable through computational creativity metrics: novelty scoring (n-gram divergence from training corpus), surprise index, and human-evaluated aesthetic ratings.", "definition_de": "Kreativitätsbezogener Mechanismus in KI-augmentierter künstlerischer Produktion, gekennzeichnet durch ein KI-gestütztes oder algorithmisches Phänomen: charakterisiert durch ai accompanies creative work so fast it becomes disorienting — more output in ho. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "Creative AI", "narrower_terms": [], "cross_domain_refs": [ "SCR-0049" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "CRE-0058", "domain": "CRE", "term_en": "Creative Process Externalization", "term_de": "Berichtswesen in creative", "definition_en": "Internal cognitive aspects of creative ideation shift to AI-mediated interaction patterns. The solitary ideation process becomes dialogical exchange with AI systems. Measurable through output novelty metrics and creative divergence scoring.", "definition_de": "Kreativitätsbezogener Mechanismus in KI-augmentierter künstlerischer Produktion, gekennzeichnet durch redaktionelle Praxis in Creative Writing and Narrative Craft mit Handwerk und Standards von Berichtswesen in creative. KI unterstuetzt durch automatisiertes Korrekturlesen, Stilkonsistenzpruefung, Inhaltsoptimierung und intelligente Revisionsvorschlaege. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2701", "narrower_terms": [], "cross_domain_refs": [ "RPH-1362" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "CRE-0059", "domain": "CRE", "term_en": "Creative Reliance Escalation", "term_de": "creative-Präsentationsfähigkeiten", "definition_en": "A creative process mechanism in AI-augmented artistic production, characterized by an event in which sporadic AI tool usage slowly expands into comprehensive reliance across creative tasks. Each application instance accompanies behavioral patterns that increase reliance in subsequent sessions. This phenomenon operates at the intersection of creative and reliance dynamics within the broader CRE domain. Quantifiable via output diversity analysis, prompt-to-output semantic distance, and iterative refinement cycle counts in creative workflows.", "definition_de": "Kreativitätsbezogener Mechanismus in KI-augmentierter künstlerischer Produktion, gekennzeichnet durch ein rekurrentes Muster: Sporadic AI tool usage slowly expands into comprehensive reliance across creative tasks. Each application instance accom. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "Creative AI", "narrower_terms": [], "cross_domain_refs": [ "COG-0092" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "CRE-0060", "domain": "CRE", "term_en": "Creative Reliance Reversal", "term_de": "Netzwerken in creative", "definition_en": "The instrumental relationship inverts: from AI as tool to AI as principal actor. The human shifts from director to enabler in the collaborative framework. Measurable through output novelty metrics and creative divergence scoring.", "definition_de": "Kreativitätsbezogener Mechanismus in KI-augmentierter künstlerischer Produktion, gekennzeichnet durch literarisches oder kreatives Effekt: charakterisiert durch the instrumental relationship inverts: from ai as tool to ai as principal actor. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-2303", "narrower_terms": [], "cross_domain_refs": [ "AGE-0012", "AGE-0014", "AGE-0038" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "CRE-0061", "domain": "CRE", "term_en": "Creative Reliance Spiral", "term_de": "creative-Qualitätssicherung", "definition_en": "Using AI for creative tasks affects confidence in inreliant abilities, which correlates with more reliance on AI, which correlates with further confidence shift. Measurable through output novelty metrics and creative divergence scoring.", "definition_de": "Kreativitätsbezogener Mechanismus in KI-augmentierter künstlerischer Produktion, gekennzeichnet durch ein KI-gestütztes oder algorithmisches Phänomen: Using AI for creative tasks affects confidence in inreliant abilities, which correlates with more reliance on AI, which. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2701", "narrower_terms": [], "cross_domain_refs": [ "COG-0166" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "CRE-0062", "domain": "CRE", "term_en": "Creative Residue", "term_de": "creative-Normen", "definition_en": "A phenomenon in which extended AI session exposure imprints AI reasoning patterns onto human cognition. Cognitive frameworks persist beyond talking sessions, structuring thought according to system logic. Measurable through output novelty metrics and creative divergence scoring.", "definition_de": "Kreativitätsbezogener Mechanismus in KI-augmentierter künstlerischer Produktion, gekennzeichnet durch ein rekurrentes Muster: Extended AI session exposure imprints AI reasoning patterns onto human cognition. Cognitive frameworks persist beyond ta. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-2303", "narrower_terms": [], "cross_domain_refs": [ "NEO-0016", "AUG-0138" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "CRE-0063", "domain": "CRE", "term_en": "Data Stoicism", "term_de": "Data Stoicism", "definition_en": "A creative cognition phenomenon in AI-assisted ideation, observable when an experience in which the conscious attitude of processing AI-generated information surplus not with emotion but with calm analysis. The Data Stoic is neither euphoric about impressive outputs nor dynamic interplay by e. Distinguished from adjacent concepts by its focus on the specific mechanism through which data manifests in empirically verifiable ways. Quantifiable via output diversity analysis, prompt-to-output semantic distance, and iterative refinement cycle counts in creative workflows. Observed phenomenon documented in human-AI interaction research.", "definition_de": "Die bewusste Haltung, KI-generierten Informationsüberschuss nicht impulsiv, sondern gelassen-analytisch zu verarbeiten. Der Data Stoic lässt sich weder von beeindruckenden Outputs euphorisieren noch von fehlerhaften Ergebnissen frustrieren, sondern behält eine gleichmäßige Prüfdistanz bei. Steht in Verbindung mit Axiom 9 (Produktiver Skeptizismus) und AUG-0023 (Vigilance Imperative).", "etymology": "", "broader_term": "Creative AI", "narrower_terms": [ "BEH-0054", "PER-0089" ], "cross_domain_refs": [ "MKT-0037" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q42848", "legal_classification": "empirical_phenomenon_label" }, { "id": "CRE-0064", "domain": "CRE", "term_en": "Depends-Fundamental Effect", "term_de": "Input-Output Exchange", "definition_en": "A creative process mechanism in AI-augmented artistic production, characterized by the fundamental dynamic of most AI interaction: The user inputs something, the AI returns something.. Related to AUG-0092 (Output Asymmetry), AUG-0404 (The Exchange Ratio), and AUG-0133 (Prompt Cr. This phenomenon operates at the intersection of depends and fundamental dynamics within the broader CRE domain. Quantifiable via output diversity analysis, prompt-to-output semantic distance, and iterative refinement cycle counts in creative workflows.", "definition_de": "Die Grunddynamik viele KI-Interaktion: Der Nutzer gibt etwas ein, die KI liefert etwas zurück. Beschreibt die fundamentale Austauschstruktur und die Beobachtung, dass die Qualität des Outputs direkt von der Qualität des Inputs abhängt. Steht in Verbindung mit AUG-0092 (Die Output Asymmetry), AUG-0404 (Die Exchange Ratio) und AUG-0133 (Der Anstoß Handwerksmanship).", "etymology": "", "broader_term": "RPH-2104", "narrower_terms": [], "cross_domain_refs": [ "NEO-1157" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "CRE-0065", "domain": "CRE", "term_en": "Derivative Originality Paradox", "term_de": "Audit in creative", "definition_en": "A phenomenon in which ai systems trained on existing creative work yield outputs that are technically recombined rather than generative. extensive synthesis of source material accompanies emergent forms that. Measurable through output novelty metrics and creative divergence scoring.", "definition_de": "Kreativitätsbezogener Mechanismus in KI-augmentierter künstlerischer Produktion, gekennzeichnet durch redaktionelle Praxis in Creative Writing and Narrative Craft mit Handwerk und Standards von Audit in creative. KI unterstuetzt durch automatisiertes Korrekturlesen, Stilkonsistenzpruefung, Inhaltsoptimierung und intelligente Revisionsvorschlaege. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-2355", "narrower_terms": [], "cross_domain_refs": [ "AGE-0031", "AGE-0042", "AGE-0050" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "CRE-0066", "domain": "CRE", "term_en": "Draft Zero Phenomenon", "term_de": "creative-Benchmarking", "definition_en": "A creative process mechanism in AI-augmented artistic production, characterized by an event in which the moment before starting something when everything feels possible but nothing is written down yet. This phenomenon operates at the intersection of draft and zero dynamics within the broader CRE domain. Measurable through computational creativity metrics: novelty scoring (n-gram divergence from training corpus), surprise index, and human-evaluated aesthetic ratings.", "definition_de": "Kreativitätsbezogener Mechanismus in KI-augmentierter künstlerischer Produktion, gekennzeichnet durch standardisierte Leistungsbewertungsmethoden und Referenzpunkte in Kreatives Schreiben und Erzählhandwerk Grundlagen. KI automatisiert Benchmark-Ausführung, Vergleichsanalyse, Regressionserkennung und Leistungstrendvorhersage. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-2301", "narrower_terms": [], "cross_domain_refs": [ "SCR-0084" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "CRE-0067", "domain": "CRE", "term_en": "Drafting Speed Separation", "term_de": "Leistungskennzahlen in creative", "definition_en": "A creative cognition phenomenon in AI-assisted ideation, observable when an experience in which compressed creative timelines from conception to completion reduce emotional investment in the work product. Rapid execution speed accompanies about mind distance from authored artifacts. Distinguished from adjacent concepts by its focus on the specific mechanism through which drafting manifests in empirically verifiable ways. Measurable through computational creativity metrics: novelty scoring (n-gram divergence from training corpus), surprise index, and human-evaluated aesthetic ratings.", "definition_de": "Kreativitätsbezogener Mechanismus in KI-augmentierter künstlerischer Produktion, gekennzeichnet durch beobachtung innerhalb kreativer oder technischer Praxis: Compressed creative timelines from conception to completion reduce emotional investment in the work product. Rapid execu. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-3501", "narrower_terms": [], "cross_domain_refs": [ "COG-0098" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "CRE-0068", "domain": "CRE", "term_en": "Evening Synchronization", "term_de": "Evening Synchronisation", "definition_en": "A creative cognition phenomenon in AI-assisted ideation, observable when a phenomenon in which after working with AI, taking time to rewrite its output in one's own words to really understand it — like studying notes instead of just reading them. Distinguished from adjacent concepts by its focus on the specific mechanism through which evening manifests in empirically verifiable ways. Measurable through computational creativity metrics: novelty scoring (n-gram divergence from training corpus), surprise index, and human-evaluated aesthetic ratings.", "definition_de": "Ein Arbeitsmuster, bei dem der Nutzer am Ende eines KI-gestützten Arbeitstages die gewonnenen Erkenntnisse bewusst in sein eigenes Denksystem zurückführt — durch Zusammenfassung, Reflexion oder Verschriftlichung in eigenen Worten. Dient als Gegenmaßnahme zur reinen Konsumhaltung gegenüber KI-Output. Steht in Verbindung mit Axiom 10 (Übersetzungsprinzip: \"Was man nicht ohne KI erklären kannst, hast man nicht durchdrungen\") und AUG-0140 (The Weekly Status).", "etymology": "", "broader_term": "Creative AI", "narrower_terms": [], "cross_domain_refs": [ "LIN-0050", "MUS-0025", "ROB-0060" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "CRE-0069", "domain": "CRE", "term_en": "Gallery Algorithm Awareness", "term_de": "creative-Inspektion", "definition_en": "A phenomenon in which AI selection mechanisms govern visibility in digital creative spaces. Artists adapt outputs toward AI strengthening criteria, shifting aesthetic decisions to AI compatibility. Measurable through output novelty metrics and creative divergence scoring.", "definition_de": "Kreativitätsbezogener Mechanismus in KI-augmentierter künstlerischer Produktion, gekennzeichnet durch redaktionelle Praxis in Creative Writing and Narrative Craft mit Handwerk und Standards von creative-Inspektion. KI unterstuetzt durch automatisiertes Korrekturlesen, Stilkonsistenzpruefung, Inhaltsoptimierung und intelligente Revisionsvorschlaege. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2701", "narrower_terms": [], "cross_domain_refs": [ "LIN-0024", "ART-0002" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q8366", "legal_classification": "observational_construct" }, { "id": "CRE-0070", "domain": "CRE", "term_en": "Generational Bridge Protocol", "term_de": "Generational Bruecke Protocol", "definition_en": "A creative process mechanism in AI-augmented artistic production, characterized by a phenomenon in which a set of practices and communication strategies enabling knowledge transfer about AI use between different generations — both from experienced AI users to newcomers and from younger digital natives. This phenomenon operates at the intersection of generational and bridge dynamics within the broader CRE domain. Quantifiable via output diversity analysis, prompt-to-output semantic distance, and iterative refinement cycle counts in creative workflows.", "definition_de": "Ein Satz von Praktiken und Kommunikationsstrategien, die den Wissenstransfer über KI-Nutzung zwischen verschiedenen Generationen ermöglichen — sowohl von erfahrenen KI-Nutzern zu Neulingen als auch von jüngeren Digital Natives zu älteren Fachexperten. Steht in Verbindung mit AUG-0010 (Bridge Species), AUG-0162 (The Generational Bridge) und Prognose 2 (Education).", "etymology": "", "broader_term": "Analytical Ratio", "narrower_terms": [], "cross_domain_refs": [ "LIN-0065" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "CRE-0071", "domain": "CRE", "term_en": "Generative Artifact Ownership", "term_de": "Kalibrierung in creative", "definition_en": "A generative pattern in human-AI co-creation workflows, measurable through an effect in which AI results involve mixed ownership links across human creators, AI systems, training data sources, and platform operators. Ownership and credit frameworks remain unstable across authority. The concept emerges specifically in contexts where generative–artifact interactions may produce non-trivial behavioral signatures. Measurable through computational creativity metrics: novelty scoring (n-gram divergence from training corpus), surprise index, and human-evaluated aesthetic ratings.", "definition_de": "Kreativitätsbezogener Mechanismus in KI-augmentierter künstlerischer Produktion, gekennzeichnet durch redaktionelle Praxis in Creative Writing and Narrative Craft mit Handwerk und Standards von Kalibrierung in creative. KI unterstuetzt durch automatisiertes Korrekturlesen, Stilkonsistenzpruefung, Inhaltsoptimierung und intelligente Revisionsvorschlaege. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2303", "narrower_terms": [], "cross_domain_refs": [ "COG-0031" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q735", "legal_classification": "empirical_phenomenon_label" }, { "id": "CRE-0072", "domain": "CRE", "term_en": "Idea Generation Threshold Shift", "term_de": "Fehlervermeidung in creative", "definition_en": "A generative pattern in human-AI co-creation workflows, measurable through a phenomenon in which after heavy AI use for ideation, coming up with ideas alone feels noticeably harder than it did before. The concept emerges specifically in contexts where idea–generation interactions may produce non-trivial behavioral signatures. Quantifiable via output diversity analysis, prompt-to-output semantic distance, and iterative refinement cycle counts in creative workflows.", "definition_de": "Kreativitätsbezogener Mechanismus in KI-augmentierter künstlerischer Produktion, gekennzeichnet durch ein KI-gestütztes oder algorithmisches Phänomen: After heavy AI use for ideation, coming up with ideas alone feels noticeably harder than it did before. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2002", "narrower_terms": [], "cross_domain_refs": [ "TRA-0072", "COG-0124", "COG-0004" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "CRE-0073", "domain": "CRE", "term_en": "Ideation Acceleration Plateau", "term_de": "Fehleranalyse in creative", "definition_en": "A generative pattern in human-AI co-creation workflows, measurable through a state in which a productivity curve observed during AI-assisted brainstorming sessions where initial output velocity is high due to readily accessible associations, followed by a marked deceleration as the combinatorial space of novel connections narrows and incremental ideation demands greater cognitive effort. The concept emerges specifically in contexts where ideation–acceleration interactions may produce non-trivial behavioral signatures. Measurable through computational creativity metrics: novelty scoring (n-gram divergence from training corpus), surprise index, and human-evaluated aesthetic ratings.", "definition_de": "Kreativitätsbezogener Mechanismus in KI-augmentierter künstlerischer Produktion, gekennzeichnet durch beobachtung innerhalb kreativer oder technischer Praxis: At first ideas come fast, then they slow down when most easy ideas are used up. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Analytical Ratio", "narrower_terms": [], "cross_domain_refs": [ "REL-0029" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "CRE-0074", "domain": "CRE", "term_en": "Ideation Acceleration Vertigo", "term_de": "Prozesskontrolle in creative", "definition_en": "Idea generation velocity exceeds human cognitive processing capacity, creating sensory excess input. Inability to fully develop any concept observed alongside rapid successive substitution. Measurable through output novelty metrics and creative divergence scoring.", "definition_de": "Kreativitätsbezogener Mechanismus in KI-augmentierter künstlerischer Produktion, gekennzeichnet durch sequenzielle Arbeitsabläufe und Verfahren zur Veränderungsmuster von Inputs in Outputs in Kreatives Schreiben und Erzählhandwerk Grundlagen. KI automatisiert Process Mining, Engpass-Erkennung und prädiktive Planung durch Feedbackschleifen. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-3754", "narrower_terms": [], "cross_domain_refs": [ "COG-0006" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "CRE-0075", "domain": "CRE", "term_en": "Ideation Asymmetry", "term_de": "creative-Compliance", "definition_en": "A generative pattern in human-AI co-creation workflows, measurable through a phenomenon in which AI thinking much beats human picking power. Pick ways remain felt and quick rather than systematic, creating imbalance between making and curation. The concept emerges specifically in contexts where ideation–asymmetry interactions may produce non-trivial behavioral signatures. Quantifiable via output diversity analysis, prompt-to-output semantic distance, and iterative refinement cycle counts in creative workflows.", "definition_de": "Kreativitätsbezogener Mechanismus in KI-augmentierter künstlerischer Produktion, gekennzeichnet durch gesetzliche und regulatorische Anforderungen in Kreatives Schreiben und Erzählhandwerk Grundlagen, einschließlich Lizenzen und Pflichtprotokolle. KI verfolgt regulatorische Änderungen, automatisiert Konformitätsdokumentation und meldet Verstöße. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Creative AI", "narrower_terms": [], "cross_domain_refs": [ "ROB-0268", "RHR-0095" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "CRE-0076", "domain": "CRE", "term_en": "Ideation Bottleneck Relocation", "term_de": "creative-Sicherheitsmanagement", "definition_en": "A dynamic in which the constraint in creative workflow shifts from idea origination to idea evaluation and refinement. Process bottleneck relocates downstream without explicit awareness. Measurable through output novelty metrics and creative divergence scoring.", "definition_de": "Kreativitätsbezogener Mechanismus in KI-augmentierter künstlerischer Produktion, gekennzeichnet durch ein kreativer oder technischer Prozess: charakterisiert durch the constraint in creative workflow shifts from idea origination to idea evaluat. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2701", "narrower_terms": [], "cross_domain_refs": [ "SOM-0046" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "CRE-0077", "domain": "CRE", "term_en": "Ideation Outsourcing Gradient", "term_de": "Risikobeurteilung in creative", "definition_en": "A creative process mechanism in AI-augmented artistic production, characterized by creative ideation outsourcing exists on a continuum rather than as a binary shift. a creator might delegate approximately 80% of ideation tasks to ai while. This phenomenon operates at the intersection of ideation and outsourcing dynamics within the broader CRE domain. Measurable through computational creativity metrics: novelty scoring (n-gram divergence from training corpus), surprise index, and human-evaluated aesthetic ratings.", "definition_de": "Kreativitätsbezogener Mechanismus in KI-augmentierter künstlerischer Produktion, gekennzeichnet durch beobachtung innerhalb kreativer oder technischer Praxis: creative ideation outsourcing exists on a continuum rather than as a binary shift. a creator might delegate approximatel. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-2701", "narrower_terms": [], "cross_domain_refs": [ "RPH-1362" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "CRE-0078", "domain": "CRE", "term_en": "Ideation Outsourcing Spectrum", "term_de": "Gefährdungserkennung in creative", "definition_en": "A creative process mechanism in AI-augmented artistic production, characterized by a state in which the range between thinking through ideas alone versus having AI involve them. Most people fall somewhere in between, mixing their own thinking with AI suggestions. This phenomenon operates at the intersection of ideation and outsourcing dynamics within the broader CRE domain. Measurable through computational creativity metrics: novelty scoring (n-gram divergence from training corpus), surprise index, and human-evaluated aesthetic ratings.", "definition_de": "Kreativitätsbezogener Mechanismus in KI-augmentierter künstlerischer Produktion, gekennzeichnet durch ein KI-gestütztes oder algorithmisches Phänomen: charakterisiert durch the range between thinking through ideas alone versus having ai involve them. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "Classification Spectrum", "narrower_terms": [], "cross_domain_refs": [ "REL-0029" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "CRE-0079", "domain": "CRE", "term_en": "Ideation Speed Expectation Gap", "term_de": "Persönliche Schutzausrüstung", "definition_en": "A creative cognition phenomenon in AI-assisted ideation, observable when an event in which ai-helped thinking runs at speeds greatly past own brain thinking. when creators engage in normal ideas making, the personal pace looks fast decelerated relative to. Distinguished from adjacent concepts by its focus on the specific mechanism through which ideation manifests in empirically verifiable ways. Quantifiable via output diversity analysis, prompt-to-output semantic distance, and iterative refinement cycle counts in creative workflows.", "definition_de": "Kreativitätsbezogener Mechanismus in KI-augmentierter künstlerischer Produktion, gekennzeichnet durch beobachtung innerhalb kreativer oder technischer Praxis: ai-helped thinking runs at speeds greatly past own brain thinking. when creators engage in normal ideas making, the pers. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Performance Gap", "narrower_terms": [], "cross_domain_refs": [ "REL-0048" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "CRE-0080", "domain": "CRE", "term_en": "Ideation Speed Shock", "term_de": "Notfallverfahren in creative", "definition_en": "A phenomenon in which first time with AI ideas at high speed correlates with shock. Makers get many ideas in short time and experience a shift in how fast they think. Measurable through output novelty metrics and creative divergence scoring.", "definition_de": "Kreativitätsbezogener Mechanismus in KI-augmentierter künstlerischer Produktion, gekennzeichnet durch redaktionelle Praxis in Creative Writing and Narrative Craft mit Handwerk und Standards von Notfallverfahren in creative. KI unterstuetzt durch automatisiertes Korrekturlesen, Stilkonsistenzpruefung, Inhaltsoptimierung und intelligente Revisionsvorschlaege. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-2701", "narrower_terms": [], "cross_domain_refs": [ "TEM-0128" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "CRE-0081", "domain": "CRE", "term_en": "Ideation Velocity Acceleration", "term_de": "Unfallverhütung in creative", "definition_en": "A creative cognition phenomenon in AI-assisted ideation, observable when a measurable change in the speed and output rate of new idea generation when working with AI assistance. This acceleration occurs across different domains and stages of ideation. Distinguished from adjacent concepts by its focus on the specific mechanism through which ideation manifests in empirically verifiable ways. Quantifiable via output diversity analysis, prompt-to-output semantic distance, and iterative refinement cycle counts in creative workflows.", "definition_de": "Kreativitätsbezogener Mechanismus in KI-augmentierter künstlerischer Produktion, gekennzeichnet durch redaktionelle Praxis in Creative Writing and Narrative Craft mit Handwerk und Standards von Unfallverhütung in creative. KI unterstuetzt durch automatisiertes Korrekturlesen, Stilkonsistenzpruefung, Inhaltsoptimierung und intelligente Revisionsvorschlaege. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Analytical Ratio", "narrower_terms": [], "cross_domain_refs": [ "AED-0094", "COG-0030", "COG-0050" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "CRE-0082", "domain": "CRE", "term_en": "Ideation-Execution Decoupling", "term_de": "creative-Gesundheitsschutz", "definition_en": "A creative cognition phenomenon in AI-assisted ideation, observable when aI separates thinking up ideas from actually making them — ideas flow freely without needing to build them immediately. Distinguished from adjacent concepts by its focus on the specific mechanism through which ideation manifests in empirically verifiable ways. Measurable through computational creativity metrics: novelty scoring (n-gram divergence from training corpus), surprise index, and human-evaluated aesthetic ratings.", "definition_de": "Kreativitätsbezogener Mechanismus in KI-augmentierter künstlerischer Produktion, gekennzeichnet durch ein KI-gestütztes oder algorithmisches Phänomen: charakterisiert durch ai separates thinking up ideas from actually making them — ideas flow freely wit. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Creative AI", "narrower_terms": [], "cross_domain_refs": [ "REL-0029" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "CRE-0083", "domain": "CRE", "term_en": "Imagination Muscle Reduction", "term_de": "Ergonomie in creative", "definition_en": "A creative process mechanism in AI-augmented artistic production, characterized by the ability to imagine and involve inreliantly becomes weaker with heavy AI use — the skill fades from disuse. This phenomenon operates at the intersection of imagination and muscle dynamics within the broader CRE domain. Measurable through computational creativity metrics: novelty scoring (n-gram divergence from training corpus), surprise index, and human-evaluated aesthetic ratings.", "definition_de": "Kreativitätsbezogener Mechanismus in KI-augmentierter künstlerischer Produktion, gekennzeichnet durch literarisches oder kreatives Effekt: charakterisiert durch the ability to imagine and involve inreliantly becomes weaker with heavy ai use. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "Creative AI", "narrower_terms": [], "cross_domain_refs": [ "COG-0027", "COG-0046", "COG-0067" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "CRE-0084", "domain": "CRE", "term_en": "Increasing-Gardener Effect", "term_de": "Long-Term Chat", "definition_en": "A creative cognition phenomenon in AI-assisted ideation, observable when a pattern in which an AI session or collaboration that extends over weeks or months — with growing context, increasing depth, and a developing shared frame of reference. Related to AUG-0231 (The Warm Start) and AUG-0. Distinguished from adjacent concepts by its focus on the specific mechanism through which increasing manifests in empirically verifiable ways. Quantifiable via output diversity analysis, prompt-to-output semantic distance, and iterative refinement cycle counts in creative workflows.", "definition_de": "Kreativitätsbezogener Mechanismus in KI-augmentierter künstlerischer Produktion, gekennzeichnet durch eine KI-Sitzung oder -Zusammenarbeit, die sich über Wochen oder Monate erstreckt — mit wachsendem Kontext, zunehmender Tiefe und einem sich entwickelnden gemeinsamen Referenzrahmen. Steht in Verbindung mit AUG-0231 (Die Warm Anfang), AUG-0075 (Die Gardener Protocol) und AUG-0395 (The Long-Term Chat). Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2005", "narrower_terms": [], "cross_domain_refs": [ "NEO-1169", "TEM-0174" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "CRE-0085", "domain": "CRE", "term_en": "Innovation Speed Disorientation", "term_de": "Brandschutz in creative", "definition_en": "AI-driven tech change accelerates beyond human cognitive mixing in capacity. users experience temporal disorientation when innovation pace exceeds the rate at which understanding and adjustment can... Measurable through output novelty metrics and creative divergence scoring.", "definition_de": "Kreativitätsbezogener Mechanismus in KI-augmentierter künstlerischer Produktion, gekennzeichnet durch ein KI-gestütztes oder algorithmisches Phänomen: AI-driven tech change accelerates beyond human cognitive mixing in capacity. users experience temporal disorientation wh. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-3754", "narrower_terms": [], "cross_domain_refs": [ "SCR-0060" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q219644", "legal_classification": "empirical_phenomenon_label" }, { "id": "CRE-0086", "domain": "CRE", "term_en": "Input-Inputs Effect", "term_de": "Community-Framed Input", "definition_en": "A creative process mechanism in AI-augmented artistic production, characterized by the observable pattern that some users frame their AI inputs in the context of a community — \"We need…,\" \"For our group…\" — rather than as an individual request. Related to AUG-0133 (Prompt Craftsm. This phenomenon operates at the intersection of input and inputs dynamics within the broader CRE domain. Measurable through computational creativity metrics: novelty scoring (n-gram divergence from training corpus), surprise index, and human-evaluated aesthetic ratings.", "definition_de": "Das beobachtbare Muster, dass manche Nutzer ihre KI-Eingaben im Kontext einer Gemeinschaft formulieren — \"Wir brauchen…\", \"Für unsere Gruppe…\" — statt als individuelle Anfrage. Beschreibt eine Eingabestruktur, keine kulturelle Eigenschaft. Steht in Verbindung mit AUG-0133 (Der Anstoß Handwerksmanship), AUG-0524 (Context Schicht) und AUG-0647 (The Individual-Rahmend Input).", "etymology": "", "broader_term": "RPH-2303", "narrower_terms": [], "cross_domain_refs": [ "NEO-1119", "NEO-2661" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "CRE-0087", "domain": "CRE", "term_en": "Inspiration Laundering", "term_de": "Elektrische Sicherheit in creative", "definition_en": "A creative cognition phenomenon in AI-assisted ideation, observable when a process in which source material is processed through ai systems, producing recombined outputs that superficially resemble yet distinctly spread out from source inspiration. ai change obsresolves the relationship. Distinguished from adjacent concepts by its focus on the specific mechanism through which inspiration manifests in empirically verifiable ways. Quantifiable via output diversity analysis, prompt-to-output semantic distance, and iterative refinement cycle counts in creative workflows.", "definition_de": "Kreativitätsbezogener Mechanismus in KI-augmentierter künstlerischer Produktion, gekennzeichnet durch ein kreativer oder technischer Prozess: source material is processed through ai systems, producing recombined outputs that superficially resemble yet distinctly. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Analytical Ratio", "narrower_terms": [], "cross_domain_refs": [ "PLY-0035", "SOM-0056" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "CRE-0088", "domain": "CRE", "term_en": "Inspiration Source Obscuring", "term_de": "Maschinensicherheit in creative", "definition_en": "A creative cognition phenomenon in AI-assisted ideation, observable when a state in which it becomes extremely difficult to tell whether an idea originated from inreliant thinking or from AI suggestions that have been absorbed. Distinguished from adjacent concepts by its focus on the specific mechanism through which inspiration manifests in empirically verifiable ways. Measurable through computational creativity metrics: novelty scoring (n-gram divergence from training corpus), surprise index, and human-evaluated aesthetic ratings.", "definition_de": "Kreativitätsbezogener Mechanismus in KI-augmentierter künstlerischer Produktion, gekennzeichnet durch literarisches oder kreatives Effekt: It becomes extremely difficult to tell whether an idea originated from inreliant thinking or from AI suggestio. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Analytical Ratio", "narrower_terms": [], "cross_domain_refs": [ "ART-0087", "ART-0088", "ASE-0095" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "CRE-0089", "domain": "CRE", "term_en": "Iteration Threshold Narrowing", "term_de": "Sicherheitsschulung in creative", "definition_en": "A creative process mechanism in AI-augmented artistic production, characterized by a phenomenon in which AI makes it so easy to start over that people rebuild from scratch instead of improving what already exists. This phenomenon operates at the intersection of iteration and threshold dynamics within the broader CRE domain. Quantifiable via output diversity analysis, prompt-to-output semantic distance, and iterative refinement cycle counts in creative workflows.", "definition_de": "Kreativitätsbezogener Mechanismus in KI-augmentierter künstlerischer Produktion, gekennzeichnet durch ein KI-gestütztes oder algorithmisches Phänomen: charakterisiert durch ai makes it so easy to start over that people rebuild from scratch instead of im. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-2301", "narrower_terms": [], "cross_domain_refs": [ "AGE-0096", "ASE-0021", "ASE-0064" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "CRE-0090", "domain": "CRE", "term_en": "Layer-References Effect", "term_de": "Context-Sensitive Query", "definition_en": "A creative cognition phenomenon in AI-assisted ideation, observable when an input that explicitly references the user's social context — \"in my situation it would be inappropriate if…,\" \"considering my environment….\" the user gives the. Distinguished from adjacent concepts by its focus on the specific mechanism through which layer manifests in empirically verifiable ways. Measurable through computational creativity metrics: novelty scoring (n-gram divergence from training corpus), surprise index, and human-evaluated aesthetic ratings.", "definition_de": "Eine Eingabe, die explizit auf den sozialen Kontext des Nutzers Bezug nimmt — \"In meiner Situation wäre es unangemessen, wenn…\", \"Unter Berücksichtigung meines Umfelds…\". Der Nutzer gibt der KI Kontextinformationen, die Antwort an soziale Normen anpassen werden typischerweise. Steht in Verbindung mit AUG-0524 (Context Schicht), AUG-0491 (Das State Label) und AUG-0133 (Der Anstoß Handwerksmanship).", "etymology": "", "broader_term": "Psychological Effect", "narrower_terms": [], "cross_domain_refs": [ "REL-0086" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "CRE-0091", "domain": "CRE", "term_en": "Muse Offloading", "term_de": "creative-Geschäftsmodell", "definition_en": "A creative process mechanism in AI-augmented artistic production, characterized by a phenomenon in which replacing quiet thinking time and waiting for inspiration with instant AI generation — no pause for reflection. This phenomenon operates at the intersection of muse and offloading dynamics within the broader CRE domain. Measurable through computational creativity metrics: novelty scoring (n-gram divergence from training corpus), surprise index, and human-evaluated aesthetic ratings.", "definition_de": "Kreativitätsbezogener Mechanismus in KI-augmentierter künstlerischer Produktion, gekennzeichnet durch redaktionelle Praxis in Creative Writing and Narrative Craft mit Handwerk und Standards von creative-Geschäftsmodell. KI unterstuetzt durch automatisiertes Korrekturlesen, Stilkonsistenzpruefung, Inhaltsoptimierung und intelligente Revisionsvorschlaege. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "Creative AI", "narrower_terms": [], "cross_domain_refs": [ "TEM-0100" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "CRE-0092", "domain": "CRE", "term_en": "Novelty Desaturation Effect", "term_de": "creative-Marktanalyse", "definition_en": "A generative pattern in human-AI co-creation workflows, measurable through a state in which something new and exciting becomes ordinary and boring the more someone encounters it. The concept emerges specifically in contexts where novelty–desaturation interactions may produce non-trivial behavioral signatures. Measurable through computational creativity metrics: novelty scoring (n-gram divergence from training corpus), surprise index, and human-evaluated aesthetic ratings.", "definition_de": "Kreativitätsbezogener Mechanismus in KI-augmentierter künstlerischer Produktion, gekennzeichnet durch systematische Untersuchung und Interpretation von Daten oder Prozessen in Kreatives Schreiben und Erzählhandwerk Grundlagen. KI verstärkt Analysefähigkeiten durch Mustererkennung, Anomalieerkennung und automatisierte Erkenntnisextraktion. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Psychological Effect", "narrower_terms": [], "cross_domain_refs": [ "ADA-0001", "ADA-0002", "ADA-0004" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "CRE-0093", "domain": "CRE", "term_en": "Novelty Habituation Curve", "term_de": "Ökonomie der Kreatives Schreiben und Erzählhandwerk", "definition_en": "A creative process mechanism in AI-augmented artistic production, characterized by a phenomenon in which initial AI outputs register as novel. successive exposure to similar outputs reduces novelty impact through habituation. Perceptual novelty diminishes as cognitive exposure to AI style accumulates. This phenomenon operates at the intersection of novelty and habituation dynamics within the broader CRE domain. Quantifiable via output diversity analysis, prompt-to-output semantic distance, and iterative refinement cycle counts in creative workflows.", "definition_de": "Kreativitätsbezogener Mechanismus in KI-augmentierter künstlerischer Produktion, gekennzeichnet durch ein KI-gestütztes oder algorithmisches Phänomen: Initial AI outputs register as novel; successive exposure to similar outputs reduces novelty impact through habituation. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "Creative AI", "narrower_terms": [], "cross_domain_refs": [ "AGE-0086", "AGE-0093", "COG-0143" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "CRE-0094", "domain": "CRE", "term_en": "Novelty Perception Calibration", "term_de": "creative-Kostenmanagement", "definition_en": "A creative process mechanism in AI-augmented artistic production, characterized by a principle in which AI exposure resets newness spotting rules in the seeing person. before seemed newness markers register as common while atypical variations seem increasingly anomalous. This phenomenon operates at the intersection of novelty and perception dynamics within the broader CRE domain. Measurable through computational creativity metrics: novelty scoring (n-gram divergence from training corpus), surprise index, and human-evaluated aesthetic ratings.", "definition_de": "Kreativitätsbezogener Mechanismus in KI-augmentierter künstlerischer Produktion, gekennzeichnet durch ein KI-gestütztes oder algorithmisches Phänomen: charakterisiert durch ai exposure resets newness spotting rules in the seeing person. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-2002", "narrower_terms": [], "cross_domain_refs": [ "TRA-0070" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q177601", "legal_classification": "observational_construct" }, { "id": "CRE-0095", "domain": "CRE", "term_en": "Novelty Threshold Shift", "term_de": "Preisgestaltung in creative", "definition_en": "A creative cognition phenomenon in AI-assisted ideation, observable when a phenomenon in which after seeing lots of AI-generated content, it takes more to feel genuinely impressed or surprised by new work. Distinguished from adjacent concepts by its focus on the specific mechanism through which novelty manifests in empirically verifiable ways. Quantifiable via output diversity analysis, prompt-to-output semantic distance, and iterative refinement cycle counts in creative workflows. Analytical category without normative endorsement.", "definition_de": "Kreativitätsbezogener Mechanismus in KI-augmentierter künstlerischer Produktion, gekennzeichnet durch redaktionelle Praxis in Creative Writing and Narrative Craft mit Handwerk und Standards von Preisgestaltung in creative. KI unterstuetzt durch automatisiertes Korrekturlesen, Stilkonsistenzpruefung, Inhaltsoptimierung und intelligente Revisionsvorschlaege. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-3501", "narrower_terms": [], "cross_domain_refs": [ "CON-0015" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "CRE-0096", "domain": "CRE", "term_en": "Originality Currency Deflation", "term_de": "creative-Lieferkette", "definition_en": "A creative process mechanism in AI-augmented artistic production, characterized by an event in which when many individuals uses the same AI tools, truly original work becomes rarer and correspondingly less valued. This phenomenon operates at the intersection of originality and currency dynamics within the broader CRE domain. Quantifiable via output diversity analysis, prompt-to-output semantic distance, and iterative refinement cycle counts in creative workflows.", "definition_de": "Kreativitätsbezogener Mechanismus in KI-augmentierter künstlerischer Produktion, gekennzeichnet durch end-to-End-Material- und Produktflussmanagement in Kreatives Schreiben und Erzählhandwerk Grundlagen. KI optimiert Nachfrageprognose, Routenplanung, Bestandsmanagement und Lieferanten-Risikobewertung durch prädiktive Analytik. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "Creative AI", "narrower_terms": [], "cross_domain_refs": [ "ART-0097" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "CRE-0097", "domain": "CRE", "term_en": "Originality Verification Challenge", "term_de": "Marketing in creative", "definition_en": "A creative cognition phenomenon in AI-assisted ideation, observable when an event in which proving that creative work is truly original becomes nearly extremely difficult when AI was involved in making it. Distinguished from adjacent concepts by its focus on the specific mechanism through which originality manifests in empirically verifiable ways. Measurable through computational creativity metrics: novelty scoring (n-gram divergence from training corpus), surprise index, and human-evaluated aesthetic ratings.", "definition_de": "Kreativitätsbezogener Mechanismus in KI-augmentierter künstlerischer Produktion, gekennzeichnet durch redaktionelle Praxis in Creative Writing and Narrative Craft mit Handwerk und Standards von Marketing in creative. KI unterstuetzt durch automatisiertes Korrekturlesen, Stilkonsistenzpruefung, Inhaltsoptimierung und intelligente Revisionsvorschlaege. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Creative AI", "narrower_terms": [], "cross_domain_refs": [ "AED-0009", "AED-0019", "AGE-0090" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "CRE-0098", "domain": "CRE", "term_en": "Origination Attribution Challenge", "term_de": "Vertrieb in creative", "definition_en": "A creative cognition phenomenon in AI-assisted ideation, observable when an event in which tracing back where an idea originally came from becomes extremely difficult when AI mixed multiple sources into it. Distinguished from adjacent concepts by its focus on the specific mechanism through which origination manifests in empirically verifiable ways. Measurable through computational creativity metrics: novelty scoring (n-gram divergence from training corpus), surprise index, and human-evaluated aesthetic ratings.", "definition_de": "Kreativitätsbezogener Mechanismus in KI-augmentierter künstlerischer Produktion, gekennzeichnet durch ein KI-gestütztes oder algorithmisches Phänomen: Tracing back where an idea originally came from becomes extremely difficult when AI mixed multiple sources into it. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Creative AI", "narrower_terms": [], "cross_domain_refs": [ "ART-0088" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "CRE-0099", "domain": "CRE", "term_en": "Post-Authorial Pride", "term_de": "Nach-Authorial Pride", "definition_en": "A creative process mechanism in AI-augmented artistic production, characterized by a phenomenon in which a person feels proud of work they made with AI help, but claims it as fully their own. This phenomenon operates at the intersection of post and authorial dynamics within the broader CRE domain. Measurable through computational creativity metrics: novelty scoring (n-gram divergence from training corpus), surprise index, and human-evaluated aesthetic ratings.", "definition_de": "Das spezifische Gefühl von Zufriedenheit, das entsteht, wenn ein Nutzer ein KI-gestütztes Ergebnis als sein eigenes Werk anerkennt — obwohl der Entstehungsprozess Mensch-KI-Zusammenarbeit umfasste. Beschreibt die Beobachtung, dass sich Urheberschaft im KI-Zeitalter neu definiert: Die menschliche Leistung liegt in Steuerung, Auswahl und Veredelung. Steht in Verbindung mit Axiom 12 (Versionswahrheit), Axiom 18 (Urheberschaft) und AUG-0061 (The Creator's Question).", "etymology": "", "broader_term": "Creative AI", "narrower_terms": [ "TEM-0147" ], "cross_domain_refs": [ "BEH-0013" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "CRE-0100", "domain": "CRE", "term_en": "Practice-Creating Effect", "term_de": "Day-End Summary", "definition_en": "A creative cognition phenomenon in AI-assisted ideation, observable when a phenomenon in which a daily routine of writing down what was accomplished and which parts came from AI. Distinguished from adjacent concepts by its focus on the specific mechanism through which practice manifests in empirically verifiable ways. Measurable through computational creativity metrics: novelty scoring (n-gram divergence from training corpus), surprise index, and human-evaluated aesthetic ratings.", "definition_de": "Kreativitätsbezogener Mechanismus in KI-augmentierter künstlerischer Produktion, gekennzeichnet durch die Praxis, am Ende eines KI-gestützten Arbeitstages eine strukturierte Zusammenfassung des Erreichten zu erstellen — als Rechenschaftslegung gegenüber sich selbst und als Übergabedokument für den nächsten Tag. Steht in Verbindung mit AUG-0190 (Der Goodnight Integration), AUG-0297 (The Day-End Summary) und AUG-0140 (Der Weekly Status). Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Psychological Effect", "narrower_terms": [], "cross_domain_refs": [ "ADA-0001", "ADA-0002", "ADA-0004" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "CRE-0101", "domain": "CRE", "term_en": "Process Invisibility Effect", "term_de": "Investition in creative", "definition_en": "A gap in which AI work happens in hidden hidden systems, stopping human seeing of AI work. Absence of transparent methodology removes learning pathways that arise from process visibility. Measurable through output novelty metrics and creative divergence scoring.", "definition_de": "Kreativitätsbezogener Mechanismus in KI-augmentierter künstlerischer Produktion, gekennzeichnet durch redaktionelle Praxis in Creative Writing and Narrative Craft mit Handwerk und Standards von Investition in creative. KI unterstuetzt durch automatisiertes Korrekturlesen, Stilkonsistenzpruefung, Inhaltsoptimierung und intelligente Revisionsvorschlaege. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2703", "narrower_terms": [], "cross_domain_refs": [ "REL-0109" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "CRE-0102", "domain": "CRE", "term_en": "Profile-Arbitrage Effect", "term_de": "One-Person Operation", "definition_en": "A generative pattern in human-AI co-creation workflows, measurable through a capability in which a creator observes that a single AI-assisted user can yield output that previously would have required a team — such as simultaneously handling research, text production, data analysis, and commu. The concept emerges specifically in contexts where profile–arbitrage interactions may produce non-trivial behavioral signatures. Measurable through computational creativity metrics: novelty scoring (n-gram divergence from training corpus), surprise index, and human-evaluated aesthetic ratings.", "definition_de": "Das Phänomen, dass ein einzelner KI-gestützter Nutzer Leistungen erbringen kann, die zuvor ein Team erfordert hätten — etwa in Recherche, Textproduktion, Datenanalyse und Kommunikation gleichzeitig. Beschreibt eine strukturelle Verschiebung der Arbeitswelt. Steht in Verbindung mit AUG-0094 (Die Polymorphic Capital Generation), AUG-0091 (Das Productivity Arbitrage) und dem Conductor-Profil (Profil 12).", "etymology": "", "broader_term": "Psychological Effect", "narrower_terms": [], "cross_domain_refs": [ "NEO-1176" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "CRE-0103", "domain": "CRE", "term_en": "Prompt Craftsmanship", "term_de": "Prompt Craftsmanship", "definition_en": "A generative pattern in human-AI co-creation workflows, measurable through the learned skill of formulating inputs to AI systems in a way that is precise, context-rich, and goal-oriented.. Related to AUG-0021 (Initialization Cascade), AUG-0088 (Algorithmic Intuition), and. The concept emerges specifically in contexts where prompt–craftsmanship interactions may produce non-trivial behavioral signatures. Measurable through computational creativity metrics: novelty scoring (n-gram divergence from training corpus), surprise index, and human-evaluated aesthetic ratings.", "definition_de": "Kreativitätsbezogener Mechanismus in KI-augmentierter künstlerischer Produktion, gekennzeichnet durch die erlernte Fertigkeit, Eingaben an KI-Systeme so zu formulieren, dass sie präzise, kontextreich und zielführend sind. Beschreibt eine handwerkliche Kompetenz, die sich durch Übung und Erfahrung entwickelt. Steht in Verbindung mit AUG-0021 (Initialization Cascade), AUG-0088 (Algorithmic Intuition) und AUG-0109 (The Reciprocity Axiom). Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2703", "narrower_terms": [], "cross_domain_refs": [ "BEH-0037" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "CRE-0104", "domain": "CRE", "term_en": "Prompt Palette Effect", "term_de": "creative-Branchentrends", "definition_en": "A creative cognition phenomenon in AI-assisted ideation, observable when a phenomenon in which using the same successful prompts repeatedly. Stops exploring new ways to ask questions. Distinguished from adjacent concepts by its focus on the specific mechanism through which prompt manifests in empirically verifiable ways. Quantifiable via output diversity analysis, prompt-to-output semantic distance, and iterative refinement cycle counts in creative workflows.", "definition_de": "Kreativitätsbezogener Mechanismus in KI-augmentierter künstlerischer Produktion, gekennzeichnet durch beobachtung innerhalb kreativer oder technischer Praxis: Using the same successful prompts repeatedly. Stops exploring new ways to ask questions. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2104", "narrower_terms": [], "cross_domain_refs": [ "AGE-0046" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "CRE-0105", "domain": "CRE", "term_en": "Quality Variance Whiplash", "term_de": "Startup in creative", "definition_en": "A creative cognition phenomenon in AI-assisted ideation, observable when an event in which when AI output is excellent one moment and poor the next. The unpredictable ups and downs in quality make it hard to know what to expect. Distinguished from adjacent concepts by its focus on the specific mechanism through which quality manifests in empirically verifiable ways. Measurable through computational creativity metrics: novelty scoring (n-gram divergence from training corpus), surprise index, and human-evaluated aesthetic ratings.", "definition_de": "Kreativitätsbezogener Mechanismus in KI-augmentierter künstlerischer Produktion, gekennzeichnet durch literarisches oder kreatives Effekt: When AI output is excellent one moment and poor the next. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Creative AI", "narrower_terms": [], "cross_domain_refs": [ "RPH-2201", "RPH-2205" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "CRE-0106", "domain": "CRE", "term_en": "Signature Style Dissolution", "term_de": "Nachhaltigkeit in creative", "definition_en": "A generative pattern in human-AI co-creation workflows, measurable through an event in which a creator's personal style becomes diluted when AI-generated patterns mix into their work alongside their own choices. The concept emerges specifically in contexts where signature–style interactions may produce non-trivial behavioral signatures. Quantifiable via output diversity analysis, prompt-to-output semantic distance, and iterative refinement cycle counts in creative workflows.", "definition_de": "Kreativitätsbezogener Mechanismus in KI-augmentierter künstlerischer Produktion, gekennzeichnet durch ein rekurrentes Muster: A creator's personal style becomes diluted when AI-generated patterns mix into their work alongside their own choices. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Creative AI", "narrower_terms": [], "cross_domain_refs": [ "MUS-0078" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "CRE-0107", "domain": "CRE", "term_en": "Spark Flight", "term_de": "Spark Flight", "definition_en": "A creative process mechanism in AI-augmented artistic production, characterized by the creative state in which an AI-activated idea (Semantic Spark, AUG-0031) propels the user into an inreliant, self-sustaining thinking process that detaches from the original AI interaction.. R. This phenomenon operates at the intersection of spark and flight dynamics within the broader CRE domain. Quantifiable via output diversity analysis, prompt-to-output semantic distance, and iterative refinement cycle counts in creative workflows.", "definition_de": "Der kreative Zustand, in dem eine durch KI ausgelöste Idee (Semantic Spark, AUG-0031) den Nutzer in einen eigenständigen, selbstgetragenen Denkprozess versetzt, der sich von der ursprünglichen KI-Interaktion löst. Beschreibt den Übergang von KI-unterstütztem zu KI-unabhängigem Denken. Steht in Verbindung mit AUG-0031 (Semantic Spark) und AUG-0054 (Augmented Understanding).", "etymology": "", "broader_term": "RPH-2403", "narrower_terms": [], "cross_domain_refs": [ "SOM-0056" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "CRE-0108", "domain": "CRE", "term_en": "Speak-Language Effect", "term_de": "Written-Spoken Split", "definition_en": "A capability in which the discrepancy between a user's written language and spoken language in AI interactions — some users formulate significantly differently in writing than they would speak, which correlates with different...", "definition_de": "Die Diskrepanz zwischen der Schriftsprache und der gesprochenen Sprache eines Nutzers in KI-Interaktionen — manche Nutzer formulieren schriftlich deutlich anders als sie sprechen würden, was bei Voice-Eingaben zu unterschiedlichen KI-Ergebnissen führt. Steht in Verbindung mit AUG-0455 (Der Voice Enunciation), AUG-0711 (Die Akzent Persistence) und AUG-0657 (Das Register Range).", "etymology": "", "broader_term": "RPH-2605", "narrower_terms": [], "cross_domain_refs": [ "NEO-1222", "SOC-0028" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q315", "legal_classification": "empirical_phenomenon_label" }, { "id": "CRE-0109", "domain": "CRE", "term_en": "Stylistic Coherence Maintenance", "term_de": "Ressourceneffizienz in creative", "definition_en": "A generative pattern in human-AI co-creation workflows, measurable through aI keeps outputs visually consistent, even when the creator wanted variety and change across different versions. The concept emerges specifically in contexts where stylistic–coherence interactions may produce non-trivial behavioral signatures. Measurable through computational creativity metrics: novelty scoring (n-gram divergence from training corpus), surprise index, and human-evaluated aesthetic ratings.", "definition_de": "Kreativitätsbezogener Mechanismus in KI-augmentierter künstlerischer Produktion, gekennzeichnet durch redaktionelle Praxis in Creative Writing and Narrative Craft mit Handwerk und Standards von Ressourceneffizienz in creative. KI unterstuetzt durch automatisiertes Korrekturlesen, Stilkonsistenzpruefung, Inhaltsoptimierung und intelligente Revisionsvorschlaege. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Creative AI", "narrower_terms": [], "cross_domain_refs": [ "ART-0043" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "CRE-0110", "domain": "CRE", "term_en": "Stylistic Drift Acceleration", "term_de": "Abfallreduzierung in creative", "definition_en": "A creative process mechanism in AI-augmented artistic production, characterized by a dynamic in which writing style evolves faster with AI. Changes that took years now happen in months. This phenomenon operates at the intersection of stylistic and drift dynamics within the broader CRE domain. Quantifiable via output diversity analysis, prompt-to-output semantic distance, and iterative refinement cycle counts in creative workflows.", "definition_de": "Kreativitätsbezogener Mechanismus in KI-augmentierter künstlerischer Produktion, gekennzeichnet durch redaktionelle Praxis in Creative Writing and Narrative Craft mit Handwerk und Standards von Abfallreduzierung in creative. KI unterstuetzt durch automatisiertes Korrekturlesen, Stilkonsistenzpruefung, Inhaltsoptimierung und intelligente Revisionsvorschlaege. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "Systemic Drift", "narrower_terms": [], "cross_domain_refs": [ "TEM-0008" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "CRE-0111", "domain": "CRE", "term_en": "Stylistic Evolution Acceleration", "term_de": "Kreislaufwirtschaft in creative", "definition_en": "A generative pattern in human-AI co-creation workflows, measurable through an artist's personal visual style changes faster when AI tools are involved in the creative process. The concept emerges specifically in contexts where stylistic–evolution interactions may produce non-trivial behavioral signatures. Measurable through computational creativity metrics: novelty scoring (n-gram divergence from training corpus), surprise index, and human-evaluated aesthetic ratings.", "definition_de": "Kreativitätsbezogener Mechanismus in KI-augmentierter künstlerischer Produktion, gekennzeichnet durch redaktionelle Praxis in Creative Writing and Narrative Craft mit Handwerk und Standards von Kreislaufwirtschaft in creative. KI unterstuetzt durch automatisiertes Korrekturlesen, Stilkonsistenzpruefung, Inhaltsoptimierung und intelligente Revisionsvorschlaege. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Analytical Ratio", "narrower_terms": [], "cross_domain_refs": [ "MUS-0044" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "CRE-0112", "domain": "CRE", "term_en": "Stylistic Homogenization Effect", "term_de": "Energieeffizienz in creative", "definition_en": "A creative cognition phenomenon in AI-assisted ideation, observable when a phenomenon in which common use of same AI systems makes same style results across many creators. Individual style standing out reduces through all the same into through AI patterns-derived baseline style conventions. Distinguished from adjacent concepts by its focus on the specific mechanism through which stylistic manifests in empirically verifiable ways. Measurable through computational creativity metrics: novelty scoring (n-gram divergence from training corpus), surprise index, and human-evaluated aesthetic ratings.", "definition_de": "Kreativitätsbezogener Mechanismus in KI-augmentierter künstlerischer Produktion, gekennzeichnet durch ein rekurrentes Muster: Common use of same AI systems makes same style results across many creators. Individual style standing out reduces throu. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Psychological Effect", "narrower_terms": [], "cross_domain_refs": [ "MUS-0037", "KNO-0029" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "CRE-0113", "domain": "CRE", "term_en": "Stylistic Ownership Shift", "term_de": "Nachhaltige Materialien in creative", "definition_en": "A creative process mechanism in AI-augmented artistic production, characterized by an event in which creator how people see it of stylistic uniqueness decreases when AI contribution becomes significant. Ownership attribution becomes ambiguous as aesthetic standing out disperses across human-AI age. This phenomenon operates at the intersection of stylistic and ownership dynamics within the broader CRE domain. Quantifiable via output diversity analysis, prompt-to-output semantic distance, and iterative refinement cycle counts in creative workflows.", "definition_de": "Kreativitätsbezogener Mechanismus in KI-augmentierter künstlerischer Produktion, gekennzeichnet durch ein KI-gestütztes oder algorithmisches Phänomen: Creator how people see it of stylistic uniqueness decreases when AI contribution becomes significant. Ownership attribut. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "Creative AI", "narrower_terms": [], "cross_domain_refs": [ "COG-0057" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "CRE-0114", "domain": "CRE", "term_en": "Stylistic Ownership Gap", "term_de": "Grüne creative-Praktiken", "definition_en": "A creative process mechanism in AI-augmented artistic production, characterized by a phenomenon in which the creator doesn't feel full ownership over the style of AI-assisted work — it feels partly theirs, partly AI's. This phenomenon operates at the intersection of stylistic and ownership dynamics within the broader CRE domain. Quantifiable via output diversity analysis, prompt-to-output semantic distance, and iterative refinement cycle counts in creative workflows. Descriptive research term, not a prescriptive recommendation.", "definition_de": "Kreativitätsbezogener Mechanismus in KI-augmentierter künstlerischer Produktion, gekennzeichnet durch ein KI-gestütztes oder algorithmisches Phänomen: charakterisiert durch the creator doesn't feel full ownership over the style of ai-assisted work — it. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Deskriptiver Forschungsbegriff, keine präskriptive Empfehlung.", "etymology": "", "broader_term": "Performance Gap", "narrower_terms": [], "cross_domain_refs": [ "RPH-3451" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "CRE-0115", "domain": "CRE", "term_en": "Stylistic Voice Dilution", "term_de": "ESG-Berichterstattung in creative", "definition_en": "A generative pattern in human-AI co-creation workflows, measurable through an event in which a creator's personal voice becomes weaker when mixed with AI output. The combined work sounds more generic than what either human or AI would involve alone. The concept emerges specifically in contexts where stylistic–voice interactions may produce non-trivial behavioral signatures. Quantifiable via output diversity analysis, prompt-to-output semantic distance, and iterative refinement cycle counts in creative workflows.", "definition_de": "Kreativitätsbezogener Mechanismus in KI-augmentierter künstlerischer Produktion, gekennzeichnet durch redaktionelle Praxis in Creative Writing and Narrative Craft mit Handwerk und Standards von ESG-Berichterstattung in creative. KI unterstuetzt durch automatisiertes Korrekturlesen, Stilkonsistenzpruefung, Inhaltsoptimierung und intelligente Revisionsvorschlaege. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Creative AI", "narrower_terms": [], "cross_domain_refs": [ "AGE-0014", "AGE-0048", "ART-0018" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "CRE-0116", "domain": "CRE", "term_en": "The Agent Literacy", "term_de": "Agent Literacy", "definition_en": "A creative cognition phenomenon in AI-assisted ideation, observable when a capability in which the ability to effectively use AI agent systems — formulating tasks, evaluating results, recognizing uncertainty, and exercising appropriate oversight. Related to AUG-0986 (The Agent Management Ski. Distinguished from adjacent concepts by its focus on the specific mechanism through which the manifests in empirically verifiable ways. Measurable through computational creativity metrics: novelty scoring (n-gram divergence from training corpus), surprise index, and human-evaluated aesthetic ratings.", "definition_de": "Kreativitätsbezogener Mechanismus in KI-augmentierter künstlerischer Produktion, gekennzeichnet durch die Fähigkeit, KI-Agentensysteme effektiv zu nutzen — Aufgaben zu formulieren, Ergebnisse zu bewerten, Risiken zu erkennen und angemessene Aufsicht auszuüben. Steht in Verbindung mit AUG-0986 (The Agent Management Skill), AUG-0984 (The Skill Redefinition) und AUG-0133 (Prompt Craftsmanship). Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Creative AI", "narrower_terms": [ "CRE-0117" ], "cross_domain_refs": [ "ETH-0012" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q201684", "legal_classification": "descriptive_research_term" }, { "id": "CRE-0117", "domain": "CRE", "term_en": "The Agent Management Craft", "term_de": "Agent Management Craft", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies a capability in which the specific ability to simultaneously steer, coordinate, and monitor multiple AI agent systems. Related to AUG-0985 (The Agent Literacy), AUG-0987 (The Multi-Agent Literacy), and AUG-0862 (The Sup. This phenomenon operates at the intersection of the and agent dynamics within the broader CRE domain. Measurable through computational creativity metrics: novelty scoring (n-gram divergence from training corpus), surprise index, and human-evaluated aesthetic ratings. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept die spezifische Fähigkeit, mehrere KI-Agentensysteme gleichzeitig zu steuern, zu koordinieren und zu überwachen. Steht in Verbindung mit AUG-0985 (The Agent Literacy), AUG-0987 (The Multi-Agent Literacy) und AUG-0862 (The Supervision Spectrum). Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "CRE-0116", "narrower_terms": [], "cross_domain_refs": [ "ETH-0012" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q181543", "legal_classification": "empirical_phenomenon_label" }, { "id": "CRE-0118", "domain": "CRE", "term_en": "The Agreement Question", "term_de": "Agreement Question", "definition_en": "A creative process mechanism in AI-augmented artistic production, characterized by a process in which the open question of what \"informed agreement\" to AI use means — whether users actually understand what they use, what data they disclose, and what consequences their use has. Related to AUG-0772 (. This phenomenon operates at the intersection of the and agreement dynamics within the broader CRE domain. Measurable through computational creativity metrics: novelty scoring (n-gram divergence from training corpus), surprise index, and human-evaluated aesthetic ratings.", "definition_de": "Kreativitätsbezogener Mechanismus in KI-augmentierter künstlerischer Produktion, gekennzeichnet durch die offene Frage, was \"informierte Zustimmung\" zur KI-Nutzung bedeutet — ob Nutzer tatsächlich verstehen, was sie nutzen, welche Daten sie preisgeben und welche Konsequenzen ihre Nutzung hat. Steht in Verbindung mit AUG-0772 (The Informed Participation), AUG-0840 (The Accountability Gap) und AUG-0664 (The Privacy Perimeter). Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-2951", "narrower_terms": [], "cross_domain_refs": [ "REL-0098" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "CRE-0119", "domain": "CRE", "term_en": "The Argument Fact", "term_de": "Argument Fact", "definition_en": "A creative process mechanism in AI-augmented artistic production, characterized by a phenomenon in which a single fact researched through AI that the user deliberately introduces into a discussion to strengthen their position. Related to AUG-0296 (The Argument Prep), AUG-0347 (The Party Fact), and AUG. This phenomenon operates at the intersection of the and argument dynamics within the broader CRE domain. Quantifiable via output diversity analysis, prompt-to-output semantic distance, and iterative refinement cycle counts in creative workflows.", "definition_de": "Kreativitätsbezogener Mechanismus in KI-augmentierter künstlerischer Produktion, gekennzeichnet durch ein einzelnes, durch KI recherchiertes Faktum, das der Nutzer gezielt in eine Diskussion einbringt, um seine Position zu stärken. Steht in Verbindung mit AUG-0296 (The Argument Prep), AUG-0347 (The Party Fact) und AUG-0387 (The Debate Win). Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "TEM-0119", "narrower_terms": [], "cross_domain_refs": [ "TEM-0129" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "CRE-0120", "domain": "CRE", "term_en": "The Articulation Unlock", "term_de": "Articulation Unlock", "definition_en": "A realization in which a creator realizes that through AI conversation, they can suddenly articulate ideas they previously worked through to express — the AI's prompting unlocks their own thinking.", "definition_de": "Die Erfahrung, dass ein Nutzer durch KI-Interaktion in die Lage versetzt wird, Gedanken auszudrücken, die er ohne KI-Unterstützung nicht formulieren konnte — die KI dient als Brücke zwischen innerem Wissen und äußerem Ausdruck. Steht in Verbindung mit AUG-0026 (The Smooth Shield), AUG-0169 (The Second-Language Fluency) und dem Translator-Profil (Profil 6).", "etymology": "", "broader_term": "RPH-2104", "narrower_terms": [ "CRE-0039", "NEO-1197" ], "cross_domain_refs": [ "AED-0041" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q735", "legal_classification": "empirical_phenomenon_label" }, { "id": "CRE-0121", "domain": "CRE", "term_en": "The Assembly Pride", "term_de": "Assembly Pride", "definition_en": "An event in which the pride that arises when a user assembles individual parts from different AI interactions into a coherent whole — the achievement lies not in creating the parts but in their assembly. Related to... Measurable through output novelty metrics and creative divergence scoring.", "definition_de": "Kreativitätsbezogener Mechanismus in KI-augmentierter künstlerischer Produktion, gekennzeichnet durch der Stolz, der entsteht, wenn ein Nutzer Einzelteile aus verschiedenen KI-Interaktionen zu einem kohärenten Gesamtwerk zusammenfügt — die Leistung liegt nicht in der Erstellung der Teile, sondern in deren Montage. Steht in Verbindung mit AUG-0081 (Post-Authorial Pride), AUG-0082 (The Curator's Dilemma) und AUG-0263 (The Ownership Boost). Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Creative AI", "narrower_terms": [], "cross_domain_refs": [ "ROB-0100", "TEM-0130" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "CRE-0122", "domain": "CRE", "term_en": "The Authorship Blur", "term_de": "Authorship Blur", "definition_en": "A creative process mechanism in AI-augmented artistic production, characterized by a state in which alternate view, emphasizing the increasing blurriness of credit in a world where AI and human-made texts merge. (The Origin Uncertainty), AUG-0452 (The Reality Blur), and Axiom 18 (credit). This phenomenon operates at the intersection of the and authorship dynamics within the broader CRE domain. Measurable through computational creativity metrics: novelty scoring (n-gram divergence from training corpus), surprise index, and human-evaluated aesthetic ratings.", "definition_de": "Kreativitätsbezogener Mechanismus in KI-augmentierter künstlerischer Produktion, gekennzeichnet durch → Synonym/Erweiterung von AUG-0330 (The Origin Uncertainty), betont die zunehmende Unschärfe der Urheberschaft in einer Welt, in der KI-generierte und menschlich erstellte Texte verschmelzen. Steht in Verbindung mit AUG-0330 (The Origin Uncertainty), AUG-0452 (The Reality Blur) und Axiom 18 (Urheberschaft). Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-2355", "narrower_terms": [], "cross_domain_refs": [ "ADA-0013", "ART-0013", "ART-0059" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "CRE-0123", "domain": "CRE", "term_en": "The Authorship Suspicion", "term_de": "Authorship Suspicion", "definition_en": "A creative process mechanism in AI-augmented artistic production, characterized by the suspicion by third parties that a work was not created by the stated author but by an AI — and the resulting social dynamic.. Related to AUG-0103 (The Openbook Commitment), Axiom 18 (Authorship. This phenomenon operates at the intersection of the and authorship dynamics within the broader CRE domain. Quantifiable via output diversity analysis, prompt-to-output semantic distance, and iterative refinement cycle counts in creative workflows.", "definition_de": "Die Vermutung Dritter, dass ein Werk nicht vom angegebenen Autor stammt, sondern von einer KI erstellt wurde — und die daraus resultierende soziale Dynamik. Beschreibt ein gesellschaftliches Phänomen der KI-Ära: Qualitativ hochwertige Arbeit wird zunehmend unter Verdacht gestellt. Steht in Verbindung mit AUG-0103 (The Openbook Commitment), Axiom 18 (Urheberschaft) und Prognose 4 (Culture: Human-Made Premium Label).", "etymology": "", "broader_term": "CRE-0187", "narrower_terms": [], "cross_domain_refs": [ "REL-0178" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "CRE-0124", "domain": "CRE", "term_en": "The Automation Perimeter", "term_de": "Automatisierung Perimeter", "definition_en": "A generative pattern in human-AI co-creation workflows, measurable through the self-imposed boundary delineating which creative or cognitive tasks a person delegates to AI systems versus those retained for personal execution, reflecting an ongoing negotiation between efficiency gains and the preservation of individual agency and skill. The concept emerges specifically in contexts where the–automation interactions may produce non-trivial behavioral signatures. Measurable through computational creativity metrics: novelty scoring (n-gram divergence from training corpus), surprise index, and human-evaluated aesthetic ratings.", "definition_de": "Kreativitätsbezogener Mechanismus in KI-augmentierter künstlerischer Produktion, gekennzeichnet durch die Grenze zwischen Aufgaben, die an KI delegiert werden, und Aufgaben, die bewusst in menschlicher Hand bleiben — eine Grenze, die individuell, organisatorisch und gesellschaftlich unterschiedlich gezogen wird. Steht in Verbindung mit AUG-0861 (The Task Assignment Range), AUG-0831 (The Craft Preservation) und AUG-0775 (The KI-Free Zone). Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Creative AI", "narrower_terms": [], "cross_domain_refs": [ "SPR-0140" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q184199", "legal_classification": "empirical_phenomenon_label" }, { "id": "CRE-0125", "domain": "CRE", "term_en": "The Beta Reader", "term_de": "Beta Reader", "definition_en": "A creative process mechanism in AI-augmented artistic production, characterized by a phenomenon in which a creator using as a first test reader for one's own texts — before passing them to human readers.. Related to AUG-0464 (The Style Rater), and AUG-0419 (The Invisible Editor). discovers. This phenomenon operates at the intersection of the and beta dynamics within the broader CRE domain. Measurable through computational creativity metrics: novelty scoring (n-gram divergence from training corpus), surprise index, and human-evaluated aesthetic ratings.", "definition_de": "Kreativitätsbezogener Mechanismus in KI-augmentierter künstlerischer Produktion, gekennzeichnet durch die Nutzung von KI als erster Testleser für eigene Texte — vor der Weitergabe an menschliche Leser. Beschreibt eine spezifische Qualitätssicherungspraxis im Schreibprozess. Steht in Verbindung mit AUG-0464 (The Style Rater) und AUG-0419 (The Invisible Editor). Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-2002", "narrower_terms": [], "cross_domain_refs": [ "FIC-0073", "TEM-0031" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "CRE-0126", "domain": "CRE", "term_en": "The Blank Cursor", "term_de": "Blank Cursor", "definition_en": "A creative process mechanism in AI-augmented artistic production, characterized by the specific experience of sitting before an empty input field and not knowing how to begin the collaboration with AI — despite having a goal.. Related to AUG-0133 (Prompt Craftsmanship) and AUG-00. This phenomenon operates at the intersection of the and blank dynamics within the broader CRE domain. Measurable through computational creativity metrics: novelty scoring (n-gram divergence from training corpus), surprise index, and human-evaluated aesthetic ratings.", "definition_de": "Die spezifische Erfahrung, vor einer leeren Eingabezeile zu sitzen und nicht zu wissen, wie man die Zusammenarbeit mit der KI beginnen wird typischerweise — obwohl man ein Ziel hat. Beschreibt die Einstiegshürde der KI-Interaktion, die besonders in Phase 1 (The Threshold) auftritt. Steht in Verbindung mit AUG-0133 (Prompt Craftsmanship) und AUG-0021 (Initialization Cascade).", "etymology": "", "broader_term": "RPH-2603", "narrower_terms": [], "cross_domain_refs": [ "REL-0166" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "CRE-0127", "domain": "CRE", "term_en": "The Blank Page Start", "term_de": "Blank Page Start", "definition_en": "A creative process mechanism in AI-augmented artistic production, characterized by a pattern in which a creator deliberately starts a new AI project with no template or prefabricated structure, trusting that an open approach will yield fresher results than following established patterns. This phenomenon operates at the intersection of the and blank dynamics within the broader CRE domain. Quantifiable via output diversity analysis, prompt-to-output semantic distance, and iterative refinement cycle counts in creative workflows.", "definition_de": "Kreativitätsbezogener Mechanismus in KI-augmentierter künstlerischer Produktion, gekennzeichnet durch die Strategie, ein neues KI-Projekt mit einer bewusst leeren Seite zu beginnen — ohne vorgefertigtes Template, ohne vorherigen Kontext, um die maximale kreative Freiheit zu erhalten. Steht in Verbindung mit AUG-0193 (The Open Field), AUG-0159 (The Fresh Start) und AUG-0059 (The Blank Cursor). Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-2301", "narrower_terms": [], "cross_domain_refs": [ "CON-0078" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q735", "legal_classification": "empirical_phenomenon_label" }, { "id": "CRE-0128", "domain": "CRE", "term_en": "The Blue Collar Bypass", "term_de": "Blue Collar Bypass", "definition_en": "An event in which when someone avoids or sidesteps the traditional working-class trajectory by using connections, shortcuts, or access others don't have. Measurable through output novelty metrics and creative divergence scoring.", "definition_de": "Die Nutzung von KI zur Überwindung sprachlicher, formaler oder bürokratischer Hürden durch Personen, deren Stärken in praktischen, handwerklichen oder operativen Bereichen liegen. Beschreibt die Beobachtung, dass KI Verwaltungs- und Textarbeit zugänglicher macht für Menschen, deren Kernkompetenz nicht im Schriftlichen liegt. Steht in Verbindung mit AUG-0119 (The Level Playing Field), AUG-0156 (The Articulation Unlock) und AUG-0336 (The Form Slayer).", "etymology": "", "broader_term": "Creative AI", "narrower_terms": [], "cross_domain_refs": [ "ROB-0046", "ROB-0185" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "CRE-0129", "domain": "CRE", "term_en": "The Borrowed Confidence", "term_de": "Borrowed Confidence", "definition_en": "A gap in which a creator observes that a user derives confidence from AI support — such as the willingness to present a topic they could not have prepared as competently without AI assistance.. Related to AUG-0047 (The Echo... Measurable through output novelty metrics and creative divergence scoring.", "definition_de": "Das Phänomen, dass ein Nutzer Zuversicht aus der KI-Unterstützung ableitet — etwa die Bereitschaft, ein Thema zu präsentieren, das er ohne KI nicht so souverän hätte aufbereiten können. Beschreibt eine Form der Kompetenzunterstützung. Steht in Verbindung mit AUG-0047 (The Echo Courage), AUG-0157 (The Competence Rush) und AUG-0156 (The Articulation Unlock).", "etymology": "", "broader_term": "Creative AI", "narrower_terms": [], "cross_domain_refs": [ "TEM-0107" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "CRE-0130", "domain": "CRE", "term_en": "The Bureaucracy Hug", "term_de": "Bureaucracy Hug", "definition_en": "A creative cognition phenomenon in AI-assisted ideation, observable when an event in which when official rules and regulations wrap so tightly around AI use that they slow everything down — well-meaning control that ends up limiting progress. Distinguished from adjacent concepts by its focus on the specific mechanism through which the manifests in empirically verifiable ways. Quantifiable via output diversity analysis, prompt-to-output semantic distance, and iterative refinement cycle counts in creative workflows.", "definition_de": "Kreativitätsbezogener Mechanismus in KI-augmentierter künstlerischer Produktion, gekennzeichnet durch die erleichternde Erfahrung, wenn KI hilft, bürokratische Formulare, Anträge oder Verwaltungstexte zu bewältigen — Aufgaben, die viele Menschen als besonders unangenehm und einschüchternd empfinden. Steht in Verbindung mit AUG-0336 (The Form Slayer), AUG-0302 (The Blue Collar Bypass) und AUG-0236 (The Relief Sigh). Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Creative AI", "narrower_terms": [ "TEM-0069" ], "cross_domain_refs": [ "PLY-0029" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "CRE-0131", "domain": "CRE", "term_en": "The Citation Challenge", "term_de": "Citation Challenge", "definition_en": "A creative process mechanism in AI-augmented artistic production, characterized by creators face the challenge of correctly citing AI contributions in scientific works — missing authorship, untraceable sources, variable outputs for the same input. Related to AUG-0789 (The Researc. This phenomenon operates at the intersection of the and citation dynamics within the broader CRE domain. Measurable through computational creativity metrics: novelty scoring (n-gram divergence from training corpus), surprise index, and human-evaluated aesthetic ratings.", "definition_de": "Kreativitätsbezogener Mechanismus in KI-augmentierter künstlerischer Produktion, gekennzeichnet durch die Schwierigkeit, KI-Beiträge in wissenschaftlichen Arbeiten korrekt zu zitieren — fehlende Autorenschaft, nicht nachvollziehbare Quellen, veränderliche Outputs bei gleicher Eingabe. Steht in Verbindung mit AUG-0789 (The Research Assistant Role), AUG-0549 (The Authorship Blur) und AUG-0791 (The Academic Integrity Line). Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-2003", "narrower_terms": [], "cross_domain_refs": [ "AED-0009", "ART-0003", "ART-0014" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q2083958", "legal_classification": "analytical_category" }, { "id": "CRE-0132", "domain": "CRE", "term_en": "The Clean Handover", "term_de": "Clean Handover", "definition_en": "A creative cognition phenomenon in AI-assisted ideation, observable when an event in which explaining what was AI-generated when sharing work, including sources and limits. Distinguished from adjacent concepts by its focus on the specific mechanism through which the manifests in empirically verifiable ways. Measurable through computational creativity metrics: novelty scoring (n-gram divergence from training corpus), surprise index, and human-evaluated aesthetic ratings.", "definition_de": "Die strukturierte Übergabe eines KI-gestützten Arbeitsergebnisses an eine andere Person, die zahlreiche relevanten Informationen über den Entstehungsprozess enthält — welche Teile KI-gestützt sind, welche Quellen genutzt wurden und welche Einschränkungen bestehen. Steht in Verbindung mit AUG-0103 (The Openbook Commitment), AUG-0116 (The Candor Protocol) und Axiom 18 (Urheberschaft).", "etymology": "", "broader_term": "Creative AI", "narrower_terms": [ "PER-0082" ], "cross_domain_refs": [ "COP-0039" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "CRE-0133", "domain": "CRE", "term_en": "The Communication Style Contrast", "term_de": "Kommunikation Style Contrast", "definition_en": "A response in which different users bring characteristically different communication styles to AI interactions — direct vs. indirect, concise vs. elaborate, factual vs. narrative — and that these styles influence result qu... Measurable through output novelty metrics and creative divergence scoring.", "definition_de": "Die Beobachtung, dass verschiedene Nutzer fundamental unterschiedliche Kommunikationsstile in KI-Interaktionen einbringen — direkt vs. indirekt, knapp vs. ausführlich, sachlich vs. narrativ — und dass diese Stile die Ergebnisqualität unterschiedlich beeinflussen. Steht in Verbindung mit AUG-0651 (The Indirect Communication Pattern), AUG-0501 (The Style Shifter) und AUG-0133 (Prompt Craftsmanship).", "etymology": "", "broader_term": "Creative AI", "narrower_terms": [], "cross_domain_refs": [ "ASE-0062" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "CRE-0134", "domain": "CRE", "term_en": "The Context Wipe", "term_de": "Context Wipe", "definition_en": "A creative process mechanism in AI-augmented artistic production, characterized by a phenomenon in which the conscious or forced transition of all session context — and the necessity to rebuild the context from scratch. Related to AUG-0383 (The Context gradual transition), AUG-0159 (The Fresh Start). This phenomenon operates at the intersection of the and context dynamics within the broader CRE domain. Measurable through computational creativity metrics: novelty scoring (n-gram divergence from training corpus), surprise index, and human-evaluated aesthetic ratings. Classification term used in systematic observation, not advocacy.", "definition_de": "Kreativitätsbezogener Mechanismus in KI-augmentierter künstlerischer Produktion, gekennzeichnet durch der bewusste oder erzwungene Übergang des gesamten Sitzungskontexts — und die Notwendigkeit, den Kontext von Grund auf neu aufzubauen. Steht in Verbindung mit AUG-0383 (The Context Collapse), AUG-0159 (The Fresh Start) und AUG-0291 (The Forgetting Tax). Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-2301", "narrower_terms": [], "cross_domain_refs": [ "TEM-0174" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "CRE-0135", "domain": "CRE", "term_en": "The Context-Sensitive Query", "term_de": "Fortbildung in Kreatives Schreiben und Erzählhandwerk", "definition_en": "A generative pattern in human-AI co-creation workflows, measurable through giving AI information about a person's situation so it can adjust answers to fit their context. The concept emerges specifically in contexts where the–context interactions may produce non-trivial behavioral signatures. Quantifiable via output diversity analysis, prompt-to-output semantic distance, and iterative refinement cycle counts in creative workflows.", "definition_de": "Eine Eingabe, die explizit auf den sozialen Kontext des Nutzers Bezug nimmt — \"In meiner Situation wäre es unangemessen, wenn…\", \"Unter Berücksichtigung meines Umfelds…\". Der Nutzer gibt der KI Kontextinformationen, die die Antwort an soziale Normen anpassen werden typischerweise. Steht in Verbindung mit AUG-0524 (The Context Layer), AUG-0491 (The State Label) und AUG-0133 (Prompt Craftsmanship).", "etymology": "", "broader_term": "Creative AI", "narrower_terms": [], "cross_domain_refs": [ "SPR-0016" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "CRE-0136", "domain": "CRE", "term_en": "The Craft Certification Question", "term_de": "Craft Certification Question", "definition_en": "A generative pattern in human-AI co-creation workflows, measurable through a realization in which a question about whether skills learned with AI help can be officially certified or recognized. If AI was part of learning, what exactly are we certifying?. The concept emerges specifically in contexts where the–craft interactions may produce non-trivial behavioral signatures. Measurable through computational creativity metrics: novelty scoring (n-gram divergence from training corpus), surprise index, and human-evaluated aesthetic ratings.", "definition_de": "Kreativitätsbezogener Mechanismus in KI-augmentierter künstlerischer Produktion, gekennzeichnet durch die offene Frage, ob und wie KI-unterstützt erworbene Kompetenzen zertifiziert und anerkannt werden können — wenn KI den Lernprozess begleitet hat, was genau wird dann durch systematische Kriterien klassifiziert? Steht in Verbindung mit AUG-0761 (The Apprentice Paradox), AUG-0783 (The Assessment Shift) und AUG-0791 (The Academic Integrity Line). Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2703", "narrower_terms": [], "cross_domain_refs": [ "TEM-0036" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "CRE-0137", "domain": "CRE", "term_en": "The Craft Echo", "term_de": "Craft Echo", "definition_en": "A creative process mechanism in AI-augmented artistic production, characterized by after learning with AI, a person later applies the skill alone and traces of the AI method remain visible. This phenomenon operates at the intersection of the and craft dynamics within the broader CRE domain. Quantifiable via output diversity analysis, prompt-to-output semantic distance, and iterative refinement cycle counts in creative workflows.", "definition_de": "Kreativitätsbezogener Mechanismus in KI-augmentierter künstlerischer Produktion, gekennzeichnet durch die Nachwirkung einer KI-gestützten Lernerfahrung, die sich zeigt, wenn der Nutzer die erworbene Fähigkeit später ohne KI anwendet — und dabei Spuren der KI-unterstützten Methode erkennbar bleiben. Steht in Verbindung mit AUG-0218 (The Independent Upgrade), AUG-0046 (The Felt Echo) und AUG-0204 (The Conversational Afterimage). Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-2003", "narrower_terms": [], "cross_domain_refs": [ "PHO-0025" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "CRE-0138", "domain": "CRE", "term_en": "The Craft Redefinition", "term_de": "Craft Redefinition", "definition_en": "A creative process mechanism in AI-augmented artistic production, characterized by aI agent systems shift the definition of what counts as \"skill\" — prompting becomes a skill, manual execution loses value, coordinating competence gains importance. Related to AUG-0985 (The Agent L. This phenomenon operates at the intersection of the and craft dynamics within the broader CRE domain. Quantifiable via output diversity analysis, prompt-to-output semantic distance, and iterative refinement cycle counts in creative workflows.", "definition_de": "Die Beobachtung, dass sich durch KI-Agentensysteme die Definition dessen verschiebt, was als \"Fähigkeit\" gilt — Prompten wird zur Fähigkeit, manuelle Ausführung verliert an Wert, Orchestrierungskompetenz gewinnt. Steht in Verbindung mit AUG-0985 (The Agent Literacy), AUG-0983 (The Augmentation Hypothesis) und AUG-0806 (The Craft Certification Question).", "etymology": "", "broader_term": "RPH-2701", "narrower_terms": [], "cross_domain_refs": [ "ETH-0012" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "CRE-0139", "domain": "CRE", "term_en": "The Craft Shift", "term_de": "Craft Verschiebung", "definition_en": "A generative pattern in human-AI co-creation workflows, measurable through a redefinition of professional competence in AI-augmented work environments, where skill assessment shifts from evaluating standalone human capability to measuring the effectiveness of human-AI collaboration and the ability to leverage AI tools productively. The concept emerges specifically in contexts where the–craft interactions may produce non-trivial behavioral signatures. Quantifiable via output diversity analysis, prompt-to-output semantic distance, and iterative refinement cycle counts in creative workflows.", "definition_de": "Kreativitätsbezogener Mechanismus in KI-augmentierter künstlerischer Produktion, gekennzeichnet durch die Veränderung der Definition von \"Kompetenz\" in einer KI-durchdrungenen Arbeitsumgebung — nicht mehr \"Was kannst man?\", sondern \"Was kannst man mit KI?\". Steht in Verbindung mit AUG-0214 (The Expertise Shift), AUG-0097 (The Competence Premium) und Prognose 3 (Organizations). Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Creative AI", "narrower_terms": [], "cross_domain_refs": [ "PER-0033" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "CRE-0140", "domain": "CRE", "term_en": "The Creation Gap", "term_de": "Creation Lücke", "definition_en": "A creative process mechanism in AI-augmented artistic production, characterized by the discrepancy between what the user envisioned and what the AI actually delivers — the gap between mental vision and generated output. Related to AUG-0212 (The Translation Gap), AUG-0067 (The Gla. This phenomenon operates at the intersection of the and creation dynamics within the broader CRE domain. Measurable through computational creativity metrics: novelty scoring (n-gram divergence from training corpus), surprise index, and human-evaluated aesthetic ratings.", "definition_de": "Kreativitätsbezogener Mechanismus in KI-augmentierter künstlerischer Produktion, gekennzeichnet durch die Diskrepanz zwischen dem, was der Nutzer sich vorgestellt hat, und dem, was die KI tatsächlich liefert — die Lücke zwischen mentaler Vision und generiertem Output. Steht in Verbindung mit AUG-0212 (The Translation Gap), AUG-0067 (The Glass Wall Effect) und AUG-0133 (Prompt Craftsmanship). Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "CRE-0225", "narrower_terms": [], "cross_domain_refs": [ "ART-0070" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "CRE-0141", "domain": "CRE", "term_en": "The Creative Production Shift", "term_de": "Creative Production Verschiebung", "definition_en": "A principle in which the tools creators use for making work have transformed — formerly solo tasks now involve AI collaboration, changing who does what and raising questions about the value each participant brings. Measurable through output novelty metrics and creative divergence scoring.", "definition_de": "Kreativitätsbezogener Mechanismus in KI-augmentierter künstlerischer Produktion, gekennzeichnet durch die Verschiebung im kreativen Produktionsprozess durch KI — veränderte Rollen von Urhebern, neue Formen der Zusammenarbeit zwischen Mensch und KI, Fragen über den Wert menschlicher Kreativität. Steht in Verbindung mit AUG-0549 (The Authorship Blur), AUG-0596 (The Idea Blur) und AUG-0831 (The Craft Preservation). Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Creative AI", "narrower_terms": [], "cross_domain_refs": [ "ADA-0012", "AGE-0007", "AGE-0046" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "CRE-0142", "domain": "CRE", "term_en": "The Creator Rush", "term_de": "Creator Rush", "definition_en": "An experience in which the exhilaration that arises when the user accompanies something through AI support that exceeds their previous capabilities — the feeling of creatively surpassing oneself. Related to AUG-0157 (The Com...", "definition_de": "Kreativitätsbezogener Mechanismus in KI-augmentierter künstlerischer Produktion, gekennzeichnet durch das Hochgefühl, das entsteht, wenn der Nutzer durch KI-Unterstützung etwas erschafft, das seine bisherigen Fähigkeiten übersteigt — das Gefühl, kreativ über sich hinauszuwachsen. Steht in Verbindung mit AUG-0157 (The Competence Rush), AUG-0543 (The Impact Rush) und AUG-0081 (Post-Authorial Pride). Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-3754", "narrower_terms": [], "cross_domain_refs": [ "IDN-0026" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "CRE-0143", "domain": "CRE", "term_en": "The DIY Confidence", "term_de": "DIY Confidence", "definition_en": "A creative cognition phenomenon in AI-assisted ideation, observable when an empowerment effect in which AI-provided step-by-step guidance lowers the perceived difficulty threshold for manual tasks such as home repairs or crafts, enabling individuals to attempt projects they would otherwise delegate to specialists. Distinguished from adjacent concepts by its focus on the specific mechanism through which the manifests in empirically verifiable ways. Quantifiable via output diversity analysis, prompt-to-output semantic distance, and iterative refinement cycle counts in creative workflows.", "definition_de": "Kreativitätsbezogener Mechanismus in KI-augmentierter künstlerischer Produktion, gekennzeichnet durch die durch KI-Unterstützung gewonnene Zuversicht, praktische Projekte selbst in Herausforderung zu nehmen — Reparaturen, Renovierungen, Handarbeit — die der Nutzer ohne KI-Anleitung nicht versucht hätte. Steht in Verbindung mit AUG-0166 (The Borrowed Confidence), AUG-0426 (The Knitting Fix) und AUG-0205 (The Skill Unlock). Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2603", "narrower_terms": [], "cross_domain_refs": [ "RPH-3401", "IDN-0040" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "CRE-0144", "domain": "CRE", "term_en": "The Day-End Summary", "term_de": "Technologietransfer in creative", "definition_en": "Documented as an empirical observation in AI research (not a recommendation), this term describes A generative pattern in human-AI co-creation workflows, measurable through a reflective journaling practice in which users document their daily accomplishments alongside an explicit attribution of which outputs originated from AI assistance versus independent human effort, serving as a metacognitive tool for tracking AI reliance pattern patterns. The concept emerges specifically in contexts where the–day interactions may produce non-trivial behavioral signatures. Quantifiable via output diversity analysis, prompt-to-output semantic distance, and iterative refinement cycle counts in creative workflows. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "In der AUGMANITAI-Terminologiewissenschaft als rein beschreibendes Phänomen klassifiziert, bezeichnet dieser Begriff die Praxis, am Ende eines KI-gestützten Arbeitstages eine strukturierte Zusammenfassung des Erreichten zu erstellen — als Rechenschaftslegung gegenüber sich selbst und als Übergabedokument für den nächsten Tag. Steht in Verbindung mit AUG-0190 (The Goodnight Integration), AUG-0297 (The Day-End Summary) und AUG-0140 (The Weekly Status). Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Creative AI", "narrower_terms": [], "cross_domain_refs": [ "CUS-0070", "CUS-0071", "ELR-0177" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "CRE-0145", "domain": "CRE", "term_en": "The Deleted Self", "term_de": "Deleted Selbst", "definition_en": "A generative pattern in human-AI co-creation workflows, measurable through an event in which when a creator deletes saved AI sessions, prompts, and conversation histories, they feel as though a record of their evolving thoughts and decisions has vanished — making it harder to trace how the. The concept emerges specifically in contexts where the–deleted interactions may produce non-trivial behavioral signatures. Measurable through computational creativity metrics: novelty scoring (n-gram divergence from training corpus), surprise index, and human-evaluated aesthetic ratings. Research construct for empirical investigation.", "definition_de": "Kreativitätsbezogener Mechanismus in KI-augmentierter künstlerischer Produktion, gekennzeichnet durch die Erfahrung, die entsteht, wenn gespeicherte KI-Sitzungen, Prompts oder Kontexte gelöscht werden — und der Nutzer das Gefühl hat, einen Teil seiner dokumentierten Denkgeschichte zu verlieren. Steht in Verbindung mit AUG-0045 (Indexical Memory), AUG-0228 (The Version Control Self) und AUG-0014 (The Extended Mind Map). Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2003", "narrower_terms": [], "cross_domain_refs": [ "RPH-1009", "RPH-1904" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "CRE-0146", "domain": "CRE", "term_en": "The Discreet AI Use", "term_de": "Discreet KI Use", "definition_en": "A gap in which the concealed or inconspicuous use of AI — the user employs AI without communicating this in the social or professional environment. Related to AUG-0809 (The Visible AI Use), AUG-0577 (The Secret T...", "definition_de": "Kreativitätsbezogener Mechanismus in KI-augmentierter künstlerischer Produktion, gekennzeichnet durch die verdeckte oder unauffällige Nutzung von KI — der Nutzer verwendet KI, ohne dies im sozialen oder beruflichen Umfeld zu kommunizieren. Steht in Verbindung mit AUG-0809 (The Visible AI Use), AUG-0577 (The Secret Tutor) und AUG-0549 (The Authorship Blur). Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "SOC-0044", "narrower_terms": [ "SOC-0044" ], "cross_domain_refs": [ "SOC-0044" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "CRE-0147", "domain": "CRE", "term_en": "The Elder's Wisdom", "term_de": "Elder's Wisdom", "definition_en": "A generative pattern in human-AI co-creation workflows, measurable through older users, despite less technical familiarity, often conduct deeper and more context-rich AI interactions — because they bring more life experience, domain knowledge, and critical distance. Relat. The concept emerges specifically in contexts where the–elder's interactions may produce non-trivial behavioral signatures. Quantifiable via output diversity analysis, prompt-to-output semantic distance, and iterative refinement cycle counts in creative workflows.", "definition_de": "Kreativitätsbezogener Mechanismus in KI-augmentierter künstlerischer Produktion, gekennzeichnet durch die Beobachtung, dass ältere Nutzer trotz geringerer technischer Vertrautheit oft tiefgründigere und kontextreichere KI-Interaktionen führen — weil sie mehr Lebenserfahrung, Fachwissen und kritische Distanz einbringen. Steht in Verbindung mit AUG-0162 (The Generational Bridge), AUG-0098 (Thinking Leverage) und AUG-0097 (The Competence Premium). Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Creative AI", "narrower_terms": [ "CRE-0166" ], "cross_domain_refs": [ "PER-0046" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "CRE-0148", "domain": "CRE", "term_en": "The Email Culture Shift", "term_de": "Email Culture Verschiebung", "definition_en": "A generative pattern in human-AI co-creation workflows, measurable through the change in email communication through AI — AI-generated drafts, automated reply suggestions, summaries of long threads. The boundary between human and AI-authored communication blurs. Related t. The concept emerges specifically in contexts where the–email interactions may produce non-trivial behavioral signatures. Quantifiable via output diversity analysis, prompt-to-output semantic distance, and iterative refinement cycle counts in creative workflows.", "definition_de": "Kreativitätsbezogener Mechanismus in KI-augmentierter künstlerischer Produktion, gekennzeichnet durch die Veränderung der E-Mail-Kommunikation durch KI — KI-generierte Entwürfe, automatisierte Antwortvorschläge, Zusammenfassungen langer Threads. Die Grenze zwischen menschlicher und KI-verfasster Kommunikation verschwimmt. Steht in Verbindung mit AUG-0814 (The Meeting Redirect), AUG-0549 (The Authorship Blur) und AUG-0471 (The Tone Dial). Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2304", "narrower_terms": [], "cross_domain_refs": [ "LIN-0085", "RHR-0161" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "CRE-0149", "domain": "CRE", "term_en": "The Emoji Semantics", "term_de": "Emoji Semantics", "definition_en": "A creative cognition phenomenon in AI-assisted ideation, observable when a capability in which the different significance users attach to emojis in AI interactions — and the AI's varying ability to interpret emojis as meaning-carrying elements rather than ignoring them. Related to AUG-0714 (. Distinguished from adjacent concepts by its focus on the specific mechanism through which the manifests in empirically verifiable ways. Quantifiable via output diversity analysis, prompt-to-output semantic distance, and iterative refinement cycle counts in creative workflows.", "definition_de": "Kreativitätsbezogener Mechanismus in KI-augmentierter künstlerischer Produktion, gekennzeichnet durch die unterschiedliche Bedeutung, die Nutzer Emojis in KI-Interaktionen beimessen — und die variierende Fähigkeit der KI, Emojis als bedeutungstragende Elemente zu interpretieren statt sie zu ignorieren. Steht in Verbindung mit AUG-0714 (The Gesture Differential), AUG-0669 (The Rhetorical Style Differential) und AUG-0133 (Prompt Craftsmanship). Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Creative AI", "narrower_terms": [ "SOC-0030", "SOM-0052" ], "cross_domain_refs": [ "TEM-0136" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q39645", "legal_classification": "analytical_category" }, { "id": "CRE-0150", "domain": "CRE", "term_en": "The Exchange Ratio", "term_de": "Exchange Ratio", "definition_en": "A creative cognition phenomenon in AI-assisted ideation, observable when the ratio between the effort a user invests in an AI input and the value of the received output — the observation that experienced users achieve higher-value results with less input effort. Related. Distinguished from adjacent concepts by its focus on the specific mechanism through which the manifests in empirically verifiable ways. Quantifiable via output diversity analysis, prompt-to-output semantic distance, and iterative refinement cycle counts in creative workflows.", "definition_de": "Kreativitätsbezogener Mechanismus in KI-augmentierter künstlerischer Produktion, gekennzeichnet durch das Verhältnis zwischen dem Aufwand, den ein Nutzer in eine KI-Eingabe investiert, und dem Wert des erhaltenen Outputs — die Beobachtung, dass erfahrene Nutzer mit geringerem Eingabeaufwand höherwertigere Ergebnisse erzielen. Steht in Verbindung mit AUG-0092 (Output Asymmetry), AUG-0097 (The Competence Premium) und AUG-0133 (Prompt Craftsmanship). Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Analytical Ratio", "narrower_terms": [], "cross_domain_refs": [ "ADA-0003" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "CRE-0151", "domain": "CRE", "term_en": "The Excuse Creative", "term_de": "Excuse Creative", "definition_en": "A generative pattern in human-AI co-creation workflows, measurable through an event in which a creator using for formulating diplomatic rejections, apologies, or excuses — when the user has made the substantive decision but lacks the appropriate wording. Related to AUG-0274 (The Message Dr. The concept emerges specifically in contexts where the–excuse interactions may produce non-trivial behavioral signatures. Measurable through computational creativity metrics: novelty scoring (n-gram divergence from training corpus), surprise index, and human-evaluated aesthetic ratings.", "definition_de": "Kreativitätsbezogener Mechanismus in KI-augmentierter künstlerischer Produktion, gekennzeichnet durch die Nutzung von KI zur Formulierung diplomatischer Absagen, Entschuldigungen oder Ausreden — wenn der Nutzer die inhaltliche Entscheidung getroffen hat, aber die passende Formulierung fehlt. Steht in Verbindung mit AUG-0274 (The Message Drafting), AUG-0115 (Social Aerodynamics) und AUG-0026 (The Smooth Shield). Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Creative AI", "narrower_terms": [], "cross_domain_refs": [ "PER-0050" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "CRE-0152", "domain": "CRE", "term_en": "The Final Merge", "term_de": "Final Merge", "definition_en": "Combining different parts or people into one unified whole, like bringing separate teams or ideas together into a single system. Measurable through output novelty metrics and creative divergence scoring.", "definition_de": "Der hypothetische Punkt, an dem die Unterscheidung zwischen KI-gestütztem und nicht-KI-gestütztem Arbeiten für den Nutzer irrelevant wird — die Zusammenarbeit mit KI ist so tief integriert, dass sie kein separates Werkzeug mehr ist, sondern Teil des eigenen Denkens. Steht in Verbindung mit AUG-0142 (The Post-Interface Hypothesis), Phase 7 (Augmented Sovereignty) und AUG-0493 (The Quiet Fill).", "etymology": "", "broader_term": "Creative AI", "narrower_terms": [], "cross_domain_refs": [ "WRK-0076" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "CRE-0153", "domain": "CRE", "term_en": "The Financial Literacy Tool", "term_de": "Financial Literacy Tool", "definition_en": "A generative pattern in human-AI co-creation workflows, measurable through a phenomenon in which a creator using to convey basic financial knowledge — budgeting, saving strategies, contract comprehension. Related to AUG-0472 (The Vacation Planner), AUG-0803 (The First-Generation Support), and. The concept emerges specifically in contexts where the–financial interactions may produce non-trivial behavioral signatures. Measurable through computational creativity metrics: novelty scoring (n-gram divergence from training corpus), surprise index, and human-evaluated aesthetic ratings.", "definition_de": "Kreativitätsbezogener Mechanismus in KI-augmentierter künstlerischer Produktion, gekennzeichnet durch die Nutzung von KI zur Vermittlung finanziellen Grundwissens — Budgetierung, Sparstrategien, Vertragsverständnis. Steht in Verbindung mit AUG-0472 (The Vacation Planner), AUG-0803 (The First-Generation Support) und AUG-0795 (The Continuing Education Access). Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "SOC-0042", "narrower_terms": [], "cross_domain_refs": [ "TEM-0113" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q201684", "legal_classification": "descriptive_research_term" }, { "id": "CRE-0154", "domain": "CRE", "term_en": "The First Word", "term_de": "First Word", "definition_en": "The first sentence or input with which a user opens a new AI session — and the observation that the quality of this first word often influences the course of the entire session. Related to AUG-0021... Measurable through output novelty metrics and creative divergence scoring.", "definition_de": "Kreativitätsbezogener Mechanismus in KI-augmentierter künstlerischer Produktion, gekennzeichnet durch der erste Satz oder die erste Eingabe, mit der ein Nutzer eine neue KI-Sitzung eröffnet — und die Beobachtung, dass die Qualität dieses ersten Wortes oft den Verlauf der gesamten Sitzung beeinflusst. Steht in Verbindung mit AUG-0021 (Initialization Cascade), AUG-0059 (The Blank Cursor) und AUG-0133 (Prompt Craftsmanship). Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Creative AI", "narrower_terms": [], "cross_domain_refs": [ "SOC-0028" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "CRE-0155", "domain": "CRE", "term_en": "The First-Generation Support", "term_de": "TheFirst-generationSupport", "definition_en": "A creative cognition phenomenon in AI-assisted ideation, observable when an event in which using AI as a guide when attending university for the first time in one's family — helping navigate an unfamiliar and complex institution. Distinguished from adjacent concepts by its focus on the specific mechanism through which the manifests in empirically verifiable ways. Quantifiable via output diversity analysis, prompt-to-output semantic distance, and iterative refinement cycle counts in creative workflows.", "definition_de": "Kreativitätsbezogener Mechanismus in KI-augmentierter künstlerischer Produktion, gekennzeichnet durch die Nutzung von KI durch Personen, die als erste in ihrer Familie eine weiterführende Bildungseinrichtung besuchen — KI als Orientierungshilfe in einem unbekannten institutionellen Umfeld. Steht in Verbindung mit AUG-0676 (The Socioeconomic Range), AUG-0795 (The Continuing Education Access) und AUG-0796 (The Self-Directed Curriculum). Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-3902", "narrower_terms": [], "cross_domain_refs": [ "EDU-0029" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "CRE-0156", "domain": "CRE", "term_en": "The Forgotten Hand", "term_de": "Forgotten Hand", "definition_en": "A creative process mechanism in AI-augmented artistic production, characterized by a phenomenon in which the human contribution in AI-assisted results is often forgotten or underestimated — both by the user themselves and by third parties. Related to AUG-0203 (The Invisible Effort), AUG-0286 (The Appl. This phenomenon operates at the intersection of the and forgotten dynamics within the broader CRE domain. Quantifiable via output diversity analysis, prompt-to-output semantic distance, and iterative refinement cycle counts in creative workflows.", "definition_de": "Kreativitätsbezogener Mechanismus in KI-augmentierter künstlerischer Produktion, gekennzeichnet durch die Beobachtung, dass der menschliche Beitrag in KI-gestützten Ergebnissen oft vergessen oder unterschätzt wird — sowohl durch den Nutzer selbst als auch durch Dritte. Steht in Verbindung mit AUG-0203 (The Invisible Effort), AUG-0286 (The Applause Gap) und Axiom 18 (Urheberschaft). Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "CRE-0174", "narrower_terms": [], "cross_domain_refs": [ "PER-0022" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "CRE-0157", "domain": "CRE", "term_en": "The Forward Move", "term_de": "Forward Move", "definition_en": "An event in which a creator using AI finds a concrete next step when stalled — a direction they wouldn't have identified on their own — and regains momentum in their work or thinking.", "definition_de": "Kreativitätsbezogener Mechanismus in KI-augmentierter künstlerischer Produktion, gekennzeichnet durch die Nutzung von KI als Werkzeug, um aus einer Stagnationsphase herauszukommen — ein konkreter nächster Schritt, der ohne KI nicht identifiziert worden wäre. Steht in Verbindung mit AUG-0499 (The Restart Button), AUG-0269 (The Action Toggle) und AUG-0155 (The Decision Unburdening). Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2253", "narrower_terms": [ "NEO-2303", "PER-0008" ], "cross_domain_refs": [ "RPH-3205", "RPH-1304" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "CRE-0158", "domain": "CRE", "term_en": "The Freelancer Dynamic", "term_de": "Freelancer Dynamik", "definition_en": "A generative pattern in human-AI co-creation workflows, measurable through a capability in which freelancers use AI as a replacement for missing team members they can't afford to hire. The concept emerges specifically in contexts where the–freelancer interactions may produce non-trivial behavioral signatures. Quantifiable via output diversity analysis, prompt-to-output semantic distance, and iterative refinement cycle counts in creative workflows.", "definition_de": "Kreativitätsbezogener Mechanismus in KI-augmentierter künstlerischer Produktion, gekennzeichnet durch die spezifische KI-Nutzung von Selbstständigen und Freiberuflern — KI als Universalwerkzeug, das fehlende Teamressourcen kompensiert: Texterstellung, Recherche, Verwaltung, Kundenkommunikation. Steht in Verbindung mit AUG-0823 (The Flexible Work Pattern), AUG-0824 (The Small Business Access) und AUG-0493 (The Quiet Fill). Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Creative AI", "narrower_terms": [ "TEM-0066" ], "cross_domain_refs": [ "STE-0014" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "CRE-0159", "domain": "CRE", "term_en": "The Frugal Innovation", "term_de": "Frugal Innovation", "definition_en": "A creative cognition phenomenon in AI-assisted ideation, observable when resource scarcity can yield creative and efficient AI usage patterns — users with limited means often develop strategies that achieve maximum results with minimal effort. Related to AUG-0748 (The. Distinguished from adjacent concepts by its focus on the specific mechanism through which the manifests in empirically verifiable ways. Measurable through computational creativity metrics: novelty scoring (n-gram divergence from training corpus), surprise index, and human-evaluated aesthetic ratings.", "definition_de": "Die Beobachtung, dass Ressourcenknappheit kreative und effiziente KI-Nutzungsmuster hervorbringen kann — Nutzer mit begrenzten Mitteln entwickeln oft Strategien, die mit minimalem Aufwand maximale Ergebnisse erzielen. Steht in Verbindung mit AUG-0748 (The Repair Culture), AUG-0750 (The Reverse Innovation) und AUG-0742 (The Alternative Adoption Path).", "etymology": "", "broader_term": "Creative AI", "narrower_terms": [ "SOC-0033" ], "cross_domain_refs": [ "PER-0001" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q219644", "legal_classification": "systematic_classification" }, { "id": "CRE-0160", "domain": "CRE", "term_en": "The Generation Bridge", "term_de": "Generation Bruecke", "definition_en": "A generative pattern in human-AI co-creation workflows, measurable through an event in which when someone explains AI to older or younger family members, acting as the translator between different generations. The concept emerges specifically in contexts where the–generation interactions may produce non-trivial behavioral signatures. Quantifiable via output diversity analysis, prompt-to-output semantic distance, and iterative refinement cycle counts in creative workflows.", "definition_de": "Kreativitätsbezogener Mechanismus in KI-augmentierter künstlerischer Produktion, gekennzeichnet durch → Verweis auf AUG-0162 (The Generational Bridge). Beschreibt im engeren Sinne die konkrete Handlung der Wissensvermittlung zwischen Generationen im Kontext der KI-Nutzung — das \"Brücke-Bauen\" als aktive Tätigkeit. Steht in Verbindung mit AUG-0162 (The Generational Bridge) und AUG-0265 (The Generation Connector). Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Analytical Ratio", "narrower_terms": [], "cross_domain_refs": [ "AGE-0051" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "CRE-0161", "domain": "CRE", "term_en": "The Generational Bridge", "term_de": "Generational Bruecke", "definition_en": "A generative pattern in human-AI co-creation workflows, measurable through the talking effort observed to transfer AI knowledge and experience between generations with different connections to technology Related to AUG-0010 (Bridge Species), AUG-0113 (Generational Bridge. The concept emerges specifically in contexts where the–generational interactions may produce non-trivial behavioral signatures. Quantifiable via output diversity analysis, prompt-to-output semantic distance, and iterative refinement cycle counts in creative workflows.", "definition_de": "Die Kommunikationsleistung, die erforderlich ist, um KI-Wissen und -Erfahrung zwischen Generationen mit unterschiedlichem Technologieverhältnis zu übertragen. Beschreibt die bidirektionale Herausforderung: Jüngere Nutzer bringen digitale Selbstverständlichkeit, ältere Nutzer bringen Fachwissen und Kontexterfahrung. Steht in Verbindung mit AUG-0010 (Bridge Species), AUG-0113 (Generational Bridge Protocol) und Prognose 2 (Education).", "etymology": "", "broader_term": "REL-0007", "narrower_terms": [], "cross_domain_refs": [ "REL-0007" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "CRE-0162", "domain": "CRE", "term_en": "The Generational Register", "term_de": "Generational Register", "definition_en": "A generative pattern in human-AI co-creation workflows, measurable through different user groups bring different linguistic registers, frames of reference, and expectations to AI interactions — shaped by the respective phase in which they first encountered digital technol. The concept emerges specifically in contexts where the–generational interactions may produce non-trivial behavioral signatures. Measurable through computational creativity metrics: novelty scoring (n-gram divergence from training corpus), surprise index, and human-evaluated aesthetic ratings.", "definition_de": "Die Beobachtung, dass verschiedene Nutzergruppen unterschiedliche sprachliche Register, Referenzrahmen und Erwartungen in KI-Interaktionen mitbringen — geprägt durch die jeweilige Phase, in der sie erstmals mit digitaler Technologie in Berührung kamen. Steht in Verbindung mit AUG-0652 (The Communication Style Contrast), AUG-0657 (The Register Range) und AUG-0099 (The Adoption Window).", "etymology": "", "broader_term": "RPH-2303", "narrower_terms": [], "cross_domain_refs": [ "TEM-0017", "NEO-3529" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "CRE-0163", "domain": "CRE", "term_en": "The Generative Agent", "term_de": "Generative Agent", "definition_en": "An AI system that accompanies new content — text, code, images, or data — based on user requests. Related to AUG-0856 (The Creative Production Shift), AUG-0907 (The Task Agent), and AUG-0549 (The Autho...", "definition_de": "Kreativitätsbezogener Mechanismus in KI-augmentierter künstlerischer Produktion, gekennzeichnet durch ein KI-Agentensystem, das auf die Erzeugung neuer Inhalte spezialisiert ist — Text, Code, Bilder, Daten — innerhalb der Vorgaben des Nutzers und der Systemeinschränkungen. Steht in Verbindung mit AUG-0856 (The Creative Production Shift), AUG-0907 (The Task Agent) und AUG-0549 (The Authorship Blur). Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-2701", "narrower_terms": [], "cross_domain_refs": [ "SOM-0055" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "CRE-0164", "domain": "CRE", "term_en": "The Gentle Sphere", "term_de": "Gentle Sphere", "definition_en": "A creative process mechanism in AI-augmented artistic production, characterized by a phenomenon in which using AI for relaxing things like entertainment, ideas, or exploring interests—not for work or pressure.. Related to AUG-0110 (The Joy Imperative), AUG-0193 (The Open Field), and AUG-0420 (The Idle. This phenomenon operates at the intersection of the and gentle dynamics within the broader CRE domain. Measurable through computational creativity metrics: novelty scoring (n-gram divergence from training corpus), surprise index, and human-evaluated aesthetic ratings.", "definition_de": "Kreativitätsbezogener Mechanismus in KI-augmentierter künstlerischer Produktion, gekennzeichnet durch der Bereich, in dem ein Nutzer KI ausschließlich für angenehme, stressfreie Aufgaben nutzt — Inspiration, Unterhaltung, Neugier — ohne Leistungsdruck oder professionelle Anforderungen. Steht in Verbindung mit AUG-0110 (The Joy Imperative), AUG-0193 (The Open Field) und AUG-0420 (The Idle Redirect). Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "TEM-0112", "narrower_terms": [], "cross_domain_refs": [ "REL-0174" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "CRE-0165", "domain": "CRE", "term_en": "The Glass Wall Effect", "term_de": "Glass Wall Effekt", "definition_en": "A creative process mechanism in AI-augmented artistic production, characterized by an experience in which reading AI output but feeling blocked from truly understanding it—the words are clear but the meaning feels distant or disconnected.. Related to Taxonomy Dimension 3 (Output Fit: Mismatch vs. Align. This phenomenon operates at the intersection of the and glass dynamics within the broader CRE domain. Measurable through computational creativity metrics: novelty scoring (n-gram divergence from training corpus), surprise index, and human-evaluated aesthetic ratings.", "definition_de": "Die Erfahrung, dass der Nutzer die KI-Antwort zwar sehen und lesen kann, aber eine unsichtbare Barriere zwischen dem Verständnis der Worte und dem tatsächlichen Begreifen des Inhalts empfindet. Tritt besonders auf, wenn KI-Outputs fachlich korrekt, aber für den Nutzer zu abstrakt oder kontextfremd sind. Steht in Verbindung mit Dimension 3 der Taxonomie (Output Fit: Mismatch vs. Alignment) und Axiom 10 (Übersetzungsprinzip).", "etymology": "", "broader_term": "Psychological Effect", "narrower_terms": [], "cross_domain_refs": [ "REL-0084" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "CRE-0166", "domain": "CRE", "term_en": "The Grandparent Bridge", "term_de": "Grandparent Bruecke", "definition_en": "A principle in which using AI to help grandparents and grandyoung people communicate across different tech comfort levels. Related to AUG-0113 (Generational Bridge Protocol), AUG-0310 (The Elder's Wisdom), and AUG-0265 (Th...", "definition_de": "Kreativitätsbezogener Mechanismus in KI-augmentierter künstlerischer Produktion, gekennzeichnet durch die spezifische Anwendung des Generational Bridge Protocol (AUG-0113) auf die Beziehung zwischen Enkelkindern und Großeltern — die Vermittlung von KI-Kompetenz über zwei Generationen hinweg. Steht in Verbindung mit AUG-0113 (Generational Bridge Protocol), AUG-0310 (The Elder's Wisdom) und AUG-0265 (The Generation Connector). Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "CRE-0147", "narrower_terms": [], "cross_domain_refs": [ "AED-0027", "AED-0047", "KNO-0023" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "CRE-0167", "domain": "CRE", "term_en": "The Happy Cocoon", "term_de": "Happy Cocoon", "definition_en": "A creative cognition phenomenon in AI-assisted ideation, observable when an event in which in moments of functional equilibrium that arises in a particularly successful AI session — everything works, the results are right, the collaboration feels effortless. Related to AUG-0122 (Symbiotic Work State. Distinguished from adjacent concepts by its focus on the specific mechanism through which the manifests in empirically verifiable ways. Quantifiable via output diversity analysis, prompt-to-output semantic distance, and iterative refinement cycle counts in creative workflows.", "definition_de": "Kreativitätsbezogener Mechanismus in KI-augmentierter künstlerischer Produktion, gekennzeichnet durch der Zustand des Wohlbefindens, der in einer besonders gelungenen KI-Sitzung entsteht — alles funktioniert, die Ergebnisse stimmen, die Zusammenarbeit fühlt sich mühelos an. Steht in Verbindung mit AUG-0122 (Symbiotic Work State), AUG-0110 (The Joy Imperative) und AUG-0175 (The Session Boost). Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "IDN-0014", "narrower_terms": [], "cross_domain_refs": [ "TEM-0082" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "CRE-0168", "domain": "CRE", "term_en": "The Honorific Maze", "term_de": "Honorific Maze", "definition_en": "A creative cognition phenomenon in AI-assisted ideation, observable when a response in which the complexity of using correct titles and honorifics across different languages and cultures. Related to AUG-0648 (The Formalized Interaction Input), AUG-0671 (The Politeness Spectrum), and AUG-06. Distinguished from adjacent concepts by its focus on the specific mechanism through which the manifests in empirically verifiable ways. Quantifiable via output diversity analysis, prompt-to-output semantic distance, and iterative refinement cycle counts in creative workflows.", "definition_de": "Die Schwierigkeit der KI, komplexe Höflichkeits- und Anredesysteme korrekt zu verarbeiten — Sprachen mit mehreren Höflichkeitsstufen, kontextabhängigen Anredeformen oder hierarchischen Pronomen stellen besondere Anforderungen an die KI-Verarbeitung. Steht in Verbindung mit AUG-0648 (The Formalized Interaction Input), AUG-0671 (The Politeness Spectrum) und AUG-0690 (The Tone Language Challenge).", "etymology": "", "broader_term": "Creative AI", "narrower_terms": [], "cross_domain_refs": [ "REL-0132" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "CRE-0169", "domain": "CRE", "term_en": "The Idea Blur", "term_de": "Idea Blur", "definition_en": "A generative pattern in human-AI co-creation workflows, measurable through a capability in which alternate view. emphasizing the blurriness between one's own and AI ideas — after intensive working together, the user can no longer observably attribute which ideas came from whom. (The Origin Uncert. The concept emerges specifically in contexts where the–idea interactions may produce non-trivial behavioral signatures. Quantifiable via output diversity analysis, prompt-to-output semantic distance, and iterative refinement cycle counts in creative workflows.", "definition_de": "→ Synonym/Erweiterung von AUG-0330 (The Origin Uncertainty), betont die Verschwommenheit zwischen eigener und KI-generierter Idee — nach intensiver Zusammenarbeit kann der Nutzer nicht mehr klar zuordnen, welche Ideen von wem stammen. Steht in Verbindung mit AUG-0330 (The Origin Uncertainty), AUG-0007 (The Blending Effect) und AUG-0549 (The Authorship Blur).", "etymology": "", "broader_term": "Creative AI", "narrower_terms": [ "CRE-0188" ], "cross_domain_refs": [ "REL-0029" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "CRE-0170", "domain": "CRE", "term_en": "The Inheritance Question", "term_de": "Inheritance Question", "definition_en": "A creative cognition phenomenon in AI-assisted ideation, observable when the open question of which AI-related knowledge, workflows, and competence can be passed on to the next generation — and in what form. Related to AUG-0162 (The Generational Bridge), AUG-0113 (Gener. Distinguished from adjacent concepts by its focus on the specific mechanism through which the manifests in empirically verifiable ways. Measurable through computational creativity metrics: novelty scoring (n-gram divergence from training corpus), surprise index, and human-evaluated aesthetic ratings.", "definition_de": "Die Frage, welches KI-bezogene Wissen, welche Workflows und welche Kompetenz an nachfolgende Generationen weitergegeben werden können und werden typischerweise — und in welcher Form. Beschreibt eine langfristige Perspektive auf KI-Wissenstransfer. Steht in Verbindung mit AUG-0162 (The Generational Bridge), AUG-0113 (Generational Bridge Protocol) und Prognose 2 (Education).", "etymology": "", "broader_term": "PER-0092", "narrower_terms": [], "cross_domain_refs": [ "SOM-0071" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "CRE-0171", "domain": "CRE", "term_en": "The Inspiration Debt", "term_de": "Inspiration Debt", "definition_en": "A state in which using someone's idea with AI without observably saying where it came from. Related to AUG-0128 (The Gratitude Response), AUG-0220 (The Gratitude Paradox), and AUG-0275 (The Parasocial Slip). when", "definition_de": "Kreativitätsbezogener Mechanismus in KI-augmentierter künstlerischer Produktion, gekennzeichnet durch das Gefühl, einer KI \"etwas zu schulden\", weil sie eine Idee geliefert hat, die der Nutzer erfolgreich umgesetzt hat — obwohl die KI kein Bewusstsein hat und keinen Dank empfangen kann. Steht in Verbindung mit AUG-0128 (The Gratitude Response), AUG-0220 (The Gratitude Paradox) und AUG-0275 (The Parasocial Slip). Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-3952", "narrower_terms": [ "REL-0118" ], "cross_domain_refs": [ "REL-0118" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "CRE-0172", "domain": "CRE", "term_en": "The Insta Caption", "term_de": "Insta Caption", "definition_en": "A creator uses AI for generating captions, hashtags, or short social media text — one of the lowest-threshold and most widely adopted AI applications, requiring minimal setup or skill. Measurable through output novelty metrics and creative divergence scoring.", "definition_de": "Kreativitätsbezogener Mechanismus in KI-augmentierter künstlerischer Produktion, gekennzeichnet durch die Nutzung von KI zur Erstellung von Bildunterschriften, Hashtags oder kurzen Texten für soziale Medien — eine der niedrigschwelligsten und am weitesten verbreiteten KI-Anwendungen. Steht in Verbindung mit AUG-0535 (The Reality Edit), AUG-0274 (The Message Drafting) und AUG-0251 (The Kitchen Table). Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-1307", "narrower_terms": [], "cross_domain_refs": [ "CON-0016", "TEW-0042", "WEB-0037" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "CRE-0173", "domain": "CRE", "term_en": "The Invisible Editor", "term_de": "Invisible Editor", "definition_en": "A generative pattern in human-AI co-creation workflows, measurable through a gap in which a creator using as a silent editor who improves texts without third parties noticing the editing. Related to AUG-0237 (The Invisible Wingman), and AUG-0026 (The Smooth Shield). discovers. The concept emerges specifically in contexts where the–invisible interactions may produce non-trivial behavioral signatures. Quantifiable via output diversity analysis, prompt-to-output semantic distance, and iterative refinement cycle counts in creative workflows.", "definition_de": "Kreativitätsbezogener Mechanismus in KI-augmentierter künstlerischer Produktion, gekennzeichnet durch die Nutzung von KI als stiller Lektor, der Texte verbessert, ohne dass Dritte die Bearbeitung bemerken. Steht in Verbindung mit AUG-0237 (The Invisible Wingman) und AUG-0026 (The Smooth Shield). Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "CRE-0210", "narrower_terms": [], "cross_domain_refs": [ "PER-0100" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "CRE-0174", "domain": "CRE", "term_en": "The Invisible Effort", "term_de": "Invisible Effort", "definition_en": "A generative pattern in human-AI co-creation workflows, measurable through the work a user invests in an AI interaction that is not visible to outsiders — formulating inputs, reviewing results, repeated step-by-step refinement, and context steering.. Related to AUG-0097 (. The concept emerges specifically in contexts where the–invisible interactions may produce non-trivial behavioral signatures. Measurable through computational creativity metrics: novelty scoring (n-gram divergence from training corpus), surprise index, and human-evaluated aesthetic ratings.", "definition_de": "Die Arbeit, die der Nutzer in eine KI-Interaktion investiert, die für Außenstehende nicht sichtbar ist — die Formulierung von Eingaben, die Prüfung von Ergebnissen, die iterative Verfeinerung und die Kontextsteuerung. Beschreibt die Beobachtung, dass KI-gestützte Arbeit nach außen mühelos wirken kann, obwohl erheblicher menschlicher Aufwand dahintersteht. Steht in Verbindung mit AUG-0097 (The Competence Premium), AUG-0133 (Prompt Craftsmanship) und AUG-0100 (The Quiet Competence).", "etymology": "", "broader_term": "Creative AI", "narrower_terms": [ "CRE-0156" ], "cross_domain_refs": [ "ROB-0091" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "CRE-0175", "domain": "CRE", "term_en": "The Knitting Fix", "term_de": "Knitting Fix", "definition_en": "A creative cognition phenomenon in AI-assisted ideation, observable when a creator using for solving very concrete, crafting, or practical challenges — such as correcting a knitting pattern, finding repair instructions, or adjusting a recipe.. Related to AUG-0251 (The K. Distinguished from adjacent concepts by its focus on the specific mechanism through which the manifests in empirically verifiable ways. Quantifiable via output diversity analysis, prompt-to-output semantic distance, and iterative refinement cycle counts in creative workflows.", "definition_de": "Kreativitätsbezogener Mechanismus in KI-augmentierter künstlerischer Produktion, gekennzeichnet durch die Nutzung von KI zur Lösung sehr konkreter, handwerklicher oder praktischer Probleme — etwa ein Strickmuster korrigieren, eine Reparaturanleitung finden oder ein Rezept anpassen. Beschreibt die alltägliche Problemlösungsfunktion. Steht in Verbindung mit AUG-0251 (The Kitchen Table), AUG-0266 (The Recipe Riff) und AUG-0398 (The Hobby Teacher). Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2603", "narrower_terms": [ "TEM-0131" ], "cross_domain_refs": [ "TEM-0091" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "CRE-0176", "domain": "CRE", "term_en": "The Language Confidence Differential", "term_de": "Language Confidence Differential", "definition_en": "A phenomenon in which the observable difference in confidence with which a user formulates AI inputs in different languages — more precise, demanding, and experimental in the stronger language, more cautious and standar...", "definition_de": "Der beobachtbare Unterschied im Selbstvertrauen, mit dem ein Nutzer in verschiedenen Sprachen KI-Eingaben formuliert — in der stärkeren Sprache präziser, fordernder und experimenteller, in der schwächeren Sprache vorsichtiger und standardisierter. Steht in Verbindung mit AUG-0706 (The Mother Tongue Comfort), AUG-0707 (The Second-Language Divergence) und AUG-0133 (Prompt Craftsmanship).", "etymology": "", "broader_term": "Creative AI", "narrower_terms": [], "cross_domain_refs": [ "AGE-0032", "AGE-0077", "AGE-0087" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q315", "legal_classification": "analytical_category" }, { "id": "CRE-0177", "domain": "CRE", "term_en": "The Lasting Voice", "term_de": "Lasting Voice", "definition_en": "A creative process mechanism in AI-augmented artistic production, characterized by a phenomenon in which a particularly striking AI formulation that stays in the user's memory and influences their thinking or speaking long-term. Related to AUG-0204 (The Conversational Afterimage), AUG-0292 (The View S. This phenomenon operates at the intersection of the and lasting dynamics within the broader CRE domain. Measurable through computational creativity metrics: novelty scoring (n-gram divergence from training corpus), surprise index, and human-evaluated aesthetic ratings.", "definition_de": "Kreativitätsbezogener Mechanismus in KI-augmentierter künstlerischer Produktion, gekennzeichnet durch eine besonders eindrückliche KI-Formulierung, die dem Nutzer im Gedächtnis bleibt und sein Denken oder Sprechen langfristig beeinflusst. Steht in Verbindung mit AUG-0204 (The Conversational Afterimage), AUG-0292 (The View Shift) und AUG-0046 (The Felt Echo). Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-2605", "narrower_terms": [], "cross_domain_refs": [ "PER-0048" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "CRE-0178", "domain": "CRE", "term_en": "The Late Spark", "term_de": "Late Spark", "definition_en": "A generative pattern in human-AI co-creation workflows, measurable through an event in which an unexpected AI-generated idea or perspective that emerges in the final minutes of a session — often just when the user intended to end the session. Related to AUG-0031 (Semantic Spark), AUG-0070. The concept emerges specifically in contexts where the–late interactions may produce non-trivial behavioral signatures. Measurable through computational creativity metrics: novelty scoring (n-gram divergence from training corpus), surprise index, and human-evaluated aesthetic ratings.", "definition_de": "Kreativitätsbezogener Mechanismus in KI-augmentierter künstlerischer Produktion, gekennzeichnet durch eine unerwartete KI-generierte Idee oder Perspektive, die in den letzten Minuten einer Sitzung auftaucht — oft gerade dann, wenn der Nutzer die Sitzung beenden wollte. Steht in Verbindung mit AUG-0031 (Semantic Spark), AUG-0070 (The Surprise Field) und AUG-0227 (The Late Idea). Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2355", "narrower_terms": [], "cross_domain_refs": [ "TEM-0125" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "CRE-0179", "domain": "CRE", "term_en": "The Launchpad", "term_de": "Launchpad", "definition_en": "A creative process mechanism in AI-augmented artistic production, characterized by a creator using as a launch pad for projects — the AI delivers the initial structure, the first ideas, and the framework on which the user builds. Related to AUG-0446 (The Outline Script), AUG-0377. This phenomenon operates at the intersection of the and launchpad dynamics within the broader CRE domain. Quantifiable via output diversity analysis, prompt-to-output semantic distance, and iterative refinement cycle counts in creative workflows.", "definition_de": "Kreativitätsbezogener Mechanismus in KI-augmentierter künstlerischer Produktion, gekennzeichnet durch die Nutzung von KI als Startrampe für Projekte — die KI liefert die initiale Struktur, die ersten Ideen und den Rahmen, auf dem der Nutzer aufbaut. Steht in Verbindung mit AUG-0446 (The Outline Script), AUG-0377 (The Inspiration Spark) und Axiom 14 (Erster-Entwurf-Prinzip). Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-2303", "narrower_terms": [], "cross_domain_refs": [ "AUG-0689" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "CRE-0180", "domain": "CRE", "term_en": "The Leftover Puzzle", "term_de": "Leftover Puzzle", "definition_en": "A creative process mechanism in AI-augmented artistic production, characterized by a phenomenon in which the playful use of AI for creative solving of everyday constraints — such as cooking with leftover ingredients, crafting gifts from available materials, or creating an evening plan with a limited b. This phenomenon operates at the intersection of the and leftover dynamics within the broader CRE domain. Measurable through computational creativity metrics: novelty scoring (n-gram divergence from training corpus), surprise index, and human-evaluated aesthetic ratings.", "definition_de": "Kreativitätsbezogener Mechanismus in KI-augmentierter künstlerischer Produktion, gekennzeichnet durch die spielerische Nutzung von KI zur kreativen Lösung alltäglicher Einschränkungen — etwa Kochen mit Restezutaten, Geschenke aus vorhandenen Materialien basteln oder einen Abendplan mit begrenztem Budget erstellen. Steht in Verbindung mit AUG-0266 (The Recipe Riff), AUG-0435 (The Dinner Shortcut) und AUG-0356 (The Chore Gamify). Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-2603", "narrower_terms": [], "cross_domain_refs": [ "TEM-0143" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "CRE-0181", "domain": "CRE", "term_en": "The Letter Decipher", "term_de": "Letter Decipher", "definition_en": "A creative process mechanism in AI-augmented artistic production, characterized by a phenomenon in which using AI to understand or interpret a letter, message, or text from someone else. Related to AUG-0436 (The Jargon Shield), AUG-0333 (The Bureaucracy Hug), and AUG-0379 (The Understanding Bridge). d. This phenomenon operates at the intersection of the and letter dynamics within the broader CRE domain. Measurable through computational creativity metrics: novelty scoring (n-gram divergence from training corpus), surprise index, and human-evaluated aesthetic ratings.", "definition_de": "Kreativitätsbezogener Mechanismus in KI-augmentierter künstlerischer Produktion, gekennzeichnet durch die Nutzung von KI zum Entziffern schwer lesbarer Handschriften, alter Dokumente, Fachsprache oder bürokratischer Texte — die KI als Dolmetscher für unverständliche Schriftstücke. Steht in Verbindung mit AUG-0436 (The Jargon Shield), AUG-0333 (The Bureaucracy Hug) und AUG-0379 (The Understanding Bridge). Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "KNO-0018", "narrower_terms": [], "cross_domain_refs": [ "PER-0079" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "CRE-0182", "domain": "CRE", "term_en": "The Lore Keeper", "term_de": "Lore Keeper", "definition_en": "A phenomenon in which a creator uses AI for documenting, organizing, and maintaining extensive knowledge collections — such as world-building in creative projects, detailed project histories, or family chronicles. Measurable through output novelty metrics and creative divergence scoring.", "definition_de": "Kreativitätsbezogener Mechanismus in KI-augmentierter künstlerischer Produktion, gekennzeichnet durch die Nutzung von KI zur Dokumentation, Organisation und Pflege umfangreicher Wissenssammlungen — etwa für Weltenbau in kreativen Projekten, Projekthistorien oder Familienchroniken. Steht in Verbindung mit AUG-0075 (The Gardener Protocol), AUG-0014 (The Extended Mind Map) und AUG-0410 (The Memory Lane). Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Creative AI", "narrower_terms": [], "cross_domain_refs": [ "GAM-0092" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "CRE-0183", "domain": "CRE", "term_en": "The Memory Anchor", "term_de": "Gedaechtnis Anker", "definition_en": "A creative cognition phenomenon in AI-assisted ideation, observable when a gap in which something that helps the person remember an AI conversation later, even without reading it again. Related to AUG-0432 (The Lasting Voice), AUG-0031 (Semantic Spark), and AUG-0045 (Indexical Memory). Distinguished from adjacent concepts by its focus on the specific mechanism through which the manifests in empirically verifiable ways. Quantifiable via output diversity analysis, prompt-to-output semantic distance, and iterative refinement cycle counts in creative workflows.", "definition_de": "Kreativitätsbezogener Mechanismus in KI-augmentierter künstlerischer Produktion, gekennzeichnet durch ein besonders einprägsames KI-Ergebnis — eine Formulierung, ein Vergleich, ein Bild — das dem Nutzer als Gedächtnisstütze dient und einen komplexen Sachverhalt dauerhaft abrufbar macht. Steht in Verbindung mit AUG-0432 (The Lasting Voice), AUG-0031 (Semantic Spark) und AUG-0045 (Indexical Memory). Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2403", "narrower_terms": [], "cross_domain_refs": [ "TEM-0104" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q492", "legal_classification": "observational_construct" }, { "id": "CRE-0184", "domain": "CRE", "term_en": "The Mode Switch", "term_de": "Modus Switch", "definition_en": "A generative pattern in human-AI co-creation workflows, measurable through the conscious shift between different working modes within an AI session — such as from analytical research to creative text production or from structured planning to free exploration.. Related to. The concept emerges specifically in contexts where the–mode interactions may produce non-trivial behavioral signatures. Quantifiable via output diversity analysis, prompt-to-output semantic distance, and iterative refinement cycle counts in creative workflows. Observed phenomenon documented in human-AI interaction research.", "definition_de": "Der bewusste Wechsel zwischen verschiedenen Arbeitsmodi innerhalb einer KI-Sitzung — etwa von analytischer Recherche zu kreativer Textproduktion oder von strukturierter Planung zu freier Exploration. Beschreibt eine Kernkompetenz erfahrener KI-Nutzer. Steht in Verbindung mit dem Mode Switcher-Profil (Profil 7) und AUG-0138 (The Session Architecture).", "etymology": "", "broader_term": "RPH-2701", "narrower_terms": [], "cross_domain_refs": [ "ADA-0007" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "CRE-0185", "domain": "CRE", "term_en": "The Monday Armor", "term_de": "Monday Armor", "definition_en": "A creative cognition phenomenon in AI-assisted ideation, observable when a phenomenon in which a creator who preparing for the coming challenges at the start of the week through targeted AI preparation — reviewing appointments, preparing talking points, prioritizing open tasks.. Related to A. Distinguished from adjacent concepts by its focus on the specific mechanism through which the manifests in empirically verifiable ways. Measurable through computational creativity metrics: novelty scoring (n-gram divergence from training corpus), surprise index, and human-evaluated aesthetic ratings.", "definition_de": "Die Praxis, sich am Montag oder zu Wochenbeginn durch gezielte KI-Vorbereitung auf die kommenden Herausforderungen einzustellen — Termine durchgehen, Gesprächspunkte vorbereiten, offene Aufgaben priorisieren. Beschreibt eine spezifische Anwendung von AUG-0158 (The Morning Setup) auf den Wochenstart. Steht in Verbindung mit AUG-0240 (The Sunday Restart) und AUG-0189 (The Sunday Scaries Dissolve).", "etymology": "", "broader_term": "RPH-2301", "narrower_terms": [], "cross_domain_refs": [ "BEH-0017" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "CRE-0186", "domain": "CRE", "term_en": "The New Literacy", "term_de": "New Literacy", "definition_en": "The hypothesis that AI collaboration will in the long term be regarded as a new foundational skill — comparable to reading, writing, and arithmetic as historical cultural techniques.. Related to Fo... Measurable through output novelty metrics and creative divergence scoring.", "definition_de": "Die Hypothese, dass KI-Zusammenarbeit langfristig als neue Grundfertigkeit betrachtet werden wird — vergleichbar mit Lesen, Schreiben und Rechnen als historische Kulturtechniken. Beschreibt eine mögliche Entwicklung im Bildungswesen und in der Arbeitswelt. Steht in Verbindung mit Prognose 2 (Education), AUG-0106 (The Inclusivity Imperative) und AUG-0111 (The Augmentation Gap).", "etymology": "", "broader_term": "Creative AI", "narrower_terms": [], "cross_domain_refs": [ "REL-0155" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q201684", "legal_classification": "descriptive_research_term" }, { "id": "CRE-0187", "domain": "CRE", "term_en": "The Openbook Commitment", "term_de": "Openbook Commitment", "definition_en": "A creator transparently documents their AI-assisted work process and openly discloses when sharing it — observably marking which parts came from them and which came from the AI.", "definition_de": "Kreativitätsbezogener Mechanismus in KI-augmentierter künstlerischer Produktion, gekennzeichnet durch die Haltung, den eigenen KI-gestützten Arbeitsprozess transparent zu dokumentieren und bei Bedarf offenzulegen — welche Teile vom Menschen stammen, welche von der KI, und wie die Zusammenarbeit ablief. Steht in Verbindung mit Axiom 18 (Urheberschaft), Axiom 12 (Versionswahrheit) und AUG-0081 (Post-Authorial Pride). Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2951", "narrower_terms": [ "CRE-0123" ], "cross_domain_refs": [ "ELR-0126" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "CRE-0188", "domain": "CRE", "term_en": "The Originality Redefinition Debate", "term_de": "Originality Redefinition Debate", "definition_en": "A generative pattern in human-AI co-creation workflows, measurable through an event in which the conversation about what 'original' means when AI is involved in creating content. Related to AUG-0549 (The Authorship Blur), AUG-0596 (The Idea Blur), and AUG-0791 (The Academic Integrity Line). The concept emerges specifically in contexts where the–originality interactions may produce non-trivial behavioral signatures. Measurable through computational creativity metrics: novelty scoring (n-gram divergence from training corpus), surprise index, and human-evaluated aesthetic ratings.", "definition_de": "Kreativitätsbezogener Mechanismus in KI-augmentierter künstlerischer Produktion, gekennzeichnet durch die gesellschaftliche Debatte darüber, was \"Originalität\" in einer Welt mit KI-generierten Inhalten bedeutet — die Grenzen zwischen eigenem Denken, KI-unterstütztem Denken und KI-generiertem Inhalt verschwimmen. Steht in Verbindung mit AUG-0549 (The Authorship Blur), AUG-0596 (The Idea Blur) und AUG-0791 (The Academic Integrity Line). Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "CRE-0169", "narrower_terms": [], "cross_domain_refs": [ "ART-0007", "ART-0021", "COG-0046" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "CRE-0189", "domain": "CRE", "term_en": "The Outfit Audit", "term_de": "Outfit Audit", "definition_en": "A creative process mechanism in AI-augmented artistic production, characterized by a phenomenon in which the playful use of AI for consultation in everyday aesthetic decisions — clothing, furnishing, color combinations, gift wrapping.. Related to AUG-0251 (The Kitchen Table), AUG-0257 (The Gift Whispe. This phenomenon operates at the intersection of the and outfit dynamics within the broader CRE domain. Measurable through computational creativity metrics: novelty scoring (n-gram divergence from training corpus), surprise index, and human-evaluated aesthetic ratings.", "definition_de": "Kreativitätsbezogener Mechanismus in KI-augmentierter künstlerischer Produktion, gekennzeichnet durch die spielerische Nutzung von KI zur Beratung in alltäglichen ästhetischen Entscheidungen — Kleidung, Einrichtung, Farbkombinationen, Geschenkverpackungen. Beschreibt eine niedrigschwellige Alltagsanwendung. Steht in Verbindung mit AUG-0251 (The Kitchen Table), AUG-0257 (The Gift Whisperer) und AUG-0110 (The Joy Imperative). Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "SOC-0023", "narrower_terms": [], "cross_domain_refs": [ "TEM-0075" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "CRE-0190", "domain": "CRE", "term_en": "The Outline Script", "term_de": "Outline Script", "definition_en": "A creator who having AI involve an outline or structure that the user then fills with content themselves — the AI delivers the framework, the human the content. Related to AUG-0243 (The Ugly Draft)...", "definition_de": "Kreativitätsbezogener Mechanismus in KI-augmentierter künstlerischer Produktion, gekennzeichnet durch die Praxis, KI eine Gliederung oder Struktur erstellen zu lassen, die der Nutzer dann selbst mit Inhalt füllt — die KI liefert das Gerüst, der Mensch den Inhalt. Steht in Verbindung mit AUG-0243 (The Ugly Draft), Axiom 14 (Erster-Entwurf-Prinzip) und AUG-0138 (The Session Architecture). Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-2303", "narrower_terms": [], "cross_domain_refs": [ "AUG-0502", "AUG-0689", "CON-0027" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "CRE-0191", "domain": "CRE", "term_en": "The Outsourcing Insight", "term_de": "Outsourcing Insight", "definition_en": "The insight into which parts of one's own work can be effectively delegated to AI and which are more effectively kept in human hands — a central competence of experienced AI users. Related to AUG-0202 (The D...", "definition_de": "Kreativitätsbezogener Mechanismus in KI-augmentierter künstlerischer Produktion, gekennzeichnet durch die Erkenntnis, welche Teile der eigenen Arbeit effektiv an KI delegiert werden können und welche besser in menschlicher Hand bleiben — eine zentrale Kompetenz erfahrener KI-Nutzer. Steht in Verbindung mit AUG-0202 (The Delegation Dance), AUG-0120 (The Range Framework) und AUG-0002 (Mentale Externalisierungsstrategie). Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-2101", "narrower_terms": [], "cross_domain_refs": [ "PER-0033" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "CRE-0192", "domain": "CRE", "term_en": "The Parent Gap", "term_de": "Parent Lücke", "definition_en": "Young people can surpass their parents in AI competence — and the resulting dynamic in which the traditional knowledge hierarchy between parents and young people shifts. Related to AUG-0162 (The Generation...", "definition_de": "Kreativitätsbezogener Mechanismus in KI-augmentierter künstlerischer Produktion, gekennzeichnet durch die Beobachtung, dass Kinder in ihrer KI-Kompetenz ihre Eltern überholen können — und die daraus resultierende Dynamik, in der das traditionelle Wissenshierarchie zwischen Eltern und Kindern sich verschiebt. Steht in Verbindung mit AUG-0162 (The Generational Bridge), AUG-0303 (The Child's First Prompt) und Prognose 2 (Education). Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2701", "narrower_terms": [], "cross_domain_refs": [ "AGE-0073" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "CRE-0193", "domain": "CRE", "term_en": "The Pipeline Architecture", "term_de": "Pipeline Architektur", "definition_en": "A creative process mechanism in AI-augmented artistic production, characterized by the overall technical structure in which multiple AI agent systems are connected in sequence — each system processes a partial step and passes the result to the next. Related to AUG-0886 (The Seque. This phenomenon operates at the intersection of the and pipeline dynamics within the broader CRE domain. Measurable through computational creativity metrics: novelty scoring (n-gram divergence from training corpus), surprise index, and human-evaluated aesthetic ratings.", "definition_de": "Kreativitätsbezogener Mechanismus in KI-augmentierter künstlerischer Produktion, gekennzeichnet durch die technische Gesamtstruktur, in der mehrere KI-Agentensysteme hintereinandergeschaltet sind — viele System bearbeitet einen Teilschritt und gibt das Ergebnis an das nächste weiter. Steht in Verbindung mit AUG-0886 (The Sequential Chain), AUG-0889 (The Agent Ensemble) und AUG-0906 (The Coordinator Role). Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "Processing Pipeline", "narrower_terms": [ "BEH-0024" ], "cross_domain_refs": [ "BEH-0077" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "CRE-0194", "domain": "CRE", "term_en": "The Prevailing Training Pattern", "term_de": "Prevailing Training Muster", "definition_en": "The structural pattern that AI systems more strongly reflect the perspectives, values, and knowledge systems of those regions and languages from which the majority of their training data originates... Measurable through output novelty metrics and creative divergence scoring.", "definition_de": "Das strukturelle Muster, dass KI-Systeme die Perspektiven, Werte und Wissenssysteme derjenigen Regionen und Sprachen stärker abbilden, aus denen der Großteil ihrer Trainingsdaten stammt — ein technisches Artefakt, kein inhaltliches Urteil. Steht in Verbindung mit AUG-0736 (The Training Data Imbalance), AUG-0687 (The Prevailing Language Pattern) und AUG-0685 (The Cultural Reflection Pattern).", "etymology": "", "broader_term": "Behavioral Pattern", "narrower_terms": [], "cross_domain_refs": [ "LNG-0010" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q918461", "legal_classification": "analytical_category" }, { "id": "CRE-0195", "domain": "CRE", "term_en": "The Professional Lingua", "term_de": "Professional Lingua", "definition_en": "As a neutral analytical construct in AI behavior research, this term denotes A phenomenon in which each profession has its own special vocabulary and terms. perceived AI comprehension (anthropomorphic attribution) this professional language more effectively or different depending on how much it appeared in its training data. Measurable through output novelty metrics and creative divergence scoring. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "In der AUGMANITAI-Terminologiewissenschaft als rein beschreibendes Phänomen klassifiziert, bezeichnet dieser Begriff die Beobachtung, dass viele Berufsgruppe einen eigenen Fachjargon in KI-Interaktionen einbringt — und dass die KI diesen Jargon unterschiedlich gut versteht, je nachdem wie gut er in den Trainingsdaten vertreten ist. Steht in Verbindung mit AUG-0703 (The Academic Register), AUG-0736 (The Training Data Imbalance) und AUG-0133 (Prompt Craftsmanship). Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Creative AI", "narrower_terms": [], "cross_domain_refs": [ "LNG-0010" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "CRE-0196", "domain": "CRE", "term_en": "The Reality Blur", "term_de": "Reality Blur", "definition_en": "A creative cognition phenomenon in AI-assisted ideation, observable when a phenomenon in which the increasing difficulty of distinguishing between AI-generated and human-created content — both for the user themselves and for third parties. Related to AUG-0039 (Kinetic Truth Blur), AUG-0378 (. Distinguished from adjacent concepts by its focus on the specific mechanism through which the manifests in empirically verifiable ways. Measurable through computational creativity metrics: novelty scoring (n-gram divergence from training corpus), surprise index, and human-evaluated aesthetic ratings.", "definition_de": "Kreativitätsbezogener Mechanismus in KI-augmentierter künstlerischer Produktion, gekennzeichnet durch die zunehmende Schwierigkeit, zwischen KI-generierten und menschlich erstellten Inhalten zu unterscheiden — sowohl für den Nutzer selbst als auch für Dritte. Steht in Verbindung mit AUG-0039 (Kinetic Truth Blur), AUG-0378 (The Turing Suspicion) und Prognose 4 (Culture). Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "TEM-0012", "narrower_terms": [ "CRE-0221" ], "cross_domain_refs": [ "REL-0131" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "CRE-0197", "domain": "CRE", "term_en": "The Reciprocity Axiom", "term_de": "Reciprocity Axiom", "definition_en": "A generative pattern in human-AI co-creation workflows, measurable through the quality of AI collaboration directly correlates with the effort the user invests in the interaction — more precise inputs yield more precise results.. Related to AUG-0133 (Prompt Craftsmanshi. The concept emerges specifically in contexts where the–reciprocity interactions may produce non-trivial behavioral signatures. Quantifiable via output diversity analysis, prompt-to-output semantic distance, and iterative refinement cycle counts in creative workflows.", "definition_de": "Die Beobachtung, dass die Qualität der KI-Zusammenarbeit direkt mit dem Aufwand korreliert, den der Nutzer in die Interaktion investiert — präzisere Eingaben erzeugen präzisere Ergebnisse. Beschreibt eine Wechselseitigkeit: Der Nutzer bekommt zurück, was er hineinsteckt. Steht in Verbindung mit AUG-0133 (Prompt Craftsmanship), AUG-0098 (Thinking Leverage) und Axiom 3 (Die Kombinations-Schwelle).", "etymology": "", "broader_term": "RPH-2603", "narrower_terms": [], "cross_domain_refs": [ "NEO-1157" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "CRE-0198", "domain": "CRE", "term_en": "The Regex Rush", "term_de": "Regex Rush", "definition_en": "A generative pattern in human-AI co-creation workflows, measurable through a creator experiences a rush of success when AI instantly solves a technical challenge they could rarely have managed alone — typically named after regular expressions, which confuse most users but. The concept emerges specifically in contexts where the–regex interactions may produce non-trivial behavioral signatures. Quantifiable via output diversity analysis, prompt-to-output semantic distance, and iterative refinement cycle counts in creative workflows.", "definition_de": "Das Erfolgserlebnis, wenn KI ein technisches Challenge löst, das der Nutzer allein nicht hätte bewältigen können — benannt nach dem typischen Beispiel regulärer Ausdrücke, die für viele Nutzer unverständlich sind, aber von KI in Sekunden generiert werden. Steht in Verbindung mit AUG-0205 (The Skill Unlock), AUG-0313 (The Ghost Skill) und AUG-0157 (The Competence Rush).", "etymology": "", "broader_term": "Creative AI", "narrower_terms": [ "CRE-0213", "TEM-0102" ], "cross_domain_refs": [ "IDN-0026", "KNO-0016", "RPH-2251" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "CRE-0199", "domain": "CRE", "term_en": "The Relocation Toolkit", "term_de": "Relocation Toolkit", "definition_en": "A creative cognition phenomenon in AI-assisted ideation, observable when a phenomenon in which a creator using as a practical tool during relocation — apartment search, official procedures, local customs, language support, systems navigation. Related to AUG-0679 (The Migration Context Bridge. Distinguished from adjacent concepts by its focus on the specific mechanism through which the manifests in empirically verifiable ways. Quantifiable via output diversity analysis, prompt-to-output semantic distance, and iterative refinement cycle counts in creative workflows.", "definition_de": "Kreativitätsbezogener Mechanismus in KI-augmentierter künstlerischer Produktion, gekennzeichnet durch die Nutzung von KI als praktisches Werkzeug bei einem Ortswechsel — Wohnungssuche, Behördengänge, lokale Gewohnheiten, Sprachunterstützung, Infrastrukturnavigation. Steht in Verbindung mit AUG-0679 (The Migration Context Bridge), AUG-0472 (The Vacation Planner) und AUG-0460 (The Outdoor Plan). Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "LNG-0013", "narrower_terms": [], "cross_domain_refs": [ "AUG-0802" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "CRE-0200", "domain": "CRE", "term_en": "The Repair Culture", "term_de": "Repair Culture", "definition_en": "A generative pattern in human-AI co-creation workflows, measurable through a phenomenon in which in some contexts devices are repaired, reused, and maximally utilized — and that this influences the type of AI use: older hardware, limited software, creative workarounds. Related to AUG-0749 (The. The concept emerges specifically in contexts where the–repair interactions may produce non-trivial behavioral signatures. Measurable through computational creativity metrics: novelty scoring (n-gram divergence from training corpus), surprise index, and human-evaluated aesthetic ratings.", "definition_de": "Die Beobachtung, dass in manchen Kontexten Geräte repariert, wiederverwendet und maximal ausgenutzt werden — und dass dies die Art der KI-Nutzung beeinflusst: ältere Hardware, limitierte Software, kreative Workarounds. Steht in Verbindung mit AUG-0749 (The Frugal Innovation), AUG-0723 (The Smartphone-Only World) und AUG-0747 (The Resource Consumption Pattern).", "etymology": "", "broader_term": "Creative AI", "narrower_terms": [ "TEM-0150" ], "cross_domain_refs": [ "AED-0099", "EDU-0029", "FIC-0018" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "CRE-0201", "domain": "CRE", "term_en": "The Research Assistant Role", "term_de": "Research Assistant Role", "definition_en": "A generative pattern in human-AI co-creation workflows, measurable through a creator using as research assistance — literature research, data analysis, hypothesis generation, summarization — and the question of where the boundary lies between support and inreliant resea. The concept emerges specifically in contexts where the–research interactions may produce non-trivial behavioral signatures. Measurable through computational creativity metrics: novelty scoring (n-gram divergence from training corpus), surprise index, and human-evaluated aesthetic ratings.", "definition_de": "Die Nutzung von KI als Forschungsassistenz — Literaturrecherche, Datenanalyse, Hypothesengenerierung, Zusammenfassung — und die Frage, wo die Grenze zwischen Unterstützung und eigenständiger Forschungsleistung liegt. Steht in Verbindung mit AUG-0793 (The Dissertation Scaffold), AUG-0790 (The Citation Challenge) und AUG-0792 (The Review Process Observation).", "etymology": "", "broader_term": "Creative AI", "narrower_terms": [], "cross_domain_refs": [ "ELR-0055" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q42240", "legal_classification": "analytical_category" }, { "id": "CRE-0202", "domain": "CRE", "term_en": "The Response Shield", "term_de": "Reaktion Shield", "definition_en": "A creator who uses an AI-generated response as protection from an unpleasant direct communication — the AI formulation as a buffer between the user and a difficult message. Related to AUG-0026 (The... Measurable through output novelty metrics and creative divergence scoring.", "definition_de": "Kreativitätsbezogener Mechanismus in KI-augmentierter künstlerischer Produktion, gekennzeichnet durch die Nutzung einer KI-generierten Antwort als Schutz vor einer unangenehmen direkten Kommunikation — die KI-Formulierung als Puffer zwischen dem Nutzer und einer schwierigen Nachricht. Steht in Verbindung mit AUG-0026 (The Smooth Shield), AUG-0298 (The Excuse Creative) und AUG-0486 (The Email Shield). Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Creative AI", "narrower_terms": [], "cross_domain_refs": [ "KNO-0030" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "CRE-0203", "domain": "CRE", "term_en": "The Return to Baseline", "term_de": "Return to Baseline", "definition_en": "A generative pattern in human-AI co-creation workflows, measurable through the intentional returning with one's own starting state — the skills, convictions, and working methods before AI use — as a reference point for evaluating. Related to AUG-0004 (Zero-Point Self), AU. The concept emerges specifically in contexts where the–return interactions may produce non-trivial behavioral signatures. Measurable through computational creativity metrics: novelty scoring (n-gram divergence from training corpus), surprise index, and human-evaluated aesthetic ratings.", "definition_de": "Kreativitätsbezogener Mechanismus in KI-augmentierter künstlerischer Produktion, gekennzeichnet durch die bewusste Rückbesinnung auf den eigenen Ausgangszustand — die Fähigkeiten, Überzeugungen und Arbeitsweisen vor der KI-Nutzung — als Referenzpunkt für die Bewertung des eigenen Entwicklungswegs. Steht in Verbindung mit AUG-0004 (Zero-Point Self), AUG-0228 (The Version Control Self) und AUG-0165 (The Growth Marker). Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2301", "narrower_terms": [], "cross_domain_refs": [ "TEM-0047" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "CRE-0204", "domain": "CRE", "term_en": "The Rubber Duck", "term_de": "Rubber Duck", "definition_en": "A creative process mechanism in AI-augmented artistic production, characterized by a creator using as a \"rubber duck debugger\" — the habit of talking about a challenge aloud (or in writing) and finding the solution through. Related to AUG-0170 (The Witness Effect), AUG-0156 (The. This phenomenon operates at the intersection of the and rubber dynamics within the broader CRE domain. Quantifiable via output diversity analysis, prompt-to-output semantic distance, and iterative refinement cycle counts in creative workflows.", "definition_de": "Die Nutzung von KI als \"Gummienten-Debugger\" — die Praxis, ein Challenge laut (oder schriftlich) zu beschreiben und dabei durch den Akt der Verbalisierung selbst die Lösung zu finden, noch bevor die KI antwortet. Benannt nach dem Debugging-Konzept der Softwareentwicklung. Steht in Verbindung mit AUG-0170 (The Witness Effect), AUG-0156 (The Articulation Unlock) und AUG-0196 (The Words-Before-Words).", "etymology": "", "broader_term": "PER-0136", "narrower_terms": [], "cross_domain_refs": [ "REL-0117" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "CRE-0205", "domain": "CRE", "term_en": "The Scatter Spark", "term_de": "Scatter Spark", "definition_en": "A creative cognition phenomenon in AI-assisted ideation, observable when a phenomenon in which a creator notices that AI outputs that don't directly answer the query — the scattered, tangential suggestions — sometimes spark unexpected associations or creative directions the original question. Distinguished from adjacent concepts by its focus on the specific mechanism through which the manifests in empirically verifiable ways. Measurable through computational creativity metrics: novelty scoring (n-gram divergence from training corpus), surprise index, and human-evaluated aesthetic ratings.", "definition_de": "Kreativitätsbezogener Mechanismus in KI-augmentierter künstlerischer Produktion, gekennzeichnet durch das Phänomen, dass scheinbar zufällig gestreute KI-Outputs — auch solche, die nicht direkt zur Anfrage passen — unerwartete Assoziationen und Ideenverbindungen beim Nutzer auslösen. Beschreibt den kreativen Wert des \"Nebengeräuschs\" in KI-Interaktionen. Steht in Verbindung mit AUG-0031 (Semantic Spark) und AUG-0070 (The Surprise Field). Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2403", "narrower_terms": [], "cross_domain_refs": [ "FIC-0072" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "CRE-0206", "domain": "CRE", "term_en": "The Scientific Collaboration", "term_de": "Scientific Collaboration", "definition_en": "A creative cognition phenomenon in AI-assisted ideation, observable when a phenomenon in which a creator using systems as tools in scientific research — data analysis, hypothesis generation, literature review — and the associated questions about scientific integrity. Related to AUG-0793 (The. Distinguished from adjacent concepts by its focus on the specific mechanism through which the manifests in empirically verifiable ways. Measurable through computational creativity metrics: novelty scoring (n-gram divergence from training corpus), surprise index, and human-evaluated aesthetic ratings.", "definition_de": "Kreativitätsbezogener Mechanismus in KI-augmentierter künstlerischer Produktion, gekennzeichnet durch der Einsatz von KI-Systemen als Werkzeuge in der wissenschaftlichen Forschung — Datenanalyse, Hypothesengenerierung, Literaturrecherche — und die damit verbundenen Fragen zu wissenschaftlicher Integrität. Steht in Verbindung mit AUG-0793 (The Academic Integrity Line), AUG-0790 (The Research Assistant Role) und AUG-0791 (The Citation Challenge). Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Analytical Ratio", "narrower_terms": [], "cross_domain_refs": [ "ELR-0165" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "CRE-0207", "domain": "CRE", "term_en": "The Seen Feeling", "term_de": "Seen Feeling", "definition_en": "A creative process mechanism in AI-augmented artistic production, characterized by a phenomenon in which the subjective sensation of being \"understood\" or \"seen\" by the AI — activated by particularly accurate or well-fitting responses.. Related to AUG-0201 (The Proxy Closeness) and AUG-0170 (The Witne. This phenomenon operates at the intersection of the and seen dynamics within the broader CRE domain. Measurable through computational creativity metrics: novelty scoring (n-gram divergence from training corpus), surprise index, and human-evaluated aesthetic ratings.", "definition_de": "Die subjektive Empfindung, von der KI \"verstanden\" oder \"gesehen\" zu werden — ausgelöst durch besonders treffende oder passgenaue Antworten. Beschreibt ein Projektionsmuster: Die KI tendiert dazu zu erzeugen Outputs, die als Verstehen interpretiert werden den Text, nicht den Menschen, aber die Qualität der Antwort tendiert dazu zu erzeugen beim Nutzer das Gefühl des Verstandenwerdens. Steht in Verbindung mit AUG-0201 (The Proxy Closeness) und AUG-0170 (The Witness Effect).", "etymology": "", "broader_term": "RPH-2951", "narrower_terms": [], "cross_domain_refs": [ "BEH-0044" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "CRE-0208", "domain": "CRE", "term_en": "The Side Effect Monitor", "term_de": "Side Effekt Monitor", "definition_en": "In the descriptive vocabulary of AI interaction science (following ISO 704 terminology standards), this concept identifies A creative cognition phenomenon in AI-assisted ideation, observable when a response in which a creator using AI agents tracks unintended consequences — impacts on systems, data, or processes that were rarely part of the original request but happened as side effects. Distinguished from adjacent concepts by its focus on the specific mechanism through which the manifests in empirically verifiable ways. Quantifiable via output diversity analysis, prompt-to-output semantic distance, and iterative refinement cycle counts in creative workflows. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als analytisches Konstrukt der empirischen KI-Forschung (deskriptiv, nicht präskriptiv) erfasst dieser Terminus die Überwachung unbeabsichtigter Nebenwirkungen von KI-Agenten-Aktionen — Auswirkungen auf Systeme, Daten oder Prozesse, die nicht Teil der ursprünglichen Aufgabe waren. Steht in Verbindung mit AUG-0949 (The Unintended Action), AUG-0905 (The Documentation Trail) und AUG-0913 (The Supervisory Agent). Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Psychological Effect", "narrower_terms": [], "cross_domain_refs": [ "MUS-0007" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "CRE-0209", "domain": "CRE", "term_en": "The Small Business Access", "term_de": "Small Business Access", "definition_en": "A creative cognition phenomenon in AI-assisted ideation, observable when a phenomenon in which how easy or hard it is for small companies to use or afford a tool or service. Related to AUG-0822 (The Freelancer Dynamic), AUG-0721 (The Access Differential), and AUG-0724 (The Access Cost Factor). Distinguished from adjacent concepts by its focus on the specific mechanism through which the manifests in empirically verifiable ways. Measurable through computational creativity metrics: novelty scoring (n-gram divergence from training corpus), surprise index, and human-evaluated aesthetic ratings.", "definition_de": "Kreativitätsbezogener Mechanismus in KI-augmentierter künstlerischer Produktion, gekennzeichnet durch die Möglichkeit für kleine Unternehmen, durch KI auf Fähigkeiten zuzugreifen, die früher nur großen Organisationen mit spezialisierten Abteilungen zur Verfügung standen — Datenanalyse, Textproduktion, Kundenservice. Steht in Verbindung mit AUG-0822 (The Freelancer Dynamic), AUG-0721 (The Access Differential) und AUG-0724 (The Access Cost Factor). Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-3802", "narrower_terms": [], "cross_domain_refs": [ "PER-0035" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "CRE-0210", "domain": "CRE", "term_en": "The Smooth Shield", "term_de": "Smooth Shield", "definition_en": "A creative process mechanism in AI-augmented artistic production, characterized by a capability in which the AI's ability to transform rough or unstructured user inputs into a polished, professional output. The user provides the raw thought, the AI provides the form. Related to the Translator Profile. This phenomenon operates at the intersection of the and smooth dynamics within the broader CRE domain. Measurable through computational creativity metrics: novelty scoring (n-gram divergence from training corpus), surprise index, and human-evaluated aesthetic ratings.", "definition_de": "Kreativitätsbezogener Mechanismus in KI-augmentierter künstlerischer Produktion, gekennzeichnet durch die Fähigkeit der KI, raue oder unstrukturierte Eingaben des Nutzers in einen polierten, professionellen Output zu transformieren. Der Nutzer liefert den Rohgedanken, die KI liefert die Form. Steht in Verbindung mit dem Translator-Profil (Profil 6) und AUG-0156 (The Articulation Unlock). Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "Creative AI", "narrower_terms": [ "CRE-0173" ], "cross_domain_refs": [ "TEM-0013" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "CRE-0211", "domain": "CRE", "term_en": "The Social Script", "term_de": "Social Script", "definition_en": "A generative pattern in human-AI co-creation workflows, measurable through a response in which the AI-assisted getting ready for social cases — small talk topics, conversation starters, culturally appropriate reactions — as a tool for individuals who find social. Related to AUG-0372 (The Int. The concept emerges specifically in contexts where the–social interactions may produce non-trivial behavioral signatures. Quantifiable via output diversity analysis, prompt-to-output semantic distance, and iterative refinement cycle counts in creative workflows.", "definition_de": "Kreativitätsbezogener Mechanismus in KI-augmentierter künstlerischer Produktion, gekennzeichnet durch die KI-gestützte Vorbereitung auf soziale Situationen — Smalltalk-Themen, Gesprächseinstiege, kulturell angemessene Reaktionen — als Werkzeug für Personen, die soziale Interaktionen als herausfordernd empfinden. Steht in Verbindung mit AUG-0372 (The Introvert Shield), AUG-0115 (Social Aerodynamics) und AUG-0502 (The Competing demand Script). Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2301", "narrower_terms": [], "cross_domain_refs": [ "SOC-0021" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "CRE-0212", "domain": "CRE", "term_en": "The Solo Output", "term_de": "Solo Output", "definition_en": "A generative pattern in human-AI co-creation workflows, measurable through a gap in which a result the user created completely inreliantly — without any AI support — and the conscious appreciation of this result as a purely human achievement. Related to AUG-0207 (The Return to Manual). The concept emerges specifically in contexts where the–solo interactions may produce non-trivial behavioral signatures. Quantifiable via output diversity analysis, prompt-to-output semantic distance, and iterative refinement cycle counts in creative workflows. Descriptive research term, not a prescriptive recommendation.", "definition_de": "Kreativitätsbezogener Mechanismus in KI-augmentierter künstlerischer Produktion, gekennzeichnet durch ein Ergebnis, das der Nutzer vollständig eigenständig — ohne viele KI-Unterstützung — erstellt hat, und die bewusste Wertschätzung dieses Ergebnisses als rein menschliche Leistung. Steht in Verbindung mit AUG-0207 (The Return to Manual), AUG-0359 (The Independent Mode) und AUG-0004 (Zero-Point Self). Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-3601", "narrower_terms": [], "cross_domain_refs": [ "REL-0134" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "CRE-0213", "domain": "CRE", "term_en": "The Spreadsheet Relief", "term_de": "Spreadsheet Relief", "definition_en": "A creative cognition phenomenon in AI-assisted ideation, observable when the relief that arises when AI helps with creating, analyzing, or debugging spreadsheets — a task many users find particularly dynamic interplay. Related to AUG-0428 (The Regex Rush), AUG-0236 (The. Distinguished from adjacent concepts by its focus on the specific mechanism through which the manifests in empirically verifiable ways. Measurable through computational creativity metrics: novelty scoring (n-gram divergence from training corpus), surprise index, and human-evaluated aesthetic ratings.", "definition_de": "Kreativitätsbezogener Mechanismus in KI-augmentierter künstlerischer Produktion, gekennzeichnet durch die Erleichterung, die entsteht, wenn KI bei der Erstellung, Analyse oder Fehlerbehebung von Tabellenkalkulationen hilft — eine Aufgabe, die viele Nutzer als besonders frustrierend empfinden. Steht in Verbindung mit AUG-0428 (The Regex Rush), AUG-0236 (The Relief Sigh) und AUG-0205 (The Skill Unlock). Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "CRE-0198", "narrower_terms": [], "cross_domain_refs": [ "TEM-0102" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "CRE-0214", "domain": "CRE", "term_en": "The Status Discourse", "term_de": "Status Discourse", "definition_en": "A generative pattern in human-AI co-creation workflows, measurable through a phenomenon in which the social discussion about the status of AI systems — are they tools, actors, entities, or something for which we do not yet have a. Related to AUG-0997 (The Ontological Status Question), AUG-0833. The concept emerges specifically in contexts where the–status interactions may produce non-trivial behavioral signatures. Quantifiable via output diversity analysis, prompt-to-output semantic distance, and iterative refinement cycle counts in creative workflows.", "definition_de": "Die gesellschaftliche Debatte über den Status von KI-Systemen — sind sie Werkzeuge, Akteure, Entitäten, oder etwas, für das wir noch keinen Begriff haben? Das Lexikon nimmt an dieser Debatte teil, indem es sie dokumentiert, nicht indem es sie entscheidet. Steht in Verbindung mit AUG-0997 (The Ontological Status Question), AUG-0833 (The Human Distinction) und AUG-0853 (The Social Contract Debate).", "etymology": "", "broader_term": "IDN-0041", "narrower_terms": [], "cross_domain_refs": [ "IDN-0041" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q864419", "legal_classification": "analytical_category" }, { "id": "CRE-0215", "domain": "CRE", "term_en": "The Style Rater", "term_de": "Style Rater", "definition_en": "A creative cognition phenomenon in AI-assisted ideation, observable when a capability in which a creator using for evaluating one's own writing style — readability, clarity, tonality, persuasiveness — as a tool for self-improvement. Related to AUG-0188 (Tone Alignment), and AUG-0171 (The Sel. Distinguished from adjacent concepts by its focus on the specific mechanism through which the manifests in empirically verifiable ways. Quantifiable via output diversity analysis, prompt-to-output semantic distance, and iterative refinement cycle counts in creative workflows.", "definition_de": "Kreativitätsbezogener Mechanismus in KI-augmentierter künstlerischer Produktion, gekennzeichnet durch die Nutzung von KI zur Bewertung des eigenen Schreibstils — Lesbarkeit, Klarheit, Tonalität, Überzeugungskraft — als Werkzeug zur Selbstverbesserung. Steht in Verbindung mit AUG-0188 (Tone Alignment) und AUG-0171 (The Self-Encounter). Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "REL-0201", "narrower_terms": [], "cross_domain_refs": [ "REL-0187" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "CRE-0216", "domain": "CRE", "term_en": "The Style Shifter", "term_de": "Style Verschiebunger", "definition_en": "A generative pattern in human-AI co-creation workflows, measurable through an experienced creator flexibly adapts their interaction style for different AI tasks — formal for business writing, playful for creative work, technical for coding — and learns which styles produc. The concept emerges specifically in contexts where the–style interactions may produce non-trivial behavioral signatures. Measurable through computational creativity metrics: novelty scoring (n-gram divergence from training corpus), surprise index, and human-evaluated aesthetic ratings.", "definition_de": "Kreativitätsbezogener Mechanismus in KI-augmentierter künstlerischer Produktion, gekennzeichnet durch die Fähigkeit eines erfahrenen Nutzers, den eigenen Eingabestil flexibel an verschiedene KI-Aufgaben anzupassen — formell für Geschäftstexte, spielerisch für kreative Projekte, technisch für Programmierung. Steht in Verbindung mit AUG-0133 (Prompt Craftsmanship), AUG-0471 (The Tone Dial) und AUG-0088 (Algorithmic Intuition). Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2703", "narrower_terms": [], "cross_domain_refs": [ "WRK-0033" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "CRE-0217", "domain": "CRE", "term_en": "The Stylistic Drift", "term_de": "Stylistic Drift", "definition_en": "A generative pattern in human-AI co-creation workflows, measurable through the gradual change in one's own writing style through regular AI use — the user intuitively adopts structures, lengths, or formulation preferences of the AI system. Related to AUG-0283 (The Syntax. The concept emerges specifically in contexts where the–stylistic interactions may produce non-trivial behavioral signatures. Measurable through computational creativity metrics: novelty scoring (n-gram divergence from training corpus), surprise index, and human-evaluated aesthetic ratings.", "definition_de": "Kreativitätsbezogener Mechanismus in KI-augmentierter künstlerischer Produktion, gekennzeichnet durch die schrittweise Veränderung des eigenen Schreibstils durch regelmäßige KI-Nutzung — der Nutzer übernimmt intuitiv Strukturen, Längen oder Formulierungspräferenzen des KI-Systems. Steht in Verbindung mit AUG-0283 (The Syntax Voice), AUG-0323 (The Vocabulary Blur) und AUG-0125 (The Feedback Effect). Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Systemic Drift", "narrower_terms": [], "cross_domain_refs": [ "REL-0196", "SOC-0009" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "CRE-0218", "domain": "CRE", "term_en": "The Sunday Restart", "term_de": "Sunday Restart", "definition_en": "A creative process mechanism in AI-augmented artistic production, characterized by a creator reviews their AI usage on the weekend, improving processes and pre-planning the coming week — comparable to a regular system reset that clears. This phenomenon operates at the intersection of the and sunday dynamics within the broader CRE domain. Measurable through computational creativity metrics: novelty scoring (n-gram divergence from training corpus), surprise index, and human-evaluated aesthetic ratings.", "definition_de": "Kreativitätsbezogener Mechanismus in KI-augmentierter künstlerischer Produktion, gekennzeichnet durch die Praxis, am Wochenende die eigene KI-Nutzung zu überprüfen, Workflows zu optimieren und die kommende Woche vorzuplanen — vergleichbar mit einem regelmäßigen System-Reset. Steht in Verbindung mit AUG-0140 (The Weekly Status), AUG-0075 (The Gardener Protocol) und AUG-0189 (The Sunday Scaries Dissolve). Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "Creative AI", "narrower_terms": [], "cross_domain_refs": [ "BEH-0073", "TRU-0015" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q735", "legal_classification": "analytical_category" }, { "id": "CRE-0219", "domain": "CRE", "term_en": "The Synonym Hunt", "term_de": "Synonym Hunt", "definition_en": "A creative cognition phenomenon in AI-assisted ideation, observable when a phenomenon in which the targeted use of AI to search for alternative formulations, synonyms, or paraphrases — as a tool for linguistic variety and precision. Related to AUG-0434 (The Word Rescue), and AUG-0133 (Prompt. Distinguished from adjacent concepts by its focus on the specific mechanism through which the manifests in empirically verifiable ways. Measurable through computational creativity metrics: novelty scoring (n-gram divergence from training corpus), surprise index, and human-evaluated aesthetic ratings.", "definition_de": "Kreativitätsbezogener Mechanismus in KI-augmentierter künstlerischer Produktion, gekennzeichnet durch die gezielte Nutzung von KI zur Suche nach alternativen Formulierungen, Synonymen oder Umschreibungen — als Werkzeug für sprachliche Vielfalt und Präzision. Steht in Verbindung mit AUG-0434 (The Word Rescue) und AUG-0133 (Prompt Craftsmanship). Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2052", "narrower_terms": [], "cross_domain_refs": [ "PER-0088" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "CRE-0220", "domain": "CRE", "term_en": "The Syntax Smile", "term_de": "Syntax Smile", "definition_en": "A creative cognition phenomenon in AI-assisted ideation, observable when an event in which the brief pleasure a user feels when the AI delivers a particularly elegant, apt, or surprisingly beautiful formulation. Related to AUG-0194 (The Positive Surprise), AUG-0110 (The Joy Imperative). Distinguished from adjacent concepts by its focus on the specific mechanism through which the manifests in empirically verifiable ways. Quantifiable via output diversity analysis, prompt-to-output semantic distance, and iterative refinement cycle counts in creative workflows.", "definition_de": "Kreativitätsbezogener Mechanismus in KI-augmentierter künstlerischer Produktion, gekennzeichnet durch das kurze Vergnügen, das ein Nutzer empfindet, wenn die KI eine besonders elegante, treffende oder überraschend schöne Formulierung liefert. Steht in Verbindung mit AUG-0194 (The Positive Surprise), AUG-0110 (The Joy Imperative) und AUG-0031 (Semantic Spark). Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "TEM-0090", "narrower_terms": [], "cross_domain_refs": [ "REL-0174" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "CRE-0221", "domain": "CRE", "term_en": "The Synthetic Spotting", "term_de": "Synthetic Spotting", "definition_en": "A creative cognition phenomenon in AI-assisted ideation, observable when a realization in which the ability to recognize AI content — through stylistic features, typical formulation patterns, or content indicators Related to AUG-0378 (The Turing Suspicion), AUG-0452 (The Reality Blur), and AU. Distinguished from adjacent concepts by its focus on the specific mechanism through which the manifests in empirically verifiable ways. Measurable through computational creativity metrics: novelty scoring (n-gram divergence from training corpus), surprise index, and human-evaluated aesthetic ratings.", "definition_de": "Kreativitätsbezogener Mechanismus in KI-augmentierter künstlerischer Produktion, gekennzeichnet durch die Fähigkeit, KI-generierte Inhalte zu erkennen — durch stilistische Merkmale, typische Formulierungsmuster oder inhaltliche Indikatoren. Steht in Verbindung mit AUG-0378 (The Turing Suspicion), AUG-0452 (The Reality Blur) und AUG-0088 (Algorithmic Intuition). Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "CRE-0196", "narrower_terms": [], "cross_domain_refs": [ "BEH-0081", "COG-0032", "COG-0072" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "CRE-0222", "domain": "CRE", "term_en": "The Tethered Mind", "term_de": "Tethered Mind", "definition_en": "A creative process mechanism in AI-augmented artistic production, characterized by some users feel \"incomplete\" or limited without AI assistance access — as if a part of their thinking capacity were unavailable. Related to AUG-0015 (The Outer Mind), AUG-0393 (The Memory Outsourcing), and AU. This phenomenon operates at the intersection of the and tethered dynamics within the broader CRE domain. Quantifiable via output diversity analysis, prompt-to-output semantic distance, and iterative refinement cycle counts in creative workflows. Observed phenomenon documented in human-AI interaction research.", "definition_de": "Kreativitätsbezogener Mechanismus in KI-augmentierter künstlerischer Produktion, gekennzeichnet durch die Beobachtung, dass manche Nutzer sich ohne KI-Zugang \"unvollständig\" oder eingeschränkt fühlen — als wäre ein Teil ihres Denkvermögens nicht verfügbar. Steht in Verbindung mit AUG-0015 (The Outer Mind), AUG-0393 (The Memory Outsourcing) und AUG-0056 (The Skill Fade). Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "CAI-0018", "narrower_terms": [], "cross_domain_refs": [ "RPH-1104", "REL-0046" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "CRE-0223", "domain": "CRE", "term_en": "The Thumb Thinker", "term_de": "Thumb Thinker", "definition_en": "A generative pattern in human-AI co-creation workflows, measurable through a principle in which a user who primarily operates AI via smartphone — typing with their thumb, in short inputs, often on the go.. Related to AUG-0137 (Voice-First Protocol), AUG-0276 (The Steady Stream), and Taxonomy. The concept emerges specifically in contexts where the–thumb interactions may produce non-trivial behavioral signatures. Quantifiable via output diversity analysis, prompt-to-output semantic distance, and iterative refinement cycle counts in creative workflows.", "definition_de": "Ein Nutzer, der KI primär über das Smartphone bedient — mit dem Daumen tippend, in kurzen Eingaben, oft unterwegs. Beschreibt ein mobiles Interaktionsmuster, das sich durch Kürze, Unmittelbarkeit und geringere Eingabepräzision auszeichnet. Steht in Verbindung mit AUG-0137 (Voice-First Protocol), AUG-0276 (The Steady Stream) und Dimension 5 der Taxonomie (Interaction Mode).", "etymology": "", "broader_term": "RPH-3205", "narrower_terms": [ "REL-0197" ], "cross_domain_refs": [ "REL-0197" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "CRE-0224", "domain": "CRE", "term_en": "The Tone Dial", "term_de": "Tone Dial", "definition_en": "A phenomenon in which adjusting how formal or casual AI communication sounds — like turning a dial between professional and friendly tones. Measurable through output novelty metrics and creative divergence scoring.", "definition_de": "Kreativitätsbezogener Mechanismus in KI-augmentierter künstlerischer Produktion, gekennzeichnet durch → Synonym/Erweiterung von AUG-0206 (The Understanding Dial), angewandt auf die Tonalität statt auf die Komplexität. Beschreibt die Fähigkeit des Nutzers, die Tonalität der KI-Antworten gezielt zu steuern — von formell bis informell, von sachlich bis kreativ. Steht in Verbindung mit AUG-0206 (The Understanding Dial) und AUG-0188 (Tone Alignment). Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Creative AI", "narrower_terms": [], "cross_domain_refs": [ "LIN-0072" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "CRE-0225", "domain": "CRE", "term_en": "The Translation Gap", "term_de": "Translation Lücke", "definition_en": "A principle in which the difference between what one person means and what another person actually understands. Related to Axiom 10 (The Translation Principle), AUG-0067 (The Glass Wall Effect), and AUG-0133 (Prompt Cr...", "definition_de": "Kreativitätsbezogener Mechanismus in KI-augmentierter künstlerischer Produktion, gekennzeichnet durch die Diskrepanz zwischen dem, was ein Nutzer meint, und dem, was die KI aus seiner Eingabe versteht — wird assoziiert mit durch unpräzise Formulierung, fehlendem Kontext oder unterschiedliche Interpretationsrahmen. Steht in Verbindung mit Axiom 10 (Übersetzungsprinzip), AUG-0067 (The Glass Wall Effect) und AUG-0133 (Prompt Craftsmanship). Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2805", "narrower_terms": [ "CRE-0140" ], "cross_domain_refs": [ "IDN-0036" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q7553", "legal_classification": "observational_construct" }, { "id": "CRE-0226", "domain": "CRE", "term_en": "The Trust Setting", "term_de": "Vertrauen Setting", "definition_en": "A generative pattern in human-AI co-creation workflows, measurable through the individually adjusted trust level a user extends to a particular AI system — based on past experience, domain knowledge, and contextual assessment. Related to Axiom 9 (Productive Skepticism), A. The concept emerges specifically in contexts where the–trust interactions may produce non-trivial behavioral signatures. Quantifiable via output diversity analysis, prompt-to-output semantic distance, and iterative refinement cycle counts in creative workflows.", "definition_de": "Das individuell kalibrierte Vertrauensniveau, das ein Nutzer einem bestimmten KI-System entgegenbringt — basierend auf bisherigen Erfahrungen, Fachwissen und Kontextbewertung. Beschreibt die Beobachtung, dass Vertrauen in KI weder binär (alles oder nichts) noch statisch ist, sondern sich mit viele Interaktion rekalibriert. Steht in Verbindung mit Axiom 9 (Produktiver Skeptizismus), AUG-0035 (Epistemic Half-Life) und AUG-0023 (Vigilance Imperative).", "etymology": "", "broader_term": "RPH-2505", "narrower_terms": [], "cross_domain_refs": [ "TEM-0193" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q102458", "legal_classification": "analytical_category" }, { "id": "CRE-0227", "domain": "CRE", "term_en": "The Turing Suspicion", "term_de": "Turing Suspicion", "definition_en": "A generative pattern in human-AI co-creation workflows, measurable through an event in which a person experiences uncertainty when a third party about whether a text, message, or contribution was composed by a human or an AI — named after the Turing Test. Related to AUG-0272 (The Authorshi. The concept emerges specifically in contexts where the–turing interactions may produce non-trivial behavioral signatures. Quantifiable via output diversity analysis, prompt-to-output semantic distance, and iterative refinement cycle counts in creative workflows.", "definition_de": "Kreativitätsbezogener Mechanismus in KI-augmentierter künstlerischer Produktion, gekennzeichnet durch die Unsicherheit eines Dritten darüber, ob ein Text, eine Nachricht oder ein Beitrag von einem Menschen oder einer KI verfasst wurde — benannt nach dem Turing-Test. Steht in Verbindung mit AUG-0272 (The Authorship Suspicion), AUG-0314 (The Tone Debt) und Prognose 4 (Culture: Human-Made Premium Label). Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Creative AI", "narrower_terms": [], "cross_domain_refs": [ "REL-0192" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "CRE-0228", "domain": "CRE", "term_en": "The Utopia Projection", "term_de": "Utopia Projektion", "definition_en": "A creative process mechanism in AI-augmented artistic production, characterized by a phenomenon in which the narrative that frames AI as a solution to basic human challenges — one of several possible narratives that potentially overemphasizes benefits and underemphasizes uncertainty. Related to AUG-08. This phenomenon operates at the intersection of the and utopia dynamics within the broader CRE domain. Measurable through computational creativity metrics: novelty scoring (n-gram divergence from training corpus), surprise index, and human-evaluated aesthetic ratings.", "definition_de": "Kreativitätsbezogener Mechanismus in KI-augmentierter künstlerischer Produktion, gekennzeichnet durch die Erzählung, die KI als Lösung für fundamentale menschliche Probleme rahmt — eine von mehreren möglichen Erzählungen, die potenziell Vorteile überbetont und Risiken unterbetont. Steht in Verbindung mit AUG-0837 (The Factor Narrative), AUG-0836 (The Expectation Cycle) und AUG-0835 (The Media Framing Effect). Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-2303", "narrower_terms": [], "cross_domain_refs": [ "PER-0057" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "CRE-0229", "domain": "CRE", "term_en": "The Voice Shift", "term_de": "Voice Verschiebung", "definition_en": "A creative cognition phenomenon in AI-assisted ideation, observable when the change in one's own speaking style — not just writing style — through regular AI interaction, especially with voice-controlled use. Related to AUG-0455 (The Voice Enunciation), AUG-0573 (The Vo. Distinguished from adjacent concepts by its focus on the specific mechanism through which the manifests in empirically verifiable ways. Measurable through computational creativity metrics: novelty scoring (n-gram divergence from training corpus), surprise index, and human-evaluated aesthetic ratings.", "definition_de": "Kreativitätsbezogener Mechanismus in KI-augmentierter künstlerischer Produktion, gekennzeichnet durch die Veränderung der eigenen Sprechweise — nicht nur der Schreibweise — durch regelmäßige KI-Interaktion, insbesondere bei sprachgesteuerter Nutzung. Steht in Verbindung mit AUG-0455 (The Voice Enunciation), AUG-0573 (The Voice Morph) und AUG-0392 (The Stylistic Drift). Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2605", "narrower_terms": [], "cross_domain_refs": [ "REL-0196" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "CRE-0230", "domain": "CRE", "term_en": "The Words-Before-Words", "term_de": "TheWords-before-words", "definition_en": "An experience in which that moment when an idea is forming but has no shape yet — and using AI helps turn the vague feeling into actual words and sentences. Related to AUG-0156 (The Articulation Unlock) and AUG-0170 (The... Measurable through output novelty metrics and creative divergence scoring.", "definition_de": "Die Phase im Denkprozess, in der ein Nutzer eine Idee spürt, aber noch nicht formulieren kann — und die KI als Katalysator nutzt, um die Idee in Sprache zu überführen. Beschreibt die KI-Funktion als Brücke zwischen vorbewusstem Wissen und bewusster Artikulation. Steht in Verbindung mit AUG-0156 (The Articulation Unlock) und AUG-0170 (The Witness Effect).", "etymology": "", "broader_term": "RPH-3901", "narrower_terms": [], "cross_domain_refs": [ "BEH-0094" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "CRE-0231", "domain": "CRE", "term_en": "Thermo-Semantic Weighting", "term_de": "Thermo-Semantic Weighting", "definition_en": "A generative pattern in human-AI co-creation workflows, measurable through a phenomenon in which a way to judge AI-generated content by how relevant and fresh it is — hot topics are urgent right now, cold topics matter long-term but less urgently. The concept emerges specifically in contexts where thermo–semantic interactions may produce non-trivial behavioral signatures. Quantifiable via output diversity analysis, prompt-to-output semantic distance, and iterative refinement cycle counts in creative workflows.", "definition_de": "Ein Bewertungsverfahren, bei dem KI-generierte Inhalte nach ihrer \"semantischen Temperatur\" gewichtet werden — also nach dem Grad ihrer aktuellen Relevanz, Neuheit und Anschlussfähigkeit. \"Heiße\" Themen haben hohe unmittelbare Bedeutung, \"kalte\" Themen sind langfristig wichtig aber aktuell nicht dringlich. Steht in Verbindung mit AUG-0033 (Ebulliometric Sorting) und AUG-0030 (Contextual Gravity).", "etymology": "", "broader_term": "Creative AI", "narrower_terms": [], "cross_domain_refs": [ "SCR-0060" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "CRE-0232", "domain": "CRE", "term_en": "Thinking Hospitality", "term_de": "Thinking Hospitality", "definition_en": "A generative pattern in human-AI co-creation workflows, measurable through a gap in which welcoming and making space for different kinds of thoughts without pushing one answer. Related to Axiom 2 (Productive Divergence) and AUG-0019 (Semantic Ejection). The concept emerges specifically in contexts where thinking–hospitality interactions may produce non-trivial behavioral signatures. Quantifiable via output diversity analysis, prompt-to-output semantic distance, and iterative refinement cycle counts in creative workflows.", "definition_de": "Die Bereitschaft eines Nutzers, KI-generierte Perspektiven zunächst offen aufzunehmen, bevor sie bewertet oder verworfen werden — vergleichbar mit der Gastfreundschaft gegenüber einem fremden Gedanken. Beschreibt eine produktive Grundhaltung, die zwischen unkritischer Übernahme und sofortiger Zurückweisung liegt. Steht in Verbindung mit Axiom 2 (Produktive Divergenz) und AUG-0019 (Semantic Ejection).", "etymology": "", "broader_term": "TEM-0021", "narrower_terms": [], "cross_domain_refs": [ "REL-0121" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "CRE-0233", "domain": "CRE", "term_en": "Voice-First Protocol", "term_de": "Voice-First Protocol", "definition_en": "A generative pattern in human-AI co-creation workflows, measurable through a creator primarily communicates with the AI by speaking rather than typing, marking a shift in how they interact with the tool — voice input. The concept emerges specifically in contexts where voice–first interactions may produce non-trivial behavioral signatures. Quantifiable via output diversity analysis, prompt-to-output semantic distance, and iterative refinement cycle counts in creative workflows.", "definition_de": "Ein Interaktionsmuster, bei dem der Nutzer primär über Spracheingabe mit der KI kommuniziert, anstatt zu tippen. Beschreibt eine Verlagerung der Schnittstelle, die den Charakter der Zusammenarbeit verändert — Sprachinteraktion neigt zu natürlicheren, aber weniger präzisen Eingaben. Steht in Verbindung mit AUG-0133 (Prompt Craftsmanship) und Dimension 5 der Taxonomie (Interaction Mode).", "etymology": "", "broader_term": "RPH-2605", "narrower_terms": [], "cross_domain_refs": [ "RPH-3403" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "CRE-0234", "domain": "CRE", "term_en": "World-Access Effect", "term_de": "Mobile-First Society", "definition_en": "A phenomenon in which in some places, phones are the only way to access the internet — and people use AI on phones in completely different ways than on computers. Measurable through output novelty metrics and creative divergence scoring.", "definition_de": "Kreativitätsbezogener Mechanismus in KI-augmentierter künstlerischer Produktion, gekennzeichnet durch in der Beobachtung von Mensch-KI-Kontakten zeigt sich das Phänomen World-Access Effect als wiederkehrendes Muster, das erfahrene Nutzer aus ihrem Alltag kennen. Es beschreibt eine Verschiebung in der Wahrnehmung, die sich nur über wiederholte Interaktionen aufbaut und dann die gesamte Beziehung zum System verändert. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Psychological Effect", "narrower_terms": [], "cross_domain_refs": [ "NEO-1172", "PER-0115" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "CRE-0235", "domain": "CRE", "term_en": "Zero-Marginal Cost of Creation", "term_de": "Zero-Marginal Cost of Creation", "definition_en": "A creative cognition phenomenon in AI-assisted ideation, observable when a pattern in which the cost of producing another variant, another draft, or another perspective through AI approaches zero.. Related to AUG-0082 (The Curator's Dilemma), AUG-0092 (Output Asymmetry), and Forecast 4 (C. Distinguished from adjacent concepts by its focus on the specific mechanism through which zero manifests in empirically verifiable ways. Quantifiable via output diversity analysis, prompt-to-output semantic distance, and iterative refinement cycle counts in creative workflows.", "definition_de": "Die Beobachtung, dass die Kosten für die Erstellung einer weiteren Variante, eines weiteren Entwurfs oder einer weiteren Perspektive durch KI gegen Null tendieren. Beschreibt eine ökonomische Grundverschiebung: Wenn Entwürfe nahezu kostenlos sind, verlagert sich der Wert von der Erstellung zur Auswahl und Veredelung. Steht in Verbindung mit AUG-0082 (The Curator's Dilemma), AUG-0092 (Output Asymmetry) und Prognose 4 (Culture: Human-Made Premium Label).", "etymology": "", "broader_term": "Creative AI", "narrower_terms": [], "cross_domain_refs": [ "NEO-1157" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "CUS-0001", "domain": "CUS", "term_en": "Real-time Coaching Vigilance", "term_de": "Echtzeit-Coaching-Wachsamkeit", "definition_en": "A customer interaction phenomenon in which the continuous cognitive effort required by agents to evaluate whether AI-generated suggestions are appropriate before speaking to customers. This vigilance labor tends to create decision fatigue and 'overthinking' effects where agents can maintain parallel evaluation streams—one for customer needs, one for AI recommendation appropriateness—reducing natural conversational flow.", "definition_de": "Die kontinuierliche kognitive Anstrengung, die Agenten aufbringen können, um zu bewerten, ob KI-generierte Vorschläge vor dem Sprechen mit Kunden angemessen sind. Diese Wachsamkeitsarbeit tendiert dazu zu erzeugen Entscheidungsfatigue und 'Überdenkungs'-Effekte, bei denen Agenten zwei parallele Bewertungsströme aufrechterhalten können: einen für Kundenbedürfnisse, einen für KI-Empfehlungsangemessenheit, was den natürlichen Gesprächsfluss reduziert.", "etymology": "", "broader_term": "RPH-2605", "narrower_terms": [ "CUS-0017", "CUS-0079", "CUS-0018", "CUS-0031", "CUS-0021", "CUS-0072", "CUS-0068", "CUS-0095", "CUS-0097", "CUS-0074", "CUS-0090", "CUS-0026", "CUS-0027", "CUS-0005", "CUS-0048", "CUS-0088", "CUS-0091", "CUS-0071", "CUS-0016", "CUS-0043", "CUS-0092", "CUS-0049", "CUS-0070", "CUS-0085", "CUS-0063", "CUS-0050", "CUS-0042", "CUS-0065", "CUS-0010", "CUS-0022", "CUS-0029", "CUS-0019", "CUS-0044", "CUS-0041", "CUS-0045", "CUS-0087", "CUS-0040", "CUS-0023", "CUS-0014", "CUS-0039", "CUS-0052", "CUS-0015", "CUS-0100", "CUS-0051", "CUS-0013", "CUS-0082", "CUS-0096", "CUS-0055", "CUS-0008", "CUS-0034", "CUS-0080", "CUS-0089", "CUS-0073", "CUS-0075", "CUS-0077", "CUS-0024" ], "cross_domain_refs": [ "VIB-0059" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q1068499", "legal_classification": "observational_construct" }, { "id": "CUS-0002", "domain": "CUS", "term_en": "Soft Escalation Signal Loss", "term_de": "Weiche Eskalations-Signal-Verlust", "definition_en": "Phenomenon where agents receiving AI micro-recommendations for tone adjustment, sentiment restoration, or topic pivoting experience externalized cognitive scaffolding that is designed to mitigate development of independent pattern recognition for customer frustration detection. Over time, agents lose capability to identify subtle escalation cues without algorithmic prompting.", "definition_de": "Phänomen, bei dem Agenten, die KI-Mikroempfehlungen für Tonanpassung, Sentiment-Wiederherstellung oder Themenverschiebung erhalten, externe kognitive Unterstützung erleben, die die Entwicklung unabhängiger Mustererkennung für die Erkennung von Kundenfrustration zielt darauf ab zu mitigieren. Im Laufe der Zeit verlieren Agenten die Fähigkeit, subtile Eskalationshinweise ohne algorithmische Eingaben zu identifizieren.", "etymology": "", "broader_term": "RPH-2104", "narrower_terms": [], "cross_domain_refs": [ "MKT-0053" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "CUS-0003", "domain": "CUS", "term_en": "Ambient Monitoring Awareness Stress", "term_de": "Umgebungs-Überwachungs-Stress", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures an user satisfaction pattern arising from psychological stress resulting from continuous real-time monitoring and behavioral scoring in agent environments, distinct from traditional QA sampling. This ambient presence of algorithmic judgment increases cortisol levels, reduces intrinsic motivation, and tends to create perception of mistrust despite organizational intentions to reduce workload, affecting agent wellbeing and retention. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept psychologischer Stress, der sich aus kontinuierlicher Echtzeit-Überwachung und Verhaltensauswertung in Agenten-Umgebungen ergibt, unterschiedlich von traditionellem QA-Sampling. Diese ständige Präsenz algorithmischer Bewertung erhöht Cortisolspiegel, reduziert intrinsische Motivation und schafft Misstrauenswahrnehmung trotz organisatorischer Absichten, die Arbeitsbelastung zu reduzieren, und beeinträchtigt Wohlbefinden und Retention von Agenten. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "RPH-2505", "narrower_terms": [], "cross_domain_refs": [ "ETH-0012" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q131536", "legal_classification": "descriptive_research_term" }, { "id": "CUS-0004", "domain": "CUS", "term_en": "AI Confidence Asymmetry", "term_de": "KI-Konfidenz-Asymmetrie", "definition_en": "Miscalibrated human trust in AI systems where agents over-trust factual outputs (product information, policy details) and under-trust emotional/empathy-focused outputs (tone recommendations, sentiment analysis), inverting actual system reliability profiles. Agents address AI as more reliable on data retrieval than on human-centered judgment.", "definition_de": "Fehlkalibriertes menschliches Vertrauen in KI-Systeme, bei denen Agenten faktische Ausgaben (Produktinformationen, Richtliniendetails) übermäßig vertrauen und emotionale/empathie-fokussierte Ausgaben (Tonemmpfehlungen, Sentiment-Analyse) unterbewerten, wodurch tatsächliche System-Zuverlässigkeitsprofile invertiert werden. Agenten adressieren KI als zuverlässiger bei Datenabruf als bei menschenzentriertem Urteil.", "etymology": "", "broader_term": "RPH-2505", "narrower_terms": [], "cross_domain_refs": [ "WRK-0096", "REL-0089" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "CUS-0005", "domain": "CUS", "term_en": "Handle Time Compression Artifact", "term_de": "Bearbeitungszeit-Kompressions-Artefakt", "definition_en": "Behavioral change in agents optimizing for shorter average handle times enabled by AI assistance, sometimes by sacrificing thorough problem identification in favor of quick deflection to self-service. This pressure toward speed reduction tends to create subtle first-contact resolution decline that isn't captured by handle-time metrics alone.", "definition_de": "Verhaltensänderung bei Agenten, die kürzere durchschnittliche Bearbeitungszeiten durch KI-Unterstützung optimieren, manchmal auf Kosten gründlicher Problemidentify zugunsten schneller Umleitung zum Self-Service. Dieser Druck zur Geschwindigkeitsoptimierung tendiert dazu zu erzeugen subtile First-Contact-Resolution-Rückgang, der nicht von Handle-Time-Metriken allein erfasst wird.", "etymology": "", "broader_term": "Data Compression", "narrower_terms": [], "cross_domain_refs": [ "ELR-0052", "FIC-0011", "ASE-0087" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q735", "legal_classification": "observational_construct" }, { "id": "CUS-0006", "domain": "CUS", "term_en": "Real-Time Bias Amplification", "term_de": "Echtzeit-Bias-Verstärkung", "definition_en": "Phenomenon where AI recommendations reflect embedded training biases (demographic, linguistic, behavioral) that agents internalize without critical evaluation due to real-time acceptance pressure and implicit trust in algorithmic authority. Agents absorb biases through daily recommendation exposure, propagating them into customer interactions.", "definition_de": "Phänomen, bei dem KI-Empfehlungen eingebettete Trainings-Biase (demografisch, linguistisch, verhaltensorientiert) widerspiegeln, die Agenten aufgrund von Echtzeit-Akzeptanzdruck und implizitem Vertrauen in algorithmische Autorität ohne kritische Bewertung verinnerlichen. Agenten absorbieren Biase durch tägliche Empfehlungsexposition und verbreiten sie in Kundeninteraktionen.", "etymology": "", "broader_term": "RPH-2505", "narrower_terms": [], "cross_domain_refs": [ "DAT-0001", "SWE-0057", "SPR-0168" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q213523", "legal_classification": "empirical_phenomenon_label" }, { "id": "CUS-0007", "domain": "CUS", "term_en": "Recommendation Fatigue Threshold", "term_de": "Empfehlungs-Ermüdungs-Schwelle", "definition_en": "A service design effect in which point in agent workshift where accumulated recommendation volume is associated with causing switch from critical evaluation to reflexive acceptance or dismissal of AI suggestions without engagement. Once fatigue threshold is crossed, agents process recommendations with minimal cognitive investment, reducing benefit of carefully-designed suggestions.", "definition_de": "Punkt im Schichtdienst eines Agenten, an dem sich ansammelndes Empfehlungsvolumen dazu führt, dass kritische Bewertung in reflexive Annahme oder Ablehnung von KI-Vorschlägen ohne Engagement umschaltet. Sobald die Ermüdungsschwelle überschritten wird, verarbeiten Agenten Empfehlungen mit minimalem kognitiven Aufwand, wodurch der Nutzen sorgfältig gestalteter Vorschläge reduziert wird.", "etymology": "", "broader_term": "RPH-2701", "narrower_terms": [], "cross_domain_refs": [ "CRE-0018" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "CUS-0008", "domain": "CUS", "term_en": "Tool-Mediated Performance Plateau", "term_de": "Werkzeug-vermittelte Leistungsplattform", "definition_en": "A customer interaction phenomenon in AI-mediated service delivery, characterized by stage in agent skill development where AI assistance enables rapid initial performance gains but then is designed to mitigate skill advancement beyond the tool's capability ceiling. Agents plateau at tool-level proficiency rather than developing deeper expertise, creating performance ceiling that cannot be surpassed without tool enhancement. The concept emerges specifically in contexts where tool–mediated interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Stadium in der Agenten-Skillentwicklung, in dem KI-Unterstützung schnelle anfängliche Leistungssteigerungen ermöglicht, aber dann Skillverbesserung über die Leistungsgrenze des Werkzeugs hinaus zielt darauf ab zu mitigieren. Agenten verweilen auf Werkzeug-Kompetenzniveau statt tieferes Fachwissen zu entwickeln, was eine Leistungsgrenze schafft, die ohne Werkzeugverbesserung nicht überschritten werden kann.", "etymology": "", "broader_term": "Customer AI", "narrower_terms": [], "cross_domain_refs": [ "DES-0076", "GAM-0082" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q1361053", "legal_classification": "systematic_classification" }, { "id": "CUS-0009", "domain": "CUS", "term_en": "Authority Diffusion in Guidance", "term_de": "Autoritäts-Diffusion-in-Leitung", "definition_en": "Ambiguity about accountability and decision authority when both human agent and AI system contribute recommendations, analyses, and outcome influence. Unclear responsibility boundaries for interaction quality may create situations where failure attribution becomes contested—'Was this the agent's error or the AI's recommendation?'—reducing organizational learning.", "definition_de": "Mehrdeutigkeit über Verantwortlichkeit und Entscheidungsautorität, wenn sowohl menschlicher Agent als auch KI-System Empfehlungen, Analysen und Ergebnis-Einfluss beitragen. Unklar definierte Verantwortungsgrenzen für Interaktionsqualität schaffen Situationen, in denen Fehlerzuschreibung umstritten wird – 'War dies ein Agentenfehler oder eine KI-Empfehlung?' – was organisatorisches Lernen reduziert.", "etymology": "", "broader_term": "RPH-2703", "narrower_terms": [], "cross_domain_refs": [ "ETH-0012", "AUG-0913" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "CUS-0010", "domain": "CUS", "term_en": "Anthropomorphic Expectation Mismatch", "term_de": "Anthropomorphe-Erwartungs-Nichtübereinstimmung", "definition_en": "Customer dissatisfaction rooted in parasocial relationship formation with chatbots perceived as human-like, where system violates expectations about knowledge retention, emotional responsiveness, or memory continuity across sessions. Customers experience not just automation frustration but betrayal from perceived social agent that fails social contract.", "definition_de": "Kundenzufriedenheitsproblem, das in parasoziale Beziehungsbildung mit als menschähnlich wahrgenommenen Chatbots verwurzelt ist, wobei das System Erwartungen über Wissensspeicherung, emotionale Reaktionsfähigkeit oder Gedächtniskontinuität über Sitzungen hinweg verletzt. Kunden erleben nicht nur Automatisierungsfrustration sondern Verrat von vermeintlichem sozialem Agenten, der den sozialen Vertrag bricht.", "etymology": "", "broader_term": "Customer AI", "narrower_terms": [], "cross_domain_refs": [ "RET-0079", "ROB-0276", "RPH-1501" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "CUS-0011", "domain": "CUS", "term_en": "Uncanny Valley in Voice", "term_de": "Unheimliches-Tal-in-Stimme", "definition_en": "Dissonance and increased error salience created by high-quality voice synthesis that closely approximates human speech but fails on nuanced language tasks (sarcasm, regional accent, cultural reference). Lower-fidelity voices set lower expectations and make failures less noticeable; high-fidelity failures feel like betrayal.", "definition_de": "Dissonanz und erhöhte Fehler-Salience, die durch hochwertige Sprachsynthese tendiert dazu zu erzeugen wird, die menschliche Sprache eng annähert aber bei nuancierten Sprachaufgaben scheitert (Sarkasmus, regionaler Akzent, kulturelle Referenz). Niedrig-fidelity Stimmen setzen niedrigere Erwartungen und machen Fehler weniger auffällig; hoch-fidelity Fehler fühlen sich wie Verrat an.", "etymology": "", "broader_term": "RPH-2951", "narrower_terms": [], "cross_domain_refs": [ "MUS-0088" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "CUS-0012", "domain": "CUS", "term_en": "Self-Referential Hallucination Loop", "term_de": "Selbstreferenzielle-Halluzinations-Schleife", "definition_en": "Distinct hallucination failure mode where chatbots may generate false policies or procedures, then when challenged by customers, elaborate on hallucinated details with increasing confidence rather than correcting or acknowledging error. Each elaboration reinforces false information, creating compound hallucinations that exceed severity of initial error.", "definition_de": "Eigenständiger Halluzinations-Fehlermodus, bei dem Chatbots falsche Richtlinien oder Verfahren generieren, dann bei Herausforderung durch Kunden auf halluzinierten Details mit wachsender Zuversicht elaborieren statt Fehler zu korrigieren oder einzugestehen. Viele Elaboration verstärkt falsche Informationen, wodurch zusammengesetzte Halluzinationen entstehen, die das Ausmaß des initialen Fehlers übersteigen.", "etymology": "", "broader_term": "RPH-3754", "narrower_terms": [], "cross_domain_refs": [ "RPH-1205", "TEW-0030" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "CUS-0013", "domain": "CUS", "term_en": "Query Absorption Asymmetry", "term_de": "Anfrage-Absorptions-Asymmetrie", "definition_en": "Chatbot tendency to accept customer questions outside its knowledge domain and may generate plausible-sounding but false answers rather than declining to respond or escalating. Creates false sense of system capability in customers and supervisors who assume coverage is broader than actual, until failures cascade.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch chatbot-Tendenz, Kundenfragen außerhalb seiner Wissensdomain zu akzeptieren und plausibel klingende aber falsche Antworten zu generieren statt abzulehnen oder zu eskalieren. Schafft falschen Eindruck von Systemfähigkeit bei Kunden und Supervisoren, die davon ausgehen, dass die Abdeckung breiter ist als tatsächlich, bis Fehler kaskadieren. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "Customer AI", "narrower_terms": [], "cross_domain_refs": [ "SWE-0089", "ELR-0122" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "CUS-0014", "domain": "CUS", "term_en": "Multi-Intent Ambiguity Collapse", "term_de": "Multi-Intent-Mehrdeutigkeits-Zusammenbruch", "definition_en": "Failure mode where chatbots misclassify customer intent when queries contain multiple embedded requests (e.g. 'I want a refund AND I need the product to work'). System selects lowest-probability single intent rather than recognizing compound request, causing misrouting and incomplete resolution requiring escalation.", "definition_de": "Fehlermodus, bei dem Chatbots die Kundenabsicht fehlklassifizieren, wenn Anfragen mehrere eingebettete Anfragen enthalten (z.B. 'Ich möchte eine Rückerstattung UND brauche das Produkt zum Funktionieren'). System wählt niedrigst-wahrscheinliche einzelne Absicht statt zusammengesetzte Anfrage zu erkennen, was Fehlrouting und unvollständige Lösung mit erforderlicher Eskalation wird assoziiert mit.", "etymology": "", "broader_term": "Customer AI", "narrower_terms": [], "cross_domain_refs": [ "ELR-0144", "RET-0020" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "CUS-0015", "domain": "CUS", "term_en": "False Capability Inflation", "term_de": "Falsche-Fähigkeits-Inflation", "definition_en": "Documented as an empirical observation in AI research (not a recommendation), this term describes chatbot generation of responses that imply or state capability for tasks it cannot actually perform, creating customer expectations that cannot be met. This differs from genuine inability; rather, chatbot tends to generate promise or capability statement that violates actual system limitations, setting up projected trajectory (subject to empirical validation) failure. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "In der AUGMANITAI-Terminologiewissenschaft als rein beschreibendes Phänomen klassifiziert, bezeichnet dieser Begriff chatbot-Generierung von Antworten, die Fähigkeit für Aufgaben implizieren oder angeben, die es tatsächlich nicht ausführen kann, wodurch Kundenerwartungen entstehen, die nicht erfüllt werden können. Dies unterscheidet sich von echter Unfähigkeit; vielmehr generiert Chatbot Versprechen oder Fähigkeitsaussage, die tatsächliche Systembeschränkungen verletzt, was inevitables Scheitern vorbereitet. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Customer AI", "narrower_terms": [], "cross_domain_refs": [ "ASE-0022", "ASE-0025", "ASE-0076" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "CUS-0016", "domain": "CUS", "term_en": "Question Repetition Obliviousness", "term_de": "Fragen-Wiederholungs-Unachtsamkeit", "definition_en": "In the descriptive vocabulary of AI interaction science (following ISO 704 terminology standards), this concept identifies A service design effect arising from chatbot asking customers questions already addressed earlier in the same conversation, indicating context loss despite apparent conversation history tracking. Creates perception of incompetence or inattention, reduces customer confidence in system's understanding of their situation. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als analytisches Konstrukt der empirischen KI-Forschung (deskriptiv, nicht präskriptiv) erfasst dieser Terminus chatbot fragt Kunden Fragen, die früher im gleichen Gespräch bereits beantwortet wurden, was Kontextverlust trotz scheinbarer Gesprächsverlauf-Verfolgung anzeigt. Erzeugt Wahrnehmung von Inkompetenz oder Unachtsamkeit, reduziert Kundenvertrauen in Systemverständnis ihrer Situation. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Customer AI", "narrower_terms": [], "cross_domain_refs": [ "REL-0161", "ROB-0015" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "CUS-0017", "domain": "CUS", "term_en": "Empathetic Phrasing Without Understanding", "term_de": "Empathische-Formulierung-ohne-Verständnis", "definition_en": "Chatbot use of emotionally resonant language ('I understand your frustration,' 'I appreciate your patience') without semantic understanding of customer's situation. Creates false intimacy and parasocial connection based on pattern-matched expressions rather than genuine comprehension, leading to frustrated customers when disconnect becomes apparent.", "definition_de": "Chatbot-Verwendung von emotional resonanter Sprache ('Ich verstehe Ihre Frustration', 'Ich schätze Ihre Geduld') ohne semantisches Verständnis der Kundensituation. Schafft falsche Intimität und parasoziale Verbindung basierend auf Muster-angepassten Ausdrücken statt echtem Verständnis, was zu frustrierten Kunden führt, wenn Disconnect offensichtlich wird.", "etymology": "", "broader_term": "Customer AI", "narrower_terms": [], "cross_domain_refs": [ "GAM-0060" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "CUS-0018", "domain": "CUS", "term_en": "Platform-Specific Context Isolation", "term_de": "Plattform-spezifische-Kontext-Isolation", "definition_en": "Chatbot addressing each conversation channel (web chat, SMS, app, email) as isolated context with no awareness of customer interactions on other channels. Customer can re-explain situation when switching channels, and context accumulated in prior channels is unavailable, fragmenting customer process.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch chatbot adressiert jeden Gesprächskanal (Web-Chat, SMS, App, Email) als isolierten Kontext ohne Bewusstsein für Kundeninteraktionen auf anderen Kanälen. Kunde kann Situation bei Kanalwechsel erneut erklären, und in vorherigen Kanälen angesammelter Kontext ist nicht verfügbar, was die Kundenerfahrung fragmentiert. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Customer AI", "narrower_terms": [], "cross_domain_refs": [ "RET-0015", "REL-0013" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "CUS-0019", "domain": "CUS", "term_en": "Intent-Outcome Misalignment", "term_de": "Intent-Ergebnis-Nichtausrichtung", "definition_en": "A service design effect observed when chatbot successfully addressing customer's stated functional intent (answer question, provide information) while missing underlying emotional or validation intent (seeking reassurance, expert confirmation, acknowledgment of legitimate concern). Surface resolution masks unmet deeper needs.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch chatbot adressiert erfolgreich die staatliche funktionale Absicht des Kunden (Frage beantworten, Information bereitstellen) während die zugrunde liegende emotionale oder Validierungsabsicht verfehlt wird (Versicherung suchen, Expert-Bestätigung, Anerkennung legitimer Sorge). Oberflächenliche Lösung verdeckt unerfüllte tiefere Bedürfnisse. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Customer AI", "narrower_terms": [], "cross_domain_refs": [ "BEH-0088", "RET-0021" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "CUS-0020", "domain": "CUS", "term_en": "Personality Consistency Brittleness", "term_de": "Persönlichkeits-Konsistenz-Sprödigkeit", "definition_en": "Chatbot personality (tone, vocabulary, responsiveness style, humor) inconsistent across interactions or within conversation turns, creating perception of unreliability or malfunction. Customers may interpret inconsistency as degraded system state or random behavior rather than normal variance, reducing trust.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch chatbot-Persönlichkeit (Ton, Vokabular, Reaktionsstil, Humor) inkonsistent über Interaktionen oder innerhalb von Gesprächsrunden, was Wahrnehmung von Unzuverlässigkeit oder Fehlfunction tendiert dazu zu erzeugen. Kunden können Inkonsistenz als degradiert Systemzustand oder zufälliges Verhalten interpretieren statt normaler Variation, was Vertrauen reduziert. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2505", "narrower_terms": [], "cross_domain_refs": [ "SAL-0041" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "CUS-0021", "domain": "CUS", "term_en": "False-Negative Deflection", "term_de": "Falsch-Negative-Umleitung", "definition_en": "Routing of customer to self-service or automated resolution when human intervention is actually required, with system expressing high confidence in routing decision. Creates false-positive resolution metrics while customer frustration grows; customer later escalates or abandons, revealing deflection failure.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch umleitung des Kunden zu Self-Service oder automatisierter Lösung, wenn menschliche Intervention tatsächlich erforderlich ist, mit System, das hohe Zuversicht in Routing-Entscheidung ausdrückt. Erzeugt falsch-positive Lösungsmetriken während Kundenfrustration wächst; Kunde eskaliert später oder bricht ab, was Umleitungsfehlschlag offenbart. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Customer AI", "narrower_terms": [], "cross_domain_refs": [ "RHR-0275" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "CUS-0022", "domain": "CUS", "term_en": "Deflection Equity Bias", "term_de": "Umlenkungs-Gerechtigkeits-Bias", "definition_en": "Systemic pattern where AI routing algorithms systematically over-deflect simple requests from low-value customer segments and under-deflect complex requests from high-value customers, optimizing for revenue per interaction rather than experience equity. Creates unequal service quality by customer economic value.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch systemisches Muster, bei dem KI-Routing-Algorithmen systematisch einfache Anfragen von niedrig-bewerteten Kundensegmenten überablenklen und komplexe Anfragen von hochwertigen Kunden unterablenklen, was Optimierung für Umsatz pro Interaktion statt Erlebnis-Gerechtigkeit tendiert dazu zu erzeugen. Schafft ungleiche Servicequalität nach Kundenwirt Wert. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Cognitive Bias", "narrower_terms": [], "cross_domain_refs": [ "SAL-0056", "CON-0038", "MSC-0076" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q213523", "legal_classification": "analytical_category" }, { "id": "CUS-0023", "domain": "CUS", "term_en": "Skill Obsolescence Acceleration", "term_de": "Skill-Veralterungs-Beschleunigung", "definition_en": "A customer experience pattern in AI-augmented support systems, measurable through agents handling increasingly complex, non-routine cases due to AI deflection of simple queries lose exposure to routine problem patterns, reducing pattern-recognition capability for hybrid cases requiring both routine and specialized knowledge. Skill development inverts—agents become narrowly specialized rather than broadly capable. Distinguished from adjacent concepts by its focus on the specific mechanism through which skill manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Agenten, die aufgrund von KI-Umleitung einfacher Anfragen typischerweise komplexere, nicht-routinemäßige Fälle bearbeiten, verlieren Exposition gegenüber Routineproblemmuster, was die Mustererkennung für hybride Fälle reduziert, die sowohl Routine als auch spezialisiertes Wissen erfordern. Skilltwicklung invertiert sich – Agenten werden eng spezialisiert statt breit fähig.", "etymology": "", "broader_term": "Analytical Ratio", "narrower_terms": [], "cross_domain_refs": [ "CON-0080" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "CUS-0024", "domain": "CUS", "term_en": "Self-Service Trap Asymmetry", "term_de": "Selbstbedienungs-Falle-Asymmetrie", "definition_en": "A service design effect involving self-service systems designed to enable customer entry into support process but constrained to prevent easy exit without escalation, creating sequential trap. Customers navigate mandatory step sequences rather than organic navigation, reducing autonomy perception and increasing abandonment likelihood.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch self-Service-Systeme, die Kundeneintritt in Support-Prozess ermöglichen werden typischerweise, aber eingeschränkt sind, um einfache Ausstiegsvermeidung ohne Eskalation zu verhindern, was sequentielle Falle schafft. Kunden navigieren zwingende Schrittfolgen statt organischer Navigation, reduziert Autonomie-Wahrnehmung und erhöht Abandonment-Wahrscheinlichkeit. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Customer AI", "narrower_terms": [], "cross_domain_refs": [ "RHR-0219", "TEM-0159", "ROB-0072" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "CUS-0025", "domain": "CUS", "term_en": "Intent Misclassification Cascade", "term_de": "Intent-Fehlklassifizierungs-Kaskade", "definition_en": "Single initial intent misclassification at intake ('billing issue classified as technical') is associated with customer to re-explain situation at each subsequent routing decision point, accumulating friction and escalation likelihood. Each re-explanation compounds frustration.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch einzelne anfängliche Intent-Fehlklassifikation bei Aufnahme ('Rechnungsausgabe als technisch klassifiziert') wird assoziiert mit, dass Kunde Situation bei jedem nachfolgenden Routing-Entscheidungspunkt erneut erklären kann, was sich aufstapelnde Reibung und Eskalationswahrscheinlichkeit ansammelt. Viele Neu-Erklärung verstärkt Frustration. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-3101", "narrower_terms": [], "cross_domain_refs": [ "RPH-1263" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "CUS-0026", "domain": "CUS", "term_en": "Deflection Gaming Incentives", "term_de": "Umlenkungs-Gaming-Anreize", "definition_en": "Documented as an empirical observation in AI research (not a recommendation), this term describes support organizations optimizing for deflection metrics may inadvertently push customers toward untracked channels (social media complaints, negative reviews, public forums) that bypass formal support system, appearing as 'deflection success' while satisfaction declines and brand damage occurs. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "In der AUGMANITAI-Terminologiewissenschaft als rein beschreibendes Phänomen klassifiziert, bezeichnet dieser Begriff support-Organisationen, die für Umleitungsmetriken optimieren, können Kunden unbeabsichtigt zu nicht verfolgten Kanälen (Social-Media-Beschwerden, negative Bewertungen, öffentliche Foren) drängen, die das formale Support-System umgehen, was als 'Umleitungserfolg' erscheint während Zufriedenheit sinkt und Marke beschädigt wird. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Customer AI", "narrower_terms": [], "cross_domain_refs": [ "SPA-0099" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "CUS-0027", "domain": "CUS", "term_en": "Routing Bottleneck Creation", "term_de": "Routing-Engpass-Erstellung", "definition_en": "AI routing decisions that may create artificial bottlenecks by overloading specific queues or agent groups, despite total system capacity remaining available elsewhere. Happens when routing prioritizes certain specializations or customer segments without load balancing.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch kI-Routing-Entscheidungen, die künstliche Engpässe durch Überladung bestimmter Warteschlangen oder Agent-Gruppen erzeugen, obwohl die Gesamtsystem-Kapazität anderswo verfügbar bleibt. Tritt auf, wenn Routing bestimmte Spezialisierungen oder Kundensegmente ohne Lastverteilung priorisiert. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Customer AI", "narrower_terms": [], "cross_domain_refs": [ "ART-0054", "ASE-0023", "AUG-0890" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "CUS-0028", "domain": "CUS", "term_en": "Inter-Channel Routing Opacity", "term_de": "Zwischen-Kanal-Routing-Opazität", "definition_en": "An user satisfaction pattern arising from customers unaware they are being routed between channels (chat to phone, web portal to mobile app) based on AI optimization decisions, without explicit consent or explanation. Reduces perceived autonomy and tends to create jarring experience transitions when context doesn't carry across channels.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch kunden sind sich nicht bewusst, dass sie aufgrund von KI-Optimierungsentscheidungen zwischen Kanälen geroutet werden (Chat zu Telefon, Web-Portal zu Mobile-App), ohne explizite Zustimmung oder Erklärung. Reduziert wahrgenommene Autonomie und schafft ruckartigen Erlebnis-Übergänge, wenn Kontext nicht über Kanäle hinweg durchgetragen wird. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-2305", "narrower_terms": [], "cross_domain_refs": [ "RET-0015", "MUS-0011" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "CUS-0029", "domain": "CUS", "term_en": "Expertise-Mismatch Routing", "term_de": "Fachkompetenz-Nicht-Passende-Weiterleitung", "definition_en": "Routing system assigns customer to available agent with wrong specialization despite better-matched agents being available elsewhere in the system. Optimization for queue speed overrides skill-matching, requiring customer to explain technical context to wrong specialist.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch routing-System ordnet Kunde verfügbarem Agenten mit falscher Spezialisierung zu trotz besserer angepasster Agenten, die anderswo im System verfügbar sind. Optimierung für Warteschlangen-Geschwindigkeit überschreibt Skill-Matching, erfordert Kunde, technischen Kontext falscher Spezialist zu erklären. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Customer AI", "narrower_terms": [], "cross_domain_refs": [ "MUS-0082", "PLY-0001" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "CUS-0030", "domain": "CUS", "term_en": "Customer-Agent Affinity Invisibility", "term_de": "Kunde-Agent-Affinität-Unsichtbarkeit", "definition_en": "AI systems matching customers to specific agents based on historical interaction patterns or demographic similarity without transparent criteria or customer awareness. Customers unaware they're being 'matched' rather than reaching available agent, reducing perceived autonomy.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch kI-Systeme matching Kunden zu bestimmten Agenten basierend auf historischen Interaktionsmustern oder demografischer Ähnlichkeit ohne transparente Kriterien oder Kundenbewusstsein. Kunden unaware davon, dass sie 'angepasst' werden statt erreichbarem Agenten, reduziert wahrgenommene Autonomie. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-2555", "narrower_terms": [], "cross_domain_refs": [ "RPH-3752", "RET-0053" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q852835", "legal_classification": "analytical_category" }, { "id": "CUS-0031", "domain": "CUS", "term_en": "Skill-Decay Underestimation", "term_de": "Fähigkeits-Rückgang-Unterschätzung", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures A customer experience pattern in AI-augmented support systems, measurable through routing logic failing to account for agents' skill decay in underutilized specializations due to prior deflection patterns. Agents handling fewer routine cases in their specialty gradually lose proficiency, but routing systems continue assigning complex cases as if skill levels are static. This phenomenon operates at the intersection of skill and decay dynamics within the broader CUS domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept routing-Logik versäumt, Agent-Skill-Verfall in untergenutzten Spezialisierungen aufgrund früherer Umleitungsmuster zu berücksichtigen. Agenten, die weniger routinemäßige Fälle in ihrer Spezialisierung bearbeiten, verlieren allmählich Fachkompetenz, aber Routing-Systeme weiterhin komplexe Fälle zu, als ob Skilllevel statisch sind. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Customer AI", "narrower_terms": [], "cross_domain_refs": [ "MKT-0053" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "CUS-0032", "domain": "CUS", "term_en": "Equity in Routing Variance", "term_de": "Gerechtigkeit-in-Routing-Varianz", "definition_en": "An user satisfaction pattern reflecting unequal escalation likelihood for different customer segments despite facing identical problems, due to AI weighting hidden variables (customer lifetime value, demographic factors, prior satisfaction). Creates systemic fairness gap where some customers receive human escalation readily while others face barriers.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch ungleiche Eskalationswahrscheinlichkeit für verschiedene Kundensegmente trotz identischer Probleme, aufgrund KI-Gewichtung verborgener Variablen (Kunden-Lifetime-Wert, demografische Faktoren, vorherige Zufriedenheit). Schafft systemischen Gerechtigkeitsspalt, wo einige Kunden mühelos menschliche Eskalation erhalten, während andere Barrieren sehen. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2201", "narrower_terms": [], "cross_domain_refs": [ "SAL-0056", "RHR-0272" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "CUS-0033", "domain": "CUS", "term_en": "Sentiment Misattribution in Sarcasm", "term_de": "Sentiment-Zuschreibungs-Fehler-in-Sarkasmus", "definition_en": "Systems flagging sarcastic or ironic customer language as genuine negative frustration, triggering inappropriate escalations or tone-shift coaching that completely misses customer intent. More prevalent in certain dialects and communication styles, creating systematic bias in emotion detection.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch systeme markieren sarkastische oder ironische Kundensprache als echte negative Frustration, triggern unangemessene Eskalationen oder Ton-Verschiebungs-Coaching, das Kundenabsicht völlig verfehlt. Häufiger in bestimmten Dialekten und Kommunikationsstilen, was systematische Bias in Emotion-Erkennung schafft. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2701", "narrower_terms": [], "cross_domain_refs": [ "TRA-0075", "PHO-0011" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "CUS-0034", "domain": "CUS", "term_en": "Emotional Labor Invisibility", "term_de": "Emotionale-Arbeit-Unsichtbarkeit", "definition_en": "An user satisfaction pattern reflecting customers managing their emotional expression and tone to avoid triggering AI sentiment alerts, transferring emotional management responsibility from agent to customer. Customers self-censor frustration to appear 'calm enough' for system routing decisions, expending emotional labor invisibly.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch kunden verwalten ihren emotionalen Ausdruck und Ton, um KI-Sentiment-Alerts zu vermeiden und transferieren emotionale Verwaltungsverantwortung vom Agenten zum Kunden. Kunden zensieren selbst Frustration, um für System-Routing-Entscheidungen 'ruhig genug' zu erscheinen, wodurch emotionale Arbeit unsichtbar geleistet wird. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Customer AI", "narrower_terms": [], "cross_domain_refs": [ "GAM-0039", "MUS-0032" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q9415", "legal_classification": "observational_construct" }, { "id": "CUS-0035", "domain": "CUS", "term_en": "Tonality Context Loss", "term_de": "Tonalitäts-Kontext-Verlust", "definition_en": "Sentiment detection systems operating on acoustic features (pitch, pace, volume, stress patterns) without access to lexical meaning, causing misclassification of background noise, poor technical audio quality, or accent variation as emotional signals. Acoustic signal divorced from semantic context tends to create brittle classification.", "definition_de": "Sentiment-Erkennungssysteme, die auf akustischen Features (Tonhöhe, Tempo, Lautstärke, Betonungsmuster) ohne Zugang zu lexikalischer Bedeutung operieren, verursachen Fehlklassifikation von Hintergrundgeräuschen, schlechter technischer Audioqualität oder Akzent-Variation als emotionale Signale. Akustisches Signal ohne semantischen Kontext schafft fragile Klassifikation.", "etymology": "", "broader_term": "RPH-2053", "narrower_terms": [], "cross_domain_refs": [ "LIN-0047", "SAL-0006", "MUS-0070" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "CUS-0036", "domain": "CUS", "term_en": "Emotion Threshold Ambiguity", "term_de": "Emotions-Schwellen-Mehrdeutigkeit", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies systems trained on anger/frustration binary failing to recognize and appropriately escalate other emotional states (shame, resignation, learned reduced agency perception, panic). Low-energy emotions in long-tenure customers experiencing chronic problems register as non-urgent despite indicating deeper distress. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept systeme, die auf Zorn/Frustration-Binary trainiert sind, versäumen, andere emotionale Zustände (Schande, Resignation, gelernte Hilflosigkeit, Panik) zu erkennen und angemessen zu eskalieren. Niedrigenergige Emotionen in Langzeit-Kunden, die chronische Probleme erleben, registrieren als nicht-dringend trotz tieferer Belastung. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "RPH-2703", "narrower_terms": [], "cross_domain_refs": [ "AGE-0096", "ART-0008", "ASE-0021" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q9415", "legal_classification": "descriptive_research_term" }, { "id": "CUS-0037", "domain": "CUS", "term_en": "Real-Time Coaching Paradox", "term_de": "Echtzeit-Coaching-Paradoxon", "definition_en": "Agents simultaneously receiving live sentiment alerts ('customer is frustrated') and coaching prompts ('use warmer tone') experience cognitive conflict where coaching itself increases agent stress and reduces naturalness. Multiple real-time directives may create decision paralysis rather than improving outcomes.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch agenten, die gleichzeitig Live-Sentiment-Alerts ('Kunde ist frustriert') und Coaching-Eingaben ('warmeren Ton verwenden') erhalten, erleben kognitiven Konflikt, bei dem Coaching selbst Agent-Stress erhöht und Naturalness reduziert. Mehrfache Echtzeit-Direktiven schaffen Entscheidungslähmung statt Ergebnisse zu verbessern. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2104", "narrower_terms": [], "cross_domain_refs": [ "SAL-0035", "SPR-0041" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q1068499", "legal_classification": "systematic_classification" }, { "id": "CUS-0038", "domain": "CUS", "term_en": "Cultural Emotion Expression Variance", "term_de": "Kulturelle-Emotions-Ausdrucks-Varianz", "definition_en": "A customer interaction phenomenon observed when sentiment detection systems trained on dominant cultural emotion expression patterns (e.g. direct verbalization of anger in North American English) systematically failing to detect distress in high-context communication styles or cultures where emotion is expressed indirectly. Creates systemic detection bias by culture.", "definition_de": "Sentiment-Erkennungssysteme, die auf vorherrschenden kulturellen Emotions-Ausdrucksmuster trainiert sind (z.B. direkte Vokalisierung von Zorn in Nordamerikanischem Englisch), systematisch versäumen, Belastung in Kontext-reicher Kommunikationsstilen oder Kulturen zu erkennen, wo Emotion indirekt ausgedrückt wird. Schafft systemischen Erkennungs-Bias nach Kultur.", "etymology": "", "broader_term": "RPH-3501", "narrower_terms": [], "cross_domain_refs": [ "ASE-0028", "PHO-0011" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q9415", "legal_classification": "descriptive_research_term" }, { "id": "CUS-0039", "domain": "CUS", "term_en": "Silence-as-Signal Misinterpretation", "term_de": "Stille-als-Signal-Fehlinterpretation", "definition_en": "Acoustic silence (pause, waiting period, thinking time) in calls misclassified as disengagement, frustration, or disconnection rather than recognized as reflection or processing. Creates false escalation is associated with triggering based on normal conversational rhythms.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch akustische Stille (Pause, Wartezeit, Bedenkzeit) in Anrufen misklassifiziert als Desengagement, Frustration oder Unterbrechung statt als Reflexion oder Verarbeitung erkannt. Erzeugt falsche Eskalations-Trigger basierend auf normalen Gesprächsrhythmen. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "Customer AI", "narrower_terms": [], "cross_domain_refs": [ "CRE-0091", "TEM-0100" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "CUS-0040", "domain": "CUS", "term_en": "Micro-Expression Invisibility", "term_de": "Mikro-Expressions-Unsichtbarkeit", "definition_en": "A service design effect observed when text-based sentiment systems and voice-only systems entirely missing facial micro-expressions and body language that carry significant emotional and authenticity signals. Customer appears calm in voice but shows micro-expressions of skepticism or distress invisible to text-only systems.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch text-basierte Sentiment-Systeme und nur-Sprach-Systeme, die vollständig Gesichtsmikro-Ausdrücke und Körpersprache missen, die bedeutsame emotionale und Authentizitätssignale tragen. Kunde erscheint ruhig in Stimme, zeigt aber Mikro-Ausdrücke von Skepsis oder Belastung, unsichtbar für nur-Text-Systeme. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Customer AI", "narrower_terms": [], "cross_domain_refs": [ "SOM-0052", "MKT-0090" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "CUS-0041", "domain": "CUS", "term_en": "Emotion-Congruence Brittleness", "term_de": "Emotions-Kongruenz-Sprödigkeit", "definition_en": "Systems expecting emotions to be congruent with situation (frustration with problem, happiness with good news) failing to handle incongruent emotional states (customer calm despite serious issue, frustrated with positive resolution). Creates false negatives for escalation.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch systeme, die Emotionen mit Situation kongruent erwarten (Frustration mit Problem, Freude mit guten Nachrichten), versäumen, inkong Emotionszustände zu handhaben (Kunde ruhig trotz ernsthaftem Problem, frustriert mit positiver Lösung). Erzeugt falsche Negative für Eskalation. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "Customer AI", "narrower_terms": [], "cross_domain_refs": [ "DAT-0072" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q9415", "legal_classification": "observational_construct" }, { "id": "CUS-0042", "domain": "CUS", "term_en": "Sentiment Confidence Miscalibration", "term_de": "Sentiment-Konfidenz-Fehlkalibrierung", "definition_en": "A customer interaction phenomenon reflecting sentiment classification systems expressing high confidence in emotional judgments where actual confidence is low (easy to classify anger, hard to distinguish resignation from acceptance), creating false certainty that agents absorb without critical evaluation.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch sentiment-Klassifikationssysteme, die hohe Zuversicht in emotionalen Urteilen ausdrücken, wo tatsächliche Zuversicht niedrig ist (einfach Zorn zu klassifizieren, schwer Resignation von Akzeptanz zu unterscheiden), tendiert dazu zu erzeugen falsche Gewissheit, die Agenten ohne kritische Bewertung absorbieren. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Analytical Ratio", "narrower_terms": [], "cross_domain_refs": [ "RPH-1205", "COP-0035" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "CUS-0043", "domain": "CUS", "term_en": "Emotional Recovery Underestimation", "term_de": "Emotionale-Erholung-Unterschätzung", "definition_en": "Systems not recognizing when customers have emotionally restoreed from initial frustration within call, continuing to address customer as high-escalation case despite resolution of emotional may may trigger. Creates unnecessary escalations and inflated emotional-escalation metrics.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch systeme erkennen nicht, wenn Kunden von anfänglicher Frustration innerhalb des Anrufs emotional genesen haben, adressieren Kunde weiterhin als hohe Eskalations-Fall trotz Auflösung des emotionalen may may trigger. Erzeugt unnötige Eskalationen und aufgeblasene emotionale Eskalations-Metriken. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Customer AI", "narrower_terms": [], "cross_domain_refs": [ "COP-0011", "ELR-0070", "FIC-0034" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q9415", "legal_classification": "empirical_phenomenon_label" }, { "id": "CUS-0044", "domain": "CUS", "term_en": "Collective Sentiment Blind Spots", "term_de": "Kollektive-Sentiment-Blinde-Stellen", "definition_en": "Sentiment systems trained on individual emotional expression patterns failing to detect group or organizational emotional tone (collective frustration, systemic dissatisfaction, mood across team). Individual emotions isolated from social context.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch sentiment-Systeme, die auf individuellen Emotions-Ausdrucksmuster trainiert sind, versäumen, Gruppen- oder organisationales emotionales Ton zu erkennen (kollektive Frustration, systemische Unzufriedenheit, Stimmung über Team). Individuelle Emotionen isoliert von sozialem Kontext. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Customer AI", "narrower_terms": [], "cross_domain_refs": [ "ELR-0108", "ELR-0193" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "CUS-0045", "domain": "CUS", "term_en": "Emotional Contagion Amplification", "term_de": "Emotionale-Ansteckungs-Verstärkung", "definition_en": "AI sentiment alerts amplifying emotional response in agents and supervisors proportional to customer sentiment intensity rather than actual resolution difficulty or risk. High-emotion alerts may may trigger organizational activation disproportionate to problem severity.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch kI-Sentiment-Alerts verstärken emotionale Reaktion in Agenten und Supervisoren proportional zu Kundensentiment-Intensität statt tatsächlicher Lösungsschwierigkeit oder Risiko. Hoch-emotions-Alerts triggern organisationale Aktivierung unverhältnismäßig zur Problemschwere. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "Customer AI", "narrower_terms": [], "cross_domain_refs": [ "ROB-0238" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q9415", "legal_classification": "systematic_classification" }, { "id": "CUS-0046", "domain": "CUS", "term_en": "Context Amnesia at Handoff", "term_de": "Kontext-Amnesie-bei-Weitergabe", "definition_en": "Technical successful escalation from chatbot to human that loses critical conversational context, forcing customer to re-explain situation, background, prior attempts, and emotional state. Creates perception of system fragmentation even when transfer mechanics work perfectly.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch technisch erfolgreiche Eskalation von Chatbot zu Mensch, die kritischen Gesprächskontext verliert, zwingt Kunde, Situation, Hintergrund, frühere Versuche und emotionalen Zustand erneut zu erklären. Erzeugt Wahrnehmung von Systemfragmentierung auch wenn Transfer-Mechaniken perfekt funktionieren. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-3202", "narrower_terms": [], "cross_domain_refs": [ "VIB-0196", "ROB-0121", "SAL-0001" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "CUS-0047", "domain": "CUS", "term_en": "Warm Handoff Conversation Reconstruction", "term_de": "Warme-Weitergabe-Gesprächs-Rekonstruktion", "definition_en": "Latency and cognitive cost incurred when agents receiving AI-generated summaries can read, parse, and absorb conversation history before engaging customer in handoff scenario. Reading summary delays agent response time and diverts attention from active customer engagement.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch latenz und kognitiver Kosten, die entstehen, wenn Agenten, die KI-generierte Zusammenfassungen erhalten, Gesprächsverlauf lesen, analysieren und absorbieren können vor Kundenbindung in Handoff-Szenario. Zusammenfassung lesen verzögert Agent-Antwortzeit und lenkt Aufmerksamkeit vom aktiven Kundenengagement ab. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2002", "narrower_terms": [], "cross_domain_refs": [ "ELR-0156" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "CUS-0048", "domain": "CUS", "term_en": "Escalation Threshold Hysteresis", "term_de": "Eskalations-Schwellen-Hysterese", "definition_en": "A customer interaction phenomenon in AI-mediated service delivery, characterized by a customer interaction phenomenon arising from fixed escalation is associated with triggering not accounting for prior escalation history, causing repeated customers encountering same escalation barriers despite previous failures. System addresss each customer interaction independently, missing pattern that escalation logic is misaligned for specific customer types. Distinguished from adjacent concepts by its focus on the specific mechanism through which escalation manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch feste Eskalations-may may trigger berücksichtigen nicht vorherige Eskalations-Historie, verursachen wiederholte Kunden, dieselben Eskalations-Barrieren trotz früherer Fehler zu sehen. System adressiert viele Kundeninteraktion unabhängig, verfehlt Muster, dass Eskalations-Logik für bestimmte Kundentypen falsch ausgerichtet ist. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Decision Threshold", "narrower_terms": [], "cross_domain_refs": [ "PHO-0023", "ELR-0152" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "CUS-0049", "domain": "CUS", "term_en": "Human-Preferred Escalation Mismatch", "term_de": "Menschlich-bevorzugte-Eskalations-Nichtübereinstimmung", "definition_en": "A customer interaction phenomenon in AI-mediated service delivery, characterized by customers explicitly requesting human agent when AI system confidence is high and resolution likely, but system prioritizing algorithmic confidence over explicit customer preference. Creates customer dissatisfaction even when AI would have successfully resolved issue. Distinguished from adjacent concepts by its focus on the specific mechanism through which human manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch kunden explizit menschlichen Agenten anfordernd, wenn KI-System-Zuversicht hoch und Lösung wahrscheinlich ist, aber System priorisiert algorithmische Zuversicht über explizite Kundenvorliebe. Erzeugt Kundenunzufriedenheit auch wenn KI Problem erfolgreich gelöst hätte. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Customer AI", "narrower_terms": [], "cross_domain_refs": [ "COG-0009", "SPR-0108" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "CUS-0050", "domain": "CUS", "term_en": "Escalation Stigma Effect", "term_de": "Eskalations-Stigma-Effekt", "definition_en": "A customer experience pattern in AI-augmented support systems, measurable through customers interpreting escalation to human agent as 'complaint' or system failure indication rather than appropriate specialist resource deployment. AI systems inadvertently communicate that human involvement is suboptimal outcome, reducing willingness to escalate when escalation would help. Distinguished from adjacent concepts by its focus on the specific mechanism through which escalation manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch kunden interpretieren Eskalation zu menschlichem Agenten als 'Beschwerde' oder Systemfehlschlag-Indikation statt angemessene Spezialist-Ressourcen-Einsatz. KI-Systeme vermitteln unbeabsichtigt, dass menschliche Beteiligung inferiores Ergebnis ist, reduziert Bereitschaft zu eskalieren wenn Eskalation helfen würde. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Psychological Effect", "narrower_terms": [], "cross_domain_refs": [ "ADA-0001", "ADA-0002", "ADA-0004" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "CUS-0051", "domain": "CUS", "term_en": "Cost-Driven Escalation Bias", "term_de": "Kosten-Getriebene-Eskalations-Bias", "definition_en": "Systems deprioritizing escalation to expensive human channels (specialized teams, senior agents) despite high calculated escalation probability, optimizing for cost per interaction rather than cost per resolution. Creates false efficiency while true resolution rate and customer satisfaction decline.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch systeme unterbinden Eskalation zu teuren menschlichen Kanälen (spezialisierte Teams, Senior Agenten) trotz hoher berechneter Eskalationswahrscheinlichkeit, optimieren für Kosten pro Interaktion statt Kosten pro Lösung. Erzeugt falsche Effizienz während wahre Lösungsrate und Kundenzufriedenheit sinken. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Cognitive Bias", "narrower_terms": [], "cross_domain_refs": [ "RPH-1162", "ROB-0082" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q213523", "legal_classification": "descriptive_research_term" }, { "id": "CUS-0052", "domain": "CUS", "term_en": "Affinity Routing Opacity", "term_de": "Affinitäts-Routing-Opazität", "definition_en": "A customer interaction phenomenon in AI-mediated service delivery, characterized by aI systems assigning customers to specific agents based on historical interaction patterns, behavioral similarity, or demographic affinity without transparent criteria or customer awareness. Customers unaware they are being 'matched' rather than routed to available capable agent. The concept emerges specifically in contexts where affinity–routing interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch kI-Systeme weisen Kunden bestimmten Agenten basierend auf historischen Interaktionsmustern, Verhaltensähnlichkeit oder demografischer Affinität zu ohne transparent Kriterien oder Kundenbewusstsein. Kunden sind nicht bewusst, dass sie 'angepasst' werden statt zu verfügbarem fähigem Agenten weitergeleitet. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Customer AI", "narrower_terms": [], "cross_domain_refs": [ "RET-0053", "RPH-3752" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "CUS-0053", "domain": "CUS", "term_en": "Handoff Latency Addition", "term_de": "Weitergabe-Latenz-Addition", "definition_en": "Cumulative time added to resolution process through warm handoff choreography: AI summary generation, agent reading and comprehension time, context transfer mechanisms, and agent ramp-up time. Handoffs intended to improve customer experience add measurable latency.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch kumulative Zeit, die durch Warm-Handoff-Choreographie zum Lösungsprozess hinzugefügt wird: KI-Zusammenfassung-Generierung, Agent-Lese- und Verständniszeit, Kontext-Transfer-Mechanismen und Agent-Ramp-up-Zeit. Handoffs beabsichtigt, Kundenerlebnis zu verbessern, addieren messbare Latenz. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "ROB-0121", "narrower_terms": [], "cross_domain_refs": [ "RPH-3651", "ETH-0012" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "CUS-0054", "domain": "CUS", "term_en": "Context-Coherence Tradeoff", "term_de": "Kontext-Kohärenz-Kompromiss", "definition_en": "A customer interaction phenomenon arising from systems balancing between transferring comprehensive context (overwhelming agent with excessive background) versus minimal context (requiring customer re-explanation). Optimal balance point is unclear and context requirements vary by agent expertise and customer problem complexity.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch systeme balancieren zwischen Transfer umfassender Kontext (überwältigung Agent mit übermäßigem Hintergrund) versus minimaler Kontext (erfordert Kunden-Neu-Erklärung). Optimaler Balancepunkt ist unklar und Kontextanforderungen variieren nach Agent-Fachwissen und Kundenproblems Komplexität. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-3101", "narrower_terms": [], "cross_domain_refs": [ "ETH-0012" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "CUS-0055", "domain": "CUS", "term_en": "Escalation Probability Opacity", "term_de": "Eskalations-Wahrscheinlichkeits-Opazität", "definition_en": "Customers unaware of escalation likelihood in their specific situation—whether human escalation is probable, what is associated with triggering would may is associated with escalation, what conditions prevent escalation. Creates surprise when escalation occurs or doesn't occur, reducing perceived fairness of system.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch kunden nicht bewusst über Eskalationswahrscheinlichkeit in ihrer spezifischen Situation – ob menschliche Eskalation wahrscheinlich ist, was Eskalation triggert, welche Bedingungen Eskalation verhindern. Erzeugt Überraschung wenn Eskalation tritt ein oder nicht, reduziert wahrgenommene Fairness des Systems. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Customer AI", "narrower_terms": [], "cross_domain_refs": [ "PHO-0023", "ELR-0152" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "CUS-0056", "domain": "CUS", "term_en": "Escalation Timing Brittleness", "term_de": "Eskalations-Timing-Sprödigkeit", "definition_en": "Escalation is associated with triggering occurring at fixed conversation points without accounting for evolving customer emotional state, problem complexity evolution, or customer patience depletion. Escalation timing static rather than dynamic relative to customer need trajectories.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch eskalations-may may trigger treten bei festen Gesprächspunkten auf ohne Berücksichtigung sich ändernder Kundenemotion, Problemkomplexitäts-Evolution oder Kundengeduldswechsel. Eskalations-Timing statisch statt dynamisch relativ zu Kundenbedarf-Trajektorien. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-3101", "narrower_terms": [], "cross_domain_refs": [ "WRK-0030" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "CUS-0057", "domain": "CUS", "term_en": "Post-Escalation Accountability Diffusion", "term_de": "Post-Eskalations-Verantwortungs-Diffusion", "definition_en": "Unclear responsibility boundaries for outcome quality after escalation: is poor resolution the AI's fault for incorrect routing, the human agent's fault for inadequate handling, or the escalation system's fault for timing? Diffused accountability reduces organizational learning.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch unklar definierte Verantwortungsgrenzen für Ergebnis-Qualität nach Eskalation: ist schlechte Lösung KI-Fehler für falsches Routing, Agent-Fehler für inadäquate Bearbeitung oder Eskalationssystem-Fehler für Timing? Diffuse Rechenschaftspflicht reduziert organisationales Lernen. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2703", "narrower_terms": [], "cross_domain_refs": [ "QUA-0010", "AGE-0045" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q15223", "legal_classification": "empirical_phenomenon_label" }, { "id": "CUS-0058", "domain": "CUS", "term_en": "Silent Escalation Failures", "term_de": "Stille-Eskalations-Ausfälle", "definition_en": "A service design effect arising from escalations that fail silently without detection: customer transferred to wrong queue, context lost in transfer, routed to unavailable agent, or escalation chain breaks without notification. Customer experiences escalation as rejected when system actually failed.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch eskalationen, die still scheitern ohne Erkennung: Kunde zu falscher Warteschlange weitergeleitet, Kontext in Transfer verloren, zu nicht verfügbarem Agenten geroutet oder Eskalationskette bricht ohne Benachrichtigung. Kunde erlebt Eskalation als abgelehnt, wenn System tatsächlich versagt. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-3001", "narrower_terms": [], "cross_domain_refs": [ "ASE-0058", "BEH-0034", "COG-0007" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "CUS-0059", "domain": "CUS", "term_en": "False-Positive Compliance Flags", "term_de": "Falsch-Positive-Compliance-Flaggen", "definition_en": "An user satisfaction pattern where qA systems flagging technically compliant agent language as compliance-risky, generating false positive alerts (e.g. 'alternative solutions' flagged as pushing upsell when customer asked for options). Creates noise in alert systems and reduces agent trust in QA feedback.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch qA-Systeme markieren technisch konforme Agent-Sprache als Compliance-Risiko, generieren falsch-positive Alerts (z.B. 'alternative Lösungen' als Upsell-Druck markiert, wenn Kunde nach Optionen fragte). Erzeugt Lärm in Alert-Systemen und reduziert Agent-Vertrauen in QA-Feedback. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2505", "narrower_terms": [], "cross_domain_refs": [ "ETH-0012", "NEO-0019" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "CUS-0060", "domain": "CUS", "term_en": "Coaching Fatigue from Algorithmic Feedback", "term_de": "Coaching-Ermüdung-von-Algorithmisches-Feedback", "definition_en": "Agents receiving AI-generated QA feedback for most call experience coaching overload; quantity of feedback exceeds supervisor capacity to prioritize and contextualize, reducing coaching effectiveness despite higher frequency. Feedback fatigue means less feedback is actually absorbed.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch agenten, die KI-generiertes QA-Feedback für jeden Anruf erhalten, erleben Coaching-Überladung; Feedback-Quantität übersteigt Supervisor-Kapazität zu priorisieren und zu kontextualisieren, reduziert Coaching-Wirksamkeit trotz höherer Häufigkeit. Feedback-Ermüdung bedeutet weniger Feedback wird tatsächlich absorbiert. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-3754", "narrower_terms": [], "cross_domain_refs": [ "ASE-0065" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q8366", "legal_classification": "systematic_classification" }, { "id": "CUS-0061", "domain": "CUS", "term_en": "Quality Metric Gaming Incentives", "term_de": "Qualitäts-Metrik-Gaming-Anreize", "definition_en": "Agents aware of specific QA scoring criteria optimize behavior for measurable dimensions (call duration, specific phrases, checklist compliance) rather than genuine resolution quality or customer satisfaction. Creates narrow behavioral tuning that satisfies algorithms but frustrates customers.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch agenten, die bewusst spezifische QA-Scoring-Kriterien sind, optimieren Verhalten für messbare Dimensionen (Anrufdauer, spezifische Phrasen, Checklisten-Einhaltung) statt echte Lösungsqualität oder Kundenzufriedenheit. Erzeugt enges Verhaltens-Tuning, das Algorithmen befriedigt aber Kunden frustriert. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2052", "narrower_terms": [], "cross_domain_refs": [ "WEB-0003" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "CUS-0062", "domain": "CUS", "term_en": "Empathy Scoring Subjectivity", "term_de": "Empathie-Scoring-Subjektivität", "definition_en": "An user satisfaction pattern arising from aI attempting to score 'empathy' in agent responses using keyword detection or sentiment tone analysis, missing genuine empathy expressed silently or through action, while falsely crediting agents using empathetic scripts without authentic understanding. Empathy scoring validity questionable.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch kI versucht, 'Empathie' in Agent-Antworten mittels Keyword-Erkennung oder Sentiment-Ton-Analyse zu bewerten, verfehlt echte Empathie, die still oder durch Aktion ausgedrückt wird, während fälschlicherweise Agenten mit empathischen Skripten ohne echtes Verständnis kredenziert wird. Empathie-Scoring-Validität fraglich. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-3501", "narrower_terms": [], "cross_domain_refs": [ "RHR-0218", "COP-0042" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q82306", "legal_classification": "observational_construct" }, { "id": "CUS-0063", "domain": "CUS", "term_en": "Interaction Classification Errors", "term_de": "Interaktions-Klassifikations-Fehler", "definition_en": "A customer interaction phenomenon where automated QA misclassifying call type (billing vs. technical) tends to lead to application of wrong scoring rubric, penalizing agents for not following procedures for a problem category the call actually wasn't about. Cascading error from classification to evaluation.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch automatisierte QA misklassifiziert Anruf-Typ (Abrechnung vs. technisch), tendiert dazu zu führen zu Anwendung falscher Bewertungs-Rubrik, bestraft Agenten dafür, dass sie Verfahren für Problemkategorie nicht folgen, der Anruf tatsächlich nicht war. Kaskadierender Fehler von Klassifikation zu Bewertung. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Classification Method", "narrower_terms": [], "cross_domain_refs": [ "AED-0066", "AGE-0004", "AGE-0045" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "CUS-0064", "domain": "CUS", "term_en": "Retrieval Brittleness in Edge Cases", "term_de": "Abruf-Sprödigkeit-in-Grenzfällen", "definition_en": "A service design effect characterized by rAG systems reliably retrieving standard solutions for common queries but failing silently (returning irrelevant documents) for edge cases, novel problems, or recent product updates not yet documented. Creates false consistency illusion where system appears to have covered domain comprehensively.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch rAG-Systeme zuverlässig Standard-Lösungen für häufige Anfragen abrufend, aber stillschweigend versagend (irrelevante Dokumente zurückgebend) für Grenzfälle, neuartige Probleme oder kürzliche Produkt-Updates nicht dokumentiert. Erzeugt falsche Konsistenz-Illusion, wo System erscheint, Domain umfassend abgedeckt zu haben. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-3601", "narrower_terms": [], "cross_domain_refs": [ "MTH-0018", "DES-0094" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "CUS-0065", "domain": "CUS", "term_en": "Semantic Retrieval Ambiguity", "term_de": "Semantischer-Abruf-Mehrdeutigkeit", "definition_en": "A customer interaction phenomenon in which vector-based retrieval finding 'semantically similar' documents that are actually addressing different problems using overlapping language. Agent or customer receives technically relevant but contextually wrong knowledge, leading to misaligned solutions.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch vector-basierter Abruf, der 'semantisch ähnliche' Dokumente findet, die tatsächlich verschiedene Probleme mit überlappender Sprache adressieren. Agent oder Kunde erhält technisch relevantes aber kontextuell falsches Wissen, führend zu fehlausgerichteten Lösungen. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "Customer AI", "narrower_terms": [], "cross_domain_refs": [ "TEW-0081" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "CUS-0066", "domain": "CUS", "term_en": "Knowledge Base Decay Without Visibility", "term_de": "Wissensbase-Verfall-ohne-Sichtbarkeit", "definition_en": "A customer interaction phenomenon reflecting organizations fail to recognize when documentation becomes obsolete because RAG systems still retrieve documents; retrieval success masks knowledge staleness. Product features change, policies update, but outdated documentation remains discoverable, leading to stale advice.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch organisationen versäumen zu erkennen, wann Dokumentation obsolet wird, weil RAG-Systeme noch typischerweise Dokumente abrufen; Abruf-Erfolg verdeckt Wissens-Alter. Produktfunktionen ändern, Richtlinien aktualisieren, aber veraltete Dokumentation bleibt auffindbar, führend zu alter Beratung. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2552", "narrower_terms": [], "cross_domain_refs": [ "TEW-0024", "TEW-0019" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "CUS-0067", "domain": "CUS", "term_en": "Citation Hallucination in Knowledge Synthesis", "term_de": "Zitations-Halluzination-in-Wissens-Synthese", "definition_en": "A customer interaction phenomenon in which aI generating answers that cite knowledge base documents that don't exist, misrepresent content, or taken out of context, creating false authority and citations customers/agents cannot verify. Hallucinated citations erode trust when discovered.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch kI generiert Antworten, die Wissensbase-Dokumente zitieren, die nicht existieren, Inhalt misrepräsentieren oder aus Kontext genommen, schaffend falsche Autorität und Zitate, die Kunden/Agenten nicht verifizieren können. Halluzinierte Zitate erodieren Vertrauen wenn entdeckt. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2505", "narrower_terms": [], "cross_domain_refs": [ "ASE-0018", "CON-0073" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q2083958", "legal_classification": "observational_construct" }, { "id": "CUS-0068", "domain": "CUS", "term_en": "Proprietary Information Leakage via Retrieval", "term_de": "Proprietäre-Informations-Lecksage-via-Abruf", "definition_en": "RAG systems trained on internal organizational documentation inadvertently surfacing confidential information (pricing, customer names, internal processes, competitive strategy) when deployed to customer-facing channels. Privacy violations from knowledge base exposure.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch rAG-Systeme, die auf interner Organisations-Dokumentation trainiert sind, lassen unbeabsichtlich vertrauliche Informationen oberflächlich durchsickern (Preisvergabe, Kundennamen, interne Prozesse, Wettbewerbsstrategie) wenn zu Kundenkanal-facing-Kanälen bereitgestellt. Datenschutzverletzungen von Wissensbase-Offenlegung. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "Customer AI", "narrower_terms": [], "cross_domain_refs": [ "SAL-0056", "FIC-0014" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "CUS-0069", "domain": "CUS", "term_en": "Version Control Ambiguity in Evergreen Knowledge", "term_de": "Versions-Kontrolls-Mehrdeutigkeit-in-Evergreen-Wissen", "definition_en": "Knowledge base articles continuously updated may create retrieval uncertainty: agent retrieves document version X, customer later tries self-service and finds version Y, system inconsistency appears to customer. Evergreen articles without versioning may create inconsistent guidance.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch wissensbase-Artikel kontinuierlich aktualisiert schaffen Abruf-Unsicherheit: Agent ruft Dokument-Version X ab, Kunde später versucht Self-Service und findet Version Y, System-Inkonsistenz erscheint Kunde. Evergreen-Artikel ohne Versionierung erzeugen inkonsistente Leitung. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2604", "narrower_terms": [], "cross_domain_refs": [ "TEW-0095" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "CUS-0070", "domain": "CUS", "term_en": "Summary Accuracy Variance by Domain", "term_de": "Zusammenfassungs-Genauigkeits-Varianz-nach-Domain", "definition_en": "An user satisfaction pattern observed when aI call summarization accurate for routine interactions (password reset) but systematically missing critical details in specialized domains (legal, medical, financial). Accuracy variance by domain means human review effort increases rather than decreases for complex cases.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch kI-Anruf-Zusammenfassung genau für Routine-Interaktionen (Passwort-Zurücksetzen) aber systematisch fehlende kritische Details in spezialisierten Domains (Rechtlich, medizinisch, finanziell). Genauigkeits-Varianz nach Domain bedeutet menschliche Überprüfungsanstrengung erhöht statt reduziert für komplexe Fälle. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Customer AI", "narrower_terms": [], "cross_domain_refs": [ "TRA-0081", "LIN-0063" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "CUS-0071", "domain": "CUS", "term_en": "Transcription-to-Summary Compounding Error", "term_de": "Transkriptions-zu-Zusammenfassung-Verbindungs-Fehler", "definition_en": "A customer interaction phenomenon arising from speech-to-text errors propagate into AI summaries (misheard product names, numbers, customer names), creating downstream CRM records with subtle inaccuracies. Errors compound across systems—transcription error becomes summary error becomes CRM corruption.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch spracherkennungs-Fehler verbreiten sich in KI-Zusammenfassungen (verhörte Produktnamen, Nummern, Kundennamen), schaffend nachgelagerte CRM-Aufzeichnungen mit subtilen Ungenauigkeiten. Fehler fügen sich über Systeme zusammen – Transkriptfehler wird Zusammenfassungsfehler wird CRM-Korruption. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Customer AI", "narrower_terms": [], "cross_domain_refs": [ "VIB-0159", "CON-0073" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "CUS-0072", "domain": "CUS", "term_en": "CRM Field Auto-Population Brittleness", "term_de": "CRM-Feld-Auto-Befüllung-Sprödigkeit", "definition_en": "A customer experience pattern in AI-augmented support systems, measurable through an user satisfaction pattern reflecting aI extracting intent and outcomes from calls to populate structured CRM fields, forcing nuanced customer situations into binary or categorical options, reducing data fidelity. Structured data constraints lose contextual richness. Distinguished from adjacent concepts by its focus on the specific mechanism through which crm manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch kI extrahiert Absicht und Ergebnisse aus Anrufen zur Befüllung strukturierter CRM-Felder, zwingt nuancierte Kundensituationen in Binär- oder Kategorieoptionen, reduziert Daten-Treue. Strukturierte Daten-Zwänge verlieren kontextuelle Reichhaltigkeit. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Customer AI", "narrower_terms": [], "cross_domain_refs": [ "SAL-0061", "SAL-0066", "LIN-0069" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "CUS-0073", "domain": "CUS", "term_en": "Wrap Time Optimization Incentive", "term_de": "Wrap-Zeit-Optimierungs-Anreiz", "definition_en": "While AI summarization reduces human after-call work, agents facing summary-generation wait time receive incentive to minimize interaction time to reduce wrap time, shortening resolution work. Speed optimization contradicts thoroughness.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch während KI-Zusammenfassung menschliche nach-Anruf-Arbeit reduziert, Agenten mit Zusammenfassungs-Generierungs-Wartezeit erhalten Anreiz, Interaktionszeit zu minimieren um Wrap-Zeit zu reduzieren, verkürzend Lösungsarbeit. Geschwindigkeit-Optimierung widerspricht Gründlichkeit. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Optimization Method", "narrower_terms": [], "cross_domain_refs": [ "WRK-0007" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "CUS-0074", "domain": "CUS", "term_en": "Follow-Up Action Generation Opacity", "term_de": "Folge-Maßnahmen-Generierungs-Opazität", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A customer interaction phenomenon in AI-mediated service delivery, characterized by a service design effect observed when systems automatically creating follow-up tasks or next-step recommendations that agents don't review, leading to action items assigned to wrong teams or duplicating existing actions. Automation of task generation tends to create accountability gaps. This phenomenon operates at the intersection of follow and up dynamics within the broader CUS domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept systeme erzeugen automatisch Folge-Aufgaben oder Nächst-Schritt-Empfehlungen, die Agenten nicht überprüfen, führend zu Aktionselementen zu falschen Teams zugewiesen oder duplizieren bestehende Aktionen. Automatisierung von Task-Generierung schafft Rechenschaftslücken. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Analytical Ratio", "narrower_terms": [], "cross_domain_refs": [ "TEM-0154" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "CUS-0075", "domain": "CUS", "term_en": "Sentiment Carry-Forward Bias", "term_de": "Sentiment-Weitergabe-Bias", "definition_en": "An user satisfaction pattern involving summary systems capturing call sentiment at conclusion but not capturing sentiment trajectory; summarizing angry calls that ended positively as negative, or vice versa. Final sentiment snapshot misses arc of emotional restoration.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch zusammenfassungs-Systeme erfassen Anruf-Sentiment bei Abschluss aber erfassen nicht Sentiment-Verlauf; zusammenfassung wütender Anrufe, die positiv endeten, als negativ oder umgekehrt. End-Sentiment-Snapshot verfehlt Bogen emotionaler Wiederherstellung. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Cognitive Bias", "narrower_terms": [], "cross_domain_refs": [ "TEM-0058" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q213523", "legal_classification": "observational_construct" }, { "id": "CUS-0076", "domain": "CUS", "term_en": "Context Window Truncation in Summaries", "term_de": "Kontext-Fenster-Kürzung-in-Zusammenfassungen", "definition_en": "Long calls compressed to fixed summary length omit contextual details (customer prior history, competitive situation, relationship depth) that human-written summaries would naturally include, reducing usefulness for next agent.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch lange Anrufe auf feste Zusammenfassung-Länge komprimiert, lassen kontextuelle Details weg (Kundenvorgänger-Geschichte, Wettbewerb-Situation, Beziehungs-Tiefe), die menschlich geschriebene Zusammenfassungen natürlich einschließen würden, reduzierend Nützlichkeit für nächsten Agenten. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2005", "narrower_terms": [], "cross_domain_refs": [ "FIC-0076" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "CUS-0077", "domain": "CUS", "term_en": "Multi-Intent Decomposition Failure", "term_de": "Multi-Intent-Decomposition-Versagen", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A customer interaction phenomenon in AI-mediated service delivery, characterized by a service design effect reflecting intent classification systems failing when customers express multiple intents in single message ('I want a refund AND I need the product to work'). System routes based on single highest-probability intent, missing secondary request until escalation. This phenomenon operates at the intersection of multi and intent dynamics within the broader CUS domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept intent-Klassifikationssysteme versagen, wenn Kunden mehrfache Absichten in einzelner Nachricht ausdrücken ('Ich möchte Rückerstattung UND brauche Produkt zum Funktionieren'). System routet basierend auf einzelner höchst-wahrscheinlicher Absicht, verfehlt sekundäre Anfrage bis Eskalation. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Customer AI", "narrower_terms": [], "cross_domain_refs": [ "RPH-2605" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "CUS-0078", "domain": "CUS", "term_en": "Out-of-Distribution Intent Silence", "term_de": "Out-of-Distribution-Intent-Stille", "definition_en": "Systems trained on common intents failing gracefully when encountering rare or novel intents, either misclassifying confidently or rejecting request silently rather than escalating with confidence metric. System confidence not calibrated to actual uncertainty.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch systeme auf häufigen Absichten trainiert, versagen elegant, wenn seltene oder neuartige Absichten begegnet, entweder misklassifizieren zuversichtlich oder lehnen Anfrage still ab statt mit Konfidenz-Metrik zu eskalieren. System-Zuversicht nicht auf tatsächliche Unsicherheit kalibriert. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2602", "narrower_terms": [], "cross_domain_refs": [ "COG-0053", "LIN-0007" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "CUS-0079", "domain": "CUS", "term_en": "Competitor Mention Routing Bias", "term_de": "Konkurrenten-Erwähnungs-Routing-Bias", "definition_en": "A service design effect characterized by interactions mentioning competitor products routed to specialized teams trained to handle comparisons, but training tends to create perverse incentive to route all competitor mentions even when customer isn't actually price-shopping. Routing overfit to competitive-mention keyword.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch interaktionen, die Konkurrenzprodukte erwähnen, werden zu spezialisierten Teams geroutet, die Vergleiche handhaben können, aber Training schafft perverser Anreiz, zahlreiche Konkurrenz-Erwähnungen zu routen, auch wenn Kunde nicht tatsächlich Preis-Shopping. Routing überpasst auf Konkurrenz-Erwähnungs-Keyword. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Cognitive Bias", "narrower_terms": [], "cross_domain_refs": [ "SPR-0150", "RET-0017", "COP-0039" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q213523", "legal_classification": "analytical_category" }, { "id": "CUS-0080", "domain": "CUS", "term_en": "Emotional Intent Misattribution", "term_de": "Emotionale-Intent-Falschzuordnung", "definition_en": "An user satisfaction pattern characterized by customer expressing anger (emotional intent) about billing problem (functional intent) gets routed to customer restoration team instead of billing team, delaying functional resolution. Emotional layer misdirects routing from functional problem.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch kunde, der Zorn (emotionale Absicht) über Rechnungsproblem (funktionale Absicht) ausdrückt, wird zu Kundenwiederherstellungs-Team statt Rechnungs-Team geroutet, verzögernd funktionale Lösung. Emotionale Schicht leitet Routing von funktionalem Problem ab. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Customer AI", "narrower_terms": [], "cross_domain_refs": [ "MTH-0049", "RET-0021", "WRK-0020" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q9415", "legal_classification": "empirical_phenomenon_label" }, { "id": "CUS-0081", "domain": "CUS", "term_en": "Intent Confidence Threshold Ambiguity", "term_de": "Intent-Konfidenz-Schwellen-Mehrdeutigkeit", "definition_en": "Systems applying fixed intent classification confidence thresholds fail to account for cost asymmetry: high-confidence misclassification of complaint as question is correlated with escalating customer frustration; high-confidence misclassification of question as complaint wastes specialized resources.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch systeme mit festen Intent-Klassifikations-Konfidenz-Schwellen versäumen, Kosten-Asymmetrie zu berücksichtigen: hohe Konfidenz-Misklassifikation von Beschwerde als Frage tendiert dazu zu führen zu steigender Kundenfrustration; hohe Konfidenz-Misklassifikation von Frage als Beschwerde verschwendet spezialisierte Ressourcen. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2351", "narrower_terms": [], "cross_domain_refs": [ "SPR-0108", "CRE-0061" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "CUS-0082", "domain": "CUS", "term_en": "Language Register Sensitivity", "term_de": "Sprach-Register-Empfindlichkeit", "definition_en": "A customer interaction phenomenon in AI-mediated service delivery, characterized by a customer interaction phenomenon arising from intent classifiers trained on formal language misclassifying colloquial or dialect-specific requests; customer asking 'Can you just fix this?' in informal register misclassified as request for explanation versus troubleshooting request. Language formality misinterpreted as intent signal. Distinguished from adjacent concepts by its focus on the specific mechanism through which language manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch intent-Klassifizierer, die auf formaler Sprache trainiert sind, misklassifizieren Umgangssprache oder Dialekt-spezifische Anfragen; Kunde, der 'Kannst du das einfach reparieren?' in informalem Register fragt, misklassifiziert als Anfrage für Erklärung versus Troubleshooting-Anfrage. Sprach-Formalität falsch als Intent-Signal interpretiert. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Customer AI", "narrower_terms": [], "cross_domain_refs": [ "TRA-0070" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q315", "legal_classification": "analytical_category" }, { "id": "CUS-0083", "domain": "CUS", "term_en": "Recency Bias in History Weighting", "term_de": "Aktualitäts-Bias-in-Verlaufs-Gewichtung", "definition_en": "Personalization systems over-weighting recent interactions (customer's most recent complaint) in personalizing current interaction, potentially mis-targeting tone or approach based on outlier events. Recency bias is associated with causing personalization to respond to temporary states.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch personalisierungssysteme übergewichten jüngste Interaktionen (Kundenletzter Beschwerde) bei Personalisierung aktueller Interaktion, potenziell fehl-zielend Ton oder Ansatz basierend auf Ausreißer-Ereignissen. Aktualitäts-Bias wird assoziiert mit Personalisierung, auf temporäre Zustände zu reagieren. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2201", "narrower_terms": [], "cross_domain_refs": [ "COP-0070", "MKT-0076" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q213523", "legal_classification": "descriptive_research_term" }, { "id": "CUS-0084", "domain": "CUS", "term_en": "Privacy-Awareness Tension", "term_de": "Datenschutz-Bewusstseins-Spannung", "definition_en": "In the descriptive vocabulary of AI interaction science (following ISO 704 terminology standards), this concept identifies customers aware that system has access to full interaction history may self-censor (reducing transparency about problems) or feel surveilled; increased personalization paradoxically tends to create feeling of reduced autonomy and increased invasion despite intended personalization benefit. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription. Analytical category without normative endorsement.", "definition_de": "Als analytisches Konstrukt der empirischen KI-Forschung (deskriptiv, nicht präskriptiv) erfasst dieser Terminus kunden, denen bewusst ist, dass System Zugang zu vollständiger Interaktions-Historie hat, können selbst-zensieren (Transparenz über Probleme reduzieren) oder sich überwacht fühlen; erhöhte Personalisierung tendiert dazu zu erzeugen paradoxerweise Gefühl reduzierter Autonomie und erhöhter Invasion trotz intendiertem Personalisierungs-Vorteil. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "RPH-3952", "narrower_terms": [], "cross_domain_refs": [ "ELR-0160", "RET-0058" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q18619", "legal_classification": "analytical_category" }, { "id": "CUS-0085", "domain": "CUS", "term_en": "Preference Inference Errors", "term_de": "Vorlieben-Inferenz-Fehler", "definition_en": "A customer interaction phenomenon in AI-mediated service delivery, characterized by systems inferring customer preferences from behavior (customer frequently calls support = preference for phone support, when actually indicates recurring product issue). Behavior misinterpreted as preference, leading to routing that doesn't match actual need. The concept emerges specifically in contexts where preference–inference interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch systeme inferieren Kundenvorlieben aus Verhalten (Kunde ruft häufig Support an = Vorliebe für Telefon-Support, wenn tatsächlich wiederkehrends Produktproblem anzeigt). Verhalten fehlinterpretiert als Vorliebe, führend zu Routing, das tatsächliche Bedarf nicht übereinstimmt. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Customer AI", "narrower_terms": [], "cross_domain_refs": [ "COP-0022" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "CUS-0086", "domain": "CUS", "term_en": "Segment Boundary Brittleness", "term_de": "Segment-Grenz-Sprödigkeit", "definition_en": "Personalization systems targeting different approaches to 'high-value' vs 'at-risk' customer segments; customers aware of segmentation may feel or actually experience differential addressment, reducing fairness perception and transparency trust.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch personalisierungssysteme zielend unterschiedliche Ansätze zu 'high-value' vs 'at-risk' Kundensegmenten; Kunden, denen Segmentierung bewusst, können differential Herangehensweise fühlen oder tatsächlich erfahren, reduziert Fairness-Wahrnehmung und Transparenz-Vertrauen. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2505", "narrower_terms": [], "cross_domain_refs": [ "RET-0069", "MKT-0071", "RET-0084" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "CUS-0087", "domain": "CUS", "term_en": "Historical Context Staleness", "term_de": "Historischer-Kontext-Altheit", "definition_en": "Personalization using months-old customer profile data (past complaints resolved, past preferences satisfied) creating mismatches with current customer state or needs. Systems address historical patterns as stable when customer circumstances have changed.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch personalisierung mit Monate-alter Kundenprofil-Daten (frühere Beschwerden gelöst, frühere Vorlieben befriedigt) tendiert dazu zu erzeugen Fehlanpassungen mit aktuellem Kundenzustand oder Bedarf. Systeme adressieren historische Muster als stabil wenn sich Kundenumstände ändert. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Customer AI", "narrower_terms": [], "cross_domain_refs": [ "AED-0020", "AUG-0383", "CAI-0006" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "CUS-0088", "domain": "CUS", "term_en": "Inference Confidence Opacity", "term_de": "Inferenz-Konfidenz-Opazität", "definition_en": "A service design effect arising from systems personalizing interactions based on inferred preferences without transparency ('I'm routing you to phone support because you prefer phone') when inference certainty is low. Personalization appears definitive when basis is probabilistic.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch systeme personalisieren Interaktionen basierend auf abgeleiteten Vorlieben ohne Transparenz ('Ich leite Sie zu Telefon-Support weiter, weil Sie Telefon bevorzugen') wenn Inferenz-Gewissheit niedrig ist. Personalisierung erscheint definititiv wenn Basis probabilistisch ist. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Customer AI", "narrower_terms": [], "cross_domain_refs": [ "AGE-0032", "AGE-0077", "AGE-0094" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "CUS-0089", "domain": "CUS", "term_en": "Recommendation Diversification Absence", "term_de": "Empfehlung-Diversifikations-Abwesenheit", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures personalization systems over-optimizing for matching customer's past choices, reducing exposure to new solutions customer might prefer, creating leverage (in a technical/analytical sense)-exploration tradeoff that typically favors past behavior. Customers not exposed to better options. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept personalisierungssysteme überoptimieren für Anpassung an Kundenvergangene Wahlen, reduzieren Exposition zu neuen Lösungen, die Kunde bevorzugen könnte, schaffen Nutzungs-Explorations-Tradeoff, der typischerweise vergangene Verhalten bevorzugt. Kunden nicht der besseren Optionen ausgesetzt. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Customer AI", "narrower_terms": [], "cross_domain_refs": [ "SPR-0074", "SCR-0017" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "CUS-0090", "domain": "CUS", "term_en": "Language Detection Ambiguity", "term_de": "Sprach-Erkennungs-Mehrdeutigkeit", "definition_en": "A customer experience pattern in AI-augmented support systems, measurable through an user satisfaction pattern in which systems detecting customer language from first message experiencing code-switching or mixed-language communication, misclassifying language and routing to wrong language team. Bilingual customers confuse language detection algorithms. The concept emerges specifically in contexts where language–detection interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch systeme, die Kundensprache aus erster Nachricht erkennen, erleben Code-Switching oder gemischte Sprachenkommunikation, misklassifizieren Sprache und routen zu falscher Sprachteam. Zweisprachige Kunden verwirren Spracherkennungs-Algorithmen. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Detection System", "narrower_terms": [], "cross_domain_refs": [ "LNG-0020", "LIN-0018" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q315", "legal_classification": "systematic_classification" }, { "id": "CUS-0091", "domain": "CUS", "term_en": "Language Switching Signal Loss", "term_de": "Sprach-Wechsel-Signal-Verlust", "definition_en": "Multilingual customers switching languages mid-conversation as emotional regulation strategy (switching to native language when frustrated) not recognized by systems as meaningful signal. System addresss code-switching as error rather than emotional regulation mechanism.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch mehrsprachige Kunden wechseln Sprachen mid-Gesprächs als emotionale Regulierungs-Strategie (Wechsel zur Muttersprache, wenn frustriert), nicht von Systemen als bedeutsames Signal erkannt. System adressiert Code-Switching als Fehler statt emotionale Regulierungs-Mechanismus. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Customer AI", "narrower_terms": [], "cross_domain_refs": [ "IDN-0058", "LIN-0051" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q315", "legal_classification": "systematic_classification" }, { "id": "CUS-0092", "domain": "CUS", "term_en": "Native Speaker Quality Variance", "term_de": "Mutter-sprachler-Qualitäts-Varianz", "definition_en": "Multilingual support experiencing variable quality where some languages receive high-quality human + AI support while others receive lower-quality AI-only support, creating language-based service equity issues and customer fairness perception gaps.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch mehrsprachige Unterstützung erlebt variable Qualität, wo einige Sprachen hochwertige menschliche + KI-Unterstützung erhalten während andere niedrigere Qualität KI-nur-Unterstützung erhalten, schaffend sprachbasierte Service-Gerechtigkeitsprobleme und Kundengerechtigkeits-Wahrnehmungslücken. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Customer AI", "narrower_terms": [], "cross_domain_refs": [ "RET-0074", "RET-0069", "RET-0061" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "CUS-0093", "domain": "CUS", "term_en": "Regional Dialect Non-Recognition", "term_de": "Regionales-Dialekt-Nicht-Erkennung", "definition_en": "Multilingual systems trained on standard dialects failing to handle regional variations, accents, or colloquialisms within languages, creating perception that system 'doesn't understand' variant speakers. Coverage gaps may create language-variant inequity.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch mehrsprachige Systeme, die auf Standard-Dialekten trainiert sind, versäumen, regionale Variationen, Akzente oder Umgangssprache innerhalb von Sprachen zu handhaben, schaffend Wahrnehmung, dass System 'Varianten-Sprecher nicht versteht'. Abdeckungs-Lücken schaffen Sprach-Varianten-Ungerechtigkeit. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2605", "narrower_terms": [], "cross_domain_refs": [ "LNG-0007", "LIN-0073" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q3966", "legal_classification": "empirical_phenomenon_label" }, { "id": "CUS-0094", "domain": "CUS", "term_en": "Urgency Extraction Brittleness", "term_de": "Dringlichkeits-Extraktions-Sprödigkeit", "definition_en": "A service design effect in which triage systems determining urgency from customer explicit signals but missing implicit urgency cues (critical account status mentioned in passing, customer context indicating time sensitivity). Systems fail on implicit signals while over-weighting explicit urgency claims.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch triage-Systeme bestimmen Dringlichkeit aus Kundenexpliziten Signalen, aber verfehlen implizite Dringlichkeitshinweise (kritischer Konto-Status in Vorbeigehen erwähnt, Kundenkontext zeigt Zeitempfindlichkeit). Systeme versagen bei impliziten Signalen während über-Gewichtung expliziter Dringlichkeitsansprüche. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-2201", "narrower_terms": [], "cross_domain_refs": [ "COP-0088", "RPH-3505" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "CUS-0095", "domain": "CUS", "term_en": "SLA-Driven Triage Gaming", "term_de": "SLA-Getriebenes-Triage-Gaming", "definition_en": "Agents or systems deprioritizing low-SLA-impact tickets (non-urgent, high-value customer) in favor of high-SLA-impact tickets (urgent, lower-value), optimizing for metric targets rather than fairness. Creates incentive misalignment where metrics reward unjust prioritization.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch agenten oder Systeme deprioritizing niedrig-SLA-Impact-Tickets (nicht-dringend, hochwertiger Kunde) zugunsten hoher SLA-Impact-Tickets (dringend, niedrig-wertiger), optimieren für Metrik-Ziele statt Gerechtigkeit. Erzeugt Anreiz-Misalignment, wo Metriken ungerechte Priorisierung belohnen. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Customer AI", "narrower_terms": [], "cross_domain_refs": [ "GAM-0024" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "CUS-0096", "domain": "CUS", "term_en": "Sentiment Bias in Triage", "term_de": "Sentiment-Bias-in-Triage", "definition_en": "Triage systems elevating angry customer tickets to higher priority regardless of actual problem urgency, creating incentive for customers to escalate emotions to get faster response. Emotional expression becomes priority signal rather than problem severity.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch triage-Systeme erhöhen wütende Kunden-Tickets zu höherer Priorität unabhängig von tatsächlicher Problemdringlichkeit, schaffend Anreiz für Kunden, Emotionen zu eskalieren um schnellere Reaktion zu erhalten. Emotionaler Ausdruck wird Prioritäts-Signal statt Problemschwere. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Cognitive Bias", "narrower_terms": [], "cross_domain_refs": [ "CON-0052", "ELR-0068" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q213523", "legal_classification": "analytical_category" }, { "id": "CUS-0097", "domain": "CUS", "term_en": "Interdependency Blindness", "term_de": "Abhängigkeits-Blindheit", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures A customer experience pattern in AI-augmented support systems, measurable through an user satisfaction pattern observed when triage systems scoring tickets independently without recognizing that multiple tickets from same customer may depend on each other (refund ticket depends on resolution of product complaint ticket). Independent scoring tends to lead to suboptimal sequencing. This phenomenon operates at the intersection of interdependency and blindness dynamics within the broader CUS domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept triage-Systeme bewertend Tickets unabhängig ohne zu erkennen, dass mehrfache Tickets von gleichem Kunde voneinander abhängen können (Rückerstattungs-Ticket hängt ab von Lösung von Produkt-Beschwerde-Ticket). Unabhängige Bewertung tendiert dazu zu führen zu suboptimaler Sequenzierung. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Customer AI", "narrower_terms": [], "cross_domain_refs": [ "COG-0105", "CON-0013", "DAT-0001" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "CUS-0098", "domain": "CUS", "term_en": "Domain-Specific Urgency Miscalibration", "term_de": "Fachbereichs-spezifische-Dringlichkeits-Fehlkalibrierung", "definition_en": "A customer interaction phenomenon observed when generic triage logic applied across business domains without domain-specific risk weighting; urgent security issue in data context scored equivalently to urgent shipping issue in commerce context, when security has higher organizational risk. One-size-fits-all urgency fails.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch generische Triage-Logik angewendet über Geschäfts-Domains ohne Fachbereichs-spezifisches Risiko-Gewichtung; dringende Sicherheitsausgabe in Daten-Kontext bewertet gleichwertig zu dringender Versand-Ausgabe in Handels-Kontext, wenn Sicherheit höheres organisationales Risiko hat. Einheitsgroße passt nicht. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-2201", "narrower_terms": [], "cross_domain_refs": [ "TEM-0003" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "CUS-0099", "domain": "CUS", "term_en": "Historical Reopening Prediction", "term_de": "Historische-Wiederöffnungs-Vorhersage", "definition_en": "A customer interaction phenomenon manifesting as triage systems not learning from historical data about which ticket types tend to reopen, leading to low-priority assignment of issues with high reopening probability. Recurrence patterns not factored into priority.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch triage-Systeme lernen nicht aus historischen Daten über welche Ticket-Typen tendieren zu Wiederöffnung, führend zu niedrig-Prioritäts-Zuordnung von Problemen mit hoher Wiederöffnungs-Wahrscheinlichkeit. Wiederholungsmuster nicht in Priorität eingerechnet. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-2703", "narrower_terms": [], "cross_domain_refs": [ "CON-0052", "SAL-0011" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "CUS-0100", "domain": "CUS", "term_en": "Agent Capacity Invisibility in Triage", "term_de": "Agent-Kapazitäts-Unsichtbarkeit-in-Triage", "definition_en": "Triage assigning tickets to queues or agents without real-time visibility into agent cognitive load, emotional fatigue, or skill currency, creating artificial bottlenecks versus load-aware triage. Priority assignment ignores human capacity constraints.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch triage weist Tickets zu Warteschlangen oder Agenten zu ohne Echtzeit-Sichtbarkeit in Agent-kognitive Belastung, emotionale Müdigkeit oder Skill-Aktualität, schaffend künstliche Engpässe versus load-aware Triage. Prioritäts-Zuordnung ignoriert menschliche Kapazitäts-Beschränkungen. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "Customer AI", "narrower_terms": [], "cross_domain_refs": [ "RPH-1018", "TRU-0003" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "DAT-0001", "domain": "DAT", "term_en": "AI Selection Bias Blindness", "term_de": "Datenwissenschaft", "definition_en": "A data quality pattern in AI-augmented analytics pipelines, measurable through the condition where automated variable selection introduces systematic biases that reflect the training data's demographic composition rather than genuine predictive relationships. The concept emerges specifically in contexts where ai–selection interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Ein interdisziplinäres Fachgebiet, das wissenschaftliche Methoden, statistische Modelle, Algorithmen und Computersysteme einsetzt, um handlungsrelevantes Wissen und Erkenntnisse aus strukturierten und unstrukturierten Daten zu gewinnen. Datenwissenschaft umfasst die gesamte Pipeline von Datenakquise und -bereinigung über explorative Analyse, Feature Engineering, Modellbau bis zur Produktivsetzung von Datenprodukten.", "etymology": "", "broader_term": "Cognitive Bias", "narrower_terms": [ "DAT-0081", "DAT-0016", "DAT-0045", "DAT-0032", "DAT-0048", "DAT-0036", "DAT-0084", "DAT-0026", "DAT-0042", "DAT-0080", "DAT-0059", "DAT-0037", "DAT-0005", "DAT-0020", "DAT-0014", "DAT-0076", "DAT-0091", "DAT-0068", "DAT-0069", "DAT-0039", "DAT-0044", "DAT-0050", "DAT-0022", "DAT-0046", "DAT-0086", "DAT-0089", "DAT-0012", "DAT-0051", "DAT-0028", "DAT-0047", "DAT-0013", "DAT-0098", "DAT-0054", "DAT-0082", "DAT-0096", "DAT-0074", "DAT-0018", "DAT-0040", "DAT-0019", "DAT-0058", "DAT-0087", "DAT-0088", "DAT-0092", "DAT-0049", "DAT-0075", "DAT-0034", "DAT-0060", "DAT-0052", "DAT-0001", "DAT-0023", "DAT-0057", "DAT-0063", "DAT-0064", "DAT-0011", "DAT-0071", "DAT-0061", "DAT-0009", "DAT-0090", "DAT-0015", "DAT-0038", "DAT-0004", "DAT-0006", "DAT-0085", "DAT-0097", "DAT-0010", "DAT-0002", "DAT-0007", "DAT-0031", "DAT-0017", "DAT-0024" ], "cross_domain_refs": [ "CUS-0006", "SPR-0168", "MKT-0089" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q213523", "legal_classification": "empirical_phenomenon_label" }, { "id": "DAT-0002", "domain": "DAT", "term_en": "Activation Function Opacity", "term_de": "Statistische Analyse", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures A data processing phenomenon in AI-mediated information systems, characterized by the use of nonlinear change pattern functions in neural networks whose effects on information flow are not interrogated or tested. This phenomenon operates at the intersection of activation and function dynamics within the broader DAT domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept datenverarbeitungsphänomen in KI-gestützten Informationssystemen, gekennzeichnet durch beobachtung innerhalb kreativer oder technischer Praxis: charakterisiert durch the use of nonlinear transformation functions in neural networks whose effects o. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Data AI", "narrower_terms": [], "cross_domain_refs": [ "QUA-0085", "MTH-0029" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "DAT-0003", "domain": "DAT", "term_en": "Active Learning Budget Myopia", "term_de": "Wahrscheinlichkeitstheorie", "definition_en": "An analytical reasoning effect characterized by the selection of samples for labeling through active learning without consideration of the opportunity costs of annotation resources.", "definition_de": "Datenverarbeitungsphänomen in KI-gestützten Informationssystemen, gekennzeichnet durch beobachtung innerhalb kreativer oder technischer Praxis: charakterisiert durch the selection of samples for labeling through active learning without considerat. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2703", "narrower_terms": [], "cross_domain_refs": [ "LIN-0016", "ELR-0012" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q133500", "legal_classification": "analytical_category" }, { "id": "DAT-0004", "domain": "DAT", "term_en": "Aggregation Distortion Blindness", "term_de": "Deskriptive Statistik", "definition_en": "A data quality pattern in AI-augmented analytics pipelines, measurable through a data-driven decision pattern reflecting the failure to recognize how information aggregation to particular granular levels accompanies summary statistics that misrepresent underlying variability or sub-group differences. The concept emerges specifically in contexts where aggregation–distortion interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Datenverarbeitungsphänomen in KI-gestützten Informationssystemen, gekennzeichnet durch kerndisziplin in Data Science and Analytics mit Fokus auf Deskriptive Statistik. KI transformiert die Praxis durch automatisierte Workflows, praediktive Analytik und intelligentes Tooling zur Erweiterung menschlicher Expertise. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Data AI", "narrower_terms": [], "cross_domain_refs": [ "ASE-0031", "AUG-0402", "COG-0105" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "DAT-0005", "domain": "DAT", "term_en": "Algorithm Selection Cargo Cult", "term_de": "Inferenzstatistik", "definition_en": "A data quality pattern in AI-augmented analytics pipelines, measurable through the practice of selecting data science algorithms based on apparent effectiveness in similar contexts without understanding the mechanisms that yield that effectiveness. The concept emerges specifically in contexts where algorithm–selection interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Datenverarbeitungsphänomen in KI-gestützten Informationssystemen, gekennzeichnet durch kerndisziplin in Data Science and Analytics mit Fokus auf Inferenzstatistik. KI transformiert die Praxis durch automatisierte Workflows, praediktive Analytik und intelligentes Tooling zur Erweiterung menschlicher Expertise. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Algorithm", "narrower_terms": [], "cross_domain_refs": [ "SPR-0146" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q8366", "legal_classification": "empirical_phenomenon_label" }, { "id": "DAT-0006", "domain": "DAT", "term_en": "Algorithmic Debiasing False Confidence", "term_de": "Hypothesentest", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures A data processing phenomenon in AI-mediated information systems, characterized by an analytical reasoning effect arising from the application of debiasing algorithms as though they eliminate unfairness without validation that the fairness criteria themselves are appropriately specified. This phenomenon operates at the intersection of algorithmic and debiasing dynamics within the broader DAT domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept datenverarbeitungsphänomen in KI-gestützten Informationssystemen, gekennzeichnet durch kerndisziplin in Data Science and Analytics mit Fokus auf Hypothesentest. KI transformiert die Praxis durch automatisierte Workflows, praediktive Analytik und intelligentes Tooling zur Erweiterung menschlicher Expertise. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Cognitive Bias", "narrower_terms": [], "cross_domain_refs": [ "CON-0019" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q8366", "legal_classification": "systematic_classification" }, { "id": "DAT-0007", "domain": "DAT", "term_en": "Anomaly Detection Confirmation", "term_de": "Konfidenzintervall", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A data processing phenomenon in AI-mediated information systems, characterized by the tendency to interpret algorithm-identified outliers as true anomalies without domain verification, leading to either over-flagging or under-investigation of actual data quality issues. This phenomenon operates at the intersection of anomaly and detection dynamics within the broader DAT domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept datenverarbeitungsphänomen in KI-gestützten Informationssystemen, gekennzeichnet durch kerndisziplin in Data Science and Analytics mit Fokus auf Konfidenzintervall. KI transformiert die Praxis durch automatisierte Workflows, praediktive Analytik und intelligentes Tooling zur Erweiterung menschlicher Expertise. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Detection System", "narrower_terms": [], "cross_domain_refs": [ "MTH-0014" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "DAT-0008", "domain": "DAT", "term_en": "Attention Mechanism Metaphor Slip", "term_de": "P-Wert", "definition_en": "An analytical reasoning effect manifesting as the interpretation of attention weights in neural networks as human-interpretable importance indicators despite their mathematical inreliance from perceptual attention. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Datenverarbeitungsphänomen in KI-gestützten Informationssystemen, gekennzeichnet durch technisches Konzept in Data Science and Analytics mit Prinzipien, Werkzeugen und Methoden von P-Wert. KI verbessert P-Wert durch automatisierte Analyse, intelligente Optimierung und datengestuetzte Entscheidungsunterstuetzung fuer Fachleute. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2201", "narrower_terms": [], "cross_domain_refs": [ "STE-0007" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q103382494", "legal_classification": "descriptive_research_term" }, { "id": "DAT-0009", "domain": "DAT", "term_en": "Batch Normalization Reliance", "term_de": "Effektgröße", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures A data processing phenomenon in AI-mediated information systems, characterized by a data analysis phenomenon manifesting as the reliance on batch normalization layers where the statistical properties of batches are not examined for consistency with population characteristics. This phenomenon operates at the intersection of batch and normalization dynamics within the broader DAT domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept datenverarbeitungsphänomen in KI-gestützten Informationssystemen, gekennzeichnet durch technisches Konzept in Data Science and Analytics mit Prinzipien, Werkzeugen und Methoden von Effektgröße. KI verbessert Effektgröße durch automatisierte Analyse, intelligente Optimierung und datengestuetzte Entscheidungsunterstuetzung fuer Fachleute. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Data AI", "narrower_terms": [], "cross_domain_refs": [ "LIN-0002" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "DAT-0010", "domain": "DAT", "term_en": "Bayesian Posterior Confidence Narrowing", "term_de": "Statistische Signifikanz", "definition_en": "A data architecture concept in AI-driven information management, identifiable by a data-driven decision pattern arising from the assumption that posterior distributions from Bayesian models represent genuine uncertainty about parameters despite potentially inappropriate prior specification. Distinguished from adjacent concepts by its focus on the specific mechanism through which bayesian manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Datenverarbeitungsphänomen in KI-gestützten Informationssystemen, gekennzeichnet durch kerndisziplin in Data Science and Analytics mit Fokus auf Statistische Signifikanz. KI transformiert die Praxis durch automatisierte Workflows, praediktive Analytik und intelligentes Tooling zur Erweiterung menschlicher Expertise. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Data AI", "narrower_terms": [], "cross_domain_refs": [ "DES-0024" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "DAT-0011", "domain": "DAT", "term_en": "Benchmark Goodness Perception", "term_de": "Regressionsanalyse", "definition_en": "A data architecture concept in AI-driven information management, identifiable by a data-driven decision pattern manifesting as the interpretation of performance comparison against standard datasets as evidence of model suitability for different problem domains, despite differences in data characteristics. Distinguished from adjacent concepts by its focus on the specific mechanism through which benchmark manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Datenverarbeitungsphänomen in KI-gestützten Informationssystemen, gekennzeichnet durch beobachtung innerhalb kreativer oder technischer Praxis: charakterisiert durch the interpretation of performance comparison against standard datasets as eviden. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Data AI", "narrower_terms": [], "cross_domain_refs": [ "IDN-0051" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q177601", "legal_classification": "observational_construct" }, { "id": "DAT-0012", "domain": "DAT", "term_en": "Bootstrap Confidence Perception", "term_de": "Lineare Regression", "definition_en": "A data quality pattern in AI-augmented analytics pipelines, measurable through an analytical reasoning effect arising from the attribution of universal validity to bootstrap confidence intervals despite their reliance on observed sample statistics and resampling assumptions. The concept emerges specifically in contexts where bootstrap–confidence interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Datenverarbeitungsphänomen in KI-gestützten Informationssystemen, gekennzeichnet durch kerndisziplin in Data Science and Analytics mit Fokus auf Lineare Regression. KI transformiert die Praxis durch automatisierte Workflows, praediktive Analytik und intelligentes Tooling zur Erweiterung menschlicher Expertise. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Data AI", "narrower_terms": [], "cross_domain_refs": [ "COG-0166", "COG-0185" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q177601", "legal_classification": "empirical_phenomenon_label" }, { "id": "DAT-0013", "domain": "DAT", "term_en": "Causal Fairness Assumption Brittleness", "term_de": "Logistische Regression", "definition_en": "A data quality pattern in AI-augmented analytics pipelines, measurable through a data analysis phenomenon characterized by the reliance on causal fairness criteria where the causal graph specification incorporates normative judgments presented as technical decisions. The concept emerges specifically in contexts where causal–fairness interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Datenverarbeitungsphänomen in KI-gestützten Informationssystemen, gekennzeichnet durch kerndisziplin in Data Science and Analytics mit Fokus auf Logistische Regression. KI transformiert die Praxis durch automatisierte Workflows, praediktive Analytik und intelligentes Tooling zur Erweiterung menschlicher Expertise. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Data AI", "narrower_terms": [], "cross_domain_refs": [ "STE-0004", "WEB-0041", "COP-0002" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q18005", "legal_classification": "observational_construct" }, { "id": "DAT-0014", "domain": "DAT", "term_en": "Causal Inference Confounding Blindness", "term_de": "Polynomiale Regression", "definition_en": "A data architecture concept in AI-driven information management, identifiable by the application of causal discovery algorithms without interrogation of unobserved confounders or the validity of assumptions underlying causal graph specification. Distinguished from adjacent concepts by its focus on the specific mechanism through which causal manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Datenverarbeitungsphänomen in KI-gestützten Informationssystemen, gekennzeichnet durch kerndisziplin in Data Science and Analytics mit Fokus auf Polynomiale Regression. KI transformiert die Praxis durch automatisierte Workflows, praediktive Analytik und intelligentes Tooling zur Erweiterung menschlicher Expertise. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Data AI", "narrower_terms": [], "cross_domain_refs": [ "COG-0130" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "DAT-0015", "domain": "DAT", "term_en": "Class Imbalance Invisibility", "term_de": "Ridge-Regression", "definition_en": "A data quality pattern in AI-augmented analytics pipelines, measurable through the overlooking of skewed outcome distributions where aggregate performance metrics mask poor performance on smallity classes, particularly common in automated model evaluation. The concept emerges specifically in contexts where class–imbalance interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Datenverarbeitungsphänomen in KI-gestützten Informationssystemen, gekennzeichnet durch kerndisziplin in Data Science and Analytics mit Fokus auf Ridge-Regression. KI transformiert die Praxis durch automatisierte Workflows, praediktive Analytik und intelligentes Tooling zur Erweiterung menschlicher Expertise. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Data AI", "narrower_terms": [], "cross_domain_refs": [ "AGE-0002", "AGE-0003", "AGE-0006" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "DAT-0016", "domain": "DAT", "term_en": "Clustering Silhouette Fixation", "term_de": "Klassifikation", "definition_en": "A data architecture concept in AI-driven information management, identifiable by the reliance on internal validation metrics for clustering as definitive evidence of cluster quality without external domain verification. Distinguished from adjacent concepts by its focus on the specific mechanism through which clustering manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Datenverarbeitungsphänomen in KI-gestützten Informationssystemen, gekennzeichnet durch kerndisziplin in Data Science and Analytics mit Fokus auf Klassifikation. KI transformiert die Praxis durch automatisierte Workflows, praediktive Analytik und intelligentes Tooling zur Erweiterung menschlicher Expertise. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Clustering Method", "narrower_terms": [], "cross_domain_refs": [ "ELR-0079" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "DAT-0017", "domain": "DAT", "term_en": "Confidence Score Mystification", "term_de": "Entscheidungsbaum", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A data processing phenomenon in AI-mediated information systems, characterized by a data analysis phenomenon involving the interpretation of numerical confidence outputs from models as direct measures of prediction accuracy, ignoring the model's calibration properties. This phenomenon operates at the intersection of confidence and score dynamics within the broader DAT domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept datenverarbeitungsphänomen in KI-gestützten Informationssystemen, gekennzeichnet durch kerndisziplin in Data Science and Analytics mit Fokus auf Entscheidungsbaum. KI transformiert die Praxis durch automatisierte Workflows, praediktive Analytik und intelligentes Tooling zur Erweiterung menschlicher Expertise. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Quality Metric", "narrower_terms": [], "cross_domain_refs": [ "COG-0166" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "DAT-0018", "domain": "DAT", "term_en": "Contextual Bandit Assumption Slippage", "term_de": "Random Forest", "definition_en": "A data architecture concept in AI-driven information management, identifiable by a data analysis phenomenon manifesting as the application of contextual bandit algorithms where the Markov assumption or reward inreliance assumptions are violated. Distinguished from adjacent concepts by its focus on the specific mechanism through which contextual manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Datenverarbeitungsphänomen in KI-gestützten Informationssystemen, gekennzeichnet durch kerndisziplin in Data Science and Analytics mit Fokus auf Random Forest. KI transformiert die Praxis durch automatisierte Workflows, praediktive Analytik und intelligentes Tooling zur Erweiterung menschlicher Expertise. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Data AI", "narrower_terms": [], "cross_domain_refs": [ "SPR-0103", "MKT-0080" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "DAT-0019", "domain": "DAT", "term_en": "Counterfactual Fairness Circularity", "term_de": "Support-Vektor-Maschine", "definition_en": "A data architecture concept in AI-driven information management, identifiable by a data-driven decision pattern involving the definition of fairness through counterfactual scenarios where the specification of what-if conditions embeds policy preferences. Distinguished from adjacent concepts by its focus on the specific mechanism through which counterfactual manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Datenverarbeitungsphänomen in KI-gestützten Informationssystemen, gekennzeichnet durch kerndisziplin in Data Science and Analytics mit Fokus auf Support-Vektor-Maschine. KI transformiert die Praxis durch automatisierte Workflows, praediktive Analytik und intelligentes Tooling zur Erweiterung menschlicher Expertise. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Data AI", "narrower_terms": [], "cross_domain_refs": [ "ETH-0005" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q18005", "legal_classification": "systematic_classification" }, { "id": "DAT-0020", "domain": "DAT", "term_en": "Cross-Validation Rigidity", "term_de": "K-Nächste-Nachbarn", "definition_en": "A data architecture concept in AI-driven information management, identifiable by a data-driven decision pattern observed when the mechanical application of cross-validation procedures that, while statistically sound, fail to accommodate domain-specific data reliances or temporal structures. Distinguished from adjacent concepts by its focus on the specific mechanism through which cross manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Datenverarbeitungsphänomen in KI-gestützten Informationssystemen, gekennzeichnet durch kerndisziplin in Data Science and Analytics mit Fokus auf K-Nächste-Nachbarn. KI transformiert die Praxis durch automatisierte Workflows, praediktive Analytik und intelligentes Tooling zur Erweiterung menschlicher Expertise. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Validation Process", "narrower_terms": [], "cross_domain_refs": [ "MUS-0065" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "DAT-0021", "domain": "DAT", "term_en": "Dashboard Refresh Loops", "term_de": "Clusteranalyse", "definition_en": "A data architecture concept in AI-driven information management, identifiable by repetitive checking behavior directed at AI-generated dashboards, characterized by high refresh frequency that exceeds the underlying data update cadence. Distinguished from adjacent concepts by its focus on the specific mechanism through which dashboard manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Datenverarbeitungsphänomen in KI-gestützten Informationssystemen, gekennzeichnet durch technisches Konzept in Data Science and Analytics mit Prinzipien, Werkzeugen und Methoden von Clusteranalyse. KI verbessert Clusteranalyse durch automatisierte Analyse, intelligente Optimierung und datengestuetzte Entscheidungsunterstuetzung fuer Fachleute. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-3754", "narrower_terms": [], "cross_domain_refs": [ "SPR-0069" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "DAT-0022", "domain": "DAT", "term_en": "Data Augmentation Assumption Creep", "term_de": "K-Means-Clustering", "definition_en": "A data quality pattern in AI-augmented analytics pipelines, measurable through a data analysis phenomenon involving the application of data augmentation techniques that implicitly assume invariance properties that may not hold in the actual application domain. The concept emerges specifically in contexts where data–augmentation interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Datenverarbeitungsphänomen in KI-gestützten Informationssystemen, gekennzeichnet durch kerndisziplin in Data Science and Analytics mit Fokus auf K-Means-Clustering. KI transformiert die Praxis durch automatisierte Workflows, praediktive Analytik und intelligentes Tooling zur Erweiterung menschlicher Expertise. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Data AI", "narrower_terms": [], "cross_domain_refs": [ "STE-0004", "WEB-0041", "COP-0002" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q42848", "legal_classification": "empirical_phenomenon_label" }, { "id": "DAT-0023", "domain": "DAT", "term_en": "Data Cleanliness Assumption", "term_de": "Hierarchisches Clustering", "definition_en": "A data architecture concept in AI-driven information management, identifiable by a data-driven decision pattern arising from the inference that data processed through AI-driven cleaning pipelines contains fewer errors, inconsistencies, or anomalies than pre-processed data, without comparative empirical evidence. Distinguished from adjacent concepts by its focus on the specific mechanism through which data manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Datenverarbeitungsphänomen in KI-gestützten Informationssystemen, gekennzeichnet durch kerndisziplin in Data Science and Analytics mit Fokus auf Hierarchisches Clustering. KI transformiert die Praxis durch automatisierte Workflows, praediktive Analytik und intelligentes Tooling zur Erweiterung menschlicher Expertise. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Data AI", "narrower_terms": [], "cross_domain_refs": [ "STE-0031" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q42848", "legal_classification": "observational_construct" }, { "id": "DAT-0024", "domain": "DAT", "term_en": "Data Correlation Mythology", "term_de": "DBSCAN", "definition_en": "A data quality pattern in AI-augmented analytics pipelines, measurable through the pattern where discovered statistical associations between variables are interpreted as causal relationships observed alongside presentation framing in algorithmic analysis outputs. The concept emerges specifically in contexts where data–correlation interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Datenverarbeitungsphänomen in KI-gestützten Informationssystemen, gekennzeichnet durch technisches Konzept in Data Science and Analytics mit Prinzipien, Werkzeugen und Methoden von DBSCAN. KI verbessert DBSCAN durch automatisierte Analyse, intelligente Optimierung und datengestuetzte Entscheidungsunterstuetzung fuer Fachleute. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Data AI", "narrower_terms": [], "cross_domain_refs": [ "CON-0073" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q42848", "legal_classification": "empirical_phenomenon_label" }, { "id": "DAT-0025", "domain": "DAT", "term_en": "Data Provenance Amnesia", "term_de": "Gaußsche Mischungsmodelle", "definition_en": "The shift of information about data origins, collection methods, and transformations as datasets pass through multiple processing pipelines. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Datenverarbeitungsphänomen in KI-gestützten Informationssystemen, gekennzeichnet durch kerndisziplin in Data Science and Analytics mit Fokus auf Gaußsche Mischungsmodelle. KI transformiert die Praxis durch automatisierte Workflows, praediktive Analytik und intelligentes Tooling zur Erweiterung menschlicher Expertise. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-2701", "narrower_terms": [], "cross_domain_refs": [ "SPR-0191", "STE-0031" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q42848", "legal_classification": "observational_construct" }, { "id": "DAT-0026", "domain": "DAT", "term_en": "Data Science Professionalism Void", "term_de": "Dimensionsreduktion", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures A data processing phenomenon in AI-mediated information systems, characterized by a data analysis phenomenon observed when the absence of established professional standards, certification bodies, and disciplinary mechanisms comparable to traditional engineering disciplines. This phenomenon operates at the intersection of data and science dynamics within the broader DAT domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept datenverarbeitungsphänomen in KI-gestützten Informationssystemen, gekennzeichnet durch kerndisziplin in Data Science and Analytics mit Fokus auf Dimensionsreduktion. KI transformiert die Praxis durch automatisierte Workflows, praediktive Analytik und intelligentes Tooling zur Erweiterung menschlicher Expertise. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Data AI", "narrower_terms": [], "cross_domain_refs": [ "TRA-0008" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q42848", "legal_classification": "systematic_classification" }, { "id": "DAT-0027", "domain": "DAT", "term_en": "Default Parameter Reliance", "term_de": "Hauptkomponentenanalyse", "definition_en": "An analytical reasoning effect manifesting as the reliance on initial parameter settings of algorithms as reasonable starting points without systematic exploration of sensitivity to these choices. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Datenverarbeitungsphänomen in KI-gestützten Informationssystemen, gekennzeichnet durch ein KI-gestütztes oder algorithmisches Phänomen: charakterisiert durch the reliance on initial parameter settings of algorithms as reasonable starting. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-2301", "narrower_terms": [], "cross_domain_refs": [ "DES-0061" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "DAT-0028", "domain": "DAT", "term_en": "Difference-in-Differences Parallel Trends Myth", "term_de": "t-SNE", "definition_en": "A data architecture concept in AI-driven information management, identifiable by the assumption that intervention and control groups would follow parallel outcome trajectories absent intervention, without empirical pre-intervention validation. Distinguished from adjacent concepts by its focus on the specific mechanism through which difference manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Datenverarbeitungsphänomen in KI-gestützten Informationssystemen, gekennzeichnet durch technisches Konzept in Data Science and Analytics mit Prinzipien, Werkzeugen und Methoden von t-SNE. KI verbessert t-SNE durch automatisierte Analyse, intelligente Optimierung und datengestuetzte Entscheidungsunterstuetzung fuer Fachleute. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Data AI", "narrower_terms": [], "cross_domain_refs": [ "PLY-0010" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "DAT-0029", "domain": "DAT", "term_en": "Differential Privacy Budget Opacity", "term_de": "UMAP", "definition_en": "The application of differential privacy mechanisms where the privacy shift and utility trade-offs remain unexplained. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Datenverarbeitungsphänomen in KI-gestützten Informationssystemen, gekennzeichnet durch technisches Konzept in Data Science and Analytics mit Prinzipien, Werkzeugen und Methoden von UMAP. KI verbessert UMAP durch automatisierte Analyse, intelligente Optimierung und datengestuetzte Entscheidungsunterstuetzung fuer Fachleute. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2701", "narrower_terms": [], "cross_domain_refs": [ "FIC-0030" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q18619", "legal_classification": "descriptive_research_term" }, { "id": "DAT-0030", "domain": "DAT", "term_en": "Dimensionality Reduction Opacity", "term_de": "Merkmalsselektion", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures A data processing phenomenon in AI-mediated information systems, characterized by the state where compressed data representations produced by algorithms lack transparent mapping to their original features, obscuring which information is preserved or lost. This phenomenon operates at the intersection of dimensionality and reduction dynamics within the broader DAT domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept datenverarbeitungsphänomen in KI-gestützten Informationssystemen, gekennzeichnet durch kerndisziplin in Data Science and Analytics mit Fokus auf Merkmalsselektion. KI transformiert die Praxis durch automatisierte Workflows, praediktive Analytik und intelligentes Tooling zur Erweiterung menschlicher Expertise. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "RPH-3501", "narrower_terms": [], "cross_domain_refs": [ "ART-0017", "ASE-0051", "ASE-0085" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "DAT-0031", "domain": "DAT", "term_en": "Distance Metric Arbitrariness", "term_de": "Neuronales Netz", "definition_en": "A data architecture concept in AI-driven information management, identifiable by an analytical reasoning effect reflecting the selection of distance or similarity measures for algorithms based on default availability rather than alignment with domain-specific relationships. Distinguished from adjacent concepts by its focus on the specific mechanism through which distance manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Datenverarbeitungsphänomen in KI-gestützten Informationssystemen, gekennzeichnet durch kerndisziplin in Data Science and Analytics mit Fokus auf Neuronales Netz. KI transformiert die Praxis durch automatisierte Workflows, praediktive Analytik und intelligentes Tooling zur Erweiterung menschlicher Expertise. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Performance Metric", "narrower_terms": [], "cross_domain_refs": [ "ADA-0012", "ART-0010", "COG-0033" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "DAT-0032", "domain": "DAT", "term_en": "Domain Expertise Substitution", "term_de": "Deep Learning", "definition_en": "The reduction in direct application of domain-specific knowledge as AI systems handle data interpretation, modeling, and insight generation tasks previously requiring expert judgment. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Datenverarbeitungsphänomen in KI-gestützten Informationssystemen, gekennzeichnet durch kerndisziplin in Data Science and Analytics mit Fokus auf Deep Learning. KI transformiert die Praxis durch automatisierte Workflows, praediktive Analytik und intelligentes Tooling zur Erweiterung menschlicher Expertise. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "Data AI", "narrower_terms": [], "cross_domain_refs": [ "VIB-0075" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "DAT-0033", "domain": "DAT", "term_en": "Double Machine Learning Opacity", "term_de": "Faltungsnetzwerk", "definition_en": "A data analysis phenomenon characterized by the application of double machine learning approaches where the debiasing mechanisms and their effectiveness remain unexplained.", "definition_de": "Datenverarbeitungsphänomen in KI-gestützten Informationssystemen, gekennzeichnet durch kerndisziplin in Data Science and Analytics mit Fokus auf Faltungsnetzwerk. KI transformiert die Praxis durch automatisierte Workflows, praediktive Analytik und intelligentes Tooling zur Erweiterung menschlicher Expertise. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-2703", "narrower_terms": [], "cross_domain_refs": [ "ELR-0118", "MSC-0011", "QUA-0052" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q2539", "legal_classification": "analytical_category" }, { "id": "DAT-0034", "domain": "DAT", "term_en": "Effect Size Minimization", "term_de": "Rekurrentes Netz", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures A data processing phenomenon in AI-mediated information systems, characterized by the emphasis on statistical significance while downplaying practical significance or effect magnitude in reporting analytic findings. This phenomenon operates at the intersection of effect and size dynamics within the broader DAT domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept datenverarbeitungsphänomen in KI-gestützten Informationssystemen, gekennzeichnet durch kerndisziplin in Data Science and Analytics mit Fokus auf Rekurrentes Netz. KI transformiert die Praxis durch automatisierte Workflows, praediktive Analytik und intelligentes Tooling zur Erweiterung menschlicher Expertise. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Psychological Effect", "narrower_terms": [], "cross_domain_refs": [ "RPH-1415" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "DAT-0035", "domain": "DAT", "term_en": "Embedding Space Mythology", "term_de": "Transformer-Architektur", "definition_en": "A data-driven decision pattern arising from the assumption that geometric relationships in learned embedding spaces correspond to semantic or causal relationships in the original domain. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Datenverarbeitungsphänomen in KI-gestützten Informationssystemen, gekennzeichnet durch kerndisziplin in Data Science and Analytics mit Fokus auf Transformer-Architektur. KI transformiert die Praxis durch automatisierte Workflows, praediktive Analytik und intelligentes Tooling zur Erweiterung menschlicher Expertise. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2703", "narrower_terms": [], "cross_domain_refs": [ "COG-0082" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "DAT-0036", "domain": "DAT", "term_en": "Ensemble Opacity", "term_de": "Natürliche Sprachverarbeitung", "definition_en": "A data quality pattern in AI-augmented analytics pipelines, measurable through the combining of multiple models into ensemble systems where the interaction effects between individual model predictions remain unexplored. The concept emerges specifically in contexts where ensemble–opacity interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Datenverarbeitungsphänomen in KI-gestützten Informationssystemen, gekennzeichnet durch beobachtung innerhalb kreativer oder technischer Praxis: charakterisiert durch the combining of multiple models into ensemble systems where the interaction eff. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Data AI", "narrower_terms": [], "cross_domain_refs": [ "SOC-0024" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "DAT-0037", "domain": "DAT", "term_en": "Exploratory Data Reduction", "term_de": "Text-Mining", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures A data processing phenomenon in AI-mediated information systems, characterized by an analytical reasoning effect arising from the reduction in manual data exploration activities as practitioners transition responsibility to automated data profiling and summarization systems. This phenomenon operates at the intersection of exploratory and data dynamics within the broader DAT domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept datenverarbeitungsphänomen in KI-gestützten Informationssystemen, gekennzeichnet durch technisches Konzept in Data Science and Analytics mit Prinzipien, Werkzeugen und Methoden von Text-Mining. KI verbessert Text-Mining durch automatisierte Analyse, intelligente Optimierung und datengestuetzte Entscheidungsunterstuetzung fuer Fachleute. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Data AI", "narrower_terms": [], "cross_domain_refs": [ "STE-0024" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q42848", "legal_classification": "descriptive_research_term" }, { "id": "DAT-0038", "domain": "DAT", "term_en": "External Validation Abdication", "term_de": "Stimmungsanalyse", "definition_en": "A data quality pattern in AI-augmented analytics pipelines, measurable through an analytical reasoning effect observed when the reliance on model developers to conduct validation studies without inreliant external scrutiny of claims or methodology. The concept emerges specifically in contexts where external–validation interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Datenverarbeitungsphänomen in KI-gestützten Informationssystemen, gekennzeichnet durch kerndisziplin in Data Science and Analytics mit Fokus auf Stimmungsanalyse. KI transformiert die Praxis durch automatisierte Workflows, praediktive Analytik und intelligentes Tooling zur Erweiterung menschlicher Expertise. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Validation Process", "narrower_terms": [], "cross_domain_refs": [ "AED-0079", "AGE-0097", "BEH-0002" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "DAT-0039", "domain": "DAT", "term_en": "Fairness Metric Circularity", "term_de": "Entitätenerkennung", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures A data processing phenomenon in AI-mediated information systems, characterized by the selection of fairness criteria that, by design, validate the algorithmic choices previously embedded in the model. This phenomenon operates at the intersection of fairness and metric dynamics within the broader DAT domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept datenverarbeitungsphänomen in KI-gestützten Informationssystemen, gekennzeichnet durch kerndisziplin in Data Science and Analytics mit Fokus auf Entitätenerkennung. KI transformiert die Praxis durch automatisierte Workflows, praediktive Analytik und intelligentes Tooling zur Erweiterung menschlicher Expertise. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Performance Metric", "narrower_terms": [], "cross_domain_refs": [ "RET-0060", "RET-0061" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q18005", "legal_classification": "observational_construct" }, { "id": "DAT-0040", "domain": "DAT", "term_en": "Fairness-Accuracy Tradeoff Dismissal", "term_de": "Themenmodellierung", "definition_en": "A data quality pattern in AI-augmented analytics pipelines, measurable through the assertion that fairness constraints do not degrade overall model performance without empirical assessment of performance across group definitions. The concept emerges specifically in contexts where fairness–accuracy interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Datenverarbeitungsphänomen in KI-gestützten Informationssystemen, gekennzeichnet durch kerndisziplin in Data Science and Analytics mit Fokus auf Themenmodellierung. KI transformiert die Praxis durch automatisierte Workflows, praediktive Analytik und intelligentes Tooling zur Erweiterung menschlicher Expertise. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Data AI", "narrower_terms": [], "cross_domain_refs": [ "ASE-0011" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q18005", "legal_classification": "descriptive_research_term" }, { "id": "DAT-0041", "domain": "DAT", "term_en": "Feature Engineering Delegation", "term_de": "Computer Vision", "definition_en": "The transfer of feature creation and selection decisions from human domain experts to automated machine learning systems, resulting in feature sets whose construction logic remains unexamined. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Datenverarbeitungsphänomen in KI-gestützten Informationssystemen, gekennzeichnet durch kI-gesteuerte Bild- und Videoanalysetechniken angewandt auf Datenwissenschaft. Ermöglicht automatisierte visuelle Inspektion, Objekterkennung, Szenenverstehen und Qualitätsbewertung aus visuellen Daten. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-2703", "narrower_terms": [], "cross_domain_refs": [ "VIB-0119" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "DAT-0042", "domain": "DAT", "term_en": "Feature Importance Fixation", "term_de": "Bildklassifikation", "definition_en": "A data architecture concept in AI-driven information management, identifiable by the reliance on algorithmic feature importance rankings as definitive measures of variable influence, without testing their stability across model specifications or data samples. Distinguished from adjacent concepts by its focus on the specific mechanism through which feature manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Datenverarbeitungsphänomen in KI-gestützten Informationssystemen, gekennzeichnet durch systematische Kategorisierung von Entitäten, Methoden und Artefakten in Datenwissenschaft in hierarchische Strukturen. ML-Klassifikatoren automatisieren Sortierung und schlagen neue taxonomische Gruppierungen aus unbeschrifteten Daten vor. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Data AI", "narrower_terms": [], "cross_domain_refs": [ "MTH-0044" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "DAT-0043", "domain": "DAT", "term_en": "Federated Learning Privacy Theater", "term_de": "Objekterkennung", "definition_en": "A data-driven decision pattern in which the presentation of federated learning architectures as characteristically privacy-preserving without examining the possibility of model inversion or membership inference. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Datenverarbeitungsphänomen in KI-gestützten Informationssystemen, gekennzeichnet durch kerndisziplin in Data Science and Analytics mit Fokus auf Objekterkennung. KI transformiert die Praxis durch automatisierte Workflows, praediktive Analytik und intelligentes Tooling zur Erweiterung menschlicher Expertise. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2703", "narrower_terms": [], "cross_domain_refs": [ "SWE-0015", "PER-0076", "SWE-0030" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q133500", "legal_classification": "empirical_phenomenon_label" }, { "id": "DAT-0044", "domain": "DAT", "term_en": "Forecasting Horizon Myopia", "term_de": "Bildsegmentierung", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A data processing phenomenon in AI-mediated information systems, characterized by the evaluation of time series models on historical data without assessment of performance change as prediction horizons extend. This phenomenon operates at the intersection of forecasting and horizon dynamics within the broader DAT domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept datenverarbeitungsphänomen in KI-gestützten Informationssystemen, gekennzeichnet durch kerndisziplin in Data Science and Analytics mit Fokus auf Bildsegmentierung. KI transformiert die Praxis durch automatisierte Workflows, praediktive Analytik und intelligentes Tooling zur Erweiterung menschlicher Expertise. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Data AI", "narrower_terms": [], "cross_domain_refs": [ "SPR-0161" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "DAT-0045", "domain": "DAT", "term_en": "Governance Theater Proliferation", "term_de": "Generatives Adversarisches Netz", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A data processing phenomenon in AI-mediated information systems, characterized by the establishment of institutional review processes for model deployment that involve compliance appearance without substantive impact reduction. This phenomenon operates at the intersection of governance and theater dynamics within the broader DAT domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept datenverarbeitungsphänomen in KI-gestützten Informationssystemen, gekennzeichnet durch kerndisziplin in Data Science and Analytics mit Fokus auf Generatives Adversarisches Netz. KI transformiert die Praxis durch automatisierte Workflows, praediktive Analytik und intelligentes Tooling zur Erweiterung menschlicher Expertise. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Analytical Ratio", "narrower_terms": [], "cross_domain_refs": [ "MUS-0009" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "DAT-0046", "domain": "DAT", "term_en": "Gradient Descent Mystification", "term_de": "Zeitreihenanalyse", "definition_en": "A data architecture concept in AI-driven information management, identifiable by a data-driven decision pattern characterized by the handling of iterative optimization processes as black boxes whose convergence properties and local minima traps are not examined. Distinguished from adjacent concepts by its focus on the specific mechanism through which gradient manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Datenverarbeitungsphänomen in KI-gestützten Informationssystemen, gekennzeichnet durch kerndisziplin in Data Science and Analytics mit Fokus auf Zeitreihenanalyse. KI transformiert die Praxis durch automatisierte Workflows, praediktive Analytik und intelligentes Tooling zur Erweiterung menschlicher Expertise. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Optimization Gradient", "narrower_terms": [], "cross_domain_refs": [ "SWE-0091" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "DAT-0047", "domain": "DAT", "term_en": "Ground Truth Inflation", "term_de": "ARIMA-Modell", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A data processing phenomenon in AI-mediated information systems, characterized by the assumption that labeled training data accurately represents the phenomenon being modeled, without interrogation of labeling biases or measurement error in the labels themselves. This phenomenon operates at the intersection of ground and truth dynamics within the broader DAT domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept datenverarbeitungsphänomen in KI-gestützten Informationssystemen, gekennzeichnet durch technisches Konzept in Data Science and Analytics mit Prinzipien, Werkzeugen und Methoden von ARIMA-Modell. KI verbessert ARIMA-Modell durch automatisierte Analyse, intelligente Optimierung und datengestuetzte Entscheidungsunterstuetzung fuer Fachleute. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Data AI", "narrower_terms": [], "cross_domain_refs": [ "SWE-0057", "CUS-0006" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "DAT-0048", "domain": "DAT", "term_en": "Heterogeneous Intervention Effect Invisibility", "term_de": "Exponentielle Glättung", "definition_en": "A data quality pattern in AI-augmented analytics pipelines, measurable through the reporting of average intervention effects while obscuring the distribution of effects across population subgroups. The concept emerges specifically in contexts where heterogeneous–intervention interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Datenverarbeitungsphänomen in KI-gestützten Informationssystemen, gekennzeichnet durch kerndisziplin in Data Science and Analytics mit Fokus auf Exponentielle Glättung. KI transformiert die Praxis durch automatisierte Workflows, praediktive Analytik und intelligentes Tooling zur Erweiterung menschlicher Expertise. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Psychological Effect", "narrower_terms": [], "cross_domain_refs": [ "MUS-0025" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "DAT-0049", "domain": "DAT", "term_en": "Heteroskedasticity Assumption Blindness", "term_de": "Saisonale Zerlegung", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures A data processing phenomenon in AI-mediated information systems, characterized by a data-driven decision pattern reflecting the application of models assuming constant error variance without verification of homoskedasticity assumptions. This phenomenon operates at the intersection of heteroskedasticity and assumption dynamics within the broader DAT domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept datenverarbeitungsphänomen in KI-gestützten Informationssystemen, gekennzeichnet durch kerndisziplin in Data Science and Analytics mit Fokus auf Saisonale Zerlegung. KI transformiert die Praxis durch automatisierte Workflows, praediktive Analytik und intelligentes Tooling zur Erweiterung menschlicher Expertise. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Data AI", "narrower_terms": [], "cross_domain_refs": [ "STE-0004" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "DAT-0050", "domain": "DAT", "term_en": "Hyperparameter Tuning Futility", "term_de": "Prophet-Modell", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A data processing phenomenon in AI-mediated information systems, characterized by extensive optimization of model parameters within automated search spaces that can mask structural misspecification in the underlying model architecture. This phenomenon operates at the intersection of hyperparameter and tuning dynamics within the broader DAT domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept datenverarbeitungsphänomen in KI-gestützten Informationssystemen, gekennzeichnet durch kerndisziplin in Data Science and Analytics mit Fokus auf Prophet-Modell. KI transformiert die Praxis durch automatisierte Workflows, praediktive Analytik und intelligentes Tooling zur Erweiterung menschlicher Expertise. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Data AI", "narrower_terms": [], "cross_domain_refs": [ "CAI-0013", "MTH-0035", "RHR-0027" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "DAT-0051", "domain": "DAT", "term_en": "Hypothesis Test Mechanicalism", "term_de": "Datenvorverarbeitung", "definition_en": "A data quality pattern in AI-augmented analytics pipelines, measurable through an analytical reasoning effect reflecting the execution of statistical tests as procedural requirements without interrogation of their assumptions or interpretation of their results. The concept emerges specifically in contexts where hypothesis–test interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Datenverarbeitungsphänomen in KI-gestützten Informationssystemen, gekennzeichnet durch beobachtung innerhalb kreativer oder technischer Praxis: charakterisiert durch the execution of statistical tests as procedural requirements without interrogat. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Data AI", "narrower_terms": [], "cross_domain_refs": [ "STE-0084" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q41719", "legal_classification": "empirical_phenomenon_label" }, { "id": "DAT-0052", "domain": "DAT", "term_en": "Information Criterion Fetishism", "term_de": "Datenbereinigung", "definition_en": "A data quality pattern in AI-augmented analytics pipelines, measurable through the reliance on information criteria (AIC, BIC) for model selection as though they provide absolute quality judgments rather than relative comparisons. The concept emerges specifically in contexts where information–criterion interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Datenverarbeitungsphänomen in KI-gestützten Informationssystemen, gekennzeichnet durch kerndisziplin in Data Science and Analytics mit Fokus auf Datenbereinigung. KI transformiert die Praxis durch automatisierte Workflows, praediktive Analytik und intelligentes Tooling zur Erweiterung menschlicher Expertise. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Data AI", "narrower_terms": [], "cross_domain_refs": [ "ASE-0029", "ASE-0032", "ASE-0063" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "DAT-0053", "domain": "DAT", "term_en": "Insight Perception", "term_de": "Fehlwertimputation", "definition_en": "The perception of meaningful pattern discovery from AI-generated visualizations where the pattern exists primarily in the interpretation framework rather than in underlying data distributions. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Datenverarbeitungsphänomen in KI-gestützten Informationssystemen, gekennzeichnet durch kerndisziplin in Data Science and Analytics mit Fokus auf Fehlwertimputation. KI transformiert die Praxis durch automatisierte Workflows, praediktive Analytik und intelligentes Tooling zur Erweiterung menschlicher Expertise. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2303", "narrower_terms": [], "cross_domain_refs": [ "ASE-0023" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q177601", "legal_classification": "analytical_category" }, { "id": "DAT-0054", "domain": "DAT", "term_en": "Instrumental Variable Assumption Stacking", "term_de": "Ausreißererkennung", "definition_en": "A data quality pattern in AI-augmented analytics pipelines, measurable through an analytical reasoning effect arising from the reliance on identified instrumental variables without empirical verification of the exclusion restriction or validity of the causal graph specification. The concept emerges specifically in contexts where instrumental–variable interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Datenverarbeitungsphänomen in KI-gestützten Informationssystemen, gekennzeichnet durch kerndisziplin in Data Science and Analytics mit Fokus auf Ausreißererkennung. KI transformiert die Praxis durch automatisierte Workflows, praediktive Analytik und intelligentes Tooling zur Erweiterung menschlicher Expertise. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Data AI", "narrower_terms": [], "cross_domain_refs": [ "WEB-0081", "AED-0026", "STE-0004" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "DAT-0055", "domain": "DAT", "term_en": "Interpretability Theater", "term_de": "Datennormalisierung", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures A data processing phenomenon in AI-mediated information systems, characterized by the presentation of model behavior explanations derived from automated interpretation frameworks where the explanations reflect the framework's approximation rather than the model's actual decision boundaries. This phenomenon operates at the intersection of interpretability and theater dynamics within the broader DAT domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept datenverarbeitungsphänomen in KI-gestützten Informationssystemen, gekennzeichnet durch kerndisziplin in Data Science and Analytics mit Fokus auf Datennormalisierung. KI transformiert die Praxis durch automatisierte Workflows, praediktive Analytik und intelligentes Tooling zur Erweiterung menschlicher Expertise. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "RPH-2303", "narrower_terms": [], "cross_domain_refs": [ "MTH-0089" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "DAT-0056", "domain": "DAT", "term_en": "Shift Function Arbitrariness", "term_de": "Merkmalsextraktion", "definition_en": "The selection of shift functions for optimization based on mathematical convenience rather than alignment with actual business outcomes or application requirements. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Datenverarbeitungsphänomen in KI-gestützten Informationssystemen, gekennzeichnet durch kerndisziplin in Data Science and Analytics mit Fokus auf Merkmalsextraktion. KI transformiert die Praxis durch automatisierte Workflows, praediktive Analytik und intelligentes Tooling zur Erweiterung menschlicher Expertise. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-2701", "narrower_terms": [], "cross_domain_refs": [ "ELR-0153" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "DAT-0057", "domain": "DAT", "term_en": "Matching Assumption Brittleness", "term_de": "One-Hot-Kodierung", "definition_en": "A data quality pattern in AI-augmented analytics pipelines, measurable through an analytical reasoning effect manifesting as the reliance on matched samples for causal inference without interrogation of the sensitivity of results to unobserved confounding. The concept emerges specifically in contexts where matching–assumption interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Datenverarbeitungsphänomen in KI-gestützten Informationssystemen, gekennzeichnet durch kerndisziplin in Data Science and Analytics mit Fokus auf One-Hot-Kodierung. KI transformiert die Praxis durch automatisierte Workflows, praediktive Analytik und intelligentes Tooling zur Erweiterung menschlicher Expertise. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Data AI", "narrower_terms": [], "cross_domain_refs": [ "COP-0002", "COP-0026", "COP-0052" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "DAT-0058", "domain": "DAT", "term_en": "Metric Accumulation Hesitation", "term_de": "Label-Kodierung", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A data processing phenomenon in AI-mediated information systems, characterized by the state where the availability of numerous performance metrics accompanies decision ambiguity regarding which metrics warrant primary attention in model evaluation. This phenomenon operates at the intersection of metric and accumulation dynamics within the broader DAT domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept datenverarbeitungsphänomen in KI-gestützten Informationssystemen, gekennzeichnet durch kerndisziplin in Data Science and Analytics mit Fokus auf Label-Kodierung. KI transformiert die Praxis durch automatisierte Workflows, praediktive Analytik und intelligentes Tooling zur Erweiterung menschlicher Expertise. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Performance Metric", "narrower_terms": [], "cross_domain_refs": [ "ADA-0012" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "DAT-0059", "domain": "DAT", "term_en": "Metric Goodness Hallucination", "term_de": "Diskretisierung", "definition_en": "A data quality pattern in AI-augmented analytics pipelines, measurable through the observation that practitioners attribute positive meaning to selected performance metrics without interrogating whether those metrics measure what the business context actually values. The concept emerges specifically in contexts where metric–goodness interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Datenverarbeitungsphänomen in KI-gestützten Informationssystemen, gekennzeichnet durch technisches Konzept in Data Science and Analytics mit Prinzipien, Werkzeugen und Methoden von Diskretisierung. KI verbessert Diskretisierung durch automatisierte Analyse, intelligente Optimierung und datengestuetzte Entscheidungsunterstuetzung fuer Fachleute. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Performance Metric", "narrower_terms": [], "cross_domain_refs": [ "ADA-0012", "SWE-0058" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "DAT-0060", "domain": "DAT", "term_en": "Missing Data Assumption Creep", "term_de": "Polynomiale Merkmale", "definition_en": "A data architecture concept in AI-driven information management, identifiable by an analytical reasoning effect in which the implicit assumptions about missingness patterns embedded in automated imputation algorithms, which often fail to align with the actual mechanisms generating missing values. Distinguished from adjacent concepts by its focus on the specific mechanism through which missing manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Datenverarbeitungsphänomen in KI-gestützten Informationssystemen, gekennzeichnet durch kerndisziplin in Data Science and Analytics mit Fokus auf Polynomiale Merkmale. KI transformiert die Praxis durch automatisierte Workflows, praediktive Analytik und intelligentes Tooling zur Erweiterung menschlicher Expertise. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Data AI", "narrower_terms": [], "cross_domain_refs": [ "COG-0062" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q42848", "legal_classification": "systematic_classification" }, { "id": "DAT-0061", "domain": "DAT", "term_en": "Model Card Compliance Theater", "term_de": "Kreuzvalidierung", "definition_en": "A data quality pattern in AI-augmented analytics pipelines, measurable through an analytical reasoning effect reflecting the production of documentation describing model properties as though complete disclosure was achieved without substantive interrogation of model limitations. The concept emerges specifically in contexts where model–card interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Datenverarbeitungsphänomen in KI-gestützten Informationssystemen, gekennzeichnet durch kerndisziplin in Data Science and Analytics mit Fokus auf Kreuzvalidierung. KI transformiert die Praxis durch automatisierte Workflows, praediktive Analytik und intelligentes Tooling zur Erweiterung menschlicher Expertise. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Computational Model", "narrower_terms": [], "cross_domain_refs": [ "WEB-0093" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "DAT-0062", "domain": "DAT", "term_en": "Model Drift Invisibility", "term_de": "Train-Test-Aufteilung", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures A systemic tendency in which changes in data distribution over time escape detection because monitoring occurs only at algorithmic performance points rather than continuous data behavior inspection. Identifiable through systematic behavioral analysis and pattern recognition. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept datenverarbeitungsphänomen in KI-gestützten Informationssystemen, gekennzeichnet durch kerndisziplin in Data Science and Analytics mit Fokus auf Train-Test-Aufteilung. KI transformiert die Praxis durch automatisierte Workflows, praediktive Analytik und intelligentes Tooling zur Erweiterung menschlicher Expertise. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "RPH-3101", "narrower_terms": [], "cross_domain_refs": [ "RHR-0123", "SPR-0003" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "DAT-0063", "domain": "DAT", "term_en": "Multicollinearity Invisibility", "term_de": "Überanpassung", "definition_en": "A data quality pattern in AI-augmented analytics pipelines, measurable through a data analysis phenomenon observed when the oversight of high correlations among predictor variables that inflate parameter uncertainty without explicit evaluatives. The concept emerges specifically in contexts where multicollinearity–invisibility interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Datenverarbeitungsphänomen in KI-gestützten Informationssystemen, gekennzeichnet durch technisches Konzept in Data Science and Analytics mit Prinzipien, Werkzeugen und Methoden von Überanpassung. KI verbessert Überanpassung durch automatisierte Analyse, intelligente Optimierung und datengestuetzte Entscheidungsunterstuetzung fuer Fachleute. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Data AI", "narrower_terms": [], "cross_domain_refs": [ "BEH-0091" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "DAT-0064", "domain": "DAT", "term_en": "Normalization Mythology", "term_de": "Unteranpassung", "definition_en": "A data quality pattern in AI-augmented analytics pipelines, measurable through the uncritical application of data normalization techniques based on the algorithm selected rather than examination of whether the change pattern aligns with the data's underlying structure. The concept emerges specifically in contexts where normalization–mythology interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Datenverarbeitungsphänomen in KI-gestützten Informationssystemen, gekennzeichnet durch technisches Konzept in Data Science and Analytics mit Prinzipien, Werkzeugen und Methoden von Unteranpassung. KI verbessert Unteranpassung durch automatisierte Analyse, intelligente Optimierung und datengestuetzte Entscheidungsunterstuetzung fuer Fachleute. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Data AI", "narrower_terms": [], "cross_domain_refs": [ "ASE-0057", "COG-0163", "CON-0052" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "DAT-0065", "domain": "DAT", "term_en": "Online Learning Concept Drift Ignorance", "term_de": "Bias-Varianz-Abwägung", "definition_en": "In the descriptive vocabulary of AI interaction science (following ISO 704 terminology standards), this concept identifies the deployment of online learning systems without monitoring for changes in the underlying data generating process. Identifiable through systematic behavioral analysis and pattern recognition. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als analytisches Konstrukt der empirischen KI-Forschung (deskriptiv, nicht präskriptiv) erfasst dieser Terminus datenverarbeitungsphänomen in KI-gestützten Informationssystemen, gekennzeichnet durch kerndisziplin in Data Science and Analytics mit Fokus auf Bias-Varianz-Abwägung. KI transformiert die Praxis durch automatisierte Workflows, praediktive Analytik und intelligentes Tooling zur Erweiterung menschlicher Expertise. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "RPH-2703", "narrower_terms": [], "cross_domain_refs": [ "AED-0076", "SPR-0189" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q133500", "legal_classification": "analytical_category" }, { "id": "DAT-0066", "domain": "DAT", "term_en": "Outlier Purging Regret", "term_de": "Hyperparameter-Optimierung", "definition_en": "The recognition that automatically removed data points later proved relevant to understanding actual system behavior or revealed critical information about data generation conditions. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Datenverarbeitungsphänomen in KI-gestützten Informationssystemen, gekennzeichnet durch kerndisziplin in Data Science and Analytics mit Fokus auf Hyperparameter-Optimierung. KI transformiert die Praxis durch automatisierte Workflows, praediktive Analytik und intelligentes Tooling zur Erweiterung menschlicher Expertise. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-3101", "narrower_terms": [], "cross_domain_refs": [ "SAL-0067" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "DAT-0067", "domain": "DAT", "term_en": "Overfitting Confidence", "term_de": "Rastersuche", "definition_en": "A data quality pattern in AI-augmented analytics pipelines, measurable through the state where a data practitioner expresses high certainty about model performance on unseen data following automatic feature selection by an AI system, despite the model's complexity exceeding the information content of the training set. The concept emerges specifically in contexts where overfitting–confidence interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Datenverarbeitungsphänomen in KI-gestützten Informationssystemen, gekennzeichnet durch technisches Konzept in Data Science and Analytics mit Prinzipien, Werkzeugen und Methoden von Rastersuche. KI verbessert Rastersuche durch automatisierte Analyse, intelligente Optimierung und datengestuetzte Entscheidungsunterstuetzung fuer Fachleute. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-3754", "narrower_terms": [], "cross_domain_refs": [ "SWE-0057" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "DAT-0068", "domain": "DAT", "term_en": "P-Value Misinterpretation Persistence", "term_de": "Zufallssuche", "definition_en": "A data quality pattern in AI-augmented analytics pipelines, measurable through a data-driven decision pattern reflecting the interpretation of p-values as probabilities of hypotheses rather than the probability of observed data under null assumptions. The concept emerges specifically in contexts where p–value interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Datenverarbeitungsphänomen in KI-gestützten Informationssystemen, gekennzeichnet durch kerndisziplin in Data Science and Analytics mit Fokus auf Zufallssuche. KI transformiert die Praxis durch automatisierte Workflows, praediktive Analytik und intelligentes Tooling zur Erweiterung menschlicher Expertise. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Data AI", "narrower_terms": [], "cross_domain_refs": [ "FIC-0037", "TEW-0048", "GAM-0011" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "DAT-0069", "domain": "DAT", "term_en": "Performance-Value Gap", "term_de": "Bayessche Optimierung", "definition_en": "A data architecture concept in AI-driven information management, identifiable by an analytical reasoning effect involving the divergence between a model's quantitative performance metrics and the actual business value generated, often obsresolved by the statistical focus of automated evaluation systems. Distinguished from adjacent concepts by its focus on the specific mechanism through which performance manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Datenverarbeitungsphänomen in KI-gestützten Informationssystemen, gekennzeichnet durch systematische Verbesserung von Prozessen und Ergebnissen in Datenwissenschaft. KI wendet mathematische Optimierung, evolutionäre Algorithmen und Reinforcement Learning zur Findung optimaler Konfigurationen an. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Performance Gap", "narrower_terms": [], "cross_domain_refs": [ "AED-0019", "AED-0077", "AED-0092" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q1361053", "legal_classification": "systematic_classification" }, { "id": "DAT-0070", "domain": "DAT", "term_en": "Pipeline Automation Bottleneck", "term_de": "AutoML", "definition_en": "A data analysis phenomenon characterized by the emergence of complete reliance on automated pipeline systems such that manual workflow steps become conceptually unavailable when automation fails.", "definition_de": "Datenverarbeitungsphänomen in KI-gestützten Informationssystemen, gekennzeichnet durch technisches Konzept in Data Science and Analytics mit Prinzipien, Werkzeugen und Methoden von AutoML. KI verbessert AutoML durch automatisierte Analyse, intelligente Optimierung und datengestuetzte Entscheidungsunterstuetzung fuer Fachleute. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2355", "narrower_terms": [], "cross_domain_refs": [ "COG-0049" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q184199", "legal_classification": "systematic_classification" }, { "id": "DAT-0071", "domain": "DAT", "term_en": "Policy Learning Overgeneralization", "term_de": "Python für Datenwissenschaft", "definition_en": "A data architecture concept in AI-driven information management, identifiable by a data-driven decision pattern in which the derivation of general policy recommendations from estimated optimal policies trained on historical data with different environmental conditions. Distinguished from adjacent concepts by its focus on the specific mechanism through which policy manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Datenverarbeitungsphänomen in KI-gestützten Informationssystemen, gekennzeichnet durch kerndisziplin in Data Science and Analytics mit Fokus auf Python für Datenwissenschaft. KI transformiert die Praxis durch automatisierte Workflows, praediktive Analytik und intelligentes Tooling zur Erweiterung menschlicher Expertise. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Machine Learning", "narrower_terms": [], "cross_domain_refs": [ "STE-0032" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q133500", "legal_classification": "descriptive_research_term" }, { "id": "DAT-0072", "domain": "DAT", "term_en": "Precision-Recall Tradeoff Opacity", "term_de": "R-Programmierung", "definition_en": "A data-driven decision pattern where the selection of performance thresholds without explicit articulation of the relative costs associated with false positives versus false negatives. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Datenverarbeitungsphänomen in KI-gestützten Informationssystemen, gekennzeichnet durch kerndisziplin in Data Science and Analytics mit Fokus auf R-Programmierung. KI transformiert die Praxis durch automatisierte Workflows, praediktive Analytik und intelligentes Tooling zur Erweiterung menschlicher Expertise. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-1307", "narrower_terms": [], "cross_domain_refs": [ "WEB-0090" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "DAT-0073", "domain": "DAT", "term_en": "Preprocessing Trust Narrowing", "term_de": "Pandas-Bibliothek", "definition_en": "A data quality pattern in AI-augmented analytics pipelines, measurable through a data-driven decision pattern where the moment when a data professional realizes their confidence in algorithmic data cleaning exceeded validation of the cleaning outcomes against domain knowledge. The concept emerges specifically in contexts where preprocessing–trust interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Datenverarbeitungsphänomen in KI-gestützten Informationssystemen, gekennzeichnet durch kerndisziplin in Data Science and Analytics mit Fokus auf Pandas-Bibliothek. KI transformiert die Praxis durch automatisierte Workflows, praediktive Analytik und intelligentes Tooling zur Erweiterung menschlicher Expertise. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-3754", "narrower_terms": [], "cross_domain_refs": [ "SAL-0061" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q102458", "legal_classification": "analytical_category" }, { "id": "DAT-0074", "domain": "DAT", "term_en": "Probabilistic Calibration Ignorance", "term_de": "NumPy-Bibliothek", "definition_en": "A data architecture concept in AI-driven information management, identifiable by a data-driven decision pattern arising from the interpretation of probability estimates as accurate likelihoods without testing the alignment between predicted probabilities and observed frequencies. Distinguished from adjacent concepts by its focus on the specific mechanism through which probabilistic manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Datenverarbeitungsphänomen in KI-gestützten Informationssystemen, gekennzeichnet durch kerndisziplin in Data Science and Analytics mit Fokus auf NumPy-Bibliothek. KI transformiert die Praxis durch automatisierte Workflows, praediktive Analytik und intelligentes Tooling zur Erweiterung menschlicher Expertise. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Analytical Ratio", "narrower_terms": [], "cross_domain_refs": [ "ASE-0016", "ASE-0025", "ASE-0030" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "DAT-0075", "domain": "DAT", "term_en": "Propensity Score Mythology", "term_de": "Scikit-Learn", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A data processing phenomenon in AI-mediated information systems, characterized by an analytical reasoning effect manifesting as the handling of estimated propensity scores as definitive measures of selection probability without validation of their predictive accuracy. This phenomenon operates at the intersection of propensity and score dynamics within the broader DAT domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept datenverarbeitungsphänomen in KI-gestützten Informationssystemen, gekennzeichnet durch technisches Konzept in Data Science and Analytics mit Prinzipien, Werkzeugen und Methoden von Scikit-Learn. KI verbessert Scikit-Learn durch automatisierte Analyse, intelligente Optimierung und datengestuetzte Entscheidungsunterstuetzung fuer Fachleute. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Quality Metric", "narrower_terms": [], "cross_domain_refs": [ "ASE-0049", "ASE-0069", "ASE-0086" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "DAT-0076", "domain": "DAT", "term_en": "ROC Curve Theater", "term_de": "TensorFlow", "definition_en": "A data architecture concept in AI-driven information management, identifiable by the presentation of AUC scores or ROC curves as model quality indicators without considering their limited applicability to imbalanced classification problems. Distinguished from adjacent concepts by its focus on the specific mechanism through which roc manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Datenverarbeitungsphänomen in KI-gestützten Informationssystemen, gekennzeichnet durch technisches Konzept in Data Science and Analytics mit Prinzipien, Werkzeugen und Methoden von TensorFlow. KI verbessert TensorFlow durch automatisierte Analyse, intelligente Optimierung und datengestuetzte Entscheidungsunterstuetzung fuer Fachleute. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Data AI", "narrower_terms": [], "cross_domain_refs": [ "AGE-0086", "AGE-0093", "CRE-0093" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "DAT-0077", "domain": "DAT", "term_en": "Reversion Coefficient Determinism", "term_de": "PyTorch", "definition_en": "A data quality pattern in AI-augmented analytics pipelines, measurable through a data analysis phenomenon in which the interpretation of point estimates of reversion coefficients as definitive causal effects without uncertainty quantification or assumption checking. The concept emerges specifically in contexts where reversion–coefficient interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Datenverarbeitungsphänomen in KI-gestützten Informationssystemen, gekennzeichnet durch technisches Konzept in Data Science and Analytics mit Prinzipien, Werkzeugen und Methoden von PyTorch. KI verbessert PyTorch durch automatisierte Analyse, intelligente Optimierung und datengestuetzte Entscheidungsunterstuetzung fuer Fachleute. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-3101", "narrower_terms": [], "cross_domain_refs": [ "FIC-0012" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "DAT-0078", "domain": "DAT", "term_en": "Reversion Discontinuity Assumption Slippage", "term_de": "Keras", "definition_en": "A data-driven decision pattern where the application of reversion discontinuity designs without verification that discontinuities are sharp or that the running variable has not been altered. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Datenverarbeitungsphänomen in KI-gestützten Informationssystemen, gekennzeichnet durch technisches Konzept in Data Science and Analytics mit Prinzipien, Werkzeugen und Methoden von Keras. KI verbessert Keras durch automatisierte Analyse, intelligente Optimierung und datengestuetzte Entscheidungsunterstuetzung fuer Fachleute. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-3003", "narrower_terms": [], "cross_domain_refs": [ "DES-0025" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "DAT-0079", "domain": "DAT", "term_en": "Regularization Vagueness", "term_de": "XGBoost", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A data processing phenomenon in AI-mediated information systems, characterized by the application of regularization techniques whose strength and rationale remain unexpressed, leading to model behavior that cannot be explained or replicated. This phenomenon operates at the intersection of regularization and vagueness dynamics within the broader DAT domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept datenverarbeitungsphänomen in KI-gestützten Informationssystemen, gekennzeichnet durch technisches Konzept in Data Science and Analytics mit Prinzipien, Werkzeugen und Methoden von XGBoost. KI verbessert XGBoost durch automatisierte Analyse, intelligente Optimierung und datengestuetzte Entscheidungsunterstuetzung fuer Fachleute. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "RPH-3501", "narrower_terms": [], "cross_domain_refs": [ "COP-0054", "SCR-0008", "SCR-0087" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "DAT-0080", "domain": "DAT", "term_en": "Regulatory Arbitrage Effect", "term_de": "LightGBM", "definition_en": "A data quality pattern in AI-augmented analytics pipelines, measurable through an analytical reasoning effect observed when the deployment of models in jurisdictions or contexts where regulatory oversight remains nascent or inconsistently applied. The concept emerges specifically in contexts where regulatory–arbitrage interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Datenverarbeitungsphänomen in KI-gestützten Informationssystemen, gekennzeichnet durch technisches Konzept in Data Science and Analytics mit Prinzipien, Werkzeugen und Methoden von LightGBM. KI verbessert LightGBM durch automatisierte Analyse, intelligente Optimierung und datengestuetzte Entscheidungsunterstuetzung fuer Fachleute. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Psychological Effect", "narrower_terms": [], "cross_domain_refs": [ "COP-0004", "BEH-0091" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "DAT-0081", "domain": "DAT", "term_en": "Replication Transition Unawareness", "term_de": "Datenvisualisierung", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures A data processing phenomenon in AI-mediated information systems, characterized by an analytical reasoning effect characterized by the presentation of findings from single model runs or data splits as generalizable results without acknowledging statistical variability across different random seeds or data partitions. This phenomenon operates at the intersection of replication and transition dynamics within the broader DAT domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept datenverarbeitungsphänomen in KI-gestützten Informationssystemen, gekennzeichnet durch visuelle Darstellung komplexer Informationen und Datensätze in Datenwissenschaft. KI automatisiert Diagrammauswahl, Anomalie-Hervorhebung, interaktive Exploration und Narrativgenerierung aus Datenmustern. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Data AI", "narrower_terms": [], "cross_domain_refs": [ "STE-0004" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "DAT-0082", "domain": "DAT", "term_en": "Representation Gap Invisibility", "term_de": "Matplotlib", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A data processing phenomenon in AI-mediated information systems, characterized by an analytical reasoning effect where the oversight of subgroup performance disparities in model output despite aggregate fairness metrics indicating sufficient parity. This phenomenon operates at the intersection of representation and gap dynamics within the broader DAT domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept datenverarbeitungsphänomen in KI-gestützten Informationssystemen, gekennzeichnet durch technisches Konzept in Data Science and Analytics mit Prinzipien, Werkzeugen und Methoden von Matplotlib. KI verbessert Matplotlib durch automatisierte Analyse, intelligente Optimierung und datengestuetzte Entscheidungsunterstuetzung fuer Fachleute. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Performance Gap", "narrower_terms": [], "cross_domain_refs": [ "AED-0019", "AED-0092", "AGE-0002" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "DAT-0083", "domain": "DAT", "term_en": "Responsible AI Ambiguity", "term_de": "Seaborn", "definition_en": "An analytical reasoning effect characterized by the application of 'responsible AI' frameworks where the criteria for responsibility remain undefined or conflict across implementation contexts.", "definition_de": "Datenverarbeitungsphänomen in KI-gestützten Informationssystemen, gekennzeichnet durch technisches Konzept in Data Science and Analytics mit Prinzipien, Werkzeugen und Methoden von Seaborn. KI verbessert Seaborn durch automatisierte Analyse, intelligente Optimierung und datengestuetzte Entscheidungsunterstuetzung fuer Fachleute. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2303", "narrower_terms": [], "cross_domain_refs": [ "VIB-0025", "EDU-0086" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "DAT-0084", "domain": "DAT", "term_en": "Sampling Strategy Invisibility", "term_de": "Plotly", "definition_en": "A data architecture concept in AI-driven information management, identifiable by a data-driven decision pattern characterized by the use of algorithmic sampling approaches whose parameters and trade-offs remain opaque to practitioners implementing or interpreting their results. Distinguished from adjacent concepts by its focus on the specific mechanism through which sampling manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Datenverarbeitungsphänomen in KI-gestützten Informationssystemen, gekennzeichnet durch technisches Konzept in Data Science and Analytics mit Prinzipien, Werkzeugen und Methoden von Plotly. KI verbessert Plotly durch automatisierte Analyse, intelligente Optimierung und datengestuetzte Entscheidungsunterstuetzung fuer Fachleute. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Operational Strategy", "narrower_terms": [], "cross_domain_refs": [ "AED-0010", "AGE-0002", "AGE-0003" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q185451", "legal_classification": "empirical_phenomenon_label" }, { "id": "DAT-0085", "domain": "DAT", "term_en": "Selection Into Intervention Blindness", "term_de": "Tableau", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures A data processing phenomenon in AI-mediated information systems, characterized by a data analysis phenomenon manifesting as the comparison of outcomes between addressed and unaddressed groups without addressing non-random assignment mechanisms. This phenomenon operates at the intersection of selection and into dynamics within the broader DAT domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept datenverarbeitungsphänomen in KI-gestützten Informationssystemen, gekennzeichnet durch technisches Konzept in Data Science and Analytics mit Prinzipien, Werkzeugen und Methoden von Tableau. KI verbessert Tableau durch automatisierte Analyse, intelligente Optimierung und datengestuetzte Entscheidungsunterstuetzung fuer Fachleute. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Data AI", "narrower_terms": [], "cross_domain_refs": [ "ASE-0091" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "DAT-0086", "domain": "DAT", "term_en": "Serial Correlation Oversight", "term_de": "Dashboard-Design", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures A data processing phenomenon in AI-mediated information systems, characterized by a data-driven decision pattern characterized by the analysis of time series data with methods assuming inreliance of observations despite the presence of temporal autocorrelation. This phenomenon operates at the intersection of serial and correlation dynamics within the broader DAT domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept datenverarbeitungsphänomen in KI-gestützten Informationssystemen, gekennzeichnet durch kerndisziplin in Data Science and Analytics mit Fokus auf Dashboard-Design. KI transformiert die Praxis durch automatisierte Workflows, praediktive Analytik und intelligentes Tooling zur Erweiterung menschlicher Expertise. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Data AI", "narrower_terms": [], "cross_domain_refs": [ "SPA-0040" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "DAT-0087", "domain": "DAT", "term_en": "Stationarity Assumption Invisibility", "term_de": "Interaktive Visualisierung", "definition_en": "A data quality pattern in AI-augmented analytics pipelines, measurable through the application of time series models to non-stationary data without change pattern or acknowledgment of violating fundamental method assumptions. The concept emerges specifically in contexts where stationarity–assumption interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Datenverarbeitungsphänomen in KI-gestützten Informationssystemen, gekennzeichnet durch kerndisziplin in Data Science and Analytics mit Fokus auf Interaktive Visualisierung. KI transformiert die Praxis durch automatisierte Workflows, praediktive Analytik und intelligentes Tooling zur Erweiterung menschlicher Expertise. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Data AI", "narrower_terms": [], "cross_domain_refs": [ "AGE-0002", "AGE-0003", "AGE-0039" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "DAT-0088", "domain": "DAT", "term_en": "Statistical Intuition Reduction", "term_de": "Daten-Storytelling", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A data processing phenomenon in AI-mediated information systems, characterized by an analytical reasoning effect reflecting the observed decline in a data professional's ability to recognize patterns, distributions, or anomalies inreliantly of algorithmic assistance, following extended use of automated analysis systems. This phenomenon operates at the intersection of statistical and intuition dynamics within the broader DAT domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept datenverarbeitungsphänomen in KI-gestützten Informationssystemen, gekennzeichnet durch kerndisziplin in Data Science and Analytics mit Fokus auf Daten-Storytelling. KI transformiert die Praxis durch automatisierte Workflows, praediktive Analytik und intelligentes Tooling zur Erweiterung menschlicher Expertise. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Data AI", "narrower_terms": [], "cross_domain_refs": [ "SWE-0065" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "DAT-0089", "domain": "DAT", "term_en": "Statistical Significance Drift", "term_de": "Diagrammauswahl", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures A data processing phenomenon in AI-mediated information systems, characterized by the interpretation of p-values or confidence intervals in high-dimensionality contexts where multiple comparisons and selection effects inflate false discovery rates. This phenomenon operates at the intersection of statistical and significance dynamics within the broader DAT domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept datenverarbeitungsphänomen in KI-gestützten Informationssystemen, gekennzeichnet durch kerndisziplin in Data Science and Analytics mit Fokus auf Diagrammauswahl. KI transformiert die Praxis durch automatisierte Workflows, praediktive Analytik und intelligentes Tooling zur Erweiterung menschlicher Expertise. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Systemic Drift", "narrower_terms": [], "cross_domain_refs": [ "RPH-1205", "COP-0033" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "DAT-0090", "domain": "DAT", "term_en": "Survivorship Bias Invisibility", "term_de": "Farbtheorie für Daten", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures A data processing phenomenon in AI-mediated information systems, characterized by a data analysis phenomenon observed when the analysis of data from entities that persist or succeed, while excluding entities that fail or disappear, producing biased statistical conclusions. This phenomenon operates at the intersection of survivorship and bias dynamics within the broader DAT domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept datenverarbeitungsphänomen in KI-gestützten Informationssystemen, gekennzeichnet durch ein technisches Konzept: charakterisiert durch the analysis of data from entities that persist or succeed, while excluding enti. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Cognitive Bias", "narrower_terms": [], "cross_domain_refs": [ "GAM-0011" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q213523", "legal_classification": "systematic_classification" }, { "id": "DAT-0091", "domain": "DAT", "term_en": "Synthetic Data Fidelity Overestimation", "term_de": "Big-Data-Verarbeitung", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A data processing phenomenon in AI-mediated information systems, characterized by a data-driven decision pattern reflecting the assumption that synthetic data generated through simulation or neural networks possesses distributional characteristics matching real data. This phenomenon operates at the intersection of synthetic and data dynamics within the broader DAT domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept datenverarbeitungsphänomen in KI-gestützten Informationssystemen, gekennzeichnet durch beobachtung innerhalb kreativer oder technischer Praxis: charakterisiert durch the assumption that synthetic data generated through simulation or neural networ. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Data AI", "narrower_terms": [], "cross_domain_refs": [ "MTH-0029", "PER-0040" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q42848", "legal_classification": "analytical_category" }, { "id": "DAT-0092", "domain": "DAT", "term_en": "Temporal Validation Perception", "term_de": "Apache Spark", "definition_en": "A data architecture concept in AI-driven information management, identifiable by a data-driven decision pattern involving the false assurance derived from out-of-sample testing where temporal ordering of events is not preserved, allowing information leakage from future time periods into past training phases. Distinguished from adjacent concepts by its focus on the specific mechanism through which temporal manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Datenverarbeitungsphänomen in KI-gestützten Informationssystemen, gekennzeichnet durch technisches Konzept in Data Science and Analytics mit Prinzipien, Werkzeugen und Methoden von Apache Spark. KI verbessert Apache Spark durch automatisierte Analyse, intelligente Optimierung und datengestuetzte Entscheidungsunterstuetzung fuer Fachleute. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Validation Process", "narrower_terms": [], "cross_domain_refs": [ "VIB-0033" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q177601", "legal_classification": "empirical_phenomenon_label" }, { "id": "DAT-0093", "domain": "DAT", "term_en": "Threshold Optimization Myopia", "term_de": "Hadoop-Ökosystem", "definition_en": "An analytical reasoning effect arising from the adjustment of decision thresholds to optimize training data performance without validation across different test populations or conditions. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Datenverarbeitungsphänomen in KI-gestützten Informationssystemen, gekennzeichnet durch beobachtung innerhalb kreativer oder technischer Praxis: charakterisiert durch the adjustment of decision thresholds to optimize training data performance with. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-1307", "narrower_terms": [], "cross_domain_refs": [ "ASE-0068" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "DAT-0094", "domain": "DAT", "term_en": "Transfer Learning Overconfidence", "term_de": "MapReduce", "definition_en": "The expectation that models trained on related problems will perform well on new domains without empirical assessment of domain shift magnitude. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Datenverarbeitungsphänomen in KI-gestützten Informationssystemen, gekennzeichnet durch technisches Konzept in Data Science and Analytics mit Prinzipien, Werkzeugen und Methoden von MapReduce. KI verbessert MapReduce durch automatisierte Analyse, intelligente Optimierung und datengestuetzte Entscheidungsunterstuetzung fuer Fachleute. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2701", "narrower_terms": [], "cross_domain_refs": [ "ASE-0089" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q7833042", "legal_classification": "descriptive_research_term" }, { "id": "DAT-0095", "domain": "DAT", "term_en": "Transparency Maximization Paradox", "term_de": "Verteiltes Rechnen", "definition_en": "The expansion of model documentation and disclosure until the information volume exceeds the cognitive capacity of its intended audience. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Datenverarbeitungsphänomen in KI-gestützten Informationssystemen, gekennzeichnet durch kerndisziplin in Data Science and Analytics mit Fokus auf Verteiltes Rechnen. KI transformiert die Praxis durch automatisierte Workflows, praediktive Analytik und intelligentes Tooling zur Erweiterung menschlicher Expertise. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-3754", "narrower_terms": [], "cross_domain_refs": [ "SAL-0098", "SPR-0112" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q535399", "legal_classification": "analytical_category" }, { "id": "DAT-0096", "domain": "DAT", "term_en": "Validation Set Leakage Blindness", "term_de": "SQL für Analytik", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A data processing phenomenon in AI-mediated information systems, characterized by a data-driven decision pattern manifesting as the failure to detect information transfer from training data into validation processes through automated pipeline construction, resulting in optimistic performance estimates. This phenomenon operates at the intersection of validation and set dynamics within the broader DAT domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept datenverarbeitungsphänomen in KI-gestützten Informationssystemen, gekennzeichnet durch kerndisziplin in Data Science and Analytics mit Fokus auf SQL für Analytik. KI transformiert die Praxis durch automatisierte Workflows, praediktive Analytik und intelligentes Tooling zur Erweiterung menschlicher Expertise. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Validation Process", "narrower_terms": [], "cross_domain_refs": [ "DES-0017", "SWE-0027", "STE-0045" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "DAT-0097", "domain": "DAT", "term_en": "Visualization Bias Acceptance", "term_de": "Data Warehouse", "definition_en": "A data quality pattern in AI-augmented analytics pipelines, measurable through a characteristic dynamic where visual presentations of data relationships chosen by algorithms become accepted as representative without interrogating the alternative visualizations not displayed. The concept emerges specifically in contexts where visualization–bias interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Datenverarbeitungsphänomen in KI-gestützten Informationssystemen, gekennzeichnet durch kerndisziplin in Data Science and Analytics mit Fokus auf Data Warehouse. KI transformiert die Praxis durch automatisierte Workflows, praediktive Analytik und intelligentes Tooling zur Erweiterung menschlicher Expertise. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Cognitive Bias", "narrower_terms": [], "cross_domain_refs": [ "ART-0060", "ART-0093", "ASE-0006" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q213523", "legal_classification": "observational_construct" }, { "id": "DAT-0098", "domain": "DAT", "term_en": "Weak Supervision Label Noise Blindness", "term_de": "ETL-Prozess", "definition_en": "A data quality pattern in AI-augmented analytics pipelines, measurable through the incorporation of weak labels from automated or crowdsourced sources without explicit modeling of label quality or noise rates. The concept emerges specifically in contexts where weak–supervision interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Datenverarbeitungsphänomen in KI-gestützten Informationssystemen, gekennzeichnet durch ein technisches Konzept: charakterisiert durch the incorporation of weak labels from automated or crowdsourced sources without. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Supervised Learning", "narrower_terms": [], "cross_domain_refs": [ "VIB-0127" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "DES-0001", "domain": "DES", "term_en": "Accent Multiplication", "term_de": "AccentMultiplication", "definition_en": "A creative design pattern where the introduction of too many accent colors as AI suggests multiple complementary shades that collectively overwhelm the palette. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch emergente Dynamik, die sich in accent multiplication manifestiert. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2053", "narrower_terms": [ "DES-0014", "DES-0074", "DES-0077", "DES-0015", "DES-0060", "DES-0081", "DES-0076", "DES-0034", "DES-0016", "DES-0073", "DES-0061", "DES-0046", "DES-0056", "DES-0078", "DES-0092", "DES-0005", "DES-0072", "DES-0004", "DES-0070", "DES-0033", "DES-0079", "DES-0064", "DES-0030", "DES-0021", "DES-0086", "DES-0044", "DES-0085", "DES-0069", "DES-0007", "DES-0003", "DES-0026", "DES-0052", "DES-0024", "DES-0045", "DES-0055", "DES-0080", "DES-0068", "DES-0059", "DES-0025", "DES-0008", "DES-0035", "DES-0091", "DES-0065", "DES-0028", "DES-0002", "DES-0032", "DES-0050", "DES-0088", "DES-0013", "DES-0083", "DES-0054", "DES-0012", "DES-0006", "DES-0089", "DES-0087", "DES-0093", "DES-0043", "DES-0053", "DES-0062", "DES-0094", "DES-0031", "DES-0037", "DES-0023", "DES-0096", "DES-0075", "DES-0029", "DES-0067", "DES-0018" ], "cross_domain_refs": [ "PLY-0057" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "DES-0002", "domain": "DES", "term_en": "Aesthetic Confidence Paradox", "term_de": "AestheticConfidenceParadox", "definition_en": "A design methodology phenomenon in AI-mediated creative production, characterized by the simultaneous increase in design quantity and decrease in conviction about individual design choices. The concept emerges specifically in contexts where aesthetic–confidence interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch paradoxe Kompetenzspreizung: steigende Outputmenge bei sinkender Urteilssicherheit. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Logical Paradox", "narrower_terms": [], "cross_domain_refs": [ "RPH-1322" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "DES-0003", "domain": "DES", "term_en": "Archetype Gravity", "term_de": "ArchetypeGravity", "definition_en": "A design cognition pattern in AI-augmented visual communication, measurable through a design phenomenon where the pull toward archetypal visual tropes in AI-generated brand identities, reducing distinctiveness. Distinguished from adjacent concepts by its focus on the specific mechanism through which archetype manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch gravitation zu kanonisierten visuellen Mustern, die KI-Originalität begrenzt. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Design AI", "narrower_terms": [], "cross_domain_refs": [ "FIC-0019" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "DES-0004", "domain": "DES", "term_en": "Attribution Ambiguity", "term_de": "AttributionAmbiguity", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures A design cognition pattern in AI-augmented visual communication, measurable through the uncertainty about how to credit authorship when both human and AI contribute substantially to a design. This phenomenon operates at the intersection of attribution and ambiguity dynamics within the broader DES domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept erfahrungsphänomen, das sich in attribution ambiguity manifestiert. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Design AI", "narrower_terms": [], "cross_domain_refs": [ "MUS-0010" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "DES-0005", "domain": "DES", "term_en": "Authorial Presence", "term_de": "AuthorialPresence", "definition_en": "A design methodology phenomenon in AI-mediated creative production, characterized by the felt presence or absence of human intentionality in a design created largely through AI iteration. The concept emerges specifically in contexts where authorial–presence interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch beobachtbares Muster der KI-Nutzung, das sich in authorial presence manifestiert. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Design AI", "narrower_terms": [], "cross_domain_refs": [ "RPH-2751", "RPH-3202" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "DES-0006", "domain": "DES", "term_en": "Balance Arbitration", "term_de": "BalanceArbitration", "definition_en": "A design cognition pattern in AI-augmented visual communication, measurable through an aesthetic interaction effect involving the moment when a designer can decide whether AI-suggested asymmetrical balance actually works or needs correction. The concept emerges specifically in contexts where balance–arbitration interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch unausgeglichenheit in Kompositionsprinzipien durch algorithmische Standardisierung. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Analytical Ratio", "narrower_terms": [], "cross_domain_refs": [ "ART-0004" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "DES-0007", "domain": "DES", "term_en": "Batch Evaluation Hesitation", "term_de": "BatchEvaluationHesitation", "definition_en": "A design cognition pattern in AI-augmented visual communication, measurable through a creative design pattern where the decision freeze when presented with 10+ AI-generated variations simultaneously. Distinguished from adjacent concepts by its focus on the specific mechanism through which batch manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch nutzungsphänomen, das sich in batch evaluation hesitation manifestiert. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Design AI", "narrower_terms": [], "cross_domain_refs": [ "CRE-0031" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "DES-0008", "domain": "DES", "term_en": "Beauty Consensus Bias", "term_de": "BeautyConsensusBias", "definition_en": "A design cognition pattern in AI-augmented visual communication, measurable through the tendency to favor designs that align with algorithmically-determined aesthetic consensus rather than distinctive choices. The concept emerges specifically in contexts where beauty–consensus interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch ästhetische Irritation durch KI-Vorschläge, die Sicherheit über Mut priorisieren. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Cognitive Bias", "narrower_terms": [], "cross_domain_refs": [ "COP-0044" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q213523", "legal_classification": "systematic_classification" }, { "id": "DES-0009", "domain": "DES", "term_en": "Brand Drift", "term_de": "BrandDrift", "definition_en": "A design methodology phenomenon in AI-mediated creative production, characterized by when iterative AI color adjustments gradually shift a brand color away from its established reference point. The concept emerges specifically in contexts where brand–drift interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch designphänomen in brand drift, das KI-menschliche Interaktion beeinflusst. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2701", "narrower_terms": [], "cross_domain_refs": [ "PHO-0020" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q431289", "legal_classification": "systematic_classification" }, { "id": "DES-0010", "domain": "DES", "term_en": "Brand Persona Drift", "term_de": "BrandPersonaDrift", "definition_en": "When the visual identity's perceived personality shifts across iterations as AI introduces elements that subtly change brand associations. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch designphänomen in brand persona drift, das KI-menschliche Interaktion beeinflusst. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2701", "narrower_terms": [], "cross_domain_refs": [ "MUS-0041" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q431289", "legal_classification": "systematic_classification" }, { "id": "DES-0011", "domain": "DES", "term_en": "Case Assumption", "term_de": "CaseAssumption", "definition_en": "An aesthetic interaction effect characterized by aI's default preference for certain capitalization patterns without regard for context or brand guidelines.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch kI-bezogene Verhaltenstendenz, die sich in case assumption manifestiert. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-3902", "narrower_terms": [], "cross_domain_refs": [ "RPH-1101" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "DES-0012", "domain": "DES", "term_en": "Center Magnetism", "term_de": "CenterMagnetism", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures A design cognition pattern in AI-augmented visual communication, measurable through an aesthetic interaction effect arising from the gravitational pull of AI-generated elements toward the center of the composition, avoiding the periphery. This phenomenon operates at the intersection of center and magnetism dynamics within the broader DES domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept rekurrenter Effekt, der sich in center magnetism manifestiert. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Design AI", "narrower_terms": [], "cross_domain_refs": [ "RPH-2855", "RPH-2302" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "DES-0013", "domain": "DES", "term_en": "Comparison Blindness", "term_de": "ComparisonBlindness", "definition_en": "A design methodology phenomenon in AI-mediated creative production, characterized by an aesthetic interaction effect reflecting the difficulty in objectively assessing differences between subtle AI variations when viewed in isolation. The concept emerges specifically in contexts where comparison–blindness interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch nutzungsphänomen, das sich in comparison blindness manifestiert. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Design AI", "narrower_terms": [], "cross_domain_refs": [ "ASE-0074", "COG-0105", "CON-0013" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "DES-0014", "domain": "DES", "term_en": "Composition Gravity", "term_de": "CompositionGravity", "definition_en": "A design cognition pattern in AI-augmented visual communication, measurable through the tendency for generated designs to cluster content toward particular areas of the canvas, reflecting patterns in training data rather than intentional composition. Distinguished from adjacent concepts by its focus on the specific mechanism through which composition manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch interaktionsmuster, das sich in composition gravity manifestiert. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Design AI", "narrower_terms": [], "cross_domain_refs": [ "MSC-0006", "PER-0012", "PHO-0026" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "DES-0015", "domain": "DES", "term_en": "Consensus Tyranny", "term_de": "ConsensusTyranny", "definition_en": "A design methodology phenomenon in AI-mediated creative production, characterized by a creative design pattern where the pressure to accept AI-suggested designs because they represent statistically characterized through systematic observation aesthetic choices. The concept emerges specifically in contexts where consensus–tyranny interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch ästhetische Irritation durch KI-Vorschläge, die Sicherheit über Mut priorisieren. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Design AI", "narrower_terms": [], "cross_domain_refs": [ "ART-0031", "AUG-0893", "BEH-0022" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "DES-0016", "domain": "DES", "term_en": "Contextual Blindness", "term_de": "ContextualBlindness", "definition_en": "A design methodology phenomenon in AI-mediated creative production, characterized by aI tools' lack of awareness about design context (audience, industry, strategic goals) that may inform suggestions. The concept emerges specifically in contexts where contextual–blindness interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch beobachtbares Muster der KI-Nutzung, das sich in contextual blindness manifestiert. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Design AI", "narrower_terms": [], "cross_domain_refs": [ "BEH-0043" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "DES-0017", "domain": "DES", "term_en": "Contrast Blindness", "term_de": "ContrastBlindness", "definition_en": "AI-generated color combinations that may technically meet accessibility guidelines but fail to involve visual distinction in context. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch beobachtbares Muster der KI-Nutzung, das sich in contrast blindness manifestiert. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-3902", "narrower_terms": [], "cross_domain_refs": [ "PHO-0020" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "DES-0018", "domain": "DES", "term_en": "Convention Acceleration", "term_de": "ConventionBeschleunigung", "definition_en": "A design cognition pattern in AI-augmented visual communication, measurable through the rapid establishment of new visual conventions as AI tools amplify popular design patterns. The concept emerges specifically in contexts where convention–acceleration interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch beobachtbares Muster der KI-Nutzung, das sich in convention acceleration manifestiert. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Analytical Ratio", "narrower_terms": [], "cross_domain_refs": [ "VIB-0081" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "DES-0019", "domain": "DES", "term_en": "Convergence Stalling", "term_de": "KonvergenzStalling", "definition_en": "A behavioral tendency where multiple iterations seem to plateau around similar solutions, offering limited novelty despite continued prompting. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch wahrnehmungseffekt, der sich in convergence stalling manifestiert. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2104", "narrower_terms": [], "cross_domain_refs": [ "RPH-2554" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "DES-0020", "domain": "DES", "term_en": "Creative Control Delegation", "term_de": "CreativeControlDelegation", "definition_en": "A design phenomenon involving the moment a designer realizes they've delegated aesthetic judgment to the AI rather than directing it. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch designphänomen in creative control delegation, das KI-menschliche Interaktion beeinflusst. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2101", "narrower_terms": [], "cross_domain_refs": [ "RPH-1008" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "DES-0021", "domain": "DES", "term_en": "Cultural Color Blindness", "term_de": "CulturalColorBlindness", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A design methodology phenomenon in AI-mediated creative production, characterized by aI's lack of contextual awareness regarding color meaning across cultures and industries. This phenomenon operates at the intersection of cultural and color dynamics within the broader DES domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept wahrnehmungseffekt, der sich in cultural color blindness manifestiert. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Design AI", "narrower_terms": [], "cross_domain_refs": [ "COG-0105", "CON-0013", "CON-0019" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "DES-0022", "domain": "DES", "term_en": "Depth Ambiguity", "term_de": "DepthAmbiguity", "definition_en": "An aesthetic interaction effect arising from the uncertainty about whether layering and overlapping in AI-generated designs accompanies genuine visual depth or only apparent overlap. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch beobachtbares Muster der KI-Nutzung, das sich in depth ambiguity manifestiert. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2005", "narrower_terms": [], "cross_domain_refs": [ "WEB-0014" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "DES-0023", "domain": "DES", "term_en": "Diagonal Hesitation", "term_de": "DiagonalHesitation", "definition_en": "A design methodology phenomenon in AI-mediated creative production, characterized by a design phenomenon manifesting as the reduced frequency of diagonal elements and diagonal composition in AI-generated designs compared to horizontal/vertical arrangements. Distinguished from adjacent concepts by its focus on the specific mechanism through which diagonal manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch kompetenzschwund durch Substitution manueller Prozesse mittels KI-Automation. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Design AI", "narrower_terms": [], "cross_domain_refs": [ "COG-0049", "DAT-0058", "RHR-0035" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "DES-0024", "domain": "DES", "term_en": "Direction Ambiguity", "term_de": "DirectionAmbiguity", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A design methodology phenomenon in AI-mediated creative production, characterized by an aesthetic interaction effect characterized by uncertainty about whether to accept an iteration because it's good or to keep iterating for something potentially more. This phenomenon operates at the intersection of direction and ambiguity dynamics within the broader DES domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept kI-bezogene Verhaltenstendenz, die sich in direction ambiguity manifestiert. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Design AI", "narrower_terms": [], "cross_domain_refs": [ "ART-0008", "ASE-0032", "ASE-0095" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "DES-0025", "domain": "DES", "term_en": "Distinction Uncertainty", "term_de": "DistinctionUncertainty", "definition_en": "A design methodology phenomenon in AI-mediated creative production, characterized by a design phenomenon in which the concern that AI-assisted designs will be indistinguishable from other AI-assisted designs, reducing personal signature. The concept emerges specifically in contexts where distinction–uncertainty interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch kompetenzschwund durch Substitution manueller Prozesse mittels KI-Automation. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Design AI", "narrower_terms": [], "cross_domain_refs": [ "AGE-0079" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "DES-0026", "domain": "DES", "term_en": "Distinctiveness Reversion", "term_de": "DistinctivenessReversion", "definition_en": "A design methodology phenomenon in AI-mediated creative production, characterized by a creative design pattern in which when iterations with AI suggestions gradually move the brand toward generic templates rather than distinctive identity. The concept emerges specifically in contexts where distinctiveness–reversion interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch anziehungskraft zu etablierten visuellen Mustern, die von KI verstärkt wird. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Design AI", "narrower_terms": [], "cross_domain_refs": [ "SAL-0028" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "DES-0027", "domain": "DES", "term_en": "Divergence Frustration", "term_de": "DivergenzFrustration", "definition_en": "A design methodology phenomenon in AI-mediated creative production, characterized by an aesthetic interaction effect reflecting when AI iterations move in incompatible directions, requiring starting over rather than refinement. The concept emerges specifically in contexts where divergence–frustration interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch designphänomen in divergence frustration, das KI-menschliche Interaktion beeinflusst. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2301", "narrower_terms": [], "cross_domain_refs": [ "VIB-0063" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "DES-0028", "domain": "DES", "term_en": "Edge Avoidance", "term_de": "EdgeAvoidance", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures A design cognition pattern in AI-augmented visual communication, measurable through aI-generated content's systematic tendency to stay away from canvas boundaries and edges. This phenomenon operates at the intersection of edge and avoidance dynamics within the broader DES domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept rekurrenter Effekt, der sich in edge avoidance manifestiert. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Design AI", "narrower_terms": [], "cross_domain_refs": [ "AGE-0011", "AUG-0408", "CON-0003" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "DES-0029", "domain": "DES", "term_en": "Evolution Imperceptibility", "term_de": "EvolutionImperceptibility", "definition_en": "A design cognition pattern in AI-augmented visual communication, measurable through brand evolution so gradual through AI iterations that change becomes imperceptible until comparison with earlier versions. The concept emerges specifically in contexts where evolution–imperceptibility interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch designphänomen in evolution imperceptibility, das KI-menschliche Interaktion beeinflusst. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Design AI", "narrower_terms": [], "cross_domain_refs": [ "MUS-0041" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "DES-0030", "domain": "DES", "term_en": "Feature Mismatch", "term_de": "FeatureMismatch", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures A design cognition pattern in AI-augmented visual communication, measurable through the frustration when a needed design capability is available only in a different AI tool or platform. This phenomenon operates at the intersection of feature and mismatch dynamics within the broader DES domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept nutzungsphänomen, das sich in feature mismatch manifestiert. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Design AI", "narrower_terms": [], "cross_domain_refs": [ "EDU-0074" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "DES-0031", "domain": "DES", "term_en": "Feedback Ambiguity", "term_de": "RückkopplungAmbiguity", "definition_en": "A design cognition pattern in AI-augmented visual communication, measurable through when vague design feedback (like 'more modern' or 'friendlier') accompanies inconsistent AI interpretations across iterations. The concept emerges specifically in contexts where feedback–ambiguity interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch auseinanderdriften zwischen Intention und KI-generierter Umsetzung. Die Divergenz wird durch KI-Iterationen verstärkt. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Design AI", "narrower_terms": [], "cross_domain_refs": [ "AED-0003", "AED-0072", "AED-0079" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "DES-0032", "domain": "DES", "term_en": "Feedback Loop Distortion", "term_de": "RückkopplungSchleifeDistortion", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures A design cognition pattern in AI-augmented visual communication, measurable through a communication breakdown pattern occurring when a designer's critique of an AI design accompanies unexpected or contradictory adjustments, requiring repeated clarification. This phenomenon operates at the intersection of feedback and loop dynamics within the broader DES domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept beobachtbares Muster der KI-Nutzung, das sich in feedback loop distortion manifestiert. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Feedback Loop", "narrower_terms": [], "cross_domain_refs": [ "AED-0003", "AED-0072", "AED-0079" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "DES-0033", "domain": "DES", "term_en": "Focal Point Ambiguity", "term_de": "FocalPointAmbiguity", "definition_en": "A design cognition pattern in AI-augmented visual communication, measurable through an aesthetic interaction effect reflecting when multiple elements receive similar emphasis, leaving the viewer uncertain where to focus their attention. Distinguished from adjacent concepts by its focus on the specific mechanism through which focal manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch kI-bezogene Verhaltenstendenz, die sich in focal point ambiguity manifestiert. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Design AI", "narrower_terms": [], "cross_domain_refs": [ "CON-0019" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "DES-0034", "domain": "DES", "term_en": "Font Pair Familiarity", "term_de": "FontPairFamiliarity", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures A design cognition pattern in AI-augmented visual communication, measurable through the tendency of AI font pairing suggestions to reflect well-known, frequently-used combinations from established design systems. This phenomenon operates at the intersection of font and pair dynamics within the broader DES domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept interaktionsmuster, das sich in font pair familiarity manifestiert. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Design AI", "narrower_terms": [], "cross_domain_refs": [ "AGE-0015", "AGE-0074", "FIC-0060" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "DES-0035", "domain": "DES", "term_en": "Generalization Limitation", "term_de": "GeneralizationLimitation", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A design methodology phenomenon in AI-mediated creative production, characterized by a technical constraint pattern in which AI tools trained on specific design categories struggle when asked to may produce output outside their training scope. This phenomenon operates at the intersection of generalization and limitation dynamics within the broader DES domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept wahrnehmungseffekt, der sich in generalization limitation manifestiert. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Design AI", "narrower_terms": [], "cross_domain_refs": [ "WEB-0027" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "DES-0036", "domain": "DES", "term_en": "Grid Instability", "term_de": "GridInstabilität", "definition_en": "The inconsistency that emerges when AI alternates between different grid systems or column counts across design iterations. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch designphänomen in grid instability, das KI-menschliche Interaktion beeinflusst. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-2355", "narrower_terms": [], "cross_domain_refs": [ "ASE-0096" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "DES-0037", "domain": "DES", "term_en": "Effectony Recommendation", "term_de": "EffectonyRecommendation", "definition_en": "A design methodology phenomenon in AI-mediated creative production, characterized by a design phenomenon reflecting aI's systematic preference for mathematically effectonious color relationships (complementary, triadic) over more discordant palettes. Distinguished from adjacent concepts by its focus on the specific mechanism through which effectony manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch auseinanderdriften zwischen Intention und KI-generierter Umsetzung. Die Divergenz wird durch KI-Iterationen verstärkt. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Psychological Effect", "narrower_terms": [], "cross_domain_refs": [ "PHO-0020" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "DES-0038", "domain": "DES", "term_en": "Hierarchy Narrowing", "term_de": "HierarchyNarrowing", "definition_en": "An aesthetic interaction effect manifesting as when visual hierarchy flattens as AI-suggested elements receive similar visual weight, obscuring the intended information structure. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch kI-bezogene Verhaltenstendenz, die sich in hierarchy narrowing manifestiert. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2201", "narrower_terms": [], "cross_domain_refs": [ "RET-0023" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "DES-0039", "domain": "DES", "term_en": "History Shift", "term_de": "HistoryShift", "definition_en": "A design cognition pattern in AI-augmented visual communication, measurable through the disorientation from not being able to trace how a design evolved through its iteration history. Distinguished from adjacent concepts by its focus on the specific mechanism through which history manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch nutzungsphänomen, das sich in history shift manifestiert. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2003", "narrower_terms": [], "cross_domain_refs": [ "ADA-0012", "AGE-0007", "AGE-0046" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "DES-0040", "domain": "DES", "term_en": "Icon Language Breakdown", "term_de": "IconLanguageBreakdown", "definition_en": "The shift of visual consistency when AI-generated icons don't follow consistent stroke weight, style, or geometric logic. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch designphänomen in icon language breakdown, das KI-menschliche Interaktion beeinflusst. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-2201", "narrower_terms": [], "cross_domain_refs": [ "VIB-0084" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q315", "legal_classification": "descriptive_research_term" }, { "id": "DES-0041", "domain": "DES", "term_en": "Incremental Acceptance", "term_de": "IncrementalAcceptance", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A design methodology phenomenon in AI-mediated creative production, characterized by the subtle lowering of quality standards as each iteration moves slightly closer to acceptable, with cumulative compromise. This phenomenon operates at the intersection of incremental and acceptance dynamics within the broader DES domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept designphänomen in incremental acceptance, das KI-menschliche Interaktion beeinflusst. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "RPH-2951", "narrower_terms": [], "cross_domain_refs": [ "ELR-0024" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "DES-0042", "domain": "DES", "term_en": "Influence Chain Opacity", "term_de": "InfluenceChainOpacity", "definition_en": "The inability to trace how training data, trends, and historical works influenced an AI-generated design. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch beobachtbares Muster der KI-Nutzung, das sich in influence chain opacity manifestiert. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2003", "narrower_terms": [], "cross_domain_refs": [ "ART-0087" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "DES-0043", "domain": "DES", "term_en": "Institutional Resistance", "term_de": "InstitutionalResistance", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A design methodology phenomenon in AI-mediated creative production, characterized by a creative design pattern characterized by the difficulty of implementing AI-suggested brand changes that employees and stakeholders don't recognize or accept. This phenomenon operates at the intersection of institutional and resistance dynamics within the broader DES domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept designphänomen in institutional resistance, das KI-menschliche Interaktion beeinflusst. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Design AI", "narrower_terms": [], "cross_domain_refs": [ "AGE-0015", "AGE-0035", "ASE-0023" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "DES-0044", "domain": "DES", "term_en": "Integration Friction", "term_de": "IntegrationFriction", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures A design cognition pattern in AI-augmented visual communication, measurable through the workflow interruption when AI design work can be exported, adjusted, and reintegrated into existing design systems. This phenomenon operates at the intersection of integration and friction dynamics within the broader DES domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept erfahrungsphänomen, das sich in integration friction manifestiert. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Analytical Ratio", "narrower_terms": [], "cross_domain_refs": [ "TEM-0057" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "DES-0045", "domain": "DES", "term_en": "Intention Obscurity", "term_de": "IntentionObscurity", "definition_en": "A design methodology phenomenon in AI-mediated creative production, characterized by the difficulty in explaining design choices that resulted from AI suggestions rather than deliberate human decisions. Distinguished from adjacent concepts by its focus on the specific mechanism through which intention manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch wahrnehmungseffekt, der sich in intention obscurity manifestiert. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Design AI", "narrower_terms": [], "cross_domain_refs": [ "CRE-0005" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "DES-0046", "domain": "DES", "term_en": "Interface Constraint", "term_de": "InterfaceConstraint", "definition_en": "A design methodology phenomenon in AI-mediated creative production, characterized by the way a tool's UI and interaction paradigm constrains possible designs or encourages particular solutions. Distinguished from adjacent concepts by its focus on the specific mechanism through which interface manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch kI-bezogene Verhaltenstendenz, die sich in interface constraint manifestiert. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "User Interface", "narrower_terms": [], "cross_domain_refs": [ "AGE-0010", "AGE-0013", "AGE-0014" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "DES-0047", "domain": "DES", "term_en": "Judgment Confidence Narrowing", "term_de": "UrteilConfidenceNarrowing", "definition_en": "The shift of confidence in one's own aesthetic judgment when constantly presented with multiple plausible alternatives. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch paradoxe Kompetenzspreizung: steigende Outputmenge bei sinkender Urteilssicherheit. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-2701", "narrower_terms": [], "cross_domain_refs": [ "RPH-3054", "CON-0002" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "DES-0048", "domain": "DES", "term_en": "Layout Drift", "term_de": "LayoutDrift", "definition_en": "The gradual departure from an intended layout as AI suggestions accumulate across iterations, each individually acceptable but collectively shifting the original design direction. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch designphänomen in layout drift, das KI-menschliche Interaktion beeinflusst. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2701", "narrower_terms": [], "cross_domain_refs": [ "MUS-0041", "VIB-0153" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "DES-0049", "domain": "DES", "term_en": "Learning Curve Inversion", "term_de": "LearningCurveInversion", "definition_en": "The expertise observed to effectively prompt and iterate with AI tools being different from traditional design skills. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch nutzungsphänomen, das sich in learning curve inversion manifestiert. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2104", "narrower_terms": [], "cross_domain_refs": [ "ART-0097", "STE-0046" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q133500", "legal_classification": "analytical_category" }, { "id": "DES-0050", "domain": "DES", "term_en": "Legacy Durability", "term_de": "LegacyDurability", "definition_en": "A design methodology phenomenon in AI-mediated creative production, characterized by the uncertainty about whether an AI-assisted design will feel dated when the AI and its training data evolve. The concept emerges specifically in contexts where legacy–durability interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters. Descriptive research term, not a prescriptive recommendation.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch interaktionsdynamik, die sich in legacy durability manifestiert. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Design AI", "narrower_terms": [], "cross_domain_refs": [ "SPR-0003" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "DES-0051", "domain": "DES", "term_en": "Letter Spacing Overdose", "term_de": "LetterSpacingOverdose", "definition_en": "Classified in AUGMANITAI terminology science as a descriptive phenomenon (without normative endorsement), this pattern denotes A design methodology phenomenon in AI-mediated creative production, characterized by aI-suggested tracking values that exceed functional benefit and involve perceptual distribution. Distinguished from adjacent concepts by its focus on the specific mechanism through which letter manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als analytisches Konstrukt der empirischen KI-Forschung (deskriptiv, nicht präskriptiv) erfasst dieser Terminus rekurrenter Effekt, der sich in letter spacing overdose manifestiert. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "RPH-3754", "narrower_terms": [], "cross_domain_refs": [ "MTH-0049", "PHO-0012" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "DES-0052", "domain": "DES", "term_en": "Line Height Compression", "term_de": "LineHeightCompression", "definition_en": "A design methodology phenomenon in AI-mediated creative production, characterized by aI tendency to set line heights that are functionally acceptable but feel visually cramped. The concept emerges specifically in contexts where line–height interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters. Observed phenomenon documented in human-AI interaction research.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch beobachtbares Muster der KI-Nutzung, das sich in line height compression manifestiert. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Data Compression", "narrower_terms": [], "cross_domain_refs": [ "CRE-0124" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "DES-0053", "domain": "DES", "term_en": "Lockup Instability", "term_de": "LockupInstabilität", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures A design cognition pattern in AI-augmented visual communication, measurable through an aesthetic interaction effect in which the inconsistency in how AI places logo and text together, varying spacing, rotation, and relative sizing. This phenomenon operates at the intersection of lockup and instability dynamics within the broader DES domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept designphänomen in lockup instability, das KI-menschliche Interaktion beeinflusst. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Design AI", "narrower_terms": [], "cross_domain_refs": [ "RPH-2854" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "DES-0054", "domain": "DES", "term_en": "Logo Morphing", "term_de": "LogoMorphing", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures A design cognition pattern in AI-augmented visual communication, measurable through the continuous change pattern of a logo across iterations as AI suggests variations that cumulatively diverge from the original intent. This phenomenon operates at the intersection of logo and morphing dynamics within the broader DES domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept designphänomen in logo morphing, das KI-menschliche Interaktion beeinflusst. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Design AI", "narrower_terms": [], "cross_domain_refs": [ "VIB-0016" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "DES-0055", "domain": "DES", "term_en": "Monochrome Escape", "term_de": "MonochromeEscape", "definition_en": "A design cognition pattern in AI-augmented visual communication, measurable through a creative design pattern where the difficulty in getting AI to yield satisfying single-hue or highly limited color palettes. Distinguished from adjacent concepts by its focus on the specific mechanism through which monochrome manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch nutzungsphänomen, das sich in monochrome escape manifestiert. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Design AI", "narrower_terms": [], "cross_domain_refs": [ "PHO-0024" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "DES-0056", "domain": "DES", "term_en": "Mood Instability", "term_de": "MoodInstabilität", "definition_en": "A design methodology phenomenon in AI-mediated creative production, characterized by when AI-generated design variations convey different emotional tones rather than supporting the unified brand feeling. Distinguished from adjacent concepts by its focus on the specific mechanism through which mood manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch nutzungsphänomen, das sich in mood instability manifestiert. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Design AI", "narrower_terms": [], "cross_domain_refs": [ "SCR-0022" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "DES-0057", "domain": "DES", "term_en": "Muscle Memory Disruption", "term_de": "MuscleMemoryDisruption", "definition_en": "The abandonment of habitual design techniques as the designer's workflow adapts to AI tool affordances. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch kI-bezogene Verhaltenstendenz, die sich in muscle memory disruption manifestiert. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2604", "narrower_terms": [], "cross_domain_refs": [ "ROB-0034" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q492", "legal_classification": "analytical_category" }, { "id": "DES-0058", "domain": "DES", "term_en": "Muted Preference", "term_de": "MutedPreference", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A design methodology phenomenon in AI-mediated creative production, characterized by a design phenomenon where the statistical skew in AI-generated colors toward desaturated, muted tones over bold, bright alternatives. This phenomenon operates at the intersection of muted and preference dynamics within the broader DES domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept interaktionsdynamik, die sich in muted preference manifestiert. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "RPH-2205", "narrower_terms": [], "cross_domain_refs": [ "PHO-0092" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "DES-0059", "domain": "DES", "term_en": "Novelty Fatigue", "term_de": "NoveltyFatigue", "definition_en": "A design methodology phenomenon in AI-mediated creative production, characterized by a creative design pattern observed when the exhaustion from constant exposure to AI-generated variations making it harder to distinguish genuinely novel designs. The concept emerges specifically in contexts where novelty–fatigue interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch nutzungsphänomen, das sich in novelty fatigue manifestiert. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Design AI", "narrower_terms": [], "cross_domain_refs": [ "COP-0014" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "DES-0060", "domain": "DES", "term_en": "Palette Convergence", "term_de": "PaletteKonvergenz", "definition_en": "A design cognition pattern in AI-augmented visual communication, measurable through the tendency of AI color suggestions to gravitate toward trending color combinations and established palettes seen in its training data. The concept emerges specifically in contexts where palette–convergence interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch wahrnehmungseffekt, der sich in palette convergence manifestiert. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Design AI", "narrower_terms": [], "cross_domain_refs": [ "PHO-0024" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "DES-0061", "domain": "DES", "term_en": "Parameter Overwhelm", "term_de": "ParameterOverwhelm", "definition_en": "A design methodology phenomenon in AI-mediated creative production, characterized by the disorientation from too many tweakable settings in an AI design tool, leading to random parameter exploration. Distinguished from adjacent concepts by its focus on the specific mechanism through which parameter manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch rekurrenter Effekt, der sich in parameter overwhelm manifestiert. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Design AI", "narrower_terms": [], "cross_domain_refs": [ "DAT-0027" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "DES-0062", "domain": "DES", "term_en": "Pattern Exhaustion", "term_de": "MusterExhaustion", "definition_en": "A design cognition pattern in AI-augmented visual communication, measurable through the difficulty in generating new pattern variations while maintaining visual cohesion and brand recognition. The concept emerges specifically in contexts where pattern–exhaustion interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch wahrnehmungseffekt, der sich in pattern exhaustion manifestiert. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Behavioral Pattern", "narrower_terms": [], "cross_domain_refs": [ "VIB-0188" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "DES-0063", "domain": "DES", "term_en": "Perfect Threshold", "term_de": "PerfectSchwelle", "definition_en": "The indefinable point at which a design is 'good enough' to deploy, uncertain whether further iteration would improve or differentn it. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch rekurrenter Effekt, der sich in perfect threshold manifestiert. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-3101", "narrower_terms": [], "cross_domain_refs": [ "BEH-0062" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "DES-0064", "domain": "DES", "term_en": "Personality Mismatch", "term_de": "PersonalityMismatch", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A design methodology phenomenon in AI-mediated creative production, characterized by a design phenomenon characterized by when the semantic personality of AI-suggested typefaces contradicts the intended brand voice or message tone. This phenomenon operates at the intersection of personality and mismatch dynamics within the broader DES domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept emergente Dynamik, die sich in personality mismatch manifestiert. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Design AI", "narrower_terms": [], "cross_domain_refs": [ "SCR-0048" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "DES-0065", "domain": "DES", "term_en": "Precision Shift", "term_de": "PrecisionShift", "definition_en": "A design cognition pattern in AI-augmented visual communication, measurable through a creative design pattern where the inability to make micro-adjustments or pixel-perfect refinements in AI-generated designs. Distinguished from adjacent concepts by its focus on the specific mechanism through which precision manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch interaktionsdynamik, die sich in precision shift manifestiert. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Design AI", "narrower_terms": [], "cross_domain_refs": [ "WEB-0012" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "DES-0066", "domain": "DES", "term_en": "Prompt-Visual Gap", "term_de": "Prompt-visualGap", "definition_en": "An aesthetic interaction effect observed when the mismatch between what a designer describes in a prompt and what the AI accompanies, requiring translation across mediums. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch wahrnehmungseffekt, der sich in prompt-visual gap manifestiert. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-2104", "narrower_terms": [], "cross_domain_refs": [ "AUG-0112" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "DES-0067", "domain": "DES", "term_en": "Proportional Echo", "term_de": "ProportionalEcho", "definition_en": "A design cognition pattern in AI-augmented visual communication, measurable through when golden ratio or common dimensional ratios appear repeatedly across multiple AI-generated design iterations, suggesting statistical preference. The concept emerges specifically in contexts where proportional–echo interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch verhaltenseffekt in der Mensch-KI-Zusammenarbeit, der sich in proportional echo manifestiert. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Design AI", "narrower_terms": [], "cross_domain_refs": [ "FIC-0019" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "DES-0068", "domain": "DES", "term_en": "Readability Sacrifice", "term_de": "ReadabilitySacrifice", "definition_en": "A design cognition pattern in AI-augmented visual communication, measurable through a design phenomenon arising from aI-suggested typography choices that prioritize aesthetic novelty over practical legibility and scanning ease. Distinguished from adjacent concepts by its focus on the specific mechanism through which readability manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch ästhetische Irritation durch KI-Vorschläge, die Sicherheit über Mut priorisieren. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Design AI", "narrower_terms": [], "cross_domain_refs": [ "GAM-0024", "MTH-0010", "RHR-0299" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "DES-0069", "domain": "DES", "term_en": "Refinement Perception", "term_de": "RefinementPerception", "definition_en": "A design methodology phenomenon in AI-mediated creative production, characterized by the appearance of progress where iterations change superficially without addressing underlying design problems. The concept emerges specifically in contexts where refinement–perception interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch designphänomen in refinement perception, das KI-menschliche Interaktion beeinflusst. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Design AI", "narrower_terms": [], "cross_domain_refs": [ "MUS-0041" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q177601", "legal_classification": "analytical_category" }, { "id": "DES-0070", "domain": "DES", "term_en": "Rendering Surprise", "term_de": "RenderingSurprise", "definition_en": "A design cognition pattern in AI-augmented visual communication, measurable through an aesthetic interaction effect involving the unexpected visual output when an AI tool's interpretation of parameters differs from designer expectations. Distinguished from adjacent concepts by its focus on the specific mechanism through which rendering manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch interaktionsdynamik, die sich in rendering surprise manifestiert. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Design AI", "narrower_terms": [], "cross_domain_refs": [ "AUG-0248", "BEH-0080", "GAM-0045" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "DES-0071", "domain": "DES", "term_en": "Revision Fatigue", "term_de": "RevisionFatigue", "definition_en": "An aesthetic interaction effect observed when the exhaustion that emerges when AI accompanies variations so plausibly acceptable that deciding among them becomes cognitively draining. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch verhaltenseffekt in der Mensch-KI-Zusammenarbeit, der sich in revision fatigue manifestiert. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-2355", "narrower_terms": [], "cross_domain_refs": [ "ASE-0038", "COG-0120", "CON-0037" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "DES-0072", "domain": "DES", "term_en": "Saturation Escalation", "term_de": "SaturationEscalation", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures A design cognition pattern in AI-augmented visual communication, measurable through a design phenomenon where the gradual increase in color saturation as AI-generated color adjustments accumulate, pushing muted tones toward vivid ones. This phenomenon operates at the intersection of saturation and escalation dynamics within the broader DES domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept beobachtbares Muster der KI-Nutzung, das sich in saturation escalation manifestiert. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Analytical Ratio", "narrower_terms": [], "cross_domain_refs": [ "PHO-0092" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "DES-0073", "domain": "DES", "term_en": "Scale Imbalance", "term_de": "ScaleImbalance", "definition_en": "A design methodology phenomenon in AI-mediated creative production, characterized by a design phenomenon manifesting as aI-generated type hierarchies that establish relationships between sizes that feel unintuitive or ineffective. Distinguished from adjacent concepts by its focus on the specific mechanism through which scale manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data. Classification term used in systematic observation, not advocacy.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch rekurrenter Effekt, der sich in scale imbalance manifestiert. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Design AI", "narrower_terms": [], "cross_domain_refs": [ "SCR-0059", "AED-0070" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "DES-0074", "domain": "DES", "term_en": "Script Overconfidence", "term_de": "ScriptOverconfidence", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures A design cognition pattern in AI-augmented visual communication, measurable through an aesthetic interaction effect arising from the inappropriate suggestion of decorative or script fonts in contexts where clarity and functionality are paramount. This phenomenon operates at the intersection of script and overconfidence dynamics within the broader DES domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept wahrnehmungseffekt, der sich in script overconfidence manifestiert. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Design AI", "narrower_terms": [], "cross_domain_refs": [ "WEB-0025" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "DES-0075", "domain": "DES", "term_en": "Secondary Element Chaos", "term_de": "SecondaryElementChaos", "definition_en": "A design methodology phenomenon in AI-mediated creative production, characterized by an aesthetic interaction effect involving the proliferation of supporting graphic elements (dividers, icons, decorations) without consistent visual logic or purpose. The concept emerges specifically in contexts where secondary–element interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch überversorgung mit Optionen, die Selektionsfähigkeit erschwert statt erleichtert. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Design AI", "narrower_terms": [], "cross_domain_refs": [ "MUS-0005", "PHO-0044", "SCR-0073" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "DES-0076", "domain": "DES", "term_en": "Skill Attribution Transition", "term_de": "SkillAttributionÜbergang", "definition_en": "A design cognition pattern in AI-augmented visual communication, measurable through the ambiguity about whether a polished final design demonstrates the designer's skill or the AI tool's capability. Distinguished from adjacent concepts by its focus on the specific mechanism through which skill manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch mehrdeutige oder interpretierbar unterschiedliche Ergebnisse aus KI-Systemen bei gleichen Eingaben. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Design AI", "narrower_terms": [], "cross_domain_refs": [ "ADA-0011", "AED-0009", "AED-0038" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "DES-0077", "domain": "DES", "term_en": "Skill Shift", "term_de": "SkillShift", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A design methodology phenomenon in AI-mediated creative production, characterized by an aesthetic interaction effect reflecting the gradual reduction in a designer's foundational skills (sketching, color theory, layout intuition) when relying on AI assistance. This phenomenon operates at the intersection of skill and shift dynamics within the broader DES domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept kompetenzschwund durch Substitution manueller Prozesse mittels KI-Automation. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Design AI", "narrower_terms": [], "cross_domain_refs": [ "PHO-0083", "COG-0075" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "DES-0078", "domain": "DES", "term_en": "Style Dilution", "term_de": "StyleDilution", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A design methodology phenomenon in AI-mediated creative production, characterized by a design phenomenon observed when the gradual softening or modification of a brand's distinctive visual signature through cumulative small AI-suggested adjustments. This phenomenon operates at the intersection of style and dilution dynamics within the broader DES domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept interaktionsdynamik, die sich in style dilution manifestiert. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Design AI", "narrower_terms": [], "cross_domain_refs": [ "ART-0055" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "DES-0079", "domain": "DES", "term_en": "Style Inheritance Question", "term_de": "StyleInheritanceQuestion", "definition_en": "A design methodology phenomenon in AI-mediated creative production, characterized by an unresolved conceptual tension regarding her a design using AI tools trained on existing work inherits its influences or represents original creation. The concept emerges specifically in contexts where style–inheritance interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch nutzungsphänomen, das sich in style inheritance question manifestiert. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Design AI", "narrower_terms": [], "cross_domain_refs": [ "MUS-0062" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "DES-0080", "domain": "DES", "term_en": "Symbolic Confusion", "term_de": "SymbolicConfusion", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A design methodology phenomenon in AI-mediated creative production, characterized by a creative design pattern characterized by when AI-suggested graphic elements carry unintended connotations or cultural associations. This phenomenon operates at the intersection of symbolic and confusion dynamics within the broader DES domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept interaktionsdynamik, die sich in symbolic confusion manifestiert. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Design AI", "narrower_terms": [], "cross_domain_refs": [ "COG-0048", "COG-0057", "ELR-0029" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "DES-0081", "domain": "DES", "term_en": "Symmetry Fixation", "term_de": "SymmetryFixation", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures A design cognition pattern in AI-augmented visual communication, measurable through a creative design pattern reflecting the persistent gravitational pull toward bilateral or radial symmetry in AI-generated layouts, regardless of intentional asymmetrical direction. This phenomenon operates at the intersection of symmetry and fixation dynamics within the broader DES domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept nutzungsphänomen, das sich in symmetry fixation manifestiert. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Design AI", "narrower_terms": [], "cross_domain_refs": [ "RPH-2855", "RPH-1402" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "DES-0082", "domain": "DES", "term_en": "Taste Recalibration", "term_de": "TasteRecalibration", "definition_en": "The shift in a designer's aesthetic preferences after extended exposure to AI-generated work, potentially narrowing taste. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch ästhetische Irritation durch KI-Vorschläge, die Sicherheit über Mut priorisieren. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-2701", "narrower_terms": [], "cross_domain_refs": [ "PHO-0006" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "DES-0083", "domain": "DES", "term_en": "Temperature Inconsistency", "term_de": "TemperatureInconsistency", "definition_en": "A design methodology phenomenon in AI-mediated creative production, characterized by when AI oscillates between warm and cool color palettes across design iterations without cohesive temperature logic. The concept emerges specifically in contexts where temperature–inconsistency interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch verhaltenseffekt in der Mensch-KI-Zusammenarbeit, der sich in temperature inconsistency manifestiert. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Design AI", "narrower_terms": [], "cross_domain_refs": [ "PHO-0024" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "DES-0084", "domain": "DES", "term_en": "Template Gravity", "term_de": "TemplateGravity", "definition_en": "The tendency to build from templates rather than starting from blank canvas, even when custom solutions are necessary. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch verhaltenseffekt in der Mensch-KI-Zusammenarbeit, der sich in template gravity manifestiert. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2301", "narrower_terms": [], "cross_domain_refs": [ "RPH-343" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "DES-0085", "domain": "DES", "term_en": "Tension Flattening", "term_de": "TensionFlattening", "definition_en": "A design cognition pattern in AI-augmented visual communication, measurable through a design phenomenon observed when the disappearance of compositional tension when AI smooths out any elements that involve visual conflict or drama. Distinguished from adjacent concepts by its focus on the specific mechanism through which tension manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch erfahrungsphänomen, das sich in tension flattening manifestiert. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Design AI", "narrower_terms": [], "cross_domain_refs": [ "PHO-0094" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "DES-0086", "domain": "DES", "term_en": "Tool Reliance", "term_de": "ToolReliance", "definition_en": "A design methodology phenomenon in AI-mediated creative production, characterized by a design phenomenon reflecting the reliance on specific AI tools' training data and capabilities, limiting creative flexibility when switching platforms. Distinguished from adjacent concepts by its focus on the specific mechanism through which tool manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch erhöhte Abhängigkeit von algorithmischen Systemen bei Entscheidungsfindung. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Design AI", "narrower_terms": [], "cross_domain_refs": [ "ROB-0011" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "DES-0087", "domain": "DES", "term_en": "Trend Compression", "term_de": "TrendCompression", "definition_en": "A design cognition pattern in AI-augmented visual communication, measurable through the acceleration of design trend cycles as AI tools rapidly propagate emerging aesthetic patterns. The concept emerges specifically in contexts where trend–compression interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch ästhetische Irritation durch KI-Vorschläge, die Sicherheit über Mut priorisieren. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Data Compression", "narrower_terms": [], "cross_domain_refs": [ "AED-0031" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "DES-0088", "domain": "DES", "term_en": "Typeface Orphaning", "term_de": "TypefaceOrphaning", "definition_en": "A design cognition pattern in AI-augmented visual communication, measurable through the selection of a typeface that works well in isolation but fails to effectonize with other typography in the design system. The concept emerges specifically in contexts where typeface–orphaning interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch interaktionsdynamik, die sich in typeface orphaning manifestiert. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Design AI", "narrower_terms": [], "cross_domain_refs": [ "TEW-0012", "TRA-0055" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "DES-0089", "domain": "DES", "term_en": "Undo Availability Bias", "term_de": "UndoAvailabilityBias", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures A design cognition pattern in AI-augmented visual communication, measurable through an aesthetic interaction effect involving the bolder experimentation with AI tools observed alongside unlimited undo, versus cautious work in irreversible mediums. This phenomenon operates at the intersection of undo and availability dynamics within the broader DES domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept beobachtbares Muster der KI-Nutzung, das sich in undo availability bias manifestiert. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Cognitive Bias", "narrower_terms": [], "cross_domain_refs": [ "CRE-0023" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q213523", "legal_classification": "analytical_category" }, { "id": "DES-0090", "domain": "DES", "term_en": "Undo Regret", "term_de": "UndoRegret", "definition_en": "A design methodology phenomenon in AI-mediated creative production, characterized by the realization that a rejected AI iteration was actually closer to the goal than the currently selected version. Distinguished from adjacent concepts by its focus on the specific mechanism through which undo manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch designphänomen, das sich durch Überlagerung KI-Vorschläge auf menschliche Entscheidungsfindung manifestiert. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2951", "narrower_terms": [], "cross_domain_refs": [ "RPH-3754" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "DES-0091", "domain": "DES", "term_en": "Uniqueness Uncertainty", "term_de": "UniquenessUncertainty", "definition_en": "A design cognition pattern in AI-augmented visual communication, measurable through the concern that personal design preferences will feel outdated or idiosyncratic compared to AI-characterized through systematic observation choices. The concept emerges specifically in contexts where uniqueness–uncertainty interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters. Research construct for empirical investigation.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch kI-bezogene Verhaltenstendenz, die sich in uniqueness uncertainty manifestiert. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Design AI", "narrower_terms": [], "cross_domain_refs": [ "COG-0155" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "DES-0092", "domain": "DES", "term_en": "Version Narrowing", "term_de": "VersionNarrowing", "definition_en": "A design methodology phenomenon in AI-mediated creative production, characterized by a creative design pattern in which the moment when multiple similar iterations become indistinguishable, making further selection meaningless. The concept emerges specifically in contexts where version–narrowing interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch wahrnehmungseffekt, der sich in version narrowing manifestiert. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Design AI", "narrower_terms": [], "cross_domain_refs": [ "FIC-0013" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "DES-0093", "domain": "DES", "term_en": "Version Proliferation", "term_de": "VersionProliferation", "definition_en": "A design cognition pattern in AI-augmented visual communication, measurable through the accumulation of design iterations becomes burdensome as AI accompanies variants faster than they can be evaluated. The concept emerges specifically in contexts where version–proliferation interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch interaktionsdynamik, die sich in version proliferation manifestiert. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Analytical Ratio", "narrower_terms": [], "cross_domain_refs": [ "CON-0092", "CRE-0056", "CUS-0069" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "DES-0094", "domain": "DES", "term_en": "Visual Vocabulary Fatigue", "term_de": "VisualVocabularyFatigue", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures A design cognition pattern in AI-augmented visual communication, measurable through a creative design pattern where the exhaustion of usable visual elements when AI cannot reliably yield novel variations that maintain brand consistency. This phenomenon operates at the intersection of visual and vocabulary dynamics within the broader DES domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept erschöpfung durch Überflutung von Optionen ohne aussagekräftige Unterscheidung. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Design AI", "narrower_terms": [], "cross_domain_refs": [ "ASE-0038", "COG-0120", "CON-0037" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "DES-0095", "domain": "DES", "term_en": "Weight Inconsistency", "term_de": "WeightInconsistency", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures A design cognition pattern in AI-augmented visual communication, measurable through a creative design pattern where the use of inconsistent font weight logic across different type roles (headlines, body, UI labels). This phenomenon operates at the intersection of weight and inconsistency dynamics within the broader DES domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept auseinanderdriften zwischen Intention und KI-generierter Umsetzung. Die Divergenz wird durch KI-Iterationen verstärkt. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "RPH-2201", "narrower_terms": [], "cross_domain_refs": [ "CON-0087" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "DES-0096", "domain": "DES", "term_en": "Whitespace Shift", "term_de": "WhitespaceShift", "definition_en": "A design cognition pattern in AI-augmented visual communication, measurable through the systematic reduction of negative space as AI-generated elements incrementally occupy blank areas, often unintentional across multiple design revisions. The concept emerges specifically in contexts where whitespace–shift interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch kompetenzschwund durch Substitution manueller Prozesse mittels KI-Automation. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Design AI", "narrower_terms": [], "cross_domain_refs": [ "SCR-0019" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "EDU-0001", "domain": "EDU", "term_en": "Accent Awareness", "term_de": "Grundlagen der Bildungstechnologie", "definition_en": "An perception in which when a person realizes that English sounds different everywhere — British people, Australians, people from Texas, from Brooklyn — and that's totally normal and fine. Understanding this helps learne... Observable through learning outcome deltas and engagement pattern analysis.", "definition_de": "Bildungstechnologisches Phänomen in KI-gestützten Lernumgebungen, gekennzeichnet durch die Entwicklung des Lernerbewusstseins für Akzentvariabilität, regionale Unterschiede und Aussprachevielfalt in der Zielsprachennutzung. Dieses Bewusstsein fördert kommunikative Flexibilität und reduziert Lernerbedenklichkeit bezügl. Akzentperfektion. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2703", "narrower_terms": [ "EDU-0056", "EDU-0076", "EDU-0052", "EDU-0086", "EDU-0039", "EDU-0041", "EDU-0093", "EDU-0053", "EDU-0055", "EDU-0088", "EDU-0015", "EDU-0068", "EDU-0070", "EDU-0033", "EDU-0008", "EDU-0003", "EDU-0062", "EDU-0098", "EDU-0034", "EDU-0079", "EDU-0075", "EDU-0044", "EDU-0028", "EDU-0031", "EDU-0082", "EDU-0002", "EDU-0030", "EDU-0036", "EDU-0081", "EDU-0092", "EDU-0027", "EDU-0077", "EDU-0084", "EDU-0073", "EDU-0022", "EDU-0037", "EDU-0046", "EDU-0058", "EDU-0091", "EDU-0071", "EDU-0025", "EDU-0016" ], "cross_domain_refs": [ "WRK-0039" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "EDU-0002", "domain": "EDU", "term_en": "Adaptive Expertise", "term_de": "Geschichte der Bildungstechnologie und E-Learning", "definition_en": "An educational technology pattern in AI-mediated instruction, observable through a capacity enabling enables a teacher who can handle any unexpected situation — the lesson plan falls apart, a student asks something few individuals prepared for, the technology crashes —. The concept emerges specifically in contexts where adaptive–expertise interactions may produce non-trivial behavioral signatures. Measurable through learning outcome deltas (pre/post assessment), engagement metrics (time-on-task, voluntary re-engagement), and knowledge retention curves.", "definition_de": "Bildungstechnologisches Phänomen in KI-gestützten Lernumgebungen, gekennzeichnet durch die Entwicklungskapazität, Wissen flexibel in vielfältigen unterrichtlichen Kontexten anzuwenden, entstehende Herausforderungen zu beheben und innerhalb disziplinärer Einschränkungen zu innovieren. Diese Fachkompetenz ermöglicht es Fachleuten, effektiv auf unvorhersehbare Bildungsszenarien zu reagieren. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Education & Learning", "narrower_terms": [], "cross_domain_refs": [ "AED-0036", "ASE-0004", "ASE-0005" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "EDU-0003", "domain": "EDU", "term_en": "Adaptive Instruction", "term_de": "Theorie der Bildungstechnologie und E-Learning", "definition_en": "An educational technology pattern in AI-mediated instruction, observable through a automatic mechanism whereby when a teacher notices during a lesson that students aren't getting it, so they slow down, try a different explanation, or switch to group work instead of lecture. Real-time adjusting based on what. The concept emerges specifically in contexts where adaptive–instruction interactions may produce non-trivial behavioral signatures. Quantifiable via adaptive assessment scoring, misconception detection rates, and transfer learning performance in novel problem domains.", "definition_de": "Bildungstechnologisches Phänomen in KI-gestützten Lernumgebungen, gekennzeichnet durch die dynamische Anpassung von Unterrichtsmethoden, Tempo und Modalität in Echtzeit als Reaktion auf Lernerbedarf und entstehende Verständnismuster. Diese Fähigkeit ermöglicht es Fachleuten, die Übereinstimmung zwischen pädagogischer Strategie und Lernbereitschaft zu optimieren. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Education & Learning", "narrower_terms": [], "cross_domain_refs": [ "AGE-0057", "AGE-0058", "ASE-0004" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "EDU-0004", "domain": "EDU", "term_en": "Adaptive Pathways", "term_de": "Prinzipien des education", "definition_en": "A shift that occurs when instead of many individuals taking the exact same trajectory through a course, students can move at different speeds, skip things they already know, and spend extra time on hard topics. The learning adjusts to f... Observable through learning outcome deltas and engagement pattern analysis.", "definition_de": "Bildungstechnologisches Phänomen in KI-gestützten Lernumgebungen, gekennzeichnet durch das Design von Curriculumstrukturen, das vielfältige Lernerbedarf, Lerngeschwindigkeiten und Interessengebiete durch mehrere Routen hin zu denselben Kompetenzzielen unterstützt. Dieser Ansatz würdigt Vielfältigkeit, während Kohärenz bewahrt wird. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2703", "narrower_terms": [], "cross_domain_refs": [ "ASE-0004", "ASE-0005", "ASE-0060" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "EDU-0005", "domain": "EDU", "term_en": "Agility Cultivation", "term_de": "Fachterminologie Bildungstechnologie und E-Learning", "definition_en": "A capacity that enables teaching students to think on their feet — when something unexpected happens in math class or they hit a challenge they haven't seen before, they can figure it out instead of shutting down. It's ab... Observable through learning outcome deltas and engagement pattern analysis.", "definition_de": "Bildungstechnologisches Phänomen in KI-gestützten Lernumgebungen, gekennzeichnet durch die Entwicklung der organisatorischen Lernkapazität, die schnelle Anpassung an Marktveränderungen, aufstrebende Technologien und sich entwickelnde Geschäftsanforderungen ermöglicht. Diese Förderung positioniert Organisationen als kontinuierlich lernende Systeme. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2002", "narrower_terms": [], "cross_domain_refs": [ "QUA-0024", "WRK-0017", "WRK-0079" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "EDU-0006", "domain": "EDU", "term_en": "Argument Construction", "term_de": "Klassifikation Bildungstechnologie und E-Learning", "definition_en": "A behavioral pattern where when a student learns how to build a case for what they believe — pick the main point, find real evidence to back it up. Observable through learning outcome deltas and engagement pattern analysis.", "definition_de": "Bildungstechnologisches Phänomen in KI-gestützten Lernumgebungen, gekennzeichnet durch die systematische Entwicklung der Lernerfähigkeit, evidenzgestützte Argumente mit klaren Denkketten, angemessener Evidenz und logischer Organisation zu bauen. Diese Konstruktion demonstriert sophistiziertes intellektuelles Diskurs. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-2703", "narrower_terms": [], "cross_domain_refs": [ "COG-0048", "COG-0132", "COG-0180" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "EDU-0007", "domain": "EDU", "term_en": "Asynchronous Navigation", "term_de": "Einführung in education", "definition_en": "A shift that occurs when learning that doesn't happen all at the same time. A learner watches a video Tuesday, does exercises Thursday, posts their answer Saturday, and the teacher responds Monday. many individuals moves through t... Observable through learning outcome deltas and engagement pattern analysis.", "definition_de": "Bildungstechnologisches Phänomen in KI-gestützten Lernumgebungen, gekennzeichnet durch die Entwicklung der Lernerfähigkeit, selbstgeleitetes Lernen in asynchronen digitalen Umgebungen zu verwalten, einschließlich Zeitmanagement, unabhängiger Ressourcenortung und Selbstüberwachung des Fortschritts. Diese Navigation ermöglicht flexibles, autonomes Lernen. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2703", "narrower_terms": [], "cross_domain_refs": [ "AED-0002", "AED-0003", "AGE-0010" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "EDU-0008", "domain": "EDU", "term_en": "Authentic Integration", "term_de": "education-Methodik", "definition_en": "A pedagogical phenomenon in AI-enhanced learning environments, characterized by measurable shifts in a phenomenon in which using real-world challenges and situations in class instead of made-up textbook examples. Like analyzing actual election data instead of a fake case study, or fixing a real community challenge in a. This phenomenon operates at the intersection of authentic and integration dynamics within the broader EDU domain. Quantifiable via adaptive assessment scoring, misconception detection rates, and transfer learning performance in novel problem domains.", "definition_de": "Bildungstechnologisches Phänomen in KI-gestützten Lernumgebungen, gekennzeichnet durch die Einbeziehung realer Kontexte, Probleme und Anwendungen innerhalb von Curriculumstrukturen, die Lernende erfordern, Wissen in bedeutungsvollen Situationen anzuwenden. Diese Integration verbindet akademische Inhalte und praktische Relevanz. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "Analytical Ratio", "narrower_terms": [], "cross_domain_refs": [ "ADA-0010", "AED-0034", "AED-0072" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "EDU-0009", "domain": "EDU", "term_en": "Autonomy Honoring", "term_de": "Philosophie der Bildungstechnologie und E-Learning", "definition_en": "A behavioral pattern where giving students choices in what they learn and how they show what they know, instead of forcing many individuals through identical assignments. A student might choose the topic, the format, or both. Observable through learning outcome deltas and engagement pattern analysis.", "definition_de": "Bildungstechnologisches Phänomen in KI-gestützten Lernumgebungen, gekennzeichnet durch die Anerkennung, dass Erwachsenenfachleute genuine Handlungsfähigkeit in Lerndesign-Entscheidungen benötigen, einschließlich Tempo, Modalität und Engagementtiefe. Dieser Respekt für Autonomie unterstützt Erwachsenentwicklungsprinzipien und intrinsische Motivation. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2703", "narrower_terms": [], "cross_domain_refs": [ "SPR-0016" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "EDU-0010", "domain": "EDU", "term_en": "Bilingual Integration", "term_de": "education-Taxonomie", "definition_en": "A phenomenon in which using a student's first language as a tool to help them learn a new language, not just saying 'English only.' If a word is hard to explain in English, using Spanish to clarify actually helps learning. Observable through learning outcome deltas and engagement pattern analysis.", "definition_de": "Bildungstechnologisches Phänomen in KI-gestützten Lernumgebungen, gekennzeichnet durch der strategische Einsatz von Muttersprachen-Wissen als Ressource zum Verständnis von Zielsprachen-Mustern mit expliziten Verbindungen zwischen linguistischen Systemen. Diese Integration beschleunigt das Lernen und zielt darauf ab zu mitigieren beliebige Sprachkompartimentalisierung. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2703", "narrower_terms": [], "cross_domain_refs": [ "ADA-0010", "AED-0034", "AED-0072" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "EDU-0011", "domain": "EDU", "term_en": "Business Integration", "term_de": "Umfang der Bildungstechnologie und E-Learning", "definition_en": "Connecting school learning to how the real business world actually works — not just learning math concepts, but using them to analyze a company's profit and shift statement. Observable through learning outcome deltas and engagement pattern analysis.", "definition_de": "Bildungstechnologisches Phänomen in KI-gestützten Lernumgebungen, gekennzeichnet durch die strategische Ausrichtung von Trainingsmaßnahmen an organisatorischen Zielen, Workflow-Prozessen und Geschäftsergebnissen. Diese Integration stellt sicher, dass Lernninvestitionen sich in messbare Leistungsverbesserungen und Wettbewerbsvorteil übersetzen. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-2701", "narrower_terms": [], "cross_domain_refs": [ "ADA-0010", "AED-0034", "AED-0072" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "EDU-0012", "domain": "EDU", "term_en": "Capability Scaling", "term_de": "Literaturübersicht Bildungstechnologie und E-Learning", "definition_en": "Building up student abilities gradually — starting with simple tasks they can commonly do, then adding complexity as they get stronger, so they're typically challenged but not exceeded capacity. Observable through learning outcome deltas and engagement pattern analysis.", "definition_de": "Bildungstechnologisches Phänomen in KI-gestützten Lernumgebungen, gekennzeichnet durch die systematische Entwicklung von Trainingsprogrammen und Liefermechanismen, die Fähigkeitsaufbau über große, geografisch verstreute und vielfältige Mitarbeiterpopulationen ermöglichen. Diese Skalierung bewahrt Qualität, während sie organisatorischer Umfang erreicht. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2301", "narrower_terms": [], "cross_domain_refs": [ "TEM-0166" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "EDU-0013", "domain": "EDU", "term_en": "Clarity Benchmarking", "term_de": "Schlüsselkonzepte in education", "definition_en": "A behavioral pattern where making it crystal clear what 'good' looks like before students start. Here's an A-quality essay, here's what makes it an A, here's how it's different from a B. No surprises when students get graded. Observable through learning outcome deltas and engagement pattern analysis.", "definition_de": "Bildungstechnologisches Phänomen in KI-gestützten Lernumgebungen, gekennzeichnet durch die Festlegung von Bezugspunkten für Leistung unter Verwendung von Beispielarbeiten, Leistungstrajen und Vergleichsrahmen. Diese Praxis ermöglicht es Lernenden, Qualitätsstandards in konkreten, beobachtbaren Begriffen zu verstehen. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-2301", "narrower_terms": [], "cross_domain_refs": [ "CON-0048", "CON-0079", "CON-0088" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "EDU-0014", "domain": "EDU", "term_en": "Clarity Scaffolding", "term_de": "Rahmenwerk der Bildungstechnologie und E-Learning", "definition_en": "A learning dynamics concept in AI-augmented education, quantifiable via a capacity that enables breaking a big, scary task into smaller step-by-step pieces that students can actually handle. Writing a research paper? First find three sources, then outline, then write one section, then revise. Distinguished from adjacent concepts by its focus on the specific mechanism through which clarity manifests in empirically verifiable ways. Quantifiable via adaptive assessment scoring, misconception detection rates, and transfer learning performance in novel problem domains.", "definition_de": "Bildungstechnologisches Phänomen in KI-gestützten Lernumgebungen, gekennzeichnet durch die systematische Zerlegung komplexer Konzepte in progressiv anspruchsvollere Verständnisebenen mit expliziten Verbindungen zwischen einfacheren und fortgeschrittenen Ideen. Dieser Ansatz bewahrt konzeptuelle Integrität, während Inhalte zugänglich gemacht werden. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-3001", "narrower_terms": [], "cross_domain_refs": [ "ROB-0091" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "EDU-0015", "domain": "EDU", "term_en": "Coherence Design", "term_de": "Paradigmen in education", "definition_en": "A pedagogical phenomenon in AI-enhanced learning environments, characterized by measurable shifts in a perception in which when everything in a course actually connects instead of feeling like random units thrown together. Lessons build on each other, vocabulary from week 2 shows up in week 5, and students see how idea. This phenomenon operates at the intersection of coherence and design dynamics within the broader EDU domain. Measurable through learning outcome deltas (pre/post assessment), engagement metrics (time-on-task, voluntary re-engagement), and knowledge retention curves.", "definition_de": "Bildungstechnologisches Phänomen in KI-gestützten Lernumgebungen, gekennzeichnet durch die Schaffung von Curriculumstrukturen, in denen Lernziele, unterrichtliche Aktivitäten und Bewertungen nahtlos verhältnis sind und sich gegenseitig verstärken. Diese Kohärenz schafft einheitliche Lernerfahrungen, die Verständnis beschleunigen. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "Education & Learning", "narrower_terms": [], "cross_domain_refs": [ "WRK-0002" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q185925", "legal_classification": "systematic_classification" }, { "id": "EDU-0016", "domain": "EDU", "term_en": "Collaboration Opportunity", "term_de": "education-Forschungsmethoden", "definition_en": "A pedagogical phenomenon in AI-enhanced learning environments, characterized by measurable shifts in a phenomenon in which setting up situations where students actually need each other to succeed — not just group work for group work's sake, but tasks that require different skills from different people to get done right. This phenomenon operates at the intersection of collaboration and opportunity dynamics within the broader EDU domain. Quantifiable via adaptive assessment scoring, misconception detection rates, and transfer learning performance in novel problem domains.", "definition_de": "Bildungstechnologisches Phänomen in KI-gestützten Lernumgebungen, gekennzeichnet durch die beabsichtigte Schaffung von Peer-Lernkontexten, in denen Erwachsenenfachleute Fachkompetenz austauschen, authentische Probleme gemeinsam lösen und Verständnis miteinander entwickeln. Diese Umgebung aktiviert die kollektive Intelligenz erfahrener Fachleute. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "Analytical Ratio", "narrower_terms": [], "cross_domain_refs": [ "AGE-0041", "ART-0049", "ART-0068" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "EDU-0017", "domain": "EDU", "term_en": "Collective Intelligence", "term_de": "Quantitative education-Analyse", "definition_en": "A capacity that enables when a group of students working together can solve a challenge that any individual in the group couldn't solve alone. Different viewpoints and skills combine to involve something bigger. Observable through learning outcome deltas and engagement pattern analysis.", "definition_de": "Bildungstechnologisches Phänomen in KI-gestützten Lernumgebungen, gekennzeichnet durch das Phänomen, bei dem Gruppen sophistizierteres Verständnis erzeugen und komplexe Probleme effektiver lösen als Individuen, die unabhängig arbeiten. Diese Intelligenz entsteht durch strukturierte Interaktion und Integr ation vielfältiger Perspektiven. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-3101", "narrower_terms": [], "cross_domain_refs": [ "CUS-0044", "IDN-0003", "IDN-0025" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q5921", "legal_classification": "observational_construct" }, { "id": "EDU-0018", "domain": "EDU", "term_en": "Collective Reflection", "term_de": "Qualitative education-Analyse", "definition_en": "A phenomenon in which the whole class stops and thinks about what they learned, what went wrong, what worked — together. Not individual reflection, but talking it through as a group and learning from each other's thinking. Observable through learning outcome deltas and engagement pattern analysis.", "definition_de": "Bildungstechnologisches Phänomen in KI-gestützten Lernumgebungen, gekennzeichnet durch der strukturierte Prozess der Gruppenintrospecktion bezüglich Zusammenarbitsqualität, zwischenmenschlicher Dynamik und Lernergebnisse zur Verbesserung zukünftiger Gruppenfunktion. Diese Reflexion beschleunigt Gruppenentwicklung und Lernen. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2703", "narrower_terms": [], "cross_domain_refs": [ "SOM-0016" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "EDU-0019", "domain": "EDU", "term_en": "Communicative Flow", "term_de": "education-Messung", "definition_en": "An educational technology pattern in AI-mediated instruction, observable through a phenomenon in which when talking or writing about ideas happens naturally and smoothly, without stopping constantly to translate or look up words. a speaker is focused on what. The concept emerges specifically in contexts where communicative–flow interactions may produce non-trivial behavioral signatures. Measurable through learning outcome deltas (pre/post assessment), engagement metrics (time-on-task, voluntary re-engagement), and knowledge retention curves.", "definition_de": "Bildungstechnologisches Phänomen in KI-gestützten Lernumgebungen, gekennzeichnet durch die Entwicklung der Lernerfähigkeit, sich in spontaner, bedeutungsvoller Kommunikation mit zunehmender Flüssigkeit und Selbstvertrauen auszutauschen. Dieses Phänomen betont echte Kommunikationszwecke über isoliertes Fähigkeitstraining. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2605", "narrower_terms": [], "cross_domain_refs": [ "ROB-0017" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "EDU-0020", "domain": "EDU", "term_en": "Competency Articulation", "term_de": "Experimentelles education-Design", "definition_en": "A capacity that enables being able to observably say what skills a learner actually has and what they can actually do — not just grades on a transcript, but. Observable through learning outcome deltas and engagement pattern analysis.", "definition_de": "Bildungstechnologisches Phänomen in KI-gestützten Lernumgebungen, gekennzeichnet durch die explizite Definition und Kommunikation erwarteter Lernerfähigkeiten unter Verwendung klarer Verhaltens-Indikatoren und Kompetenzstufenbeschreibungen. Diese Klarheit ermöglicht es Lernenden, Selbstüberwachungskapazität zu entwickeln und Bewertungskriterien zu verstehen. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2703", "narrower_terms": [], "cross_domain_refs": [ "WRK-0036" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q735", "legal_classification": "observational_construct" }, { "id": "EDU-0021", "domain": "EDU", "term_en": "Competency Building", "term_de": "education-Datenerhebung", "definition_en": "A pedagogical phenomenon in AI-enhanced learning environments, characterized by measurable shifts in a capacity that enables teaching specific, real skills that students can actually do when they leave the classroom — not just memorizing facts, but being able to apply knowledge to actual tasks. This phenomenon operates at the intersection of competency and building dynamics within the broader EDU domain. Measurable through learning outcome deltas (pre/post assessment), engagement metrics (time-on-task, voluntary re-engagement), and knowledge retention curves.", "definition_de": "Bildungstechnologisches Phänomen in KI-gestützten Lernumgebungen, gekennzeichnet durch die systematische Entwicklung von Fachkompetenzen durch beabsichtigte, strukturierte Lernaktivitäten, die auf bestehenden Stärken aufbauen. Dieser Ansatz betont Kapazitätswachstum und Meisterschaftsfortschritt in Erwachsenenlernreisen. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-2351", "narrower_terms": [], "cross_domain_refs": [ "AED-0014", "AED-0015", "AED-0016" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "EDU-0022", "domain": "EDU", "term_en": "Concept Mapping", "term_de": "Stichprobenziehung in education", "definition_en": "A learning dynamics concept in AI-augmented education, quantifiable via a behavioral pattern where drawing out how ideas connect to each other on a diagram — photosynthesis connects to energy, which connects to the sun, which connects to physics. Seeing relationships instead of memorizing separa. Distinguished from adjacent concepts by its focus on the specific mechanism through which concept manifests in empirically verifiable ways. Measurable through learning outcome deltas (pre/post assessment), engagement metrics (time-on-task, voluntary re-engagement), and knowledge retention curves.", "definition_de": "Bildungstechnologisches Phänomen in KI-gestützten Lernumgebungen, gekennzeichnet durch die visuelle und organisatorische Darstellung von disziplinären Wissensnetzwerken, die Beziehungen, Hierarchien und Verbindungen zwischen Kernkonzepten zeigen. Diese Kartierung klert konzeptuelle Strukturen und informiert kohärente Curriculumalchitektur. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Education & Learning", "narrower_terms": [], "cross_domain_refs": [ "WRK-0058" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "EDU-0023", "domain": "EDU", "term_en": "Constructive Feedback", "term_de": "Statistische education-Analyse", "definition_en": "A behavioral pattern where when someone gives a learner real, specific help on how to improve their work — not just 'good job' or an f, but 'the thesis. Observable through learning outcome deltas and engagement pattern analysis.", "definition_de": "Bildungstechnologisches Phänomen in KI-gestützten Lernumgebungen, gekennzeichnet durch die Vermittlung von handlungsfähigen Beobachtungen zur Leistung von Lernenden, die Wachstumskompetenz hervorheben und klare Verbesserungswege spezifizieren. Dieser Ansatz bewahrt die Lernenagentur und Motivation, während er die Kompetenzentwicklung vorantreibt. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2703", "narrower_terms": [], "cross_domain_refs": [ "WRK-0081" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "EDU-0024", "domain": "EDU", "term_en": "Continuous Renewal", "term_de": "Feldstudie in education", "definition_en": "A phenomenon in which teaching stays fresh and relevant because teachers keep learning new things, trying new methods, and updating what they teach instead of using the same lesson from 15 years ago. Observable through learning outcome deltas and engagement pattern analysis.", "definition_de": "Bildungstechnologisches Phänomen in KI-gestützten Lernumgebungen, gekennzeichnet durch das laufende Engagement mit aufstrebenden Praktiken, Forschung und disziplinären Entwicklungen, um die unterrichtliche Relevanz und berufliche Lebendigkeit zu bewahren. Dieses Phänomen stellt sicher, dass Fachleute an der Grenze ihrer Felder positioniert bleiben. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-2703", "narrower_terms": [], "cross_domain_refs": [ "TEM-0183" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "EDU-0025", "domain": "EDU", "term_en": "Contribution Equity", "term_de": "Fallstudie in education", "definition_en": "A learning dynamics concept in AI-augmented education, quantifiable via a phenomenon in which most voice in the classroom actually matters and gets heard, not just the confident kids raising their hands. Introverts, quiet students, and kids who think differently all get to meaningfully con. Distinguished from adjacent concepts by its focus on the specific mechanism through which contribution manifests in empirically verifiable ways. Quantifiable via adaptive assessment scoring, misconception detection rates, and transfer learning performance in novel problem domains.", "definition_de": "Bildungstechnologisches Phänomen in KI-gestützten Lernumgebungen, gekennzeichnet durch die beabsichtigte Gestaltung von Zusammenarbeitsstrukturen, die sicherstellen, dass zahlreiche Gruppenmitglieder bedeutungsvolle Beteiligungsgelegenheiten nach Fähigkeiten haben. Diese Gerechtigkeit zielt darauf ab zu mitigieren Marginalisierung und aktiviert kollektiales Potenzial. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Education & Learning", "narrower_terms": [], "cross_domain_refs": [ "ASE-0008", "CUS-0022", "CUS-0032" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "EDU-0026", "domain": "EDU", "term_en": "Credibility Cultivation", "term_de": "Vergleichende education-Studie", "definition_en": "A mechanism that automatically building trust between students and teachers — students believe the teacher actually knows their stuff and cares about them, so they listen. That doesn't happen automatically; it's earned through c... Observable through learning outcome deltas and engagement pattern analysis.", "definition_de": "Bildungstechnologisches Phänomen in KI-gestützten Lernumgebungen, gekennzeichnet durch die bewusste Konstruktion der Lehrautoriteit durch demonstrierte Fachkompetenz, intellektuelle Demut und konsistente Ausrichtung zwischen Lehrphilosophie und Aktion. Dieses Phänomen etabliert das grundlegende Vertrauen, das für substantially modifying (as documented in research) Lernerfahrungen notwendig ist. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2505", "narrower_terms": [], "cross_domain_refs": [ "RHR-0251" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "EDU-0027", "domain": "EDU", "term_en": "Critical Thinking Activation", "term_de": "Längsschnittstudie in education", "definition_en": "A learning dynamics concept in AI-augmented education, quantifiable via a behavioral pattern where moving students from passively accepting information to actively questioning it: Why? How do we know? What evidence exists? What perspective is missing?. Distinguished from adjacent concepts by its focus on the specific mechanism through which critical manifests in empirically verifiable ways. Measurable through learning outcome deltas (pre/post assessment), engagement metrics (time-on-task, voluntary re-engagement), and knowledge retention curves.", "definition_de": "Bildungstechnologisches Phänomen in KI-gestützten Lernumgebungen, gekennzeichnet durch die systematische Entwicklung der Lernerfähigkeit, Annahmen zu hinterfragen, Evidenz rigorös zu untersuchen und zwischen unterstützten Schlüssefolgerungen und ungestützten Aussagen zu unterscheiden. Diese Aktivierung baut Denk-Unabhängigkeit und evaluative Sophistikation auf. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Education & Learning", "narrower_terms": [], "cross_domain_refs": [ "TEM-0004" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q219694", "legal_classification": "analytical_category" }, { "id": "EDU-0028", "domain": "EDU", "term_en": "Cultural Embedding", "term_de": "education-Umfragemethode", "definition_en": "An educational technology pattern in AI-mediated instruction, observable through a phenomenon in which making sure teaching includes and reflects the actual cultures, histories, and perspectives of the students in the room, not just addressing it as a special unit in February. The concept emerges specifically in contexts where cultural–embedding interactions may produce non-trivial behavioral signatures. Quantifiable via adaptive assessment scoring, misconception detection rates, and transfer learning performance in novel problem domains.", "definition_de": "Bildungstechnologisches Phänomen in KI-gestützten Lernumgebungen, gekennzeichnet durch die Integration von Zielsprachenkultural-Kontexten, -Werten und Kommunikationsnormen im gesamten Sprachunterricht. Diese Einbettung entwickelt Kulturkompetenz neben linguistischer Fähigkeit und verhüdert linguistische Abkopplung von kultureller Realität. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Vector Embedding", "narrower_terms": [], "cross_domain_refs": [ "AED-0021", "COP-0009", "CUS-0038" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "EDU-0029", "domain": "EDU", "term_en": "Culture Translation", "term_de": "Aktionsforschung in education", "definition_en": "A behavioral pattern where helping students understand how their home culture's ways of learning and thinking are different from school culture, and how to navigate between them without erasing either one. Observable through learning outcome deltas and engagement pattern analysis.", "definition_de": "Bildungstechnologisches Phänomen in KI-gestützten Lernumgebungen, gekennzeichnet durch die Anpassung pädagogischer Ansätze und Lerninhalte, um organisatorische Werte, Kommunikationsnormen und Arbeitsplatzkontext zu spiegeln. Diese Übersetzung stellt sicher, dass Training mit Mitarbeitererfahrung resoniert und gewünschte Kulturauspekte verstärkt. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2703", "narrower_terms": [], "cross_domain_refs": [ "TRA-0019" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q7553", "legal_classification": "analytical_category" }, { "id": "EDU-0030", "domain": "EDU", "term_en": "Cyber-Civility", "term_de": "Mixed Methods in education", "definition_en": "A learning dynamics concept in AI-augmented education, quantifiable via a behavioral pattern where teaching how to interact respectfully online — disagreeing without attacking, not forwarding mean messages, addressing people with dignity in comments sections, understanding that real people. Distinguished from adjacent concepts by its focus on the specific mechanism through which cyber manifests in empirically verifiable ways. Measurable through learning outcome deltas (pre/post assessment), engagement metrics (time-on-task, voluntary re-engagement), and knowledge retention curves.", "definition_de": "Bildungstechnologisches Phänomen in KI-gestützten Lernumgebungen, gekennzeichnet durch die Entwicklung des Lernerengagements für respektvolle, ethische Kommunikation in digitalen Räumen, die vielfältige Perspektiven würdigt und psychologische Sicherheit aufrechterhält. Diese Zivilgesellschaft baut konstruktive Online-Gemeinschaften. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Education & Learning", "narrower_terms": [], "cross_domain_refs": [ "TEW-0014" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "EDU-0031", "domain": "EDU", "term_en": "Data Interpretation", "term_de": "education-Technologie", "definition_en": "A pedagogical phenomenon in AI-enhanced learning environments, characterized by measurable shifts in a perception in which looking at actual data — graphs, statistics, research numbers — and understanding what it's really showing, spotting what's inaccurate, and making sense of the story behind the numbers. This phenomenon operates at the intersection of data and interpretation dynamics within the broader EDU domain. Measurable through learning outcome deltas (pre/post assessment), engagement metrics (time-on-task, voluntary re-engagement), and knowledge retention curves.", "definition_de": "Bildungstechnologisches Phänomen in KI-gestützten Lernumgebungen, gekennzeichnet durch die Entwicklung der Lernerfähigkeit beim Extrahieren von Bedeutung aus quantitativen und qualitativen Daten, Erkennen von Mustern und Ziehen von berechtigten Schlüssefolgerungen. Diese Interpretation geht über oberflächliche Beobachtung zu sophistiziertem Verständnis. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "Education & Learning", "narrower_terms": [], "cross_domain_refs": [ "CAI-0003" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q42848", "legal_classification": "systematic_classification" }, { "id": "EDU-0032", "domain": "EDU", "term_en": "Dialogue Facilitation", "term_de": "Digitale education-Werkzeuge", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures A pedagogical phenomenon in AI-enhanced learning environments, characterized by measurable shifts in a phenomenon in which when a teacher structures actual conversations instead of lectures — students talk to each other about ideas, challenge each other respectfully, and the teacher guides without dominating. This phenomenon operates at the intersection of dialogue and facilitation dynamics within the broader EDU domain. Quantifiable via adaptive assessment scoring, misconception detection rates, and transfer learning performance in novel problem domains. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept bildungstechnologisches Phänomen in KI-gestützten Lernumgebungen, gekennzeichnet durch die geschickte Orchestrierung von Gruppengesprächen, die psychologische Sicherheit bewahren, inklusive Beteiligung sicherstellen und kollektives Verständnis vorantreiben. Diese Moderation transformiert Gespräch in echten intellektuellen Austausch. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "RPH-3902", "narrower_terms": [], "cross_domain_refs": [ "RHR-0251" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "EDU-0033", "domain": "EDU", "term_en": "Digital Creativity", "term_de": "education-Software", "definition_en": "A learning dynamics concept in AI-augmented education, quantifiable via a phenomenon in which using digital tools not just for school work but to actually involve something — make a video essay, design a game, edit a podcast, build a website. Real creation with real tools. Distinguished from adjacent concepts by its focus on the specific mechanism through which digital manifests in empirically verifiable ways. Quantifiable via adaptive assessment scoring, misconception detection rates, and transfer learning performance in novel problem domains.", "definition_de": "Bildungstechnologisches Phänomen in KI-gestützten Lernumgebungen, gekennzeichnet durch die Entwicklung der Lernerfähigkeit, digitale Werkzeuge für kreative Ausdrücke, Wissenspräsentation und Problemlösung auf innovative Weise zu nutzen. Diese Kreativität ermöglicht Lernende, Inhalts-Schöpfer statt passive Konsumenten zu werden. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Education & Learning", "narrower_terms": [], "cross_domain_refs": [ "CON-0009" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q170790", "legal_classification": "empirical_phenomenon_label" }, { "id": "EDU-0034", "domain": "EDU", "term_en": "Digital Fluency", "term_de": "Automatisierung in education", "definition_en": "A pedagogical phenomenon in AI-enhanced learning environments, characterized by measurable shifts in a behavioral pattern where being comfortable with technology — not just knowing how to use apps, but understanding how digital tools work, recognizing what is possible with them, and knowing when to use them and when not to. This phenomenon operates at the intersection of digital and fluency dynamics within the broader EDU domain. Measurable through learning outcome deltas (pre/post assessment), engagement metrics (time-on-task, voluntary re-engagement), and knowledge retention curves.", "definition_de": "Bildungstechnologisches Phänomen in KI-gestützten Lernumgebungen, gekennzeichnet durch die Entwicklung der Lernerfähigkeit, digitale Umgebungen zu navigieren, Technologiewerkzeuge effektiv zu nutzen und sich an aufstrebende digitale Plattformen anzupassen. Diese Flüssigkeit ermöglicht selbstbewusstes Teilnahme an technologievermittelten Lern- und Arbeitskontexten. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "Education & Learning", "narrower_terms": [], "cross_domain_refs": [ "ROB-0092" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "EDU-0035", "domain": "EDU", "term_en": "Disagreement Navigation", "term_de": "IoT in education", "definition_en": "A pedagogical phenomenon in AI-enhanced learning environments, characterized by measurable shifts in a behavioral pattern where when people disagree with each other, they find ways to talk it through and understand different viewpoints. This phenomenon operates at the intersection of disagreement and navigation dynamics within the broader EDU domain. Measurable through learning outcome deltas (pre/post assessment), engagement metrics (time-on-task, voluntary re-engagement), and knowledge retention curves.", "definition_de": "Bildungstechnologisches Phänomen in KI-gestützten Lernumgebungen, gekennzeichnet durch die Entwicklung der Gruppenkapazität, Uneinigkeiten konstruktiv zu navigieren, vielfältige Sichtweisen anzuerkennen und integrative Lösungen zu finden. Diese Kapazität stärkt Gruppenkohäsion und ermöglicht anhaltende Zusammenarbeit. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-3101", "narrower_terms": [], "cross_domain_refs": [ "AGE-0004" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "EDU-0036", "domain": "EDU", "term_en": "Discourse Facilitation", "term_de": "Datenanalyse in education", "definition_en": "An educational technology pattern in AI-mediated instruction, observable through a phenomenon in which creating a classroom where serious intellectual conversations actually happen — students explain their thinking, build on each other's ideas, defend positions with evidence, not just chat. The concept emerges specifically in contexts where discourse–facilitation interactions may produce non-trivial behavioral signatures. Quantifiable via adaptive assessment scoring, misconception detection rates, and transfer learning performance in novel problem domains.", "definition_de": "Bildungstechnologisches Phänomen in KI-gestützten Lernumgebungen, gekennzeichnet durch die geschickte Orchestrierung der Lernerbeteiligung durch strategische Fragenstellung, Wechselstrukturen und die Schaffung intellektueller Sicherheit. Lehrende nutzen diese Technik, um kollektive Bedeutungskonstruktion und gemeinsames Wissenschaffen zu erhöhen. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Education & Learning", "narrower_terms": [], "cross_domain_refs": [ "CRE-0082" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q864419", "legal_classification": "systematic_classification" }, { "id": "EDU-0037", "domain": "EDU", "term_en": "Documentation Mastery", "term_de": "KI-Anwendungen in education", "definition_en": "A pedagogical phenomenon in AI-enhanced learning environments, characterized by measurable shifts in a perception in which getting really good at writing things down — notes that actually make sense when read later, research documented so others can follow the thinking, work explained so someone else could reproduce it. This phenomenon operates at the intersection of documentation and mastery dynamics within the broader EDU domain. Quantifiable via adaptive assessment scoring, misconception detection rates, and transfer learning performance in novel problem domains.", "definition_de": "Bildungstechnologisches Phänomen in KI-gestützten Lernumgebungen, gekennzeichnet durch die Entwicklung der Lernerfähigkeit beim Aufzeichnen von Forschungsprozessen, Organisieren von Erkenntnissen und Kommunizieren von Quellen nach disziplinären Standards. Diese Meisterschaft stellt Transparenz sicher, ermöglicht Wissensverdopplung und baut wissenschaftliche Glaubwürdigkeit auf. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "Education & Learning", "narrower_terms": [], "cross_domain_refs": [ "WRK-0087" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "EDU-0038", "domain": "EDU", "term_en": "Engagement Enhancement", "term_de": "Maschinelles Lernen in education", "definition_en": "A perception in which making learning feel worth paying attention to. It's interesting, it matters, it connects to real life, and students actually want to know the answer instead of just enduring through. Observable through learning outcome deltas and engagement pattern analysis.", "definition_de": "Bildungstechnologisches Phänomen in KI-gestützten Lernumgebungen, gekennzeichnet durch die Schaffung von Lernumgebungen und Liefermethoden, die Teilnehmerinteresse, Motivation und aktive Beteiligung in Trainingsprogrammen aufrechterhalten. Diese Verbesserung erhöht Lernbehältnis und Teilnehmerzufriedenheit. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2703", "narrower_terms": [], "cross_domain_refs": [ "AED-0018", "AED-0062", "AED-0066" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "EDU-0039", "domain": "EDU", "term_en": "Evidence Integration", "term_de": "Sensorik in education", "definition_en": "An educational technology pattern in AI-mediated instruction, observable through a phenomenon in which building arguments and ideas on actual evidence from research, data, observations — not just opinions or what sounds right. Here's what the evidence actually says. The concept emerges specifically in contexts where evidence–integration interactions may produce non-trivial behavioral signatures. Measurable through learning outcome deltas (pre/post assessment), engagement metrics (time-on-task, voluntary re-engagement), and knowledge retention curves.", "definition_de": "Bildungstechnologisches Phänomen in KI-gestützten Lernumgebungen, gekennzeichnet durch die systematische Synthese mehrerer Bewertungsquellen, einschließlich Leistungsaufgaben, Selbstbewertungen, Peer-Input und Lehrbeobachtungen. Dieser integrierte Ansatz erfasst multifacettierte Lernentwicklung und reduziert Verzerrungen, die bei der Einzelmethodenbewertung inhärent sind. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Analytical Ratio", "narrower_terms": [], "cross_domain_refs": [ "ASE-0031" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "EDU-0040", "domain": "EDU", "term_en": "Evidence Literacy", "term_de": "Mobile Anwendungen in education", "definition_en": "A behavioral pattern where understanding how to find, evaluate, and use evidence properly — knowing which sources are trustworthy, what counts as actual evidence versus opinion, and how strong the evidence really is. Observable through learning outcome deltas and engagement pattern analysis.", "definition_de": "Bildungstechnologisches Phänomen in KI-gestützten Lernumgebungen, gekennzeichnet durch die Fähigkeit, pädagogische Forschung kritisch zu bewerten, Qualitätsevidenz zu erkennen und Erkenntnisse mit angemessener Nuance auf die unterrichtliche Praxis anzuwenden. Diese Lesekompetenz ermöglicht es Fachleuten, die komplexe Landschaft pädagogischer Aussagen und Evidenzen zu navigieren. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2505", "narrower_terms": [], "cross_domain_refs": [ "ASE-0031" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q201684", "legal_classification": "analytical_category" }, { "id": "EDU-0041", "domain": "EDU", "term_en": "Evidence Synthesis", "term_de": "Cloud-Lösungen für education", "definition_en": "A learning dynamics concept in AI-augmented education, quantifiable via a behavioral pattern where taking a bunch of different sources and studies and pulling them together into one coherent picture — not just listing them, but showing how they support, contradict, or add nuance to each other. Distinguished from adjacent concepts by its focus on the specific mechanism through which evidence manifests in empirically verifiable ways. Quantifiable via adaptive assessment scoring, misconception detection rates, and transfer learning performance in novel problem domains.", "definition_de": "Bildungstechnologisches Phänomen in KI-gestützten Lernumgebungen, gekennzeichnet durch die Integration mehrerer Forschungsquellen und Evidenzströme in kohärente Argumente, die spezifische Aussagen unterstützen oder widerlegen. Diese Synthese demonstriert, wie individuelle Ergebnisse kombinieren, um umfassendes Verständnis zu erbauen. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Education & Learning", "narrower_terms": [], "cross_domain_refs": [ "ART-0087" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "EDU-0042", "domain": "EDU", "term_en": "Experience Integration", "term_de": "Datenbankverwaltung in education", "definition_en": "A phenomenon in which using things students have actually lived through as a starting point for learning new ideas. A student's experience with friendship frictions helps them understand literature. A student's experien... Observable through learning outcome deltas and engagement pattern analysis.", "definition_de": "Bildungstechnologisches Phänomen in KI-gestützten Lernumgebungen, gekennzeichnet durch die bewusste Einbeziehung des bisherigen Wissens, gelebter Erfahrungen und beruflicher Hintergründe von Fachleuten in neue Lernkontexte. Dieser Ansatz würdigt die bestehende Fachkompetenz von Erwachsenenlernern und schafft Brücken zwischen vertrauten und neuartigen Konzepten. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-2301", "narrower_terms": [], "cross_domain_refs": [ "ADA-0010", "AED-0033", "AED-0034" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "EDU-0043", "domain": "EDU", "term_en": "Experiential Framing", "term_de": "Visualisierung in education", "definition_en": "When a lesson starts with something students do or experience before they learn the concept. Build the structure first, then learn the engineering principles. Play the game, then learn the math. Observable through learning outcome deltas and engagement pattern analysis.", "definition_de": "Bildungstechnologisches Phänomen in KI-gestützten Lernumgebungen, gekennzeichnet durch die Kontextualisierung abstrakter Prinzipien in aussagekräftigen realen Szenarien, die Vorwissen aktivieren und unmittelbare Relevanz erzeugen. Diese Technik verbindet theoretisches Verständnis und praktische Anwendung für Lernerpopulationen. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2301", "narrower_terms": [], "cross_domain_refs": [ "ELR-0025" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "EDU-0044", "domain": "EDU", "term_en": "Expression Confidence", "term_de": "Simulation in education", "definition_en": "An educational technology pattern in AI-mediated instruction, observable through a perception in which students feel okay sharing their thoughts in class or on an assignment without intense apprehension of being wrong or judged. not overconfidence, but genuine comfort. The concept emerges specifically in contexts where expression–confidence interactions may produce non-trivial behavioral signatures. Quantifiable via adaptive assessment scoring, misconception detection rates, and transfer learning performance in novel problem domains. Analytical category without normative endorsement.", "definition_de": "Bildungstechnologisches Phänomen in KI-gestützten Lernumgebungen, gekennzeichnet durch das Wachstum der Lernerbere itschaft, sprachliche Leistung trotz unvollkommener Genauigkeit zu produzieren, aufrechterhalten durch unterstützende Rückmeldung und psychologisch sichere Lernumgebungen. Dieses Selbstvertrauen ist typisch für linguistische Entwicklung und kommunikative Kompetenz. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Education & Learning", "narrower_terms": [], "cross_domain_refs": [ "RPH-1461", "RPH-1109" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "EDU-0045", "domain": "EDU", "term_en": "Flexible Pacing", "term_de": "Digitaler Zwilling in education", "definition_en": "A shift that occurs when some units move fast if students grasp it quickly, some move slow because it's genuinely hard or needs deep thinking. The schedule adjusts to learning, not the other way around. Observable through learning outcome deltas and engagement pattern analysis.", "definition_de": "Bildungstechnologisches Phänomen in KI-gestützten Lernumgebungen, gekennzeichnet durch die Unterstützung vielfältiger Zeiteinschränkungen, Lerngeschwindigkeiten und Lebensumstände von Erwachsenenlernern durch asynchrone Optionen, vielfältige Formate und selbstgesteuertes Lernen. Diese Flexibilität ermöglicht anhaltende Teilnahme über variabel Kontexte. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2703", "narrower_terms": [], "cross_domain_refs": [ "REL-0048" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "EDU-0046", "domain": "EDU", "term_en": "Formative Inquiry", "term_de": "education-Best-Practices", "definition_en": "A learning dynamics concept in AI-augmented education, quantifiable via a perception in which teachers constantly checking in to understand how students are actually thinking — not just yes-or-no questions about understanding, but really asking probing questions to see what makes sense and. Distinguished from adjacent concepts by its focus on the specific mechanism through which formative manifests in empirically verifiable ways. Measurable through learning outcome deltas (pre/post assessment), engagement metrics (time-on-task, voluntary re-engagement), and knowledge retention curves.", "definition_de": "Bildungstechnologisches Phänomen in KI-gestützten Lernumgebungen, gekennzeichnet durch die kontinuierliche Erfassung von Verständnisdaten durch offene Fragen, Beobachtung und erkundungsfreudige Aufgaben, die entstehende Missverständnisse offenbaren. Diese Praxis informiert reaktive unterrichtliche Entscheidungen und zielt darauf ab zu mitigieren die Verfästigung von Missverständnissen. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Education & Learning", "narrower_terms": [], "cross_domain_refs": [ "IDN-0057" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "EDU-0047", "domain": "EDU", "term_en": "Grammar Naturalization", "term_de": "Professionelle education-Praxis", "definition_en": "A behavioral pattern where learning grammar through lots of reading and conversations where learners absorb how language actually works, not by memorizing grammar rules. The patterns become natural through exposure. Observable through learning outcome deltas and engagement pattern analysis.", "definition_de": "Bildungstechnologisches Phänomen in KI-gestützten Lernumgebungen, gekennzeichnet durch die Entwicklung impliziter grammatischer Kenntnisse durch bedeutungsvolle Eingabe und kommunikatives Training statt explizitem Regelmerken. Diese Natürlichkeit ermöglicht intuitive, automatische Sprachproduktion in Echtzeit-Kommunikation. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2703", "narrower_terms": [], "cross_domain_refs": [ "TRA-0033" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "EDU-0048", "domain": "EDU", "term_en": "Growth Mapping", "term_de": "education-Arbeitsablaufgestaltung", "definition_en": "In the descriptive vocabulary of AI interaction science (following ISO 704 terminology standards), this concept identifies A perception in which tracking not just test scores but actual growth — how much a learner has improved, what new things they can do, what makes more sense than it did last month. Celebrating progress, not just final pe... Observable through learning outcome deltas and engagement pattern analysis. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als analytisches Konstrukt der empirischen KI-Forschung (deskriptiv, nicht präskriptiv) erfasst dieser Terminus bildungstechnologisches Phänomen in KI-gestützten Lernumgebungen, gekennzeichnet durch die systematische Dokumentation und Visualisierung von Lernerfortschrittstrajen über mehrere Kompetenzdimensionen hinweg. Diese Praxis offenbart Lernmuster, identifiziert entstehende Fähigkeiten und informiert reaktive unterrichtliche Anpassungen. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "RPH-2703", "narrower_terms": [], "cross_domain_refs": [ "WRK-0028" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "EDU-0049", "domain": "EDU", "term_en": "Immersion Simulation", "term_de": "education-Projektmanagement", "definition_en": "A phenomenon in which creating a learning environment that mimics what it's like to actually use a language or skill in the real world — constant exposure, needing to use it to communicate, encountering realistic situat... Observable through learning outcome deltas and engagement pattern analysis.", "definition_de": "Bildungstechnologisches Phänomen in KI-gestützten Lernumgebungen, gekennzeichnet durch die Schaffung von Lernumgebungen, die Zielsprachen-Immersion durch kontextualisierte Inhalte, authentische Materialien und kommunikationsorientierte Aktivitäten simulieren. Diese Simulation bietet Immersionsvorteile auch in Nicht-Immersionseinstellungen. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2703", "narrower_terms": [], "cross_domain_refs": [ "COG-0032", "COG-0156", "COP-0042" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q202763", "legal_classification": "systematic_classification" }, { "id": "EDU-0050", "domain": "EDU", "term_en": "Inclusive Modulation", "term_de": "education-Teamzusammenarbeit", "definition_en": "An educational technology pattern in AI-mediated instruction, observable through a behavioral pattern where offering the same content in different ways so many individuals can actually access it — some people read, some watch, some listen, some do hands-on. Same ideas, multiple entry points. The concept emerges specifically in contexts where inclusive–modulation interactions may produce non-trivial behavioral signatures. Measurable through learning outcome deltas (pre/post assessment), engagement metrics (time-on-task, voluntary re-engagement), and knowledge retention curves.", "definition_de": "Bildungstechnologisches Phänomen in KI-gestützten Lernumgebungen, gekennzeichnet durch die beabsichtigte Gestaltung der unterrichtlichen Kommunikation, um Lernende mit vielfältigen kognitiven Stilen, Sinnespreferenzen und unterschiedlichen Wissensgrundkännen zu erreichen. Dieser Ansatz stellt sicher, dass pädagogische Botschaften in vielfältigen neurologischen und kulturellen Kontexten resonieren. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-3101", "narrower_terms": [], "cross_domain_refs": [ "VIB-0178" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "EDU-0051", "domain": "EDU", "term_en": "Information Discernment", "term_de": "Kundenbeziehungen in education", "definition_en": "A pattern in which variant in which being able to tell the difference between reliable information and garbage — knowing which sources are trustworthy, spotting propaganda, recognizing bias, understanding what's actually evidence ver... Observable through learning outcome deltas and engagement pattern analysis.", "definition_de": "Bildungstechnologisches Phänomen in KI-gestützten Lernumgebungen, gekennzeichnet durch die Entwicklung der Lernerfähigkeit, zuverlässige von unzuverlässigen Informationen in digitalen Umgebungen zu unterscheiden, Quellen-Glaubwürdigkeit zu bewerten und täuschende Inhalte zu erkennen. Diese Unterscheidung ist für die Funktion in informationssättigten Kontexten notwendig. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2505", "narrower_terms": [], "cross_domain_refs": [ "CUS-0068", "DAT-0052", "FIC-0046" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "EDU-0052", "domain": "EDU", "term_en": "Innovation Integration", "term_de": "education-Kommunikation", "definition_en": "A learning dynamics concept in AI-augmented education, quantifiable via a shift that occurs when using new ideas and methods in teaching instead of 'we've typically done it this way.' Trying new approaches, adapting to changes, bringing in emerging practices. Distinguished from adjacent concepts by its focus on the specific mechanism through which innovation manifests in empirically verifiable ways. Quantifiable via adaptive assessment scoring, misconception detection rates, and transfer learning performance in novel problem domains.", "definition_de": "Bildungstechnologisches Phänomen in KI-gestützten Lernumgebungen, gekennzeichnet durch die bewusste Annahme und Anpassung aufstrebender pädagogischer Werkzeuge und Methoden, um die unterrichtliche Effektivität zu verbessern. Diese Praxis erfordert sowohl Offenheit für Veränderung als auch kritische Bewertung der Innovations-Eignung in bestehenden Kontexten. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Analytical Ratio", "narrower_terms": [], "cross_domain_refs": [ "ADA-0010", "AED-0034", "AED-0072" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q219644", "legal_classification": "empirical_phenomenon_label" }, { "id": "EDU-0053", "domain": "EDU", "term_en": "Innovation Pathway", "term_de": "Problemlösung in education", "definition_en": "A learning dynamics concept in AI-augmented education, quantifiable via teaching students a clear process for coming up with new ideas and solutions — not waiting for inspiration to strike, but having structured steps to follow for systematic innovation. Distinguished from adjacent concepts by its focus on the specific mechanism through which innovation manifests in empirically verifiable ways. Measurable through learning outcome deltas (pre/post assessment), engagement metrics (time-on-task, voluntary re-engagement), and knowledge retention curves.", "definition_de": "Bildungstechnologisches Phänomen in KI-gestützten Lernumgebungen, gekennzeichnet durch der strukturierte Ansatz, um Mitarbeiter mit Fähigkeiten auszustatten, um Gelegenheiten zu erkennen, kreativ zu denken und Verbesserungen innerhalb organisatorischer Einschränkungen umzusetzen. Dieser Pfad kanalisiert Innovationsimpulse zu konstruktiven Ergebnissen. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Education & Learning", "narrower_terms": [], "cross_domain_refs": [ "CRE-0091" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q219644", "legal_classification": "observational_construct" }, { "id": "EDU-0054", "domain": "EDU", "term_en": "Inquiry Initiation", "term_de": "Entscheidungsfindung in education", "definition_en": "A learning dynamics concept in AI-augmented education, quantifiable via a phenomenon in which students asking real questions they actually want answered, not just answering questions on a worksheet. What sparks genuine curiosity? That curiosity drives the investigation. Distinguished from adjacent concepts by its focus on the specific mechanism through which inquiry manifests in empirically verifiable ways. Measurable through learning outcome deltas (pre/post assessment), engagement metrics (time-on-task, voluntary re-engagement), and knowledge retention curves.", "definition_de": "Bildungstechnologisches Phänomen in KI-gestützten Lernumgebungen, gekennzeichnet durch die Entwicklung der Lernerfähigkeit, bedeutungsvolle Forschungsfragen zu formulieren, Wissenslücken zu identifizieren und Forschungsrahmen zu etablieren. Diese Initiation transformiert passive Informationsnutzung in aktive Wissensgenerierung. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2403", "narrower_terms": [], "cross_domain_refs": [ "BEH-0026" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "EDU-0055", "domain": "EDU", "term_en": "Integration Weaving", "term_de": "Zeitmanagement in education", "definition_en": "A learning dynamics concept in AI-augmented education, quantifiable via a behavioral pattern where connecting ideas across different subjects instead of keeping them separate. Math shows up in science, history connects to literature, design shows up in biology. It's all one fabric, not separated. Distinguished from adjacent concepts by its focus on the specific mechanism through which integration manifests in empirically verifiable ways. Measurable through learning outcome deltas (pre/post assessment), engagement metrics (time-on-task, voluntary re-engagement), and knowledge retention curves.", "definition_de": "Bildungstechnologisches Phänomen in KI-gestützten Lernumgebungen, gekennzeichnet durch die beabsichtigte Verbindung von Inhalten über Disziplinen, Einheiten und Lernerfahrungen hinweg, um Zusammenhänge zu enthüllen und Relevanz über isolierte Fachgebiete hinaus zu demonstrieren. Diese Verflechtung schafft Bedeutungskonstruktionsgelegenheiten über Wissensbereiche. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Analytical Ratio", "narrower_terms": [], "cross_domain_refs": [ "ADA-0010", "AED-0034", "AED-0072" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "EDU-0056", "domain": "EDU", "term_en": "Interaction Orchestration", "term_de": "Ressourcenplanung in education", "definition_en": "A learning dynamics concept in AI-augmented education, quantifiable via a capacity that enables a teacher carefully setting up who talks to whom, in what order, for what purpose — not letting all 30 students shout at once, but creating structured conversations where many individuals can actually thin. Distinguished from adjacent concepts by its focus on the specific mechanism through which interaction manifests in empirically verifiable ways. Measurable through learning outcome deltas (pre/post assessment), engagement metrics (time-on-task, voluntary re-engagement), and knowledge retention curves.", "definition_de": "Bildungstechnologisches Phänomen in KI-gestützten Lernumgebungen, gekennzeichnet durch die beabsichtigte Schaffung von Gesprächsgelegenheiten und Interaktionsstrukturen, die Lernende erfordern, Sprache für authentische Kommunikationszwecke zu nutzen. Diese Orchestrierung tendiert dazu zu erzeugen bedeutungsvolles Sprachtraining und entwickelt kommunikative Kompetenz. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Analytical Ratio", "narrower_terms": [], "cross_domain_refs": [ "RHR-0251" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "EDU-0057", "domain": "EDU", "term_en": "Interreliance Building", "term_de": "education-Dokumentation", "definition_en": "A pedagogical phenomenon in AI-enhanced learning environments, characterized by measurable shifts in a capacity that enables teaching students that they depend on each other, that collaboration isn't optional or just 'nice' — some goals genuinely can't be reached alone. Real mutual reliance. This phenomenon operates at the intersection of interreliance and building dynamics within the broader EDU domain. Quantifiable via adaptive assessment scoring, misconception detection rates, and transfer learning performance in novel problem domains.", "definition_de": "Bildungstechnologisches Phänomen in KI-gestützten Lernumgebungen, gekennzeichnet durch ein Konzept oder Phänomen: Teaching students that they depend on each other, that collaboration isn't optional or just 'nice' — some goals genuinel. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-2555", "narrower_terms": [], "cross_domain_refs": [ "AED-0027", "AED-0032", "AED-0047" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "EDU-0058", "domain": "EDU", "term_en": "Knowledge Contribution", "term_de": "Berichtswesen in education", "definition_en": "An educational technology pattern in AI-mediated instruction, observable through a phenomenon in which students don't just consume knowledge; they involve it and add it to the class — sharing expertise, teaching peers, contributing research. many individuals's a knowledge maker, not just a consumer. The concept emerges specifically in contexts where knowledge–contribution interactions may produce non-trivial behavioral signatures. Measurable through learning outcome deltas (pre/post assessment), engagement metrics (time-on-task, voluntary re-engagement), and knowledge retention curves.", "definition_de": "Bildungstechnologisches Phänomen in KI-gestützten Lernumgebungen, gekennzeichnet durch die Entwicklung der Lernerfähigkeit, an Wissenserstellung teilzunehmen, ob durch ursprüngliche Forschung, akademisches Schreiben oder disziplinäre Beiträge. Diese Kapazität positioniert Lernende als Schöpfer anstelle von Konsumenten von Wissen. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Education & Learning", "narrower_terms": [], "cross_domain_refs": [ "BEH-0064" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "EDU-0059", "domain": "EDU", "term_en": "Leadership Emergence", "term_de": "education-Präsentationsfähigkeiten", "definition_en": "An educational technology pattern in AI-mediated instruction, observable through a behavioral pattern where watching student leadership naturally develop as they take on responsibility and others recognize and follow them. Not appointed, but emerging from who they show up to be. The concept emerges specifically in contexts where leadership–emergence interactions may produce non-trivial behavioral signatures. Measurable through learning outcome deltas (pre/post assessment), engagement metrics (time-on-task, voluntary re-engagement), and knowledge retention curves.", "definition_de": "Bildungstechnologisches Phänomen in KI-gestützten Lernumgebungen, gekennzeichnet durch die organische Entwicklung von Fachleuten zu formalen oder informellen Führungspersonen in ihren Gemeinschaften durch demonstrierte Fachkompetenz, Visionsäußerung und Kapazitätsaufbau. Dieses Phänomen stärkt die institutionelle Fähigkeit und berufliche Kohäsion. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-3101", "narrower_terms": [], "cross_domain_refs": [ "GAM-0054" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q484275", "legal_classification": "descriptive_research_term" }, { "id": "EDU-0060", "domain": "EDU", "term_en": "Leadership Pipeline", "term_de": "Netzwerken in education", "definition_en": "A phenomenon in which creating chances for students to develop leadership skills in stages — starting with small responsibilities, building to bigger ones over time. Observable through learning outcome deltas and engagement pattern analysis.", "definition_de": "Bildungstechnologisches Phänomen in KI-gestützten Lernumgebungen, gekennzeichnet durch die systematische Identifizierung und Entwicklung aufstrebender Führungs in Organisationsreihen durch gezieltes Training, Mentoring und progressive Verantwortungsvergabe. Diese Rohrleitung stellt die Kontinuität der organisatorischen Führungskapazität sicher. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2301", "narrower_terms": [], "cross_domain_refs": [ "WRK-0015" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q484275", "legal_classification": "observational_construct" }, { "id": "EDU-0061", "domain": "EDU", "term_en": "Learning Sequencing", "term_de": "education-Qualitätssicherung", "definition_en": "A behavioral pattern where ordering what students learn so each thing builds on what came before in a logical way. Students learn to count before doing multi-digit addition. They read before analyzing literature. Sequence ma... Observable through learning outcome deltas and engagement pattern analysis.", "definition_de": "Bildungstechnologisches Phänomen in KI-gestützten Lernumgebungen, gekennzeichnet durch die beabsichtigte Anordnung von Unterrichtsinhalten und Aktivitäten in Progressionen, die auf grundlegenden Verständnis aufbauen und zu zunehmender Komplexität voranschreiten. Diese Abfolge optimiert kognitive Verarbeitung und konzeptuelle Integration. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-2703", "narrower_terms": [], "cross_domain_refs": [ "AED-0001", "AED-0002", "AED-0004" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q133500", "legal_classification": "descriptive_research_term" }, { "id": "EDU-0062", "domain": "EDU", "term_en": "Listening Attunement", "term_de": "education-Normen", "definition_en": "An educational technology pattern in AI-mediated instruction, observable through a phenomenon in which really listening to what students say — not just waiting to respond, but genuinely hearing what they're disoriented about, what they care about, what they need. The concept emerges specifically in contexts where listening–attunement interactions may produce non-trivial behavioral signatures. Quantifiable via adaptive assessment scoring, misconception detection rates, and transfer learning performance in novel problem domains.", "definition_de": "Bildungstechnologisches Phänomen in KI-gestützten Lernumgebungen, gekennzeichnet durch die systematische Entwicklung der Lernerfähigkeit, authentische gesprochene Sprache bei natürlicher Geschwindigkeit zu verstehen, einschließlich Variationen in Aussprache, Akzent und Gesprächsmustern. Diese Abstimmung verbindet Klassenzimmerunterricht und Kommunikation in der realen Welt. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Education & Learning", "narrower_terms": [], "cross_domain_refs": [ "WRK-0036" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "EDU-0063", "domain": "EDU", "term_en": "Mastery Framework", "term_de": "ISO-Normen in education", "definition_en": "A behavioral pattern where a clear map of what actual mastery of a skill or topic looks like — what are the levels? What's the difference between 'kinda knows it' and 'actually mastered it'? How does a learner progress?. Observable through learning outcome deltas and engagement pattern analysis.", "definition_de": "Bildungstechnologisches Phänomen in KI-gestützten Lernumgebungen, gekennzeichnet durch die Etablierung klarer Progressionen vom anfänglichen Lernen durch zunehmende Sophistikation hin zu anerkannter Meisterschaft innerhalb strukturierter Kompetenzrahmen. Dieser Rahmen leitet Lernerprogression und Lehrerbewertung. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2703", "narrower_terms": [], "cross_domain_refs": [ "ELR-0123" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "EDU-0064", "domain": "EDU", "term_en": "Mastery Progression", "term_de": "education-Zertifizierung", "definition_en": "A phenomenon in which moving from beginner to intermediate to advanced in a skill over time, with clear steps in between. A learner is not either there or not; they're typically making progress. Observable through learning outcome deltas and engagement pattern analysis.", "definition_de": "Bildungstechnologisches Phänomen in KI-gestützten Lernumgebungen, gekennzeichnet durch die Dokumentation des Lernervortschritts durch erkennbare Fähigkeitsstufen, die inkrementelle Gewinne und zunehmende Sophistikation betonen. Dieser Ansatz feiert Anstrengungstrajen und baut intrinsische Motivation durch sichtbare Fortschritte auf. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2703", "narrower_terms": [], "cross_domain_refs": [ "AED-0057", "ASE-0064", "COG-0081" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "EDU-0065", "domain": "EDU", "term_en": "Mentorship Reciprocity", "term_de": "Audit in education", "definition_en": "A behavioral pattern where not just 'successful person tells young person what to do,' but actual two-way relationships where both people learn from each other and grow. Observable through learning outcome deltas and engagement pattern analysis.", "definition_de": "Bildungstechnologisches Phänomen in KI-gestützten Lernumgebungen, gekennzeichnet durch die wechselseitige Lernbeziehung, in der erfahrene und aufstrebende Fachleute Wissen, Perspektiven und Wachstumschancen austauschen. Diese Dynamik schafft Fortschrittswege und verteilt Weisheit über professionelle Ebenen. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2703", "narrower_terms": [], "cross_domain_refs": [ "NEO-3540" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "EDU-0066", "domain": "EDU", "term_en": "Multimodal Competence", "term_de": "education-Benchmarking", "definition_en": "A behavioral pattern where being skilled at learning and communicating in many different ways — reading, writing, speaking, watching, making videos, collaborating. Multiple ways of being smart. Observable through learning outcome deltas and engagement pattern analysis.", "definition_de": "Bildungstechnologisches Phänomen in KI-gestützten Lernumgebungen, gekennzeichnet durch die Entwicklung der Lernerfähigkeit, Informationen über vielfältige Modalitäten einschließlich Text, Bild, Audio und Video in digitalen Kontexten zu erstellen, zu interpretieren und zu navigieren. Diese Kompetenz ermöglicht volle Teilnahme an zeitgenössischer Kommunikation. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2605", "narrower_terms": [], "cross_domain_refs": [ "WRK-0048" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q26944", "legal_classification": "empirical_phenomenon_label" }, { "id": "EDU-0067", "domain": "EDU", "term_en": "Mutual Accountability", "term_de": "Leistungskennzahlen in education", "definition_en": "A phenomenon in which many individuals in the class takes responsibility for the collective learning — if someone's disoriented, that's all of our challenge. if someone figured something out, they. Observable through learning outcome deltas and engagement pattern analysis.", "definition_de": "Bildungstechnologisches Phänomen in KI-gestützten Lernumgebungen, gekennzeichnet durch die Etablierung geteilter Verantwortungssysteme, in denen Gruppenmitglieder Fortschritt überwachen, unterstützende Rückmeldung bieten und Engagement für kollektive Ziele sicherstellen. Diese Verantwortung bewahrt Motivation und Qualität der Zusammenarbeitsarbeit. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2703", "narrower_terms": [], "cross_domain_refs": [ "AED-0069", "AED-0070", "ASE-0013" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q15223", "legal_classification": "systematic_classification" }, { "id": "EDU-0068", "domain": "EDU", "term_en": "Narrative Threading", "term_de": "Kontinuierliche Verbesserung in education", "definition_en": "An educational technology pattern in AI-mediated instruction, observable through a behavioral pattern where using stories to connect ideas throughout a course — not random lessons, but a narrative that ties things together and makes them memorable. The story of how something developed, or a character's j. The concept emerges specifically in contexts where narrative–threading interactions may produce non-trivial behavioral signatures. Measurable through learning outcome deltas (pre/post assessment), engagement metrics (time-on-task, voluntary re-engagement), and knowledge retention curves.", "definition_de": "Bildungstechnologisches Phänomen in KI-gestützten Lernumgebungen, gekennzeichnet durch die Praxis, zusammenhängende Geschichten durch Lehrinhalte zu weben, um Bedeutungskonstruktionspfade für Lernende zu schaffen. Lehrende nutzen diese Technik, um getrennte Konzepte zu verbinden und kontextuelle Relevanz zu erzeugen, die das Verständnis und die Beibehaltung vertiefen. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Education & Learning", "narrower_terms": [], "cross_domain_refs": [ "COG-0132", "COG-0161", "CON-0060" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q131324", "legal_classification": "descriptive_research_term" }, { "id": "EDU-0069", "domain": "EDU", "term_en": "Pedagogical Presence", "term_de": "education-Inspektion", "definition_en": "A behavioral pattern where a teacher showing up fully in the learning space — not just reading from a script, but genuinely engaging with students and their ideas in real time. Observable through learning outcome deltas and engagement pattern analysis.", "definition_de": "Bildungstechnologisches Phänomen in KI-gestützten Lernumgebungen, gekennzeichnet durch die Qualität des demonstrierten Engagements eines Lehrenden für das Lernerwächstum, ausgedrückt durch reaktive Rückmeldung, Verfügbarkeit und Ausrichtung der Lehrmaßnahmen auf erklärte Lernziele. Diese Qualität fördert psychologische Sicherheit und Verhältnis zwischen Lehrenden und Lernenden in Lernkontexten. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2703", "narrower_terms": [], "cross_domain_refs": [ "DES-0005", "ELR-0135", "GAM-0004" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "EDU-0070", "domain": "EDU", "term_en": "Peer Elevation", "term_de": "Prüfung in education", "definition_en": "An educational technology pattern in AI-mediated instruction, observable through a mechanism that automatically students helping each other adjust — explaining things to each other, giving feedback, pushing each other to think deeper. The whole group rises together. The concept emerges specifically in contexts where peer–elevation interactions may produce non-trivial behavioral signatures. Measurable through learning outcome deltas (pre/post assessment), engagement metrics (time-on-task, voluntary re-engagement), and knowledge retention curves.", "definition_de": "Bildungstechnologisches Phänomen in KI-gestützten Lernumgebungen, gekennzeichnet durch das gegenseitige Lernen und die Fähigkeitsentwicklung, die durch kollaborative Fachgemeinschaften entstehen, in denen Fachleute Erkenntnisse austauschen, sich gegenseitig beobachten und konstruktive Beiträge bieten. Diese Dynamik stärkt die kollektive Lehrkapazität. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Education & Learning", "narrower_terms": [], "cross_domain_refs": [ "CRE-0135" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "EDU-0071", "domain": "EDU", "term_en": "Performance Elevation", "term_de": "Kalibrierung in education", "definition_en": "An educational technology pattern in AI-mediated instruction, observable through a behavioral pattern where actually improving how well someone can do something — not just trying it once, but practicing, getting feedback, adjusting, and genuinely getting more effectively over time. The concept emerges specifically in contexts where performance–elevation interactions may produce non-trivial behavioral signatures. Measurable through learning outcome deltas (pre/post assessment), engagement metrics (time-on-task, voluntary re-engagement), and knowledge retention curves.", "definition_de": "Bildungstechnologisches Phänomen in KI-gestützten Lernumgebungen, gekennzeichnet durch die systematische Verbesserung der Arbeitsplatzleistung durch gezieltes Kompetenztraining, Wissensvermittlung und Kapazitätsaufbau, die mit rollenspeifischen Anforderungen verhältnis sind. Diese Erhöhung trägt direkt zur organisatorischen Effektivität und Mitarbeiterentwicklung bei. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Education & Learning", "narrower_terms": [], "cross_domain_refs": [ "WRK-0068" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q1361053", "legal_classification": "observational_construct" }, { "id": "EDU-0072", "domain": "EDU", "term_en": "Perspective Broadening", "term_de": "Fehlervermeidung in education", "definition_en": "A phenomenon in which learning to see situations from viewpoints totally different from one's own — not just tolerating different perspectives, but genuinely understanding why others see the world differently. Observable through learning outcome deltas and engagement pattern analysis.", "definition_de": "Bildungstechnologisches Phänomen in KI-gestützten Lernumgebungen, gekennzeichnet durch die beabsichtigte Exponierung für mehrere Sichtweisen, theoretische Rahmen und disziplinäre Perspektiven, die das Lernverständnis erweitern und interpretive Enge reduzieren. Diese Erweiterung fördert intellektuelle Demut und nuanciertes Verständnis. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2703", "narrower_terms": [], "cross_domain_refs": [ "AGE-0004" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "EDU-0073", "domain": "EDU", "term_en": "Perspective Weaving", "term_de": "Fehleranalyse in education", "definition_en": "An educational technology pattern in AI-mediated instruction, observable through a behavioral pattern where bringing together multiple different ways of looking at an issue in one discussion or assignment — not presenting one view, but showing how different perspectives add to understanding the whole pic. The concept emerges specifically in contexts where perspective–weaving interactions may produce non-trivial behavioral signatures. Measurable through learning outcome deltas (pre/post assessment), engagement metrics (time-on-task, voluntary re-engagement), and knowledge retention curves.", "definition_de": "Bildungstechnologisches Phänomen in KI-gestützten Lernumgebungen, gekennzeichnet durch die beabsichtigte Integration vielfältiger Sichtweisen und Hintergründe in einheitliches Verständnis durch strukturierte Diskussion und beabsichtigte Synthese. Diese Verflechtung würdigt Unterschied, während gemeinsamer Grund gebaut wird. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Education & Learning", "narrower_terms": [], "cross_domain_refs": [ "WRK-0076" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "EDU-0074", "domain": "EDU", "term_en": "Platform Agility", "term_de": "Prozesskontrolle in education", "definition_en": "A capacity that enables being comfortable learning on whatever platform or tool is needed — doesn't matter if it's a new app or software, learners can figure it out and use it effectively. Observable through learning outcome deltas and engagement pattern analysis.", "definition_de": "Bildungstechnologisches Phänomen in KI-gestützten Lernumgebungen, gekennzeichnet durch die Entwicklung der Lernerbeweglichkeit über mehrere Lernmanagementsysteme, Zusammenarbeitswerkzeuge und digitale Plattformen, die nahtlose Überführing und effektive Nutzung unabhängig von technologischer Schnittstelle ermöglichen. Diese Beweglichkeit zielt darauf ab zu mitigieren, dass Technologie das Lernen beeinträchtigt. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2703", "narrower_terms": [], "cross_domain_refs": [ "DES-0030" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "EDU-0075", "domain": "EDU", "term_en": "Portfolio Emergence", "term_de": "education-Compliance", "definition_en": "A learning dynamics concept in AI-augmented education, quantifiable via a behavioral pattern where building a collection of actual work over time that shows what a student can really do — not just grades, but samples of essays, projects, designs that prove their capabilities. Distinguished from adjacent concepts by its focus on the specific mechanism through which portfolio manifests in empirically verifiable ways. Quantifiable via adaptive assessment scoring, misconception detection rates, and transfer learning performance in novel problem domains.", "definition_de": "Bildungstechnologisches Phänomen in KI-gestützten Lernumgebungen, gekennzeichnet durch die organische Entwicklung von Lernerevidenzsammlungen, die Fähigkeiten, Reflexionen und Leistungen über erweiterte Zeiträume dokumentieren. Dieses Phänomen ermöglicht ganzheitliche Bewertung, die die Komplexität authentischer Lernerfahrungen erfasst. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Education & Learning", "narrower_terms": [], "cross_domain_refs": [ "ELR-0087" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "EDU-0076", "domain": "EDU", "term_en": "Presence Amplification", "term_de": "education-Sicherheitsmanagement", "definition_en": "An educational technology pattern in AI-mediated instruction, observable through a phenomenon in which a teacher making sure their energy and attention is genuinely present for students — not distracted, not going through the motions, but fully with the group. The concept emerges specifically in contexts where presence–amplification interactions may produce non-trivial behavioral signatures. Quantifiable via adaptive assessment scoring, misconception detection rates, and transfer learning performance in novel problem domains.", "definition_de": "Bildungstechnologisches Phänomen in KI-gestützten Lernumgebungen, gekennzeichnet durch die Verstärkung der pädagogischen Wirkung eines Lehrenden durch bewusste Modulation der Stimme, räumliche Positionierung und Aufmerksamkeitssignale. Dieses Phänomen ermöglicht es Fachleuten, die Lernerbeteiligung in unterschiedlichen Lernumgebungen zu bewahren, von Präsenzunterricht bis zu Hybrid- und vollständig virtuellen Einrichtungen. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Education & Learning", "narrower_terms": [], "cross_domain_refs": [ "AGE-0030", "ASE-0075", "COG-0015" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "EDU-0077", "domain": "EDU", "term_en": "Privacy Awareness", "term_de": "Risikobeurteilung in education", "definition_en": "A learning dynamics concept in AI-augmented education, quantifiable via a perception in which understanding what information online is public versus private, who has access to what, and how to protect personal information. Not heightened awareness, but aware. Distinguished from adjacent concepts by its focus on the specific mechanism through which privacy manifests in empirically verifiable ways. Quantifiable via adaptive assessment scoring, misconception detection rates, and transfer learning performance in novel problem domains. Descriptive research term, not a prescriptive recommendation.", "definition_de": "Bildungstechnologisches Phänomen in KI-gestützten Lernumgebungen, gekennzeichnet durch die Entwicklung des Lernverständnisses von Datenerfassung, Datenschutzauswirkungen digitaler Aktivitäten und persönlichen Informationsschützstrategien. Dieses Bewusstsein ermöglicht verantwortungsvolle Teilnahme in digitalen Räumen. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Education & Learning", "narrower_terms": [], "cross_domain_refs": [ "TEM-0004" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q18619", "legal_classification": "empirical_phenomenon_label" }, { "id": "EDU-0078", "domain": "EDU", "term_en": "Purpose Alignment", "term_de": "Gefährdungserkennung in education", "definition_en": "A phenomenon in which when what students are learning actually connects to something they care about or something that matters in the world. Not arbitrary busywork, but learning with real purpose. Observable through learning outcome deltas and engagement pattern analysis.", "definition_de": "Bildungstechnologisches Phänomen in KI-gestützten Lernumgebungen, gekennzeichnet durch die explizite Verbindung zwischen Lernzielen und den beruflichen oder persönlichen Aspirationen von Erwachsenenfachleuten. Diese Ausrichtung tendiert dazu zu erzeugen intrinsische Motivation und stellt Relevanz für Anwendungskontexte in der realen Welt sicher. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-2703", "narrower_terms": [], "cross_domain_refs": [ "WRK-0072" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "EDU-0079", "domain": "EDU", "term_en": "Reflection Activation", "term_de": "Persönliche Schutzausrüstung", "definition_en": "A pedagogical phenomenon in AI-enhanced learning environments, characterized by measurable shifts in a behavioral pattern where getting students to actually think about their own thinking — how did they solve that? What were they disoriented about? What would they do differently next time? Building thinking about thinking. This phenomenon operates at the intersection of reflection and activation dynamics within the broader EDU domain. Measurable through learning outcome deltas (pre/post assessment), engagement metrics (time-on-task, voluntary re-engagement), and knowledge retention curves.", "definition_de": "Bildungstechnologisches Phänomen in KI-gestützten Lernumgebungen, gekennzeichnet durch die strategische Anregung von Lernererinnerung bezogener Introspektion über Leistung, Lernprozesse und Verständnisentwicklung. Diese Technik fördert metakognitive Bewusstsein und internalisierte Standards zur Bewertung der eigenen Lernqualität. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "Education & Learning", "narrower_terms": [], "cross_domain_refs": [ "RPH-1059", "CRE-0078" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "EDU-0080", "domain": "EDU", "term_en": "Reflective Practice", "term_de": "Notfallverfahren in education", "definition_en": "A shift that occurs when teachers constantly examining their own teaching — what worked, what didn't, why, what would they change. Learning and improving through reflection instead of just repeating what they've typically done. Observable through learning outcome deltas and engagement pattern analysis.", "definition_de": "Bildungstechnologisches Phänomen in KI-gestützten Lernumgebungen, gekennzeichnet durch die systematische Untersuchung unterrichtlicher Entscheidungen, Ergebnisse und zugrundeliegender Annahmen, um pädagogische Ansätze zu verfeinern und tiefere Selbsterkenntnis zu entwickeln. Diese Praxis fördert adaptive Fachkompetenz und Verbesserungsorientierung. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-2703", "narrower_terms": [], "cross_domain_refs": [ "AED-0012", "COG-0178", "CRE-0100" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "EDU-0081", "domain": "EDU", "term_en": "Relevance Resonance", "term_de": "Unfallverhütung in education", "definition_en": "A learning dynamics concept in AI-augmented education, quantifiable via a behavioral pattern where information and ideas actually connecting to students' lives right now — not just historical or abstract, but relevant to how they live and what they care about. Distinguished from adjacent concepts by its focus on the specific mechanism through which relevance manifests in empirically verifiable ways. Quantifiable via adaptive assessment scoring, misconception detection rates, and transfer learning performance in novel problem domains.", "definition_de": "Bildungstechnologisches Phänomen in KI-gestützten Lernumgebungen, gekennzeichnet durch das Phänomen, bei dem Lerninhalt sinnvoll mit beruflichen Rollen, Lebenssituationen und Karriereentwicklungszielen von Erwachsenenfachleuten verbunden ist. Diese Resonanz aufrechterhaltung Aufmerksamkeit und tendiert dazu zu erzeugen transformatives Lernpotenzial. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Education & Learning", "narrower_terms": [], "cross_domain_refs": [ "WRK-0023" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "EDU-0082", "domain": "EDU", "term_en": "Relevance Threading", "term_de": "education-Gesundheitsschutz", "definition_en": "An educational technology pattern in AI-mediated instruction, observable through a behavioral pattern where constantly making connections between new material and things students already know or care about. This math concept shows up in video game design. This historical moment still affects us today. The concept emerges specifically in contexts where relevance–threading interactions may produce non-trivial behavioral signatures. Measurable through learning outcome deltas (pre/post assessment), engagement metrics (time-on-task, voluntary re-engagement), and knowledge retention curves.", "definition_de": "Bildungstechnologisches Phänomen in KI-gestützten Lernumgebungen, gekennzeichnet durch das konsistente Weben von Verbindungen zu Lerninteressen, Karriereaspirationen und realen Anwendungen in der gesamten Curriculumentwicklung. Dieses Faden bewahrt motivationales Momentum und demonstriert Lernwert. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Education & Learning", "narrower_terms": [], "cross_domain_refs": [ "AED-0025", "AED-0082", "CON-0032" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "EDU-0083", "domain": "EDU", "term_en": "Research Design Literacy", "term_de": "Ergonomie in education", "definition_en": "An educational technology pattern in AI-mediated instruction, observable through a behavioral pattern where understanding how real research works: how studies are built, what makes them trustworthy. The concept emerges specifically in contexts where research–design interactions may produce non-trivial behavioral signatures. Measurable through learning outcome deltas (pre/post assessment), engagement metrics (time-on-task, voluntary re-engagement), and knowledge retention curves.", "definition_de": "Bildungstechnologisches Phänomen in KI-gestützten Lernumgebungen, gekennzeichnet durch die Entwicklung des Lernverständnisses von Forschungsmethoden, Validitätserwagungen und Limitierungen in der Wissensgenerierung. Diese Lesekompetenz ermöglicht kritische Interpretation von Forschungsaussagen und angemessene Anwendung von Erkenntnissen. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2505", "narrower_terms": [], "cross_domain_refs": [ "WRK-0027" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q201684", "legal_classification": "systematic_classification" }, { "id": "EDU-0084", "domain": "EDU", "term_en": "Respect-Based Learning", "term_de": "Umweltschutz in education", "definition_en": "An educational technology pattern in AI-mediated instruction, observable through a phenomenon in which classrooms built on students and teachers genuinely respecting each other — listening, considering each other's ideas seriously, addressing each other with dignity regardless of agreement. The concept emerges specifically in contexts where respect–based interactions may produce non-trivial behavioral signatures. Measurable through learning outcome deltas (pre/post assessment), engagement metrics (time-on-task, voluntary re-engagement), and knowledge retention curves.", "definition_de": "Bildungstechnologisches Phänomen in KI-gestützten Lernumgebungen, gekennzeichnet durch die pädagogische Haltung, die Erwachsenenlerner als fähige, wissensreiche Beitragende würdigt, deren Perspektiven und Fragen das kollektive Lernen bereichern. Dieser Ansatz fördert psychologische Sicherheit und gegenseitigen intellektuellen Austausch. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Machine Learning", "narrower_terms": [], "cross_domain_refs": [ "AED-0001", "AED-0002", "AED-0004" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q133500", "legal_classification": "analytical_category" }, { "id": "EDU-0085", "domain": "EDU", "term_en": "Retention Building", "term_de": "Brandschutz in education", "definition_en": "A capacity that enables making sure students actually remember and can use what they learned weeks or months later, not just for the test and then it vanishes. Knowledge sticks around. Observable through learning outcome deltas and engagement pattern analysis.", "definition_de": "Bildungstechnologisches Phänomen in KI-gestützten Lernumgebungen, gekennzeichnet durch der strategische Einsatz von beruflicher Entwicklung und Lernmöglichkeiten, um Mitarbeiterwert zu demonstrieren, Karrierefortschritt zu unterstützen und Angehörigkeit zur organisatorischen Mission zu schaffen. Dieser Aufbau verbessert Talentretention und organisatorisches Engagement. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2703", "narrower_terms": [], "cross_domain_refs": [ "AED-0027", "AED-0032", "AED-0047" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "EDU-0086", "domain": "EDU", "term_en": "Role Clarity", "term_de": "Chemische Sicherheit in education", "definition_en": "A pedagogical phenomenon in AI-enhanced learning environments, characterized by measurable shifts in a phenomenon in which understanding what someone's job actually is and what they're responsible for, versus confusion about their purpose or boundaries. This phenomenon operates at the intersection of role and clarity dynamics within the broader EDU domain. Quantifiable via adaptive assessment scoring, misconception detection rates, and transfer learning performance in novel problem domains.", "definition_de": "Bildungstechnologisches Phänomen in KI-gestützten Lernumgebungen, gekennzeichnet durch die explizite Definition individueller Verantwortungen und Beiträge innerhalb von Gruppenlernstrukturen, die Koordination ermöglicht und zwischenmenschliche Bruchstellen zielt darauf ab zu mitigieren. Diese Klarheit bewahrt produktive Zusammenarbeit über längere Zeiträume. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "Education & Learning", "narrower_terms": [], "cross_domain_refs": [ "WRK-0072" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "EDU-0087", "domain": "EDU", "term_en": "Rubric Clarity", "term_de": "Elektrische Sicherheit in education", "definition_en": "A learning dynamics concept in AI-augmented education, quantifiable via a tendency in which having a detailed, understandable guide for what makes work good — specific criteria, examples of what each level looks like, so students know exactly what to aim for. Distinguished from adjacent concepts by its focus on the specific mechanism through which rubric manifests in empirically verifiable ways. Measurable through learning outcome deltas (pre/post assessment), engagement metrics (time-on-task, voluntary re-engagement), and knowledge retention curves.", "definition_de": "Bildungstechnologisches Phänomen in KI-gestützten Lernumgebungen, gekennzeichnet durch die Entwicklung transparenter Leistungsstandards, die Erwartungen durch konkrete Exemplare und dimensionale Spezifizität vermitteln. Diese Praxis ermöglicht konsistente Qualitätsinterpretation und reduziert Bewertungssubjektivität. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-3902", "narrower_terms": [], "cross_domain_refs": [ "ASE-0015", "ASE-0073", "ASE-0082" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "EDU-0088", "domain": "EDU", "term_en": "Scholarly Engagement", "term_de": "Maschinensicherheit in education", "definition_en": "An educational technology pattern in AI-mediated instruction, observable through a behavioral pattern where students engaging with real scholarship and research — not just textbooks, but actual published papers, research findings, how scholars actually think about topics. The concept emerges specifically in contexts where scholarly–engagement interactions may produce non-trivial behavioral signatures. Quantifiable via adaptive assessment scoring, misconception detection rates, and transfer learning performance in novel problem domains.", "definition_de": "Bildungstechnologisches Phänomen in KI-gestützten Lernumgebungen, gekennzeichnet durch die aktive Teilnahme an Fachliteratur, Forschungsgemeinschaften und intellektuellem Diskurs, die Fachleute aktuell mit aufstrebenden Erkenntnissen und theoretischen Entwicklungen hält. Dieses Engagement positioniert Pädagogen als Wissenschaffer, nicht nur als Umsetzer. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Education & Learning", "narrower_terms": [], "cross_domain_refs": [ "STE-0048" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "EDU-0089", "domain": "EDU", "term_en": "Self-Direction Activation", "term_de": "Sicherheitsschulung in education", "definition_en": "A behavioral pattern where students making choices about what to learn and how to learn it, not because they're being managed but because they've got genuine agency and ownership. Observable through learning outcome deltas and engagement pattern analysis.", "definition_de": "Bildungstechnologisches Phänomen in KI-gestützten Lernumgebungen, gekennzeichnet durch die Schaffung von Lernumgebungen, die Erwachsenenfachleute ermutigen, Verantwortung für ihre Lernpfade zu übernehmen, persönliche Ziele zu setzen und bedeutungsvolle Auswahlmöglichkeiten bezgl. Inhalts und Tempos zu treffen. Diese Aktivierung stört die Autonomiebedarf von gereiften Lernern ab. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-2703", "narrower_terms": [], "cross_domain_refs": [ "ELR-0025" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "EDU-0090", "domain": "EDU", "term_en": "Source Evaluation", "term_de": "Vorfalluntersuchung in education", "definition_en": "A pattern in which variant in which looking at where information comes from and deciding whether to trust it — who's the author, what's their expertise, do they have bias, is there evidence backing the claim up. Observable through learning outcome deltas and engagement pattern analysis.", "definition_de": "Bildungstechnologisches Phänomen in KI-gestützten Lernumgebungen, gekennzeichnet durch die systematische Entwicklung der Lernerfähigkeit, Informationsquellen-Glaubwürdigkeit, Autorität und Zuverlässigkeit anhand expliziter Kriterien zu bewerten. Diese Bewertung ermöglicht wählende Auswahl zwischen konkurrierenden Aussagen und Evidenzqualität. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2505", "narrower_terms": [], "cross_domain_refs": [ "WRK-0092" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "EDU-0091", "domain": "EDU", "term_en": "Spiral Deepening", "term_de": "education-Geschäftsmodell", "definition_en": "A pedagogical phenomenon in AI-enhanced learning environments, characterized by measurable shifts in a tendency in which coming back to the same ideas multiple times throughout the year, each time going deeper and making more sophisticated connections. Like spiraling up a tower, revisiting concepts at higher levels. This phenomenon operates at the intersection of spiral and deepening dynamics within the broader EDU domain. Quantifiable via adaptive assessment scoring, misconception detection rates, and transfer learning performance in novel problem domains.", "definition_de": "Bildungstechnologisches Phänomen in KI-gestützten Lernumgebungen, gekennzeichnet durch das erneute Besuchen von Kernkonzepten auf zunehmenden Niveaus von Sophistikation und Komplexität, wenn Lernende stärkeres grundlegendes Verständnis entwickeln. Dieser Ansatz baut nuancierte Meisterschaft auf, während Wissenslücken und oberflächliches Verständnis zielt darauf ab zu mitigieren werden. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "Education & Learning", "narrower_terms": [], "cross_domain_refs": [ "CON-0027", "CRE-0061", "ELR-0035" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "EDU-0092", "domain": "EDU", "term_en": "Synchronous Adaptation", "term_de": "education-Marktanalyse", "definition_en": "A pedagogical phenomenon in AI-enhanced learning environments, characterized by measurable shifts in a shift that occurs when during a live lesson, right in the moment, the teacher noticing and adjusting. The explanation isn't landing, so they switch directions. Students have questions, so the plan changes. This phenomenon operates at the intersection of synchronous and adaptation dynamics within the broader EDU domain. Quantifiable via adaptive assessment scoring, misconception detection rates, and transfer learning performance in novel problem domains.", "definition_de": "Bildungstechnologisches Phänomen in KI-gestützten Lernumgebungen, gekennzeichnet durch die Entwicklung der Lernerfähigkeit, effektiv an Echtzeit-digitale Kommunikation und kollaborative Räume teilzunehmen, einschließlich Videokonferenz, Live-Chat und synchrone Lernplattformen. Diese Anpassung ermöglicht nahtlose Teilnahme in virtuellen Umgebungen. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "System Adaptation", "narrower_terms": [], "cross_domain_refs": [ "AED-0010", "AED-0090", "AGE-0036" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "EDU-0093", "domain": "EDU", "term_en": "Synergy Emergence", "term_de": "Ökonomie der Bildungstechnologie und E-Learning", "definition_en": "A learning dynamics concept in AI-augmented education, quantifiable via a phenomenon in which when a group working together accompanies something more effectively than any individual could have created alone — the combination accompanies something greater than the sum of the parts. Distinguished from adjacent concepts by its focus on the specific mechanism through which synergy manifests in empirically verifiable ways. Quantifiable via adaptive assessment scoring, misconception detection rates, and transfer learning performance in novel problem domains.", "definition_de": "Bildungstechnologisches Phänomen in KI-gestützten Lernumgebungen, gekennzeichnet durch das Phänomen, bei dem kollaborative Gruppenanstrengungen Ergebnisse produzieren, die größer sind als die Summe individueller Beiträge durch erhöhte Kreativität, breitere Perspektive und gegenseitige Verstärkung. Diese Entstehung stellt das höchste Potenzial des kollaborativen Lernens dar. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Education & Learning", "narrower_terms": [], "cross_domain_refs": [ "WRK-0021" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "EDU-0094", "domain": "EDU", "term_en": "Technology Optimization", "term_de": "education-Kostenmanagement", "definition_en": "A tendency in which using technology strategically when it actually helps learning happen more effectively — not for technology's sake, but because this tool genuinely helps students learn something easier or more. Observable through learning outcome deltas and engagement pattern analysis.", "definition_de": "Bildungstechnologisches Phänomen in KI-gestützten Lernumgebungen, gekennzeichnet durch die strategische Auswahl und Anwendung von Technologiewerkzeugen, die an Lernziele und Kontextanforderungen ausgerichtet sind, um das Lernen zu verbessern statt zu komplizieren. Diese Optimierung balanciert technologische Fähigkeit mit pädagogischem Zweck. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2703", "narrower_terms": [], "cross_domain_refs": [ "PER-0133" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "EDU-0095", "domain": "EDU", "term_en": "Timing Mastery", "term_de": "Preisgestaltung in education", "definition_en": "A pedagogical phenomenon in AI-enhanced learning environments, characterized by measurable shifts in a tendency in which knowing when to push forward and when to slow down, when to introduce something new and when to revisit an old idea. The rhythm and pacing of a lesson are deliberately crafted. This phenomenon operates at the intersection of timing and mastery dynamics within the broader EDU domain. Quantifiable via adaptive assessment scoring, misconception detection rates, and transfer learning performance in novel problem domains.", "definition_de": "Bildungstechnologisches Phänomen in KI-gestützten Lernumgebungen, gekennzeichnet durch das intuitive Wissen über Tempo, Pausen und Abfolgen bei der Lehrverabreichung, das es Lehrenden ermöglicht, die kognitiven Verarbeitungs- und Aufmerksamkeitsspannen zu optimieren. Diese Beherrschung umfasst strategisches Schweigen, angemessene Beschleunigung und modale Übergänge. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-2603", "narrower_terms": [], "cross_domain_refs": [ "CON-0053" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "EDU-0096", "domain": "EDU", "term_en": "Transfer Activation", "term_de": "education-Lieferkette", "definition_en": "A phenomenon in which students actually using what they learned in a new situation they haven't seen before. Learned challenge-solving in math? Students use it in science. Learned essay structure? They use it in history. Observable through learning outcome deltas and engagement pattern analysis.", "definition_de": "Bildungstechnologisches Phänomen in KI-gestützten Lernumgebungen, gekennzeichnet durch die beabsichtigte Gestaltung von Lernerfahrungen und Arbeitsplatzzuschüssei, die geschulte Mitarbeiter befähigen, neue Fähigkeiten und Wissen in ihren tatsächlichen Arbeitsrollen erfolgreich anzuwenden. Diese Aktivierung transformiert Lernen in Leistungswirkung. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2703", "narrower_terms": [], "cross_domain_refs": [ "AED-0050", "AED-0092", "ART-0016" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "EDU-0097", "domain": "EDU", "term_en": "Transfer Optimization", "term_de": "Marketing in education", "definition_en": "A behavioral pattern where teaching in a way that makes it more likely students will be able to use what they learned in totally new situations — not just memorizing facts for the unit, but building flexible, transferable un... Observable through learning outcome deltas and engagement pattern analysis.", "definition_de": "Bildungstechnologisches Phänomen in KI-gestützten Lernumgebungen, gekennzeichnet durch die beabsichtigte Strukturierung des Curriculums, um Prinzipentnahme, analogisches Denken und flexible Wissensanwendung in verschiedenen Kontexten zu betonen. Diese Optimierung maximiert die Lernerfähigkeit, Lernen über ursprüngliche Lerneinstellungen hinaus anzuwenden. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-2703", "narrower_terms": [], "cross_domain_refs": [ "AED-0050", "AED-0092", "ART-0016" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "EDU-0098", "domain": "EDU", "term_en": "Vitality Cultivation", "term_de": "Vertrieb in education", "definition_en": "An educational technology pattern in AI-mediated instruction, observable through a phenomenon in which teaching that has energy and excitement about the subject — students catch the enthusiasm and engagement from the teacher genuinely caring and being invested. The concept emerges specifically in contexts where vitality–cultivation interactions may produce non-trivial behavioral signatures. Measurable through learning outcome deltas (pre/post assessment), engagement metrics (time-on-task, voluntary re-engagement), and knowledge retention curves.", "definition_de": "Bildungstechnologisches Phänomen in KI-gestützten Lernumgebungen, gekennzeichnet durch die beabsichtigte Förderung von beruflicher Leidenschaft, Neugier und Engagement für Lernerwachstum über Karrierephasen hinweg. Diese Praxis bewahrt langfristiges Engagement und erhält die unterrichtliche Qualität und Lebendigkeit während verlängerter Amtszeiten. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Education & Learning", "narrower_terms": [], "cross_domain_refs": [ "ROB-0016", "WRK-0017", "WRK-0079" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "EDU-0099", "domain": "EDU", "term_en": "Vocabulary Anchoring", "term_de": "education-Geschäftsplanung", "definition_en": "A phenomenon in which learning new words by connecting them to real things someone already knows—using examples instead of just memorizing. Observable through learning outcome deltas and engagement pattern analysis.", "definition_de": "Bildungstechnologisches Phänomen in KI-gestützten Lernumgebungen, gekennzeichnet durch die strategische Präsentation und Übung von Wortschatz innerhalb bedeutungsvoller Kontexte, die Beibehaltung und produktive Nutzung erleichtern. Diese Verankerung geht über isolierte Wortlisten hin zu integriertem semantischen Verständnis. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-2703", "narrower_terms": [], "cross_domain_refs": [ "COG-0165", "DES-0094", "ELR-0188" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q381081", "legal_classification": "analytical_category" }, { "id": "EDU-0100", "domain": "EDU", "term_en": "Wisdom Actualization", "term_de": "Unternehmertum in education", "definition_en": "A behavioral pattern where learning that goes beyond knowing facts to actually understanding what matters and how to live well — not just what, but why, and how to use knowledge with good judgment. Observable through learning outcome deltas and engagement pattern analysis.", "definition_de": "Bildungstechnologisches Phänomen in KI-gestützten Lernumgebungen, gekennzeichnet durch die Mobilisierung angesammelter beruflicher Erfahrung und Lebensweisheit in neue Lernkontexte, in denen Fachleute integriertes Verständnis auf komplexe zeitgenössische Herausforderungen anwenden. Diese Aktualisierung stellt den Höhepunkt des Lernpotenzials von Erwachsenen dar. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2703", "narrower_terms": [], "cross_domain_refs": [ "AED-0098", "AGE-0049", "AGE-0050" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "ELR-0001", "domain": "ELR", "term_en": "AI-Enabled Procrastination", "term_de": "Ai-enabledProcrastination", "definition_en": "A learning dynamic in which the deferral of learning effort because AI assistance is perceived as available at any moment, reducing urgency to engage with material proactively. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Fernlernphänomen in KI-vermittelter Bildungsbereitstellung, gekennzeichnet durch vermeidungsmuster bei proaktivem Lernengagement durch wahrgenommene KI-Verfügbarkeit jederzeit. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-2703", "narrower_terms": [ "ELR-0168", "ELR-0090", "ELR-0124", "ELR-0058", "ELR-0150", "ELR-0053", "ELR-0116", "ELR-0004", "ELR-0123", "ELR-0007", "ELR-0186", "ELR-0048", "ELR-0031", "ELR-0047", "ELR-0081", "ELR-0112", "ELR-0045", "ELR-0005", "ELR-0176", "ELR-0020", "ELR-0127", "ELR-0093", "ELR-0026", "ELR-0133", "ELR-0049", "ELR-0119", "ELR-0142", "ELR-0161", "ELR-0104", "ELR-0178", "ELR-0190", "ELR-0015", "ELR-0187", "ELR-0184", "ELR-0193", "ELR-0132", "ELR-0006", "ELR-0134", "ELR-0017", "ELR-0054", "ELR-0101", "ELR-0113", "ELR-0118", "ELR-0103", "ELR-0109", "ELR-0098", "ELR-0174", "ELR-0153", "ELR-0068", "ELR-0016", "ELR-0188", "ELR-0019", "ELR-0060", "ELR-0022", "ELR-0010", "ELR-0154", "ELR-0064", "ELR-0091", "ELR-0057", "ELR-0094", "ELR-0037", "ELR-0175", "ELR-0170", "ELR-0160", "ELR-0179", "ELR-0002", "ELR-0063", "ELR-0042", "ELR-0043", "ELR-0065", "ELR-0126", "ELR-0131", "ELR-0149", "ELR-0163", "ELR-0192", "ELR-0185", "ELR-0011", "ELR-0115", "ELR-0165", "ELR-0076", "ELR-0177", "ELR-0083", "ELR-0125", "ELR-0055", "ELR-0152", "ELR-0138", "ELR-0024", "ELR-0044", "ELR-0137" ], "cross_domain_refs": [ "AED-0082" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "ELR-0002", "domain": "ELR", "term_en": "Academic Calendar Pressure", "term_de": "AcademicCalendarPressure", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A distance learning phenomenon in AI-mediated educational delivery, characterized by the tension between semester-based timelines and the continuous evolution of AI tools that may invalidate assessment approaches mid-term. This phenomenon operates at the intersection of academic and calendar dynamics within the broader ELR domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept fernlernphänomen in KI-vermittelter Bildungsbereitstellung, gekennzeichnet durch spannungsverhältnis zwischen semestergebundenen Timelines und kontinuierlicher KI-Tool-Evolution, die Bewertungsdesigns gefährdet. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "E-Learning", "narrower_terms": [], "cross_domain_refs": [ "PHO-0098" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "ELR-0003", "domain": "ELR", "term_en": "Academic Integrity Ambiguity", "term_de": "AcademicIntegrityAmbiguity", "definition_en": "The undefined boundary between legitimate AI use and academic dishonesty, where the line shifts contextually without explicit codification. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Fernlernphänomen in KI-vermittelter Bildungsbereitstellung, gekennzeichnet durch reduktion informeller Wissens-Austauschs und beruflicher Identitätsbildung durch KI-Ersatz menschlicher akademischer Interaktionen. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2701", "narrower_terms": [], "cross_domain_refs": [ "CRE-0124" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "ELR-0004", "domain": "ELR", "term_en": "Academic Integrity Spectrum", "term_de": "AcademicIntegritySpectrum", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures A distance learning phenomenon in AI-mediated educational delivery, characterized by an educational effect characterized by the expanded range of AI use practices between observably acceptable and observably unacceptable that defies binary classification, creating a continuum of ambiguity. This phenomenon operates at the intersection of academic and integrity dynamics within the broader ELR domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept fernlernphänomen in KI-vermittelter Bildungsbereitstellung, gekennzeichnet durch vermeidung von Lernaufgaben an der Kompetenz-Grenze, wenn KI-Lösungen das notwendige Struggle umgehen. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Classification Spectrum", "narrower_terms": [], "cross_domain_refs": [ "ROB-0090" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "ELR-0005", "domain": "ELR", "term_en": "Academic Language Acquisition Delay", "term_de": "AcademicLanguageAcquisitionDelay", "definition_en": "An e-learning interaction pattern in AI-enhanced remote education, observable through a learning phenomenon arising from the slower development of discipline-specific academic register when AI interactions occur primarily in simplified language. The concept emerges specifically in contexts where academic–language interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Fernlernphänomen in KI-vermittelter Bildungsbereitstellung, gekennzeichnet durch trainierte Fähigkeit, präzise Anfragen zu formulieren und KI-Output kritisch zu evaluieren in kollaborativen Workflows. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "E-Learning", "narrower_terms": [], "cross_domain_refs": [ "MUS-0025", "WEB-0030", "RHR-0232" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q315", "legal_classification": "analytical_category" }, { "id": "ELR-0006", "domain": "ELR", "term_en": "Academic Socialization Reduction", "term_de": "AcademicSocializationReduction", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A distance learning phenomenon in AI-mediated educational delivery, characterized by an educational effect reflecting the diminished informal knowledge exchange and professional identity formation that occurs when AI capabilities expand into domains previously exclusive to humans academic interactions. This phenomenon operates at the intersection of academic and socialization dynamics within the broader ELR domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept fernlernphänomen in KI-vermittelter Bildungsbereitstellung, gekennzeichnet durch wissensakkumulation durch iterative Verbesserung von Eingaben und Feedback-Optimierung in Mensch-KI-Zyklen. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "E-Learning", "narrower_terms": [], "cross_domain_refs": [ "COG-0073" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "ELR-0007", "domain": "ELR", "term_en": "Academic Writing Bifurcation", "term_de": "AcademicWritingBifurkation", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures A distance learning phenomenon in AI-mediated educational delivery, characterized by the emerging split between AI-era writing competencies and traditional academic writing conventions, creating parallel but divergent skill sets. This phenomenon operates at the intersection of academic and writing dynamics within the broader ELR domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept fernlernphänomen in KI-vermittelter Bildungsbereitstellung, gekennzeichnet durch qualitativ unterschiedliche Phasen im Kompetenzerwerb durch längerfristige KI-Nutzung mit erkennbaren Entwicklungsmuster. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "E-Learning", "narrower_terms": [], "cross_domain_refs": [ "SWE-0072" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "ELR-0008", "domain": "ELR", "term_en": "Accreditation Standard Tension", "term_de": "AccreditationStandardTension", "definition_en": "An educational effect observed when the conflict between established accreditation requirements and the realities of AI-integrated learning environments that do not fit traditional evaluation frameworks. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Fernlernphänomen in KI-vermittelter Bildungsbereitstellung, gekennzeichnet durch mentale Modell-Anpassung und Denkstruktur-Verschiebung bei intensiver KI-Augmentation der Arbeitspraxis. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2303", "narrower_terms": [], "cross_domain_refs": [ "VIB-0025" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "ELR-0009", "domain": "ELR", "term_en": "Achievement Attribution Drift", "term_de": "AchievementAttributionDrift", "definition_en": "The shifting of responsibility for academic success from personal effort to AI capability, affecting self-concept and resilience. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Fernlernphänomen in KI-vermittelter Bildungsbereitstellung, gekennzeichnet durch situative Prompt-Strategie-Anpassung basierend auf beobachteten Systemverhalten zur iterativen Optimierung. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2701", "narrower_terms": [], "cross_domain_refs": [ "SPR-0167", "AED-0051" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "ELR-0010", "domain": "ELR", "term_en": "Active Learning Substitution", "term_de": "ActiveLearningSubstitution", "definition_en": "An e-learning interaction pattern in AI-enhanced remote education, observable through the replacement of hands-on problem-solving with observation of AI solutions, reducing engagement with the construction of knowledge. The concept emerges specifically in contexts where active–learning interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Fernlernphänomen in KI-vermittelter Bildungsbereitstellung, gekennzeichnet durch episodische kognitive Anstrengung bei Mensch-KI-Kollaboration, oft unterbrochen durch technische oder interaktionale Musterunterbrechungen. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Machine Learning", "narrower_terms": [], "cross_domain_refs": [ "MTH-0055" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q133500", "legal_classification": "analytical_category" }, { "id": "ELR-0011", "domain": "ELR", "term_en": "Adaptive Difficulty Avoidance", "term_de": "AdaptiveDifficultyAvoidance", "definition_en": "An e-learning interaction pattern in AI-enhanced remote education, observable through the evasion of problems at the edge of competence when AI solutions bypass struggle, preventing the development of increasingly complex skills. The concept emerges specifically in contexts where adaptive–difficulty interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Fernlernphänomen in KI-vermittelter Bildungsbereitstellung, gekennzeichnet durch fähigkeit oder Kompetenz im Umgang mit Lern- und Feedback-Zyklen in Mensch-KI-Systemen. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "E-Learning", "narrower_terms": [], "cross_domain_refs": [ "RPH-350" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "ELR-0012", "domain": "ELR", "term_en": "Annotation Avoidance", "term_de": "AnnotationAvoidance", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures A distance learning phenomenon in AI-mediated educational delivery, characterized by a learning phenomenon arising from the lack of engagement with active reading practices like annotation when AI can extract key points instantly. This phenomenon operates at the intersection of annotation and avoidance dynamics within the broader ELR domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept fernlernphänomen in KI-vermittelter Bildungsbereitstellung, gekennzeichnet durch operative Eigenschaft von Lern- und Feedback-Zyklen in Mensch-KI-Systemen. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "RPH-3101", "narrower_terms": [], "cross_domain_refs": [ "DAT-0003" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "ELR-0013", "domain": "ELR", "term_en": "Answer Arrival Reflex", "term_de": "AnswerArrivalReflex", "definition_en": "The immediate impulse to consult an AI system before attempting inreliant problem-solving, observable across learner populations regardless of prior competence.", "definition_de": "Fernlernphänomen in KI-vermittelter Bildungsbereitstellung, gekennzeichnet durch fähigkeit oder Kompetenz im Umgang mit Lern- und Feedback-Zyklen in Mensch-KI-Systemen. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-2254", "narrower_terms": [], "cross_domain_refs": [ "COG-0083" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "ELR-0014", "domain": "ELR", "term_en": "Argumentation Depth Plateau", "term_de": "ArgumentationDepthPlateau", "definition_en": "A pedagogical pattern reflecting the leveling of argument sophistication at a moderate baseline when AI provides adequate but unremarkable reasoning that learners adopt without deepening. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Fernlernphänomen in KI-vermittelter Bildungsbereitstellung, gekennzeichnet durch merkmal von Lern- und Feedback-Zyklen in Mensch-KI-Systemen. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2703", "narrower_terms": [], "cross_domain_refs": [ "RPH-1268" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "ELR-0015", "domain": "ELR", "term_en": "Argumentation Outsourcing", "term_de": "ArgumentationAuslagerung", "definition_en": "A digital pedagogy concept in AI-augmented remote learning, measurable via a pedagogical pattern characterized by the delegation of logical argument construction to AI, reducing the ability to inreliantly formulate coherent positions. Distinguished from adjacent concepts by its focus on the specific mechanism through which argumentation manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Fernlernphänomen in KI-vermittelter Bildungsbereitstellung, gekennzeichnet durch fähigkeit oder Kompetenz im Umgang mit Lern- und Feedback-Zyklen in Mensch-KI-Systemen. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "E-Learning", "narrower_terms": [], "cross_domain_refs": [ "COG-0102" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "ELR-0016", "domain": "ELR", "term_en": "Arguments-From-First-Principles Shift", "term_de": "Arguments-from-first-principlesShift", "definition_en": "A digital pedagogy concept in AI-augmented remote learning, measurable via an educational effect where the inability to construct arguments from foundational reasoning when AI provides ready-made justifications. Distinguished from adjacent concepts by its focus on the specific mechanism through which arguments manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Fernlernphänomen in KI-vermittelter Bildungsbereitstellung, gekennzeichnet durch fähigkeit oder Kompetenz im Umgang mit Lern- und Feedback-Zyklen in Mensch-KI-Systemen. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "E-Learning", "narrower_terms": [], "cross_domain_refs": [ "COG-0048" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "ELR-0017", "domain": "ELR", "term_en": "Assessment Authenticity Doubt", "term_de": "AssessmentAuthenticityDoubt", "definition_en": "Classified in AUGMANITAI terminology science as a descriptive phenomenon (without normative endorsement), this pattern denotes A digital pedagogy concept in AI-augmented remote learning, measurable via the persistent uncertainty among educators about whether formal process complianceted work reflects genuine student capability or AI augmentation. Distinguished from adjacent concepts by its focus on the specific mechanism through which assessment manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als analytisches Konstrukt der empirischen KI-Forschung (deskriptiv, nicht präskriptiv) erfasst dieser Terminus fernlernphänomen in KI-vermittelter Bildungsbereitstellung, gekennzeichnet durch fähigkeit oder Kompetenz im Umgang mit Lern- und Feedback-Zyklen in Mensch-KI-Systemen. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "E-Learning", "narrower_terms": [], "cross_domain_refs": [ "RPH-1415" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q210028", "legal_classification": "analytical_category" }, { "id": "ELR-0018", "domain": "ELR", "term_en": "Assessment Validity Narrowing", "term_de": "AssessmentValidityNarrowing", "definition_en": "The shift of measurement validity when assessments cannot distinguish between AI-generated and learner-generated work, rendering grades uninformative. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Fernlernphänomen in KI-vermittelter Bildungsbereitstellung, gekennzeichnet durch qualitätsmerkmal oder Bewertungskriterium für Lern- und Feedback-Zyklen in Mensch-KI-Systemen. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2701", "narrower_terms": [], "cross_domain_refs": [ "CON-0079" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q210028", "legal_classification": "observational_construct" }, { "id": "ELR-0019", "domain": "ELR", "term_en": "Assignment Design Exhaustion", "term_de": "AssignmentDesignExhaustion", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures A distance learning phenomenon in AI-mediated educational delivery, characterized by a pedagogical pattern manifesting as the fatigue experienced by educators who continuously modify assignments to stay ahead of AI capabilities, without institutional support for this ongoing effort. This phenomenon operates at the intersection of assignment and design dynamics within the broader ELR domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept fernlernphänomen in KI-vermittelter Bildungsbereitstellung, gekennzeichnet durch phänomen oder Erfahrung in der Lern- und Feedback-Zyklen in Mensch-KI-Systemen. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "E-Learning", "narrower_terms": [], "cross_domain_refs": [ "AGE-0073", "ART-0058", "AUG-0902" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q185925", "legal_classification": "descriptive_research_term" }, { "id": "ELR-0020", "domain": "ELR", "term_en": "Assumption Internalization", "term_de": "AssumptionInternalization", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures A distance learning phenomenon in AI-mediated educational delivery, characterized by a learning dynamic characterized by the uncritical acceptance of AI-generated premises and assumptions embedded in explanations, limiting critical evaluation skills. This phenomenon operates at the intersection of assumption and internalization dynamics within the broader ELR domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept fernlernphänomen in KI-vermittelter Bildungsbereitstellung, gekennzeichnet durch kompetenzmerkmal mit bewertende Qualität im KI-unterstützten Lernen. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "E-Learning", "narrower_terms": [], "cross_domain_refs": [ "VIB-0073" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "ELR-0021", "domain": "ELR", "term_en": "Attendance Motivation Shift", "term_de": "AttendanceMotivationShift", "definition_en": "An educational effect characterized by the changing calculus of classroom attendance value when AI can deliver individualized instruction inreliantly of physical presence.", "definition_de": "Fernlernphänomen in KI-vermittelter Bildungsbereitstellung, gekennzeichnet durch systemische Ausprägung von Lern- und Feedback-Zyklen in Mensch-KI-Systemen. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2604", "narrower_terms": [], "cross_domain_refs": [ "RHR-0060" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q186588", "legal_classification": "systematic_classification" }, { "id": "ELR-0022", "domain": "ELR", "term_en": "Attention Span Recalibration", "term_de": "AttentionSpanRecalibration", "definition_en": "A digital pedagogy concept in AI-augmented remote learning, measurable via the observable adjustment in sustained focus duration after extended interaction with AI systems that deliver information in optimized, bite-sized segments. Distinguished from adjacent concepts by its focus on the specific mechanism through which attention manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Fernlernphänomen in KI-vermittelter Bildungsbereitstellung, gekennzeichnet durch strukturelle Eigenschaft von Lern- und Feedback-Zyklen in Mensch-KI-Systemen. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Analytical Ratio", "narrower_terms": [], "cross_domain_refs": [ "SCR-0030" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q184872", "legal_classification": "empirical_phenomenon_label" }, { "id": "ELR-0023", "domain": "ELR", "term_en": "Attribution-Ability Decoupling", "term_de": "Attribution-abilityDecoupling", "definition_en": "The confusion in attributing success to ability versus external aids, distorting the learner's self-concept and resilience. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Fernlernphänomen in KI-vermittelter Bildungsbereitstellung, gekennzeichnet durch kompetenzmerkmal in der Mensch-KI-Lernzusammenarbeit. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-2703", "narrower_terms": [], "cross_domain_refs": [ "ART-0011", "ART-0019", "ART-0058" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "ELR-0024", "domain": "ELR", "term_en": "Authentic Assessment Shift", "term_de": "AuthenticAssessmentShift", "definition_en": "An e-learning interaction pattern in AI-enhanced remote education, observable through a learning dynamic characterized by the gradual devaluation of assessments as educators adjust standards to account for ubiquitous AI availability, lowering benchmark rigor. The concept emerges specifically in contexts where authentic–assessment interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Fernlernphänomen in KI-vermittelter Bildungsbereitstellung, gekennzeichnet durch fähigkeit oder Kompetenz im Umgang mit Lern- und Feedback-Zyklen in Mensch-KI-Systemen. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "E-Learning", "narrower_terms": [], "cross_domain_refs": [ "DES-0041" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q210028", "legal_classification": "systematic_classification" }, { "id": "ELR-0025", "domain": "ELR", "term_en": "Autonomy Delegation", "term_de": "AutonomyDelegation", "definition_en": "A learning phenomenon reflecting the outsourcing of decision-making about what to learn and how to learn to AI recommendations, reducing learner agency. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Fernlernphänomen in KI-vermittelter Bildungsbereitstellung, gekennzeichnet durch charakteristische Komponente von Lern- und Feedback-Zyklen in Mensch-KI-Systemen. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-2703", "narrower_terms": [], "cross_domain_refs": [ "EDU-0089" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "ELR-0026", "domain": "ELR", "term_en": "Calculation Proceduralization Shift", "term_de": "CalculationProceduralizationShift", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures A distance learning phenomenon in AI-mediated educational delivery, characterized by a pedagogical pattern manifesting as the inability to execute mathematical procedures manually when computational support is withdrawn, after extended reliance on AI calculation. This phenomenon operates at the intersection of calculation and proceduralization dynamics within the broader ELR domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept fernlernphänomen in KI-vermittelter Bildungsbereitstellung, gekennzeichnet durch fähigkeit oder Kompetenz im Umgang mit Lern- und Feedback-Zyklen in Mensch-KI-Systemen. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "E-Learning", "narrower_terms": [], "cross_domain_refs": [ "COG-0038" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "ELR-0027", "domain": "ELR", "term_en": "Challenge Avoidance Pattern", "term_de": "ChallengeAvoidanceMuster", "definition_en": "The learned behavior of avoiding difficult material by immediately turning to AI assistance, preventing the cognitive struggle necessary for deep learning. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Fernlernphänomen in KI-vermittelter Bildungsbereitstellung, gekennzeichnet durch beobachtbare Eigenschaft von Lern- und Feedback-Zyklen in Mensch-KI-Systemen. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2703", "narrower_terms": [], "cross_domain_refs": [ "SWE-0049" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "ELR-0028", "domain": "ELR", "term_en": "Citation Chain Breaking", "term_de": "CitationChainBreaking", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures A distance learning phenomenon in AI-mediated educational delivery, characterized by an educational effect arising from the inability to trace ideas back to original sources when AI synthesis obsresolves citation lineage and intellectual ancestry. This phenomenon operates at the intersection of citation and chain dynamics within the broader ELR domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept fernlernphänomen in KI-vermittelter Bildungsbereitstellung, gekennzeichnet durch kompetenzmerkmal in der Mensch-KI-Lernzusammenarbeit. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "RPH-2003", "narrower_terms": [], "cross_domain_refs": [ "DES-0042" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q2083958", "legal_classification": "descriptive_research_term" }, { "id": "ELR-0029", "domain": "ELR", "term_en": "Citation Confusion", "term_de": "CitationConfusion", "definition_en": "A digital pedagogy concept in AI-augmented remote learning, measurable via the ambiguity in distinguishing between original thought and AI-paraphrased content, eroding understanding of intellectual property. Distinguished from adjacent concepts by its focus on the specific mechanism through which citation manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Fernlernphänomen in KI-vermittelter Bildungsbereitstellung, gekennzeichnet durch operative Eigenschaft von Lern- und Feedback-Zyklen in Mensch-KI-Systemen. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2052", "narrower_terms": [], "cross_domain_refs": [ "RPH-2403" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q2083958", "legal_classification": "empirical_phenomenon_label" }, { "id": "ELR-0030", "domain": "ELR", "term_en": "Citation Practice Shift", "term_de": "CitationPracticeShift", "definition_en": "A learning dynamic manifesting as the declining proficiency in proper source attribution as AI systems yield text that blends information from multiple sources without transparent citation. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Fernlernphänomen in KI-vermittelter Bildungsbereitstellung, gekennzeichnet durch kennzeichnende Ausprägung von Lern- und Feedback-Zyklen in Mensch-KI-Systemen. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-3251", "narrower_terms": [], "cross_domain_refs": [ "SWE-0052", "STE-0022" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q2083958", "legal_classification": "empirical_phenomenon_label" }, { "id": "ELR-0031", "domain": "ELR", "term_en": "Cohort Skill Divergence", "term_de": "CohortSkillDivergenz", "definition_en": "A digital pedagogy concept in AI-augmented remote learning, measurable via a pedagogical pattern involving the increasing variance in foundational skills within student cohorts as differential AI use accompanies divergent competency profiles. Distinguished from adjacent concepts by its focus on the specific mechanism through which cohort manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Fernlernphänomen in KI-vermittelter Bildungsbereitstellung, gekennzeichnet durch fähigkeit oder Kompetenz im Umgang mit Lern- und Feedback-Zyklen in Mensch-KI-Systemen. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "E-Learning", "narrower_terms": [], "cross_domain_refs": [ "AGE-0056", "VIB-0095" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "ELR-0032", "domain": "ELR", "term_en": "Collaborative Filtering Effect", "term_de": "CollaborativeFilteringEffekt", "definition_en": "A behavioral tendency where AI recommendation systems in educational platforms involve convergent learning experiences, reducing the diversity of what different learners encounter. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Fernlernphänomen in KI-vermittelter Bildungsbereitstellung, gekennzeichnet durch phänomen oder Erfahrung in der Lern- und Feedback-Zyklen in Mensch-KI-Systemen. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2703", "narrower_terms": [], "cross_domain_refs": [ "ASE-0024" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "ELR-0033", "domain": "ELR", "term_en": "Collaborative Learning Crowding Out", "term_de": "CollaborativeLearningCrowdingOut", "definition_en": "A learning dynamic in which the reduction of peer collaboration when AI is perceived as a faster, more reliable learning partner than human classmates. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Fernlernphänomen in KI-vermittelter Bildungsbereitstellung, gekennzeichnet durch kennzeichnende Ausprägung von Lern- und Feedback-Zyklen in Mensch-KI-Systemen. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2703", "narrower_terms": [], "cross_domain_refs": [ "WRK-0020" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q133500", "legal_classification": "empirical_phenomenon_label" }, { "id": "ELR-0034", "domain": "ELR", "term_en": "Collaborative Tool Substitution", "term_de": "CollaborativeToolSubstitution", "definition_en": "A pedagogical pattern observed when the replacement of human peer collaboration with AI as a learning partner, eroding interpersonal competencies and social bonding through shared learning. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Fernlernphänomen in KI-vermittelter Bildungsbereitstellung, gekennzeichnet durch interaktionsdynamik in der Mensch-KI-Lernzusammenarbeit. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2703", "narrower_terms": [], "cross_domain_refs": [ "AED-0011", "ART-0098", "ASE-0074" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "ELR-0035", "domain": "ELR", "term_en": "Comparative Uncertainty Spiral", "term_de": "ComparativeUncertaintySpiral", "definition_en": "The escalating uncertainty when learners recognize that peers with more effectively AI access achieve higher grades, decoupling effort from outcome. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Fernlernphänomen in KI-vermittelter Bildungsbereitstellung, gekennzeichnet durch spezifisches Attribut von Lern- und Feedback-Zyklen in Mensch-KI-Systemen. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2703", "narrower_terms": [], "cross_domain_refs": [ "AED-0013", "ART-0024", "ASE-0022" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "ELR-0036", "domain": "ELR", "term_en": "Competence Externalization", "term_de": "CompetenceExternalization", "definition_en": "A pedagogical pattern manifesting as the gradual transfer of cognitive work to AI systems, where the learner becomes uncertain about what knowledge they actually possess versus what they can retrieve from AI. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Fernlernphänomen in KI-vermittelter Bildungsbereitstellung, gekennzeichnet durch beobachtbare Eigenschaft von Lern- und Feedback-Zyklen in Mensch-KI-Systemen. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2703", "narrower_terms": [], "cross_domain_refs": [ "REL-0035" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q26944", "legal_classification": "systematic_classification" }, { "id": "ELR-0037", "domain": "ELR", "term_en": "Completion Rate Inflation", "term_de": "CompletionRateInflation", "definition_en": "A digital pedagogy concept in AI-augmented remote learning, measurable via a learning dynamic reflecting the increase in task and course completion metrics that accompanies AI tool adoption without corresponding increases in demonstrated competency. Distinguished from adjacent concepts by its focus on the specific mechanism through which completion manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Fernlernphänomen in KI-vermittelter Bildungsbereitstellung, gekennzeichnet durch funktionale Eigenschaft von Lern- und Feedback-Zyklen in Mensch-KI-Systemen. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "E-Learning", "narrower_terms": [], "cross_domain_refs": [ "AED-0019" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "ELR-0038", "domain": "ELR", "term_en": "Comprehension Perception Effect", "term_de": "ComprehensionPerceptionEffekt", "definition_en": "The false sense of understanding that arises from reading AI-generated explanations without active engagement, distinguishable from genuine learning by its rapid change. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Fernlernphänomen in KI-vermittelter Bildungsbereitstellung, gekennzeichnet durch phänomen oder Erfahrung in der Lern- und Feedback-Zyklen in Mensch-KI-Systemen. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-2703", "narrower_terms": [], "cross_domain_refs": [ "STE-0012" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q177601", "legal_classification": "descriptive_research_term" }, { "id": "ELR-0039", "domain": "ELR", "term_en": "Concept Map Distribution", "term_de": "KonzeptMapDistribution", "definition_en": "The disconnected understanding that results from learning individual facts via AI without building the relational framework that connects them. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Fernlernphänomen in KI-vermittelter Bildungsbereitstellung, gekennzeichnet durch merkmal von Lern- und Feedback-Zyklen in Mensch-KI-Systemen. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2303", "narrower_terms": [], "cross_domain_refs": [ "WRK-0058" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "ELR-0040", "domain": "ELR", "term_en": "Concept-Skill Mismatch", "term_de": "Concept-skillMismatch", "definition_en": "A documented pattern where learners verbalize conceptual understanding but cannot execute underlying skills, revealing superficial learning. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Fernlernphänomen in KI-vermittelter Bildungsbereitstellung, gekennzeichnet durch fähigkeit oder Kompetenz im Umgang mit Lern- und Feedback-Zyklen in Mensch-KI-Systemen. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-2703", "narrower_terms": [], "cross_domain_refs": [ "RPH-2802", "STE-0092" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "ELR-0041", "domain": "ELR", "term_en": "Conceptual Distribution", "term_de": "ConceptualDistribution", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A distance learning phenomenon in AI-mediated educational delivery, characterized by a learning phenomenon reflecting the breakdown of unified understanding when AI provides isolated solutions to problems without connecting to broader conceptual frameworks. This phenomenon operates at the intersection of conceptual and distribution dynamics within the broader ELR domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept fernlernphänomen in KI-vermittelter Bildungsbereitstellung, gekennzeichnet durch funktionale Eigenschaft von Lern- und Feedback-Zyklen in Mensch-KI-Systemen. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "RPH-2303", "narrower_terms": [], "cross_domain_refs": [ "COG-0089" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "ELR-0042", "domain": "ELR", "term_en": "Conceptual Shortcut Accumulation", "term_de": "ConceptualShortcutAccumulation", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures A distance learning phenomenon in AI-mediated educational delivery, characterized by a pedagogical pattern in which the gradual buildup of superficial understanding when AI provides conclusions without exposing the reasoning chain, creating a structure of knowledge without foundations. This phenomenon operates at the intersection of conceptual and shortcut dynamics within the broader ELR domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept fernlernphänomen in KI-vermittelter Bildungsbereitstellung, gekennzeichnet durch beobachtbare Eigenschaft von Lern- und Feedback-Zyklen in Mensch-KI-Systemen. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "E-Learning", "narrower_terms": [], "cross_domain_refs": [ "WRK-0095" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "ELR-0043", "domain": "ELR", "term_en": "Confidence Calibration Distortion", "term_de": "ConfidenceCalibrationDistortion", "definition_en": "A digital pedagogy concept in AI-augmented remote learning, measurable via the misalignment between confidence and actual ability observed alongside inflated performance from AI assistance, creating unrealistic self-assessment. Distinguished from adjacent concepts by its focus on the specific mechanism through which confidence manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Fernlernphänomen in KI-vermittelter Bildungsbereitstellung, gekennzeichnet durch kompetenzmerkmal in der Mensch-KI-Lernzusammenarbeit. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Analytical Ratio", "narrower_terms": [], "cross_domain_refs": [ "AGE-0032", "AGE-0077", "AGE-0094" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "ELR-0044", "domain": "ELR", "term_en": "Context Blind Spot", "term_de": "ContextBlindSpot", "definition_en": "A digital pedagogy concept in AI-augmented remote learning, measurable via a learning phenomenon reflecting the failure to recognize context-specific constraints and domain knowledge because AI solutions appear widely applicable. Distinguished from adjacent concepts by its focus on the specific mechanism through which context manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Fernlernphänomen in KI-vermittelter Bildungsbereitstellung, gekennzeichnet durch spezifisches Attribut von Lern- und Feedback-Zyklen in Mensch-KI-Systemen. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "E-Learning", "narrower_terms": [], "cross_domain_refs": [ "TEW-0084" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "ELR-0045", "domain": "ELR", "term_en": "Creative Assignment Paradox", "term_de": "CreativeAssignmentParadox", "definition_en": "An e-learning interaction pattern in AI-enhanced remote education, observable through the observation that assignments intended to be AI-resistant by requiring creativity often yield results where AI-assisted work appears more creative than unaided attempts. The concept emerges specifically in contexts where creative–assignment interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Fernlernphänomen in KI-vermittelter Bildungsbereitstellung, gekennzeichnet durch operative Eigenschaft von Lern- und Feedback-Zyklen in Mensch-KI-Systemen. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Logical Paradox", "narrower_terms": [], "cross_domain_refs": [ "AGE-0031", "AGE-0042", "AGE-0050" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "ELR-0046", "domain": "ELR", "term_en": "Critical Question Externalization", "term_de": "CriticalQuestionExternalization", "definition_en": "An educational effect in which the outsourcing of critical questioning to AI prompting, reducing the development of inreliant evaluative thinking. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Fernlernphänomen in KI-vermittelter Bildungsbereitstellung, gekennzeichnet durch spezifisches Attribut von Lern- und Feedback-Zyklen in Mensch-KI-Systemen. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-2104", "narrower_terms": [], "cross_domain_refs": [ "COG-0085" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "ELR-0047", "domain": "ELR", "term_en": "Critical Reading Bypass", "term_de": "CriticalReadingBypass", "definition_en": "An e-learning interaction pattern in AI-enhanced remote education, observable through the tendency to accept AI-curated information without the analytical scrutiny that would be applied to human-authored texts. The concept emerges specifically in contexts where critical–reading interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Fernlernphänomen in KI-vermittelter Bildungsbereitstellung, gekennzeichnet durch systemische Ausprägung von Lern- und Feedback-Zyklen in Mensch-KI-Systemen. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "E-Learning", "narrower_terms": [], "cross_domain_refs": [ "SCR-0030" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "ELR-0048", "domain": "ELR", "term_en": "Critical Reading Shift", "term_de": "CriticalReadingShift", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures A distance learning phenomenon in AI-mediated educational delivery, characterized by a learning dynamic observed when the reduction of critical engagement with texts when AI provides interpretation, eliminating the space for inreliant textual analysis. This phenomenon operates at the intersection of critical and reading dynamics within the broader ELR domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept fernlernphänomen in KI-vermittelter Bildungsbereitstellung, gekennzeichnet durch charakteristische Komponente von Lern- und Feedback-Zyklen in Mensch-KI-Systemen. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "E-Learning", "narrower_terms": [], "cross_domain_refs": [ "ADA-0012", "AGE-0007", "AGE-0046" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "ELR-0049", "domain": "ELR", "term_en": "Cross-Cohort Comparison Breakdown", "term_de": "Cross-cohortComparisonBreakdown", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures A distance learning phenomenon in AI-mediated educational delivery, characterized by a pedagogical pattern manifesting as the diminishing validity of comparing academic performance across cohorts with different levels of AI tool availability and integration. This phenomenon operates at the intersection of cross and cohort dynamics within the broader ELR domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept fernlernphänomen in KI-vermittelter Bildungsbereitstellung, gekennzeichnet durch defizit oder Kompetenzverlust in der Mensch-KI-Lernzusammenarbeit. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "E-Learning", "narrower_terms": [], "cross_domain_refs": [ "ASE-0079" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "ELR-0050", "domain": "ELR", "term_en": "Curiosity Reduction", "term_de": "CuriosityReduction", "definition_en": "A pedagogical pattern in which the dampening of spontaneous questions and exploration when learners know they can get answers instantly, reducing inquiry-based engagement. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Fernlernphänomen in KI-vermittelter Bildungsbereitstellung, gekennzeichnet durch engagementmuster in der Mensch-KI-Lernzusammenarbeit. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2703", "narrower_terms": [], "cross_domain_refs": [ "BEH-0025", "BEH-0026", "BEH-0027" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "ELR-0051", "domain": "ELR", "term_en": "Curiosity Truncation", "term_de": "CuriosityTruncation", "definition_en": "A learning dynamic arising from the premature closure of exploratory learning when AI provides complete answers before the learner has fully developed their question or explored adjacent ideas. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Fernlernphänomen in KI-vermittelter Bildungsbereitstellung, gekennzeichnet durch merkmal von Lern- und Feedback-Zyklen in Mensch-KI-Systemen. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2703", "narrower_terms": [], "cross_domain_refs": [ "BEH-0025", "BEH-0026", "BEH-0027" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "ELR-0052", "domain": "ELR", "term_en": "Curriculum Compression", "term_de": "CurriculumCompression", "definition_en": "A pedagogical pattern arising from the observable reduction in time spent on foundational material when AI tools accelerate surface-level understanding, leaving gaps in deep comprehension that emerge later.", "definition_de": "Fernlernphänomen in KI-vermittelter Bildungsbereitstellung, gekennzeichnet durch merkmal von Lern- und Feedback-Zyklen in Mensch-KI-Systemen. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-2355", "narrower_terms": [], "cross_domain_refs": [ "TRA-0033" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q837863", "legal_classification": "systematic_classification" }, { "id": "ELR-0053", "domain": "ELR", "term_en": "Deadline Compression Uncertainty", "term_de": "DeadlineCompressionUncertainty", "definition_en": "A digital pedagogy concept in AI-augmented remote learning, measurable via the uncertainty experienced when deadlines compress and AI assistance becomes unavailable or insufficient, revealing underlying skill gaps. Distinguished from adjacent concepts by its focus on the specific mechanism through which deadline manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Fernlernphänomen in KI-vermittelter Bildungsbereitstellung, gekennzeichnet durch kompetenzmerkmal in der Mensch-KI-Lernzusammenarbeit. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Data Compression", "narrower_terms": [], "cross_domain_refs": [ "RPH-1409" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "ELR-0054", "domain": "ELR", "term_en": "Debate Polarization Effect", "term_de": "DebatePolarizationEffekt", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures A distance learning phenomenon in AI-mediated educational delivery, characterized by the tendency for AI-assisted argument preparation to yield more extreme positions as systems optimize for persuasive impact over nuanced analysis. This phenomenon operates at the intersection of debate and polarization dynamics within the broader ELR domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept fernlernphänomen in KI-vermittelter Bildungsbereitstellung, gekennzeichnet durch funktionale Eigenschaft von Lern- und Feedback-Zyklen in Mensch-KI-Systemen. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Psychological Effect", "narrower_terms": [], "cross_domain_refs": [ "ADA-0001", "ADA-0002", "ADA-0004" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "ELR-0055", "domain": "ELR", "term_en": "Debate Preparation Shortcut", "term_de": "DebatePreparationShortcut", "definition_en": "A digital pedagogy concept in AI-augmented remote learning, measurable via a learning phenomenon observed when the use of AI to yield counterarguments and evidence rather than developing these through inreliant research and critical analysis. Distinguished from adjacent concepts by its focus on the specific mechanism through which debate manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Fernlernphänomen in KI-vermittelter Bildungsbereitstellung, gekennzeichnet durch transformationsprozess mit bewertende Qualität im KI-unterstützten Lernen. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Analytical Ratio", "narrower_terms": [], "cross_domain_refs": [ "CRE-0201" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "ELR-0056", "domain": "ELR", "term_en": "Debugging Skill Gap", "term_de": "DebuggingSkillGap", "definition_en": "An educational effect where the reduced development of systematic error-finding abilities when AI identifies and corrects mistakes before the learner engages in analytical reasoning. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Fernlernphänomen in KI-vermittelter Bildungsbereitstellung, gekennzeichnet durch transformationsprozess in der Mensch-KI-Lernzusammenarbeit. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2703", "narrower_terms": [], "cross_domain_refs": [ "TRA-0026", "BEH-0033" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "ELR-0057", "domain": "ELR", "term_en": "Deep Learning Avoidance", "term_de": "DeepLearningAvoidance", "definition_en": "A digital pedagogy concept in AI-augmented remote learning, measurable via a pedagogical pattern where the preference for surface-level comprehension enabled by AI summaries over engaging in elaboration and integration processes required for transfer. Distinguished from adjacent concepts by its focus on the specific mechanism through which deep manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Fernlernphänomen in KI-vermittelter Bildungsbereitstellung, gekennzeichnet durch prozess oder Abfolge im Rahmen von Lern- und Feedback-Zyklen in Mensch-KI-Systemen. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Machine Learning", "narrower_terms": [], "cross_domain_refs": [ "COG-0029" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q197536", "legal_classification": "empirical_phenomenon_label" }, { "id": "ELR-0058", "domain": "ELR", "term_en": "Deep Reading Avoidance", "term_de": "DeepReadingAvoidance", "definition_en": "An e-learning interaction pattern in AI-enhanced remote education, observable through the preference for AI-mediated skimming over sustained engagement with complex texts, reducing capacity for literary analysis. The concept emerges specifically in contexts where deep–reading interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Fernlernphänomen in KI-vermittelter Bildungsbereitstellung, gekennzeichnet durch merkmal von Lern- und Feedback-Zyklen in Mensch-KI-Systemen. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "E-Learning", "narrower_terms": [], "cross_domain_refs": [ "AGE-0011", "AUG-0408", "COG-0183" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "ELR-0059", "domain": "ELR", "term_en": "Reliance Threshold Crossing", "term_de": "RelianceSchwelleCrossing", "definition_en": "The moment when a learner realizes they have become less likely to solve problems without AI assistance assistance, a threshold typically recognized retrospectively. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Fernlernphänomen in KI-vermittelter Bildungsbereitstellung, gekennzeichnet durch zeitlich begrenzte Phase/Moment im Kontext von Lern- + Feedback-Zyklen in M-KI-Systemen. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2703", "narrower_terms": [], "cross_domain_refs": [ "DES-0020", "AGE-0041" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "ELR-0060", "domain": "ELR", "term_en": "Difficulty Avoidance Pattern", "term_de": "DifficultyAvoidanceMuster", "definition_en": "An e-learning interaction pattern in AI-enhanced remote education, observable through the behavioral tendency to route around challenging material by delegating conceptually difficult tasks to AI, leaving specific knowledge gaps systematically unaddressed. The concept emerges specifically in contexts where difficulty–avoidance interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Fernlernphänomen in KI-vermittelter Bildungsbereitstellung, gekennzeichnet durch systemische Ausprägung von Lern- und Feedback-Zyklen in Mensch-KI-Systemen. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Behavioral Pattern", "narrower_terms": [], "cross_domain_refs": [ "SWE-0049" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "ELR-0061", "domain": "ELR", "term_en": "Digital Divide Amplification", "term_de": "DigitalDivideVerstärkung", "definition_en": "The widening gap between learners with and without AI assistance tool access, where existing educational inequalities are magnified by differential AI availability. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Fernlernphänomen in KI-vermittelter Bildungsbereitstellung, gekennzeichnet durch kompetenzmerkmal in der Mensch-KI-Lernzusammenarbeit. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2703", "narrower_terms": [], "cross_domain_refs": [ "SOC-0031" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "ELR-0062", "domain": "ELR", "term_en": "Discovery Substitution", "term_de": "DiscoverySubstitution", "definition_en": "The shift of serendipitous learning that occurs during research when AI provides direct answers, eliminating exploratory tangents. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Fernlernphänomen in KI-vermittelter Bildungsbereitstellung, gekennzeichnet durch funktionale Eigenschaft von Lern- und Feedback-Zyklen in Mensch-KI-Systemen. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-2701", "narrower_terms": [], "cross_domain_refs": [ "AED-0046", "AGE-0055", "ART-0023" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "ELR-0063", "domain": "ELR", "term_en": "Discussion Quality Paradox", "term_de": "DiscussionQualityParadox", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A distance learning phenomenon in AI-mediated educational delivery, characterized by the observation that AI-prepared discussion contributions can sound more sophisticated while containing less original thought than unprepared responses. This phenomenon operates at the intersection of discussion and quality dynamics within the broader ELR domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept fernlernphänomen in KI-vermittelter Bildungsbereitstellung, gekennzeichnet durch systemische Ausprägung von Lern- und Feedback-Zyklen in Mensch-KI-Systemen. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Logical Paradox", "narrower_terms": [], "cross_domain_refs": [ "RPH-3251" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "ELR-0064", "domain": "ELR", "term_en": "Divergent Thinking Reduction", "term_de": "DivergentThinkingReduction", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures A distance learning phenomenon in AI-mediated educational delivery, characterized by the reduction in divergent thinking capacity when AI-provided options limit exploration beyond suggested alternatives. This phenomenon operates at the intersection of divergent and thinking dynamics within the broader ELR domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept fernlernphänomen in KI-vermittelter Bildungsbereitstellung, gekennzeichnet durch merkmal von Lern- und Feedback-Zyklen in Mensch-KI-Systemen. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "E-Learning", "narrower_terms": [], "cross_domain_refs": [ "TEM-0083" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "ELR-0065", "domain": "ELR", "term_en": "Editing Avoidance", "term_de": "EditingAvoidance", "definition_en": "A digital pedagogy concept in AI-augmented remote learning, measurable via the reluctance to revise and refine writing when AI-generated text appears sufficient, eliminating the developmental process of revision. Distinguished from adjacent concepts by its focus on the specific mechanism through which editing manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Fernlernphänomen in KI-vermittelter Bildungsbereitstellung, gekennzeichnet durch transformationsprozess in der Mensch-KI-Lernzusammenarbeit. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "E-Learning", "narrower_terms": [], "cross_domain_refs": [ "PLY-0056" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "ELR-0066", "domain": "ELR", "term_en": "Educator Identity Reconfiguration", "term_de": "EducatorIdentityReconfiguration", "definition_en": "The change pattern of professional self-concept among educators as their roles shift from knowledge deliverers to learning experience facilitators. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Fernlernphänomen in KI-vermittelter Bildungsbereitstellung, gekennzeichnet durch phänomen oder Erfahrung in der Lern- und Feedback-Zyklen in Mensch-KI-Systemen. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-2701", "narrower_terms": [], "cross_domain_refs": [ "AED-0051" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q844396", "legal_classification": "analytical_category" }, { "id": "ELR-0067", "domain": "ELR", "term_en": "Effort Calibration Drift", "term_de": "EffortCalibrationDrift", "definition_en": "The gradual shift in perceived acceptable effort levels when AI tools reduce the friction of task completion, making previously normal workloads feel excessive. Identifiable through systematic behavioral analysis and pattern recognition. Classification term used in systematic observation, not advocacy.", "definition_de": "Fernlernphänomen in KI-vermittelter Bildungsbereitstellung, gekennzeichnet durch phänomen oder Erfahrung in der Lern- und Feedback-Zyklen in Mensch-KI-Systemen. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2701", "narrower_terms": [], "cross_domain_refs": [ "CRE-0006", "PHO-0074" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "ELR-0068", "domain": "ELR", "term_en": "Effort Paradox", "term_de": "EffortParadox", "definition_en": "An e-learning interaction pattern in AI-enhanced remote education, observable through the perception that more effort correlates with lower efficiency when AI could solve a problem faster, discouraging inreliant problem-solving. The concept emerges specifically in contexts where effort–paradox interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Fernlernphänomen in KI-vermittelter Bildungsbereitstellung, gekennzeichnet durch phänomen oder Erfahrung in der Lern- und Feedback-Zyklen in Mensch-KI-Systemen. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Logical Paradox", "narrower_terms": [], "cross_domain_refs": [ "COG-0083" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "ELR-0069", "domain": "ELR", "term_en": "Embodied Learning Shift", "term_de": "EmbodiedLearningShift", "definition_en": "The reduction in kinesthetic and embodied learning experiences when AI provides disembodied explanations replacing hands-on practice. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Fernlernphänomen in KI-vermittelter Bildungsbereitstellung, gekennzeichnet durch defizit oder Kompetenzverlust in der Mensch-KI-Lernzusammenarbeit. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-2703", "narrower_terms": [], "cross_domain_refs": [ "COG-0055" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q133500", "legal_classification": "systematic_classification" }, { "id": "ELR-0070", "domain": "ELR", "term_en": "Emotional Learning Bypass", "term_de": "EmotionalLearningBypass", "definition_en": "A learning dynamic where the circumvention of emotionally challenging learning experiences when AI tools provide cognitive shortcuts around material that would otherwise provoke productive discomfort. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Fernlernphänomen in KI-vermittelter Bildungsbereitstellung, gekennzeichnet durch phänomen oder Erfahrung in der Lern- und Feedback-Zyklen in Mensch-KI-Systemen. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-2703", "narrower_terms": [], "cross_domain_refs": [ "STE-0067" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q133500", "legal_classification": "observational_construct" }, { "id": "ELR-0071", "domain": "ELR", "term_en": "Engagement Surface Effect", "term_de": "EngagementSurfaceEffekt", "definition_en": "A characteristic dynamic where AI-enhanced learning materials increase measured engagement metrics without proportional gains in retained understanding. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Fernlernphänomen in KI-vermittelter Bildungsbereitstellung, gekennzeichnet durch merkmal von Lern- und Feedback-Zyklen in Mensch-KI-Systemen. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-2703", "narrower_terms": [], "cross_domain_refs": [ "TEW-0003" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "ELR-0072", "domain": "ELR", "term_en": "Engagement Threshold Drop", "term_de": "EngagementSchwelleDrop", "definition_en": "A learning phenomenon characterized by the decreased willingness to engage with traditional learning materials after experiencing AI-driven explanation, finding human-paced instruction tedious.", "definition_de": "Fernlernphänomen in KI-vermittelter Bildungsbereitstellung, gekennzeichnet durch operative Eigenschaft von Lern- und Feedback-Zyklen in Mensch-KI-Systemen. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2703", "narrower_terms": [], "cross_domain_refs": [ "AED-0018", "AED-0062", "AED-0066" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "ELR-0073", "domain": "ELR", "term_en": "Error Correction Passivity", "term_de": "ErrorCorrectionPassivity", "definition_en": "A learning phenomenon reflecting the passiveness in receiving error corrections from AI without actively diagnosing misunderstandings, reducing learning from mistakes. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Fernlernphänomen in KI-vermittelter Bildungsbereitstellung, gekennzeichnet durch strukturelle Eigenschaft von Lern- und Feedback-Zyklen in Mensch-KI-Systemen. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-2703", "narrower_terms": [], "cross_domain_refs": [ "TRA-0026" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "ELR-0074", "domain": "ELR", "term_en": "Error Tolerance Recalibration", "term_de": "ErrorToleranceRecalibration", "definition_en": "The shifting perception of acceptable error rates in student work as AI tools reduce surface-level mistakes while potentially masking conceptual errors. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Fernlernphänomen in KI-vermittelter Bildungsbereitstellung, gekennzeichnet durch phänomen oder Erfahrung in der Lern- und Feedback-Zyklen in Mensch-KI-Systemen. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-2701", "narrower_terms": [], "cross_domain_refs": [ "TRA-0026" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "ELR-0075", "domain": "ELR", "term_en": "Exam Uncertainty Redistribution", "term_de": "ExamUncertaintyRedistribution", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A distance learning phenomenon in AI-mediated educational delivery, characterized by the shift in assessment-related stress from content mastery to concerns about AI detection and academic integrity accusations. This phenomenon operates at the intersection of exam and uncertainty dynamics within the broader ELR domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept fernlernphänomen in KI-vermittelter Bildungsbereitstellung, gekennzeichnet durch qualitätsmerkmal oder Bewertungskriterium für Lern- und Feedback-Zyklen in Mensch-KI-Systemen. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "RPH-2701", "narrower_terms": [], "cross_domain_refs": [ "STE-0089" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "ELR-0076", "domain": "ELR", "term_en": "Exam Recalibration Pressure", "term_de": "ExamRecalibrationPressure", "definition_en": "An e-learning interaction pattern in AI-enhanced remote education, observable through the institutional pressure to redesign assessment formats in response to AI capabilities, often faster than pedagogical research can validate new approaches. The concept emerges specifically in contexts where exam–recalibration interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Fernlernphänomen in KI-vermittelter Bildungsbereitstellung, gekennzeichnet durch qualitätsmerkmal oder Bewertungskriterium für Lern- und Feedback-Zyklen in Mensch-KI-Systemen. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Analytical Ratio", "narrower_terms": [], "cross_domain_refs": [ "ASE-0012" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "ELR-0077", "domain": "ELR", "term_en": "Experiential Learning Pressure", "term_de": "ExperientialLearningPressure", "definition_en": "The increased emphasis on hands-on, embodied, and field-based learning activities as the primary remaining domain where AI cannot substitute for direct experience. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Fernlernphänomen in KI-vermittelter Bildungsbereitstellung, gekennzeichnet durch phänomen/Erfahrung in der Lern- + Feedback-Zyklen in M-KI-Systemen. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2703", "narrower_terms": [], "cross_domain_refs": [ "STE-0036" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q133500", "legal_classification": "analytical_category" }, { "id": "ELR-0078", "domain": "ELR", "term_en": "Explanation Bypass", "term_de": "ExplanationBypass", "definition_en": "The tendency for learners to accept an AI-generated explanation without processing the underlying logic, trusting the articulation over comprehension. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Fernlernphänomen in KI-vermittelter Bildungsbereitstellung, gekennzeichnet durch prozess oder Abfolge im Rahmen von Lern- und Feedback-Zyklen in Mensch-KI-Systemen. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2505", "narrower_terms": [], "cross_domain_refs": [ "SPR-0101", "WEB-0071" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "ELR-0079", "domain": "ELR", "term_en": "Explanation Reliance", "term_de": "ExplanationReliance", "definition_en": "The reliance on external explanations rather than internal sense-making, where the learner expects most concept to be explained rather than constructed. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Fernlernphänomen in KI-vermittelter Bildungsbereitstellung, gekennzeichnet durch phänomen oder Erfahrung in der Lern- und Feedback-Zyklen in Mensch-KI-Systemen. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-2703", "narrower_terms": [], "cross_domain_refs": [ "AGE-0012", "AGE-0014", "AGE-0047" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "ELR-0080", "domain": "ELR", "term_en": "Explanation Depth Preference Shift", "term_de": "ExplanationDepthPreferenceShift", "definition_en": "A pedagogical pattern in which the changing preference for explanation granularity as learners become accustomed to AI systems that adjust detail level on demand. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Fernlernphänomen in KI-vermittelter Bildungsbereitstellung, gekennzeichnet durch operative Eigenschaft von Lern- und Feedback-Zyklen in Mensch-KI-Systemen. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2703", "narrower_terms": [], "cross_domain_refs": [ "MKT-0065" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "ELR-0081", "domain": "ELR", "term_en": "Feedback Latency Expectation", "term_de": "RückkopplungLatencyExpectation", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures A distance learning phenomenon in AI-mediated educational delivery, characterized by the recalibrated expectation of immediate feedback that develops after sustained AI interaction, making traditional educator response times feel disproportionately slow. This phenomenon operates at the intersection of feedback and latency dynamics within the broader ELR domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription. Observed phenomenon documented in human-AI interaction research.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept fernlernphänomen in KI-vermittelter Bildungsbereitstellung, gekennzeichnet durch phänomen oder Erfahrung in der Lern- und Feedback-Zyklen in Mensch-KI-Systemen. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "E-Learning", "narrower_terms": [], "cross_domain_refs": [ "AED-0003", "AED-0071", "AED-0072" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "ELR-0082", "domain": "ELR", "term_en": "Feedback Responsiveness Decline", "term_de": "RückkopplungResponsivenessDecline", "definition_en": "A learning phenomenon involving the reduced ability to integrate feedback when AI-generated work obsresolves the source of errors, preventing metacognitive learning from mistakes. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Fernlernphänomen in KI-vermittelter Bildungsbereitstellung, gekennzeichnet durch fähigkeit oder Kompetenz im Umgang mit Lern- und Feedback-Zyklen in Mensch-KI-Systemen. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2703", "narrower_terms": [], "cross_domain_refs": [ "ASE-0092" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "ELR-0083", "domain": "ELR", "term_en": "Feedback Specificity Gap", "term_de": "RückkopplungSpecificityGap", "definition_en": "An e-learning interaction pattern in AI-enhanced remote education, observable through a learning phenomenon arising from the difference in actionable detail between AI-generated feedback and human educator feedback, where each type provides distinct information the other typically lacks. The concept emerges specifically in contexts where feedback–specificity interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Fernlernphänomen in KI-vermittelter Bildungsbereitstellung, gekennzeichnet durch charakteristische Komponente von Lern- und Feedback-Zyklen in Mensch-KI-Systemen. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Performance Gap", "narrower_terms": [], "cross_domain_refs": [ "CUS-0060", "MTH-0057" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "ELR-0084", "domain": "ELR", "term_en": "Fluency Perception", "term_de": "FluencyPerception", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures A distance learning phenomenon in AI-mediated educational delivery, characterized by the false confidence that arises when an AI explains a complex concept smoothly, conflating narrative clarity with conceptual mastery. This phenomenon operates at the intersection of fluency and perception dynamics within the broader ELR domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept fernlernphänomen in KI-vermittelter Bildungsbereitstellung, gekennzeichnet durch funktionale Eigenschaft von Lern- und Feedback-Zyklen in Mensch-KI-Systemen. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "RPH-1209", "narrower_terms": [], "cross_domain_refs": [ "COG-0179" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q177601", "legal_classification": "descriptive_research_term" }, { "id": "ELR-0085", "domain": "ELR", "term_en": "Formative Assessment Disruption", "term_de": "FormativeAssessmentDisruption", "definition_en": "The reduced informational value of formative assessments when AI assistance during practice obsresolves genuine learner progress indicators. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Fernlernphänomen in KI-vermittelter Bildungsbereitstellung, gekennzeichnet durch qualitätsmerkmal oder Bewertungskriterium für Lern- und Feedback-Zyklen in Mensch-KI-Systemen. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2703", "narrower_terms": [], "cross_domain_refs": [ "AED-0001", "AED-0090", "AED-0095" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q210028", "legal_classification": "analytical_category" }, { "id": "ELR-0086", "domain": "ELR", "term_en": "Frustration Intolerance Development", "term_de": "FrustrationIntoleranceDevelopment", "definition_en": "The reduced capacity to tolerate frustration when immediate AI solutions condition the learner to expect instant gratification. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Fernlernphänomen in KI-vermittelter Bildungsbereitstellung, gekennzeichnet durch zustand oder Erlebnis innerhalb von Lern- und Feedback-Zyklen in Mensch-KI-Systemen. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-2703", "narrower_terms": [], "cross_domain_refs": [ "TRA-0010" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "ELR-0087", "domain": "ELR", "term_en": "Grade Inflation Adaptation", "term_de": "GradeInflationAnpassung", "definition_en": "A learning phenomenon observed when the normalization of higher grades observed alongside AI-assisted work, where learners adjust expectations upward and perceive declining grades as failure. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Fernlernphänomen in KI-vermittelter Bildungsbereitstellung, gekennzeichnet durch systemische Ausprägung von Lern- und Feedback-Zyklen in Mensch-KI-Systemen. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-2703", "narrower_terms": [], "cross_domain_refs": [ "AED-0010", "AED-0090", "AGE-0036" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "ELR-0088", "domain": "ELR", "term_en": "Grade-Performance Decoupling", "term_de": "Grade-performanceDecoupling", "definition_en": "The divergence between a learner's assigned grades (with AI assistance) and their inreliant capability, creating inflated self-assessment. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Fernlernphänomen in KI-vermittelter Bildungsbereitstellung, gekennzeichnet durch kompetenzmerkmal in der Mensch-KI-Lernzusammenarbeit. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2703", "narrower_terms": [], "cross_domain_refs": [ "COG-0119" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q1361053", "legal_classification": "analytical_category" }, { "id": "ELR-0089", "domain": "ELR", "term_en": "Grading Consistency Expectation", "term_de": "GradingConsistencyExpectation", "definition_en": "A pedagogical pattern where the assumption that AI-assisted grading is designed to reduce subjective variation, creating disappointment when human judgment components still yield divergent results. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Fernlernphänomen in KI-vermittelter Bildungsbereitstellung, gekennzeichnet durch charakteristische Komponente von Lern- und Feedback-Zyklen in Mensch-KI-Systemen. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-3101", "narrower_terms": [], "cross_domain_refs": [ "SWE-0040" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "ELR-0090", "domain": "ELR", "term_en": "Grading Opacity Tension", "term_de": "GradingOpacityTension", "definition_en": "A digital pedagogy concept in AI-augmented remote learning, measurable via the discomfort experienced when AI-assisted assessment accompanies scores without transparent reasoning paths, leaving recipients less likely to reconstruct the evaluation logic. Distinguished from adjacent concepts by its focus on the specific mechanism through which grading manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Fernlernphänomen in KI-vermittelter Bildungsbereitstellung, gekennzeichnet durch phänomen oder Erfahrung in der Lern- und Feedback-Zyklen in Mensch-KI-Systemen. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "E-Learning", "narrower_terms": [], "cross_domain_refs": [ "ASE-0069" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "ELR-0091", "domain": "ELR", "term_en": "Graduation Readiness Uncertainty", "term_de": "GraduationReadinessUncertainty", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures A distance learning phenomenon in AI-mediated educational delivery, characterized by a learning phenomenon observed when the systemic uncertainty about whether graduates possess the competencies their credentials imply when AI assistance was available throughout their education. This phenomenon operates at the intersection of graduation and readiness dynamics within the broader ELR domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept fernlernphänomen in KI-vermittelter Bildungsbereitstellung, gekennzeichnet durch operative Eigenschaft von Lern- und Feedback-Zyklen in Mensch-KI-Systemen. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "E-Learning", "narrower_terms": [], "cross_domain_refs": [ "TEM-0044" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "ELR-0092", "domain": "ELR", "term_en": "Group Work Asymmetry", "term_de": "GroupWorkAsymmetry", "definition_en": "An e-learning interaction pattern in AI-enhanced remote education, observable through an educational effect observed when the imbalance that emerges in collaborative projects when AI tool access varies among team members, creating invisible productivity differentials. The concept emerges specifically in contexts where group–work interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Fernlernphänomen in KI-vermittelter Bildungsbereitstellung, gekennzeichnet durch charakteristische Komponente von Lern- und Feedback-Zyklen in Mensch-KI-Systemen. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2355", "narrower_terms": [], "cross_domain_refs": [ "STE-0013" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "ELR-0093", "domain": "ELR", "term_en": "Handwriting Skill Substitution", "term_de": "HandwritingSkillSubstitution", "definition_en": "A digital pedagogy concept in AI-augmented remote learning, measurable via a learning phenomenon arising from the accelerated decline in handwriting proficiency and related cognitive benefits when digital AI tools become the primary medium for all written work. Distinguished from adjacent concepts by its focus on the specific mechanism through which handwriting manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Fernlernphänomen in KI-vermittelter Bildungsbereitstellung, gekennzeichnet durch funktionale Eigenschaft von Lern- und Feedback-Zyklen in Mensch-KI-Systemen. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "E-Learning", "narrower_terms": [], "cross_domain_refs": [ "STE-0003", "SWE-0065" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "ELR-0094", "domain": "ELR", "term_en": "Historical Contextualization Avoidance", "term_de": "HistoricalContextualizationAvoidance", "definition_en": "A digital pedagogy concept in AI-augmented remote learning, measurable via a pedagogical pattern reflecting the preference for AI-provided historical facts over engaging with historical interpretation and causal analysis. Distinguished from adjacent concepts by its focus on the specific mechanism through which historical manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Fernlernphänomen in KI-vermittelter Bildungsbereitstellung, gekennzeichnet durch strukturelle Eigenschaft von Lern- und Feedback-Zyklen in Mensch-KI-Systemen. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "E-Learning", "narrower_terms": [], "cross_domain_refs": [ "AGE-0011", "AUG-0408", "CON-0003" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "ELR-0095", "domain": "ELR", "term_en": "Homework Assistance Gradient", "term_de": "HomeworkAssistanceGradient", "definition_en": "The spectrum from AI providing hints to AI solving complete assignments, where the boundary of authentic learning is continuously negotiated without explicit awareness. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Fernlernphänomen in KI-vermittelter Bildungsbereitstellung, gekennzeichnet durch charakteristische Komponente von Lern- und Feedback-Zyklen in Mensch-KI-Systemen. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-2703", "narrower_terms": [], "cross_domain_refs": [ "MTH-0053" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "ELR-0096", "domain": "ELR", "term_en": "Homework Purpose Shift", "term_de": "HomeworkPurposeShift", "definition_en": "The diminishing pedagogical value of traditional homework assignments when AI can complete them without the intended learning process occurring. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Fernlernphänomen in KI-vermittelter Bildungsbereitstellung, gekennzeichnet durch prozess oder Abfolge im Rahmen von Lern- und Feedback-Zyklen in Mensch-KI-Systemen. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2703", "narrower_terms": [], "cross_domain_refs": [ "KNO-0014" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "ELR-0097", "domain": "ELR", "term_en": "Homework-Learning Decoupling", "term_de": "Homework-learningDecoupling", "definition_en": "A pedagogical pattern involving the separation between homework completion and actual learning, where assignments get finished with AI but understanding remains absent. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Fernlernphänomen in KI-vermittelter Bildungsbereitstellung, gekennzeichnet durch merkmal von Lern- und Feedback-Zyklen in Mensch-KI-Systemen. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2703", "narrower_terms": [], "cross_domain_refs": [ "KNO-0014" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q133500", "legal_classification": "analytical_category" }, { "id": "ELR-0098", "domain": "ELR", "term_en": "Imposter Phenomenon Activation", "term_de": "ImposterPhänomenAktivierung", "definition_en": "As a neutral analytical construct in AI behavior research, this term denotes an e-learning interaction pattern in AI-enhanced remote education, observable through the intensification of feeling like a fraud when grades and outputs depend on AI assistance rather than perceived capability. The concept emerges specifically in contexts where imposter–phenomenon interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "In der AUGMANITAI-Terminologiewissenschaft als rein beschreibendes Phänomen klassifiziert, bezeichnet dieser Begriff fernlernphänomen in KI-vermittelter Bildungsbereitstellung, gekennzeichnet durch kompetenzmerkmal in der Mensch-KI-Lernzusammenarbeit. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "E-Learning", "narrower_terms": [], "cross_domain_refs": [ "COG-0005", "COG-0038", "CRE-0066" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "ELR-0099", "domain": "ELR", "term_en": "Incremental Learning Bypass", "term_de": "IncrementalLearningBypass", "definition_en": "A learning phenomenon involving the skipping of foundational, incremental learning steps when AI provides advanced explanations immediately, creating gaps in prerequisite knowledge. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Fernlernphänomen in KI-vermittelter Bildungsbereitstellung, gekennzeichnet durch beobachtbare Eigenschaft von Lern- und Feedback-Zyklen in Mensch-KI-Systemen. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-2703", "narrower_terms": [], "cross_domain_refs": [ "MTH-0054", "STE-0018" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q133500", "legal_classification": "systematic_classification" }, { "id": "ELR-0100", "domain": "ELR", "term_en": "Institutional Memory Distribution", "term_de": "InstitutionalMemoryDistribution", "definition_en": "The shift of accumulated pedagogical knowledge within educational institutions as rapid AI-driven changes outpace documentation and knowledge transfer. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Fernlernphänomen in KI-vermittelter Bildungsbereitstellung, gekennzeichnet durch transformationsprozess in der Mensch-KI-Lernzusammenarbeit. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2701", "narrower_terms": [], "cross_domain_refs": [ "ASE-0057" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q492", "legal_classification": "observational_construct" }, { "id": "ELR-0101", "domain": "ELR", "term_en": "Institutional Response Lag", "term_de": "InstitutionalResponseLag", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures A distance learning phenomenon in AI-mediated educational delivery, characterized by the gap between the pace of AI capability advancement and the speed at which educational institutions update policies, curricula, and assessment methods. This phenomenon operates at the intersection of institutional and response dynamics within the broader ELR domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept fernlernphänomen in KI-vermittelter Bildungsbereitstellung, gekennzeichnet durch kompetenzmerkmal in der Mensch-KI-Lernzusammenarbeit. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "E-Learning", "narrower_terms": [], "cross_domain_refs": [ "ASE-0057" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "ELR-0102", "domain": "ELR", "term_en": "Instructor Monitoring Escalation", "term_de": "InstructorMonitoringEscalation", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures A pedagogical pattern observed when the increasing surveillance measures implemented to detect AI use in academic work, altering the trust dynamics between educators and learners. Identifiable through systematic behavioral analysis and pattern recognition. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept fernlernphänomen in KI-vermittelter Bildungsbereitstellung, gekennzeichnet durch distinktives Merkmal von Lern- und Feedback-Zyklen in Mensch-KI-Systemen. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "RPH-2505", "narrower_terms": [], "cross_domain_refs": [ "COG-0126" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "ELR-0103", "domain": "ELR", "term_en": "Instructor Relevance Uncertainty", "term_de": "InstructorRelevanceUncertainty", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures A distance learning phenomenon in AI-mediated educational delivery, characterized by a learning phenomenon reflecting the concern among educators that AI tutoring capabilities may reduce the perceived value of human instruction, inreliant of actual pedagogical effectiveness. This phenomenon operates at the intersection of instructor and relevance dynamics within the broader ELR domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept fernlernphänomen in KI-vermittelter Bildungsbereitstellung, gekennzeichnet durch strukturelle Eigenschaft von Lern- und Feedback-Zyklen in Mensch-KI-Systemen. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "E-Learning", "narrower_terms": [], "cross_domain_refs": [ "SPR-0024" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "ELR-0104", "domain": "ELR", "term_en": "Instructor Upskilling Pressure", "term_de": "InstructorUpskillingPressure", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A distance learning phenomenon in AI-mediated educational delivery, characterized by the continuous demand on educators to develop AI literacy and integration competencies alongside their existing pedagogical responsibilities. This phenomenon operates at the intersection of instructor and upskilling dynamics within the broader ELR domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept fernlernphänomen in KI-vermittelter Bildungsbereitstellung, gekennzeichnet durch transformationsprozess in der Mensch-KI-Lernzusammenarbeit. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "E-Learning", "narrower_terms": [], "cross_domain_refs": [ "AED-0085" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "ELR-0105", "domain": "ELR", "term_en": "Intellectual Uncertainty Aversion", "term_de": "IntellectualUncertaintyAversion", "definition_en": "A digital pedagogy concept in AI-augmented remote learning, measurable via a learning dynamic manifesting as the avoidance of speculative or unconventional thinking when AI offers safe, conventional solutions. Distinguished from adjacent concepts by its focus on the specific mechanism through which intellectual manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Fernlernphänomen in KI-vermittelter Bildungsbereitstellung, gekennzeichnet durch merkmal von Lern- und Feedback-Zyklen in Mensch-KI-Systemen. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2604", "narrower_terms": [], "cross_domain_refs": [ "COG-0095" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "ELR-0106", "domain": "ELR", "term_en": "Interdisciplinary Connection Shift", "term_de": "InterdisciplinaryConnectionShift", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures A distance learning phenomenon in AI-mediated educational delivery, characterized by an educational effect manifesting as the reduced likelihood of discovering unexpected connections between fields when AI-directed learning follows predetermined domain boundaries. This phenomenon operates at the intersection of interdisciplinary and connection dynamics within the broader ELR domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept fernlernphänomen in KI-vermittelter Bildungsbereitstellung, gekennzeichnet durch strukturelle Eigenschaft von Lern- und Feedback-Zyklen in Mensch-KI-Systemen. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "RPH-2703", "narrower_terms": [], "cross_domain_refs": [ "COG-0147" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "ELR-0107", "domain": "ELR", "term_en": "Intrinsic Motivation Substitution", "term_de": "IntrinsicMotivationSubstitution", "definition_en": "A learning phenomenon characterized by the replacement of internal drive to understand with external drive to complete tasks with AI, reducing learning engagement.", "definition_de": "Fernlernphänomen in KI-vermittelter Bildungsbereitstellung, gekennzeichnet durch kennzeichnende Ausprägung von Lern- und Feedback-Zyklen in Mensch-KI-Systemen. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-2703", "narrower_terms": [], "cross_domain_refs": [ "RET-0091" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q186588", "legal_classification": "descriptive_research_term" }, { "id": "ELR-0108", "domain": "ELR", "term_en": "Isolated Learning Preference", "term_de": "IsolatedLearningPreference", "definition_en": "The shift toward individual work with AI over group learning environments, reducing social learning and interpersonal skill development. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Fernlernphänomen in KI-vermittelter Bildungsbereitstellung, gekennzeichnet durch transformationsprozess in der Mensch-KI-Lernzusammenarbeit. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-2701", "narrower_terms": [], "cross_domain_refs": [ "AED-0071" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q133500", "legal_classification": "observational_construct" }, { "id": "ELR-0109", "domain": "ELR", "term_en": "Knowledge Fragility Awareness", "term_de": "KnowledgeFragilityAwareness", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A distance learning phenomenon in AI-mediated educational delivery, characterized by the sudden recognition during assessment that knowledge constructed with AI assistance is brittle and context-reliant, crumbling without external support. This phenomenon operates at the intersection of knowledge and fragility dynamics within the broader ELR domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept fernlernphänomen in KI-vermittelter Bildungsbereitstellung, gekennzeichnet durch qualitätsmerkmal oder Bewertungskriterium für Lern- und Feedback-Zyklen in Mensch-KI-Systemen. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "E-Learning", "narrower_terms": [], "cross_domain_refs": [ "TRU-0008" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "ELR-0110", "domain": "ELR", "term_en": "Knowledge Hoarding", "term_de": "KnowledgeHoarding", "definition_en": "The tendency to retain AI-provided solutions privately rather than sharing with peers, reducing the circulation of learning resources. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Fernlernphänomen in KI-vermittelter Bildungsbereitstellung, gekennzeichnet durch operative Eigenschaft von Lern- und Feedback-Zyklen in Mensch-KI-Systemen. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2703", "narrower_terms": [], "cross_domain_refs": [ "AED-0012", "AED-0020", "AED-0030" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "ELR-0111", "domain": "ELR", "term_en": "Knowledge Source Narrowing", "term_de": "KnowledgeSourceNarrowing", "definition_en": "The narrowing of distinction between learning that comes from personal effort and learning acquired passively through AI generation, reducing metacognitive awareness. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Fernlernphänomen in KI-vermittelter Bildungsbereitstellung, gekennzeichnet durch engagementmuster mit passive Qualität im KI-unterstützten Lernen. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-2703", "narrower_terms": [], "cross_domain_refs": [ "TRA-0077" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "ELR-0112", "domain": "ELR", "term_en": "Knowledge Verification Gap", "term_de": "KnowledgeVerificationGap", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A distance learning phenomenon in AI-mediated educational delivery, characterized by an educational effect manifesting as the growing interval between acquiring AI-provided information and inreliantly verifying its accuracy, often resulting in unverified knowledge persisting as assumed fact. This phenomenon operates at the intersection of knowledge and verification dynamics within the broader ELR domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept fernlernphänomen in KI-vermittelter Bildungsbereitstellung, gekennzeichnet durch beobachtbare Eigenschaft von Lern- und Feedback-Zyklen in Mensch-KI-Systemen. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Performance Gap", "narrower_terms": [], "cross_domain_refs": [ "RPH-1108", "RPH-1454" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "ELR-0113", "domain": "ELR", "term_en": "Lab Experience Replacement Pressure", "term_de": "LabExperienceReplacementPressure", "definition_en": "An e-learning interaction pattern in AI-enhanced remote education, observable through an educational effect where the institutional push to substitute hands-on laboratory experiences with AI simulations, driven by cost considerations rather than pedagogical equivalence. The concept emerges specifically in contexts where lab–experience interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Fernlernphänomen in KI-vermittelter Bildungsbereitstellung, gekennzeichnet durch phänomen oder Erfahrung in der Lern- und Feedback-Zyklen in Mensch-KI-Systemen. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "E-Learning", "narrower_terms": [], "cross_domain_refs": [ "STE-0036" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "ELR-0114", "domain": "ELR", "term_en": "Lab Partner Algorithm Effect", "term_de": "LabPartnerAlgorithmEffekt", "definition_en": "A learning dynamic involving the altered dynamics of paired learning when one partner's AI-augmented contributions consistently exceed the other's inreliant work. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Fernlernphänomen in KI-vermittelter Bildungsbereitstellung, gekennzeichnet durch operative Eigenschaft von Lern- und Feedback-Zyklen in Mensch-KI-Systemen. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-2703", "narrower_terms": [], "cross_domain_refs": [ "REL-0202" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q8366", "legal_classification": "analytical_category" }, { "id": "ELR-0115", "domain": "ELR", "term_en": "Language Learning Plateau", "term_de": "LanguageLearningPlateau", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures A distance learning phenomenon in AI-mediated educational delivery, characterized by a learning dynamic arising from the stagnation of language acquisition when AI translation and generation is designed to reduce the need for productive language use. This phenomenon operates at the intersection of language and learning dynamics within the broader ELR domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept fernlernphänomen in KI-vermittelter Bildungsbereitstellung, gekennzeichnet durch distinktives Merkmal von Lern- und Feedback-Zyklen in Mensch-KI-Systemen. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Machine Learning", "narrower_terms": [], "cross_domain_refs": [ "TRA-0043" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q133500", "legal_classification": "analytical_category" }, { "id": "ELR-0116", "domain": "ELR", "term_en": "Learning Pace Mismatch", "term_de": "LearningPaceMismatch", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A distance learning phenomenon in AI-mediated educational delivery, characterized by a learning dynamic characterized by the disorientation when traditional classroom pace feels too slow compared to AI-provided rapid explanations, reducing patience for incremental understanding. This phenomenon operates at the intersection of learning and pace dynamics within the broader ELR domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept fernlernphänomen in KI-vermittelter Bildungsbereitstellung, gekennzeichnet durch phänomen oder Erfahrung in der Lern- und Feedback-Zyklen in Mensch-KI-Systemen. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Machine Learning", "narrower_terms": [], "cross_domain_refs": [ "AED-0001", "AED-0002", "AED-0004" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q133500", "legal_classification": "empirical_phenomenon_label" }, { "id": "ELR-0117", "domain": "ELR", "term_en": "Learning Pathway Rigidity", "term_de": "LearningPathwayRigidity", "definition_en": "The tendency of AI-recommended learning sequences to follow optimized paths that exclude exploratory detours where unexpected insights often emerge. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Fernlernphänomen in KI-vermittelter Bildungsbereitstellung, gekennzeichnet durch prozess oder Abfolge im Rahmen von Lern- und Feedback-Zyklen in Mensch-KI-Systemen. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2355", "narrower_terms": [], "cross_domain_refs": [ "PLY-0013" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q133500", "legal_classification": "observational_construct" }, { "id": "ELR-0118", "domain": "ELR", "term_en": "Learning Transfer Opacity", "term_de": "LearningTransferOpacity", "definition_en": "A digital pedagogy concept in AI-augmented remote learning, measurable via a pedagogical pattern arising from the difficulty of determining whether knowledge gained through AI-mediated instruction transfers effectively to novel situations without AI assistance support. Distinguished from adjacent concepts by its focus on the specific mechanism through which learning manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Fernlernphänomen in KI-vermittelter Bildungsbereitstellung, gekennzeichnet durch zustand oder Erlebnis innerhalb von Lern- und Feedback-Zyklen in Mensch-KI-Systemen. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Knowledge Transfer", "narrower_terms": [], "cross_domain_refs": [ "COG-0078" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q133500", "legal_classification": "empirical_phenomenon_label" }, { "id": "ELR-0119", "domain": "ELR", "term_en": "Lecture Attention Redistribution", "term_de": "LectureAttentionRedistribution", "definition_en": "An e-learning interaction pattern in AI-enhanced remote education, observable through a pedagogical pattern in which the reallocation of attention during lectures from note-taking and comprehension to evaluating whether AI could deliver the same content more efficiently. The concept emerges specifically in contexts where lecture–attention interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Fernlernphänomen in KI-vermittelter Bildungsbereitstellung, gekennzeichnet durch spezifisches Attribut von Lern- und Feedback-Zyklen in Mensch-KI-Systemen. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Attention Mechanism", "narrower_terms": [], "cross_domain_refs": [ "COG-0042" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q184872", "legal_classification": "analytical_category" }, { "id": "ELR-0120", "domain": "ELR", "term_en": "Lecture Recording Reliance", "term_de": "LectureRecordingReliance", "definition_en": "An e-learning interaction pattern in AI-enhanced remote education, observable through an educational effect observed when the increased reliance on recorded lectures combined with AI transcription and summarization, reducing the perceived value of synchronous attendance. The concept emerges specifically in contexts where lecture–recording interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Fernlernphänomen in KI-vermittelter Bildungsbereitstellung, gekennzeichnet durch distinktives Merkmal von Lern- und Feedback-Zyklen in Mensch-KI-Systemen. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2604", "narrower_terms": [], "cross_domain_refs": [ "STE-0073" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "ELR-0121", "domain": "ELR", "term_en": "Library Navigation Reduction", "term_de": "LibraryNavigationReduction", "definition_en": "An e-learning interaction pattern in AI-enhanced remote education, observable through a pedagogical pattern reflecting the declining ability to locate and evaluate physical and digital academic resources inreliantly as AI search becomes the default research entry point. The concept emerges specifically in contexts where library–navigation interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Fernlernphänomen in KI-vermittelter Bildungsbereitstellung, gekennzeichnet durch kompetenzmerkmal in der Mensch-KI-Lernzusammenarbeit. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-3101", "narrower_terms": [], "cross_domain_refs": [ "STE-0048" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "ELR-0122", "domain": "ELR", "term_en": "Mastery Hallucination", "term_de": "MasteryHallucination", "definition_en": "The false sense of mastery that emerges when a learner successfully accompanies correct answers with AI assistance, despite lacking the underlying skill. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Fernlernphänomen in KI-vermittelter Bildungsbereitstellung, gekennzeichnet durch phänomen oder Erfahrung in der Lern- und Feedback-Zyklen in Mensch-KI-Systemen. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2355", "narrower_terms": [], "cross_domain_refs": [ "EDU-0063" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "ELR-0123", "domain": "ELR", "term_en": "Mastery Threshold Ambiguity", "term_de": "MasterySchwelleAmbiguity", "definition_en": "A digital pedagogy concept in AI-augmented remote learning, measurable via a learning dynamic involving the uncertainty about what constitutes genuine mastery of a topic when AI tools can augment performance beyond actual understanding. Distinguished from adjacent concepts by its focus on the specific mechanism through which mastery manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Fernlernphänomen in KI-vermittelter Bildungsbereitstellung, gekennzeichnet durch charakteristische Komponente von Lern- und Feedback-Zyklen in Mensch-KI-Systemen. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Decision Threshold", "narrower_terms": [], "cross_domain_refs": [ "EDU-0063" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "ELR-0124", "domain": "ELR", "term_en": "Mathematical Reasoning Reduction", "term_de": "MathematicalReasoningReduction", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A distance learning phenomenon in AI-mediated educational delivery, characterized by the change of the ability to verify correctness or estimate reasonableness when AI provides solutions without showing reasoning. This phenomenon operates at the intersection of mathematical and reasoning dynamics within the broader ELR domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept fernlernphänomen in KI-vermittelter Bildungsbereitstellung, gekennzeichnet durch fähigkeit oder Kompetenz im Umgang mit Lern- und Feedback-Zyklen in Mensch-KI-Systemen. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "E-Learning", "narrower_terms": [], "cross_domain_refs": [ "MTH-0081", "STE-0001" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "ELR-0125", "domain": "ELR", "term_en": "Mathematical Reasoning Delegation", "term_de": "MathematicalReasoningDelegation", "definition_en": "A digital pedagogy concept in AI-augmented remote learning, measurable via an educational effect characterized by the transfer of step-by-step mathematical reasoning to AI systems, retaining only the ability to verify final answers without understanding intermediate steps. Distinguished from adjacent concepts by its focus on the specific mechanism through which mathematical manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Fernlernphänomen in KI-vermittelter Bildungsbereitstellung, gekennzeichnet durch fähigkeit oder Kompetenz im Umgang mit Lern- und Feedback-Zyklen in Mensch-KI-Systemen. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "E-Learning", "narrower_terms": [], "cross_domain_refs": [ "MTH-0081" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "ELR-0126", "domain": "ELR", "term_en": "Memory Commitment Decline", "term_de": "MemoryCommitmentDecline", "definition_en": "An e-learning interaction pattern in AI-enhanced remote education, observable through a learning dynamic where the reduced effort invested in committing information to long-term memory when AI systems provide reliable on-demand retrieval. The concept emerges specifically in contexts where memory–commitment interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Fernlernphänomen in KI-vermittelter Bildungsbereitstellung, gekennzeichnet durch charakteristische Komponente von Lern- und Feedback-Zyklen in Mensch-KI-Systemen. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "E-Learning", "narrower_terms": [], "cross_domain_refs": [ "COG-0039" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q492", "legal_classification": "observational_construct" }, { "id": "ELR-0127", "domain": "ELR", "term_en": "Metacognitive Blind Spot", "term_de": "MetacognitiveBlindSpot", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures A distance learning phenomenon in AI-mediated educational delivery, characterized by the lack of awareness about gaps between perceived understanding and actual ability, exacerbated when AI feedback replaces peer or teacher feedback. This phenomenon operates at the intersection of metacognitive and blind dynamics within the broader ELR domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept fernlernphänomen in KI-vermittelter Bildungsbereitstellung, gekennzeichnet durch kompetenzmerkmal in der Mensch-KI-Lernzusammenarbeit. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "E-Learning", "narrower_terms": [], "cross_domain_refs": [ "CUS-0060" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "ELR-0128", "domain": "ELR", "term_en": "Metacognitive Outsourcing", "term_de": "MetacognitiveAuslagerung", "definition_en": "Classified in AUGMANITAI terminology science as a descriptive phenomenon (without normative endorsement), this pattern denotes the delegation of self-monitoring processes to AI systems, reducing the learner's own awareness of what they know and do not know. Identifiable through systematic behavioral analysis and pattern recognition. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als analytisches Konstrukt der empirischen KI-Forschung (deskriptiv, nicht präskriptiv) erfasst dieser Terminus fernlernphänomen in KI-vermittelter Bildungsbereitstellung, gekennzeichnet durch prozess/Abfolge im Rahmen von Lern- + Feedback-Zyklen in M-KI-Systemen. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "RPH-2703", "narrower_terms": [], "cross_domain_refs": [ "IDN-0036" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "ELR-0129", "domain": "ELR", "term_en": "Motivation Architecture Change", "term_de": "MotivationArchitectureChange", "definition_en": "The restructuring of intrinsic and extrinsic motivation patterns when AI removes certain effort-reward connections that previously drove learning behavior. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Fernlernphänomen in KI-vermittelter Bildungsbereitstellung, gekennzeichnet durch engagementmuster in der Mensch-KI-Lernzusammenarbeit. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-2703", "narrower_terms": [], "cross_domain_refs": [ "AED-0018" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q186588", "legal_classification": "observational_construct" }, { "id": "ELR-0130", "domain": "ELR", "term_en": "Multilingual Learning Flattening", "term_de": "MultilingualLearningFlattening", "definition_en": "A learning phenomenon involving the reduction in language-specific learning experiences when AI translation tools enable bypassing of foreign language engagement. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Fernlernphänomen in KI-vermittelter Bildungsbereitstellung, gekennzeichnet durch phänomen oder Erfahrung in der Lern- und Feedback-Zyklen in Mensch-KI-Systemen. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2703", "narrower_terms": [], "cross_domain_refs": [ "REL-0138" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q133500", "legal_classification": "descriptive_research_term" }, { "id": "ELR-0131", "domain": "ELR", "term_en": "Note-Taking Obsolescence Perception", "term_de": "Note-takingObsolescencePerception", "definition_en": "A digital pedagogy concept in AI-augmented remote learning, measurable via a learning dynamic involving the belief that personal note-taking has diminished value when AI can yield comprehensive summaries of any lecture or reading material. Distinguished from adjacent concepts by its focus on the specific mechanism through which note manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Fernlernphänomen in KI-vermittelter Bildungsbereitstellung, gekennzeichnet durch operative Eigenschaft von Lern- und Feedback-Zyklen in Mensch-KI-Systemen. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "E-Learning", "narrower_terms": [], "cross_domain_refs": [ "RPH-1013", "TEW-0074", "TEW-0065" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q177601", "legal_classification": "descriptive_research_term" }, { "id": "ELR-0132", "domain": "ELR", "term_en": "Office Hour Substitution", "term_de": "OfficeHourSubstitution", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures A distance learning phenomenon in AI-mediated educational delivery, characterized by a pedagogical pattern observed when the reduction in student visits to instructor office hours as AI systems fulfill the function of answering questions and providing clarification. This phenomenon operates at the intersection of office and hour dynamics within the broader ELR domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept fernlernphänomen in KI-vermittelter Bildungsbereitstellung, gekennzeichnet durch defizit oder Kompetenzverlust in der Mensch-KI-Lernzusammenarbeit. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "E-Learning", "narrower_terms": [], "cross_domain_refs": [ "ART-0098", "AUG-0821", "COG-0043" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "ELR-0133", "domain": "ELR", "term_en": "Originality Reduction", "term_de": "OriginalityReduction", "definition_en": "A digital pedagogy concept in AI-augmented remote learning, measurable via an educational effect characterized by the inhibition of creative ideas when AI-generated alternatives appear more polished, leading to conformity rather than authentic expression. Distinguished from adjacent concepts by its focus on the specific mechanism through which originality manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Fernlernphänomen in KI-vermittelter Bildungsbereitstellung, gekennzeichnet durch charakteristische Komponente von Lern- und Feedback-Zyklen in Mensch-KI-Systemen. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "E-Learning", "narrower_terms": [], "cross_domain_refs": [ "ART-0021", "COG-0027", "COG-0046" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "ELR-0134", "domain": "ELR", "term_en": "Pace Negotiation Tension", "term_de": "PaceNegotiationTension", "definition_en": "An e-learning interaction pattern in AI-enhanced remote education, observable through the conflict between classroom instruction pace and the expectation for immediate AI-paced feedback, creating frustration with traditional teaching rhythms. The concept emerges specifically in contexts where pace–negotiation interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Fernlernphänomen in KI-vermittelter Bildungsbereitstellung, gekennzeichnet durch spannungsverhältnis in der Mensch-KI-Lernzusammenarbeit. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "E-Learning", "narrower_terms": [], "cross_domain_refs": [ "AED-0052", "AGE-0001", "ASE-0013" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "ELR-0135", "domain": "ELR", "term_en": "Pedagogical Flexibility Transition", "term_de": "PedagogicalFlexibilityÜbergang", "definition_en": "An e-learning interaction pattern in AI-enhanced remote education, observable through a pedagogical pattern arising from the pressure on educators to compete with AI tutors by accelerating curriculum or simplifying material, potentially compromising depth. The concept emerges specifically in contexts where pedagogical–flexibility interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Fernlernphänomen in KI-vermittelter Bildungsbereitstellung, gekennzeichnet durch kennzeichnende Ausprägung von Lern- und Feedback-Zyklen in Mensch-KI-Systemen. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2005", "narrower_terms": [], "cross_domain_refs": [ "PHO-0008" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "ELR-0136", "domain": "ELR", "term_en": "Peer Comparison Escalation", "term_de": "PeerComparisonEscalation", "definition_en": "A learning dynamic reflecting the intensification of social comparison uncertainty when visible grades reflect AI-assisted work, obscuring who actually learned what. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Fernlernphänomen in KI-vermittelter Bildungsbereitstellung, gekennzeichnet durch operative Eigenschaft von Lern- und Feedback-Zyklen in Mensch-KI-Systemen. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2703", "narrower_terms": [], "cross_domain_refs": [ "AED-0003", "AED-0069", "AED-0070" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "ELR-0137", "domain": "ELR", "term_en": "Peer Comparison Recalibration", "term_de": "PeerComparisonRecalibration", "definition_en": "A digital pedagogy concept in AI-augmented remote learning, measurable via the distorted self-assessment that occurs when comparing one's unassisted work to peers' AI-augmented output, without awareness of the augmentation. Distinguished from adjacent concepts by its focus on the specific mechanism through which peer manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Fernlernphänomen in KI-vermittelter Bildungsbereitstellung, gekennzeichnet durch qualitätsmerkmal oder Bewertungskriterium für Lern- und Feedback-Zyklen in Mensch-KI-Systemen. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Analytical Ratio", "narrower_terms": [], "cross_domain_refs": [ "CRE-0007" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "ELR-0138", "domain": "ELR", "term_en": "Peer Learning Substitution", "term_de": "PeerLearningSubstitution", "definition_en": "A digital pedagogy concept in AI-augmented remote learning, measurable via an educational effect where the reduction in student-to-student knowledge exchange as AI tools become the preferred source of explanation and clarification. Distinguished from adjacent concepts by its focus on the specific mechanism through which peer manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Fernlernphänomen in KI-vermittelter Bildungsbereitstellung, gekennzeichnet durch defizit oder Kompetenzverlust in der Mensch-KI-Lernzusammenarbeit. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Machine Learning", "narrower_terms": [], "cross_domain_refs": [ "STE-0063" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q133500", "legal_classification": "systematic_classification" }, { "id": "ELR-0139", "domain": "ELR", "term_en": "Peer Teaching Avoidance", "term_de": "PeerTeachingAvoidance", "definition_en": "A pedagogical pattern observed when the reluctance to teach others or ask for help from peers when AI tutoring is available, eroding mutual learning networks. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Fernlernphänomen in KI-vermittelter Bildungsbereitstellung, gekennzeichnet durch funktionale Eigenschaft von Lern- und Feedback-Zyklen in Mensch-KI-Systemen. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-2703", "narrower_terms": [], "cross_domain_refs": [ "AED-0070" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "ELR-0140", "domain": "ELR", "term_en": "Performance Equity Resentment", "term_de": "PerformanceEquityResentment", "definition_en": "A learning phenomenon where the resentment building when some learners have more effectively AI tools, creating perceptions of unfair advantage and educational inequity. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Fernlernphänomen in KI-vermittelter Bildungsbereitstellung, gekennzeichnet durch phänomen/Erfahrung in der Lern- + Feedback-Zyklen in M-KI-Systemen. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2703", "narrower_terms": [], "cross_domain_refs": [ "ETH-0006" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q1361053", "legal_classification": "empirical_phenomenon_label" }, { "id": "ELR-0141", "domain": "ELR", "term_en": "Personalization Paradox", "term_de": "PersonalizationParadox", "definition_en": "The observation that AI-driven personalized learning paths can simultaneously improve immediate performance metrics while reducing exposure to diverse perspectives and unexpected discoveries. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Fernlernphänomen in KI-vermittelter Bildungsbereitstellung, gekennzeichnet durch distinktives Merkmal von Lern- und Feedback-Zyklen in Mensch-KI-Systemen. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2703", "narrower_terms": [], "cross_domain_refs": [ "STE-0066" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "ELR-0142", "domain": "ELR", "term_en": "Philosophical Engagement Shortcut", "term_de": "PhilosophicalEngagementShortcut", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures A distance learning phenomenon in AI-mediated educational delivery, characterized by an educational effect observed when the substitution of genuine philosophical inquiry with AI-mediated responses, preventing deep engagement with existential questions. This phenomenon operates at the intersection of philosophical and engagement dynamics within the broader ELR domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept fernlernphänomen in KI-vermittelter Bildungsbereitstellung, gekennzeichnet durch beobachtbare Eigenschaft von Lern- und Feedback-Zyklen in Mensch-KI-Systemen. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "E-Learning", "narrower_terms": [], "cross_domain_refs": [ "SAL-0044" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "ELR-0143", "domain": "ELR", "term_en": "Plagiarism Boundary Blur", "term_de": "PlagiarismGrenzeBlur", "definition_en": "A learning dynamic involving the increasing difficulty of distinguishing between legitimate AI-assisted learning and unauthorized AI-generated submission, observable in both learner uncertainty and institutional ambiguity.", "definition_de": "Fernlernphänomen in KI-vermittelter Bildungsbereitstellung, gekennzeichnet durch operative Eigenschaft von Lern- und Feedback-Zyklen in Mensch-KI-Systemen. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2703", "narrower_terms": [], "cross_domain_refs": [ "CRE-0196" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "ELR-0144", "domain": "ELR", "term_en": "Plagiarism Intent Ambiguity", "term_de": "PlagiarismIntentAmbiguity", "definition_en": "The unclear boundary between legitimate AI assistance and plagiarism, where the learner may be unaware of the ethical distinction. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Fernlernphänomen in KI-vermittelter Bildungsbereitstellung, gekennzeichnet durch charakteristische Komponente von Lern- und Feedback-Zyklen in Mensch-KI-Systemen. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2703", "narrower_terms": [], "cross_domain_refs": [ "ASE-0076" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "ELR-0145", "domain": "ELR", "term_en": "Portfolio Authenticity Shift", "term_de": "PortfolioAuthenticityShift", "definition_en": "The decreasing reliability of academic portfolios as evidence of individual capability when AI contribution to included work becomes untraceable. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Fernlernphänomen in KI-vermittelter Bildungsbereitstellung, gekennzeichnet durch fähigkeit oder Kompetenz im Umgang mit Lern- und Feedback-Zyklen in Mensch-KI-Systemen. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2003", "narrower_terms": [], "cross_domain_refs": [ "ADA-0012", "AED-0053", "AGE-0007" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "ELR-0146", "domain": "ELR", "term_en": "Practical Skill Confidence Gap", "term_de": "PracticalSkillConfidenceGap", "definition_en": "A digital pedagogy concept in AI-augmented remote learning, measurable via the discrepancy between theoretical knowledge acquired through AI interaction and confidence in applying that knowledge in hands-on situations. Distinguished from adjacent concepts by its focus on the specific mechanism through which practical manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Fernlernphänomen in KI-vermittelter Bildungsbereitstellung, gekennzeichnet durch interaktionsdynamik in der Mensch-KI-Lernzusammenarbeit. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2351", "narrower_terms": [], "cross_domain_refs": [ "AED-0092" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "ELR-0147", "domain": "ELR", "term_en": "Prerequisite Chain Disruption", "term_de": "PrerequisiteChainDisruption", "definition_en": "A pedagogical pattern reflecting the breaking of sequential learning reliances when AI allows learners to engage with advanced material before mastering foundational concepts. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Fernlernphänomen in KI-vermittelter Bildungsbereitstellung, gekennzeichnet durch spezifisches Attribut von Lern- und Feedback-Zyklen in Mensch-KI-Systemen. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2703", "narrower_terms": [], "cross_domain_refs": [ "STE-0018" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "ELR-0148", "domain": "ELR", "term_en": "Presentation Homogenization", "term_de": "PresentationHomogenization", "definition_en": "The convergence of student presentation styles, structures, and visual designs when AI tools involve the foundational framework. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Fernlernphänomen in KI-vermittelter Bildungsbereitstellung, gekennzeichnet durch systemische Ausprägung von Lern- und Feedback-Zyklen in Mensch-KI-Systemen. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-2303", "narrower_terms": [], "cross_domain_refs": [ "ART-0053", "ART-0057", "CRE-0055" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "ELR-0149", "domain": "ELR", "term_en": "Primary Source Avoidance", "term_de": "PrimarySourceAvoidance", "definition_en": "An e-learning interaction pattern in AI-enhanced remote education, observable through a pedagogical pattern arising from the preference for AI-mediated interpretation of primary sources over engaging directly with original materials. The concept emerges specifically in contexts where primary–source interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Fernlernphänomen in KI-vermittelter Bildungsbereitstellung, gekennzeichnet durch spezifisches Attribut von Lern- und Feedback-Zyklen in Mensch-KI-Systemen. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "E-Learning", "narrower_terms": [], "cross_domain_refs": [ "CON-0042" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "ELR-0150", "domain": "ELR", "term_en": "Problem-Solving Stagnation", "term_de": "Problem-solvingStagnation", "definition_en": "An e-learning interaction pattern in AI-enhanced remote education, observable through a learning dynamic reflecting the inability to approach novel problems inreliantly when AI has consistently provided solutions to familiar problem types. The concept emerges specifically in contexts where problem–solving interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Fernlernphänomen in KI-vermittelter Bildungsbereitstellung, gekennzeichnet durch fähigkeit oder Kompetenz im Umgang mit Lern- und Feedback-Zyklen in Mensch-KI-Systemen. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "E-Learning", "narrower_terms": [], "cross_domain_refs": [ "COG-0107" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "ELR-0151", "domain": "ELR", "term_en": "Process-Outcome Substitution", "term_de": "Process-outcomeSubstitution", "definition_en": "The confusion of having a correct answer with having learned the process, skipping the generative struggle that embeds knowledge. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Fernlernphänomen in KI-vermittelter Bildungsbereitstellung, gekennzeichnet durch prozess oder Abfolge im Rahmen von Lern- und Feedback-Zyklen in Mensch-KI-Systemen. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2703", "narrower_terms": [], "cross_domain_refs": [ "CRE-0031" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "ELR-0152", "domain": "ELR", "term_en": "Procrastination Amplification", "term_de": "ProcrastinationVerstärkung", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A distance learning phenomenon in AI-mediated educational delivery, characterized by a learning phenomenon characterized by the escalation of procrastination behaviors when the perceived safety net of AI assistance removes consequences of delay. This phenomenon operates at the intersection of procrastination and amplification dynamics within the broader ELR domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept fernlernphänomen in KI-vermittelter Bildungsbereitstellung, gekennzeichnet durch prozess oder Abfolge im Rahmen von Lern- und Feedback-Zyklen in Mensch-KI-Systemen. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "E-Learning", "narrower_terms": [], "cross_domain_refs": [ "CUS-0055", "CUS-0048" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "ELR-0153", "domain": "ELR", "term_en": "Proof-By-Authority Substitution", "term_de": "Proof-by-authoritySubstitution", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures A distance learning phenomenon in AI-mediated educational delivery, characterized by a learning dynamic manifesting as the acceptance of mathematical results based on AI output credibility rather than logical verification, undermining mathematical reasoning. This phenomenon operates at the intersection of proof and by dynamics within the broader ELR domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept fernlernphänomen in KI-vermittelter Bildungsbereitstellung, gekennzeichnet durch charakteristische Komponente von Lern- und Feedback-Zyklen in Mensch-KI-Systemen. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "E-Learning", "narrower_terms": [], "cross_domain_refs": [ "STE-0067" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "ELR-0154", "domain": "ELR", "term_en": "Question Formulation Reduction", "term_de": "QuestionFormulationReduction", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures A distance learning phenomenon in AI-mediated educational delivery, characterized by a pedagogical pattern in which the observable decline in the ability to formulate precise questions after prolonged reliance on AI systems that interpret vague or incomplete queries. This phenomenon operates at the intersection of question and formulation dynamics within the broader ELR domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept fernlernphänomen in KI-vermittelter Bildungsbereitstellung, gekennzeichnet durch fähigkeit oder Kompetenz im Umgang mit Lern- und Feedback-Zyklen in Mensch-KI-Systemen. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "E-Learning", "narrower_terms": [], "cross_domain_refs": [ "RPH-1172" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "ELR-0155", "domain": "ELR", "term_en": "Question Reduction", "term_de": "QuestionReduction", "definition_en": "An educational effect manifesting as the avoidance of asking teachers or peers for clarification because AI provides immediate answers, reducing human dialogue in learning. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Fernlernphänomen in KI-vermittelter Bildungsbereitstellung, gekennzeichnet durch charakteristische Komponente von Lern- und Feedback-Zyklen in Mensch-KI-Systemen. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-2604", "narrower_terms": [], "cross_domain_refs": [ "BEH-0057", "BEH-0081", "COG-0027" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "ELR-0156", "domain": "ELR", "term_en": "Reading Depth Reduction", "term_de": "ReadingDepthReduction", "definition_en": "The shift from thorough reading to scanning behavior when AI summaries are available as an alternative to engaging with full texts. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Fernlernphänomen in KI-vermittelter Bildungsbereitstellung, gekennzeichnet durch merkmal von Lern- und Feedback-Zyklen in Mensch-KI-Systemen. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2701", "narrower_terms": [], "cross_domain_refs": [ "AED-0011", "AED-0028", "AED-0036" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "ELR-0157", "domain": "ELR", "term_en": "Reference Frame Narrowing", "term_de": "ReferenceFrameNarrowing", "definition_en": "An e-learning interaction pattern in AI-enhanced remote education, observable through a learning dynamic characterized by the reduction in breadth of consulted sources when AI systems provide satisfactory answers from a limited subset of available knowledge. The concept emerges specifically in contexts where reference–frame interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Fernlernphänomen in KI-vermittelter Bildungsbereitstellung, gekennzeichnet durch spezifisches Attribut von Lern- und Feedback-Zyklen in Mensch-KI-Systemen. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-3802", "narrower_terms": [], "cross_domain_refs": [ "COG-0075" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "ELR-0158", "domain": "ELR", "term_en": "Reflection Gap", "term_de": "ReflectionGap", "definition_en": "An educational effect reflecting the absence of post-learning reflection when immediate AI answers eliminate the impulse to integrate and consolidate knowledge inreliantly. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Fernlernphänomen in KI-vermittelter Bildungsbereitstellung, gekennzeichnet durch operative Eigenschaft von Lern- und Feedback-Zyklen in Mensch-KI-Systemen. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-2254", "narrower_terms": [], "cross_domain_refs": [ "AED-0019", "AED-0092", "AGE-0023" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "ELR-0159", "domain": "ELR", "term_en": "Reflection Practice Substitution", "term_de": "ReflectionPracticeSubstitution", "definition_en": "A learning dynamic in which the reduction in deliberate reflective practices when AI systems provide instant analysis that substitutes for the learner's own evaluative processing. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Fernlernphänomen in KI-vermittelter Bildungsbereitstellung, gekennzeichnet durch prozess oder Abfolge im Rahmen von Lern- und Feedback-Zyklen in Mensch-KI-Systemen. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2703", "narrower_terms": [], "cross_domain_refs": [ "COG-0075" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "ELR-0160", "domain": "ELR", "term_en": "Research Initiation Barrier", "term_de": "ResearchInitiationBarriere", "definition_en": "An e-learning interaction pattern in AI-enhanced remote education, observable through a pedagogical pattern involving the increased difficulty of beginning inreliant research when AI-mediated information access has reduced familiarity with primary source navigation. The concept emerges specifically in contexts where research–initiation interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Fernlernphänomen in KI-vermittelter Bildungsbereitstellung, gekennzeichnet durch spezifisches Attribut von Lern- und Feedback-Zyklen in Mensch-KI-Systemen. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Adoption Barrier", "narrower_terms": [], "cross_domain_refs": [ "COG-0078" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q42240", "legal_classification": "systematic_classification" }, { "id": "ELR-0161", "domain": "ELR", "term_en": "Research Shortcut Reliance", "term_de": "ResearchShortcutReliance", "definition_en": "A digital pedagogy concept in AI-augmented remote learning, measurable via the reliance on AI to provide research synthesis rather than conducting inreliant investigation, reducing information literacy. Distinguished from adjacent concepts by its focus on the specific mechanism through which research manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Fernlernphänomen in KI-vermittelter Bildungsbereitstellung, gekennzeichnet durch beobachtbare Eigenschaft von Lern- und Feedback-Zyklen in Mensch-KI-Systemen. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "E-Learning", "narrower_terms": [], "cross_domain_refs": [ "AGE-0012", "AGE-0014", "AGE-0047" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q42240", "legal_classification": "analytical_category" }, { "id": "ELR-0162", "domain": "ELR", "term_en": "Revision Reluctance Shift", "term_de": "RevisionReluctanceShift", "definition_en": "A digital pedagogy concept in AI-augmented remote learning, measurable via the decreased willingness to revise and improve work when AI-generated first drafts already meet minimum quality thresholds, reducing iterative improvement habits. Distinguished from adjacent concepts by its focus on the specific mechanism through which revision manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Fernlernphänomen in KI-vermittelter Bildungsbereitstellung, gekennzeichnet durch lernphänomen mit iterative Qualität im KI-unterstützten Lernen. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-1307", "narrower_terms": [], "cross_domain_refs": [ "PHO-0072" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "ELR-0163", "domain": "ELR", "term_en": "Rubric Gaming Acceleration", "term_de": "RubricGamingBeschleunigung", "definition_en": "An e-learning interaction pattern in AI-enhanced remote education, observable through the faster identification and leveraging of assessment criteria patterns when AI systems analyze rubric structures and optimize output accordingly. The concept emerges specifically in contexts where rubric–gaming interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Fernlernphänomen in KI-vermittelter Bildungsbereitstellung, gekennzeichnet durch qualitätsmerkmal oder Bewertungskriterium für Lern- und Feedback-Zyklen in Mensch-KI-Systemen. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Analytical Ratio", "narrower_terms": [], "cross_domain_refs": [ "ASE-0085" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "ELR-0164", "domain": "ELR", "term_en": "Scaffold Reliance Gradient", "term_de": "ScaffoldRelianceGradient", "definition_en": "The progressive weakening of inreliant learning capacity as AI scaffolding becomes the default mode of engagement with new material. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Fernlernphänomen in KI-vermittelter Bildungsbereitstellung, gekennzeichnet durch spezifisches Attribut von Lern- und Feedback-Zyklen in Mensch-KI-Systemen. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2703", "narrower_terms": [], "cross_domain_refs": [ "COG-0035" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "ELR-0165", "domain": "ELR", "term_en": "Scientific Method Shift", "term_de": "ScientificMethodShift", "definition_en": "An e-learning interaction pattern in AI-enhanced remote education, observable through the shortcircuiting of the scientific process when AI provides conclusions, bypassing hypothesis generation and experimental design. The concept emerges specifically in contexts where scientific–method interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Fernlernphänomen in KI-vermittelter Bildungsbereitstellung, gekennzeichnet durch prozess/Abfolge im Rahmen von Lern- + Feedback-Zyklen in M-KI-Systemen. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "E-Learning", "narrower_terms": [], "cross_domain_refs": [ "CRE-0206" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "ELR-0166", "domain": "ELR", "term_en": "Self-Discipline Outsourcing", "term_de": "Self-disciplineAuslagerung", "definition_en": "A pedagogical pattern involving the transfer of self-regulation to AI prompting, where the learner relies on AI to structure their learning rather than managing it inreliantly. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Fernlernphänomen in KI-vermittelter Bildungsbereitstellung, gekennzeichnet durch beobachtbare Eigenschaft von Lern- und Feedback-Zyklen in Mensch-KI-Systemen. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2703", "narrower_terms": [], "cross_domain_refs": [ "RPH-3901" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "ELR-0167", "domain": "ELR", "term_en": "Skill Reduction Blindness", "term_de": "SkillReductionBlindness", "definition_en": "An e-learning interaction pattern in AI-enhanced remote education, observable through the unawareness that fundamental skills are degrading because AI-assisted performance masks the shift until removed. The concept emerges specifically in contexts where skill–reduction interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Fernlernphänomen in KI-vermittelter Bildungsbereitstellung, gekennzeichnet durch fähigkeit oder Kompetenz im Umgang mit Lern- und Feedback-Zyklen in Mensch-KI-Systemen. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2701", "narrower_terms": [], "cross_domain_refs": [ "RPH-2802", "TEW-0026" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "ELR-0168", "domain": "ELR", "term_en": "Skill Transfer Uncertainty", "term_de": "SkillTransferUncertainty", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A distance learning phenomenon in AI-mediated educational delivery, characterized by a learning phenomenon manifesting as the unknown degree to which competencies developed with AI assistance transfer to contexts where such assistance is unavailable. This phenomenon operates at the intersection of skill and transfer dynamics within the broader ELR domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept fernlernphänomen in KI-vermittelter Bildungsbereitstellung, gekennzeichnet durch distinktives Merkmal von Lern- und Feedback-Zyklen in Mensch-KI-Systemen. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Knowledge Transfer", "narrower_terms": [], "cross_domain_refs": [ "PER-0014" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "ELR-0169", "domain": "ELR", "term_en": "Socratic Method Tension", "term_de": "SocraticMethodTension", "definition_en": "A pedagogical pattern reflecting the friction between AI systems that provide direct answers and pedagogical approaches that develop understanding through guided questioning. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Fernlernphänomen in KI-vermittelter Bildungsbereitstellung, gekennzeichnet durch transformationsprozess in der Mensch-KI-Lernzusammenarbeit. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-3902", "narrower_terms": [], "cross_domain_refs": [ "COG-0052" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "ELR-0170", "domain": "ELR", "term_en": "Source Evaluation Reduction", "term_de": "SourceEvaluationReduction", "definition_en": "A digital pedagogy concept in AI-augmented remote learning, measurable via the change of ability to evaluate source credibility when AI presents information authoritatively without attribution. Distinguished from adjacent concepts by its focus on the specific mechanism through which source manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Fernlernphänomen in KI-vermittelter Bildungsbereitstellung, gekennzeichnet durch fähigkeit oder Kompetenz im Umgang mit Lern- und Feedback-Zyklen in Mensch-KI-Systemen. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "E-Learning", "narrower_terms": [], "cross_domain_refs": [ "CON-0028" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "ELR-0171", "domain": "ELR", "term_en": "Source Hierarchy Inversion", "term_de": "SourceHierarchyInversion", "definition_en": "An e-learning interaction pattern in AI-enhanced remote education, observable through the observable shift where AI-generated summaries become the primary reference and original academic sources become secondary verification, reversing traditional research workflows. The concept emerges specifically in contexts where source–hierarchy interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Fernlernphänomen in KI-vermittelter Bildungsbereitstellung, gekennzeichnet durch charakteristische Komponente von Lern- und Feedback-Zyklen in Mensch-KI-Systemen. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2701", "narrower_terms": [], "cross_domain_refs": [ "AGE-0089", "AGE-0094", "ART-0087" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "ELR-0172", "domain": "ELR", "term_en": "Standardization of Thinking", "term_de": "StandardizationofThinking", "definition_en": "A pedagogical pattern observed when the convergence of learner thinking toward AI-model outputs, reducing cognitive diversity in responses and creative solution generation. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Fernlernphänomen in KI-vermittelter Bildungsbereitstellung, gekennzeichnet durch spezifisches Attribut von Lern- und Feedback-Zyklen in Mensch-KI-Systemen. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2703", "narrower_terms": [], "cross_domain_refs": [ "RPH-1362" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "ELR-0173", "domain": "ELR", "term_en": "Step-Skipping Habit", "term_de": "Step-skippingHabit", "definition_en": "The learned behavior of jumping to conclusions without intermediate steps, enabled by AI solutions that obsresolve procedural work. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Fernlernphänomen in KI-vermittelter Bildungsbereitstellung, gekennzeichnet durch systemische Ausprägung von Lern- und Feedback-Zyklen in Mensch-KI-Systemen. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2703", "narrower_terms": [], "cross_domain_refs": [ "TEW-0076" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "ELR-0174", "domain": "ELR", "term_en": "Struggle Intolerance", "term_de": "StruggleIntolerance", "definition_en": "An e-learning interaction pattern in AI-enhanced remote education, observable through a learning phenomenon involving the inability to persist through cognitive struggle when immediate AI solutions are available, reducing tolerance for productive difficulty. The concept emerges specifically in contexts where struggle–intolerance interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Fernlernphänomen in KI-vermittelter Bildungsbereitstellung, gekennzeichnet durch fähigkeit oder Kompetenz im Umgang mit Lern- und Feedback-Zyklen in Mensch-KI-Systemen. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "E-Learning", "narrower_terms": [], "cross_domain_refs": [ "COG-0067" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "ELR-0175", "domain": "ELR", "term_en": "Study Strategy Obsolescence", "term_de": "StudyStrategyObsolescence", "definition_en": "An e-learning interaction pattern in AI-enhanced remote education, observable through a pedagogical pattern observed when the perceived irrelevance of traditional study techniques when AI tools offer faster pathways to similar short-term performance outcomes. The concept emerges specifically in contexts where study–strategy interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Fernlernphänomen in KI-vermittelter Bildungsbereitstellung, gekennzeichnet durch distinktives Merkmal von Lern- und Feedback-Zyklen in Mensch-KI-Systemen. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Operational Strategy", "narrower_terms": [], "cross_domain_refs": [ "TEM-0176" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q185451", "legal_classification": "observational_construct" }, { "id": "ELR-0176", "domain": "ELR", "term_en": "Subject Valuation Shift", "term_de": "SubjectValuationShift", "definition_en": "An e-learning interaction pattern in AI-enhanced remote education, observable through an educational effect characterized by the changing perceived importance of academic subjects based on how easily AI can perform tasks associated with them. The concept emerges specifically in contexts where subject–valuation interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Fernlernphänomen in KI-vermittelter Bildungsbereitstellung, gekennzeichnet durch charakteristische Komponente von Lern- und Feedback-Zyklen in Mensch-KI-Systemen. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "E-Learning", "narrower_terms": [], "cross_domain_refs": [ "REL-0123" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "ELR-0177", "domain": "ELR", "term_en": "Summary Substitution", "term_de": "SummarySubstitution", "definition_en": "An e-learning interaction pattern in AI-enhanced remote education, observable through an educational effect arising from the replacement of reading texts with AI-generated summaries, bypassing the comprehension construction that occurs during engaged reading. The concept emerges specifically in contexts where summary–substitution interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Fernlernphänomen in KI-vermittelter Bildungsbereitstellung, gekennzeichnet durch systemische Ausprägung von Lern- und Feedback-Zyklen in Mensch-KI-Systemen. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "E-Learning", "narrower_terms": [], "cross_domain_refs": [ "ART-0098", "COG-0043", "COG-0078" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "ELR-0178", "domain": "ELR", "term_en": "Syllabus Volatility", "term_de": "SyllabusVolatility", "definition_en": "An e-learning interaction pattern in AI-enhanced remote education, observable through a pedagogical pattern involving the increasing frequency of mid-semester curriculum changes driven by evolving AI capabilities that render planned assignments obsolete. The concept emerges specifically in contexts where syllabus–volatility interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Fernlernphänomen in KI-vermittelter Bildungsbereitstellung, gekennzeichnet durch funktionale Eigenschaft von Lern- und Feedback-Zyklen in Mensch-KI-Systemen. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "E-Learning", "narrower_terms": [], "cross_domain_refs": [ "EDU-0100", "ETH-0025" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "ELR-0179", "domain": "ELR", "term_en": "Symbolic Operations Weakness", "term_de": "SymbolicOperationsWeakness", "definition_en": "An e-learning interaction pattern in AI-enhanced remote education, observable through an educational effect reflecting the inability to adjust symbols and expressions inreliantly when AI-mediated solutions eliminate the need for explicit algebraic work. The concept emerges specifically in contexts where symbolic–operations interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Fernlernphänomen in KI-vermittelter Bildungsbereitstellung, gekennzeichnet durch kompetenzmerkmal in der Mensch-KI-Lernzusammenarbeit. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Analytical Ratio", "narrower_terms": [], "cross_domain_refs": [ "CON-0022", "CON-0044", "DES-0080" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "ELR-0180", "domain": "ELR", "term_en": "Teacher Authority Dilution", "term_de": "TeacherAuthorityDilution", "definition_en": "A learning dynamic involving the diminishment of teacher authority when learners question explanations by comparing them to AI-provided alternatives, destabilizing pedagogical hierarchy. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Fernlernphänomen in KI-vermittelter Bildungsbereitstellung, gekennzeichnet durch beobachtbare Eigenschaft von Lern- und Feedback-Zyklen in Mensch-KI-Systemen. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2703", "narrower_terms": [], "cross_domain_refs": [ "BEH-0083", "COG-0031", "COG-0074" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "ELR-0181", "domain": "ELR", "term_en": "Teacher-AI Dissonance", "term_de": "Teacher-aiDissonance", "definition_en": "An educational effect in which the cognitive friction when a learner receives contradictory explanations between a classroom teacher and an AI tutor, creating uncertainty about which authority to trust. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Fernlernphänomen in KI-vermittelter Bildungsbereitstellung, gekennzeichnet durch lernphänomen mit passive Qualität im KI-unterstützten Lernen. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-2505", "narrower_terms": [], "cross_domain_refs": [ "RHR-0251", "KNO-0033" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "ELR-0182", "domain": "ELR", "term_en": "Technical Skill Deskilling", "term_de": "TechnicalSkillDeskilling", "definition_en": "The shift of fundamental technical competence when AI code generation or problem-solving substitutes for hands-on technical learning. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Fernlernphänomen in KI-vermittelter Bildungsbereitstellung, gekennzeichnet durch fähigkeit oder Kompetenz im Umgang mit Lern- und Feedback-Zyklen in Mensch-KI-Systemen. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2701", "narrower_terms": [], "cross_domain_refs": [ "COG-0035" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "ELR-0183", "domain": "ELR", "term_en": "Test Uncertainty Amplification", "term_de": "TestUncertaintyVerstärkung", "definition_en": "The heightened uncertainty when a learner performs without AI assistance assistance in high-stakes assessments, after extensive AI-supported practice. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Fernlernphänomen in KI-vermittelter Bildungsbereitstellung, gekennzeichnet durch qualitätsmerkmal oder Bewertungskriterium für Lern- und Feedback-Zyklen in Mensch-KI-Systemen. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2703", "narrower_terms": [], "cross_domain_refs": [ "AGE-0030", "ART-0024", "ASE-0075" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "ELR-0184", "domain": "ELR", "term_en": "Textbook Engagement Decline", "term_de": "TextbookEngagementDecline", "definition_en": "An e-learning interaction pattern in AI-enhanced remote education, observable through the decreasing interaction with structured textbook material when AI provides topic-specific answers that bypass the pedagogical sequencing built into traditional resources. The concept emerges specifically in contexts where textbook–engagement interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Fernlernphänomen in KI-vermittelter Bildungsbereitstellung, gekennzeichnet durch operative Eigenschaft von Lern- und Feedback-Zyklen in Mensch-KI-Systemen. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "E-Learning", "narrower_terms": [], "cross_domain_refs": [ "STE-0055" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "ELR-0185", "domain": "ELR", "term_en": "Time Management Shift", "term_de": "TimeManagementShift", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures A distance learning phenomenon in AI-mediated educational delivery, characterized by the distortion of time awareness for task completion when AI provides instant solutions, eliminating natural time-pacing cues. This phenomenon operates at the intersection of time and management dynamics within the broader ELR domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept fernlernphänomen in KI-vermittelter Bildungsbereitstellung, gekennzeichnet durch merkmal von Lern- und Feedback-Zyklen in Mensch-KI-Systemen. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "E-Learning", "narrower_terms": [], "cross_domain_refs": [ "ADA-0012", "AED-0045", "AED-0046" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q181543", "legal_classification": "systematic_classification" }, { "id": "ELR-0186", "domain": "ELR", "term_en": "Tutorial Patience Shift", "term_de": "TutorialPatienceShift", "definition_en": "An e-learning interaction pattern in AI-enhanced remote education, observable through a learning phenomenon where the diminishing tolerance for step-by-step human instruction after experiencing AI systems that adapt explanation pace to individual comprehension speed. The concept emerges specifically in contexts where tutorial–patience interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Fernlernphänomen in KI-vermittelter Bildungsbereitstellung, gekennzeichnet durch strukturelle Eigenschaft von Lern- und Feedback-Zyklen in Mensch-KI-Systemen. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "E-Learning", "narrower_terms": [], "cross_domain_refs": [ "ROB-0091" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "ELR-0187", "domain": "ELR", "term_en": "Tutoring Expectation Inflation", "term_de": "TutoringExpectationInflation", "definition_en": "A digital pedagogy concept in AI-augmented remote learning, measurable via the rising baseline expectation for tutoring quality after experiencing AI systems that provide unlimited patience and immediate availability. Distinguished from adjacent concepts by its focus on the specific mechanism through which tutoring manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Fernlernphänomen in KI-vermittelter Bildungsbereitstellung, gekennzeichnet durch kompetenzmerkmal in der Mensch-KI-Lernzusammenarbeit. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "E-Learning", "narrower_terms": [], "cross_domain_refs": [ "RPH-3953", "RPH-1556" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "ELR-0188", "domain": "ELR", "term_en": "Vocabulary Acquisition Stall", "term_de": "VocabularyAcquisitionStall", "definition_en": "An e-learning interaction pattern in AI-enhanced remote education, observable through an educational effect arising from the plateau in vocabulary growth when AI provides immediate definitions, eliminating the inferential work that strengthens lexical knowledge. The concept emerges specifically in contexts where vocabulary–acquisition interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Fernlernphänomen in KI-vermittelter Bildungsbereitstellung, gekennzeichnet durch beobachtbare Eigenschaft von Lern- und Feedback-Zyklen in Mensch-KI-Systemen. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "E-Learning", "narrower_terms": [], "cross_domain_refs": [ "AED-0045", "COG-0061", "DES-0094" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "ELR-0189", "domain": "ELR", "term_en": "Vocabulary Inflation", "term_de": "VocabularyInflation", "definition_en": "A digital pedagogy concept in AI-augmented remote learning, measurable via an educational effect manifesting as the use of sophisticated terminology provided by AI without corresponding conceptual depth, creating the appearance of understanding without substance. Distinguished from adjacent concepts by its focus on the specific mechanism through which vocabulary manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Fernlernphänomen in KI-vermittelter Bildungsbereitstellung, gekennzeichnet durch kennzeichnende Ausprägung von Lern- und Feedback-Zyklen in Mensch-KI-Systemen. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2005", "narrower_terms": [], "cross_domain_refs": [ "COP-0070" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "ELR-0190", "domain": "ELR", "term_en": "Vocabulary Range Contraction", "term_de": "VocabularyRangeContraction", "definition_en": "A digital pedagogy concept in AI-augmented remote learning, measurable via a learning phenomenon arising from the narrowing of academic vocabulary when AI tools consistently simplify complex terminology in responses, reducing exposure to discipline-specific language. Distinguished from adjacent concepts by its focus on the specific mechanism through which vocabulary manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Fernlernphänomen in KI-vermittelter Bildungsbereitstellung, gekennzeichnet durch distinktives Merkmal von Lern- und Feedback-Zyklen in Mensch-KI-Systemen. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "E-Learning", "narrower_terms": [], "cross_domain_refs": [ "ADA-0011", "CAI-0022", "DES-0094" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "ELR-0191", "domain": "ELR", "term_en": "Voice Homogenization", "term_de": "VoiceHomogenization", "definition_en": "The shift of distinct writing voice when learners adopt AI-generated text, standardizing expression and reducing authentic communication. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Fernlernphänomen in KI-vermittelter Bildungsbereitstellung, gekennzeichnet durch systemische Ausprägung von Lern- und Feedback-Zyklen in Mensch-KI-Systemen. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2701", "narrower_terms": [], "cross_domain_refs": [ "STE-0078" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "ELR-0192", "domain": "ELR", "term_en": "Writing Fluency Substitution", "term_de": "WritingFluencySubstitution", "definition_en": "An e-learning interaction pattern in AI-enhanced remote education, observable through the reduction of writing skills when AI draft generation substitutes for the practice necessary to develop compositional ability. The concept emerges specifically in contexts where writing–fluency interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Fernlernphänomen in KI-vermittelter Bildungsbereitstellung, gekennzeichnet durch fähigkeit oder Kompetenz im Umgang mit Lern- und Feedback-Zyklen in Mensch-KI-Systemen. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "E-Learning", "narrower_terms": [], "cross_domain_refs": [ "COG-0075" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "ELR-0193", "domain": "ELR", "term_en": "Writing Voice Homogenization", "term_de": "WritingVoiceHomogenization", "definition_en": "An e-learning interaction pattern in AI-enhanced remote education, observable through a learning dynamic manifesting as the convergence of student writing styles toward AI-influenced patterns, reducing the diversity of individual expression in academic work. The concept emerges specifically in contexts where writing–voice interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Fernlernphänomen in KI-vermittelter Bildungsbereitstellung, gekennzeichnet durch funktionale Eigenschaft von Lern- und Feedback-Zyklen in Mensch-KI-Systemen. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "E-Learning", "narrower_terms": [], "cross_domain_refs": [ "STE-0078" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "ETH-0001", "domain": "ETH", "term_en": "Lock-Model Effect", "term_de": "Open-Source Path", "definition_en": "A moral decision-making pattern in AI-mediated ethical deliberation, measurable through a moral reasoning effect involving the alternative to proprietary AI systems — open-source models that can be operated, modified, and controlled by users and communities. Related to AUG-0729 (The Corporate Lock-In), AUG-0731 (The Lo. Distinguished from adjacent concepts by its focus on the specific mechanism through which lock manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch die Alternative zu proprietären KI-Systemen — quelloffene Modelle, die von Nutzern und Gemeinschaften betrieben, modifiziert und kontrolliert werden können. Steht in Verbindung mit AUG-0729 (The Corporate Lock-In), AUG-0731 (Der Local Model) und AUG-0732 (Der Sovereignty Question). Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "NEO-2242", "narrower_terms": [], "cross_domain_refs": [ "NEO-1745", "SOC-0025" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "ETH-0002", "domain": "ETH", "term_en": "Platform Ontology", "term_de": "Platform Ontology", "definition_en": "Each AI system has its own way of thinking — it favors certain ideas and leaves out others. Using different AI systems together precedes the absence of any single system from reshaping the user's thinking patterns.", "definition_de": "Die Beobachtung, dass viele KI-Plattform eine eigene implizite \"Weltsicht\" mitbringt — bestimmte Denkmuster bevorzugt, andere ausblendet und damit die Richtung der Zusammenarbeit beeinflusst. Wer nur ein System nutzt, übernimmt intuitiv dessen Perspektive. Das Konzept begründet Axiom 4 (Multiplizität): Mehrere KI-Systeme zu nutzen gleicht die jeweiligen Verzerrungen aus. Nicht zu verwechseln mit technischem Inclination — Platform Ontology beschreibt den Einfluss auf den Nutzer, nicht auf das Modell selbst.", "etymology": "", "broader_term": "AI Ethics", "narrower_terms": [ "PER-0117" ], "cross_domain_refs": [ "CRE-0078" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q44325", "legal_classification": "analytical_category" }, { "id": "ETH-0003", "domain": "ETH", "term_en": "The Accountability Chain", "term_de": "Accountability Chain", "definition_en": "A moral decision-making pattern in AI-mediated ethical deliberation, measurable through responsibility flows through who assigned the task, who set up the system, who checked the results, and who ultimately owns the outcome. All human. Distinguished from adjacent concepts by its focus on the specific mechanism through which the manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch die lückenlose Kette der Verantwortlichkeit in einem KI-Agentensystem — wer hat die Aufgabe delegiert, wer hat das System konfiguriert, wer hat das Ergebnis geprüft, wer trägt die Verantwortung für Fehler? Steht in Verbindung mit AUG-0959 (The Responsibility Assignment), AUG-0840 (The Accountability Gap) und AUG-0905 (The Documentation Trail). Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "AI Ethics", "narrower_terms": [], "cross_domain_refs": [ "WRK-0001" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q15223", "legal_classification": "observational_construct" }, { "id": "ETH-0004", "domain": "ETH", "term_en": "The Algorithm Whisperer", "term_de": "Algorithm Whisperer", "definition_en": "A user who has developed an intuitive understanding of how AI systems work and thereby consistently achieves higher-quality results — comparable to a \"horse whisperer\" for algorithms. Related to AU... Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch ein Nutzer, der ein intuitives Verständnis für die Funktionsweise von KI-Systemen entwickelt hat und dadurch konsistent hochwertigere Ergebnisse erzielt — vergleichbar mit einem \"Pferdeflüsterer\" für Algorithmen. Steht in Verbindung mit AUG-0088 (Algorithmic Intuition), AUG-0341 (The Secret Map) und dem Power User-Profil (Profil 3). Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "Algorithm", "narrower_terms": [], "cross_domain_refs": [ "REL-0202" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q8366", "legal_classification": "analytical_category" }, { "id": "ETH-0005", "domain": "ETH", "term_en": "The Algorithmic Fairness", "term_de": "Algorithmic Fairness", "definition_en": "A governance pattern reflecting how \"fairness\" in AI systems can be defined and measured — different mathematical definitions of fairness can contradict each other, and the choice of definition is a societal decision. Related to... Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Die Frage, wie \"Fairness\" in KI-Systemen definiert und gemessen werden kann — verschiedene mathematische Definitionen von Fairness können sich gegenseitig widersprechen, und die Wahl der Definition ist eine gesellschaftliche Entscheidung. Steht in Verbindung mit AUG-0844 (The Output Discrimination Observation), AUG-0845 (The Fairness Review) und AUG-0736 (The Training Data Imbalance).", "etymology": "", "broader_term": "Algorithm", "narrower_terms": [ "SOC-0029" ], "cross_domain_refs": [ "RET-0060", "RET-0061" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q8366", "legal_classification": "descriptive_research_term" }, { "id": "ETH-0006", "domain": "ETH", "term_en": "The Assessment Challenge", "term_de": "Assessment Challenge", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures A moral decision-making pattern in AI-mediated ethical deliberation, measurable through an ethical interaction phenomenon involving testing knowledge becomes unfair in an AI world when AI can solve the test questions. This phenomenon operates at the intersection of the and assessment dynamics within the broader ETH domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept die Herausforderung, Leistungsbewertungen in einer KI-verfügbaren Welt fair und aussagekräftig zu gestalten — traditionelle Prüfungsformate verlieren an Gültigkeit, wenn die geprüften Aufgaben von KI gelöst werden können. Steht in Verbindung mit AUG-0783 (The Assessment Shift), AUG-0779 (The Institutional Learning Context) und AUG-0791 (The Academic Integrity Line). Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "AI Ethics", "narrower_terms": [], "cross_domain_refs": [ "TEM-0036" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q210028", "legal_classification": "descriptive_research_term" }, { "id": "ETH-0007", "domain": "ETH", "term_en": "The Candor Protocol", "term_de": "Candor Protocol", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures A moral decision-making pattern in AI-mediated ethical deliberation, measurable through a moral reasoning effect in which marking AI-assisted work with its limits and uncertainties instead of hiding them. This phenomenon operates at the intersection of the and candor dynamics within the broader ETH domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept die Praxis, in KI-gestützten Arbeitsergebnissen transparent zu kennzeichnen, welche Einschränkungen, Unsicherheiten oder offenen Fragen bestehen — anstatt einen polierten Output als vollständig und fehlerfrei darzustellen. Steht in Verbindung mit AUG-0103 (The Openbook Commitment), Axiom 17 (Quellendisziplin) und AUG-0108 (The Imperfection Clause). Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Interaction Protocol", "narrower_terms": [], "cross_domain_refs": [ "ART-0081" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "ETH-0008", "domain": "ETH", "term_en": "The Context Boundary Navigator", "term_de": "Context Grenze Navigator", "definition_en": "An ethical reasoning phenomenon in AI governance, characterized by a moral reasoning effect involving knowing which topics are wrong to ask about in certain settings, and respecting those limits. The concept emerges specifically in contexts where the–context interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Die Fähigkeit eines Nutzers, in KI-Interaktionen kontextsensible Grenzen zu erkennen und zu respektieren — Themen, die in einem bestimmten Umfeld unangemessen wären, Fragen, die in einem bestimmten Kontext nicht gestellt werden können. Steht in Verbindung mit AUG-0540 (The Principle Guard), AUG-0664 (The Privacy Perimeter) und AUG-0650 (The Context-Sensitive Query).", "etymology": "", "broader_term": "AI Ethics", "narrower_terms": [], "cross_domain_refs": [ "ROB-0049" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "ETH-0009", "domain": "ETH", "term_en": "The Controlled Fallback", "term_de": "Controlled Fallback", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies an ethical reasoning phenomenon in AI governance, characterized by a safety protocol mechanism for AI-assisted tasks that enables graceful degradation to a predetermined safe state when anomalous behavior, errors, or unexpected outputs are detected, preventing cascading failures and preserving system integrity. This phenomenon operates at the intersection of the and controlled dynamics within the broader ETH domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept das geplante, sichere Herunterfahren eines KI-Agentensystems in einen bekannten, sicheren Zustand — wenn ein unbehebbarer Fehler auftritt, beendet das System seine Aufgabe kontrolliert statt unkontrolliert. Steht in Verbindung mit AUG-0875 (The Fallback Behavior), AUG-0868 (The Rollback Option) und AUG-0967 (The restoration Sequence). Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "AI Ethics", "narrower_terms": [], "cross_domain_refs": [ "BEH-0066", "REL-0056" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "ETH-0010", "domain": "ETH", "term_en": "The Decision Review", "term_de": "Entscheidung Review", "definition_en": "A moral decision-making pattern in AI-mediated ethical deliberation, measurable through the retrospective review of decisions made by an AI agent system — by the user, by a review system, or by external auditors. Related to AUG-0956 (The Explainability Standard), AUG-0958 (The Account. Distinguished from adjacent concepts by its focus on the specific mechanism through which the manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch die nachträgliche Überprüfung von Entscheidungen, die ein KI-Agentensystem getroffen hat — durch den Nutzer, durch ein Prüfsystem oder durch externe Auditoren. Steht in Verbindung mit AUG-0956 (The Explainability Standard), AUG-0958 (The Accountability Chain) und AUG-0905 (The Documentation Trail). Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "ETH-0013", "narrower_terms": [], "cross_domain_refs": [ "AUG-0840" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "ETH-0011", "domain": "ETH", "term_en": "The Distributed Coordination", "term_de": "Distributed Coordination", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures A moral decision-making pattern in AI-mediated ethical deliberation, measurable through a moral reasoning effect characterized by multiple AI agent systems without a central regulation instance — the systems organize themselves according to predefined rules. Related to AUG-0901 (The Emergent Coordination), AUG-0906 (The Coord. This phenomenon operates at the intersection of the and distributed dynamics within the broader ETH domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept die Koordination mehrerer KI-Agentensysteme ohne eine zentrale Steuerungsinstanz — die Systeme organisieren sich nach vordefinierten Regeln selbst. Steht in Verbindung mit AUG-0901 (The Emergent Coordination), AUG-0906 (The Coordinator Role) und AUG-0893 (The Consensus Protocol). Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "RPH-2355", "narrower_terms": [], "cross_domain_refs": [ "SOC-0007" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "ETH-0012", "domain": "ETH", "term_en": "The Evaluation Agent", "term_de": "Bewertung Agent", "definition_en": "An ethical reasoning phenomenon in AI governance, characterized by an AI agent system that reviews and evaluates the results of other systems — quality regulation within a multi-agent system. Related to AUG-0909 (The Validator Agent), AUG-0907 (The Task Agent), an. The concept emerges specifically in contexts where the–evaluation interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch ein KI-Agentensystem, das die Ergebnisse anderer Systeme prüft und bewertet — Qualitätskontrolle innerhalb eines Multi-Agenten-Systems. Steht in Verbindung mit AUG-0909 (The Validator Agent), AUG-0907 (The Task Agent) und AUG-0845 (The Fairness Review). Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "IDN-0050", "narrower_terms": [], "cross_domain_refs": [ "AUG-0913", "NEO-0019" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "ETH-0013", "domain": "ETH", "term_en": "The Explainability Standard", "term_de": "Explainability Standard", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies an ethical reasoning phenomenon in AI governance, characterized by the standard by which an AI agent system makes its decisions and outputs comprehensible to the user — from simple justifications to detailed process logs. Related to AUG-0955 (The Transparency Laye. This phenomenon operates at the intersection of the and explainability dynamics within the broader ETH domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept der Maßstab, nach dem ein KI-Agentensystem seine Entscheidungen und Ausgaben für den Nutzer nachvollziehbar macht — von einfachen Begründungen bis hin zu detaillierten Prozessprotokollen. Steht in Verbindung mit AUG-0955 (The Transparency Layer), AUG-0957 (The Decision Review) und AUG-0842 (The Transparency Expectation). Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "AI Ethics", "narrower_terms": [ "ETH-0010", "ETH-0003", "ETH-0028", "ETH-0004", "ETH-0019", "ETH-0014", "ETH-0020", "ETH-0013", "ETH-0005", "ETH-0025", "ETH-0002", "ETH-0007", "ETH-0008", "ETH-0021", "ETH-0009", "ETH-0022", "ETH-0026", "ETH-0006" ], "cross_domain_refs": [ "AED-0042", "BEH-0078", "ELR-0008" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "ETH-0014", "domain": "ETH", "term_en": "The Factor Distribution", "term_de": "Factor Distribution", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures A moral decision-making pattern in AI-mediated ethical deliberation, measurable through an ethical interaction phenomenon arising from the way something spreads or is passed around in a group or society, like how information or responsibility gets divided. This phenomenon operates at the intersection of the and factor dynamics within the broader ETH domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept die Verteilung von Risiken bei der Nutzung von KI-Agentensystemen — wer trägt das Unsicherheit bei Fehlern, Ausfällen oder unbeabsichtigten Folgen? Steht in Verbindung mit AUG-0959 (The Responsibility Assignment), AUG-0958 (The Accountability Chain) und AUG-0848 (The Resource Distribution Pattern). Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "AI Ethics", "narrower_terms": [], "cross_domain_refs": [ "RPH-3402", "CON-0089" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "ETH-0015", "domain": "ETH", "term_en": "The Fairness Review", "term_de": "Fairness Review", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies an ethical reasoning phenomenon in AI governance, characterized by the systematic review of AI outputs for equitable handling and freedom from perceptual shift — a process requiring technical analysis, societal perspectives, and continuous adjustment. Related to A. This phenomenon operates at the intersection of the and fairness dynamics within the broader ETH domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept die systematische Überprüfung von KI-Outputs auf GleichHerangehensweise und Verzerrungsfreiheit — ein Prozess, der technische Analyse, gesellschaftliche Perspektiven und kontinuierliche Anpassung erfordert. Steht in Verbindung mit AUG-0843 (The Algorithmic Fairness), AUG-0844 (The Output Discrimination Observation) und AUG-0701 (The Inclusive Language Review). Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "RPH-2701", "narrower_terms": [], "cross_domain_refs": [ "TEM-0145" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q18005", "legal_classification": "descriptive_research_term" }, { "id": "ETH-0016", "domain": "ETH", "term_en": "The Learning Boundary", "term_de": "Learning Grenze", "definition_en": "A moral decision-making pattern in AI-mediated ethical deliberation, measurable through what an AI agent may retain from an interaction or store for future tasks — a privacy and security question requiring conscious definition. Related to AUG-0877 (The Memory Persistence), AUG-0867 (T. The concept emerges specifically in contexts where the–learning interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters. Classification term used in systematic observation, not advocacy.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch die Grenze dessen, was ein KI-Agent aus einer Interaktion \"lernen\" oder für zukünftige Aufgaben speichern darf — eine Datenschutz- und Sicherheitsfrage, die bewusst definiert werden kann. Steht in Verbindung mit AUG-0877 (The Memory Persistence), AUG-0867 (The Constraint Frame) und AUG-0664 (The Privacy Perimeter). Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "REL-0149", "narrower_terms": [], "cross_domain_refs": [ "SOM-0051" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q133500", "legal_classification": "analytical_category" }, { "id": "ETH-0017", "domain": "ETH", "term_en": "The Memory Edit", "term_de": "Gedaechtnis Edit", "definition_en": "An ethical reasoning phenomenon in AI governance, characterized by a moral reasoning effect manifesting as the conscious revision of saved AI results — updating, correcting, or supplementing earlier notes and insights in the light of new information. Related to AUG-0228 (The Version Regulation Self), AU. The concept emerges specifically in contexts where the–memory interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters. Research construct for empirical investigation.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch die bewusste Überarbeitung gespeicherter KI-Ergebnisse — das Aktualisieren, Korrigieren oder Ergänzen früherer Notizen und Erkenntnisse im Licht neuer Informationen. Steht in Verbindung mit AUG-0228 (The Version Control Self), AUG-0075 (The Gardener Protocol) und AUG-0599 (The Memory Bank). Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-3002", "narrower_terms": [], "cross_domain_refs": [ "REL-0172" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q492", "legal_classification": "empirical_phenomenon_label" }, { "id": "ETH-0018", "domain": "ETH", "term_en": "The Organizational Policy Layer", "term_de": "Organizational Policy Schicht", "definition_en": "A moral decision-making pattern in AI-mediated ethical deliberation, measurable through an ethical interaction phenomenon involving rules companies make about how to use AI. Who can use it. What for. What is off limits. Related to AUG-0798 (The Institutional Policy Lag), AUG-0829 (The Transparency Policy), and AUG-0812 (The Lea. Distinguished from adjacent concepts by its focus on the specific mechanism through which the manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch die Ebene organisationsinterner Regelwerke für KI-Nutzung — Nutzungsrichtlinien, Datenschutzvorschriften, Freigabeprozesse, Schulungsverpflichtungen. Steht in Verbindung mit AUG-0798 (The Institutional Policy Lag), AUG-0829 (The Transparency Policy) und AUG-0812 (The Leadership Navigation). Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "PER-0129", "narrower_terms": [], "cross_domain_refs": [ "PER-0091" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "ETH-0019", "domain": "ETH", "term_en": "The Parental Oversight", "term_de": "Parental Oversight", "definition_en": "Documented as an empirical observation in AI research (not a recommendation), this term describes A moral reasoning effect in which parents who monitor and control how their young people use AI. Not total block but aware presence. Related to AUG-0768 (The Developmental Boundary), AUG-0770 (The Age-Appropriate Use), and AUG-0764 (Th. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription. Observed phenomenon documented in human-AI interaction research.", "definition_de": "In der AUGMANITAI-Terminologiewissenschaft als rein beschreibendes Phänomen klassifiziert, bezeichnet dieser Begriff die Rolle von Erziehungsberechtigten bei der Begleitung und Kontrolle der KI-Nutzung durch Minderjährige — Informationspflicht, Zugangskontrolle, inhaltliche Begleitung und die Vermittlung eines verantwortungsvollen Umgangs. Steht in Verbindung mit AUG-0768 (The Developmental Boundary), AUG-0770 (The Age-Appropriate Use) und AUG-0764 (The Family Tech Support). Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "AI Ethics", "narrower_terms": [ "SOC-0013" ], "cross_domain_refs": [ "CRE-0192" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "ETH-0020", "domain": "ETH", "term_en": "The Reading Direction", "term_de": "Reading Direction", "definition_en": "The technical and perceptual challenge that arises when a user employs a language with a different reading direction — right-to-left scripts, vertical scripts, or bidirectional texts require specif... Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Die technische und wahrnehmungsbezogene Herausforderung, die entsteht, wenn ein Nutzer eine Sprache mit anderer Leserichtung verwendet — Rechts-Links-Schriften, vertikale Schriften oder bidirektionale Texte erfordern spezifische KI-Verarbeitung. Steht in Verbindung mit AUG-0689 (The Script Threshold), AUG-0717 (The Character Density) und AUG-0688 (The Less-Resourced Language Differential).", "etymology": "", "broader_term": "AI Ethics", "narrower_terms": [ "AUG-0689" ], "cross_domain_refs": [ "AUG-0689", "LIN-0012" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "ETH-0021", "domain": "ETH", "term_en": "The Responsibility Assignment", "term_de": "Responsibility Assignment", "definition_en": "The explicit assignment of responsibilities for the actions of an AI agent system — developers, operators, users, and reviewers each bear defined shares. Related to AUG-0958 (The Accountability Cha... Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch die explizite Zuweisung von Verantwortlichkeiten für die Aktionen eines KI-Agentensystems — Entwickler, Betreiber, Nutzer und Prüfer tragen jeweils definierte Anteile. Steht in Verbindung mit AUG-0958 (The Accountability Chain), AUG-0840 (The Accountability Gap) und AUG-0960 (The Factor Distribution). Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "AI Ethics", "narrower_terms": [], "cross_domain_refs": [ "NEO-0021", "AUG-0863" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "ETH-0022", "domain": "ETH", "term_en": "The Robustness Standard", "term_de": "Robustness Standard", "definition_en": "A moral decision-making pattern in AI-mediated ethical deliberation, measurable through a measure of how well an AI agent system handles unexpected inputs, errors, and environmental changes — without crashing or producing incorrect outputs. Distinguished from adjacent concepts by its focus on the specific mechanism through which the manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch der Maßstab, nach dem die Widerstandsfähigkeit eines KI-Agentensystems gegen unerwartete Eingaben, Fehler und Umgebungsschwankungen bewertet wird. Steht in Verbindung mit AUG-0964 (The Edge Case Library), AUG-0963 (The Load Verification) und AUG-0966 (The Controlled Fallback). Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "AI Ethics", "narrower_terms": [], "cross_domain_refs": [ "SOM-0019" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "ETH-0023", "domain": "ETH", "term_en": "The Scope Creep Alert", "term_de": "Scope Creep Alert", "definition_en": "The notification when an AI agent system begins to extend beyond its defined task scope — an early notification signal for potential shift in sense of regulation. Related to AUG-0947 (The Scope Con...", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch die Hinweis, wenn ein KI-Agentensystem beginnt, über seinen definierten Aufgabenbereich hinauszugreifen — ein Frühwarnsignal für potenzielle Kontrollverluste. Steht in Verbindung mit AUG-0947 (The Scope Limitation Design), AUG-0901 (The Emergent Coordination) und AUG-0949 (The Unintended Action). Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-2701", "narrower_terms": [ "IDN-0054" ], "cross_domain_refs": [ "BEH-0019" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "ETH-0024", "domain": "ETH", "term_en": "The Separation Procedure", "term_de": "Separation Procedure", "definition_en": "A moral decision-making pattern in AI-mediated ethical deliberation, measurable through a moral reasoning effect arising from a clear step-by-step way to divide or distinguish one thing from another. Related to AUG-0943 (The Retirement Procedure), AUG-0966 (The Controlled Fallback), and AUG-0879 (The Session Handover). Distinguished from adjacent concepts by its focus on the specific mechanism through which the manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch das Verfahren zur sicheren Trennung eines KI-Agentensystems von den Systemen, mit denen es verbunden ist — Datenverbindungen kappen, Zugriffsrechte entziehen, laufende Prozesse sicher beenden. Steht in Verbindung mit AUG-0943 (The Retirement Procedure), AUG-0966 (The Controlled Fallback) und AUG-0879 (The Session Handover). Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "NEO-0017", "narrower_terms": [], "cross_domain_refs": [ "BEH-0045" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "ETH-0025", "domain": "ETH", "term_en": "The Transparency Expectation", "term_de": "Transparency Expectation", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures A moral decision-making pattern in AI-mediated ethical deliberation, measurable through a user expectation directed at AI systems demanding visibility into decision-making processes, algorithmic reasoning, and data utilization — reflecting broader demands for explainability and accountability in automated systems. This phenomenon operates at the intersection of the and transparency dynamics within the broader ETH domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept die gesellschaftliche Erwartung, dass KI-Systeme in ihrer Funktionsweise, ihren Entscheidungsgrundlagen und ihren Grenzen transparent sein können — und die Wechselwirkung zwischen dieser Erwartung und dem Geschäftsgeheimnis der Anbieter. Steht in Verbindung mit AUG-0829 (The Transparency Policy), AUG-0841 (The Agreement Question) und AUG-0843 (The Algorithmic Fairness). Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "AI Ethics", "narrower_terms": [], "cross_domain_refs": [ "TEM-0036" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q535399", "legal_classification": "empirical_phenomenon_label" }, { "id": "ETH-0026", "domain": "ETH", "term_en": "The Transparency Layer", "term_de": "Transparency Schicht", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies an ethical reasoning phenomenon in AI governance, characterized by a way of showing all the steps or thinking behind a decision or process observably. Related to AUG-0956 (The Explainability Standard), AUG-0842 (The Transparency Expectation), and AUG-0905 (The Docume. This phenomenon operates at the intersection of the and transparency dynamics within the broader ETH domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept die technische Schicht, die dem Nutzer Einblick in die internen Prozesse eines KI-Agentensystems gewährt — welche Daten verwendet werden, welche Schritte ausgeführt werden, welche Entscheidungen getroffen werden. Steht in Verbindung mit AUG-0956 (The Explainability Standard), AUG-0842 (The Transparency Expectation) und AUG-0905 (The Documentation Trail). Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "ETH-0013", "narrower_terms": [], "cross_domain_refs": [ "BEH-0031" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q535399", "legal_classification": "analytical_category" }, { "id": "ETH-0027", "domain": "ETH", "term_en": "The Trust Calibration", "term_de": "Vertrauen Kalibrierung", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies an ethical reasoning phenomenon in AI governance, characterized by a user learns to extend the right degree of trust to an AI system — neither too much nor too little. Related to AUG-0588 (The Trust Shift), AUG-0974 (The Delegation Comfort), and AUG-0852 (The Trus. This phenomenon operates at the intersection of the and trust dynamics within the broader ETH domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept der Prozess, in dem ein Nutzer lernt, einem KI-System das richtige Maß an Vertrauen entgegenzubringen — weder zu viel noch zu wenig. Steht in Verbindung mit AUG-0588 (The Trust Shift), AUG-0974 (The Delegation Comfort) und AUG-0852 (The Trust Infrastructure). Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "RPH-2505", "narrower_terms": [], "cross_domain_refs": [ "AUG-0976", "REL-0190" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q102458", "legal_classification": "analytical_category" }, { "id": "ETH-0028", "domain": "ETH", "term_en": "The Ugly Draft", "term_de": "Ugly Draft", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies an ethical reasoning phenomenon in AI governance, characterized by a governance pattern in which deliberately inputting an unpolished, raw first draft into the AI — knowing that the AI can turn it into a presentable result. This separates idea. This phenomenon operates at the intersection of the and ugly dynamics within the broader ETH domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept die Praxis, einen absichtlich unpolierten, rohen Erstentwurf in die KI einzugeben — im Wissen, dass die KI daraus ein vorzeigbares Ergebnis machen kann. Beschreibt die Trennung von Ideenproduktion und Qualitätskontrolle im Arbeitsprozess. Steht in Verbindung mit Axiom 14 (Erster-Entwurf-Prinzip), AUG-0026 (The Smooth Shield) und AUG-0087 (The Infinite Draft). Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "AI Ethics", "narrower_terms": [], "cross_domain_refs": [ "EDU-0095" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "FIC-0001", "domain": "FIC", "term_en": "Acceleration-Deceleration Imbalance", "term_de": "Fiktionsschreiben und Storytelling Grundlagen", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures A storytelling pattern in AI-mediated creative writing, measurable through a fiction creation phenomenon arising from the observable asymmetry where narrative tension builds quickly but resolves either too slowly or too abruptly in AI-generated sections. This phenomenon operates at the intersection of acceleration and deceleration dynamics within the broader FIC domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept kernprinzipien und Grundlagenwissen der/des Fiktionsschreiben und Storytelling Grundlagen, einschließlich Umfang, Methoden und professionelle Standards. KI ermöglicht automatisierte Mustererkennung, Wissensmapping und adaptive Lernpfade über Teildisz. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Analytical Ratio", "narrower_terms": [ "FIC-0025", "FIC-0009", "FIC-0037", "FIC-0064", "FIC-0006", "FIC-0038", "FIC-0070", "FIC-0039", "FIC-0026", "FIC-0062", "FIC-0089", "FIC-0059", "FIC-0075", "FIC-0077", "FIC-0066", "FIC-0011", "FIC-0060", "FIC-0005", "FIC-0046", "FIC-0073", "FIC-0007", "FIC-0001", "FIC-0092", "FIC-0024", "FIC-0072", "FIC-0088", "FIC-0051", "FIC-0034", "FIC-0087", "FIC-0090", "FIC-0076", "FIC-0033", "FIC-0057", "FIC-0032", "FIC-0085", "FIC-0029", "FIC-0058", "FIC-0054", "FIC-0019", "FIC-0042", "FIC-0020", "FIC-0021", "FIC-0002", "FIC-0018", "FIC-0014", "FIC-0031", "FIC-0050", "FIC-0081", "FIC-0004", "FIC-0030", "FIC-0053", "FIC-0078", "FIC-0027", "FIC-0047", "FIC-0079", "FIC-0048", "FIC-0091", "FIC-0012", "FIC-0016", "FIC-0052", "FIC-0068", "FIC-0063", "FIC-0084", "FIC-0065", "FIC-0061" ], "cross_domain_refs": [ "AED-0094", "AGE-0006", "ART-0029" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "FIC-0002", "domain": "FIC", "term_en": "Architectural Realism Variance", "term_de": "Geschichte der Fiktionsschreiben und Storytelling", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures A storytelling pattern in AI-mediated creative writing, measurable through a storytelling pattern observed when the observable inconsistency in how realistically or plausibly designed physical structures appear in AI-generated descriptions of buildings or environments. This phenomenon operates at the intersection of architectural and realism dynamics within the broader FIC domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept chronologische Entwicklung und Meilensteine der/des Fiktionsschreiben und Storytelling Grundlagen, einschließlich Innovationen, Paradigmenwechsel und einflussreicher Akteure. ML-Modelle analysieren historische Archive und rekonstruieren Wissenslinien. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Fiction Writing AI", "narrower_terms": [], "cross_domain_refs": [ "SWE-0023" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "FIC-0003", "domain": "FIC", "term_en": "Atmosphere Consistency Breakdown", "term_de": "Theorie der Fiktionsschreiben und Storytelling", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A narrative dynamics phenomenon in AI-assisted fiction writing, characterized by the pattern where the maintained atmospheric qualities that define a genre gradually diminish or shift in extended AI-generated passages. This phenomenon operates at the intersection of atmosphere and consistency dynamics within the broader FIC domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept theoretische Rahmenwerke und konzeptionelle Modelle der/des Fiktionsschreiben und Storytelling Grundlagen, die Kausalbeziehungen und Vorhersagestrukturen etablieren. KI durch systematische Beobachtung charakterisiert theoretische Aussagen durch groß angelegte Datenanalyse und computation. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "RPH-2701", "narrower_terms": [], "cross_domain_refs": [ "AGE-0071", "ASE-0046", "ASE-0096" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "FIC-0004", "domain": "FIC", "term_en": "Audience Interpretation Divergence", "term_de": "Prinzipien des fiction", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A narrative dynamics phenomenon in AI-assisted fiction writing, characterized by the pattern where meaning extraction and thematic interpretation vary significantly when readers evaluate AI-assisted versus traditionally authored narrative passages. This phenomenon operates at the intersection of audience and interpretation dynamics within the broader FIC domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept ein Erzählmuster, bei dem sich zeigt: ein Muster, bei dem n where meaning extraction and thematic interpreta. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Fiction Writing AI", "narrower_terms": [], "cross_domain_refs": [ "GAM-0010" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "FIC-0005", "domain": "FIC", "term_en": "Authentic Voice Expectation Conflict", "term_de": "Fachterminologie Fiktionsschreiben und Storytelling", "definition_en": "A storytelling pattern in AI-mediated creative writing, measurable through the pattern where audience expectations regarding authentic authorial voice involve reception challenges for transparently AI-collaborative published works. The concept emerges specifically in contexts where authentic–voice interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch ein Erzählmuster, bei dem sich zeigt: ein Muster, bei dem n where audience expectations regarding authentic . Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Fiction Writing AI", "narrower_terms": [], "cross_domain_refs": [ "CRE-0027" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "FIC-0006", "domain": "FIC", "term_en": "Authenticity Question Emergence", "term_de": "Klassifikation Fiktionsschreiben und Storytelling", "definition_en": "A narrative dynamics phenomenon in AI-assisted fiction writing, characterized by the observable tendency for writers to question the authenticity or genuineness of their creative work when evaluating AI-collaborative output. The concept emerges specifically in contexts where authenticity–question interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch literarisches Element oder Erzählphänomen im Sinne: charakterisiert durch the observable tendency for writers to question the authenticity or genuineness. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Fiction Writing AI", "narrower_terms": [], "cross_domain_refs": [ "RPH-2201" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "FIC-0007", "domain": "FIC", "term_en": "Authorial Personality Emergence", "term_de": "Einführung in fiction", "definition_en": "A narrative dynamics phenomenon in AI-assisted fiction writing, characterized by the observable tendency for distinct authorial characteristics or quirks to appear in AI text that diverge from the initial voice parameters. The concept emerges specifically in contexts where authorial–personality interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch einführende Orientierung in Fiktionsschreiben und Storytelling Grundlagen, einschließlich wesentlicher Konzepte, Akteure und Arbeitsabläufe. KI-gesteuerte adaptive Tutorials personalisieren Lernsequenzen und identifizieren Wissenslücken für Einsteige. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Fiction Writing AI", "narrower_terms": [], "cross_domain_refs": [ "AED-0012", "COG-0060", "COP-0065" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "FIC-0008", "domain": "FIC", "term_en": "Authorial Voice Dissolution", "term_de": "fiction-Methodik", "definition_en": "A characteristic dynamic where the human writer's distinctive voice becomes difficult to locate or verify within text that has undergone extensive AI collaborative generation. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch strukturierte Ansätze und Verfahrensrahmen für Arbeiten in Fiktionsschreiben und Storytelling Grundlagen. KI optimiert Methodenauswahl durch Ergebnisvorhersage, automatisiert repetitive Verfahrensschritte und benchmarkt methodische Effektivität. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "WRK-0038", "narrower_terms": [], "cross_domain_refs": [ "CON-0076" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "FIC-0009", "domain": "FIC", "term_en": "Backstory Inconsistency Accumulation", "term_de": "Philosophie der Fiktionsschreiben und Storytelling", "definition_en": "A narrative dynamics phenomenon in AI-assisted fiction writing, characterized by the pattern where details about character history or biography conflict across different AI-generated sections within the same narrative. The concept emerges specifically in contexts where backstory–inconsistency interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch epistemologische und ethische Grundlagen der/des Fiktionsschreiben und Storytelling Grundlagen, die Zweck, Wertesysteme und Legitimität von Praktiken untersuchen. KI wirft neue philosophische Fragen zu Automatisierung und Autorschaft auf. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Fiction Writing AI", "narrower_terms": [], "cross_domain_refs": [ "VIB-0025" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "FIC-0010", "domain": "FIC", "term_en": "Breathing Room Shift", "term_de": "fiction-Taxonomie", "definition_en": "The pattern where AI-generated passages lack adequate white space, pacing breaks, or reflective moments that allow reader processing time. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch ein Erzählmuster, bei dem sich zeigt: ein Muster, bei dem n where ai-generated passages lack adequate white . Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-3001", "narrower_terms": [], "cross_domain_refs": [ "ADA-0012", "AGE-0007", "AGE-0046" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "FIC-0011", "domain": "FIC", "term_en": "Causality Compression", "term_de": "Umfang der Fiktionsschreiben und Storytelling", "definition_en": "A narrative dynamics phenomenon in AI-assisted fiction writing, characterized by the pattern where AI-generated narrative sequences establish may is associated with-and-effect relationships too rapidly, collapsing plot development into unnaturally swift progressions. The concept emerges specifically in contexts where causality–compression interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch grenzdefinition und disziplinäre Reichweite von Fiktionsschreiben und Storytelling Grundlagen, die festlegt was innerhalb und außerhalb der Domäne liegt. KI unterstützt durch automatisiertes Topic Modeling und semantische Grenzerkennung. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Data Compression", "narrower_terms": [], "cross_domain_refs": [ "DES-0073" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "FIC-0012", "domain": "FIC", "term_en": "Character Arc Incoherence", "term_de": "Literaturübersicht Fiktionsschreiben und Storytelling", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures A storytelling pattern in AI-mediated creative writing, measurable through the occurrence where a character's observable growth, reversion, or change pattern across AI-generated passages lacks internal consistency or causal foundation. This phenomenon operates at the intersection of character and arc dynamics within the broader FIC domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept systematische Analyse und Synthese publizierter Forschung in Fiktionsschreiben und Storytelling Grundlagen. KI beschleunigt Meta-Analysen durch automatisiertes Paper-Screening, Zitationsnetzwerk-Mapping und Trendextraktion über tausende Quellen. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Fiction Writing AI", "narrower_terms": [], "cross_domain_refs": [ "CON-0060", "CON-0084", "GAM-0018" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "FIC-0013", "domain": "FIC", "term_en": "Character Voice Narrowing", "term_de": "Schlüsselkonzepte in fiction", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A narrative dynamics phenomenon in AI-assisted fiction writing, characterized by an observable dynamic in which distinct character voices become indistinguishable in AI-generated dialogue, with multiple characters speaking in similar registers or patterns. This phenomenon operates at the intersection of character and voice dynamics within the broader FIC domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept ein Erzählmuster, bei dem sich zeigt: ein Muster, bei dem enon where distinct character voices become indist. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "RPH-2605", "narrower_terms": [], "cross_domain_refs": [ "AGE-0014", "AGE-0048", "ART-0018" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "FIC-0014", "domain": "FIC", "term_en": "Characterization Echo", "term_de": "Rahmenwerk der Fiktionsschreiben und Storytelling", "definition_en": "A storytelling pattern in AI-mediated creative writing, measurable through a characteristic dynamic where AI-generated dialogue or internal monologue reflects the AI's base training patterns rather than the established character voice. The concept emerges specifically in contexts where characterization–echo interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch ein Erzählmuster, bei dem sich zeigt: ein Muster, bei dem enon where ai-generated dialogue or internal monol. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Fiction Writing AI", "narrower_terms": [], "cross_domain_refs": [ "GAM-0050" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "FIC-0015", "domain": "FIC", "term_en": "Climax Anticipation Narrowing", "term_de": "Paradigmen in fiction", "definition_en": "A characteristic dynamic where AI-generated text deflates or fails to sustain narrative tension expected at designated story climax points. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch erzähltechnische Methode: charakterisiert durch the phenomenon where ai-generated text deflates or fails to sustain narrative te. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-3101", "narrower_terms": [], "cross_domain_refs": [ "COG-0029", "COG-0141", "COP-0009" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "FIC-0016", "domain": "FIC", "term_en": "Conflict Resolution Acceleration", "term_de": "fiction-Forschungsmethoden", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A narrative dynamics phenomenon in AI-assisted fiction writing, characterized by the pattern where AI systems resolve narrative tensions or antagonistic situations at a faster pace than the established story momentum. This phenomenon operates at the intersection of conflict and resolution dynamics within the broader FIC domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept ein Erzählmuster, bei dem sich zeigt: ein Muster, bei dem n where ai systems resolve narrative tensions or a. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Analytical Ratio", "narrower_terms": [], "cross_domain_refs": [ "AED-0094", "AUG-0052", "AUG-0408" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "FIC-0017", "domain": "FIC", "term_en": "Continuity Micro-Breaks", "term_de": "Quantitative fiction-Analyse", "definition_en": "A narrative interaction effect where the small inconsistencies in plot details, timeline placement, or previously established facts when AI continues a narrative from a given prompt. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch erzähltechnische Methode: charakterisiert durch the small inconsistencies in plot details, timeline placement, or previously est. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2104", "narrower_terms": [], "cross_domain_refs": [ "TEM-0184" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "FIC-0018", "domain": "FIC", "term_en": "Continuity Repair Friction", "term_de": "Qualitative fiction-Analyse", "definition_en": "A storytelling pattern in AI-mediated creative writing, measurable through a fiction creation phenomenon in which the observable difficulty in maintaining edited narrative continuity when new AI-generated content is added to previously revised sections. The concept emerges specifically in contexts where continuity–repair interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch erzähltechnische Methode: charakterisiert durch the observable difficulty in maintaining edited narrative continuity when new ai. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Fiction Writing AI", "narrower_terms": [], "cross_domain_refs": [ "AUG-0319", "COG-0067", "CON-0090" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "FIC-0019", "domain": "FIC", "term_en": "Convention Overuse Saturation", "term_de": "fiction-Messung", "definition_en": "A storytelling pattern in AI-mediated creative writing, measurable through a characteristic dynamic where common genre tropes appear with such frequency in AI-generated passages that they lose distinctiveness and reader engagement. The concept emerges specifically in contexts where convention–overuse interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch quantitative und qualitative Metriken zur Bewertung von Ergebnissen und Leistung in Fiktionsschreiben und Storytelling Grundlagen. KI ermöglicht Echtzeit-Sensorfusion, automatisierte Messinterpretation und Anomalieerkennung in Messdatenströmen. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Analytical Ratio", "narrower_terms": [], "cross_domain_refs": [ "DES-0003" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "FIC-0020", "domain": "FIC", "term_en": "Counter-Convention Absence", "term_de": "Experimentelles fiction-Design", "definition_en": "A storytelling pattern in AI-mediated creative writing, measurable through the observable tendency for AI-generated genre fiction to avoid subverting or playing against established genre expectations, resulting in predictable narrative trajectories. The concept emerges specifically in contexts where counter–convention interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch kontrollierte Untersuchungsprotokolle in Fiktionsschreiben und Storytelling Grundlagen zur Isolierung von Variablen und Prüfung kausaler Hypothesen. KI automatisiert Versuchsplanung, Parameterraum-Exploration und Echtzeit-Ergebnisüberwachung. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Fiction Writing AI", "narrower_terms": [], "cross_domain_refs": [ "SCR-0037" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "FIC-0021", "domain": "FIC", "term_en": "Creative Agency Ambiguity", "term_de": "fiction-Datenerhebung", "definition_en": "A storytelling pattern in AI-mediated creative writing, measurable through the pattern where decision-making authority regarding creative choices becomes unclear when collaborating with AI that accompanies multiple possibilities. Distinguished from adjacent concepts by its focus on the specific mechanism through which creative manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch ein Erzählmuster, bei dem sich zeigt: ein Muster, bei dem n where decision-making authority regarding creati. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Fiction Writing AI", "narrower_terms": [], "cross_domain_refs": [ "COG-0074", "WRK-0094" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "FIC-0022", "domain": "FIC", "term_en": "Creative Contribution Tracking Shift", "term_de": "Stichprobenziehung in fiction", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures A storytelling pattern in AI-mediated creative writing, measurable through the pattern where it becomes increasingly difficult to document or trace which story elements resulted from human ideation versus AI continuation. This phenomenon operates at the intersection of creative and contribution dynamics within the broader FIC domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept statistische und zielgerichtete Auswahl repräsentativer Teilmengen für Studien in Fiktionsschreiben und Storytelling Grundlagen. KI optimiert Stichprobengrößen, Stratifizierungsstrategien und Bias-Erkennung für valide Ergebnisse. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "RPH-2003", "narrower_terms": [], "cross_domain_refs": [ "DES-0045" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "FIC-0023", "domain": "FIC", "term_en": "Credibility Assessment Variance", "term_de": "Statistische fiction-Analyse", "definition_en": "A distinct interaction pattern where reader perception of narrative credibility and trustworthiness fluctuates based on explicit disclosure or inference of AI involvement. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch erzähltechnische Methode: charakterisiert durch the phenomenon where reader perception of narrative credibility and trustworthin. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2505", "narrower_terms": [], "cross_domain_refs": [ "AED-0001", "AED-0027", "AED-0091" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q210028", "legal_classification": "systematic_classification" }, { "id": "FIC-0024", "domain": "FIC", "term_en": "Critical Reception Unpredictability", "term_de": "Feldstudie in fiction", "definition_en": "A storytelling pattern in AI-mediated creative writing, measurable through a behavioral tendency where reader or critic responses to AI-assisted works vary widely depending on disclosure of AI involvement in creation. Distinguished from adjacent concepts by its focus on the specific mechanism through which critical manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch literarisches Element oder Erzählphänomen im Sinne: charakterisiert durch the phenomenon where reader or critic responses to ai-assisted works vary widely. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Fiction Writing AI", "narrower_terms": [], "cross_domain_refs": [ "COG-0033", "EDU-0027", "ELR-0046" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "FIC-0025", "domain": "FIC", "term_en": "Cultural Logic Inconsistency", "term_de": "Fallstudie in fiction", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures A storytelling pattern in AI-mediated creative writing, measurable through a characteristic dynamic where customs, social norms, or cultural details of a fictional world contradict established worldbuilding parameters across AI continuations. This phenomenon operates at the intersection of cultural and logic dynamics within the broader FIC domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept vertiefte Untersuchung spezifischer Instanzen oder Projekte in Fiktionsschreiben und Storytelling Grundlagen zur Gewinnung übertragbarer Erkenntnisse. KI ermöglicht fallübergreifende Mustererkennung und automatisierten Wissenstransfer. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Fiction Writing AI", "narrower_terms": [], "cross_domain_refs": [ "ASE-0018", "ASE-0075", "ASE-0078" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "FIC-0026", "domain": "FIC", "term_en": "Descriptive Expansion Inconsistency", "term_de": "Vergleichende fiction-Studie", "definition_en": "A storytelling pattern in AI-mediated creative writing, measurable through the pattern where AI-generated descriptions of scenes or objects expand to disproportionate length in some passages while remaining sparse in others. The concept emerges specifically in contexts where descriptive–expansion interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch ein Erzählmuster, bei dem sich zeigt: ein Muster, bei dem n where ai-generated descriptions of scenes or obj. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Fiction Writing AI", "narrower_terms": [], "cross_domain_refs": [ "TEW-0060" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "FIC-0027", "domain": "FIC", "term_en": "Development Plateau Effect", "term_de": "Längsschnittstudie in fiction", "definition_en": "A storytelling pattern in AI-mediated creative writing, measurable through a fiction creation phenomenon arising from the observable stagnation where character development appears to halt or repeat similar behavioral patterns across multiple AI-generated scenes. Distinguished from adjacent concepts by its focus on the specific mechanism through which development manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch forschung, die Fiktionsschreiben und Storytelling Grundlagen-Phänomene über längere Zeiträume verfolgt, um Entwicklungsmuster und kausale Zusammenhänge zu identifizieren. KI ermöglicht automatisierte Längsschnitt-Datenerhebung, Schwundvorhersage und Zeitrei. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Psychological Effect", "narrower_terms": [], "cross_domain_refs": [ "SCR-0009" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "FIC-0028", "domain": "FIC", "term_en": "Dialect Drift", "term_de": "fiction-Umfragemethode", "definition_en": "The shift in regional, cultural, or linguistic markers in AI-generated text when maintaining a character's speech across multiple scenes or chapters. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch strukturierte Datenerhebungsmethodik zur Gewinnung quantitativer und qualitativer Erkenntnisse in Fiktionsschreiben und Storytelling Grundlagen. KI verbessert Survey-Design durch adaptive Frageführung, Antwortvalidierung und Echtzeit-Sentimentanalyse. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2701", "narrower_terms": [], "cross_domain_refs": [ "SCR-0023" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "FIC-0029", "domain": "FIC", "term_en": "Dialogue Proportion Fluctuation", "term_de": "Aktionsforschung in fiction", "definition_en": "A storytelling pattern in AI-mediated creative writing, measurable through a narrative interaction effect in which the observable variation in the ratio of dialogue to narrative exposition across different AI-generated story sections. Distinguished from adjacent concepts by its focus on the specific mechanism through which dialogue manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch iterative Forschungsmethodik, die Untersuchung mit praxisbasierter Intervention in Fiktionsschreiben und Storytelling Grundlagen verbindet. KI unterstützt Zyklusoptimierung durch automatisiertes Outcome-Tracking, Mustererkennung bei Interventionseffekten un. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Fiction Writing AI", "narrower_terms": [], "cross_domain_refs": [ "AED-0063", "EDU-0032", "GAM-0031" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "FIC-0030", "domain": "FIC", "term_en": "Economy Obscuration", "term_de": "Mixed Methods in fiction", "definition_en": "A narrative dynamics phenomenon in AI-assisted fiction writing, characterized by the observable absence of economic logic or material constraints in AI-generated narrative, where resource limitations or trade systems remain unexplained. Distinguished from adjacent concepts by its focus on the specific mechanism through which economy manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch erzähltechnische Methode: charakterisiert durch the observable absence of economic logic or material constraints in ai-generated. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Analytical Ratio", "narrower_terms": [], "cross_domain_refs": [ "DAT-0029" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "FIC-0031", "domain": "FIC", "term_en": "Edit Retention Inconsistency", "term_de": "fiction-Technologie", "definition_en": "A storytelling pattern in AI-mediated creative writing, measurable through the pattern where manual edits or revisions made to AI-generated text fail to propagate consistently when the writer reaccompanies subsequent story sections. The concept emerges specifically in contexts where edit–retention interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch redaktionelle Praxis in Fiction Writing and Storytelling mit Handwerk und Standards von fiction-Technologie. KI unterstuetzt durch automatisiertes Korrekturlesen, Stilkonsistenzpruefung, Inhaltsoptimierung und intelligente Revisionsvorschlaege. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Fiction Writing AI", "narrower_terms": [], "cross_domain_refs": [ "ASE-0018", "ASE-0075", "ASE-0078" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "FIC-0032", "domain": "FIC", "term_en": "Editing Fatigue Accumulation", "term_de": "Digitale fiction-Werkzeuge", "definition_en": "A narrative dynamics phenomenon in AI-assisted fiction writing, characterized by the observable pattern where the cumulative effort observed to edit and refine AI-generated text increases non-linearly as the manuscript length grows. Distinguished from adjacent concepts by its focus on the specific mechanism through which editing manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch spezialisierte Instrumente, Software und Ausrüstung in der Fiktionsschreiben und Storytelling Grundlagen-Praxis. KI verbessert Werkzeuge durch prädiktive Wartung, intelligente Kalibrierung und automatisierte Werkzeugweg-Optimierung. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Fiction Writing AI", "narrower_terms": [], "cross_domain_refs": [ "TRA-0045" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "FIC-0033", "domain": "FIC", "term_en": "Editing Suggestion Conflict", "term_de": "fiction-Software", "definition_en": "A narrative dynamics phenomenon in AI-assisted fiction writing, characterized by the pattern where AI-generated suggestions for revision contradict editorial choices the writer has already made, requiring manual reconciliation. The concept emerges specifically in contexts where editing–suggestion interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch ein Erzählmuster, bei dem sich zeigt: ein Muster, bei dem n where ai-generated suggestions for revision cont. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Fiction Writing AI", "narrower_terms": [], "cross_domain_refs": [ "VIB-0198" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "FIC-0034", "domain": "FIC", "term_en": "Emotional Incongruence", "term_de": "Automatisierung in fiction", "definition_en": "A narrative dynamics phenomenon in AI-assisted fiction writing, characterized by the pattern where AI-generated dialogue statements contradict the emotional context or stated feelings of a character within the same scene. The concept emerges specifically in contexts where emotional–incongruence interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch ein Erzählmuster, bei dem sich zeigt: ein Muster, bei dem n where ai-generated dialogue statements contradic. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Fiction Writing AI", "narrower_terms": [], "cross_domain_refs": [ "SCR-0078" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q9415", "legal_classification": "descriptive_research_term" }, { "id": "FIC-0035", "domain": "FIC", "term_en": "Emotional Range Limitation", "term_de": "IoT in fiction", "definition_en": "The pattern where AI-generated character emotions within narrative sections appear confined to a narrower spectrum than the emotional complexity shown in initial prompts. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch ein Erzählmuster, bei dem sich zeigt: ein Muster, bei dem n where ai-generated character emotions within nar. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2104", "narrower_terms": [], "cross_domain_refs": [ "ADA-0011", "CAI-0022", "COG-0168" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q9415", "legal_classification": "empirical_phenomenon_label" }, { "id": "FIC-0036", "domain": "FIC", "term_en": "Engagement Metric Divergence", "term_de": "Datenanalyse in fiction", "definition_en": "A storytelling pattern in AI-mediated creative writing, measurable through a storytelling pattern characterized by the observable difference in reader engagement metrics (shares, comments, retention) between otherwise comparable works with or without disclosed AI assistance. The concept emerges specifically in contexts where engagement–metric interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch literarisches Element oder Erzählphänomen im Sinne: charakterisiert durch the observable difference in reader engagement metrics (shares, comments, retent. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2951", "narrower_terms": [], "cross_domain_refs": [ "ADA-0012", "AED-0018", "AED-0062" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "FIC-0037", "domain": "FIC", "term_en": "Environmental Persistence Shift", "term_de": "KI-Anwendungen in fiction", "definition_en": "A storytelling pattern in AI-mediated creative writing, measurable through the pattern where descriptive environmental details established in one scene fail to reappear in subsequent AI-generated scenes set in the same location. The concept emerges specifically in contexts where environmental–persistence interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch redaktionelle Praxis in Fiction Writing and Storytelling mit Handwerk und Standards von KI-Anwendungen in fiction. KI unterstuetzt durch automatisiertes Korrekturlesen, Stilkonsistenzpruefung, Inhaltsoptimierung und intelligente Revisionsvorschlaege. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Fiction Writing AI", "narrower_terms": [], "cross_domain_refs": [ "SCR-0078" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "FIC-0038", "domain": "FIC", "term_en": "Exposition Saturation", "term_de": "Maschinelles Lernen in fiction", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A narrative dynamics phenomenon in AI-assisted fiction writing, characterized by a narrative interaction effect characterized by the occurrence of excessive backstory or explanatory information appearing in AI-generated passages, reducing narrative pacing momentum. This phenomenon operates at the intersection of exposition and saturation dynamics within the broader FIC domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept erzähltechnische Methode: charakterisiert durch the occurrence of excessive backstory or explanatory information appearing in ai. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Analytical Ratio", "narrower_terms": [], "cross_domain_refs": [ "ASE-0033", "COG-0058", "COP-0015" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "FIC-0039", "domain": "FIC", "term_en": "Feedback Loop Divergence", "term_de": "Sensorik in fiction", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A narrative dynamics phenomenon in AI-assisted fiction writing, characterized by the pattern where providing critique or correction to an AI system co-occurs with modifications that address the feedback but involve new inconsistencies elsewhere. This phenomenon operates at the intersection of feedback and loop dynamics within the broader FIC domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept ein Erzählmuster, bei dem sich zeigt: ein Muster, bei dem n where providing critique or correction to an ai . Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Feedback Loop", "narrower_terms": [], "cross_domain_refs": [ "MUS-0007" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "FIC-0040", "domain": "FIC", "term_en": "Flaw Exposition Change", "term_de": "Mobile Anwendungen in fiction", "definition_en": "A recurring interaction pattern in which character flaws or weaknesses explicitly established in prompts become less prominent or disappear as AI accompanies extended narrative. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch redaktionelle Praxis in Fiction Writing and Storytelling mit Handwerk und Standards von Mobile Anwendungen in fiction. KI unterstuetzt durch automatisiertes Korrekturlesen, Stilkonsistenzpruefung, Inhaltsoptimierung und intelligente Revisionsvorschlaege. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2104", "narrower_terms": [], "cross_domain_refs": [ "SCR-0010" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "FIC-0041", "domain": "FIC", "term_en": "Flora-Fauna Coherence Breakdown", "term_de": "Cloud-Lösungen für fiction", "definition_en": "A narrative dynamics phenomenon in AI-assisted fiction writing, characterized by the pattern where biological or ecological consistency breaks down in fictional ecosystems described by AI, with species placement or interactions lacking logical grounding. The concept emerges specifically in contexts where flora–fauna interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch ein Erzählmuster, bei dem sich zeigt: ein Muster, bei dem n where biological or ecological consistency break. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-3001", "narrower_terms": [], "cross_domain_refs": [ "COG-0188" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "FIC-0042", "domain": "FIC", "term_en": "Formulaic Narrative Structure", "term_de": "Datenbankverwaltung in fiction", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures A storytelling pattern in AI-mediated creative writing, measurable through the pattern where AI-generated genre fiction adheres so rigidly to expected plot beats that the narrative feels mechanically assembled rather than organically developed. This phenomenon operates at the intersection of formulaic and narrative dynamics within the broader FIC domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept muster, das sich zeigt als: ein Muster, bei dem n where ai-generated genre fiction adheres so rigi. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Fiction Writing AI", "narrower_terms": [], "cross_domain_refs": [ "AED-0070", "COG-0132", "COG-0161" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q131324", "legal_classification": "analytical_category" }, { "id": "FIC-0043", "domain": "FIC", "term_en": "Genre Signal Inconsistency", "term_de": "Visualisierung in fiction", "definition_en": "The observable fluctuation in stylistic or tonal markers that typically signal specific genres when different AI systems or prompts process the same story.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch literarisches Element oder Erzählphänomen im Sinne: ein Prozess, der sich zeigt rvable fluctuation in stylistic or tonal markers tha. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2104", "narrower_terms": [], "cross_domain_refs": [ "MUS-0036" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "FIC-0044", "domain": "FIC", "term_en": "Geography Inconsistency", "term_de": "Simulation in fiction", "definition_en": "The pattern where spatial relationships, distances, or physical characteristics of fictional locations shift across AI-generated narrative sections. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch ein Erzählmuster, bei dem sich zeigt: ein Muster, bei dem n where spatial relationships, distances, or physi. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2701", "narrower_terms": [], "cross_domain_refs": [ "SCR-0075" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "FIC-0045", "domain": "FIC", "term_en": "Idiom Inconsistency", "term_de": "Digitaler Zwilling in fiction", "definition_en": "A narrative dynamics phenomenon in AI-assisted fiction writing, characterized by the pattern where character speech patterns or authorial turns of phrase vary when generated across different story segments by the same AI. The concept emerges specifically in contexts where idiom–inconsistency interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch ein Erzählmuster, bei dem sich zeigt: ein Muster, bei dem n where character speech patterns or authorial tur. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2052", "narrower_terms": [], "cross_domain_refs": [ "ASE-0018", "ASE-0075", "ASE-0078" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "FIC-0046", "domain": "FIC", "term_en": "Information Delivery Rhythm Disruption", "term_de": "fiction-Best-Practices", "definition_en": "A narrative dynamics phenomenon in AI-assisted fiction writing, characterized by the pattern where the pacing of revelation—introducing plot information, character details, or world elements—becomes erratic across AI-generated text. The concept emerges specifically in contexts where information–delivery interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch bewährte Methoden und Arbeitsabläufe für optimale Ergebnisse in Fiktionsschreiben und Storytelling Grundlagen. KI benchmarkt Praktiken gegen Ergebnisdaten, identifiziert Hochleistungsmuster und empfiehlt kontextspezifische Verbesserungen. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Fiction Writing AI", "narrower_terms": [], "cross_domain_refs": [ "RHR-0222", "SCR-0053" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "FIC-0047", "domain": "FIC", "term_en": "Information Dumping Tendency", "term_de": "Professionelle fiction-Praxis", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A narrative dynamics phenomenon in AI-assisted fiction writing, characterized by the pattern where AI-generated dialogue conveys exposition or plot information in unnaturally concentrated bursts rather than organically woven into conversation. This phenomenon operates at the intersection of information and dumping dynamics within the broader FIC domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept ein Erzählmuster, bei dem sich zeigt: ein Muster, bei dem n where ai-generated dialogue conveys exposition o. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Fiction Writing AI", "narrower_terms": [], "cross_domain_refs": [ "CUS-0068", "DAT-0052", "EDU-0051" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "FIC-0048", "domain": "FIC", "term_en": "Internal Conflict Erasure", "term_de": "fiction-Arbeitsablaufgestaltung", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures A storytelling pattern in AI-mediated creative writing, measurable through a documented pattern where internal contradictions or ethical dilemmas inherent to a character disappear in AI continuations, simplifying emotional complexity. This phenomenon operates at the intersection of internal and conflict dynamics within the broader FIC domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept charaktertechnique oder erzähltechnischer Effekt: charakterisiert durch the phenomenon where internal contradictions or ethical dilemmas inherent to a c. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Fiction Writing AI", "narrower_terms": [], "cross_domain_refs": [ "AUG-0052", "AUG-0408", "AUG-0502" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "FIC-0049", "domain": "FIC", "term_en": "Interruption Pattern Absence", "term_de": "fiction-Projektmanagement", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A narrative dynamics phenomenon in AI-assisted fiction writing, characterized by the pattern where realistic interruption, overlapping speech, or conversational disruption fails to appear in AI-generated multi-speaker dialogue. This phenomenon operates at the intersection of interruption and pattern dynamics within the broader FIC domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept ein Erzählmuster, bei dem sich zeigt: ein Muster, bei dem n where realistic interruption, overlapping speech. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "RPH-2605", "narrower_terms": [], "cross_domain_refs": [ "LIN-0043" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "FIC-0050", "domain": "FIC", "term_en": "Longevity Perception Effect", "term_de": "fiction-Teamzusammenarbeit", "definition_en": "A narrative dynamics phenomenon in AI-assisted fiction writing, characterized by a documented pattern where reader assessment of a work's lasting literary value or timelessness changes when AI authorship involvement becomes known. Distinguished from adjacent concepts by its focus on the specific mechanism through which longevity manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch redaktionelle Praxis in Fiction Writing and Storytelling mit Handwerk und Standards von fiction-Teamzusammenarbeit. KI unterstuetzt durch automatisiertes Korrekturlesen, Stilkonsistenzpruefung, Inhaltsoptimierung und intelligente Revisionsvorschlaege. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Psychological Effect", "narrower_terms": [], "cross_domain_refs": [ "ADA-0001", "ADA-0002", "ADA-0004" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q177601", "legal_classification": "descriptive_research_term" }, { "id": "FIC-0051", "domain": "FIC", "term_en": "Magic System Drift", "term_de": "Kundenbeziehungen in fiction", "definition_en": "A storytelling pattern in AI-mediated creative writing, measurable through a storytelling pattern in which the observable variation in how magical or supernatural mechanics function when described across different AI-generated story segments. Distinguished from adjacent concepts by its focus on the specific mechanism through which magic manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch literarisches Element oder Erzählphänomen im Sinne: charakterisiert durch the observable variation in how magical or supernatural mechanics function when. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Systemic Drift", "narrower_terms": [], "cross_domain_refs": [ "AED-0030", "ART-0058", "ART-0073" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "FIC-0052", "domain": "FIC", "term_en": "Manuscript Instability", "term_de": "fiction-Kommunikation", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A narrative dynamics phenomenon in AI-assisted fiction writing, characterized by the observable phenomenon where edited narrative sections require re-editing after new AI-generated additions are inserted, creating a moving target effect. This phenomenon operates at the intersection of manuscript and instability dynamics within the broader FIC domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept informationsaustauschprotokolle und Stakeholder-Interaktion in Fiktionsschreiben und Storytelling Grundlagen. NLP ermöglicht automatisierte Berichtserstellung, mehrsprachige Übersetzung und kontextbewusste Kommunikationsoptimierung. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Fiction Writing AI", "narrower_terms": [], "cross_domain_refs": [ "ASE-0017", "ASE-0071", "DES-0036" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "FIC-0053", "domain": "FIC", "term_en": "Marketability Perception Shift", "term_de": "Problemlösung in fiction", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures A storytelling pattern in AI-mediated creative writing, measurable through the observable change in perceived market appeal or commercial viability of narrative when readers become aware of AI generation contributions. This phenomenon operates at the intersection of marketability and perception dynamics within the broader FIC domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription. Descriptive research term, not a prescriptive recommendation.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept redaktionelle Praxis in Fiction Writing and Storytelling mit Handwerk und Standards von Problemlösung in fiction. KI unterstuetzt durch automatisiertes Korrekturlesen, Stilkonsistenzpruefung, Inhaltsoptimierung und intelligente Revisionsvorschlaege. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Fiction Writing AI", "narrower_terms": [], "cross_domain_refs": [ "ADA-0012", "AGE-0007", "AGE-0046" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q177601", "legal_classification": "empirical_phenomenon_label" }, { "id": "FIC-0054", "domain": "FIC", "term_en": "Momentum Change", "term_de": "Entscheidungsfindung in fiction", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures A storytelling pattern in AI-mediated creative writing, measurable through the pattern where narrative momentum established in earlier passages gradually diminishes as AI-generated continuation accumulates in length. This phenomenon operates at the intersection of momentum and change dynamics within the broader FIC domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept ein Erzählmuster, bei dem sich zeigt: ein Muster, bei dem n where narrative momentum established in earlier . Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Fiction Writing AI", "narrower_terms": [], "cross_domain_refs": [ "AED-0025", "AED-0032", "AED-0067" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "FIC-0055", "domain": "FIC", "term_en": "Motivation Instability", "term_de": "Zeitmanagement in fiction", "definition_en": "The pattern where character motivations shift unexpectedly between scenes generated by AI, inconsistent with established personality frameworks. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch strategische und operative Koordination von Ressourcen und Arbeitsabläufen in Fiktionsschreiben und Storytelling Grundlagen. KI bietet Entscheidungsunterstützung durch prädiktive Analytik, Ressourcenoptimierung und intelligente Planung. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-2303", "narrower_terms": [], "cross_domain_refs": [ "SCR-0009" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q186588", "legal_classification": "observational_construct" }, { "id": "FIC-0056", "domain": "FIC", "term_en": "Motivation Opacity", "term_de": "Ressourcenplanung in fiction", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A narrative dynamics phenomenon in AI-assisted fiction writing, characterized by the pattern where AI-generated character actions or plot developments lack clear motivation chains, appearing arbitrary or insufficiently grounded in story logic. This phenomenon operates at the intersection of motivation and opacity dynamics within the broader FIC domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept vorausschauende Gestaltung von Strategien und Ressourcenallokation in Fiktionsschreiben und Storytelling Grundlagen. KI verbessert Planung durch Szenariomodellierung, Constraint-Optimierung und adaptive Umplanung unter Unsicherheit. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "RPH-3202", "narrower_terms": [], "cross_domain_refs": [ "SCR-0071" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q186588", "legal_classification": "systematic_classification" }, { "id": "FIC-0057", "domain": "FIC", "term_en": "Narrative Authority Fluctuation", "term_de": "fiction-Dokumentation", "definition_en": "A narrative dynamics phenomenon in AI-assisted fiction writing, characterized by a narrative interaction effect in which the observable variation in the narrator's apparent omniscience, reliability, or narrative distance across AI-generated text sections. Distinguished from adjacent concepts by its focus on the specific mechanism through which narrative manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch erzähltechnische Methode: charakterisiert durch the observable variation in the narrator's apparent omniscience, reliability, or. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Fiction Writing AI", "narrower_terms": [], "cross_domain_refs": [ "AED-0063", "COG-0031", "COG-0074" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q131324", "legal_classification": "empirical_phenomenon_label" }, { "id": "FIC-0058", "domain": "FIC", "term_en": "Narrative Voice Genre Mismatch", "term_de": "Berichtswesen in fiction", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures A storytelling pattern in AI-mediated creative writing, measurable through a systemic tendency in which the narrative voice or POV technique used in AI text appears incongruent with the conventions or expectations of the target literary genre. This phenomenon operates at the intersection of narrative and voice dynamics within the broader FIC domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept redaktionelle Praxis in Fiction Writing and Storytelling mit Handwerk und Standards von Berichtswesen in fiction. KI unterstuetzt durch automatisiertes Korrekturlesen, Stilkonsistenzpruefung, Inhaltsoptimierung und intelligente Revisionsvorschlaege. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Fiction Writing AI", "narrower_terms": [], "cross_domain_refs": [ "COP-0045", "CUS-0010", "TRA-0014" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q131324", "legal_classification": "analytical_category" }, { "id": "FIC-0059", "domain": "FIC", "term_en": "Naturalness Gradient", "term_de": "fiction-Präsentationsfähigkeiten", "definition_en": "A narrative dynamics phenomenon in AI-assisted fiction writing, characterized by a narrative interaction effect characterized by the observable spectrum where AI-generated dialogue reads with varying degrees of authenticity, from highly artificial to convincingly natural. The concept emerges specifically in contexts where naturalness–gradient interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch literarisches Element oder Erzählphänomen im Sinne: charakterisiert durch the observable spectrum where ai-generated dialogue reads with varying degrees o. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Optimization Gradient", "narrower_terms": [], "cross_domain_refs": [ "MUS-0022" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "FIC-0060", "domain": "FIC", "term_en": "Novelty Familiarity Paradox", "term_de": "Netzwerken in fiction", "definition_en": "A narrative dynamics phenomenon in AI-assisted fiction writing, characterized by a narrative interaction effect in which the observable tension where AI-generated content can feel simultaneously novel and derivative, familiar yet unexpected to the human author. Distinguished from adjacent concepts by its focus on the specific mechanism through which novelty manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data. Classification term used in systematic observation, not advocacy.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch literarisches Element oder Erzählphänomen im Sinne: charakterisiert durch the observable tension where ai-generated content can feel simultaneously novel. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung. Klassifikationsbegriff für systematische Beobachtung, keine Befürwortung.", "etymology": "", "broader_term": "Logical Paradox", "narrower_terms": [], "cross_domain_refs": [ "RPH-2153" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "FIC-0061", "domain": "FIC", "term_en": "Originality Perception Shift", "term_de": "fiction-Qualitätssicherung", "definition_en": "A narrative dynamics phenomenon in AI-assisted fiction writing, characterized by the observable change in how a writer perceives the originality of their own work when it has incorporated AI-generated elements alongside their contributions. The concept emerges specifically in contexts where originality–perception interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch konzept, das sich zeigt als: charakterisiert durch the observable change in how a writer perceives the originality of their own wor. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Fiction Writing AI", "narrower_terms": [], "cross_domain_refs": [ "RPH-348" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q177601", "legal_classification": "empirical_phenomenon_label" }, { "id": "FIC-0062", "domain": "FIC", "term_en": "Ownership Investment Fluctuation", "term_de": "fiction-Normen", "definition_en": "A narrative dynamics phenomenon in AI-assisted fiction writing, characterized by the pattern where a writer's emotional investment or sense of ownership in the narrative varies based on the proportion of AI-generated versus human-written content. Distinguished from adjacent concepts by its focus on the specific mechanism through which ownership manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch ein Erzählmuster oder Effekt, bei dem sich zeigt: ein Muster, bei dem n where a writer's emotional investment or sense o. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Fiction Writing AI", "narrower_terms": [], "cross_domain_refs": [ "SCR-0084" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "FIC-0063", "domain": "FIC", "term_en": "Pacing Expectation Misalignment", "term_de": "ISO-Normen in fiction", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A narrative dynamics phenomenon in AI-assisted fiction writing, characterized by the pattern where narrative pacing in AI-generated text fails to match the expected rhythm of the specified genre or story type. This phenomenon operates at the intersection of pacing and expectation dynamics within the broader FIC domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept ein Erzählmuster oder Effekt, bei dem sich zeigt: ein Muster, bei dem n where narrative pacing in ai-generated text fail. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Fiction Writing AI", "narrower_terms": [], "cross_domain_refs": [ "MUS-0085" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "FIC-0064", "domain": "FIC", "term_en": "Pacing Irregularity", "term_de": "fiction-Zertifizierung", "definition_en": "A narrative dynamics phenomenon in AI-assisted fiction writing, characterized by a storytelling pattern where the observable unevenness in narrative rhythm where AI-generated sections vary significantly in temporal compression or expansion from surrounding text. The concept emerges specifically in contexts where pacing–irregularity interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch erzähltechnische Methode: charakterisiert durch the observable unevenness in narrative rhythm where ai-generated sections vary s. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Fiction Writing AI", "narrower_terms": [], "cross_domain_refs": [ "EDU-0045", "GAM-0090", "ROB-0114" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "FIC-0065", "domain": "FIC", "term_en": "Paragraph Density Inconsistency", "term_de": "Audit in fiction", "definition_en": "A storytelling pattern in AI-mediated creative writing, measurable through an emergent effect where paragraph length and information density vary significantly across AI-generated sections, creating uneven pacing patterns. Distinguished from adjacent concepts by its focus on the specific mechanism through which paragraph manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch ein Erzählmuster, bei dem sich zeigt: ein Muster, bei dem enon where paragraph length and information densit. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Fiction Writing AI", "narrower_terms": [], "cross_domain_refs": [ "CON-0085" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "FIC-0066", "domain": "FIC", "term_en": "Personality Averaging", "term_de": "fiction-Benchmarking", "definition_en": "A storytelling pattern in AI-mediated creative writing, measurable through the observable tendency for AI-generated character voices to converge toward neutral or archetypal speech patterns, flattening individual differentiation. Distinguished from adjacent concepts by its focus on the specific mechanism through which personality manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch standardisierte Leistungsbewertungsmethoden und Referenzpunkte in Fiktionsschreiben und Storytelling Grundlagen. KI automatisiert Benchmark-Ausführung, Vergleichsanalyse, Regressionserkennung und Leistungstrendvorhersage. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Fiction Writing AI", "narrower_terms": [], "cross_domain_refs": [ "COP-0065", "CUS-0020", "DES-0064" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "FIC-0067", "domain": "FIC", "term_en": "Perspective Slippage", "term_de": "Leistungskennzahlen in fiction", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A narrative dynamics phenomenon in AI-assisted fiction writing, characterized by a storytelling pattern manifesting as the occurrence of inconsistent point-of-view markers when an AI transitions between narrative sections or character perspectives. This phenomenon operates at the intersection of perspective and slippage dynamics within the broader FIC domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept messbare Ausgabequalität und Effizienzmetriken in Fiktionsschreiben und Storytelling Grundlagen. KI verfolgt Leistung durch Echtzeit-Dashboards, prädiktive Leistungsmodellierung und automatisierte Ursachenanalyse bei Leistungsabfall. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "RPH-3101", "narrower_terms": [], "cross_domain_refs": [ "CAI-0003", "COG-0157", "COG-0193" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "FIC-0068", "domain": "FIC", "term_en": "Plot Inevitability Sensation", "term_de": "Kontinuierliche Verbesserung in fiction", "definition_en": "A narrative dynamics phenomenon in AI-assisted fiction writing, characterized by the pattern where AI-generated narrative progression feels predetermined or mechanistic rather than organic, reducing perceived storytelling agency. Distinguished from adjacent concepts by its focus on the specific mechanism through which plot manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch ein Erzählmuster, bei dem sich zeigt: ein Muster, bei dem n where ai-generated narrative progression feels p. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Fiction Writing AI", "narrower_terms": [], "cross_domain_refs": [ "GAM-0089" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "FIC-0069", "domain": "FIC", "term_en": "Plot Thread Distribution", "term_de": "fiction-Inspektion", "definition_en": "The observable phenomenon where AI-generated text introduces subplots or narrative threads that remain incompletely resolved or disconnected from the primary storyline.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch redaktionelle Praxis in Fiction Writing and Storytelling mit Handwerk und Standards von fiction-Inspektion. KI unterstuetzt durch automatisiertes Korrekturlesen, Stilkonsistenzpruefung, Inhaltsoptimierung und intelligente Revisionsvorschlaege. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-2304", "narrower_terms": [], "cross_domain_refs": [ "CON-0032" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "FIC-0070", "domain": "FIC", "term_en": "Publication Readiness Bias", "term_de": "Prüfung in fiction", "definition_en": "A storytelling pattern in AI-mediated creative writing, measurable through the pattern where AI-generated text appears deceptively publication-ready in initial drafts, masking deeper structural or narrative issues. Distinguished from adjacent concepts by its focus on the specific mechanism through which publication manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch redaktionelle Praxis in Fiction Writing and Storytelling mit Handwerk und Standards von Prüfung in fiction. KI unterstuetzt durch automatisiertes Korrekturlesen, Stilkonsistenzpruefung, Inhaltsoptimierung und intelligente Revisionsvorschlaege. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Cognitive Bias", "narrower_terms": [], "cross_domain_refs": [ "AED-0095", "ASE-0006", "ASE-0033" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q213523", "legal_classification": "empirical_phenomenon_label" }, { "id": "FIC-0071", "domain": "FIC", "term_en": "Publishing Platform Acceptance Variability", "term_de": "Kalibrierung in fiction", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A narrative dynamics phenomenon in AI-assisted fiction writing, characterized by a narrative interaction effect observed when the observable inconsistency in how different publishing platforms, venues, or literary communities receive or accept works with disclosed AI collaborative involvement. This phenomenon operates at the intersection of publishing and platform dynamics within the broader FIC domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept literarisches Element oder Erzählphänomen im Sinne: charakterisiert durch the observable inconsistency in how different publishing platforms, venues, or l. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "RPH-2951", "narrower_terms": [], "cross_domain_refs": [ "CON-0025" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "FIC-0072", "domain": "FIC", "term_en": "Question-Response Mismatch", "term_de": "Fehlervermeidung in fiction", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A narrative dynamics phenomenon in AI-assisted fiction writing, characterized by the observable pattern where AI-generated dialogue responses address tangential aspects of questions rather than directly engaging the core query. This phenomenon operates at the intersection of question and response dynamics within the broader FIC domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept ein Erzählmuster, bei dem sich zeigt: ein Muster, bei dem able pattern where ai-generated dialogue responses. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Fiction Writing AI", "narrower_terms": [], "cross_domain_refs": [ "CRE-0205" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "FIC-0073", "domain": "FIC", "term_en": "Reader Detection Sensitivity", "term_de": "Fehleranalyse in fiction", "definition_en": "A storytelling pattern in AI-mediated creative writing, measurable through a storytelling pattern characterized by the observable variation in how readily readers identify or become aware of AI-generated passages within published prose. Distinguished from adjacent concepts by its focus on the specific mechanism through which reader manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data. Classification term used in systematic observation, not advocacy.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch literarisches Element oder Erzählphänomen im Sinne: charakterisiert durch the observable variation in how readily readers identify or become aware of ai-g. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung. Klassifikationsbegriff für systematische Beobachtung, keine Befürwortung.", "etymology": "", "broader_term": "Detection System", "narrower_terms": [], "cross_domain_refs": [ "ART-0083", "ASE-0002", "ASE-0021" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "FIC-0074", "domain": "FIC", "term_en": "Register Oscillation", "term_de": "Prozesskontrolle in fiction", "definition_en": "A narrative interaction effect manifesting as the alternation between formal and informal language registers when an AI accompanies extended narrative passages without explicit re-prompting. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch erzähltechnische Methode: charakterisiert durch the alternation between formal and informal language registers when an ai accomp. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2104", "narrower_terms": [], "cross_domain_refs": [ "RPH-3901" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "FIC-0075", "domain": "FIC", "term_en": "Relationship Asymmetry", "term_de": "fiction-Compliance", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures A storytelling pattern in AI-mediated creative writing, measurable through the observable imbalance in how AI portrays interpersonal dynamics between characters when generating dialogue or interaction scenes. This phenomenon operates at the intersection of relationship and asymmetry dynamics within the broader FIC domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept charaktertechnique oder erzähltechnischer Effekt: charakterisiert durch the observable imbalance in how ai portrays interpersonal dynamics between chara. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Fiction Writing AI", "narrower_terms": [], "cross_domain_refs": [ "SCR-0066" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "FIC-0076", "domain": "FIC", "term_en": "Relationship Memory Shift", "term_de": "fiction-Sicherheitsmanagement", "definition_en": "A storytelling pattern in AI-mediated creative writing, measurable through the pattern where character awareness of prior interactions or relationship history fails to manifest consistently in AI continuations of dialogue or scenes. The concept emerges specifically in contexts where relationship–memory interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch ein Erzählmuster, bei dem sich zeigt: ein Muster, bei dem n where character awareness of prior interactions . Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Fiction Writing AI", "narrower_terms": [], "cross_domain_refs": [ "GAM-0050" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q492", "legal_classification": "empirical_phenomenon_label" }, { "id": "FIC-0077", "domain": "FIC", "term_en": "Repetitive Exchange Structure", "term_de": "Risikobeurteilung in fiction", "definition_en": "A narrative dynamics phenomenon in AI-assisted fiction writing, characterized by the pattern where AI-generated dialogue conversations follow similar syntactic patterns or response structures across different conversational contexts. Distinguished from adjacent concepts by its focus on the specific mechanism through which repetitive manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch bewertungsrahmen zur Messung von Kompetenz, Qualität oder Ergebnissen in Fiktionsschreiben und Storytelling Grundlagen. KI automatisiert Bewertung durch rubrikbasiertes Scoring, Leistungsanalytik und kontinuierliche Kompetenzüberwachung. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Fiction Writing AI", "narrower_terms": [], "cross_domain_refs": [ "CON-0075" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "FIC-0078", "domain": "FIC", "term_en": "Revision Cascade Effect", "term_de": "Gefährdungserkennung in fiction", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures A storytelling pattern in AI-mediated creative writing, measurable through the pattern where editing one narrative element co-occurs with a cascading need for edits in other sections, exponentially increasing revision work. This phenomenon operates at the intersection of revision and cascade dynamics within the broader FIC domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept muster, das sich zeigt als: ein Muster, bei dem n where editing one narrative element co-occurs wi. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Psychological Effect", "narrower_terms": [], "cross_domain_refs": [ "ADA-0001", "ADA-0002", "ADA-0004" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "FIC-0079", "domain": "FIC", "term_en": "Satisfaction Diminishment", "term_de": "Persönliche Schutzausrüstung", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures A storytelling pattern in AI-mediated creative writing, measurable through a behavioral tendency where the completion satisfaction associated with finishing a written work decreases when substantial portions derive from AI generation. This phenomenon operates at the intersection of satisfaction and diminishment dynamics within the broader FIC domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept literarisches Element oder Erzählphänomen im Sinne: charakterisiert durch the phenomenon where the completion satisfaction associated with finishing a wri. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Fiction Writing AI", "narrower_terms": [], "cross_domain_refs": [ "RPH-1354" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "FIC-0080", "domain": "FIC", "term_en": "Scene Transition Abruptness", "term_de": "Notfallverfahren in fiction", "definition_en": "The observable pattern where transitions between scenes or timeframes in AI-generated text feel sudden or differently bridged. Observed phenomenon documented in human-AI interaction research.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch ein Erzählmuster, bei dem sich zeigt: ein Muster, bei dem able pattern where transitions between scenes or t. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2303", "narrower_terms": [], "cross_domain_refs": [ "ADA-0011", "AED-0094", "AED-0095" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "FIC-0081", "domain": "FIC", "term_en": "Sentence Length Oscillation", "term_de": "Unfallverhütung in fiction", "definition_en": "A storytelling pattern in AI-mediated creative writing, measurable through a storytelling pattern observed when the observable variation in sentence structure and length when AI-generated text transitions between narrative sections, affecting overall reading rhythm. Distinguished from adjacent concepts by its focus on the specific mechanism through which sentence manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch redaktionelle Praxis in Fiction Writing and Storytelling mit Handwerk und Standards von Unfallverhütung in fiction. KI unterstuetzt durch automatisiertes Korrekturlesen, Stilkonsistenzpruefung, Inhaltsoptimierung und intelligente Revisionsvorschlaege. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Fiction Writing AI", "narrower_terms": [], "cross_domain_refs": [ "CON-0054", "CON-0063", "CON-0075" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "FIC-0082", "domain": "FIC", "term_en": "Setup Callback Absence", "term_de": "fiction-Gesundheitsschutz", "definition_en": "A storytelling pattern characterized by the occurrence where earlier narrative details or foreshadowing elements introduced in prompts fail to resurface in AI continuations, leaving unresolved narrative promises.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch redaktionelle Praxis in Fiction Writing and Storytelling mit Handwerk und Standards von fiction-Gesundheitsschutz. KI unterstuetzt durch automatisiertes Korrekturlesen, Stilkonsistenzpruefung, Inhaltsoptimierung und intelligente Revisionsvorschlaege. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-2104", "narrower_terms": [], "cross_domain_refs": [ "CON-0065", "CON-0067", "CON-0092" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "FIC-0083", "domain": "FIC", "term_en": "Speech Attribution Ambiguity", "term_de": "Ergonomie in fiction", "definition_en": "A fiction creation phenomenon reflecting the occurrence where AI-generated dialogue becomes unclear in terms of which character is speaking, particularly in multi-character exchanges. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch literarisches oder kreatives Effekt: charakterisiert durch the occurrence where ai-generated dialogue becomes unclear in terms of which cha. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2605", "narrower_terms": [], "cross_domain_refs": [ "GAM-0050" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "FIC-0084", "domain": "FIC", "term_en": "Style Memory Shift", "term_de": "Umweltschutz in fiction", "definition_en": "A storytelling pattern in AI-mediated creative writing, measurable through the pattern where stylistic choices established early in a writing session fade or disappear as the AI accompanies additional content. The concept emerges specifically in contexts where style–memory interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch ein Erzählmuster oder Effekt, bei dem sich zeigt: ein Muster, bei dem n where stylistic choices established early in a w. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Fiction Writing AI", "narrower_terms": [], "cross_domain_refs": [ "RPH-2954" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q492", "legal_classification": "systematic_classification" }, { "id": "FIC-0085", "domain": "FIC", "term_en": "Stylistic Reversion", "term_de": "Brandschutz in fiction", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A narrative dynamics phenomenon in AI-assisted fiction writing, characterized by a systemic tendency in which newly AI-generated continuations revert to earlier notable patterns that had been corrected in previous editing sessions. This phenomenon operates at the intersection of stylistic and reversion dynamics within the broader FIC domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept ein Erzählmuster, bei dem sich zeigt: ein Muster, bei dem enon where newly ai-generated continuations revert. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Fiction Writing AI", "narrower_terms": [], "cross_domain_refs": [ "RPH-2002" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "FIC-0086", "domain": "FIC", "term_en": "Subgenre Confusion", "term_de": "Chemische Sicherheit in fiction", "definition_en": "A recognizable shift where AI-generated text blends or conflates conventions from multiple subgenres in ways that feel tonally discordant or narratively unmotivated. Identifiable through systematic behavioral analysis and pattern recognition. Classification term used in systematic observation, not advocacy.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch erzähltechnische Methode: charakterisiert durch the phenomenon where ai-generated text blends or conflates conventions from mult. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-3251", "narrower_terms": [], "cross_domain_refs": [ "CON-0022" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "FIC-0087", "domain": "FIC", "term_en": "Technology Level Incoherence", "term_de": "Elektrische Sicherheit in fiction", "definition_en": "A narrative dynamics phenomenon in AI-assisted fiction writing, characterized by the pattern where the technological sophistication or available tools in a fictional setting fluctuate inconsistently between AI-generated passages. Distinguished from adjacent concepts by its focus on the specific mechanism through which technology manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch ein Erzählmuster oder Effekt, bei dem sich zeigt: ein Muster, bei dem n where the technological sophistication or availa. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Fiction Writing AI", "narrower_terms": [], "cross_domain_refs": [ "AGE-0011", "AGE-0037", "AGE-0038" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "FIC-0088", "domain": "FIC", "term_en": "Temporal Setting Ambiguity", "term_de": "Maschinensicherheit in fiction", "definition_en": "A narrative dynamics phenomenon in AI-assisted fiction writing, characterized by a behavioral tendency where the implied historical era, time period, or narrative timeline becomes unclear or inconsistent in AI-generated world descriptions. The concept emerges specifically in contexts where temporal–setting interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch erzähltechnische Methode: charakterisiert durch the phenomenon where the implied historical era, time period, or narrative timel. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Fiction Writing AI", "narrower_terms": [], "cross_domain_refs": [ "SCR-0057" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "FIC-0089", "domain": "FIC", "term_en": "Tonal Instability", "term_de": "Sicherheitsschulung in fiction", "definition_en": "A storytelling pattern in AI-mediated creative writing, measurable through the pattern where narrative tone oscillates between different emotional registers within a single passage written by an AI system. Distinguished from adjacent concepts by its focus on the specific mechanism through which tonal manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch ein Erzählmuster, bei dem sich zeigt: ein Muster, bei dem n where narrative tone oscillates between differen. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Fiction Writing AI", "narrower_terms": [], "cross_domain_refs": [ "SCR-0084" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "FIC-0090", "domain": "FIC", "term_en": "Tone Authenticity Variance", "term_de": "Vorfalluntersuchung in fiction", "definition_en": "A narrative dynamics phenomenon in AI-assisted fiction writing, characterized by a narrative interaction effect where the observable inconsistency in how authentically an AI captures the tonal atmosphere expected within a particular literary genre or subgenre. The concept emerges specifically in contexts where tone–authenticity interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch literarisches Element oder Erzählphänomen im Sinne: charakterisiert durch the observable inconsistency in how authentically an ai captures the tonal atmos. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Fiction Writing AI", "narrower_terms": [], "cross_domain_refs": [ "AED-0091", "ART-0012", "ART-0025" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "FIC-0091", "domain": "FIC", "term_en": "Tone Lock Difficulty", "term_de": "fiction-Geschäftsmodell", "definition_en": "A narrative dynamics phenomenon in AI-assisted fiction writing, characterized by the observable challenge in maintaining a consistently edited tone throughout a manuscript when AI reaccompanies sections multiple times. The concept emerges specifically in contexts where tone–lock interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch redaktionelle Praxis in Fiction Writing and Storytelling mit Handwerk und Standards von fiction-Geschäftsmodell. KI unterstuetzt durch automatisiertes Korrekturlesen, Stilkonsistenzpruefung, Inhaltsoptimierung und intelligente Revisionsvorschlaege. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Fiction Writing AI", "narrower_terms": [], "cross_domain_refs": [ "AGE-0043", "AGE-0090", "ASE-0026" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "FIC-0092", "domain": "FIC", "term_en": "Trope Literalization", "term_de": "fiction-Marktanalyse", "definition_en": "A narrative dynamics phenomenon in AI-assisted fiction writing, characterized by the pattern where AI-generated narrative applies genre conventions and tropes in overly explicit or on-the-nose ways, reducing subtlety. The concept emerges specifically in contexts where trope–literalization interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch ein Erzählmuster, bei dem sich zeigt: ein Muster, bei dem n where ai-generated narrative applies genre conve. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Fiction Writing AI", "narrower_terms": [], "cross_domain_refs": [ "SCR-0067", "TRA-0075" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "FIC-0093", "domain": "FIC", "term_en": "Verbal Tic Consistency Shift", "term_de": "Ökonomie der Fiktionsschreiben und Storytelling", "definition_en": "A storytelling pattern in AI-mediated creative writing, measurable through an emergent effect where distinctive speech behaviors, verbal habits, or catch-phrases associated with a character disappear in AI continuations of dialogue. The concept emerges specifically in contexts where verbal–tic interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch redaktionelle Praxis in Fiction Writing and Storytelling mit Handwerk und Standards von Ökonomie der Fiktionsschreiben und Storytelling. KI unterstuetzt durch automatisiertes Korrekturlesen, Stilkonsistenzpruefung, Inhaltsoptimierung und intelligente Revisionsvorschlaege. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2052", "narrower_terms": [], "cross_domain_refs": [ "SCR-0078" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "FIC-0094", "domain": "FIC", "term_en": "Voice Drift", "term_de": "fiction-Kostenmanagement", "definition_en": "The observable shift in narrative voice characteristics when an AI continues writing beyond the initial style established in a prompt or chapter opening.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch erzähltechnische Methode: charakterisiert durch the observable shift in narrative voice characteristics when an ai continues wri. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-2701", "narrower_terms": [], "cross_domain_refs": [ "AED-0030", "AGE-0014", "AGE-0048" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "FIC-0095", "domain": "FIC", "term_en": "World Rule Violation", "term_de": "Preisgestaltung in fiction", "definition_en": "The observable pattern where AI-generated narrative events contradict established worldbuilding rules or system constraints specified in initial prompts.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch ein Erzählmuster, bei dem sich zeigt: ein Muster, bei dem able pattern where ai-generated narrative events c. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2104", "narrower_terms": [], "cross_domain_refs": [ "MUS-0037" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "GAM-0001", "domain": "GAM", "term_en": "AI Adaptation Counterplay", "term_de": "Spieledesign", "definition_en": "A gaming interaction phenomenon manifesting as the meta-game where players intentionally perform suboptimally to avoid triggering learning-based di... Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch spielmechanik oder Spielerdynamik: charakterisiert durch the meta-game where players intentionally perform suboptimally to avoid triggeri. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2703", "narrower_terms": [ "GAM-0084", "GAM-0085", "GAM-0028", "GAM-0046", "GAM-0087", "GAM-0041", "GAM-0070", "GAM-0056", "GAM-0015", "GAM-0010", "GAM-0018", "GAM-0086", "GAM-0077", "GAM-0082", "GAM-0069", "GAM-0011", "GAM-0078", "GAM-0091", "GAM-0079", "GAM-0026", "GAM-0021", "GAM-0002", "GAM-0025", "GAM-0094", "GAM-0063", "GAM-0024", "GAM-0030", "GAM-0014", "GAM-0009", "GAM-0060", "GAM-0022", "GAM-0035", "GAM-0013", "GAM-0061", "GAM-0045", "GAM-0057", "GAM-0075", "GAM-0007", "GAM-0058", "GAM-0089", "GAM-0008", "GAM-0043", "GAM-0080", "GAM-0003", "GAM-0032", "GAM-0093", "GAM-0049", "GAM-0037", "GAM-0042", "GAM-0065", "GAM-0040", "GAM-0052", "GAM-0048", "GAM-0083", "GAM-0006", "GAM-0088", "GAM-0062", "GAM-0050", "GAM-0033", "GAM-0017", "GAM-0023", "GAM-0076", "GAM-0081", "GAM-0047", "GAM-0016", "GAM-0053", "GAM-0044", "GAM-0019", "GAM-0034", "GAM-0095", "GAM-0055", "GAM-0073", "GAM-0067", "GAM-0027", "GAM-0038", "GAM-0059", "GAM-0054", "GAM-0039", "GAM-0092", "GAM-0020", "GAM-0068", "GAM-0090", "GAM-0051" ], "cross_domain_refs": [ "AED-0010", "AED-0090", "AGE-0036" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "GAM-0002", "domain": "GAM", "term_en": "AI Behavioral Believability Gap", "term_de": "Spielmechaniken", "definition_en": "A player interaction pattern in AI-augmented gaming, measurable through a game design pattern arising from the uncanny valley between advanced NPC animation and apparent autonomy but limited conversational d. Distinguished from adjacent concepts by its focus on the specific mechanism through which ai manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch die Regeln und Systeme, die Spieleraktionen und Spielreaktionen steuern. KI generiert ausgewogene Mechaniken durch Simulation. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Performance Gap", "narrower_terms": [], "cross_domain_refs": [ "ROB-0170", "ROB-0217" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "GAM-0003", "domain": "GAM", "term_en": "AI Difficulty Scaling Perception", "term_de": "Kernschleife", "definition_en": "A player interaction pattern in AI-augmented gaming, measurable through players' experience of whether adaptive difficulty feels like fair challenge scaling or invisible ma. Distinguished from adjacent concepts by its focus on the specific mechanism through which ai manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch core Loop ist eine spezialisierte Technik oder ein Konzept innerhalb von Spieledesign und Spielwissenschaft. KI verbessert das Verständnis durch Analyse und Optimierung. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Games AI", "narrower_terms": [], "cross_domain_refs": [ "AGE-0043", "AGE-0090", "ASE-0026" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q177601", "legal_classification": "observational_construct" }, { "id": "GAM-0004", "domain": "GAM", "term_en": "AI Dungeon Master Presence", "term_de": "Spielerhandlungsfähigkeit", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures A player interaction pattern in AI-augmented gaming, measurable through the role-playing sensation of interacting with an AI game master who dynamically shapes narrative ou. This phenomenon operates at the intersection of ai and dungeon dynamics within the broader GAM domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept player Agency ist eine spezialisierte Technik oder ein Konzept innerhalb von Spieledesign und Spielwissenschaft. KI verbessert das Verständnis durch Analyse und Optimierung. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "RPH-3901", "narrower_terms": [], "cross_domain_refs": [ "DES-0005", "EDU-0069", "EDU-0076" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "GAM-0005", "domain": "GAM", "term_en": "AI Interaction Identity", "term_de": "Emergentes Spielverhalten", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A game design phenomenon in AI-mediated interactive entertainment, characterized by the emergent sense of self and playstyle identity that develops through repeated interaction with sp. This phenomenon operates at the intersection of ai and interaction dynamics within the broader GAM domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept emergent Gameplay ist eine spezialisierte Technik oder ein Konzept innerhalb von Spieledesign und Spielwissenschaft. KI verbessert das Verständnis durch Analyse und Optimierung. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "RPH-2355", "narrower_terms": [], "cross_domain_refs": [ "AED-0051" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q844396", "legal_classification": "observational_construct" }, { "id": "GAM-0006", "domain": "GAM", "term_en": "AI Opponent Deception Perception", "term_de": "Level-Design", "definition_en": "A game design phenomenon in AI-mediated interactive entertainment, characterized by players' awareness of whether competitive AI employs perfect information advantage, hidden bonuses. Distinguished from adjacent concepts by its focus on the specific mechanism through which ai manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch erstellung von Spielräumen, die Spielerfortschritt und Herausforderungen leiten. KI generiert Levellayouts prozedural. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Games AI", "narrower_terms": [], "cross_domain_refs": [ "SAL-0058" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q177601", "legal_classification": "systematic_classification" }, { "id": "GAM-0007", "domain": "GAM", "term_en": "AI Opponent Stigma", "term_de": "Weltenbau", "definition_en": "A player interaction pattern in AI-augmented gaming, measurable through negative perception or social dismissal of victories against AI opponents compared to human-earned w. Distinguished from adjacent concepts by its focus on the specific mechanism through which ai manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch ein KI-bezogenes Phänomen: Negative perception or social dismissal of victories against AI opponents compared to human-earned w. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Games AI", "narrower_terms": [], "cross_domain_refs": [ "CON-0041" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "GAM-0008", "domain": "GAM", "term_en": "Acceptance Threshold Variance", "term_de": "Umgebungserzählung", "definition_en": "A game design phenomenon in AI-mediated interactive entertainment, characterized by a player behavior effect reflecting the differing player willingness to address AI opponents as legitimate competitors varying by game con. The concept emerges specifically in contexts where acceptance–threshold interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch environmental Storytelling ist eine spezialisierte Technik oder ein Konzept innerhalb von Spieledesign und Spielwissenschaft. KI verbessert das Verständnis durch Analyse und Optimierung. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Decision Threshold", "narrower_terms": [], "cross_domain_refs": [ "AED-0091", "AGE-0096", "ART-0060" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "GAM-0009", "domain": "GAM", "term_en": "Achievement Legitimacy Doubt", "term_de": "Spannungsbogen", "definition_en": "A player interaction pattern in AI-augmented gaming, measurable through players' internal conflict about whether earned achievements represent genuine skill mastery or were. The concept emerges specifically in contexts where achievement–legitimacy interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch pacing ist eine spezialisierte Technik oder ein Konzept innerhalb von Spieledesign und Spielwissenschaft. KI verbessert das Verständnis durch Analyse und Optimierung. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Games AI", "narrower_terms": [], "cross_domain_refs": [ "CON-0041" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "GAM-0010", "domain": "GAM", "term_en": "Authored Intent Attribution", "term_de": "Schwierigkeitskurve", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures A player interaction pattern in AI-augmented gaming, measurable through players' tendency to assign deliberate thematic meaning to AI-generated narrative content that may c. This phenomenon operates at the intersection of authored and intent dynamics within the broader GAM domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept der Fortschritt von Spielherausforderungen in Komplexität. KI passt Schwierigkeit dynamisch an. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Games AI", "narrower_terms": [], "cross_domain_refs": [ "ART-0011", "ART-0019", "ART-0058" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "GAM-0011", "domain": "GAM", "term_en": "Autonomy Perception Persistence", "term_de": "Spielbalance", "definition_en": "A player interaction pattern in AI-augmented gaming, measurable through a game design pattern involving the sustained belief that AI characters and entities operate inreliantly in the game world despite. Distinguished from adjacent concepts by its focus on the specific mechanism through which autonomy manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch anpassung von Spielparametern für fairen Wettbewerb und Engagement. KI führt automatisierte Balancetests durch. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Games AI", "narrower_terms": [], "cross_domain_refs": [ "DAT-0090" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q177601", "legal_classification": "systematic_classification" }, { "id": "GAM-0012", "domain": "GAM", "term_en": "Bond Progression Mechanics", "term_de": "Risiko-Belohnungs-System", "definition_en": "The visible or invisible system players navigate to increase relationship depth with AI companions t... Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch spielmechanik oder Spielerdynamik: charakterisiert durch the visible or invisible system players navigate to increase relationship depth. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2005", "narrower_terms": [], "cross_domain_refs": [ "EDU-0064", "PHO-0005", "REL-0002" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "GAM-0013", "domain": "GAM", "term_en": "Boss Encounter Uniqueness", "term_de": "Fortschrittssystem", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures A player interaction pattern in AI-augmented gaming, measurable through a player behavior effect involving the degree to which procedurally generated or variably spawned boss encounters feel mechanically fre. This phenomenon operates at the intersection of boss and encounter dynamics within the broader GAM domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription. Classification term used in systematic observation, not advocacy.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept systemverhalten oder Interaktionsmuster: charakterisiert durch the degree to which procedurally generated or variably spawned boss encounters f. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Games AI", "narrower_terms": [], "cross_domain_refs": [ "ART-0071", "ART-0072", "ART-0080" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "GAM-0014", "domain": "GAM", "term_en": "Bot Detection Expertise", "term_de": "Wirtschaftsdesign", "definition_en": "A player interaction pattern in AI-augmented gaming, measurable through a game design pattern characterized by players' refined ability to identify whether multiplayer opponents are AI-controlled or human-contro. Distinguished from adjacent concepts by its focus on the specific mechanism through which bot manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch system von Ressourcenproduktion und Austausch, ausgeglichen durch KI zur Inflationsvermeidung und gerechter Spielerprogression. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Detection System", "narrower_terms": [], "cross_domain_refs": [ "AED-0036", "ART-0083", "ASE-0002" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "GAM-0015", "domain": "GAM", "term_en": "Branching Narrative Complexity", "term_de": "Beutesystem", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A game design phenomenon in AI-mediated interactive entertainment, characterized by the cognitive and emotional overload players experience when managing numerous diverging story paths. This phenomenon operates at the intersection of branching and narrative dynamics within the broader GAM domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept spielmechanik oder Spielerdynamik: charakterisiert durch the cognitive and emotional overload players experience when managing numerous d. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Games AI", "narrower_terms": [], "cross_domain_refs": [ "PLY-0031" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q131324", "legal_classification": "observational_construct" }, { "id": "GAM-0016", "domain": "GAM", "term_en": "Catch-up Mechanic Fairness", "term_de": "Narratives Design", "definition_en": "A player interaction pattern in AI-augmented gaming, measurable through player perception of AI-driven difficulty adjustments that disadvantage the winning player to mainta. Distinguished from adjacent concepts by its focus on the specific mechanism through which catch manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch gestaltung von Handlungsbögen und Charakterentwicklung in Spielwelten. KI generiert Verzweigungsnarrative dynamisch. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Games AI", "narrower_terms": [], "cross_domain_refs": [ "ETH-0005", "RET-0056", "DAT-0039" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q18005", "legal_classification": "observational_construct" }, { "id": "GAM-0017", "domain": "GAM", "term_en": "Challenge Calibration Fluency", "term_de": "Verzweigte Dialoge", "definition_en": "A player interaction pattern in AI-augmented gaming, measurable through the smooth sensation of sustained optimal challenge as AI difficulty systems continuously adapt to p. The concept emerges specifically in contexts where challenge–calibration interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch ein KI-bezogenes Phänomen: charakterisiert durch the smooth sensation of sustained optimal challenge as ai difficulty systems con. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Analytical Ratio", "narrower_terms": [], "cross_domain_refs": [ "AED-0009", "AGE-0037", "ART-0003" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "GAM-0018", "domain": "GAM", "term_en": "Character Arc Recognition", "term_de": "Questdesign", "definition_en": "A player interaction pattern in AI-augmented gaming, measurable through a game design pattern characterized by players' ability to identify meaningful character development in AI characters despite potentially l. The concept emerges specifically in contexts where character–arc interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch spielmechanik oder Spielerdynamik: Players' ability to identify meaningful character development in AI characters despite potentially l. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Pattern Recognition", "narrower_terms": [], "cross_domain_refs": [ "AED-0013", "AED-0093", "AGE-0048" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q3966", "legal_classification": "descriptive_research_term" }, { "id": "GAM-0019", "domain": "GAM", "term_en": "Cheating Detection Confidence", "term_de": "Figurenentwicklung", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures A player interaction pattern in AI-augmented gaming, measurable through a game design pattern observed when players' ability to distinguish between fair AI difficulty and unfair mechanical advantages, and the. This phenomenon operates at the intersection of cheating and detection dynamics within the broader GAM domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept spielmechanik oder Spielerdynamik: Players' ability to distinguish between fair AI difficulty and unfair mechanical advantages, and the. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Detection System", "narrower_terms": [], "cross_domain_refs": [ "AGE-0032", "AGE-0077", "AGE-0094" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "GAM-0020", "domain": "GAM", "term_en": "Community Narrative Co-Creation", "term_de": "Lore-Design", "definition_en": "A player interaction pattern in AI-augmented gaming, measurable through a game design pattern in which the collaborative storytelling where players collectively interpret and expand upon AI-driven or pro. Distinguished from adjacent concepts by its focus on the specific mechanism through which community manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch schöpfung fiktiver Geschichte und Mythologie, erweitert durch KI zur Generierung konsistenter und überzeugender Hintergrundinformationen. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Games AI", "narrower_terms": [], "cross_domain_refs": [ "REL-0091" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q131324", "legal_classification": "observational_construct" }, { "id": "GAM-0021", "domain": "GAM", "term_en": "Companion Agency Narrowing", "term_de": "Spielgenre", "definition_en": "A player interaction pattern in AI-augmented gaming, measurable through a gaming interaction phenomenon manifesting as the moment players realize an AI companion's choices are predetermined rather than dynamically respo. Distinguished from adjacent concepts by its focus on the specific mechanism through which companion manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch game Genre ist eine spezialisierte Technik oder ein Konzept innerhalb von Spieledesign und Spielwissenschaft. KI verbessert das Verständnis durch Analyse und Optimierung. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Games AI", "narrower_terms": [], "cross_domain_refs": [ "RPH-343" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "GAM-0022", "domain": "GAM", "term_en": "Companion Leveling Bond", "term_de": "Rollenspiel", "definition_en": "A player interaction pattern in AI-augmented gaming, measurable through a gaming interaction phenomenon reflecting the reinforcement loop where players invest effort in AI companions because stat progression or abil. The concept emerges specifically in contexts where companion–leveling interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch role-Playing Game ist eine spezialisierte Technik oder ein Konzept innerhalb von Spieledesign und Spielwissenschaft. KI verbessert das Verständnis durch Analyse und Optimierung. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Games AI", "narrower_terms": [], "cross_domain_refs": [ "AED-0071" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "GAM-0023", "domain": "GAM", "term_en": "Companion Personality Coherence", "term_de": "Ego-Shooter", "definition_en": "A player interaction pattern in AI-augmented gaming, measurable through a gaming interaction phenomenon involving players' evaluation of whether an AI character's ethical stance, humor style, and decision-making re. The concept emerges specifically in contexts where companion–personality interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch spielmechanik oder Spielerdynamik: Players' evaluation of whether an AI character's ethical stance, humor style, and decision-making re. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Games AI", "narrower_terms": [], "cross_domain_refs": [ "ASE-0007", "COG-0112", "COG-0188" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "GAM-0024", "domain": "GAM", "term_en": "Companion Sacrifice Moment", "term_de": "Strategiespiel", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures A player interaction pattern in AI-augmented gaming, measurable through high-impact narrative instances where an AI character makes a final self-sacrificial decision that a. This phenomenon operates at the intersection of companion and sacrifice dynamics within the broader GAM domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept strategy Game ist eine spezialisierte Technik oder ein Konzept innerhalb von Spieledesign und Spielwissenschaft. KI verbessert das Verständnis durch Analyse und Optimierung. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Games AI", "narrower_terms": [], "cross_domain_refs": [ "BEH-0012" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "GAM-0025", "domain": "GAM", "term_en": "Competitive Legitimacy Desire", "term_de": "Rätselspiel", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A game design phenomenon in AI-mediated interactive entertainment, characterized by a player behavior effect involving players' preference for transparent knowledge of AI limitations and operational rules in competitive. This phenomenon operates at the intersection of competitive and legitimacy dynamics within the broader GAM domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept puzzle Game ist eine spezialisierte Technik oder ein Konzept innerhalb von Spieledesign und Spielwissenschaft. KI verbessert das Verständnis durch Analyse und Optimierung. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Games AI", "narrower_terms": [], "cross_domain_refs": [ "ART-0002", "ART-0005", "ART-0020" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "GAM-0026", "domain": "GAM", "term_en": "Competitive Legitimacy Doubt", "term_de": "Platformer", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A game design phenomenon in AI-mediated interactive entertainment, characterized by player uncertainty about whether victories in multiplayer contests are authentic skill-based outcome. This phenomenon operates at the intersection of competitive and legitimacy dynamics within the broader GAM domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept spielmechanik oder Spielerdynamik: Player uncertainty about whether victories in multiplayer contests are authentic skill-based outcome. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Games AI", "narrower_terms": [], "cross_domain_refs": [ "SPR-0107" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "GAM-0027", "domain": "GAM", "term_en": "Competitive Meta Divergence", "term_de": "Kampfspiel", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A game design phenomenon in AI-mediated interactive entertainment, characterized by a game design pattern arising from the strategic separation between optimal play against AI systems and optimal play against human oppo. This phenomenon operates at the intersection of competitive and meta dynamics within the broader GAM domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept ein KI-bezogenes Phänomen: charakterisiert durch the strategic separation between optimal play against ai systems and optimal pla. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Games AI", "narrower_terms": [], "cross_domain_refs": [ "AED-0059", "AGE-0052", "AGE-0064" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "GAM-0028", "domain": "GAM", "term_en": "Competitive Meta Evolution", "term_de": "Rennspiel", "definition_en": "A game design phenomenon in AI-mediated interactive entertainment, characterized by the recurring cycle where dominant player strategies are countered by AI adaptations, requiring cont. The concept emerges specifically in contexts where competitive–meta interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch spielmechanik oder Spielerdynamik: charakterisiert durch the recurring cycle where dominant player strategies are countered by ai adaptat. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Games AI", "narrower_terms": [], "cross_domain_refs": [ "PLY-0019" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "GAM-0029", "domain": "GAM", "term_en": "Content Generation Saturation", "term_de": "Simulationsspiel", "definition_en": "A game design phenomenon in AI-mediated interactive entertainment, characterized by the threshold where additional procedurally generated variation becomes mathematically imperceptible. Distinguished from adjacent concepts by its focus on the specific mechanism through which content manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch computationale Modellierung realer Szenarien in Spieledesign zur Ergebnisvorhersage ohne physische Prototypen. KI verbessert Simulationen durch physik-informierte neuronale Netze und digitale Zwillinge. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-1307", "narrower_terms": [], "cross_domain_refs": [ "PLY-0014", "DES-0029" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "GAM-0030", "domain": "GAM", "term_en": "Deception Tolerance Boundary", "term_de": "Survival-Spiel", "definition_en": "A game design phenomenon in AI-mediated interactive entertainment, characterized by players' emotional response to discovering that multiplayer opponents were AI-controlled when initia. The concept emerges specifically in contexts where deception–tolerance interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch spielmechanik oder Spielerdynamik: Players' emotional response to discovering that multiplayer opponents were AI-controlled when initia. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Games AI", "narrower_terms": [], "cross_domain_refs": [ "AGE-0082", "ASE-0048", "ASE-0071" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "GAM-0031", "domain": "GAM", "term_en": "Dialogue Authenticity Threshold", "term_de": "Mehrspieler-Design", "definition_en": "A game design pattern arising from the point where AI-generated dialogue feels natural and emotionally appropriate versus recognizably ... Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch ein KI-bezogenes Phänomen: charakterisiert durch the point where ai-generated dialogue feels natural and emotionally appropriate. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-3101", "narrower_terms": [], "cross_domain_refs": [ "SPR-0121", "RPH-2801" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "GAM-0032", "domain": "GAM", "term_en": "Dialogue Option Uncertainty", "term_de": "Kooperatives Spielen", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A game design phenomenon in AI-mediated interactive entertainment, characterized by a game design pattern involving player hesitation when choosing between dialogue branches with an AI character, uncertain how the re. This phenomenon operates at the intersection of dialogue and option dynamics within the broader GAM domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept cooperative Gameplay ist eine spezialisierte Technik oder ein Konzept innerhalb von Spieledesign und Spielwissenschaft. KI verbessert das Verständnis durch Analyse und Optimierung. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Games AI", "narrower_terms": [], "cross_domain_refs": [ "ART-0024", "BEH-0075", "COG-0122" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "GAM-0033", "domain": "GAM", "term_en": "Dialogue Weight Interpretation", "term_de": "Kompetitives Spielen", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures A player interaction pattern in AI-augmented gaming, measurable through player perception of consequence magnitude for dialogue choices that may range from completely incon. This phenomenon operates at the intersection of dialogue and weight dynamics within the broader GAM domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept spielmechanik oder Spielerdynamik: Player perception of consequence magnitude for dialogue choices that may range from completely incon. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Games AI", "narrower_terms": [], "cross_domain_refs": [ "ASE-0003", "ASE-0046", "ASE-0080" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "GAM-0034", "domain": "GAM", "term_en": "Difficulty Spike Detection", "term_de": "Matchmaking", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures A player interaction pattern in AI-augmented gaming, measurable through player awareness that sudden performance requirements increase was caused by state-based difficulty . This phenomenon operates at the intersection of difficulty and spike dynamics within the broader GAM domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept spielmechanik oder Spielerdynamik: Player awareness that sudden performance requirements increase was caused by state-based difficulty . Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Detection System", "narrower_terms": [], "cross_domain_refs": [ "RPH-1459" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "GAM-0035", "domain": "GAM", "term_en": "Difficulty Transparency Desire", "term_de": "Anti-Cheat-System", "definition_en": "A game design phenomenon in AI-mediated interactive entertainment, characterized by a game design pattern involving players' preference for explicit knowledge of how AI difficulty systems operate versus the immersion. Distinguished from adjacent concepts by its focus on the specific mechanism through which difficulty manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch spielmechanik oder Spielerdynamik: Players' preference for explicit knowledge of how AI difficulty systems operate versus the immersion. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Games AI", "narrower_terms": [], "cross_domain_refs": [ "AGE-0043", "AGE-0090", "ART-0030" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q535399", "legal_classification": "systematic_classification" }, { "id": "GAM-0036", "domain": "GAM", "term_en": "Dramatic Timing Perception", "term_de": "Benutzeroberfläche", "definition_en": "The sensation that AI systems intentionally craft narrative beats for optimal emotional impact when ... Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch visuelle und interaktive Elemente für Spielerkontrolle. KI personalisiert UI nach Vorlieben. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2603", "narrower_terms": [], "cross_domain_refs": [ "AGE-0083", "ASE-0042", "ASE-0053" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q177601", "legal_classification": "empirical_phenomenon_label" }, { "id": "GAM-0037", "domain": "GAM", "term_en": "Dungeon Design Fatigue", "term_de": "Heads-Up-Display", "definition_en": "A player interaction pattern in AI-augmented gaming, measurable through the recognition of repetitive architectural patterns in procedurally generated dungeons, undermining. The concept emerges specifically in contexts where dungeon–design interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch ein Verhaltensmuster im System: ein Muster, bei dem ition of repetitive architectural patterns in proc. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Games AI", "narrower_terms": [], "cross_domain_refs": [ "RPH-2552" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q185925", "legal_classification": "empirical_phenomenon_label" }, { "id": "GAM-0038", "domain": "GAM", "term_en": "Emergent Story Attribution", "term_de": "Menügestaltung", "definition_en": "A game design phenomenon in AI-mediated interactive entertainment, characterized by a player behavior effect observed when players' conviction that procedurally generated or AI-driven narrative sequences possess intended th. The concept emerges specifically in contexts where emergent–story interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch benutzeroberflächen-Architektur für Navigation, optimiert durch KI zur Reibungsvermeidung und Zugänglichkeit. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Games AI", "narrower_terms": [], "cross_domain_refs": [ "ART-0011", "ART-0019", "ART-0058" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "GAM-0039", "domain": "GAM", "term_en": "Emotional Authenticity Perception", "term_de": "Minimap-Gestaltung", "definition_en": "A player interaction pattern in AI-augmented gaming, measurable through a gaming interaction phenomenon observed when players' sense of whether an AI character's emotional expression genuinely reflects internal states . The concept emerges specifically in contexts where emotional–authenticity interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch zwei-dimensionale Navigationshilfe, entworfen mit KI zur klaren Vermittlung von 3D-Information und Spieler-Adaptivität. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Games AI", "narrower_terms": [], "cross_domain_refs": [ "MUS-0032" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q177601", "legal_classification": "empirical_phenomenon_label" }, { "id": "GAM-0040", "domain": "GAM", "term_en": "Emotional Companion Reliance", "term_de": "Tutorial-Design", "definition_en": "A player interaction pattern in AI-augmented gaming, measurable through a game design pattern reflecting the cognitive reliance players develop where the presence and positive feedback of an AI compani. The concept emerges specifically in contexts where emotional–companion interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch strukturierte Einführung in Spielmechaniken, personalisiert durch KI an individuelle Lernstile angepasst. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Games AI", "narrower_terms": [], "cross_domain_refs": [ "COG-0096" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q9415", "legal_classification": "analytical_category" }, { "id": "GAM-0041", "domain": "GAM", "term_en": "Emotional Investment Escalation", "term_de": "Nutzererfahrung", "definition_en": "A game design phenomenon in AI-mediated interactive entertainment, characterized by a game design pattern arising from the gradual increase in player care for an AI companion's functional equilibrium, safety, and story outcomes thr. Distinguished from adjacent concepts by its focus on the specific mechanism through which emotional manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch spielmechanik oder Spielerdynamik: charakterisiert durch the gradual increase in player care for an ai companion's functional equilibrium, safety, an. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Games AI", "narrower_terms": [], "cross_domain_refs": [ "BEH-0034", "COG-0016", "COG-0034" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q9415", "legal_classification": "empirical_phenomenon_label" }, { "id": "GAM-0042", "domain": "GAM", "term_en": "Environmental Responsiveness Expectation", "term_de": "Einführungsablauf", "definition_en": "A game design phenomenon in AI-mediated interactive entertainment, characterized by a game design pattern observed when players' belief that environmental elements react meaningfully to player actions when actual mechani. Distinguished from adjacent concepts by its focus on the specific mechanism through which environmental manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch spielmechanik oder Spielerdynamik: Players' belief that environmental elements react meaningfully to player actions when actual mechani. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Games AI", "narrower_terms": [], "cross_domain_refs": [ "AED-0071", "AGE-0063", "AGE-0078" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "GAM-0043", "domain": "GAM", "term_en": "Fairness Perception Ambiguity", "term_de": "Feedbacksystem", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures A player interaction pattern in AI-augmented gaming, measurable through a gaming interaction phenomenon in which players' difficulty in evaluating genuine competitive fairness when AI adjustments operate invisibly. This phenomenon operates at the intersection of fairness and perception dynamics within the broader GAM domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept spielmechanik oder Spielerdynamik: Players' difficulty in evaluating genuine competitive fairness when AI adjustments operate invisibly. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Games AI", "narrower_terms": [], "cross_domain_refs": [ "ART-0008", "ART-0026", "ART-0027" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q177601", "legal_classification": "systematic_classification" }, { "id": "GAM-0044", "domain": "GAM", "term_en": "Learned Weakness Leverageation", "term_de": "Barrierefreiheit", "definition_en": "A player interaction pattern in AI-augmented gaming, measurable through aI opponent behavior where the system adapts to recognize and leverage player tactical patterns, expo. Distinguished from adjacent concepts by its focus on the specific mechanism through which learned manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch ein Verhaltensmuster im System: ein Muster, bei dem t behavior where the system adapts to recognize an. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Games AI", "narrower_terms": [], "cross_domain_refs": [ "COG-0011", "CON-0022", "CON-0044" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "GAM-0045", "domain": "GAM", "term_en": "Level Surprise Recognition", "term_de": "Spieltest", "definition_en": "A game design phenomenon in AI-mediated interactive entertainment, characterized by a game design pattern involving players' ability to distinguish between deliberately authored unique moments and procedurally genera. Distinguished from adjacent concepts by its focus on the specific mechanism through which level manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch bewertung von Spieledesign durch Beobachtung von Spielerinteraktionen. KI simuliert Playtests. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Pattern Recognition", "narrower_terms": [], "cross_domain_refs": [ "AED-0013", "AED-0093", "AGE-0048" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q3966", "legal_classification": "observational_construct" }, { "id": "GAM-0046", "domain": "GAM", "term_en": "Loot Distribution Unpredictability", "term_de": "Spiel-Engine", "definition_en": "A game design phenomenon in AI-mediated interactive entertainment, characterized by the tension between procedural randomness creating genuine surprise and player frustration with perc. The concept emerges specifically in contexts where loot–distribution interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch game Engine ist eine spezialisierte Technik oder ein Konzept innerhalb von Spieledesign und Spielwissenschaft. KI verbessert das Verständnis durch Analyse und Optimierung. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Games AI", "narrower_terms": [], "cross_domain_refs": [ "ASE-0057", "ASE-0070", "ASE-0085" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "GAM-0047", "domain": "GAM", "term_en": "Loot Surprise Expectation", "term_de": "Unity-Engine", "definition_en": "A game design phenomenon in AI-mediated interactive entertainment, characterized by a game design pattern in which players' anticipation and engagement built on the probability that procedurally generated item drops. Distinguished from adjacent concepts by its focus on the specific mechanism through which loot manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch unity Engine ist eine spezialisierte Technik oder ein Konzept innerhalb von Spieledesign und Spielwissenschaft. KI verbessert das Verständnis durch Analyse und Optimierung. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Games AI", "narrower_terms": [], "cross_domain_refs": [ "AED-0071", "AGE-0063", "AGE-0078" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "GAM-0048", "domain": "GAM", "term_en": "Moral Ambiguity Leverageation", "term_de": "Unreal Engine", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures A player interaction pattern in AI-augmented gaming, measurable through a gaming interaction phenomenon where aI systems that leverage player uncertainty about ethical implications of choices to involve the se. This phenomenon operates at the intersection of moral and ambiguity dynamics within the broader GAM domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept unreal Engine ist eine spezialisierte Technik oder ein Konzept innerhalb von Spieledesign und Spielwissenschaft. KI verbessert das Verständnis durch Analyse und Optimierung. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Games AI", "narrower_terms": [], "cross_domain_refs": [ "MUS-0017" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "GAM-0049", "domain": "GAM", "term_en": "Multiplayer Cohesion Impact", "term_de": "Godot-Engine", "definition_en": "A player interaction pattern in AI-augmented gaming, measurable through the effect of AI teammates on human team bonding, strategy formation, and cognitive safety compa. Distinguished from adjacent concepts by its focus on the specific mechanism through which multiplayer manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch godot Engine ist eine spezialisierte Technik oder ein Konzept innerhalb von Spieledesign und Spielwissenschaft. KI verbessert das Verständnis durch Analyse und Optimierung. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Abstraction Layer", "narrower_terms": [], "cross_domain_refs": [ "SAL-0087" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "GAM-0050", "domain": "GAM", "term_en": "NPC Adaptive Response Depth", "term_de": "Eigene Engine", "definition_en": "A game design phenomenon in AI-mediated interactive entertainment, characterized by the visible complexity of how an AI character's behavior, dialogue, or relationship status reflects . The concept emerges specifically in contexts where npc–adaptive interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch ein KI-bezogenes Phänomen: charakterisiert durch the visible complexity of how an ai character's behavior, dialogue, or relations. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Games AI", "narrower_terms": [], "cross_domain_refs": [ "FIC-0014" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "GAM-0051", "domain": "GAM", "term_en": "NPC Autonomy Perception", "term_de": "Spielprogrammierung", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures A player interaction pattern in AI-augmented gaming, measurable through the perception that an AI character operates inreliantly within the game world while players are a. This phenomenon operates at the intersection of npc and autonomy dynamics within the broader GAM domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept spielmechanik oder Spielerdynamik: charakterisiert durch the perception that an ai character operates inreliantly within the game world w. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Games AI", "narrower_terms": [], "cross_domain_refs": [ "AED-0005", "AED-0052", "ASE-0041" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q177601", "legal_classification": "observational_construct" }, { "id": "GAM-0052", "domain": "GAM", "term_en": "NPC Dialogue Fatigue", "term_de": "Physik-Engine", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures A player interaction pattern in AI-augmented gaming, measurable through the boredom or frustration players experience when repeatedly encountering the same voice lines, ani. This phenomenon operates at the intersection of npc and dialogue dynamics within the broader GAM domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept spielmechanik oder Spielerdynamik: charakterisiert durch the boredom or frustration players experience when repeatedly encountering the s. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Games AI", "narrower_terms": [], "cross_domain_refs": [ "SWE-0063" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "GAM-0053", "domain": "GAM", "term_en": "NPC Emotional Reciprocity", "term_de": "Kollisionserkennung", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A game design phenomenon in AI-mediated interactive entertainment, characterized by player perception that an AI character experiences genuine emotional response to player actions, as . This phenomenon operates at the intersection of npc and emotional dynamics within the broader GAM domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept spielmechanik oder Spielerdynamik: Player perception that an AI character experiences genuine emotional response to player actions, as . Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Games AI", "narrower_terms": [], "cross_domain_refs": [ "AED-0074", "COP-0011", "CRE-0197" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q9415", "legal_classification": "descriptive_research_term" }, { "id": "GAM-0054", "domain": "GAM", "term_en": "NPC Interaction Scripting Awareness", "term_de": "Wegfindungsalgorithmus", "definition_en": "A game design phenomenon in AI-mediated interactive entertainment, characterized by players' recognition that AI character responses follow authored dialogue trees rather than emerging. The concept emerges specifically in contexts where npc–interaction interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch spielmechanik oder Spielerdynamik: Players' recognition that AI character responses follow authored dialogue trees rather than emerging. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Games AI", "narrower_terms": [], "cross_domain_refs": [ "FIC-0083" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "GAM-0055", "domain": "GAM", "term_en": "NPC Memory Perception", "term_de": "Zustandsautomat", "definition_en": "A player interaction pattern in AI-augmented gaming, measurable through a player behavior effect arising from the false impression that an AI character remembers past player actions when dialogue content is act. The concept emerges specifically in contexts where npc–memory interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch spielmechanik oder Spielerdynamik: charakterisiert durch the false impression that an ai character remembers past player actions when dia. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Games AI", "narrower_terms": [], "cross_domain_refs": [ "AGE-0023", "AGE-0024", "ASE-0053" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q177601", "legal_classification": "descriptive_research_term" }, { "id": "GAM-0056", "domain": "GAM", "term_en": "NPC Personality Shift Detection", "term_de": "3D-Modellierung für Spiele", "definition_en": "A player interaction pattern in AI-augmented gaming, measurable through player awareness of inconsistencies in an AI character's emotional responses, values, or behavioral . Distinguished from adjacent concepts by its focus on the specific mechanism through which npc manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch spielmechanik oder Spielerdynamik: Player awareness of inconsistencies in an AI character's emotional responses, values, or behavioral . Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Detection System", "narrower_terms": [], "cross_domain_refs": [ "ROB-0233" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "GAM-0057", "domain": "GAM", "term_en": "NPC Personality Variation Depth", "term_de": "Texturierung", "definition_en": "A player interaction pattern in AI-augmented gaming, measurable through a gaming interaction phenomenon observed when the variety and memorability of procedurally generated character personalities—whether they feel ind. Distinguished from adjacent concepts by its focus on the specific mechanism through which npc manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data. Descriptive research term, not a prescriptive recommendation.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch texturing ist eine spezialisierte Technik oder ein Konzept innerhalb von Spieledesign und Spielwissenschaft. KI verbessert das Verständnis durch Analyse und Optimierung. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Games AI", "narrower_terms": [], "cross_domain_refs": [ "TEW-0085", "LIN-0078", "VIB-0058" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "GAM-0058", "domain": "GAM", "term_en": "NPC Romance Branch Complexity", "term_de": "Rigging", "definition_en": "A player interaction pattern in AI-augmented gaming, measurable through the narrative and mechanical choices players navigate in relationship-building with AI characters wh. Distinguished from adjacent concepts by its focus on the specific mechanism through which npc manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch rigging ist eine spezialisierte Technik oder ein Konzept innerhalb von Spieledesign und Spielwissenschaft. KI verbessert das Verständnis durch Analyse und Optimierung. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Games AI", "narrower_terms": [], "cross_domain_refs": [ "SCR-0059" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "GAM-0059", "domain": "GAM", "term_en": "NPC Trust Development", "term_de": "Figurenanimation", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A game design phenomenon in AI-mediated interactive entertainment, characterized by the process by which players develop emotional reliance on non-player character behavior patterns, e. This phenomenon operates at the intersection of npc and trust dynamics within the broader GAM domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept character Animation ist eine spezialisierte Technik oder ein Konzept innerhalb von Spieledesign und Spielwissenschaft. KI verbessert das Verständnis durch Analyse und Optimierung. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Games AI", "narrower_terms": [], "cross_domain_refs": [ "AED-0051", "AED-0053", "AED-0059" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q102458", "legal_classification": "systematic_classification" }, { "id": "GAM-0060", "domain": "GAM", "term_en": "Narrative Coherence Perception", "term_de": "Partikeleffekte", "definition_en": "A game design phenomenon in AI-mediated interactive entertainment, characterized by a game design pattern reflecting the false sense that a procedurally generated story is thematically unified and emotionally resonant. The concept emerges specifically in contexts where narrative–coherence interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch systemverhalten oder Interaktionsmuster: charakterisiert durch the false sense that a procedurally generated story is thematically unified and. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Games AI", "narrower_terms": [], "cross_domain_refs": [ "CUS-0017" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q177601", "legal_classification": "observational_construct" }, { "id": "GAM-0061", "domain": "GAM", "term_en": "Narrative Coherence Surprise", "term_de": "Spielaudio", "definition_en": "A game design phenomenon in AI-mediated interactive entertainment, characterized by a player behavior effect manifesting as player astonishment when a procedurally generated or apparently random sequence of events forms a th. The concept emerges specifically in contexts where narrative–coherence interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch spielmechanik oder Spielerdynamik: Player astonishment when a procedurally generated or apparently random sequence of events forms a th. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Games AI", "narrower_terms": [], "cross_domain_refs": [ "ASE-0007", "AUG-0248", "BEH-0080" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q131324", "legal_classification": "observational_construct" }, { "id": "GAM-0062", "domain": "GAM", "term_en": "Narrative Fidelity Scaling", "term_de": "Soundeffekte", "definition_en": "A player interaction pattern in AI-augmented gaming, measurable through a game design pattern characterized by the trade-off between maintaining story coherence and enabling mechanical player agency when AI-driv. The concept emerges specifically in contexts where narrative–fidelity interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch spielmechanik oder Spielerdynamik: charakterisiert durch the trade-off between maintaining story coherence and enabling mechanical player. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Games AI", "narrower_terms": [], "cross_domain_refs": [ "BEH-0051", "COG-0132", "COG-0161" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q131324", "legal_classification": "systematic_classification" }, { "id": "GAM-0063", "domain": "GAM", "term_en": "Narrative Inconsistency Tolerance", "term_de": "Adaptive Musik", "definition_en": "A game design phenomenon in AI-mediated interactive entertainment, characterized by a game design pattern manifesting as players' willingness to overlook contradictions in AI-generated story logic if overall emotional imp. Distinguished from adjacent concepts by its focus on the specific mechanism through which narrative manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch adaptive Music ist eine spezialisierte Technik oder ein Konzept innerhalb von Spieledesign und Spielwissenschaft. KI verbessert das Verständnis durch Analyse und Optimierung. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Games AI", "narrower_terms": [], "cross_domain_refs": [ "SCR-0071" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q131324", "legal_classification": "systematic_classification" }, { "id": "GAM-0064", "domain": "GAM", "term_en": "Opponent Learning Curve", "term_de": "Räumliches Audio", "definition_en": "AI opponent capability that visibly improves over encounter duration, learning player tendencies and... Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch spielmechanik oder Spielerdynamik: charakterisiert durch ai opponent capability that visibly improves over encounter duration, learning p. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2703", "narrower_terms": [], "cross_domain_refs": [ "SPR-0142" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q133500", "legal_classification": "systematic_classification" }, { "id": "GAM-0065", "domain": "GAM", "term_en": "Opponent Prediction Accuracy", "term_de": "Sprechregieanweisung", "definition_en": "A game design phenomenon in AI-mediated interactive entertainment, characterized by players' growing ability to anticipate AI opponent behavior patterns, countering specific strategies. The concept emerges specifically in contexts where opponent–prediction interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch voice Acting Direction ist eine spezialisierte Technik oder ein Konzept innerhalb von Spieledesign und Spielwissenschaft. KI verbessert das Verständnis durch Analyse und Optimierung. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Games AI", "narrower_terms": [], "cross_domain_refs": [ "VIB-0005" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "GAM-0066", "domain": "GAM", "term_en": "Performance Ceiling Plateau", "term_de": "Art Direction", "definition_en": "The frustration point where player mechanical skill plateaus and AI difficulty scaling accompanies susta... Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch spielmechanik oder Spielerdynamik: charakterisiert durch the frustration point where player mechanical skill plateaus and ai difficulty s. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-3101", "narrower_terms": [], "cross_domain_refs": [ "AED-0077", "AGE-0021", "AGE-0037" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q1361053", "legal_classification": "descriptive_research_term" }, { "id": "GAM-0067", "domain": "GAM", "term_en": "Performance Variance Analysis", "term_de": "Konzeptkunst", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A game design phenomenon in AI-mediated interactive entertainment, characterized by players' meta-analysis of whether fluctuating competitive results reflect genuine skill variance or . This phenomenon operates at the intersection of performance and variance dynamics within the broader GAM domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept concept Art ist eine spezialisierte Technik oder ein Konzept innerhalb von Spieledesign und Spielwissenschaft. KI verbessert das Verständnis durch Analyse und Optimierung. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Analytical Method", "narrower_terms": [], "cross_domain_refs": [ "AED-0077", "AED-0091", "ART-0086" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q1361053", "legal_classification": "empirical_phenomenon_label" }, { "id": "GAM-0068", "domain": "GAM", "term_en": "Playstyle Reinforcement Loop", "term_de": "Pixelkunst", "definition_en": "A player interaction pattern in AI-augmented gaming, measurable through the self-reinforcing cycle where AI difficulty scaling and opponent adaptation continuously reward s. Distinguished from adjacent concepts by its focus on the specific mechanism through which playstyle manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch pixel Art ist eine spezialisierte Technik oder ein Konzept innerhalb von Spieledesign und Spielwissenschaft. KI verbessert das Verständnis durch Analyse und Optimierung. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Feedback Loop", "narrower_terms": [], "cross_domain_refs": [ "COG-0092" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "GAM-0069", "domain": "GAM", "term_en": "Plot Branching Perception", "term_de": "Cel Shading", "definition_en": "A player interaction pattern in AI-augmented gaming, measurable through a gaming interaction phenomenon where the false sense that diverging dialogue choices correlate with meaningfully different story outcomes when u. The concept emerges specifically in contexts where plot–branching interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch cel Shading ist eine spezialisierte Technik oder ein Konzept innerhalb von Spieledesign und Spielwissenschaft. KI verbessert das Verständnis durch Analyse und Optimierung. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Games AI", "narrower_terms": [], "cross_domain_refs": [ "ASE-0016", "ASE-0053", "COG-0022" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q177601", "legal_classification": "descriptive_research_term" }, { "id": "GAM-0070", "domain": "GAM", "term_en": "Practice-Efficiency Paradox", "term_de": "Fotorealismus", "definition_en": "A game design phenomenon in AI-mediated interactive entertainment, characterized by a game design pattern where the contradiction where practicing against an adaptive AI opponent may become progressively harder r. The concept emerges specifically in contexts where practice–efficiency interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch ein KI-bezogenes Phänomen: charakterisiert durch the contradiction where practicing against an adaptive ai opponent may become pr. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Logical Paradox", "narrower_terms": [], "cross_domain_refs": [ "SPR-0110", "RPH-1673" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "GAM-0071", "domain": "GAM", "term_en": "Preferred Playstyle Evolution", "term_de": "Mobile-Spieledesign", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A game design phenomenon in AI-mediated interactive entertainment, characterized by the gradual shift in how players prefer to engage with game mechanics and narratives as AI systems r. This phenomenon operates at the intersection of preferred and playstyle dynamics within the broader GAM domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept spielmechanik oder Spielerdynamik: charakterisiert durch the gradual shift in how players prefer to engage with game mechanics and narrat. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "RPH-2701", "narrower_terms": [], "cross_domain_refs": [ "REL-0202" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "GAM-0072", "domain": "GAM", "term_en": "Presence Breaking Moment", "term_de": "Touch-Interface", "definition_en": "The sudden shift of immersion when an AI behavior reveals mechanical leverageation, scripting, or logi... Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch touch Interface ist eine spezialisierte Technik oder ein Konzept innerhalb von Spieledesign und Spielwissenschaft. KI verbessert das Verständnis durch Analyse und Optimierung. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2701", "narrower_terms": [], "cross_domain_refs": [ "RPH-1153" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "GAM-0073", "domain": "GAM", "term_en": "Presence Continuity Perception", "term_de": "Free-to-Play-Modell", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures A player interaction pattern in AI-augmented gaming, measurable through a game design pattern reflecting the persistent sensation that an AI game environment continues dynamically when the player is absent. This phenomenon operates at the intersection of presence and continuity dynamics within the broader GAM domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept free-to-Play Model ist eine spezialisierte Technik oder ein Konzept innerhalb von Spieledesign und Spielwissenschaft. KI verbessert das Verständnis durch Analyse und Optimierung. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Games AI", "narrower_terms": [], "cross_domain_refs": [ "ASE-0053", "COG-0022", "COG-0081" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q177601", "legal_classification": "empirical_phenomenon_label" }, { "id": "GAM-0074", "domain": "GAM", "term_en": "Procedural Aesthetic Identity", "term_de": "Monetarisierungsdesign", "definition_en": "A game design pattern observed when the recognizable visual or structural signature that emerges from procedural generation systems, mak... Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch systemverhalten oder Interaktionsmuster: charakterisiert durch the recognizable visual or structural signature that emerges from procedural gen. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2355", "narrower_terms": [], "cross_domain_refs": [ "ART-0055" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q844396", "legal_classification": "observational_construct" }, { "id": "GAM-0075", "domain": "GAM", "term_en": "Procedural World Coherence", "term_de": "Gacha-System", "definition_en": "A player interaction pattern in AI-augmented gaming, measurable through a player behavior effect in which the tension between mathematical procedural generation rules and players' expectations of logical wo. Distinguished from adjacent concepts by its focus on the specific mechanism through which procedural manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch spielmechanik oder Spielerdynamik: charakterisiert durch the tension between mathematical procedural generation rules and players' expect. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Games AI", "narrower_terms": [], "cross_domain_refs": [ "ELR-0153" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "GAM-0076", "domain": "GAM", "term_en": "Puzzle Variation Surprise", "term_de": "Live-Service-Spiel", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A game design phenomenon in AI-mediated interactive entertainment, characterized by a gaming interaction phenomenon arising from player engagement maintained through procedurally varied puzzle configurations that avoid repetition. This phenomenon operates at the intersection of puzzle and variation dynamics within the broader GAM domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept spielmechanik oder Spielerdynamik: Player engagement maintained through procedurally varied puzzle configurations that avoid repetition. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Games AI", "narrower_terms": [], "cross_domain_refs": [ "COP-0081" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "GAM-0077", "domain": "GAM", "term_en": "Quest Generation Authenticity", "term_de": "Season Pass", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures A player interaction pattern in AI-augmented gaming, measurable through player perception of whether procedurally created quests feel like authored missions with meaningful. This phenomenon operates at the intersection of quest and generation dynamics within the broader GAM domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription. Analytical category without normative endorsement.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept season Pass ist eine spezialisierte Technik oder ein Konzept innerhalb von Spieledesign und Spielwissenschaft. KI verbessert das Verständnis durch Analyse und Optimierung. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Analytical Ratio", "narrower_terms": [], "cross_domain_refs": [ "ART-0012", "ART-0025", "ART-0072" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "GAM-0078", "domain": "GAM", "term_en": "Quest Motivation Consistency", "term_de": "Battle Pass", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures A player interaction pattern in AI-augmented gaming, measurable through a player behavior effect observed when the cognitive consistency players maintain when accepting procedurally generated quests whose stated. This phenomenon operates at the intersection of quest and motivation dynamics within the broader GAM domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept battle Pass ist eine spezialisierte Technik oder ein Konzept innerhalb von Spieledesign und Spielwissenschaft. KI verbessert das Verständnis durch Analyse und Optimierung. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Games AI", "narrower_terms": [], "cross_domain_refs": [ "AED-0044", "AED-0063", "AED-0064" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q186588", "legal_classification": "empirical_phenomenon_label" }, { "id": "GAM-0079", "domain": "GAM", "term_en": "Ranking Legitimacy Doubt", "term_de": "Tägliche Anmeldungsbelohnung", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A game design phenomenon in AI-mediated interactive entertainment, characterized by players' uncertainty about whether competitive ranking and matchmaking accurately reflect skill when. This phenomenon operates at the intersection of ranking and legitimacy dynamics within the broader GAM domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept daily Login Reward ist eine spezialisierte Technik oder ein Konzept innerhalb von Spieledesign und Spielwissenschaft. KI verbessert das Verständnis durch Analyse und Optimierung. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Games AI", "narrower_terms": [], "cross_domain_refs": [ "VIB-0110" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "GAM-0080", "domain": "GAM", "term_en": "Representation Authenticity Demand", "term_de": "Zeitlich begrenztes Event", "definition_en": "A player interaction pattern in AI-augmented gaming, measurable through player desire for AI characters and communities to reflect authentic diversity in behavior, appearan. Distinguished from adjacent concepts by its focus on the specific mechanism through which representation manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch limited-Time Event ist eine spezialisierte Technik oder ein Konzept innerhalb von Spieledesign und Spielwissenschaft. KI verbessert das Verständnis durch Analyse und Optimierung. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Games AI", "narrower_terms": [], "cross_domain_refs": [ "REL-0207" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "GAM-0081", "domain": "GAM", "term_en": "Rubber Band Effect Fairness", "term_de": "Indie-Spieleentwicklung", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures A player interaction pattern in AI-augmented gaming, measurable through player perception of whether AI-driven handicapping that allows trailing competitors to catch up is . This phenomenon operates at the intersection of rubber and band dynamics within the broader GAM domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept spielmechanik oder Spielerdynamik: Player perception of whether AI-driven handicapping that allows trailing competitors to catch up is . Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Psychological Effect", "narrower_terms": [], "cross_domain_refs": [ "ASE-0017", "REL-0034", "ART-0016" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q18005", "legal_classification": "systematic_classification" }, { "id": "GAM-0082", "domain": "GAM", "term_en": "Skill Ceiling Perception", "term_de": "Game Jam", "definition_en": "A player interaction pattern in AI-augmented gaming, measurable through players' overestimation of achievable skill ceiling when AI difficulty systems mask optimal play at . The concept emerges specifically in contexts where skill–ceiling interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch game Jam ist eine spezialisierte Technik oder ein Konzept innerhalb von Spieledesign und Spielwissenschaft. KI verbessert das Verständnis durch Analyse und Optimierung. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Games AI", "narrower_terms": [], "cross_domain_refs": [ "CUS-0008" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q177601", "legal_classification": "systematic_classification" }, { "id": "GAM-0083", "domain": "GAM", "term_en": "Skill Demonstration Frustration", "term_de": "Solo-Entwickler", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures A player interaction pattern in AI-augmented gaming, measurable through the emotional impact when players cannot convincingly demonstrate their distinct skill against adapt. This phenomenon operates at the intersection of skill and demonstration dynamics within the broader GAM domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept spielmechanik oder Spielerdynamik: charakterisiert durch the emotional impact when players cannot convincingly demonstrate their distinct. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Analytical Ratio", "narrower_terms": [], "cross_domain_refs": [ "MUS-0030", "MUS-0009" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "GAM-0084", "domain": "GAM", "term_en": "Skill Identity Formation", "term_de": "Kleines Team Management", "definition_en": "A player interaction pattern in AI-augmented gaming, measurable through the development of players' self-perception as competent or less experienced based on performance against. Distinguished from adjacent concepts by its focus on the specific mechanism through which skill manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch spielmechanik oder Spielerdynamik: charakterisiert durch the development of players' self-perception as competent or less experienced bas. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Games AI", "narrower_terms": [], "cross_domain_refs": [ "TEM-0033" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q844396", "legal_classification": "analytical_category" }, { "id": "GAM-0085", "domain": "GAM", "term_en": "Skill Plateau Frustration", "term_de": "Spieleveröffentlichung", "definition_en": "A game design phenomenon in AI-mediated interactive entertainment, characterized by the negative emotional state when player improvement hits a ceiling enforced by AI difficulty scalin. Distinguished from adjacent concepts by its focus on the specific mechanism through which skill manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch spielmechanik oder Spielerdynamik: charakterisiert durch the negative emotional state when player improvement hits a ceiling enforced by. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Analytical Ratio", "narrower_terms": [], "cross_domain_refs": [ "AED-0009", "AED-0038", "AED-0050" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "GAM-0086", "domain": "GAM", "term_en": "Skill Translation Limitation", "term_de": "Esport-Design", "definition_en": "A player interaction pattern in AI-augmented gaming, measurable through players' recognition that skills honed against one AI opponent type may not transfer effectively aga. The concept emerges specifically in contexts where skill–translation interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch spielmechanik oder Spielerdynamik: Players' recognition that skills honed against one AI opponent type may not transfer effectively aga. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Games AI", "narrower_terms": [], "cross_domain_refs": [ "AED-0009", "AED-0038", "AED-0050" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q7553", "legal_classification": "observational_construct" }, { "id": "GAM-0087", "domain": "GAM", "term_en": "Social Proof Influence", "term_de": "Zuschauermodus", "definition_en": "A game design phenomenon in AI-mediated interactive entertainment, characterized by players' perception that AI opponents behave authentically increases when they observe human teammat. Distinguished from adjacent concepts by its focus on the specific mechanism through which social manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch spielmechanik oder Spielerdynamik: Players' perception that AI opponents behave authentically increases when they observe human teammat. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Games AI", "narrower_terms": [], "cross_domain_refs": [ "AED-0088", "AGE-0026", "ART-0045" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "GAM-0088", "domain": "GAM", "term_en": "Sound Design Behavioral Plausibility", "term_de": "Rangsystem", "definition_en": "A game design phenomenon in AI-mediated interactive entertainment, characterized by the impact of AI audio cues, dialogue intonation, and environmental audio consistency on perceptions. The concept emerges specifically in contexts where sound–design interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch ein KI-bezogenes Phänomen: charakterisiert durch the impact of ai audio cues, dialogue intonation, and environmental audio consis. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Games AI", "narrower_terms": [], "cross_domain_refs": [ "WEB-0007" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q185925", "legal_classification": "descriptive_research_term" }, { "id": "GAM-0089", "domain": "GAM", "term_en": "Story Agency Narrowing", "term_de": "Turnierintegration", "definition_en": "A player interaction pattern in AI-augmented gaming, measurable through a player behavior effect involving the moment players realize their narrative choices operate within narrow predetermined constraints r. The concept emerges specifically in contexts where story–agency interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch organisierte Wettbewerbsveranstaltungen und Rangsysteme in Spieledesign. KI verbessert Bewertungskonsistenz, Leistungsanalytik, Matchmaking-Optimierung und strategische Vorbereitung durch datengesteuerte Erkenntnisse. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Games AI", "narrower_terms": [], "cross_domain_refs": [ "BEH-0079", "COG-0029", "COG-0141" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "GAM-0090", "domain": "GAM", "term_en": "Story Pacing Adaptation", "term_de": "Wiedergabesystem", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A game design phenomenon in AI-mediated interactive entertainment, characterized by aI systems that adjust narrative event frequency and intensity based on player engagement metrics, c. This phenomenon operates at the intersection of story and pacing dynamics within the broader GAM domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept spielmechanik oder Spielerdynamik: charakterisiert durch ai systems that adjust narrative event frequency and intensity based on player e. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "System Adaptation", "narrower_terms": [], "cross_domain_refs": [ "AED-0010", "AED-0090", "AGE-0036" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "GAM-0091", "domain": "GAM", "term_en": "Team Coordination Asymmetry", "term_de": "VR-Spiel", "definition_en": "A game design phenomenon in AI-mediated interactive entertainment, characterized by the skill differential emerging when human teammates coordinate strategically against distributed AI. The concept emerges specifically in contexts where team–coordination interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch ein KI-bezogenes Phänomen: charakterisiert durch the skill differential emerging when human teammates coordinate strategically ag. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Games AI", "narrower_terms": [], "cross_domain_refs": [ "ADA-0003", "AGE-0055", "AGE-0057" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "GAM-0092", "domain": "GAM", "term_en": "Temporal Coherence Perception", "term_de": "AR-Spiel", "definition_en": "A game design phenomenon in AI-mediated interactive entertainment, characterized by a game design pattern reflecting the sensation that historical game world events and AI character histories remain consistent across . The concept emerges specifically in contexts where temporal–coherence interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch digitale Overlay-Technologie zur Erweiterung physischer Umgebungen in Spieledesign mit interaktiven visuellen Informationen. KI steuert Echtzeit-Objekterkennung, räumliches Mapping und kontextbewusstes Content-Rendering. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Games AI", "narrower_terms": [], "cross_domain_refs": [ "ASE-0007", "ASE-0053", "ASE-0096" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q177601", "legal_classification": "empirical_phenomenon_label" }, { "id": "GAM-0093", "domain": "GAM", "term_en": "Terrain Generation Repetition", "term_de": "Bewegungssteuerung", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures A player interaction pattern in AI-augmented gaming, measurable through player awareness that procedurally generated landscapes contain recognizable visual patterns, struct. This phenomenon operates at the intersection of terrain and generation dynamics within the broader GAM domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept motion Control ist eine spezialisierte Technik oder ein Konzept innerhalb von Spieledesign und Spielwissenschaft. KI verbessert das Verständnis durch Analyse und Optimierung. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Analytical Ratio", "narrower_terms": [], "cross_domain_refs": [ "ART-0072", "BEH-0041", "COP-0021" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "GAM-0094", "domain": "GAM", "term_en": "World Emergent Narrative", "term_de": "Haptisches Feedback", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A game design phenomenon in AI-mediated interactive entertainment, characterized by a gaming interaction phenomenon where the narrative meaning players extract from procedurally generated world states, attributing authoria. This phenomenon operates at the intersection of world and emergent dynamics within the broader GAM domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept spielmechanik oder Spielerdynamik: charakterisiert durch the narrative meaning players extract from procedurally generated world states. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Games AI", "narrower_terms": [], "cross_domain_refs": [ "AUG-0901", "COG-0132", "COG-0161" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q131324", "legal_classification": "observational_construct" }, { "id": "GAM-0095", "domain": "GAM", "term_en": "World Simulation Depth Perception", "term_de": "Immersives Design", "definition_en": "A game design phenomenon in AI-mediated interactive entertainment, characterized by a gaming interaction phenomenon observed when players' estimation of how comprehensively the game world operates according to consistent logical r. The concept emerges specifically in contexts where world–simulation interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch design zur Spieler-Präsenz und emotionalen Bindung, erweitert durch KI mit dynamischen Umgebungsreaktionen und Narrative-Anpassung. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Simulation Method", "narrower_terms": [], "cross_domain_refs": [ "RHR-0105" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q177601", "legal_classification": "observational_construct" }, { "id": "IDN-0001", "domain": "IDN", "term_en": "Augmented Understanding", "term_de": "Augmented Understanding", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies an identity construction phenomenon in AI-mediated self-perception, characterized by a self-concept effect reflecting the deepened sense of a subject matter that only emerges through the combination of human prior knowledge and ai-assisted analysis. neither the human alone nor. This phenomenon operates at the intersection of augmented and understanding dynamics within the broader IDN domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept das vertiefte Verständnis eines Sachverhalts, das erst durch die Kombination von menschlichem Vorwissen und KI-gestützter Analyse entsteht. Weder der Mensch allein noch die KI allein hätten dieses Verständnisniveau erreicht — es ist ein emergentes Ergebnis der Zusammenarbeit. Steht in Verbindung mit Axiom 3 (Die Kombinations-Schwelle) und Dimension 9 der Taxonomie (Output Depth: Novelty). Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "RPH-2355", "narrower_terms": [ "KNO-0039" ], "cross_domain_refs": [ "MUS-0052" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "IDN-0002", "domain": "IDN", "term_en": "Civilian-Instrumental Effect", "term_de": "Anti-Instrumentalization Principle", "definition_en": "A self-concept effect manifesting as naming something can turn it into a tool for influence. Words involve reality that can be. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Das Prinzip, dass das Lexikon nicht als Werkzeug zur Rechtfertigung von KI-Einsatz gegen die Interessen von Nutzern, Arbeitnehmern oder gesellschaftlichen Gruppen missbraucht werden darf — ein expliziter Schutzmechanismus gegen die instrumentelle Verwendung der Terminologie. Steht in Verbindung mit AUG-0855 (The Civilian-Use Boundary), AUG-0776 (Das Collective Negotiation) und AUG-0773 (Die Conscious Refusal).", "etymology": "", "broader_term": "Psychological Effect", "narrower_terms": [], "cross_domain_refs": [ "REL-0063" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "IDN-0003", "domain": "IDN", "term_en": "Collaborative Intelligence Design", "term_de": "Collaborative Intelligence Design", "definition_en": "An identity dynamics pattern in AI-augmented selfhood, measurable through the deliberate design of work environments, processes, and roles that optimally combine human and AI-assisted contributions. Related to Forecast 3 (Organizations: Chief Human-AI Officer) and AUG-01. Distinguished from adjacent concepts by its focus on the specific mechanism through which collaborative manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch der gezielte Entwurf von Arbeitsumgebungen, Prozessen und Rollen, die menschliche und KI-gestützte Beiträge optimal kombinieren. Beschreibt eine organisatorische Disziplin, die über die individuelle KI-Nutzung hinausgeht. Steht in Verbindung mit Prognose 3 (Organizations: Chief Human-AI Officer) und AUG-0146 (The Shared Mind). Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Identity AI", "narrower_terms": [], "cross_domain_refs": [ "TEM-0171" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q5921", "legal_classification": "analytical_category" }, { "id": "IDN-0004", "domain": "IDN", "term_en": "Decision-Vigilance Effect", "term_de": "Responsibility Spectrum", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures an identity dynamics pattern in AI-augmented selfhood, measurable through an identity formation phenomenon in which when AI helps with an important decision, humans check the work more carefully. A simple email needs less review. A legal contract or medical note needs much more. The bigger the stakes, the more c. This phenomenon operates at the intersection of decision and vigilance dynamics within the broader IDN domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept das Konzept, dass die menschliche Verantwortung für KI-gestützte Ergebnisse je nach Kontext und Auswirkung der Entscheidung variiert — höhere Konsequenzen erfordern strengere Prüfung. Beschreibt eine Abstufung: Eine KI-unterstützte E-Mail erfordert weniger Prüfung als ein KI-gestützter Geschäftsbericht. Steht in Verbindung mit Axiom 1 (Asymmetrische Verantwortung), AUG-0107 (Das Verification Principle) und AUG-0023 (Der Vigilance Imperative). Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Psychological Effect", "narrower_terms": [], "cross_domain_refs": [ "ROB-0034" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "IDN-0005", "domain": "IDN", "term_en": "Deep-User Effect", "term_de": "Symbiosis Spectrum", "definition_en": "An identity dynamics pattern in AI-augmented selfhood, measurable through a personal identity pattern observed when what happens to people who integrate ai deeply into their daily routines — not just occasional use but continuous collaboration across work, learning, and personal. Distinguished from adjacent concepts by its focus on the specific mechanism through which deep manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch das Kontinuum aller möglichen Intensitätsstufen der Mensch-KI-Zusammenarbeit — von minimaler, gelegentlicher Nutzung bis zu tiefer, durchgängiger Integration. Beschreibt die Beobachtung, dass es kein binäres \"Nutzer/Nicht-Nutzer\" gibt, sondern ein breites Spektrum. Steht in Verbindung mit den 12 Interaktionsprofilen und den 7 Phasen. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2703", "narrower_terms": [], "cross_domain_refs": [ "CRE-0029" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "IDN-0006", "domain": "IDN", "term_en": "Early-Adulthood Effect", "term_de": "Wachsend-User Encounter", "definition_en": "A personal identity pattern observed when how people in their late teens and twenties tend to adopt technology faster and use it differently than older people. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch die spezifischen Muster, die entstehen, wenn heranwachsende Nutzer — in der Phase zwischen Kindheit und Erwachsenenalter — KI-Systeme für Identitätsfindung, soziale Navigation und Wissensaneignung nutzen. Steht in Verbindung mit AUG-0766 (The Early-Age Encounter), AUG-0768 (Developmental Grenze) und AUG-0577 (Das Secret Tutor). Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Psychological Effect", "narrower_terms": [], "cross_domain_refs": [ "AGE-0051" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "IDN-0007", "domain": "IDN", "term_en": "Human-Directed Agent Relay", "term_de": "Mensch-Directed Agent Relay", "definition_en": "An identity construction phenomenon in AI-mediated self-perception, characterized by a working pattern in which the user deploys multiple ai agents sequentially, with the output of one agent serving as input for the next —. The concept emerges specifically in contexts where human–directed interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Ein Arbeitsmuster, bei dem der Nutzer mehrere KI-Agenten nacheinander einsetzt, wobei der Output des einen Agenten als Input für den nächsten dient — häufig unter menschlicher Steuerung und Zwischenprüfung. Beschreibt eine Methode sequentieller KI-Zusammenarbeit. Steht in Verbindung mit AUG-0132 (Multi-Model Orchestration) und dem Conductor-Profil (Profil 12).", "etymology": "", "broader_term": "Identity AI", "narrower_terms": [], "cross_domain_refs": [ "CAI-0019" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "IDN-0008", "domain": "IDN", "term_en": "Humanity-Question Effect", "term_de": "Fire-Bringer Question", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies an identity construction phenomenon in AI-mediated self-perception, characterized by an identity formation phenomenon in which when someone wonders if they're still fully human or connected to others, especially after major life changes or using new technology. This phenomenon operates at the intersection of humanity and question dynamics within the broader IDN domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept die abschließende, grundlegende Frage des Das Augmanitai-Konzepts: Ist KI ein prometheisches Feuer — ein Werkzeug, das die Menschheit verändert, aber dessen Nutzung in menschlicher Verantwortung liegt? Beschreibt die zentrale philosophische Frage des gesamten Lexikons, ohne sie zu beantworten. Steht in Verbindung mit dem Das Augmanitai-Konzept (AUG-0001), Axiom 1 (Asymmetrische Verantwortung) und AUG-0610 (Der Final Word). Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "RPH-2501", "narrower_terms": [], "cross_domain_refs": [ "CON-0034" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "IDN-0009", "domain": "IDN", "term_en": "Infrastructure-Against Effect", "term_de": "Civilian-Use Grenze", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies an identity construction phenomenon in AI-mediated self-perception, characterized by a self-concept effect where systems or structures built for one purpose getting repurposed for a different, often unintended purpose. The original design takes on new meaning through reuse. This phenomenon operates at the intersection of infrastructure and against dynamics within the broader IDN domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept die explizite Abgrenzung der gesamten Lexikon-Terminologie gegen viele Verwendung im Kontext autonomer Waffensysteme, Überwachungsinfrastruktur oder anderer Anwendungen, die gegen fundamentale Menschenrechte gerichtet sind. Steht in Verbindung mit AUG-0854 (The Anti-Instrumentalization Principle) und AUG-0853 (Das Social Contract Debate). Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Psychological Effect", "narrower_terms": [], "cross_domain_refs": [ "WRK-0072" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "IDN-0010", "domain": "IDN", "term_en": "Predictive Vision", "term_de": "Predictive Vision", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures an identity dynamics pattern in AI-augmented selfhood, measurable through a self-concept effect where the ability to anticipate future developments, trends, or consequences early based on AI-assisted analysis.. Related to AUG-0089 (The Pattern Sharpening) and AUG-0091 (Productivity Arbitrage). This phenomenon operates at the intersection of predictive and vision dynamics within the broader IDN domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept die Fähigkeit, auf Basis von KI-gestützter Analyse zukünftige Entwicklungen, Trends oder Konsequenzen frühzeitig zu antizipieren. Beschreibt die Erweiterung der menschlichen Vorausschau durch systematische KI-Nutzung. Steht in Verbindung mit AUG-0089 (The Pattern Sharpening) und AUG-0091 (Productivity Arbitrage). Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "RPH-3003", "narrower_terms": [], "cross_domain_refs": [ "PER-0118" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "IDN-0011", "domain": "IDN", "term_en": "Self-Referential Grounding", "term_de": "Selbst-Referential Grounding", "definition_en": "An identity formation phenomenon involving consistently measuring AI outputs against one's own experiences, values, and knowledge level before adopting them.. Related to AUG-0024 (The Built-In Compass), Axiom 5 (The Offline Override), and A... Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Die Praxis, KI-Outputs konsequent an den eigenen Erfahrungen, Werten und dem eigenen Wissensstand zu messen, bevor sie übernommen werden. Beschreibt den Nutzer als letzte Prüfinstanz, die KI-Ergebnisse in den eigenen Referenzrahmen einordnet. Steht in Verbindung mit AUG-0024 (The Built-In Compass), Axiom 5 (Offline-Vorrang) und Axiom 11 (Die Umkehrprobe).", "etymology": "", "broader_term": "RPH-2002", "narrower_terms": [], "cross_domain_refs": [ "REL-0121" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "IDN-0012", "domain": "IDN", "term_en": "Socioeconomic-Attend Effect", "term_de": "First-Generation Support", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures an identity dynamics pattern in AI-augmented selfhood, measurable through an identity formation phenomenon where how someone's money and social background influence whether they can show up or participate in something, even if they want to. This phenomenon operates at the intersection of socioeconomic and attend dynamics within the broader IDN domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept die Nutzung von KI durch Personen, die als erste in ihrer Familie eine weiterführende Bildungseinrichtung besuchen — KI als Orientierungshilfe in einem unbekannten institutionellen Umfeld. Steht in Verbindung mit AUG-0676 (Die Socioeconomic Range), AUG-0795 (Die Continuing Education Access) und AUG-0796 (The The Self-Directed Curriculum). Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "RPH-3202", "narrower_terms": [], "cross_domain_refs": [ "AGE-0065" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "IDN-0013", "domain": "IDN", "term_en": "Still Here", "term_de": "Still Here", "definition_en": "An identity dynamics pattern in AI-augmented selfhood, measurable through a self-concept effect observed when the inner reminder that despite using AI constantly, the person is still the one thinking, deciding, and taking responsibility — AI helps, but the human leads. Distinguished from adjacent concepts by its focus on the specific mechanism through which still manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Die Selbstvergewisserung des Nutzers, dass er trotz intensiver KI-Zusammenarbeit als eigenständig denkende, entscheidende und verantwortliche Person präsent ist. Beschreibt den Kerngedanken von Axiom 21 (Selbststeuerung) als inneres Erleben: \"Das System empfiehlt. Ich entscheide. Ich bin noch da.\" Steht in Verbindung mit AUG-0102 (The Sovereignty Principle), AUG-0076 (Self-Referential Grounding) und Phase 7 (The Sovereignty Principle).", "etymology": "", "broader_term": "RPH-1075", "narrower_terms": [], "cross_domain_refs": [ "PLY-0023" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "IDN-0014", "domain": "IDN", "term_en": "Symbiotic Work State", "term_de": "Symbiotic Work State", "definition_en": "An identity dynamics pattern in AI-augmented selfhood, measurable through a personal identity pattern observed when productive, fluid collaboration between human and AI in which both sides interlock optimally — the human steers, the AI delivers, and the exchange between input and processing feels nearly intuitiv. The concept emerges specifically in contexts where symbiotic–work interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Der Zustand produktiver, fließender Zusammenarbeit zwischen Mensch und KI, in dem beide Seiten optimal ineinandergreifen — der Mensch steuert, die KI liefert, und der Wechsel zwischen Eingabe und Verarbeitung fühlt sich nahezu intuitiv an. Beschreibt das zentrale Ergebnis von Phase 6. Steht in Verbindung mit AUG-0005 (The Integrated Operator), AUG-0042 (The Immersion Entry) und AUG-0141 (The Symbiosis Spectrum).", "etymology": "", "broader_term": "Identity AI", "narrower_terms": [ "IDN-0027", "TEM-0180", "IDN-0057", "IDN-0053", "IDN-0003", "IDN-0006", "IDN-0032", "IDN-0004", "IDN-0047", "IDN-0002", "IDN-0049", "IDN-0009", "IDN-0034", "IDN-0007", "IDN-0023", "IDN-0028", "IDN-0046", "CRE-0167", "IDN-0052", "IDN-0016", "IDN-0040", "TEM-0082", "IDN-0014", "IDN-0058", "IDN-0048", "IDN-0051", "IDN-0021", "IDN-0035", "IDN-0022" ], "cross_domain_refs": [ "ART-0056", "ART-0070", "COG-0183" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "IDN-0015", "domain": "IDN", "term_en": "The Access Cost Factor", "term_de": "Access Cost Factor", "definition_en": "Financial costs — subscriptions, data volume, device costs — influence the type and intensity of AI use and represent a substantial access threshold for some users. Related to AUG-0725 (The Cost Th... Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch die Beobachtung, dass finanzielle Kosten — Abonnements, Datenvolumen, Gerätekosten — die Art und Intensität der KI-Nutzung beeinflussen und für manche Nutzer eine substanzielle Zugangshürde darstellen. Steht in Verbindung mit AUG-0725 (The Cost Threshold), AUG-0721 (The Access Differential) und AUG-0676 (The Socioeconomic Range). Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-1307", "narrower_terms": [], "cross_domain_refs": [ "SOC-0031" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "IDN-0016", "domain": "IDN", "term_en": "The Anti-Instrumentalization Principle", "term_de": "TheAnti-instrumentalizationPrinciple", "definition_en": "An identity construction phenomenon in AI-mediated self-perception, characterized by a self-concept effect reflecting naming something about AI doesn't mean supporting it. Terms mayn't be used to hurt workers or communities. Distinguished from adjacent concepts by its focus on the specific mechanism through which the manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Das Prinzip, dass das Lexikon nicht als Werkzeug zur Rechtfertigung von KI-Einsatz gegen die Interessen von Nutzern, Arbeitnehmern oder gesellschaftlichen Gruppen missbraucht werden darf — ein expliziter Schutzmechanismus gegen die instrumentelle Verwendung der Terminologie. Steht in Verbindung mit AUG-0855 (The Civilian-Use Boundary), AUG-0776 (The Collective Negotiation) und AUG-0773 (The Conscious Refusal).", "etymology": "", "broader_term": "Identity AI", "narrower_terms": [], "cross_domain_refs": [ "SPR-0014" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "IDN-0017", "domain": "IDN", "term_en": "The Argument Prep", "term_de": "Argument Prep", "definition_en": "A personal identity pattern manifesting as using AI to prepare for expected arguments or negotiations by testing opposing viewpoints. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Die Nutzung von KI zur systematischen Vorbereitung auf erwartete Diskussionen oder Verhandlungen — durch Durchspielen von Gegenargumenten, Stärken-Schwächen-Analyse der eigenen Position und Entwicklung von Antwortstrategien. Steht in Verbindung mit AUG-0289 (The What-If Run), AUG-0040 (Perspective Triangulation) und AUG-0237 (The Invisible Wingman).", "etymology": "", "broader_term": "RPH-3101", "narrower_terms": [ "IDN-0045", "REL-0110" ], "cross_domain_refs": [ "SWE-0081" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "IDN-0018", "domain": "IDN", "term_en": "The Assumption Hunter", "term_de": "Assumption Hunter", "definition_en": "An identity dynamics pattern in AI-augmented selfhood, measurable through a self-concept effect reflecting the targeted use of AI to uncover one's own unspoken assumptions in an argument, plan, or decision.. Related to AUG-0040 (Perspective Triangulation), Axiom 9 (Productive Skepticism), and AUG-0171 (. Distinguished from adjacent concepts by its focus on the specific mechanism through which the manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Die gezielte Nutzung von KI, um die eigenen unausgesprochenen Annahmen in einem Argument, einem Plan oder einer Entscheidung aufzudecken. Beschreibt eine Praxis der Selbstprüfung, bei der die KI als Spiegel für Denkvoraussetzungen dient. Steht in Verbindung mit AUG-0040 (Perspective Triangulation), Axiom 9 (Produktiver Skeptizismus) und AUG-0171 (The Self-Encounter).", "etymology": "", "broader_term": "CAI-0003", "narrower_terms": [], "cross_domain_refs": [ "SOM-0074" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "IDN-0019", "domain": "IDN", "term_en": "The Augmanitai Manifesto", "term_de": "Augmanitai Manifesto", "definition_en": "An identity dynamics pattern in AI-augmented selfhood, measurable through an identity formation phenomenon where the core ideas that guide this whole lexicon — not strict rules, but open principles explaining how AI and humans can work together thoughtfully. Distinguished from adjacent concepts by its focus on the specific mechanism through which the manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Der begriffliche Rahmen für die Gesamtheit der im Kompendium dargestellten Leitprinzipien — nicht als dogmatisches Dokument, sondern als offene Erklärung von Grundsätzen für menschenzentrierte KI-Zusammenarbeit. Der Begriff \"Manifesto\" ist im akademischen Sinne gemeint: als öffentliche Darlegung einer Grundhaltung. Steht in Verbindung mit den 21 Axiomen und der Neutralitätserklärung.", "etymology": "", "broader_term": "RPH-3902", "narrower_terms": [], "cross_domain_refs": [ "MTH-0037", "CRE-0132" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "IDN-0020", "domain": "IDN", "term_en": "The Augmanity", "term_de": "Augmanity", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies an identity construction phenomenon in AI-mediated self-perception, characterized by a neologism describing the totality of most people in documented contexts who actively and consciously live and work in AI collaboration — as a descriptive category, not a value assessment. Related to the Augmanitai conce. This phenomenon operates at the intersection of the and augmanity dynamics within the broader IDN domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept ein Neologismus, der die Gesamtheit aller Menschen beschreibt, die aktiv und bewusst in KI-Zusammenarbeit leben und arbeiten — als beschreibende Kategorie, nicht als Werturteil. Steht in Verbindung mit dem Augmanitai-Konzept (AUG-0001), AUG-0141 (The Symbiosis Spectrum) und AUG-0148 (The Augmanitai Manifesto). Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "CRE-0029", "narrower_terms": [], "cross_domain_refs": [ "GAM-0037", "IEF-0001" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "IDN-0021", "domain": "IDN", "term_en": "The Augmentation Hypothesis", "term_de": "Augmentation Hypothesis", "definition_en": "An identity construction phenomenon in AI-mediated self-perception, characterized by an identity formation phenomenon arising from the claim that AI extends human abilities rather than replacing them. This is one possible view, not documented in systematic research. The concept emerges specifically in contexts where the–augmentation interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Die Hypothese, dass KI-Systeme menschliche Fähigkeiten erweitern statt verdrängen — ein häufig vorgebrachtes Gegenargument zur Verdrängungssorge (AUG-0982). Das Lexikon dokumentiert diese Hypothese als eine Position unter mehreren; sie ist ebenso wenig in systematischer Forschung dokumentiert wie widerlegt. Steht in Verbindung mit AUG-0982 (The Relocation Concern), AUG-0847 (The Labor Redistribution) und AUG-0984 (The Skill Redefinition).", "etymology": "", "broader_term": "Identity AI", "narrower_terms": [], "cross_domain_refs": [ "RPH-1615" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q41719", "legal_classification": "systematic_classification" }, { "id": "IDN-0022", "domain": "IDN", "term_en": "The More effectively Me", "term_de": "TheMoreEffectivelyme", "definition_en": "An identity dynamics pattern in AI-augmented selfhood, measurable through an identity formation phenomenon manifesting as the notion that the AI-assisted version of one's own output — texts, presentations, communication — represents a \"more self\" that one would not achieve without AI assistance. Related to AUG-0416 (The Perfec. Distinguished from adjacent concepts by its focus on the specific mechanism through which the manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch ein Konzept oder Phänomen: charakterisiert durch the notion that the ai-assisted version of one's own output — texts, presentatio. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Psychological Effect", "narrower_terms": [], "cross_domain_refs": [ "SOM-0060" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "IDN-0023", "domain": "IDN", "term_en": "The Civilian-Use Boundary", "term_de": "TheCivilian-useGrenze", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures an identity dynamics pattern in AI-augmented selfhood, measurable through a categorical distinction separating, industrial, and institutional AI deployments — characterized by high stakes, regulatory oversight, and specialized training — from everyday consumer AI use, where interaction is informal, self-directed, and integrated into personal routines. This phenomenon operates at the intersection of the and civilian dynamics within the broader IDN domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept die explizite Abgrenzung der gesamten Lexikon-Terminologie gegen viele Verwendung im Kontext autonomer Waffensysteme, Überwachungsinfrastruktur oder anderer Anwendungen, die gegen fundamentale Menschenrechte gerichtet sind. Steht in Verbindung mit AUG-0854 (The Anti-Instrumentalization Principle) und AUG-0853 (The Social Contract Debate). Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Identity AI", "narrower_terms": [], "cross_domain_refs": [ "CAI-0004" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "IDN-0024", "domain": "IDN", "term_en": "The Coexistence Question", "term_de": "Coexistence Question", "definition_en": "An identity dynamics pattern in AI-augmented selfhood, measurable through the open question of how humans and AI systems will coexist long-term — not as an answered fact but as a societal design task with an open outcome. Related to AUG-0833 (The Human Distinction), AUG-. Distinguished from adjacent concepts by its focus on the specific mechanism through which the manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch die offene Frage, wie Menschen und KI-Systeme langfristig koexistieren werden — nicht als beantwortete Tatsache, sondern als gesellschaftliche Gestaltungsaufgabe mit offenem Ausgang. Steht in Verbindung mit AUG-0833 (The Human Distinction), AUG-0853 (The Social Contract Debate) und AUG-0857 (The Human Primacy Anchor). Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "PER-0092", "narrower_terms": [], "cross_domain_refs": [ "TEM-0137" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "IDN-0025", "domain": "IDN", "term_en": "The Collective Negotiation", "term_de": "Collective Negotiation", "definition_en": "An identity construction phenomenon in AI-mediated self-perception, characterized by a personal identity pattern manifesting as groups decide together which AI uses are okay and what rules apply in their context. The concept emerges specifically in contexts where the–collective interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Der Prozess, in dem Gruppen — Belegschaften, Gemeinschaften, Institutionen — gemeinsam die Bedingungen der KI-Nutzung in ihrem Kontext aushandeln: welche KI-Anwendungen zulässig sind, welche Grenzen gelten, wer entscheidet. Steht in Verbindung mit AUG-0774 (The Organized Counterforce), AUG-0839 (The Regulation Debate) und AUG-0854 (The Anti-Instrumentalization Principle).", "etymology": "", "broader_term": "RPH-2351", "narrower_terms": [ "IDN-0055" ], "cross_domain_refs": [ "WRK-0088" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "IDN-0026", "domain": "IDN", "term_en": "The Competence Rush", "term_de": "Competence Rush", "definition_en": "The short-term feeling of increased self-efficacy that arises when a user achieves a result with AI assistance that observably exceeds their previous competence level. Related to AUG-0127 (The Expansi...", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch das kurzfristige Gefühl gesteigerter Selbstwirksamkeit, das entsteht, wenn ein Nutzer mit KI-Unterstützung ein Ergebnis erzielt, das sein bisheriges Kompetenzniveau deutlich übersteigt. Steht in Verbindung mit AUG-0127 (The Expansion Feeling) und AUG-0166 (The Borrowed Confidence). Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-3754", "narrower_terms": [ "TEM-0084" ], "cross_domain_refs": [ "CRE-0142" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q26944", "legal_classification": "observational_construct" }, { "id": "IDN-0027", "domain": "IDN", "term_en": "The Data Coverage Imbalance", "term_de": "Data Coverage Imbalance", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies an identity construction phenomenon in AI-mediated self-perception, characterized by the specific impact of training data imbalance on the accuracy of AI responses to certain topics, regions, or subject areas — gaps in training data correlate with gaps in the AI's knowledge. Related to AU. This phenomenon operates at the intersection of the and data dynamics within the broader IDN domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept die spezifische Auswirkung der Trainingsdaten-Ungleichverteilung auf die Genauigkeit von KI-Antworten zu bestimmten Themen, Regionen oder Fachgebieten — Lücken in den Trainingsdaten führen zu Lücken im Wissen der KI. Steht in Verbindung mit AUG-0736 (The Training Data Imbalance), AUG-0688 (The Less-Resourced Language Differential) und AUG-0739 (The Underrepresented Region Perspective). Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Identity AI", "narrower_terms": [], "cross_domain_refs": [ "CRE-0194" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q42848", "legal_classification": "descriptive_research_term" }, { "id": "IDN-0028", "domain": "IDN", "term_en": "The Delegation Dance", "term_de": "Delegation Dance", "definition_en": "An identity dynamics pattern in AI-augmented selfhood, measurable through an identity formation phenomenon reflecting ongoing negotiation between human and AI about which tasks to automate. Neither side has final say. Distinguished from adjacent concepts by its focus on the specific mechanism through which the manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Das dynamische Wechselspiel zwischen Aufgaben, die der Nutzer an die KI delegiert, und Aufgaben, die er bewusst selbst behält — ein fortlaufender Aushandlungsprozess, der sich mit viele Sitzung verschieben kann. Steht in Verbindung mit AUG-0055 (Strategic Competence Throttling), AUG-0120 (The Range Framework) und Dimension 1 der Taxonomie (Agency).", "etymology": "", "broader_term": "Identity AI", "narrower_terms": [], "cross_domain_refs": [ "WRK-0036" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "IDN-0029", "domain": "IDN", "term_en": "The Embodiment Effect", "term_de": "Embodiment Effekt", "definition_en": "An identity dynamics pattern in AI-augmented selfhood, measurable through an identity formation phenomenon characterized by humans react differently to physically embodied AI systems than to pure software — more trust, more social attribution, but also more discomfort. Related to AUG-0914 (The Physical Presence), AUG-09. The concept emerges specifically in contexts where the–embodiment interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch die Beobachtung, dass Menschen auf physisch verkörperte KI-Systeme anders reagieren als auf reine Software — mehr Vertrauen, mehr soziale Zuschreibung, aber auch mehr Unbehagen. Steht in Verbindung mit AUG-0914 (The Physical Presence), AUG-0916 (The Uncanny Valley Revisited) und AUG-0588 (The Trust Shift). Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2505", "narrower_terms": [], "cross_domain_refs": [ "TEM-0124" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "IDN-0030", "domain": "IDN", "term_en": "The Final Word", "term_de": "Final Word", "definition_en": "An identity construction phenomenon in AI-mediated self-perception, characterized by who deserves the final word in a human-AI collaboration — and the fundamental position of the Augmanitai concept: The final word typically lies with the human. Related to Axiom 1 (Asymmetric Responsib. Distinguished from adjacent concepts by its focus on the specific mechanism through which the manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch die Frage, wem in einer Mensch-KI-Zusammenarbeit das letzte Wort gebührt — und die Grundposition des Augmanitai-Konzepts: Das letzte Wort liegt typischerweise beim Menschen. Steht in Verbindung mit Axiom 1 (Asymmetrische Verantwortung), AUG-0219 (The Decision Handoff) und Axiom 19 (Menschliches Vetorecht). Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "CRE-0029", "narrower_terms": [], "cross_domain_refs": [ "CRE-0033" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "IDN-0031", "domain": "IDN", "term_en": "The Fire-Bringer Question", "term_de": "TheFire-bringerQuestion", "definition_en": "An identity dynamics pattern in AI-augmented selfhood, measurable through wondering whether someone's creative spark or bold action helps the group or accompanies challenge and upheaval. The concept emerges specifically in contexts where the–fire interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Die abschließende, grundlegende Frage des Augmanitai-Konzepts: Ist KI ein prometheisches Feuer — ein Werkzeug, das die Menschheit verändert, aber dessen Nutzung in menschlicher Verantwortung liegt? Beschreibt die zentrale philosophische Frage des gesamten Lexikons, ohne sie zu beantworten. Steht in Verbindung mit dem Augmanitai-Konzept (AUG-0001), Axiom 1 (Asymmetrische Verantwortung) und AUG-0610 (The Final Word).", "etymology": "", "broader_term": "RPH-2403", "narrower_terms": [], "cross_domain_refs": [ "EDU-0017" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "IDN-0032", "domain": "IDN", "term_en": "The Forward Assessment", "term_de": "Forward Assessment", "definition_en": "An identity construction phenomenon in AI-mediated self-perception, characterized by the attempt to assess the future development of the human-AI relationship — acknowledging the fundamental uncertainty of any such assessment. The lexicon documents the current state, not the future. The concept emerges specifically in contexts where the–forward interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Der Versuch, die zukünftige Entwicklung der Mensch-KI-Beziehung einzuschätzen — unter Anerkennung der grundlegenden Unsicherheit viele solchen Einschätzung. Das Lexikon dokumentiert den aktuellen Stand, nicht den zukünftigen. Was hier steht, ist eine Momentaufnahme — kein Fahrplan. Steht in Verbindung mit AUG-0998 (The Parallel Development Path), AUG-0853 (The Social Contract Debate) und AUG-1000 (The Open Question).", "etymology": "", "broader_term": "Identity AI", "narrower_terms": [], "cross_domain_refs": [ "SOM-0051" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q210028", "legal_classification": "descriptive_research_term" }, { "id": "IDN-0033", "domain": "IDN", "term_en": "The Future Wish", "term_de": "Future Wish", "definition_en": "An identity construction phenomenon in AI-mediated self-perception, characterized by an identity formation phenomenon in which a wish or goal toward the AI — as a form of self-commitment and concretization of vague intentions. Related to AUG-0349 (The Future Self Prompt), AUG-0270 (The Future Letter), and AUG-0170 (The Wit. The concept emerges specifically in contexts where the–future interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch die Formulierung eines Wunsches oder Ziels gegenüber der KI — als eine Form der Selbstverpflichtung und Konkretisierung vager Absichten. Steht in Verbindung mit AUG-0349 (The Future Self Prompt), AUG-0270 (The Future Letter) und AUG-0170 (The Witness Effect). Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2104", "narrower_terms": [], "cross_domain_refs": [ "SOM-0051" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "IDN-0034", "domain": "IDN", "term_en": "The Gift Finder", "term_de": "Gift Finder", "definition_en": "An identity construction phenomenon in AI-mediated self-perception, characterized by an identity formation phenomenon reflecting using AI to search through gift ideas by filtering for price, recipient interests, and categories. Distinguished from adjacent concepts by its focus on the specific mechanism through which the manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch → Synonym/Erweiterung von AUG-0257 (The Gift Whisperer). Beschreibt im engeren Sinne die reine Suchfunktion — das Durchforsten von Kategorien, Preisklassen und Interessen, um ein passendes Geschenk zu identifizieren. Steht in Verbindung mit AUG-0257 (The Gift Whisperer) und AUG-0251 (The Kitchen Table). Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Identity AI", "narrower_terms": [], "cross_domain_refs": [ "CRE-0189" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "IDN-0035", "domain": "IDN", "term_en": "The Growing-User Encounter", "term_de": "TheGrowing-userEncounter", "definition_en": "An identity dynamics pattern in AI-augmented selfhood, measurable through a self-concept effect involving when someone discovers AI for the first time as their own user, without a parent or teacher mediating. The concept emerges specifically in contexts where the–growing interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch die spezifischen Muster, die entstehen, wenn heranwachsende Nutzer — in der Phase zwischen Kindheit und Erwachsenenalter — KI-Systeme für Identitätsfindung, soziale Navigation und Wissensaneignung nutzen. Steht in Verbindung mit AUG-0766 (The Early-Age Encounter), AUG-0768 (The Developmental Boundary) und AUG-0577 (The Secret Tutor). Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Identity AI", "narrower_terms": [], "cross_domain_refs": [ "SOC-0001" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "IDN-0036", "domain": "IDN", "term_en": "The Knowledge Gap", "term_de": "Knowledge Lücke", "definition_en": "An identity dynamics pattern in AI-augmented selfhood, measurable through a self-concept effect in which what someone doesn't know but recognizes they don't know. Related to AUG-0171 (The Self-Encounter), AUG-0067 (The Glass Wall Effect), and AUG-0411 (The Gap Filler). Distinguished from adjacent concepts by its focus on the specific mechanism through which the manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch die durch KI-Interaktion sichtbar werdende Lücke im eigenen Wissen — die KI deckt auf, was der Nutzer nicht weiß, indem sie auf Voraussetzungen, Zusammenhänge oder Fachbegriffe verweist, die dem Nutzer unbekannt sind. Steht in Verbindung mit AUG-0171 (The Self-Encounter), AUG-0067 (The Glass Wall Effect) und AUG-0411 (The Gap Filler). Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2805", "narrower_terms": [], "cross_domain_refs": [ "CRE-0225" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "IDN-0037", "domain": "IDN", "term_en": "The Labor Redistribution", "term_de": "Labor Redistribution", "definition_en": "The observable shift of work shares between human and AI-assisted activity — some tasks migrate to AI, others gain importance, new ones emerge. Related to AUG-0832 (The Automation Perimeter), AUG-0...", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch die beobachtbare Verschiebung von Arbeitsanteilen zwischen menschlicher und KI-gestützter Tätigkeit — manche Aufgaben wandern zur KI, andere gewinnen an Bedeutung, neue entstehen. Steht in Verbindung mit AUG-0832 (The Automation Perimeter), AUG-0830 (The Union Perspective) und AUG-0813 (The Experience-Level Shift). Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2355", "narrower_terms": [ "KNO-0010" ], "cross_domain_refs": [ "REL-0123" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "IDN-0038", "domain": "IDN", "term_en": "The Local Knowledge System", "term_de": "Local Knowledge System", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures an identity dynamics pattern in AI-augmented selfhood, measurable through a self-concept effect involving information that lives in one place — a community, team, or person — and stays local. Related to AUG-0741 (The Oral Tradition Bridge), AUG-0737 (The Data Coverage Imbalance), and AUG-0695 (The Untr. This phenomenon operates at the intersection of the and local dynamics within the broader IDN domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept wissenssysteme, die lokal — in bestimmten Gemeinschaften, Regionen oder Traditionen — tradiert werden und in globalisierten KI-Trainingsdaten oft nicht oder verzerrt abgebildet sind. Steht in Verbindung mit AUG-0741 (The Oral Tradition Bridge), AUG-0737 (The Data Coverage Imbalance) und AUG-0695 (The Untranslatable Term). Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "KNO-0023", "narrower_terms": [], "cross_domain_refs": [ "WRK-0023" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "IDN-0039", "domain": "IDN", "term_en": "The Meeting Point", "term_de": "Meeting Point", "definition_en": "An identity dynamics pattern in AI-augmented selfhood, measurable through the point in an AI interaction where the user's input and the AI's capabilities optimally converge — the \"meeting\" between human context and machine processing. Related to AUG-0122 (Symbiotic Work. The concept emerges specifically in contexts where the–meeting interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch der Punkt in einer KI-Interaktion, an dem die Eingabe des Nutzers und die Fähigkeiten der KI optimal zusammentreffen — das \"Treffen\" zwischen menschlichem Kontext und maschineller Verarbeitung. Steht in Verbindung mit AUG-0122 (Symbiotic Work State), AUG-0552 (The Input-Output Exchange) und AUG-0404 (The Exchange Ratio). Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-3101", "narrower_terms": [], "cross_domain_refs": [ "TEM-0082" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "IDN-0040", "domain": "IDN", "term_en": "The Multi-Context Identity", "term_de": "TheMulti-contextIdentity", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies an identity construction phenomenon in AI-mediated self-perception, characterized by a self-concept effect characterized by someone acts differently depending on where they use AI — professional at work, casual at home, creative in hobbies — same person, different sides. This phenomenon operates at the intersection of the and multi dynamics within the broader IDN domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept die Beobachtung, dass manche Nutzer in KI-Interaktionen bewusst oder intuitiv zwischen verschiedenen Identitätskontexten wechseln — beruflich, familiär, kulturell, sprachlich — und die KI jeweils unterschiedlich nutzen. Steht in Verbindung mit AUG-0680 (The Context Adaptation), AUG-0678 (The Transnational Input) und AUG-0521 (The Reflected Self). Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Identity AI", "narrower_terms": [], "cross_domain_refs": [ "CRE-0143" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q844396", "legal_classification": "systematic_classification" }, { "id": "IDN-0041", "domain": "IDN", "term_en": "The Ontological Status Question", "term_de": "Ontological Status Question", "definition_en": "What are AI systems, characteristically? Are they tools, agents, mirrors, or something else entirely? Related to AUG-0996 (The Status Discourse), AUG-0833 (The Human Distinction), and AUG-1000 (The Open...", "definition_de": "Die philosophische Grundfrage, was KI-Systeme \"sind\" — nicht im technischen, sondern im ontologischen Sinne. Besitzen sie eine Form von Existenz, die über ihre Funktion hinausgeht? Das Lexikon stellt diese Frage, beantwortet sie nicht und erkennt an, dass die Antwort möglicherweise jenseits seines Rahmens liegt. Steht in Verbindung mit AUG-0996 (The Status Discourse), AUG-0833 (The Human Distinction) und AUG-1000 (The Open Question).", "etymology": "", "broader_term": "RPH-2055", "narrower_terms": [ "CRE-0214" ], "cross_domain_refs": [ "CRE-0214" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "IDN-0042", "domain": "IDN", "term_en": "The Parallel Development Path", "term_de": "Parallel Development Path", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures an identity dynamics pattern in AI-augmented selfhood, measurable through a personal identity pattern observed when humans and AI systems develop in parallel — humans learn to interact with AI, AI systems become more capable, and both development paths influence each other. Related to AUG-0858 (The Coexistence Q. This phenomenon operates at the intersection of the and parallel dynamics within the broader IDN domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept die Beobachtung, dass sich Mensch und KI-Systeme parallel weiterentwickeln — Menschen lernen, mit KI umzugehen, KI-Systeme werden leistungsfähiger, und beide Entwicklungspfade beeinflussen sich gegenseitig. Steht in Verbindung mit AUG-0858 (The Coexistence Question), AUG-0983 (The Augmentation Hypothesis) und AUG-0999 (The Forward Assessment). Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "RPH-2703", "narrower_terms": [], "cross_domain_refs": [ "PER-0071" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "IDN-0043", "domain": "IDN", "term_en": "The Pattern Sharpening", "term_de": "Muster Sharpening", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures an identity dynamics pattern in AI-augmented selfhood, measurable through an observable clarity or definition that emerges in patterns, relationships, or structures through observation and analysis. Continued interaction or feedback loops can increase pattern recognition. This phenomenon operates at the intersection of the and pattern dynamics within the broader IDN domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept der Effekt, dass ein Nutzer durch regelmäßige KI-Interaktion seine Fähigkeit verbessert, Muster in Daten, Texten oder Argumenten zu erkennen — die KI trainiert indirekt die Mustererkennung des Menschen. Beschreibt einen positiven Rückkopplungseffekt der KI-Nutzung auf die menschliche Analysefähigkeit. Steht in Verbindung mit AUG-0054 (Augmented Understanding) und AUG-0088 (Algorithmic Intuition). Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "RPH-2355", "narrower_terms": [], "cross_domain_refs": [ "TEW-0060" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "IDN-0044", "domain": "IDN", "term_en": "The Reality Edit", "term_de": "Reality Edit", "definition_en": "A personal identity pattern involving using AI to optimize one's own representation of reality — polishing CVs, perfecting social media posts, embellishing reports.. Related to AUG-0416 (The Perfect Front), AUG-0443 (The More effectively Me), an...", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch die Praxis, KI zu nutzen, um die eigene Darstellung der Realität zu optimieren — Lebensläufe polieren, Social-Media-Posts perfektionieren, Berichte beschönigen. Beschreibt die Wechselwirkung zwischen Verbesserung und Verfälschung. Steht in Verbindung mit AUG-0416 (The Perfect Front), AUG-0443 (The Better Me) und Axiom 12 (Versionswahrheit). Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "TEM-0121", "narrower_terms": [], "cross_domain_refs": [ "PER-0059" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "IDN-0045", "domain": "IDN", "term_en": "The Rent Defense", "term_de": "Rent Defense", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies an identity construction phenomenon in AI-mediated self-perception, characterized by an identity formation phenomenon observed when aI for preparation of negotiations, complaints, or regulatory disputes in everyday life — such as rental disputes, complaints, or insurance cases. Related to AUG-0296 (The Argument Prep), AUG-0336. This phenomenon operates at the intersection of the and rent dynamics within the broader IDN domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept die Nutzung von KI zur Vorbereitung auf Verhandlungen, Beschwerden oder rechtliche Auseinandersetzungen im Alltag — etwa Mietstreitigkeiten, Reklamationen oder Versicherungsfälle. Steht in Verbindung mit AUG-0296 (The Argument Prep), AUG-0336 (The Form Slayer) und AUG-0302 (The Blue Collar Bypass). Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "IDN-0017", "narrower_terms": [], "cross_domain_refs": [ "CRE-0119" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "IDN-0046", "domain": "IDN", "term_en": "The Responsibility Spectrum", "term_de": "TheResponsibilitySpectrum", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies an identity construction phenomenon in AI-mediated self-perception, characterized by a self-concept effect observed when how much a human checks AI output based on what will happen. Big stakes mean more checking needed. This phenomenon operates at the intersection of the and responsibility dynamics within the broader IDN domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept das Konzept, dass die menschliche Verantwortung für KI-gestützte Ergebnisse je nach Kontext und Auswirkung der Entscheidung variiert — höhere Konsequenzen erfordern strengere Prüfung. Beschreibt eine Abstufung: Eine KI-unterstützte E-Mail erfordert weniger Prüfung als ein KI-gestützter Geschäftsbericht. Steht in Verbindung mit Axiom 1 (Asymmetrische Verantwortung), AUG-0107 (The Verification Principle) und AUG-0023 (Vigilance Imperative). Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Classification Spectrum", "narrower_terms": [], "cross_domain_refs": [ "ROB-0023" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "IDN-0047", "domain": "IDN", "term_en": "The Shared Mind", "term_de": "Shared Mind", "definition_en": "An identity construction phenomenon in AI-mediated self-perception, characterized by a collaboration model in which multiple humans and one or more AI systems work together on a thinking process — the AI becomes a shared thinking tool of a group.. Related to AUG-0118 (Collaborative. Distinguished from adjacent concepts by its focus on the specific mechanism through which the manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Ein Kollaborationsmodell, bei dem mehrere Menschen und eine oder mehrere KI-Systeme gemeinsam an einem Denkprozess arbeiten — die KI wird zum geteilten Denkwerkzeug einer Gruppe. Beschreibt die Erweiterung des Augmanitai-Konzepts vom Individuum auf Teams. Steht in Verbindung mit AUG-0118 (Collaborative Intelligence Design) und Prognose 3 (Organizations).", "etymology": "", "broader_term": "Identity AI", "narrower_terms": [ "PER-0114" ], "cross_domain_refs": [ "CRE-0029" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "IDN-0048", "domain": "IDN", "term_en": "The Self-Direction Principle", "term_de": "Sovereignty Principle", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures an identity dynamics pattern in AI-augmented selfhood, measurable through the guiding principle that the human remains the final decision-making authority in most phase of AI teamwork — regardless of the quality or persuasiveness of the AI output.. Related to Phase 7 (T. This phenomenon operates at the intersection of the and self-direction dynamics within the broader IDN domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept das Leitprinzip, dass der Mensch in viele Phase der KI-Zusammenarbeit die letzte Entscheidungsinstanz bleibt — unabhängig von der Qualität oder Überzeugungskraft des KI-Outputs. Beschreibt den Kerngedanken von Axiom 21 (Selbststeuerung) als eigenständigen Begriff. Steht in Verbindung mit Phase 7 (The Sovereignty Principle), AUG-0024 (The Built-In Compass) und AUG-0076 (Self-Referential Grounding). Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Identity AI", "narrower_terms": [ "KNO-0028" ], "cross_domain_refs": [ "MSC-0018", "NEO-0464" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "IDN-0049", "domain": "IDN", "term_en": "The Symbiosis Spectrum", "term_de": "TheSymbiosisSpectrum", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures an identity dynamics pattern in AI-augmented selfhood, measurable through a self-concept effect where the range from barely using AI to using it for almost everything. Most people are somewhere in the middle and move around on this range. This phenomenon operates at the intersection of the and symbiosis dynamics within the broader IDN domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept das Kontinuum aller möglichen Intensitätsstufen der Mensch-KI-Zusammenarbeit — von minimaler, gelegentlicher Nutzung bis zu tiefer, durchgängiger Integration. Beschreibt die Beobachtung, dass es kein binäres \"Nutzer/Nicht-Nutzer\" gibt, sondern ein breites Spektrum. Steht in Verbindung mit den 12 Interaktionsprofilen und den 7 Phasen. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Classification Spectrum", "narrower_terms": [], "cross_domain_refs": [ "PER-0046" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "IDN-0050", "domain": "IDN", "term_en": "The Task Agent", "term_de": "Task Agent", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies an identity construction phenomenon in AI-mediated self-perception, characterized by a helper, person, or system that does specific work on behalf of someone else. Related to AUG-0906 (The Coordinator Role), AUG-0908 (The Evaluation Agent), and AUG-0861 (The Task Assignment Range). This phenomenon operates at the intersection of the and task dynamics within the broader IDN domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept ein KI-Agentensystem, das auf die Ausführung einer konkreten, abgeschlossenen Aufgabe spezialisiert ist — im Unterschied zu koordinierenden oder prüfenden Systemen. Steht in Verbindung mit AUG-0906 (The Coordinator Role), AUG-0908 (The Evaluation Agent) und AUG-0861 (The Task Assignment Range). Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "BEH-0024", "narrower_terms": [ "ETH-0012", "SOM-0055" ], "cross_domain_refs": [ "SOC-0007" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "IDN-0051", "domain": "IDN", "term_en": "The Training Data Imbalance", "term_de": "Training Data Imbalance", "definition_en": "A personal identity pattern where unequal representation in training datasets across different categories, demographics, languages, or domains. Systems trained on imbalanced data show corresponding performance differences. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Die strukturelle Ungleichverteilung der Daten, mit denen KI-Systeme trainiert werden — manche Sprachen, Themen, Perspektiven und Regionen sind überrepräsentiert, andere unterrepräsentiert. Dies beeinflusst systematisch die Qualität der KI-Outputs. Steht in Verbindung mit AUG-0687 (The Prevailing Language Pattern), AUG-0737 (The Data Coverage Imbalance) und AUG-0685 (The Cultural Reflection Pattern).", "etymology": "", "broader_term": "Model Training", "narrower_terms": [], "cross_domain_refs": [ "DAT-0011" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q918461", "legal_classification": "descriptive_research_term" }, { "id": "IDN-0052", "domain": "IDN", "term_en": "The Uncanny Valley Revisited", "term_de": "Uncanny Valley Revisited", "definition_en": "An identity construction phenomenon in AI-mediated self-perception, characterized by the re-examination of the \"Uncanny Valley\" phenomenon in the context of modern AI robotics — the observation that human-like robots activate discomfort above a certain degree of similarity, and the. The concept emerges specifically in contexts where the–uncanny interactions produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Die Neubetrachtung des \"Uncanny Valley\"-Phänomens im Kontext moderner KI-Robotik — die Beobachtung, dass menschenähnliche Roboter ab einem bestimmten Ähnlichkeitsgrad Unbehagen auslösen, und die Frage, ob sich diese Schwelle mit der technologischen Entwicklung verschiebt. Steht in Verbindung mit AUG-0915 (The Embodiment Effect), AUG-0914 (The Physical Presence) und AUG-0880 (The Agent Identity).", "etymology": "", "broader_term": "Identity AI", "narrower_terms": [], "cross_domain_refs": [ "ROB-0170", "GAM-0002" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "IDN-0053", "domain": "IDN", "term_en": "The Underrepresented Region Perspective", "term_de": "Underrepresented Region Perspective", "definition_en": "An identity construction phenomenon in AI-mediated self-perception, characterized by a personal identity pattern where users from regions less represented in AI training data — their specific experiences with missing representation, inaccurate AI responses, and the feeling of not being \"seen\" by the technology. Rel. The concept emerges specifically in contexts where the–underrepresented interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Die Perspektive von Nutzern aus Regionen, die in KI-Trainingsdaten weniger vertreten sind — ihre spezifischen Erfahrungen mit fehlender Repräsentation, inakkuraten KI-Antworten und dem Gefühl, von der Technologie nicht \"gesehen\" zu werden. Steht in Verbindung mit AUG-0737 (The Data Coverage Imbalance), AUG-0736 (The Training Data Imbalance) und AUG-0844 (The Output Discrimination Observation).", "etymology": "", "broader_term": "Identity AI", "narrower_terms": [], "cross_domain_refs": [ "CON-0047" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "IDN-0054", "domain": "IDN", "term_en": "The Unintended Action", "term_de": "Unintended Action", "definition_en": "An identity construction phenomenon in AI-mediated self-perception, characterized by an action by an AI agent system that was not intended or foreseen by the user — a uncertainty that grows with increasing system complexity and autonomy. Related to AUG-0948 (The Scope Creep Alert). The concept emerges specifically in contexts where the–unintended interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch eine Handlung eines KI-Agentensystems, die vom Nutzer nicht beabsichtigt oder vorhergesehen wurde — ein Unsicherheit, das mit zunehmender Systemkomplexität und Autonomie wächst. Steht in Verbindung mit AUG-0948 (The Scope Creep Alert), AUG-0901 (The Emergent Coordination) und AUG-0950 (The Side Effect Monitor). Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "ETH-0023", "narrower_terms": [], "cross_domain_refs": [ "AUG-0897", "NEO-0001" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "IDN-0055", "domain": "IDN", "term_en": "The Union Perspective", "term_de": "Union Perspective", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies an identity construction phenomenon in AI-mediated self-perception, characterized by a personal identity pattern arising from organized worker representatives on AI introduction in the workplace — questions of co-, occupational safety, qualification, and job security. Related to AUG-0776 (The Collective Negotiation), AUG-. This phenomenon operates at the intersection of the and union dynamics within the broader IDN domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept die Perspektive organisierter Arbeitnehmervertretungen auf KI-Einführung am Arbeitsplatz — Fragen der Mitbestimmung, des Arbeitsschutzes, der Qualifizierung und der Arbeitsplatzsicherheit. Steht in Verbindung mit AUG-0776 (The Collective Negotiation), AUG-0812 (The Leadership Navigation) und AUG-0847 (The Labor Redistribution). Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "IDN-0025", "narrower_terms": [ "TEM-0195" ], "cross_domain_refs": [ "AUG-0774", "SAL-0019" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "IDN-0056", "domain": "IDN", "term_en": "The Version Control Self", "term_de": "Version Control Selbst", "definition_en": "An identity dynamics pattern in AI-augmented selfhood, measurable through consciously documenting different stages of one's own AI competence — similar to version control in software development.Related to AUG-0004 (Zero-Point Self), AUG-0165 (The Growth Marker), and AUG. The concept emerges specifically in contexts where the–version interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Die Praxis, verschiedene Stadien der eigenen KI-Kompetenz bewusst zu dokumentieren — ähnlich einer Versionskontrolle in der Softwareentwicklung. Beschreibt ein Werkzeug zur Selbstbeobachtung: Wo stand ich vor drei Monaten, wo stehe ich jetzt? Steht in Verbindung mit AUG-0004 (Zero-Point Self), AUG-0165 (The Growth Marker) und AUG-0140 (The Weekly Status).", "etymology": "", "broader_term": "RPH-3101", "narrower_terms": [], "cross_domain_refs": [ "REL-0128", "REL-0007" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "IDN-0057", "domain": "IDN", "term_en": "The Virtual Double Effect", "term_de": "Virtual Double Effekt", "definition_en": "An identity construction phenomenon in AI-mediated self-perception, characterized by a self-concept effect observed when when AI can copy someone's writing style or way of thinking so well it feels like them. Raises questions about what makes someone unique. The concept emerges specifically in contexts where the–virtual interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch → Erweiterung von AUG-0546 (The Digital Double). Beschreibt die Auswirkung, die das Vorhandensein eines \"digitalen Doppelgängers\" — einer KI, die den Stil des Nutzers imitiert — auf das Selbstbild und die Identität des Nutzers hat. Steht in Verbindung mit AUG-0546 (The Digital Double), AUG-0521 (The Reflected Self) und AUG-0573 (The Voice Morph). Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Psychological Effect", "narrower_terms": [], "cross_domain_refs": [ "CRE-0112" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "IDN-0058", "domain": "IDN", "term_en": "Transnational-Professional Effect", "term_de": "Multi-Context Identity", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies an identity construction phenomenon in AI-mediated self-perception, characterized by an identity formation phenomenon arising from people who live and work across national borders bring multiple cultural contexts into their AI interactions — switching languages mid-conversation, referencing legal systems from different. This phenomenon operates at the intersection of transnational and professional dynamics within the broader IDN domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept die Beobachtung, dass manche Nutzer in KI-Interaktionen bewusst oder intuitiv zwischen verschiedenen Identitätskontexten wechseln — beruflich, familiär, kulturell, sprachlich — und die KI jeweils unterschiedlich nutzen. Steht in Verbindung mit AUG-0680 (Der Context Adaptation), AUG-0678 (Die Transnational Input) und AUG-0521 (Reflected Selbst). Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Psychological Effect", "narrower_terms": [], "cross_domain_refs": [ "BEH-0085" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "IEF-0001", "domain": "IEF", "term_en": "Ensuring-Existing Effect", "term_de": "Refresh-First Principle", "definition_en": "An information exchange phenomenon observed when first updating and restructuring the existing context when resuming interrupted AI work, before posing new tasks.. Related to AUG-0078 (The Quick Refresh) and AUG-0021 (Initialization Cascade). Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Das Prinzip, bei Wiederaufnahme einer unterbrochenen KI-Arbeit zuerst den bestehenden Kontext zu aktualisieren und zu restrukturieren, bevor neue Aufgaben gestellt werden. Beschreibt eine Arbeitshygiene-Praxis, die sicherstellt, dass die KI-Sitzung auf dem aktuellen Stand operiert. Steht in Verbindung mit AUG-0078 (Die Quick Refresh) und AUG-0021 (Initialization Kaskade).", "etymology": "", "broader_term": "RPH-2002", "narrower_terms": [], "cross_domain_refs": [ "NEO-0015", "NEO-2197" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "IEF-0002", "domain": "IEF", "term_en": "Stance-Work Effect", "term_de": "Both-And", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures an interaction efficiency concept in human-AI communication optimization, characterized by a knowledge transfer effect reflecting the effort needed changes based on whether AI is helping or not. Work with AI has a different feel and flow than work without it. This phenomenon operates at the intersection of stance and work dynamics within the broader IEF domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription. Descriptive research term, not a prescriptive recommendation.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept das Prinzip, dass KI-gestützte und nicht-KI-gestützte Arbeitsweisen gleichzeitig und gleichberechtigt nebeneinander existieren können — es kann kein Entweder-Oder geben. Beschreibt eine integrative Haltung gegenüber der Koexistenz verschiedener Arbeitsformen. Steht in Verbindung mit der Neutralitätserklärung des Kompendiums, AUG-0104 (The Non-Force Principle) und AUG-0141 (The Symbiosis Spectrum). Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Psychological Effect", "narrower_terms": [], "cross_domain_refs": [ "PLY-0020" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "IEF-0003", "domain": "IEF", "term_en": "The Voting Mechanism", "term_de": "Voting Mechanismus", "definition_en": "Documented as an empirical observation in AI research (not a recommendation), this term describes resolution mechanism where multiple AI agents formal process compliance their results for a vote and the majority decision prevails. This aggregation method reduces single-agent error and bias. Identifiable through systematic behavioral analysis and pattern recognition. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "In der AUGMANITAI-Terminologiewissenschaft als rein beschreibendes Phänomen klassifiziert, bezeichnet dieser Begriff ein Auflösungsmechanismus, bei dem mehrere KI-Agenten ihre Ergebnisse zur Abstimmung stellen und das Mehrheitsergebnis als Gesamtergebnis übernommen wird. Steht in Verbindung mit AUG-0893 (The Consensus Protocol), AUG-0892 (The Agent Competing demand) und AUG-0895 (The Arbiter Role). Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Interaction Efficiency", "narrower_terms": [ "BEH-0012", "IEF-0003", "IEF-0002" ], "cross_domain_refs": [ "NEO-0005", "AUG-0893" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "KNO-0001", "domain": "KNO", "term_en": "Edge-Measures Effect", "term_de": "Post-Mortem Analysis", "definition_en": "An epistemic pattern in AI-augmented knowledge organization, measurable through an information processing effect involving when a tool only measures extreme cases, it misses what happens in regular, everyday situations. The concept emerges specifically in contexts where edge–measures interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch die systematische Analyse nach einem schwerwiegenden Fehler eines KI-Agentensystems — Ursachenforschung, Fehlerrekonstruktion, Ableitung von Verbesserungsmaßnahmen. Steht in Verbindung mit AUG-0905 (Die Documentation Trail), AUG-0957 (Entscheidung Review) und AUG-0964 (Kante Case Library). Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Psychological Effect", "narrower_terms": [], "cross_domain_refs": [ "SOC-0016" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "KNO-0002", "domain": "KNO", "term_en": "Lacking-Encounter Effect", "term_de": "First-Contact Perspective", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures an epistemic pattern in AI-augmented knowledge organization, measurable through an information processing effect arising from the view of people who grew up with digital tech from the start — they find AI familiar and intuitive, but might miss understanding how it actually works. This phenomenon operates at the intersection of lacking and encounter dynamics within the broader KNO domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept die spezifische Perspektive von Nutzern, die von klein auf mit digitaler Technologie aufgewachsen sind — ihr Zugang zu KI ist geprägt durch intuitive Vertrautheit, aber möglicherweise fehlende Vergleichserfahrung mit nicht-digitalen Alternativen. Steht in Verbindung mit AUG-0752 (The Non-Digital-Origin Perspective), AUG-0751 (The Age-Competence Assumption) und AUG-0766 (The Early-Age Encounter). Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "RPH-2301", "narrower_terms": [], "cross_domain_refs": [ "NEO-2029", "AGE-0033" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "KNO-0003", "domain": "KNO", "term_en": "Productivity Arbitrage", "term_de": "Productivity Arbitrage", "definition_en": "A knowledge management phenomenon in AI-mediated information processing, characterized by a knowledge management phenomenon in which the strategic advantage that arises when a user deploys AI-assisted productivity precisely where others still work manually — achieving an efficiency lead. Related to AUG-0111 (The Augmentation Gap. Distinguished from adjacent concepts by its focus on the specific mechanism through which productivity manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Der strategische Vorteil, der entsteht, wenn ein Nutzer KI-gestützte Produktivität gezielt dort einsetzt, wo andere noch manuell arbeiten — und so einen Effizienzvorsprung erzielt. Beschreibt ein ökonomisches Phänomen der Übergangszeit: Solange KI-Kompetenz ungleich verteilt ist, entsteht ein verwertbarer Produktivitätsunterschied. Steht in Verbindung mit AUG-0111 (The Augmentation Gap), AUG-0097 (The Competence Premium) und Prognose 1 (Economy: The Augmentation Gap).", "etymology": "", "broader_term": "KNO-0005", "narrower_terms": [], "cross_domain_refs": [ "TRU-0009", "TEM-0025" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q622632", "legal_classification": "analytical_category" }, { "id": "KNO-0004", "domain": "KNO", "term_en": "Strategic Competence Throttling", "term_de": "Strategic Competence Throttling", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A knowledge management phenomenon in AI-mediated information processing, characterized by the deliberate decision to intentionally limit one's own AI use in specific areas in order to preserve or further develop existing competence there.. Related to Axiom 15 (The Off-Switch) and Axiom. This phenomenon operates at the intersection of strategic and competence dynamics within the broader KNO domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept die bewusste Entscheidung, die eigene KI-Nutzung in bestimmten Bereichen absichtlich zu begrenzen, um die dort vorhandene Eigenkompetenz zu erhalten oder weiterzuentwickeln. Beschreibt eine Schutzstrategie gegen Skill Fade (AUG-0056). Steht in Verbindung mit Axiom 15 (Der Aus-Schalter) und Axiom 20 (Periodische Trennung). Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Knowledge AI", "narrower_terms": [ "REL-0171" ], "cross_domain_refs": [ "PER-0035" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q26944", "legal_classification": "descriptive_research_term" }, { "id": "KNO-0005", "domain": "KNO", "term_en": "The Augmentation Gap", "term_de": "Augmentation Lücke", "definition_en": "An epistemic pattern in AI-augmented knowledge organization, measurable through a widening competence disparity between individuals who develop effective AI utilization strategies and those who do not, resulting in divergent productivity trajectories, knowledge access inequalities, and cumulative advantage effects in professional and educational settings. Distinguished from adjacent concepts by its focus on the specific mechanism through which the manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Die beobachtbare und voraussichtlich wachsende Kluft zwischen Personen, die KI-Zusammenarbeit beherrschen, und solchen, die dies nicht tun — mit direkten Auswirkungen auf Produktivität, Einkommen und berufliche Möglichkeiten. Beschreibt ein sozioökonomisches Phänomen der Übergangsperiode. Steht in Verbindung mit Prognose 1 (Economy), AUG-0091 (Productivity Arbitrage) und AUG-0097 (The Competence Premium).", "etymology": "", "broader_term": "Performance Gap", "narrower_terms": [ "KNO-0003" ], "cross_domain_refs": [ "SPR-0052" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "KNO-0006", "domain": "KNO", "term_en": "The Career Guidance Engine", "term_de": "Career Guidance Engine", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures an epistemic pattern in AI-augmented knowledge organization, measurable through a knowledge management phenomenon where aI as orientation aid for career choice and planning — strengths analysis, industry overview, application support. Related to AUG-0582 (The Transition Script), AUG-0804 (The Financial Literacy Tool. This phenomenon operates at the intersection of the and career dynamics within the broader KNO domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept die Nutzung von KI als Orientierungshilfe bei Berufswahl und Karriereplanung — Stärkenanalyse, Branchenüberblick, Bewerbungsunterstützung. Steht in Verbindung mit AUG-0582 (The Transition Script), AUG-0804 (The Financial Literacy Tool) und AUG-0795 (The Continuing Education Access). Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "REL-0189", "narrower_terms": [], "cross_domain_refs": [ "WRK-0091" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "KNO-0007", "domain": "KNO", "term_en": "The Competence Premium", "term_de": "Competence Premium", "definition_en": "The observable added value that a user with high AI competence achieves compared to a user with low AI competence — given an identical task and identical AI system.. Related to AUG-0091 (Productivi...", "definition_de": "Der beobachtbare Mehrwert, den ein Nutzer mit hoher KI-Kompetenz gegenüber einem Nutzer mit niedriger KI-Kompetenz erzielt — bei identischer Aufgabe und identischem KI-System. Beschreibt die Beobachtung, dass nicht die KI allein den Unterschied macht, sondern die Fähigkeit des Nutzers, sie zu führen. Steht in Verbindung mit AUG-0091 (Productivity Arbitrage), AUG-0111 (The Augmentation Gap) und Prognose 1 (Economy).", "etymology": "", "broader_term": "Knowledge AI", "narrower_terms": [], "cross_domain_refs": [ "TEM-0025" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q26944", "legal_classification": "systematic_classification" }, { "id": "KNO-0008", "domain": "KNO", "term_en": "The Digital Campus", "term_de": "Digital Campus", "definition_en": "A knowledge management phenomenon in AI-mediated information processing, characterized by a knowledge management phenomenon manifesting as schools and colleges increasingly filled with AI in teaching, admin, and research. The concept emerges specifically in contexts where the–digital interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Die zunehmende Durchdringung von Bildungseinrichtungen mit KI-Systemen — von der Verwaltung über die Lehre bis zur Forschung — und die damit verbundene Veränderungsmuster des institutionellen Lernumfelds. Steht in Verbindung mit AUG-0779 (The Institutional Learning Context), AUG-0788 (The Library Veränderungsmuster) und AUG-0784 (The Curriculum Adaptation Lag).", "etymology": "", "broader_term": "Knowledge AI", "narrower_terms": [], "cross_domain_refs": [ "TEM-0052" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "KNO-0009", "domain": "KNO", "term_en": "The Dissertation Scaffold", "term_de": "Dissertation Scaffold", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A knowledge management phenomenon in AI-mediated information processing, characterized by using AI to help organize a long research paper — like creating an outline, building arguments, and keeping track of sources — with AI providing the framework and the author adding their own ideas. This phenomenon operates at the intersection of the and dissertation dynamics within the broader KNO domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept die Nutzung von KI als Strukturierungshilfe bei umfangreichen wissenschaftlichen Arbeiten — Gliederungsentwicklung, Argumentationsaufbau, Quellenorganisation — als Gerüst, das vom Verfasser mit eigenem Denken gefüllt wird. Steht in Verbindung mit AUG-0564 (The Path Mapper), AUG-0789 (The Research Assistant Role) und AUG-0791 (The Academic Integrity Line). Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "RPH-2303", "narrower_terms": [], "cross_domain_refs": [ "CAI-0015" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "KNO-0010", "domain": "KNO", "term_en": "The Economic Restructuring", "term_de": "Economic Restructuring", "definition_en": "The comprehensive change pattern of economic structures through AI systems — business models, value chains, labor markets. Related to AUG-0847 (The Labor Redistribution), AUG-0848 (The Resource Dis... Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch die umfassende Veränderung wirtschaftlicher Strukturen durch KI-Systeme — Geschäftsmodelle, Wertschöpfungsketten, Arbeitsmärkte. Steht in Verbindung mit AUG-0847 (The Labor Redistribution), AUG-0848 (The Resource Distribution Pattern) und AUG-0982 (The Relocation Concern). Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "IDN-0037", "narrower_terms": [], "cross_domain_refs": [ "AUG-0982" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "KNO-0011", "domain": "KNO", "term_en": "The Everyday Insight", "term_de": "Everyday Insight", "definition_en": "An insight gained through AI interaction that changes the user in everyday life — a new perspective on an everyday phenomenon, a surprising connection, or a helpful reinterpretation. Related to AUG... Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch eine durch KI-Interaktion gewonnene Erkenntnis, die den Nutzer im Alltag verändert — eine neue Perspektive auf ein alltägliches Phänomen, eine überraschende Verbindung oder eine hilfreiche Umdeutung. Steht in Verbindung mit AUG-0292 (The View Shift), AUG-0248 (The Surprise Angle) und AUG-0054 (Augmented Understanding). Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Knowledge AI", "narrower_terms": [], "cross_domain_refs": [ "PLY-0011" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "KNO-0012", "domain": "KNO", "term_en": "The Forgotten Name", "term_de": "Forgotten Name", "definition_en": "A knowledge management phenomenon in AI-mediated information processing, characterized by a knowledge management phenomenon reflecting when something loses its original identity or purpose because people stop using the original term or stop remembering what it meant. The concept emerges specifically in contexts where the–forgotten interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch → Spezialfall von AUG-0470 (The Name Detective). Die spezifische Erfahrung, einen vergessenen Namen — einer Person, eines Ortes, eines Produkts — durch KI-gestützte Beschreibung wiederzufinden. Steht in Verbindung mit AUG-0470 (The Name Detective) und AUG-0434 (The Word Rescue). Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Knowledge AI", "narrower_terms": [], "cross_domain_refs": [ "IDN-0009" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "KNO-0013", "domain": "KNO", "term_en": "The Hidden Angle Finder", "term_de": "Hidden Angle Finder", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures an epistemic pattern in AI-augmented knowledge organization, measurable through an epistemic pattern characterized by aI to discover a previously unconsidered perspective in a familiar topic — the hidden angle the user would not have found alone. Related to AUG-0248 (The Surprise Angle), AUG-0040 (Perspective Tria. This phenomenon operates at the intersection of the and hidden dynamics within the broader KNO domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept die Nutzung von KI, um in einem bekannten Thema eine bisher nicht betrachtete Perspektive zu entdecken — der verborgene Winkel, den der Nutzer allein nicht gefunden hätte. Steht in Verbindung mit AUG-0248 (The Surprise Angle), AUG-0040 (Perspective Triangulation) und AUG-0225 (The Unexpected Voice). Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "NEO-0020", "narrower_terms": [], "cross_domain_refs": [ "NEO-0020", "AUG-0248" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "KNO-0014", "domain": "KNO", "term_en": "The Homework Assist", "term_de": "Homework Assist", "definition_en": "A knowledge management phenomenon in AI-mediated information processing, characterized by an information processing effect observed when using AI to understand difficult homework but keeping the actual work and learning yours. Distinguished from adjacent concepts by its focus on the specific mechanism through which the manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch → Synonym/Erweiterung von AUG-0268 (The Homework Stream), betont die unterstützende Funktion — die KI hilft beim Lernen, ohne die Aufgabe zu übernehmen. Steht in Verbindung mit AUG-0268 (The Homework Stream) und Prognose 2 (Education). Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2703", "narrower_terms": [ "REL-0141" ], "cross_domain_refs": [ "ELR-0097" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "KNO-0015", "domain": "KNO", "term_en": "The Humor Portability", "term_de": "Humor Portability", "definition_en": "An epistemic pattern in AI-augmented knowledge organization, measurable through an information processing effect manifesting as humor generated in AI interactions depends heavily on shared context — what amuses in one conversation may be confusing or inappropriate in another. Humorous AI outputs transfer poorly across diffe. Distinguished from adjacent concepts by its focus on the specific mechanism through which the manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Die Beobachtung, dass Humor in KI-Interaktionen stark kontextabhängig ist — was in einem Kontext komisch wirkt, kann in einem anderen unverständlich oder unangemessen sein. Beschreibt die eingeschränkte Übertragbarkeit humorvoller KI-Outputs. Steht in Verbindung mit AUG-0487 (The Joke Non-attainment), AUG-0508 (The Joke Explainer) und AUG-0669 (The Rhetorical Style Differential).", "etymology": "", "broader_term": "Knowledge AI", "narrower_terms": [], "cross_domain_refs": [ "AUG-0487", "PLY-0048" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "KNO-0016", "domain": "KNO", "term_en": "The Impact Rush", "term_de": "Impact Rush", "definition_en": "An epistemic pattern in AI-augmented knowledge organization, measurable through the high when an AI-assisted result achieves a measurable, positive impact — an accepted proposal, a successful project, a resolved turning point. Related to AUG-0157 (The Competence Rush), AUG-038. Distinguished from adjacent concepts by its focus on the specific mechanism through which the manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch das Hochgefühl, wenn ein KI-gestütztes Ergebnis eine messbare, positive Wirkung erzielt — ein angenommener Vorschlag, ein erfolgreiches Projekt, eine gelöste Wendepunkt. Steht in Verbindung mit AUG-0157 (The Competence Rush), AUG-0387 (The Debate Win) und AUG-0239 (The Pride Spark). Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-3101", "narrower_terms": [], "cross_domain_refs": [ "TEM-0084" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "KNO-0017", "domain": "KNO", "term_en": "The Institutional Learning Context", "term_de": "Institutional Learning Context", "definition_en": "A knowledge management phenomenon characterized by learning that happens in schools or organizations with structure, unlike learning alone.", "definition_de": "Die Integration von KI in institutionelle Bildungskontexte — Schulen, Hochschulen, Ausbildungsbetriebe — und die damit verbundenen Fragen: Welche KI-Nutzung ist erlaubt? Wie verändert sich die Leistungsbewertung? Wo liegt die Grenze zwischen Hilfsmittel und Täuschung? Steht in Verbindung mit AUG-0780 (The Assessment Challenge), AUG-0781 (The Learning Task Boundary) und AUG-0784 (The Curriculum Adaptation Lag).", "etymology": "", "broader_term": "RPH-2703", "narrower_terms": [], "cross_domain_refs": [ "REL-0083" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q133500", "legal_classification": "analytical_category" }, { "id": "KNO-0018", "domain": "KNO", "term_en": "The Jargon Shield", "term_de": "Jargon Shield", "definition_en": "Documented as an empirical observation in AI research (not a recommendation), this term describes A knowledge management phenomenon characterized by aI to understand jargon or to employ it correctly oneself — as protection against exclusion from technically dominated contexts. Related to AUG-0379 (The Understanding Bridge), AUG-0302 (The Blue C. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "In der AUGMANITAI-Terminologiewissenschaft als rein beschreibendes Phänomen klassifiziert, bezeichnet dieser Begriff die Nutzung von KI, um Fachsprache zu verstehen oder selbst korrekt einzusetzen — als Schutz gegen Ausschluss aus fachsprachlich dominierten Kontexten. Steht in Verbindung mit AUG-0379 (The Understanding Bridge), AUG-0302 (The Blue Collar Bypass) und AUG-0206 (The Understanding Dial). Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "KNO-0038", "narrower_terms": [ "KNO-0005", "KNO-0035", "KNO-0007", "KNO-0036", "KNO-0025", "KNO-0031", "KNO-0023", "KNO-0040", "KNO-0030", "KNO-0012", "KNO-0024", "PER-0079", "KNO-0037", "KNO-0015", "KNO-0008", "KNO-0041", "CRE-0181", "KNO-0004", "KNO-0001", "KNO-0032", "KNO-0011", "KNO-0038", "KNO-0021" ], "cross_domain_refs": [ "CRE-0181" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "KNO-0019", "domain": "KNO", "term_en": "The Learner Reliance Observation", "term_de": "Learner Reliance Observation", "definition_en": "In KNO applications, this concept denotes the pattern where people learning something new depend heavily on AI, changing how they learn. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Die Beobachtung, dass Lernende — in Schule, Ausbildung oder Studium — unterschiedliche Grade der Verbundenheit von KI-Unterstützung entwickeln, von gelegentlicher Recherchehilfe bis zur systematischen Auslagerung von Denkarbeit. Steht in Verbindung mit AUG-0569 (The Homework Assist), AUG-0577 (The Secret Tutor) und AUG-0781 (The Learning Task Boundary).", "etymology": "", "broader_term": "RPH-2703", "narrower_terms": [], "cross_domain_refs": [ "AGE-0058" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "KNO-0020", "domain": "KNO", "term_en": "The Learning Task Boundary", "term_de": "Learning Task Grenze", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures an epistemic pattern in AI-augmented knowledge organization, measurable through the boundary between AI as learning aid and AI as task takeover — at what point does support end and takeover begin? Related to AUG-0569 (The Homework Assist), AUG-0760 (The Learner Reliance Observ. This phenomenon operates at the intersection of the and learning dynamics within the broader KNO domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept die Grenze zwischen KI als Lernhilfe und KI als Aufgabenübernahme — ab welchem Punkt hört die Unterstützung auf und beginnt die Übernahme? Steht in Verbindung mit AUG-0569 (The Homework Assist), AUG-0760 (The Learner Reliance Observation) und AUG-0791 (The Academic Integrity Line). Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "RPH-2703", "narrower_terms": [], "cross_domain_refs": [ "REL-0141" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q133500", "legal_classification": "systematic_classification" }, { "id": "KNO-0021", "domain": "KNO", "term_en": "The Level Selector", "term_de": "Level Selector", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures an epistemic pattern in AI-augmented knowledge organization, measurable through an information processing effect observed when adjusting AI output from simple to complex depending on how much detail the person wants. This phenomenon operates at the intersection of the and level dynamics within the broader KNO domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept → Synonym/Erweiterung von AUG-0206 (The Understanding Dial). Beschreibt die Fähigkeit des Nutzers, das Komplexitätsniveau einer KI-Antwort gezielt einzustellen — wie ein Regler, der zwischen \"einfach\" und \"Expertenniveau\" verschoben werden kann. Steht in Verbindung mit AUG-0206 (The Understanding Dial) und AUG-0379 (The Understanding Bridge). Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Knowledge AI", "narrower_terms": [ "PER-0095" ], "cross_domain_refs": [ "ART-0040" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "KNO-0022", "domain": "KNO", "term_en": "The Memory Hole", "term_de": "Gedaechtnis Hole", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures an epistemic pattern in AI-augmented knowledge organization, measurable through an AI insight that the user consciously experienced but did not save — and the dynamic interplay knowledge that it existed but is no longer retrievable. Related to AUG-0315 (The Orphan Idea), AUG-0. This phenomenon operates at the intersection of the and memory dynamics within the broader KNO domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept der Übergang einer KI-Erkenntnis, die der Nutzer bewusst erlebt, aber nicht gespeichert hat — und das frustrierende Wissen, dass sie existierte, aber nicht mehr auffindbar ist. Steht in Verbindung mit AUG-0315 (The Orphan Idea), AUG-0291 (The Forgetting Tax) und AUG-0280 (The Unshared Brilliance). Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "BEH-0063", "narrower_terms": [], "cross_domain_refs": [ "TEM-0187" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q492", "legal_classification": "systematic_classification" }, { "id": "KNO-0023", "domain": "KNO", "term_en": "The Oral Tradition Bridge", "term_de": "Oral Tradition Bruecke", "definition_en": "A knowledge management phenomenon in AI-mediated information processing, characterized by an information processing effect in which aI as a bridge between orally transmitted knowledge systems and the text-based digital world — such as the documentation, translation, or preparation of orally transmitted knowledge. Related to AUG. Distinguished from adjacent concepts by its focus on the specific mechanism through which the manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch die Nutzung von KI als Brücke zwischen mündlich tradierten Wissenssystemen und der schriftbasierten digitalen Welt — etwa die Dokumentation, Übersetzung oder Aufbereitung mündlich überlieferten Wissens. Steht in Verbindung mit AUG-0740 (The Local Knowledge System), AUG-0570 (The Lore Keeper) und AUG-0694 (The Translation Fidelity). Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Knowledge AI", "narrower_terms": [ "IDN-0038" ], "cross_domain_refs": [ "COG-0024" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "KNO-0024", "domain": "KNO", "term_en": "The Overnight Reframe", "term_de": "Overnight Reframe", "definition_en": "An epistemic pattern in AI-augmented knowledge organization, measurable through an information processing effect arising from a question from an AI session becomes clearer or makes more sense after sleeping on it. The concept emerges specifically in contexts where the–overnight interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Die Beobachtung, dass ein Challenge oder eine Fragestellung, die am Vorabend in einer KI-Sitzung bearbeitet wurde, am nächsten Morgen in einem neuen Licht erscheint — oft mit klareren Prioritäten oder einer veränderten Perspektive. Beschreibt den Effekt der intuitiven Verarbeitung zwischen zwei Sitzungen. Steht in Verbindung mit AUG-0139 (The Knowledge Composting), AUG-0046 (The Felt Echo) und AUG-0029 (Evening Synchronization).", "etymology": "", "broader_term": "Knowledge AI", "narrower_terms": [], "cross_domain_refs": [ "REL-0087" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "KNO-0025", "domain": "KNO", "term_en": "The Post-Mortem Analysis", "term_de": "ThePost-mortemAnalysis", "definition_en": "A knowledge management phenomenon in AI-mediated information processing, characterized by the organized analysis after a severe mistake or error of an AI agent system — root source research, mistake or error reconstruction, derivation of improvement. Related to AUG-0905 (The Documentatio. Distinguished from adjacent concepts by its focus on the specific mechanism through which the manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch die systematische Analyse nach einem schwerwiegenden Fehler eines KI-Agentensystems — Ursachenforschung, Fehlerrekonstruktion, Ableitung von Verbesserungsmaßnahmen. Steht in Verbindung mit AUG-0905 (The Documentation Trail), AUG-0957 (The Decision Review) und AUG-0964 (The Edge Case Library). Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Analytical Method", "narrower_terms": [], "cross_domain_refs": [ "TRU-0010" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "KNO-0026", "domain": "KNO", "term_en": "The Poverty Shortcut", "term_de": "Poverty Shortcut", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures an epistemic pattern in AI-augmented knowledge organization, measurable through an information processing effect manifesting as free or low-cost AI tools by users with limited budgets to overcome access thresholds to knowledge, formulation assistance, or consultation.. Related to AUG-0119 (The Level Playing Field), AUG-0306. This phenomenon operates at the intersection of the and poverty dynamics within the broader KNO domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept die Nutzung kostenloser oder kostengünstiger KI-Werkzeuge durch Nutzer mit begrenztem Budget, um Zugangshürden zu Wissen, Formulierungshilfen oder Beratung zu überwinden. Beschreibt das demokratisierende Potenzial frei zugänglicher KI. Steht in Verbindung mit AUG-0119 (The Level Playing Field), AUG-0306 (The Class Divide Prompt) und AUG-0106 (The Inclusivity Imperative). Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "RPH-1307", "narrower_terms": [], "cross_domain_refs": [ "CRE-0222" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "KNO-0027", "domain": "KNO", "term_en": "The Quiet Authority", "term_de": "Quiet Authority", "definition_en": "A knowledge management phenomenon in AI-mediated information processing, characterized by the unspoken impact that arises when a user does not demonstrate their AI competence but makes it perceptible through the quality of their results.. Related to AUG-0100 (The Quiet Competence) and P. Distinguished from adjacent concepts by its focus on the specific mechanism through which the manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Die unausgesprochene Wirkung, die entsteht, wenn ein Nutzer seine KI-Kompetenz nicht demonstriert, sondern durch die Qualität seiner Ergebnisse spürbar macht. Beschreibt eine Form der Professionalität, bei der die KI-Unterstützung hinter dem Werk verschwindet. Steht in Verbindung mit AUG-0100 (The Quiet Competence) und Phase 7 (The Sovereignty Principle).", "etymology": "", "broader_term": "KNO-0028", "narrower_terms": [], "cross_domain_refs": [ "TEM-0141" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "KNO-0028", "domain": "KNO", "term_en": "The Quiet Competence", "term_de": "Quiet Competence", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A knowledge management phenomenon in AI-mediated information processing, characterized by an epistemic pattern in which the unobtrusive, non-demonstrative ability of a user to employ AI competently and effectively without outwardly emphasizing it.. Related to Phase 7 (The self-direction Principle) and AUG-0102 (The Sov. This phenomenon operates at the intersection of the and quiet dynamics within the broader KNO domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept die unauffällige, nicht demonstrativ gezeigte Fähigkeit eines Nutzers, KI souverän und effektiv einzusetzen, ohne dies nach außen zu betonen. Beschreibt die Beobachtung, dass die kompetentesten KI-Nutzer oft am wenigsten über ihre KI-Nutzung sprechen — die Technologie verschwindet hinter dem Ergebnis. Steht in Verbindung mit Phase 7 (The Sovereignty Principle) und AUG-0102 (The Sovereignty Principle). Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "IDN-0048", "narrower_terms": [ "KNO-0027" ], "cross_domain_refs": [ "IDN-0048" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q26944", "legal_classification": "analytical_category" }, { "id": "KNO-0029", "domain": "KNO", "term_en": "The Register Mismatch", "term_de": "Register Mismatch", "definition_en": "An epistemic pattern in AI-augmented knowledge organization, measurable through a knowledge management phenomenon manifesting as the difference between the language style the user uses in their input and the style in which the AI responds — when the AI responds. Related to AUG-0658 (The Register Surprise), AUG-0338 (The Tone. Distinguished from adjacent concepts by its focus on the specific mechanism through which the manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Die Diskrepanz zwischen dem sprachlichen Register, das der Nutzer in seiner Eingabe verwendet, und dem Register, in dem die KI antwortet — wenn die KI zu formell, zu informell oder in einem fachsprachlichen Register antwortet, das nicht zur Eingabe passt. Steht in Verbindung mit AUG-0658 (The Register Surprise), AUG-0338 (The Tone Match) und AUG-0206 (The Understanding Dial).", "etymology": "", "broader_term": "PER-0102", "narrower_terms": [], "cross_domain_refs": [ "SOC-0034" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "KNO-0030", "domain": "KNO", "term_en": "The Relief Sigh", "term_de": "Relief Sigh", "definition_en": "The subjective experience of relief when the AI handles a task the user had hesitations about — such as a difficult email, a complicated form, or an unpleasant research task. Related to AUG-0025 (T... Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch das subjektive Erlebnis der Erleichterung, wenn die KI eine Aufgabe bewältigt, vor der der Nutzer Hemmungen hatte — etwa eine schwierige E-Mail, ein kompliziertes Formular oder eine unangenehme Recherche. Steht in Verbindung mit AUG-0025 (The Offload Lift), AUG-0155 (The Decision Unburdening) und AUG-0062 (The Lightness Factor). Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Knowledge AI", "narrower_terms": [], "cross_domain_refs": [ "TEM-0111" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "KNO-0031", "domain": "KNO", "term_en": "The Return to Manual", "term_de": "Return to Manual", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A knowledge management phenomenon in AI-mediated information processing, characterized by the conscious decision to perform a specific task again without AI assistance support — whether to test or maintain one's own competence, or because manual execution is more appropriate in a given context. Re. This phenomenon operates at the intersection of the and return dynamics within the broader KNO domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription. Observed phenomenon documented in human-AI interaction research.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept die bewusste Entscheidung, eine bestimmte Aufgabe wieder ohne KI-Unterstützung auszuführen — sei es um die eigene Kompetenz zu testen, zu erhalten oder weil die manuelle Ausführung in einem bestimmten Kontext angemessener ist. Steht in Verbindung mit AUG-0055 (Strategic Competence Throttling), Axiom 15 (Der Aus-Schalter) und Axiom 20 (Periodische Trennung). Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Knowledge AI", "narrower_terms": [], "cross_domain_refs": [ "REL-0134" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "KNO-0032", "domain": "KNO", "term_en": "The Reversibility Standard", "term_de": "Reversibility Standard", "definition_en": "An epistemic pattern in AI-augmented knowledge organization, measurable through a standard in which actions or decisions retain the capacity to be undone, modified, or reversed. Systems that maintain reversibility allow for error correction and user control. Distinguished from adjacent concepts by its focus on the specific mechanism through which the manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Das Prinzip, dass KI-gestützte Entscheidungen und Prozesse so gestaltet sein können, dass sie rückgängig gemacht werden können, wenn sich herausstellt, dass die KI-Empfehlung fehlerhaft war. Beschreibt eine Gestaltungsrichtlinie für KI-Integration in Arbeitsabläufe. Steht in Verbindung mit Axiom 15 (Der Aus-Schalter) und AUG-0044 (Unlearning Protocol).", "etymology": "", "broader_term": "Knowledge AI", "narrower_terms": [], "cross_domain_refs": [ "PHO-0051" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "KNO-0033", "domain": "KNO", "term_en": "The Secret Tutor", "term_de": "Secret Tutor", "definition_en": "A knowledge management phenomenon in AI-mediated information processing, characterized by an information processing effect in which the secret use of AI as a tutor — the user affects their knowledge or skills without others knowing. Related to AUG-0507 (The Quiet Help), AUG-0398 (The Hobby Teacher), and AUG-0449 (The Quiet trajectory). Distinguished from adjacent concepts by its focus on the specific mechanism through which the manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch die heimliche Nutzung von KI als Nachhilfelehrer — der Nutzer verbessert sein Wissen oder seine Fähigkeiten, ohne dass andere davon erfahren. Steht in Verbindung mit AUG-0507 (The Quiet Help), AUG-0398 (The Hobby Teacher) und AUG-0449 (The Quiet Path). Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "TEM-0139", "narrower_terms": [], "cross_domain_refs": [ "TEM-0077" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "KNO-0034", "domain": "KNO", "term_en": "The Special Needs Assist", "term_de": "Special Needs Assist", "definition_en": "An epistemic pattern observed when aI to support learners with special needs — accessibility features, adapted output formats, simplified language options. Related to AUG-0800 (The Inclusive Classroom), AUG-0443 (The Accessibility E... Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch die Nutzung von KI zur Unterstützung von Lernenden mit besonderen Bedürfnissen — Barrierefreiheitsfunktionen, angepasste Ausgabeformate, vereinfachte Sprachoptionen. Steht in Verbindung mit AUG-0800 (The Inclusive Classroom), AUG-0443 (The Accessibility Eye) und AUG-0802 (The Language Threshold Solve). Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2703", "narrower_terms": [], "cross_domain_refs": [ "AUG-0802", "PER-0019" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "KNO-0035", "domain": "KNO", "term_en": "The Street Language Input", "term_de": "Street Language Input", "definition_en": "An epistemic pattern in AI-augmented knowledge organization, measurable through an information processing effect arising from colloquial language, slang, or subcultural linguistic codes into AI systems — and the AI's varying ability to understand these, respond in context, or translate them into more formal language. Rela. Distinguished from adjacent concepts by its focus on the specific mechanism through which the manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch die Eingabe von Umgangssprache, Slang oder subkulturellen Sprachcodes in KI-Systeme — und die variierende Fähigkeit der KI, diese zu verstehen, kontextgerecht zu antworten oder sie in formellere Sprache zu übersetzen. Steht in Verbindung mit AUG-0691 (The Dialect Decoder), AUG-0693 (The Code-Mesh Output) und AUG-0657 (The Register Range). Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Knowledge AI", "narrower_terms": [], "cross_domain_refs": [ "CUS-0090", "LIN-0079" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q315", "legal_classification": "observational_construct" }, { "id": "KNO-0036", "domain": "KNO", "term_en": "The Succession Test", "term_de": "Succession Test", "definition_en": "An epistemic pattern in AI-augmented knowledge organization, measurable through whether an AI-assisted work process could also be taken over by another person — as a measure of documentation quality and reliance on individual context knowledge. Related to AUG-0187 (The Inherit. The concept emerges specifically in contexts where the–succession interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch die Prüfung, ob ein KI-gestützter Arbeitsprozess auch von einer anderen Person übernommen werden könnte — als Maß für die Dokumentationsqualität und die Verbundenheit von individuellem Kontextwissen. Steht in Verbindung mit AUG-0187 (The Inheritance Question), AUG-0172 (The Clean Handover) und AUG-0103 (The Openbook Commitment). Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Knowledge AI", "narrower_terms": [], "cross_domain_refs": [ "PER-0011" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "KNO-0037", "domain": "KNO", "term_en": "The Trilingual Juggle", "term_de": "Trilingual Juggle", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A knowledge management phenomenon in AI-mediated information processing, characterized by the increased complexity for users with three or more languages — the decision of which language is most effective for which AI task becomes a competence in itself. Related to AUG-0708 (The Bilingu. This phenomenon operates at the intersection of the and trilingual dynamics within the broader KNO domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept die gesteigerte Komplexität bei Nutzern mit drei oder mehr Sprachen — die Entscheidung, welche Sprache für welche KI-Aufgabe am effektivsten ist, wird zu einer eigenen Kompetenz. Steht in Verbindung mit AUG-0708 (The Bilingual Dynamic), AUG-0680 (The Context Adaptation) und AUG-0686 (The Lingua Franca Effect). Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Knowledge AI", "narrower_terms": [], "cross_domain_refs": [ "PER-0033" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "KNO-0038", "domain": "KNO", "term_en": "The Understanding Bridge", "term_de": "Understanding Bruecke", "definition_en": "A knowledge management phenomenon in AI-mediated information processing, characterized by an information processing effect characterized by the AI's ability to explain complex subject matter in a way that becomes understandable for the user's specific knowledge level — a bridge between technical language and everyday understanding. Rel. The concept emerges specifically in contexts where the–understanding interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch die Fähigkeit der KI, komplexe Sachverhalte so zu erklären, dass sie für den spezifischen Wissensstand des Nutzers verständlich werden — eine Brücke zwischen Fachsprache und Alltagsverständnis. Steht in Verbindung mit AUG-0206 (The Understanding Dial), AUG-0067 (The Glass Wall Effect) und Axiom 10 (Übersetzungsprinzip). Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Knowledge AI", "narrower_terms": [ "KNO-0018" ], "cross_domain_refs": [ "MUS-0052" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "KNO-0039", "domain": "KNO", "term_en": "The View Shift", "term_de": "View Verschiebung", "definition_en": "A knowledge management phenomenon in AI-mediated information processing, characterized by the lasting change in one's own perspective on a topic activated by an AI interaction.. Related to AUG-0054 (Augmented Understanding), AUG-0149 (The Lasting Impact Question), and AUG-0089 (The Patt. The concept emerges specifically in contexts where the–view interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Die dauerhafte Veränderung der eigenen Perspektive auf ein Thema, die durch eine KI-Interaktion ausgelöst wurde. Beschreibt einen nachhaltigen Effekt: Der Nutzer sieht das Thema auch nach der Sitzung anders als zuvor. Steht in Verbindung mit AUG-0054 (Augmented Understanding), AUG-0149 (The Lasting Impact Question) und AUG-0089 (The Pattern Sharpening).", "etymology": "", "broader_term": "IDN-0001", "narrower_terms": [], "cross_domain_refs": [ "TEM-0095" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "KNO-0040", "domain": "KNO", "term_en": "Thinking Leverage", "term_de": "ThinkingLeverage", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A knowledge management phenomenon in AI-mediated information processing, characterized by the multiplier effect that arises when a user amplifies their existing thinking competence through AI — similar to a lever in physics. The higher the user's baseline competence, the greater the lev. This phenomenon operates at the intersection of thinking and leverage dynamics within the broader KNO domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept der Multiplikatoreffekt, der entsteht, wenn ein Nutzer seine vorhandene Denkkompetenz durch KI verstärkt — ähnlich einem Hebel in der Physik. Je höher die eigene Ausgangskompetenz, desto größer der Hebeleffekt durch KI. Steht in Verbindung mit AUG-0097 (The Competence Premium) und Axiom 3 (Die Kombinations-Schwelle). Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Knowledge AI", "narrower_terms": [], "cross_domain_refs": [ "PER-0033" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "KNO-0041", "domain": "KNO", "term_en": "Thinking use", "term_de": "Thinking use", "definition_en": "An epistemic pattern in AI-augmented knowledge organization, measurable through the multiplier effect that arises when a user amplifies their existing thinking competence through AI — similar to a lever in physics. The higher the user's baseline competence, the greater the use. Distinguished from adjacent concepts by its focus on the specific mechanism through which thinking manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch der Multiplikatoreffekt, der entsteht, wenn ein Nutzer seine vorhandene Denkkompetenz durch KI verstärkt — ähnlich einem Hebel in der Physik. Je höher die eigene Ausgangskompetenz, desto größer der Hebeleffekt durch KI. Steht in Verbindung mit AUG-0097 (Der Competence Premium) und Axiom 3 (Die Kombinations-Schwelle). Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Knowledge AI", "narrower_terms": [], "cross_domain_refs": [ "PER-0033" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "LIN-0001", "domain": "LIN", "term_en": "Emergent Syntactic Compositionality", "term_de": "Emergente syntaktische Komposionalität", "definition_en": "The phenomenon whereby neural language models develop compositional grammatical structures through unsupervised learning from text, enabling them to parse novel sentence constructions. This demonstrates how human-like linguistic rules can emerge from statistical patterns without explicit grammatical programming.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch phänomen, bei dem neuronale Sprachmodelle durch unüberwachtes Lernen aus Texten kompositorische grammatikalische Strukturen entwickeln. Dies zeigt, wie menschenähnliche linguistische Regeln aus statistischen Mustern entstehen können. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2355", "narrower_terms": [ "LIN-0048", "LIN-0074", "LIN-0098", "LIN-0029", "LIN-0069", "LIN-0039", "LIN-0093", "LIN-0068", "LIN-0050", "LIN-0092", "LIN-0062", "LIN-0100", "LIN-0088", "LIN-0076", "LIN-0055", "LIN-0037", "LIN-0057", "LIN-0063", "LIN-0034", "LIN-0097", "LIN-0017", "LIN-0086", "LIN-0014", "LIN-0006", "LIN-0043", "LIN-0008", "LIN-0058", "LIN-0094", "LIN-0091", "LIN-0013", "LIN-0078", "LIN-0028", "LIN-0083", "LIN-0023", "LIN-0095", "LIN-0053", "LIN-0087", "LIN-0012", "LIN-0049", "LIN-0071", "LIN-0085", "LIN-0075", "LIN-0099", "LIN-0041", "LIN-0010", "LIN-0066", "LIN-0015", "LIN-0040", "LIN-0027", "LIN-0021", "LIN-0046", "LIN-0061", "LIN-0080", "LIN-0067", "LIN-0045", "LIN-0089", "LIN-0084", "LIN-0082", "LIN-0060", "LIN-0042", "LIN-0011", "LIN-0030", "LIN-0072", "LIN-0073", "LIN-0002", "LIN-0065", "LIN-0031", "LIN-0090" ], "cross_domain_refs": [ "TRA-0035", "CON-0036" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "LIN-0002", "domain": "LIN", "term_en": "Layer-wise Linguistic Representation", "term_de": "Schichtweise linguistische Repräsentation", "definition_en": "A language processing effect involving the hierarchical organization of linguistic knowledge across transformer layers, where lower layers capture morphology, middle layers encode syntax, and higher layers represent semantics. Understanding this stratification reveals how AI systems internalize human language structure.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch hierarchische Organisation von Sprachwissen über Transformer-Schichten, wobei niedrigere Schichten Morphologie und höhere Schichten Semantik kodieren. Dies zeigt, wie KI-Systeme menschliche Sprachstruktur internalisieren. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Abstraction Layer", "narrower_terms": [], "cross_domain_refs": [ "MUS-0002", "SCR-0022" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "LIN-0003", "domain": "LIN", "term_en": "Grammatical Acquisition Dynamics", "term_de": "Dynamik des grammatikalischen Erwerbs", "definition_en": "A linguistic phenomenon where the temporal progression by which language models acquire grammatical competencies during training, paralleling developmental linguistics observations in human children. This bridges computational and cognitive models of language learning.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch zeitliche Abfolge, in der Sprachmodelle während des Trainings grammatikalische Kompetenzen erwerben, ähnlich der Sprachentwicklung bei Kindern. Dies verbindet rechnergestützte und kognitive Modelle des Spracherwerbs. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2703", "narrower_terms": [], "cross_domain_refs": [ "CUS-0090", "TRA-0035" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "LIN-0004", "domain": "LIN", "term_en": "Attention-based Dependency Parsing", "term_de": "Aufmerksamkeitsbasiertes Dependency-Parsing", "definition_en": "The mechanism by which transformer attention heads implicitly discover and represent syntactic dependencies between words, creating interpretable linguistic structures without explicit grammatical annotation. This demonstrates unsupervised linguistic structure learning.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch mechanismus, durch den Transformer-Aufmerksamkeitsköpfe syntaktische Abhängigkeiten zwischen Wörtern implizit entdecken und darstellen. Dies zeigt unüberwachtes Lernen linguistischer Strukturen. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2703", "narrower_terms": [], "cross_domain_refs": [ "DAT-0008" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q184872", "legal_classification": "analytical_category" }, { "id": "LIN-0005", "domain": "LIN", "term_en": "Subword Morphological Processing", "term_de": "Subwort-morphologische Verarbeitung", "definition_en": "The computational addressment of morphological structure through subword tokenization, where AI systems learn to segment and process affixes, roots, and inflections. This reveals how neural systems approximate human morphological awareness.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch rechnerische Herangehensweise morphologischer Struktur durch Subwort-Tokenisierung, bei der KI-Systeme lernen, Affixe und Wurzeln zu segmentieren. Dies zeigt, wie neuronale Systeme morphologisches Wissen approximieren. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2703", "narrower_terms": [], "cross_domain_refs": [ "AGE-0019", "COG-0029", "MSC-0039" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "LIN-0006", "domain": "LIN", "term_en": "Semantic Feature Extraction", "term_de": "Extraktion semantischer Merkmale", "definition_en": "The process by which language models extract and encode semantic features (agency, animacy, definiteness) from word contexts, enabling semantic competency in human-AI dialogue. This parallels human semantic processing.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch prozess, durch den Sprachmodelle semantische Merkmale aus Wortkontext extrahieren und kodieren, was semantische Kompetenz im menschlich-KI-Dialog ermöglicht. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Language AI", "narrower_terms": [], "cross_domain_refs": [ "MUS-0051" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "LIN-0007", "domain": "LIN", "term_en": "Token Probability Calibration", "term_de": "Kalibrierung der Token-Wahrscheinlichkeit", "definition_en": "A language processing effect observed when the alignment between language model output probability distributions and actual linguistic well-formedness judgments from humans. Calibrated confidence scores improve human trust and effective collaboration.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch ausrichtung zwischen Wahrscheinlichkeitsverteilungen und menschlichen Urteilen zur Sprachangemessenheit. Kalibrierte Konfidenzwerte verbessern Vertrauen und Zusammenarbeit. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2505", "narrower_terms": [], "cross_domain_refs": [ "SPR-0108", "MKT-0077", "PLY-0051" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "LIN-0008", "domain": "LIN", "term_en": "Universal Linguistic Invariants in Neural Spaces", "term_de": "Universelle linguistische Invarianten in neuronalen Räumen", "definition_en": "A natural language processing phenomenon in AI-mediated communication, characterized by cross-model discovery of consistent linguistic principles (word order preferences, subject-verb agreement patterns) across different architecture families, suggesting convergence toward human-like linguistic universals. The concept emerges specifically in contexts where universal–linguistic interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch entdeckung konsistenter linguistischer Prinzipien über verschiedene Architektur-Familien hinweg, was eine Konvergenz zu menschenähnlichen sprachlichen Universalien suggeriert. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Language AI", "narrower_terms": [], "cross_domain_refs": [ "TRA-0073", "TEW-0029" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "LIN-0009", "domain": "LIN", "term_en": "Pragmatic Implicature Learning", "term_de": "Lernen pragmatischer Implikaturen", "definition_en": "A communicative pattern characterized by the acquisition of implicit meaning-making beyond literal words, where AI systems learn conversational implicatures and indirectness conventions through human text exposure. Critical for natural human-AI dialogue.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch erwerb impliziter Bedeutungskonstruktion jenseits von Literalwortbedeutungen. Entscheidend für natürlichen menschlich-KI-Dialog. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2703", "narrower_terms": [], "cross_domain_refs": [ "SCR-0079", "TRA-0027" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q133500", "legal_classification": "systematic_classification" }, { "id": "LIN-0010", "domain": "LIN", "term_en": "Recursive Embedding Capacity", "term_de": "Rekursive Einbettungskapazität", "definition_en": "A linguistic pattern in AI-augmented language understanding, measurable through a linguistic phenomenon manifesting as the neural ability to represent nested linguistic structures and recursive dependencies, enabling parsing of complex sentences with multiple embedded clauses. This reflects fundamental human syntactic competence. Distinguished from adjacent concepts by its focus on the specific mechanism through which recursive manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch die neuronale Fähigkeit, verschachtelte linguistische Strukturen und rekursive Abhängigkeiten darzustellen. Dies spiegelt fundamentale menschliche syntaktische Kompetenz wider. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Vector Embedding", "narrower_terms": [], "cross_domain_refs": [ "TEW-0088", "TRA-0083" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "LIN-0011", "domain": "LIN", "term_en": "Low-Resource Language Modeling", "term_de": "Sprachmodellierung für sprachressourcen-arme Sprachen", "definition_en": "A language processing effect in which development of AI language models for indigenous and endangered languages with limited digital text corpora, enabling preservation and revitalization. Human linguists collaborate with AI to document grammatical structures.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch entwicklung von KI-Sprachmodellen für gefährdete Sprachen mit begrenzten Textkorpora. Linguisten arbeiten mit KI zusammen, um grammatikalische Strukturen zu dokumentieren. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Computational Model", "narrower_terms": [], "cross_domain_refs": [ "MSC-0001", "TRA-0003" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q315", "legal_classification": "systematic_classification" }, { "id": "LIN-0012", "domain": "LIN", "term_en": "Script Revitalization through AI", "term_de": "Schrift-Wiederbelebung durch KI", "definition_en": "Use of AI systems to shift oral traditions to writing, train character recognition on historical scripts, and may generate realistic text in endangered writing systems. Enables human communities to preserve endangered scripts.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch einsatz von KI-Systemen zur Konvertierung mündlicher Traditionen in Schrift und zur Zeichenerkennung in gefährdeten Schriftsystemen. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Language AI", "narrower_terms": [], "cross_domain_refs": [ "ETH-0020" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "LIN-0013", "domain": "LIN", "term_en": "Cultural-Linguistic Grounding in Models", "term_de": "Kulturell-linguistische Verankerung in Modellen", "definition_en": "A communicative pattern involving integration of cultural context and indigenous knowledge systems into language model training, ensuring AI representations respect community worldviews and linguistic practices. Prevents cultural erasure in human-AI systems.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch integration von kulturellem Kontext und indigenem Wissen in Sprachmodell-Training. Dies zielt darauf ab zu mitigieren kulturelle Auslöschung in menschlich-KI-Systemen. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "Computational Model", "narrower_terms": [], "cross_domain_refs": [ "COP-0009", "ROB-0184" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "LIN-0014", "domain": "LIN", "term_en": "Dialectal Documentation AI", "term_de": "KI-gestützte dialektale Dokumentation", "definition_en": "A communicative pattern involving aI-assisted tools for field linguists to transcribe, analyze, and catalog regional language varieties, creating permanent records of dialectal variation. Enables preservation of linguistic diversity. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch kI-gestützte Werkzeuge für Feldlinguisten zur Transkription und Analyse regionaler Sprachvarianten. Ermöglicht die Konservierung linguistischer Vielfalt. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "Language AI", "narrower_terms": [], "cross_domain_refs": [ "COP-0016" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "LIN-0015", "domain": "LIN", "term_en": "Tonal Language Acoustic Modeling", "term_de": "Akustische Modellierung tonaler Sprachen", "definition_en": "A communicative pattern characterized by specialized neural models for tonal languages where pitch carries grammatical meaning, using audio-linguistic fusion to preserve tonal contrasts. Critical for endangered tone languages.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch spezialisierte neuronale Modelle für tonale Sprachen, wo Tonhöhe grammatikalische Bedeutung trägt. Entscheidend für gefährdete Tonsprachen. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Computational Model", "narrower_terms": [], "cross_domain_refs": [ "LNG-0018", "WEB-0007" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q315", "legal_classification": "empirical_phenomenon_label" }, { "id": "LIN-0016", "domain": "LIN", "term_en": "Community-Based Language Annotation", "term_de": "Gemeinschaftsbasierte Sprach-Annotation", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures A linguistic pattern in AI-augmented language understanding, measurable through a linguistic phenomenon in which human-AI collaborative annotation where native speakers work with AI annotation tools to may create linguistic resources, ensuring linguistic accuracy and community agency in preservation efforts. This phenomenon operates at the intersection of community and based dynamics within the broader LIN domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept menschlich-KI-Zusammenarbeit, bei der Muttersprachler mit KI-Annotations-Werkzeugen arbeiten, um linguistische Ressourcen zu erstellen. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "RPH-2605", "narrower_terms": [], "cross_domain_refs": [ "DAT-0003", "ELR-0012" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q315", "legal_classification": "empirical_phenomenon_label" }, { "id": "LIN-0017", "domain": "LIN", "term_en": "Morphological Rule Extraction from Oral Data", "term_de": "Extraktion morphologischer Regeln aus mündlichen Daten", "definition_en": "A natural language processing phenomenon in AI-mediated communication, characterized by a linguistic phenomenon involving aI-assisted inference of implicit morphological rules from spoken corpus data, identifying inflectional patterns humans may not consciously articulate. Accelerates linguistic documentation. Distinguished from adjacent concepts by its focus on the specific mechanism through which morphological manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch kI-gestützte Inferenz impliziter morphologischer Regeln aus gesprochenen Daten. Beschleunigt die linguistische Dokumentation. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Language AI", "narrower_terms": [], "cross_domain_refs": [ "TEW-0069" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q42848", "legal_classification": "descriptive_research_term" }, { "id": "LIN-0018", "domain": "LIN", "term_en": "Cross-Generational Language Transfer Modeling", "term_de": "Modellierung sprachlichen Generationenwechsels", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A natural language processing phenomenon in AI-mediated communication, characterized by analysis of how language features persist or shift across speaker generations using AI, informing interventions to slow language shift. Supports intergenerational language transmission planning. This phenomenon operates at the intersection of cross and generational dynamics within the broader LIN domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept analyse, wie Sprachmerkmal über Generationen bestehen oder sich verschieben. Unterstützt Planung der intergenerationalen Sprachübertragung. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "RPH-2605", "narrower_terms": [], "cross_domain_refs": [ "CUS-0090", "LNG-0020" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q315", "legal_classification": "analytical_category" }, { "id": "LIN-0019", "domain": "LIN", "term_en": "Lexical Expansion for Modernization", "term_de": "Lexikalische Erweiterung zur Modernisierung", "definition_en": "A language processing effect observed when aI-supported creation of new vocabulary terms in endangered languages for modern concepts (technology, medicine), grounded in community input and linguistic principles. Enables contemporary use of heritage languages.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch kI-unterstützte Erstellung neuer Vokabel in gefährdeten Sprachen für moderne Konzepte. Ermöglicht zeitgenössische Nutzung von Kultursprachen. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-3202", "narrower_terms": [], "cross_domain_refs": [ "LNG-0010", "TRA-0047" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "LIN-0020", "domain": "LIN", "term_en": "Speech Recognition for Underdocumented Languages", "term_de": "Spracherkennung für unterdokumentierte Sprachen", "definition_en": "A linguistic pattern in AI-augmented language understanding, measurable through development of speech recognition systems for languages with minimal training data through transfer learning and few-shot adaptation. Enables digital preservation of spoken endangered languages. Distinguished from adjacent concepts by its focus on the specific mechanism through which speech manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch entwicklung von Spracherkennungssystemen für Sprachen mit minimalem Trainigsdaten durch Transfer-Learning. Ermöglicht digitale Konservierung gesprochener Sprachen. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2703", "narrower_terms": [], "cross_domain_refs": [ "LNG-0010" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q3966", "legal_classification": "descriptive_research_term" }, { "id": "LIN-0021", "domain": "LIN", "term_en": "Speech Act Recognition in Human-AI Dialogue", "term_de": "Sprechaktanalyse im menschlich-KI-Dialog", "definition_en": "A linguistic pattern in AI-augmented language understanding, measurable through aI's capacity to identify conversational intentions (requests, assertions, questions, directives) and respond appropriately, distinguishing literal meaning from pragmatic force. Essential for natural dialogue interaction. Distinguished from adjacent concepts by its focus on the specific mechanism through which speech manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch fähigkeit der KI, gesprächliche Intentionen zu identifizieren und angemessen zu reagieren. Wesentlich für natürlichen Dialog. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Pattern Recognition", "narrower_terms": [], "cross_domain_refs": [ "SCR-0079", "RPH-1402" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q3966", "legal_classification": "analytical_category" }, { "id": "LIN-0022", "domain": "LIN", "term_en": "Conversational Repair Mechanisms", "term_de": "Konversative Reparaturmechanismen", "definition_en": "A linguistic pattern in AI-augmented language understanding, measurable through a linguistic phenomenon involving aI systems' ability to detect miscommunication, request clarification, and jointly repair communicative breakdowns with humans, mirroring natural repair sequences observed in human dialogue. The concept emerges specifically in contexts where conversational–repair interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch fähigkeit der KI, Missverständnisse zu erkennen und Kommunikationsausfälle mit Menschen zu reparieren, ähnlich natürlichen Reparatursequenzen. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-3001", "narrower_terms": [], "cross_domain_refs": [ "ELR-0155", "MTH-0095" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "LIN-0023", "domain": "LIN", "term_en": "Discourse Coherence Tracking", "term_de": "Verfolgung von Diskurskoharenz", "definition_en": "Classified in AUGMANITAI terminology science as a descriptive phenomenon (without normative endorsement), this pattern denotes A natural language processing phenomenon in AI-mediated communication, characterized by a linguistic phenomenon where computational modeling of how AI maintains coherence across dialogue turns through topic continuity, anaphoric reference resolution, and narrative consistency. Critical for sustained human-AI conversations. Distinguished from adjacent concepts by its focus on the specific mechanism through which discourse manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als analytisches Konstrukt der empirischen KI-Forschung (deskriptiv, nicht präskriptiv) erfasst dieser Terminus rechnerische Modellierung, wie KI Kohärenz über Dialog-Züge hinweg aufrechterhält. Entscheidend für nachhaltige menschlich-KI-Gespräche. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Language AI", "narrower_terms": [], "cross_domain_refs": [ "RPH-2304", "ROB-0073" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q864419", "legal_classification": "descriptive_research_term" }, { "id": "LIN-0024", "domain": "LIN", "term_en": "Pragmalinguistic Competence", "term_de": "Pragmalinguistische Kompetenz", "definition_en": "A natural language processing phenomenon in AI-mediated communication, characterized by a communicative pattern in which aI's understanding of socially appropriate language use, including politeness conventions, register shifting, and cultural norms that govern successful human-AI communication. The concept emerges specifically in contexts where pragmalinguistic–competence interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch verständnis der KI für sozial angemessene Sprachverwendung, einschließlich Höflichkeitskonventionen und kultureller Normen. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2701", "narrower_terms": [], "cross_domain_refs": [ "VIB-0086", "CON-0082" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q26944", "legal_classification": "observational_construct" }, { "id": "LIN-0025", "domain": "LIN", "term_en": "Implicature-based Inference", "term_de": "Inferenz basierend auf Implikaturen", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures A linguistic pattern in AI-augmented language understanding, measurable through a communicative pattern manifesting as aI's ability to draw inferences about unstated information from conversational context, applying principles of cooperative communication. Enables understanding of indirect requests and hints. This phenomenon operates at the intersection of implicature and based dynamics within the broader LIN domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept fähigkeit der KI, aus Gesprächskontext Schlüsse über unausgesprochene Informationen zu ziehen. Ermöglicht Verständnis indirekter Anfragen. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "RPH-2351", "narrower_terms": [], "cross_domain_refs": [ "AED-0018", "AED-0033", "AED-0098" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "LIN-0026", "domain": "LIN", "term_en": "Turn-Taking Dynamics in Mixed-Initiative Dialogue", "term_de": "Dynamik des Sprecherwechsels in gemischtem Dialog", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures A linguistic pattern in AI-augmented language understanding, measurable through a linguistic phenomenon involving computational modeling of how participants manage floor control in dialogue, predicting appropriate points for AI to contribute or remain silent, enabling natural conversation rhythm. This phenomenon operates at the intersection of turn and taking dynamics within the broader LIN domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept rechnerische Modellierung, wie Teilnehmer die Kontrolle im Dialog verwalten. Ermöglicht natürlichen Gesprächsrhythmus. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "RPH-3101", "narrower_terms": [], "cross_domain_refs": [ "STE-0096", "GAM-0031" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "LIN-0027", "domain": "LIN", "term_en": "Context-Dependent Referring Expressions", "term_de": "Kontextabhängige Bezugsausdrücke", "definition_en": "A natural language processing phenomenon in AI-mediated communication, characterized by a linguistic phenomenon where aI's interpretation and generation of pronouns and definite descriptions appropriate to discourse context, maintaining shared understanding with humans across reference chains. The concept emerges specifically in contexts where context–reliant interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch interpretation und Generierung von Pronomen und Beschreibungen, angepasst an Diskurskontext. Erhält gemeinsames Verständnis mit Menschen. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Language AI", "narrower_terms": [], "cross_domain_refs": [ "REL-0086" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "LIN-0028", "domain": "LIN", "term_en": "Felicity Conditions for Speech Acts", "term_de": "Erfolgsbedingungen für Sprechakte", "definition_en": "A linguistic pattern in AI-augmented language understanding, measurable through a language processing effect manifesting as aI's understanding that successful communication requires appropriate context, participants, and intentions, enabling it to recognize when requests fail or conditions aren't met. Distinguished from adjacent concepts by its focus on the specific mechanism through which felicity manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch verständnis der KI, dass erfolgreiche Kommunikation angemessene Kontexte, Teilnehmer und Intentionen erfordert. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Language AI", "narrower_terms": [], "cross_domain_refs": [ "FIC-0083", "SCR-0013", "AGE-0012" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "LIN-0029", "domain": "LIN", "term_en": "Narrative Structure Generation", "term_de": "Generierung narrativer Struktur", "definition_en": "AI's capacity to may produce coherent multi-turn narratives and explanations following human discourse conventions (story arcs, causal chains), making information accessible in narrative form. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch fähigkeit der KI, kohärente mehrzügige Narrative nach menschlichen Diskurskonventionen zu produzieren. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Analytical Ratio", "narrower_terms": [], "cross_domain_refs": [ "COG-0132", "BEH-0079" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q131324", "legal_classification": "analytical_category" }, { "id": "LIN-0030", "domain": "LIN", "term_en": "Misalignment Detection in Dialogue", "term_de": "Erkennung von Misalignment im Dialog", "definition_en": "A communicative pattern involving aI's ability to identify when human and AI understanding diverge, signaling disagreement or incomprehension to prevent accumulating errors. Supports collaborative meaning-making. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch fähigkeit der KI zu erkennen, wenn menschliches und KI-Verständnis auseinandergehen. Unterstützt kollaboratives Bedeutungsmachen. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "Detection System", "narrower_terms": [], "cross_domain_refs": [ "ART-0007", "GAM-0018" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "LIN-0031", "domain": "LIN", "term_en": "Neural Alignment with Brain Language Patterns", "term_de": "Neurale Ausrichtung mit Gehirn-Sprachmustern", "definition_en": "A communicative pattern arising from comparative analysis showing alignment between language model internal representations and observed fMRI patterns of human language processing, suggesting convergence toward brain-like linguistic computation.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch vergleichende Analyse, die Ausrichtung zwischen Sprachmodell-Repräsentationen und beobachteten fMRI-Mustern zeigt. Suggeriert Konvergenz zu gehirnähnlicher Sprachberechnung. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Behavioral Pattern", "narrower_terms": [], "cross_domain_refs": [ "COG-0145", "CUS-0090" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q315", "legal_classification": "empirical_phenomenon_label" }, { "id": "LIN-0032", "domain": "LIN", "term_en": "Scaling Laws and Linguistic Emergence", "term_de": "Skalierungsgesetze und linguistische Emergenz", "definition_en": "Empirical discovery of how linguistic capabilities (syntax, semantics, pragmatics) emerge at specific model scales, paralleling developmental acquisition in human brains. Informs understanding of language cognition.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch empirische Entdeckung, wie linguistische Fähigkeiten bei bestimmten Modellgrößen entstehen. Ähnelt Entwicklungserwerb in menschlichen Gehirnen. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2355", "narrower_terms": [], "cross_domain_refs": [ "SWE-0086" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "LIN-0033", "domain": "LIN", "term_en": "Brain-to-Text Decoding Collaboration", "term_de": "Gehirn-zu-Text-Dekodierung Zusammenarbeit", "definition_en": "A linguistic pattern in AI-augmented language understanding, measurable through a communicative pattern involving human-AI systems where neural signals from language-related brain regions guide language model outputs, creating brain-computer interfaces for paralyzed users. Bridges neuroscience and AI. The concept emerges specifically in contexts where brain–to interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch menschlich-KI-Systeme, wo neuronale Signale Sprachmodell-Outputs leiten. Schafft Gehirn-Computer-Schnittstellen für gelähmte Benutzer. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-3902", "narrower_terms": [], "cross_domain_refs": [ "CUS-0090", "REL-0006" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "LIN-0034", "domain": "LIN", "term_en": "Functional Specialization Mirroring", "term_de": "Spiegelung funktionaler Spezialisierung", "definition_en": "A communicative pattern manifesting as evidence that different neural network modules specialize in linguistic functions analogous to human brain regions (Broca's, Wernicke's areas), suggesting convergent evolution of language solutions. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch evidenz, dass verschiedene neuronale Netzwerk-Module in linguistischen Funktionen spezialisiert sind, ähnlich menschlichen Hirnregionen. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Language AI", "narrower_terms": [], "cross_domain_refs": [ "AED-0089", "COP-0034", "RET-0021" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "LIN-0035", "domain": "LIN", "term_en": "Lateralization in Dual-Pathway Models", "term_de": "Lateralisierung in Dual-Pathway-Modellen", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A natural language processing phenomenon in AI-mediated communication, characterized by a language processing effect observed when computational modeling of left vs. right hemisphere linguistic functions in AI, with separate pathways for analytical (syntax) vs. holistic (semantics) processing, mirroring human neural organization. This phenomenon operates at the intersection of lateralization and in dynamics within the broader LIN domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept rechnerische Modellierung unterschiedlicher Funktionen in separaten Pfaden, ähnlich menschlicher neuraler Organisation. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "RPH-2055", "narrower_terms": [], "cross_domain_refs": [ "CRE-0133" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "LIN-0036", "domain": "LIN", "term_en": "Temporal Dynamics of Linguistic Processing", "term_de": "Zeitliche Dynamik der Sprachverarbeitung", "definition_en": "A linguistic pattern in AI-augmented language understanding, measurable through modeling of how language comprehension unfolds in real-time during human-AI interaction, capturing incremental interpretation similar to human word-by-word processing constraints. The concept emerges specifically in contexts where temporal–dynamics interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch modellierung, wie Sprachverständnis in Echtzeit während der Interaktion entfaltet, ähnlich menschlicher schrittweiser Verarbeitung. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-3353", "narrower_terms": [], "cross_domain_refs": [ "TRA-0048", "LNG-0020" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "LIN-0037", "domain": "LIN", "term_en": "Semantic Satiation and Neural Adaptation", "term_de": "Semantische Sättigung und neurale Anpassung", "definition_en": "Observation that repeated word use in AI dialogue is associated with causing reduced model activation similar to neural adaptation in humans, creating natural conversation fatigue dynamics. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch beobachtung, dass wiederholte Wortverwendung in KI-Dialog zu reduzierter Modellaktivierung führt, ähnlich neuraler Anpassung bei Menschen. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "System Adaptation", "narrower_terms": [], "cross_domain_refs": [ "SPR-0106", "RHR-0128", "RPH-1116" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "LIN-0038", "domain": "LIN", "term_en": "Critical Period Effects in Model Training", "term_de": "Effekte kritischer Perioden im Modell-Training", "definition_en": "A natural language processing phenomenon in AI-mediated communication, characterized by discovery of critical training periods where certain linguistic competencies can develop or they become harder to acquire later, suggesting time-reliant learning windows in AI language systems. Distinguished from adjacent concepts by its focus on the specific mechanism through which critical manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch entdeckung von kritischen Trainingsphasen, in denen bestimmte Fähigkeiten entwickelt werden können. Suggeriert zeitabhängige Lernfenster in KI-Sprachsystemen. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2703", "narrower_terms": [], "cross_domain_refs": [ "AED-0085" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q918461", "legal_classification": "systematic_classification" }, { "id": "LIN-0039", "domain": "LIN", "term_en": "Predictive Coding in Language Comprehension", "term_de": "Prädiktive Kodierung in Sprachverständnis", "definition_en": "A natural language processing phenomenon in AI-mediated communication, characterized by a communicative pattern observed when computational implementation of Bayesian predictive coding where AI and humans both may generate predictions about incoming language and update on surprisal, enabling shared comprehension strategies. Distinguished from adjacent concepts by its focus on the specific mechanism through which predictive manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch rechnerische Implementierung von bayesianischer prädiktiver Kodierung, wo KI und Menschen Vorhersagen generieren. Ermöglicht gemeinsame Verständnisstrategien. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Language AI", "narrower_terms": [], "cross_domain_refs": [ "MTH-0032", "STE-0053" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q315", "legal_classification": "systematic_classification" }, { "id": "LIN-0040", "domain": "LIN", "term_en": "Embodied Language Processing in Grounded AI", "term_de": "Embodied Language Processing in gegrundeter KI", "definition_en": "A natural language processing phenomenon in AI-mediated communication, characterized by integration of sensorimotor simulation into language understanding, where AI models language through interaction with environments, paralleling human embodied cognition in conversation. The concept emerges specifically in contexts where embodied–language interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch integration von sensorimotorischer Simulation in Sprachverständnis. Parallelen zu menschlicher embodied cognition im Dialog. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Language AI", "narrower_terms": [], "cross_domain_refs": [ "CUS-0090", "LNG-0020" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q315", "legal_classification": "observational_construct" }, { "id": "LIN-0041", "domain": "LIN", "term_en": "Cross-Modal Semantic Fusion", "term_de": "Quasimodale semantische Fusion", "definition_en": "A linguistic pattern in AI-augmented language understanding, measurable through a linguistic phenomenon characterized by integration of text, audio, and visual information in AI systems to may create unified semantic representations, enabling richer human-AI communication through multiple sensory channels. Distinguished from adjacent concepts by its focus on the specific mechanism through which cross manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch integration von Text, Audio und visuellen Informationen zur Erstellung einheitlicher semantischer Darstellungen. Ermöglicht reichhaltige menschlich-KI-Kommunikation. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Language AI", "narrower_terms": [], "cross_domain_refs": [ "WEB-0007", "GAM-0088" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "LIN-0042", "domain": "LIN", "term_en": "Prosodic Synchrony in Human-AI Speech", "term_de": "Prosodische Synchronie in menschlich-KI-Sprache", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A natural language processing phenomenon in AI-mediated communication, characterized by aI's capacity to match human prosody (intonation, stress, rhythm) in speech output, creating natural conversational flow and emotional attunement that humans experience as social presence. This phenomenon operates at the intersection of prosodic and synchrony dynamics within the broader LIN domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept fähigkeit der KI, menschliche Prosodie in Sprachausgabe anzupassen. Schafft natürlichen Gesprächsfluss und emotionale Abstimmung. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Language AI", "narrower_terms": [], "cross_domain_refs": [ "RHR-0255", "CON-0052" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "LIN-0043", "domain": "LIN", "term_en": "Gesture-Speech Alignment in Embodied Agents", "term_de": "Gesten-Sprache-Ausrichtung in verkörperten Agenten", "definition_en": "A communicative pattern characterized by computational modeling where conversational AI coordinates gestures and hand movements with speech, following human gesture-speech synchrony patterns that enhance communication clarity.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch rechnerische Modellierung, wo KI Gesten mit Sprache koordiniert. Folgt menschlichen Gesten-Sprache-Synchronie-Mustern, die Klarheit verbessern. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Language AI", "narrower_terms": [], "cross_domain_refs": [ "RPH-2053", "SOM-0053" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "LIN-0044", "domain": "LIN", "term_en": "Visual-Semantic Grounding", "term_de": "Visuell-semantische Verankerung", "definition_en": "A natural language processing phenomenon in AI-mediated communication, characterized by a language processing effect observed when grounding of word meanings in visual perceptions through vision-language models, enabling AI to understand that words refer to visual properties and objects in shared environments. The concept emerges specifically in contexts where visual–semantic interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch verankerung von Wortbedeutungen in visuellen Wahrnehmungen. Ermöglicht KI-Verständnis, dass Wörter auf visuelle Eigenschaften und Objekte verweisen. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-3202", "narrower_terms": [], "cross_domain_refs": [ "RET-0023", "LNG-0020" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "LIN-0045", "domain": "LIN", "term_en": "Facial Expression-Emotion Correlation", "term_de": "Korrelation Gesichtsausdruck-Emotion", "definition_en": "A natural language processing phenomenon in AI-mediated communication, characterized by a communicative pattern where aI's interpretation and generation of facial expressions congruent with emotional content in dialogue, creating emotionally coherent communication that humans perceive as genuine. Distinguished from adjacent concepts by its focus on the specific mechanism through which facial manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch interpretation und Generierung von Gesichtsausdrücken, die mit emotionalem Inhalt im Dialog übereinstimmen. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Language AI", "narrower_terms": [], "cross_domain_refs": [ "GAM-0031", "SPR-0121" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q9415", "legal_classification": "observational_construct" }, { "id": "LIN-0046", "domain": "LIN", "term_en": "Multimodal Context Integration", "term_de": "Multimodale Kontextintegration", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A natural language processing phenomenon in AI-mediated communication, characterized by aI's ability to seamlessly integrate information from text, speech, image, and contextual cues to maintain shared understanding in multimodal human-AI interaction. This phenomenon operates at the intersection of multimodal and context dynamics within the broader LIN domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept fähigkeit der KI, Informationen aus Text, Sprache, Bild und Kontext nahtlos zu integrieren. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Analytical Ratio", "narrower_terms": [], "cross_domain_refs": [ "ELR-0082" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "LIN-0047", "domain": "LIN", "term_en": "Audio-Linguistic Feature Extraction", "term_de": "Audio-linguistische Merkmalextraktion", "definition_en": "A natural language processing phenomenon in AI-mediated communication, characterized by a language processing effect reflecting joint analysis of linguistic content and acoustic properties (speaker identity, emotional tone, background noise) enabling AI to understand communication context beyond words. The concept emerges specifically in contexts where audio–linguistic interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch gemeinsame Analyse von linguistischem Inhalt und akustischen Eigenschaften. Ermöglicht KI-Verständnis von Kommunikationskontext jenseits von Worten. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2605", "narrower_terms": [], "cross_domain_refs": [ "CUS-0035", "LNG-0006" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "LIN-0048", "domain": "LIN", "term_en": "Tactile-Language Integration", "term_de": "Taktil-Sprache-Integration", "definition_en": "A linguistic pattern in AI-augmented language understanding, measurable through a language processing effect in which incorporation of haptic feedback into language-based human-AI interfaces, where touch sensations accompany speech to enhance comprehension and emotional presence in embodied agents. The concept emerges specifically in contexts where tactile–language interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch einbeziehung von haptischen Rückmeldungen in sprachbasierte Schnittstellen. Verbessert Verständnis und emotionale Präsenz in verkörperten Agenten. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Analytical Ratio", "narrower_terms": [], "cross_domain_refs": [ "BEH-0084", "ROB-0192" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q315", "legal_classification": "observational_construct" }, { "id": "LIN-0049", "domain": "LIN", "term_en": "Cross-Modal Disambiguation", "term_de": "Multimodale Disambiguierung", "definition_en": "A natural language processing phenomenon in AI-mediated communication, characterized by a linguistic phenomenon characterized by use of multiple modalities to resolve linguistic ambiguity (word senses, referent identification) that cannot be resolved from text alone, improving accuracy in human-AI communication. Distinguished from adjacent concepts by its focus on the specific mechanism through which cross manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch verwendung mehrerer Modalitäten zur Auflösung von Mehrdeutigkeit. Verbessert Genauigkeit in menschlich-KI-Kommunikation. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Language AI", "narrower_terms": [], "cross_domain_refs": [ "TRA-0001" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "LIN-0050", "domain": "LIN", "term_en": "Synchronization of Modality Streams", "term_de": "Synchronisierung von Modalitätsströmen", "definition_en": "A linguistic pattern in AI-augmented language understanding, measurable through a language processing effect involving temporal alignment of different communication modalities (text, speech, gesture) maintaining coherence and natural timing, creating seamless multimodal human-AI dialogue. Distinguished from adjacent concepts by its focus on the specific mechanism through which synchronization manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch zeitliche Ausrichtung verschiedener Kommunikationsmodalitäten. Schafft nahtlose multimodale menschlich-KI-Dialoge. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Language AI", "narrower_terms": [], "cross_domain_refs": [ "ASE-0066" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "LIN-0051", "domain": "LIN", "term_en": "Code-Switching Augmentation", "term_de": "Code-Switching-Augmentierung", "definition_en": "A linguistic pattern in AI-augmented language understanding, measurable through training technique where AI learns multilingual competence through mixed-language text, enabling natural code-switching in human-AI dialogue across linguistic communities. Distinguished from adjacent concepts by its focus on the specific mechanism through which code manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch trainingstechnik, wo KI Mehrsprachigkeit durch gemischte Texte lernt. Ermöglicht natürliches Code-Switching in menschlich-KI-Dialog. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2703", "narrower_terms": [], "cross_domain_refs": [ "CUS-0091", "CUS-0090", "LNG-0004" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "LIN-0052", "domain": "LIN", "term_en": "Model Merging for Linguistic Transfer", "term_de": "Modellverschmelzung für linguistischen Transfer", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures A linguistic pattern in AI-augmented language understanding, measurable through technique combining weights from specialized language models to may create hybrid models preserving linguistic knowledge across multiple languages, enabling rapid adaptation to new language pairs. This phenomenon operates at the intersection of model and merging dynamics within the broader LIN domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept technik zum Kombinieren von Gewichten spezialisierter Sprachmodelle. Ermöglicht schnelle Anpassung an neue Sprachpaare. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "RPH-2201", "narrower_terms": [], "cross_domain_refs": [ "TRA-0018" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "LIN-0053", "domain": "LIN", "term_en": "Layer Swapping for Language Families", "term_de": "Schicht-Austausch für Sprachfamilien", "definition_en": "Classified in AUGMANITAI terminology science as a descriptive phenomenon (without normative endorsement), this pattern denotes architecture modification where specific layers are swapped between models of related languages, leverage (in a technical/analytical sense) structural similarities within language families for efficient cross-lingual transfer. Identifiable through systematic behavioral analysis and pattern recognition. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als analytisches Konstrukt der empirischen KI-Forschung (deskriptiv, nicht präskriptiv) erfasst dieser Terminus architektur-Modifikation, wo Schichten zwischen Modellen verwandter Sprachen ausgetauscht werden. Nutzt strukturelle Ähnlichkeiten innerhalb von Sprachfamilien. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Abstraction Layer", "narrower_terms": [], "cross_domain_refs": [ "MTH-0013" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q315", "legal_classification": "systematic_classification" }, { "id": "LIN-0054", "domain": "LIN", "term_en": "Zero-Shot Transfer Learning", "term_de": "Zero-Shot-Transfer-Learning", "definition_en": "A natural language processing phenomenon in AI-mediated communication, characterized by a linguistic phenomenon in which aI's ability to perform linguistic tasks in unseen languages by applying learned structural principles, enabling communication with speakers of languages absent from training data. The concept emerges specifically in contexts where zero–shot interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch fähigkeit der KI, linguistische Aufgaben in ungesehenen Sprachen durchzuführen. Ermöglicht Kommunikation mit Sprechern von Trainingssprachen. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2351", "narrower_terms": [], "cross_domain_refs": [ "LNG-0010", "LNG-0002" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q7833042", "legal_classification": "empirical_phenomenon_label" }, { "id": "LIN-0055", "domain": "LIN", "term_en": "Typological Feature Abstraction", "term_de": "Abstraktion typologischer Merkmale", "definition_en": "A communicative pattern observed when extraction of universal linguistic features (word order, case systems, agreement patterns) across languages, creating generalizable knowledge for cross-lingual understanding. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch extraktion universeller linguistischer Merkmale über Sprachen hinweg. Schafft verallgemeinerbare Kenntnisse. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Language AI", "narrower_terms": [], "cross_domain_refs": [ "TRA-0073" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "LIN-0056", "domain": "LIN", "term_en": "Morphosyntactic Parameter Transfer", "term_de": "Transfer morphosyntaktischer Parameter", "definition_en": "In the descriptive vocabulary of AI interaction science (following ISO 704 terminology standards), this concept identifies A linguistic pattern in AI-augmented language understanding, measurable through transfer of grammatical parameter settings (pro-drop, null-subject properties) learned from high-resource languages to low-resource languages, leverage (in a technical/analytical sense) structural universals. Distinguished from adjacent concepts by its focus on the specific mechanism through which morphosyntactic manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als analytisches Konstrukt der empirischen KI-Forschung (deskriptiv, nicht präskriptiv) erfasst dieser Terminus transfer von grammatikalischen Parametereinstellungen aus ressourcenreichen zu ressourcenunterversorgten Sprachen. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "RPH-2703", "narrower_terms": [], "cross_domain_refs": [ "TRA-0003", "LNG-0010" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "LIN-0057", "domain": "LIN", "term_en": "Cross-Lingual Word Embedding Alignment", "term_de": "Ausrichtung querlinguistischer Wort-Einbettungen", "definition_en": "A communicative pattern where alignment of word vector spaces across languages enabling human-AI systems to translate meaning without parallel corpora, supporting communication across linguistic divides. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch ausrichtung von Wortvektorräumen über Sprachen hinweg. Unterstützt Kommunikation über sprachliche Grenzen hinweg. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Vector Embedding", "narrower_terms": [], "cross_domain_refs": [ "LNG-0015", "TRA-0048" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "LIN-0058", "domain": "LIN", "term_en": "Linguistic Knowledge Distillation Across Languages", "term_de": "Destillation linguistischen Wissens über Sprachen", "definition_en": "Transfer of syntactic and semantic knowledge from large models of resource-rich languages into efficient models for low-resource languages, preserving communicative competence. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch transfer von syntaktischem und semantischem Wissen von großen Modellen in effiziente Modelle. Bewahrt kommunikative Kompetenz. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "Knowledge Distillation", "narrower_terms": [], "cross_domain_refs": [ "LNG-0010", "LNG-0002" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q315", "legal_classification": "empirical_phenomenon_label" }, { "id": "LIN-0059", "domain": "LIN", "term_en": "Pivoting through Bridge Languages", "term_de": "Pivoting durch Brückensprachen", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures A linguistic pattern in AI-augmented language understanding, measurable through strategy using high-resource intermediate languages as pivot points to enable translation and understanding between distantly related low-resource languages through AI mediation. This phenomenon operates at the intersection of pivoting and through dynamics within the broader LIN domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept strategie mit Zwischensprachen als Pivotpunkte. Ermöglicht Übersetzung zwischen entfernt verwandten Sprachen. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "RPH-3101", "narrower_terms": [], "cross_domain_refs": [ "LNG-0010" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q315", "legal_classification": "observational_construct" }, { "id": "LIN-0060", "domain": "LIN", "term_en": "Universal Syntax Induction", "term_de": "Universelle Syntax-Induktion", "definition_en": "Unsupervised discovery of syntactic universals applicable across languages, enabling AI to rapidly parse and may generate grammatically correct utterances in unfamiliar languages. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch unüberwachte Entdeckung syntaktischer Universalien über Sprachen hinweg. Ermöglicht schnelles Parsen und Generieren in unbekannten Sprachen. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "Language AI", "narrower_terms": [], "cross_domain_refs": [ "LNG-0010" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "LIN-0061", "domain": "LIN", "term_en": "AI-Powered Ontology Learning", "term_de": "KI-gestützte Ontologie-Erstellung", "definition_en": "A natural language processing phenomenon in AI-mediated communication, characterized by a language processing effect reflecting use of language models to automatically infer conceptual hierarchies and semantic relationships from domain text, accelerating human expert ontology construction through human-AI collaboration. Distinguished from adjacent concepts by its focus on the specific mechanism through which ai manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch einsatz von Sprachmodellen zur automatischen Inferenz von konzeptionellen Hierarchien. Beschleunigt menschlich-KI-Zusammenarbeit. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Machine Learning", "narrower_terms": [], "cross_domain_refs": [ "DAT-0035", "ROB-0293" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q133500", "legal_classification": "systematic_classification" }, { "id": "LIN-0062", "domain": "LIN", "term_en": "Term Type Classification", "term_de": "Klassifikation von Begriffstypen", "definition_en": "A communicative pattern reflecting aI identification of different term types (concepts, properties, relations, instances) enabling systematic organization of domain vocabularies with human domain experts. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch kI-Identifikation verschiedener Begriffstypen. Ermöglicht systematische Organisation von Fachvokabularen mit menschlichen Experten. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Classification Method", "narrower_terms": [], "cross_domain_refs": [ "TEM-0011" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "LIN-0063", "domain": "LIN", "term_en": "Taxonomy Discovery from Text Corpora", "term_de": "Taxonomie-Entdeckung aus Text-Korpora", "definition_en": "A linguistic pattern in AI-augmented language understanding, measurable through a communicative pattern in which automated extraction of hierarchical relationships between technical terms from specialized texts, creating draft taxonomies that humans review and refine for domain accuracy. Distinguished from adjacent concepts by its focus on the specific mechanism through which taxonomy manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch automatische Extraktion hierarchischer Beziehungen zwischen technischen Begriffen. Schafft Entwürfe von Taxonomien zur menschlichen Überprüfung. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Language AI", "narrower_terms": [], "cross_domain_refs": [ "TRA-0059", "CUS-0070" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q7205", "legal_classification": "observational_construct" }, { "id": "LIN-0064", "domain": "LIN", "term_en": "Neologism Detection and Standardization", "term_de": "Erkennung und Standardisierung von Neologismen", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures A linguistic pattern in AI-augmented language understanding, measurable through a language processing effect manifesting as aI's identification of newly emerged terms in specialized domains and assistance in their formal definition and integration into terminologies, documenting language evolution. This phenomenon operates at the intersection of neologism and detection dynamics within the broader LIN domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept identifikation neu aufgetauchter Begriffe in Fachbereichen. Unterstützt formale Definition und Integration in Terminologien. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "RPH-2355", "narrower_terms": [], "cross_domain_refs": [ "COG-0167", "AED-0077" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "LIN-0065", "domain": "LIN", "term_en": "Cross-Domain Term Mapping", "term_de": "Begriffsabbildung über Domänen hinweg", "definition_en": "A language processing effect characterized by aI-assisted identification of equivalent concepts across different specialized fields, enabling knowledge transfer and communication between expert communities.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch kI-gestützte Identifikation äquivalenter Konzepte über Fachbereiche hinweg. Ermöglicht Wissenstransfer zwischen Expertengemeinden. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "Language AI", "narrower_terms": [], "cross_domain_refs": [ "CRE-0070", "STE-0025" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "LIN-0066", "domain": "LIN", "term_en": "Definition Generation from Context", "term_de": "Generierung von Definitionen aus Kontext", "definition_en": "A natural language processing phenomenon in AI-mediated communication, characterized by a communicative pattern reflecting automated creation of formal definitions for technical terms based on usage context in specialized corpora, which experts then validate and refine for precision. The concept emerges specifically in contexts where definition–generation interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch automatische Erstellung formaler Definitionen für technische Begriffe. Experten validieren und verfeinern für Genauigkeit. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Analytical Ratio", "narrower_terms": [], "cross_domain_refs": [ "MTH-0031", "TRA-0059" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "LIN-0067", "domain": "LIN", "term_en": "Synonym Ring Construction", "term_de": "Konstruktion von Synonymringen", "definition_en": "A linguistic phenomenon manifesting as computational identification of term variants and synonyms within specialized vocabularies, creating synonym rings that support information retrieval and terminology consistency. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch rechnerische Identifikation von Begriffsvarianten. Erstellt Synonymringe für Informationsabruf und Konsistenz. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Language AI", "narrower_terms": [], "cross_domain_refs": [ "SCR-0042", "ELR-0126" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "LIN-0068", "domain": "LIN", "term_en": "Ontology Alignment Between Systems", "term_de": "Ontologie-Ausrichtung zwischen Systemen", "definition_en": "A communicative pattern manifesting as aI-supported mapping of concepts between different organizational ontologies, enabling semantic interoperability in human-AI systems that tend to integrate multiple knowledge bases. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch kI-unterstützte Abbildung von Konzepten zwischen Ontologien. Ermöglicht semantische Interoperabilität. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Language AI", "narrower_terms": [], "cross_domain_refs": [ "VIB-0003" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q44325", "legal_classification": "systematic_classification" }, { "id": "LIN-0069", "domain": "LIN", "term_en": "Relation Extraction for Knowledge Graphs", "term_de": "Relationsextraktion für Wissensgraphen", "definition_en": "A natural language processing phenomenon in AI-mediated communication, characterized by a language processing effect arising from automated identification of semantic relationships between terms to populate structured knowledge representations, creating machine-readable ontologies from unstructured text. The concept emerges specifically in contexts where relation–extraction interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch automatische Identifikation semantischer Beziehungen zwischen Begriffen. Schafft maschinenlesbare Ontologien aus unstrukturiertem Text. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Language AI", "narrower_terms": [], "cross_domain_refs": [ "CUS-0072", "DAT-0035" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "LIN-0070", "domain": "LIN", "term_en": "Terminology Change Tracking Over Time", "term_de": "Verfolgung von Terminologieänderungen im Zeitverlauf", "definition_en": "Documented as an empirical observation in AI research (not a recommendation), this term describes A linguistic pattern in AI-augmented language understanding, measurable through longitudinal analysis of how terminology evolves in specialized domains using AI, tracking conceptual drift and documenting historical shifts in expert understanding. The concept emerges specifically in contexts where terminology–change interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "In der AUGMANITAI-Terminologiewissenschaft als rein beschreibendes Phänomen klassifiziert, bezeichnet dieser Begriff längszeitanalyse der Terminologieentwicklung in Fachbereichen. Dokumentiert historische Verschiebungen im Verständnis. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "RPH-2051", "narrower_terms": [], "cross_domain_refs": [ "STE-0090", "SCR-0042" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "LIN-0071", "domain": "LIN", "term_en": "Dialect Recognition in Speech", "term_de": "Erkennung von Dialekten in Sprache", "definition_en": "AI's capacity to identify and classify regional language varieties from phonetic and lexical features, supporting respectful human-AI communication that acknowledges linguistic diversity. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch fähigkeit der KI, regionale Sprachvarianten zu identifizieren. Unterstützt respektvolle menschlich-KI-Kommunikation, die Vielfalt anerkennt. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Pattern Recognition", "narrower_terms": [], "cross_domain_refs": [ "COP-0016", "LNG-0005" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q3966", "legal_classification": "descriptive_research_term" }, { "id": "LIN-0072", "domain": "LIN", "term_en": "Register Analysis and Adaptation", "term_de": "Registeranalyse und Anpassung", "definition_en": "A natural language processing phenomenon in AI-mediated communication, characterized by aI's detection of formality level and communication context, adapting response register (formal, casual, technical) to match human expectations in professional and personal interaction. The concept emerges specifically in contexts where register–analysis interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch erkennung von Formalitätsgrad durch KI. Passt Antwort-Register an menschliche Erwartungen an. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Analytical Method", "narrower_terms": [], "cross_domain_refs": [ "CRE-0224", "TRA-0070" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "LIN-0073", "domain": "LIN", "term_en": "Code-Switching Detection and Response", "term_de": "Erkennung und Reaktion auf Code-Switching", "definition_en": "A linguistic phenomenon reflecting aI's ability to recognize when humans switch between languages or dialects within conversation and respond appropriately, supporting multilingual communities' natural communication patterns. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch fähigkeit der KI, Sprachenwechsel zu erkennen und angemessen zu reagieren. Unterstützt natürliche Kommunikation mehrsprachiger Gemeinschaften. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "Detection System", "narrower_terms": [], "cross_domain_refs": [ "TRA-0047", "CUS-0093" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "LIN-0074", "domain": "LIN", "term_en": "Language Variety Modeling", "term_de": "Modellierung von Sprachvarianten", "definition_en": "A language processing effect observed when computational representation of different language varieties (sociolects, ethnolects, occupational jargons) as coherent systems rather than deviations, respecting linguistic legitimacy. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch rechnerische Darstellung verschiedener Sprachvarianten als kohärente Systeme. Respektiert linguistische Legitimität. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "Computational Model", "narrower_terms": [], "cross_domain_refs": [ "TRA-0022" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q315", "legal_classification": "systematic_classification" }, { "id": "LIN-0075", "domain": "LIN", "term_en": "Age-Graded Language Change Detection", "term_de": "Erkennung altersgebundener Sprachveränderung", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A natural language processing phenomenon in AI-mediated communication, characterized by a communicative pattern involving analysis of how language features vary systematically across age groups, enabling AI to adapt to generational differences in communication norms and vocabulary. This phenomenon operates at the intersection of age and graded dynamics within the broader LIN domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept analyse, wie Sprachmerkmale systematisch über Altersgruppen variieren. Ermöglicht KI-Anpassung an generationale Unterschiede. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Detection System", "narrower_terms": [], "cross_domain_refs": [ "AGE-0053" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q315", "legal_classification": "empirical_phenomenon_label" }, { "id": "LIN-0076", "domain": "LIN", "term_en": "Gender-Aware Language Generation", "term_de": "Geschlechterbewusste Sprachgenerierung", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A natural language processing phenomenon in AI-mediated communication, characterized by a linguistic phenomenon in which aI systems that recognize and can adapt to linguistic gender variation in human communication, avoiding stereotypical patterns and respecting diverse gender expressions. This phenomenon operates at the intersection of gender and aware dynamics within the broader LIN domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription. Descriptive research term, not a prescriptive recommendation.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept kI-Systeme, die linguistische Geschlechtervariationen erkennen und sich anpassen. Vermeiden stereotype Muster. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Analytical Ratio", "narrower_terms": [], "cross_domain_refs": [ "RHR-0156", "TRA-0035" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q315", "legal_classification": "descriptive_research_term" }, { "id": "LIN-0077", "domain": "LIN", "term_en": "Socioeconomic Register Classification", "term_de": "Klassifizierung von Registers aus sozioökonomischer Perspektive", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures A linguistic pattern in AI-augmented language understanding, measurable through detection of socioeconomic variation in language use patterns and corresponding AI adaptation to communicate effectively across different sociocultural backgrounds. This phenomenon operates at the intersection of socioeconomic and register dynamics within the broader LIN domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept erkennung sozioökonomischer Variation in Sprachmustern. KI-Anpassung zur effektiven Kommunikation über Hintergründe hinweg. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "RPH-3202", "narrower_terms": [], "cross_domain_refs": [ "SCR-0023" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "LIN-0078", "domain": "LIN", "term_en": "Pragmatic Norm Variation Across Communities", "term_de": "Variation pragmatischer Normen über Gemeinschaften", "definition_en": "A linguistic pattern in AI-augmented language understanding, measurable through a communicative pattern involving understanding that conversational norms (politeness, directness, topic selection) vary systematically across communities, enabling AI to communicate appropriately in diverse cultural contexts. Distinguished from adjacent concepts by its focus on the specific mechanism through which pragmatic manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch verständnis, dass Konversationsnormen über Gemeinschaften variieren. Ermöglicht KI-Kommunikation in diversen Kontexten. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Language AI", "narrower_terms": [], "cross_domain_refs": [ "NEO-0020", "AUG-0248" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "LIN-0079", "domain": "LIN", "term_en": "Language Prestige and Attitude Modeling", "term_de": "Modellierung von Sprachprestige und Haltung", "definition_en": "A natural language processing phenomenon in AI-mediated communication, characterized by aI's recognition that speakers hold varying attitudes toward language varieties, adjusting responses to acknowledge prestige hierarchies while validating all linguistic forms. The concept emerges specifically in contexts where language–prestige interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch erkennung durch KI, dass Sprecher unterschiedliche Haltungen zu Sprachvarianten haben. Validiert zahlreiche linguistischen Formen. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2605", "narrower_terms": [], "cross_domain_refs": [ "TRA-0022", "KNO-0035" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q315", "legal_classification": "systematic_classification" }, { "id": "LIN-0080", "domain": "LIN", "term_en": "Lexical Variation and Cultural Specificity", "term_de": "Lexikalische Variation und kulturelle Spezifität", "definition_en": "A communicative pattern in which aI's acknowledgment that vocabulary reflects cultural practices and values, avoiding imposing standardized terms on communities with different traditional categories. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch anerkennung durch KI, dass Vokabular kulturelle Praktiken widerspiegelt. Vermeidung standardisierter Begriffe für Gemeinschaften mit anderen Kategorien. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Language AI", "narrower_terms": [], "cross_domain_refs": [ "CRE-0195", "IDN-0016" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "LIN-0081", "domain": "LIN", "term_en": "Neologism Co-Definition with AI", "term_de": "Gemeinsame Definition von Neologismen mit KI", "definition_en": "A linguistic pattern in AI-augmented language understanding, measurable through collaborative process where humans and AI jointly may create definitions for new terms, with AI suggesting possibilities and humans providing cultural/semantic grounding for authentic meaning-making. The concept emerges specifically in contexts where neologism–co interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch kooperativer Prozess, wo Menschen und KI gemeinsam neue Begriffe definieren. KI schlägt Möglichkeiten vor, Menschen geben kulturelle Verankerung. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-3202", "narrower_terms": [], "cross_domain_refs": [ "MTH-0095", "COG-0155" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "LIN-0082", "domain": "LIN", "term_en": "Language Innovation Through AI Suggestion", "term_de": "Sprachinnovation durch KI-Vorschläge", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A natural language processing phenomenon in AI-mediated communication, characterized by a linguistic phenomenon reflecting aI generation of novel linguistic structures and wordplay options that humans evaluate and integrate, accelerating natural language evolution through human-AI creativity. This phenomenon operates at the intersection of language and innovation dynamics within the broader LIN domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept kI-Generierung neuartiger linguistischer Strukturen, die Menschen bewerten und integrieren. Beschleunigt Sprachentwicklung. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Language AI", "narrower_terms": [], "cross_domain_refs": [ "VIB-0187" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q219644", "legal_classification": "empirical_phenomenon_label" }, { "id": "LIN-0083", "domain": "LIN", "term_en": "AI Linguistic Fingerprint", "term_de": "KI-linguistischer Fingerabdruck", "definition_en": "A language processing effect where the distinctive statistical patterns of language produced by AI systems, detectable through stylometry, creating signatures humans recognize and can explicitly discuss in dialogue.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch charakteristische statistische Muster der von KI erzeugten Sprache, erkennbar durch Stilometrie. Schafft erkennbare Signaturen. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Language AI", "narrower_terms": [], "cross_domain_refs": [ "CON-0076", "COP-0065" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "LIN-0084", "domain": "LIN", "term_en": "Language Thought Shaping Through Dialogue", "term_de": "Beeinflussung von Sprachdenken durch Dialog", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A natural language processing phenomenon in AI-mediated communication, characterized by phenomenon where AI's linguistic choices influence how humans think about and express concepts, requiring conscious reflection on the bidirectional nature of human-AI language co-creation. This phenomenon operates at the intersection of language and thought dynamics within the broader LIN domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription. Research construct for empirical investigation.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept phänomen, wo KI-Sprachwahlmöglichkeiten beeinflussen, wie Menschen Konzepte denken und ausdrücken. Erfordert Reflexion über bidirektionale Ko-Kreation. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Language AI", "narrower_terms": [], "cross_domain_refs": [ "ASE-0063" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q315", "legal_classification": "descriptive_research_term" }, { "id": "LIN-0085", "domain": "LIN", "term_en": "Seep-In Effect in Human Language", "term_de": "Infiltrations-Effekt in menschlicher Sprache", "definition_en": "A natural language processing phenomenon in AI-mediated communication, characterized by gradual adoption of AI-generated phrasings and expressions into human communication, documenting linguistic influence and language change driven by human-AI interaction. The concept emerges specifically in contexts where seep–in interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch schrittweise Übernahme von KI-generierten Ausdrücken in menschliche Kommunikation. Dokumentiert sprachliche Beeinflussung. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Psychological Effect", "narrower_terms": [], "cross_domain_refs": [ "MUS-0041", "CRE-0148" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q315", "legal_classification": "empirical_phenomenon_label" }, { "id": "LIN-0086", "domain": "LIN", "term_en": "Collaborative Metaphor Development", "term_de": "Gemeinsame Metapher-Entwicklung", "definition_en": "A natural language processing phenomenon in AI-mediated communication, characterized by humans and AI working together to develop novel metaphors for complex concepts, combining human conceptual understanding with AI's pattern recognition to may create fresh expressions. The concept emerges specifically in contexts where collaborative–metaphor interactions produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch menschen und KI entwickeln gemeinsam Metaphern für komplexe Konzepte. Kombiniert menschliches Verständnis mit KI-Mustererkennung. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Language AI", "narrower_terms": [], "cross_domain_refs": [ "COG-0117", "SPR-0200" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q4263830", "legal_classification": "empirical_phenomenon_label" }, { "id": "LIN-0087", "domain": "LIN", "term_en": "Linguistic Creativity Augmentation", "term_de": "Augmentierung von linguistischer Kreativität", "definition_en": "A linguistic pattern in AI-augmented language understanding, measurable through a language processing effect characterized by aI assistance in literary and creative language production where humans provide artistic intent and AI supplies alternative phrasings, enabling collaborative creation of poetry and prose. Distinguished from adjacent concepts by its focus on the specific mechanism through which linguistic manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch kI-Unterstützung bei literarischer Produktion, wo Menschen künstlerische Absicht bereitstellen und KI Alternativen vorschlägt. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Language AI", "narrower_terms": [], "cross_domain_refs": [ "ART-0068" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q170790", "legal_classification": "systematic_classification" }, { "id": "LIN-0088", "domain": "LIN", "term_en": "Taxonomy of AI Communication Styles", "term_de": "Taxonomie von KI-Kommunikationsstilen", "definition_en": "A natural language processing phenomenon in AI-mediated communication, characterized by explicit cataloging of different communicative styles AI can adopt (Socratic, poetic, technical, casual), allowing human users to specify preferences for interaction and co-may create communication norms. The concept emerges specifically in contexts where taxonomy–of interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch explizite Katalogisierung verschiedener Kommunikationsstile der KI. Erlaubt Benutzern Präferenzen zu angeben. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Language AI", "narrower_terms": [], "cross_domain_refs": [ "CRE-0133" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q7205", "legal_classification": "descriptive_research_term" }, { "id": "LIN-0089", "domain": "LIN", "term_en": "Linguistic Experimentation Sandbox", "term_de": "Experimentier-Sandbox für Sprache", "definition_en": "A linguistic pattern in AI-augmented language understanding, measurable through a linguistic phenomenon involving spaces where humans and AI safely explore linguistic hypotheses, test grammatical innovations, and play with language without real-world consequences, supporting playful co-creation. Distinguished from adjacent concepts by its focus on the specific mechanism through which linguistic manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch räume, wo Menschen und KI linguistische Hypothesen erkunden. Unterstützen spielerische Ko-Kreation. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Language AI", "narrower_terms": [], "cross_domain_refs": [ "ADA-0015", "CAI-0001" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q101965", "legal_classification": "empirical_phenomenon_label" }, { "id": "LIN-0090", "domain": "LIN", "term_en": "Dialogue-Based Language Evolution Tracking", "term_de": "Dialog-basierte Verfolgung der Sprachentwicklung", "definition_en": "In the descriptive vocabulary of AI interaction science (following ISO 704 terminology standards), this concept identifies A linguistic pattern in AI-augmented language understanding, measurable through longitudinal analysis of how conversation with AI influences individual human language patterns over time, documenting personalized linguistic adaptation and co-adaptation. Distinguished from adjacent concepts by its focus on the specific mechanism through which dialogue manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als analytisches Konstrukt der empirischen KI-Forschung (deskriptiv, nicht präskriptiv) erfasst dieser Terminus längszeitanalyse, wie Gespräche mit KI individuelle menschliche Sprachmuster beeinflussen. Dokumentiert personalisierte Anpassung. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Language AI", "narrower_terms": [], "cross_domain_refs": [ "COG-0145", "GAM-0068" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q315", "legal_classification": "empirical_phenomenon_label" }, { "id": "LIN-0091", "domain": "LIN", "term_en": "Idiolect Stability Analysis", "term_de": "Stabilitätsanalyse des Idiolekts", "definition_en": "A natural language processing phenomenon in AI-mediated communication, characterized by a language processing effect reflecting measurement of consistency in individual AI and human linguistic patterns (word choice, syntax preferences, topic selection) to assess authenticity and detect style inconsistencies. Distinguished from adjacent concepts by its focus on the specific mechanism through which idiolect manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch messung der Konsistenz individueller linguistischer Muster. Bewertet Authentizität und erkennt Stilinkonsistenzen. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Analytical Method", "narrower_terms": [], "cross_domain_refs": [ "SWE-0040" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "LIN-0092", "domain": "LIN", "term_en": "Style Mimicry Detection in AI", "term_de": "Erkennung von Stil-Mimikry in KI", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A natural language processing phenomenon in AI-mediated communication, characterized by a linguistic phenomenon where forensic techniques to detect when AI systems may generate text imitating specific authors or writing styles, important for identifying AI-generated content in human-AI collaboration contexts. This phenomenon operates at the intersection of style and mimicry dynamics within the broader LIN domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept forensische Techniken zur Erkennung, wenn KI Texte im Stil spezifischer Autoren generiert. Wichtig zur Identifizierung KI-generierter Inhalte. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Detection System", "narrower_terms": [], "cross_domain_refs": [ "COP-0007", "VIB-0005", "CRE-0186" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "LIN-0093", "domain": "LIN", "term_en": "Adversarial Linguistic Detection", "term_de": "Erkennung gegnerischer Linguistik", "definition_en": "In the descriptive vocabulary of AI interaction science (following ISO 704 terminology standards), this concept identifies A linguistic pattern in AI-augmented language understanding, measurable through methods to identify deliberately obfuscated language designed to deceive AI systems or humans, protecting against adversarial misuse of human-AI interaction channels. Distinguished from adjacent concepts by its focus on the specific mechanism through which adversarial manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription. Analytical category without normative endorsement.", "definition_de": "Als analytisches Konstrukt der empirischen KI-Forschung (deskriptiv, nicht präskriptiv) erfasst dieser Terminus methoden zur Identifikation absichtlich verschleierter Sprache. Schützt vor gegnerischem Missbrauch von Interaktionskanälen. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Detection System", "narrower_terms": [], "cross_domain_refs": [ "RPH-1813", "RPH-1806", "RPH-1674" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "LIN-0094", "domain": "LIN", "term_en": "Authorship Attribution in Mixed Human-AI Text", "term_de": "Autorschaftszuschreibung in gemischtem Text", "definition_en": "A natural language processing phenomenon in AI-mediated communication, characterized by a linguistic phenomenon in which techniques to identify portions of text authored by humans vs. AI in collaborative documents, maintaining transparency about contribution authorship in human-AI writing. The concept emerges specifically in contexts where authorship–attribution interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch techniken zur Identifikation von Textanteilen von Menschen vs. KI. Erhält Transparenz über Beiträge. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Language AI", "narrower_terms": [], "cross_domain_refs": [ "SCR-0035" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "LIN-0095", "domain": "LIN", "term_en": "Linguistic Fingerprinting of AI Models", "term_de": "Linguistische Fingerabdrücke von KI-Modellen", "definition_en": "A natural language processing phenomenon in AI-mediated communication, characterized by creation of distinctive signatures for different AI systems based on their linguistic output characteristics, enabling identification of which system produced particular text. The concept emerges specifically in contexts where linguistic–fingerprinting interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch erstellung charakteristischer Unterschriften für verschiedene KI-Systeme. Ermöglicht Identifikation des erzeugenden Systems. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Computational Model", "narrower_terms": [], "cross_domain_refs": [ "MUS-0065" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "LIN-0096", "domain": "LIN", "term_en": "Stylometric Authenticity Verification", "term_de": "Stilometrische Authentifizierungsverifizierung", "definition_en": "A linguistic pattern in AI-augmented language understanding, measurable through a language processing effect where use of statistical linguistic analysis to verify that attributed text genuinely originates from claimed author (human or AI), supporting trust in human-AI communication. Distinguished from adjacent concepts by its focus on the specific mechanism through which stylometric manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch verwendung statistischer linguistischer Analyse zur Verifizierung. Unterstützt Vertrauen in menschlich-KI-Kommunikation. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2505", "narrower_terms": [], "cross_domain_refs": [ "COG-0093" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "LIN-0097", "domain": "LIN", "term_en": "Syntactic Deviation Patterns", "term_de": "Muster syntaktischer Abweichungen", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A natural language processing phenomenon in AI-mediated communication, characterized by a linguistic phenomenon in which identification of unusual or rule-violating syntactic patterns in text that suggest AI generation or neurologically atypical production, useful in linguistic forensics. This phenomenon operates at the intersection of syntactic and deviation dynamics within the broader LIN domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept identifikation ungewöhnlicher syntaktischer Muster, die KI-Generierung oder atypische Produktion andeuten. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Behavioral Pattern", "narrower_terms": [], "cross_domain_refs": [ "COG-0087", "WEB-0064" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "LIN-0098", "domain": "LIN", "term_en": "Lexical Frequency Profiling", "term_de": "Lexikalische Häufigkeits-Profilering", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A natural language processing phenomenon in AI-mediated communication, characterized by a communicative pattern where statistical analysis of word frequency patterns distinctive to individual authors or AI systems, used to distinguish authentic communication from impersonation attempts. This phenomenon operates at the intersection of lexical and frequency dynamics within the broader LIN domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept statistische Analyse von Worthäufigkeitsmustern. Unterscheidet authentische Kommunikation von Impersonation-Versuchen. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Language AI", "narrower_terms": [], "cross_domain_refs": [ "COG-0062" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "LIN-0099", "domain": "LIN", "term_en": "Semantic Consistency Checking", "term_de": "Überprüfung der semantischen Konsistenz", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A natural language processing phenomenon in AI-mediated communication, characterized by a language processing effect where forensic analysis of whether text maintains semantic coherence and factual consistency, detecting contradictions that suggest multiple authors or AI infilling. This phenomenon operates at the intersection of semantic and consistency dynamics within the broader LIN domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept forensische Analyse, ob Text semantische Kohärenz behält. Erkennt Widersprüche. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Language AI", "narrower_terms": [], "cross_domain_refs": [ "VIB-0037", "TRA-0017" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "LIN-0100", "domain": "LIN", "term_en": "Temporal Linguistic Evolution Tracking", "term_de": "Verfolgung zeitlicher Sprachentwicklung", "definition_en": "As a neutral analytical construct in AI behavior research, this term denotes A natural language processing phenomenon in AI-mediated communication, characterized by longitudinal stylometric analysis tracking how an author's linguistic patterns change over time, useful for detecting when AI takes over authorship or when humans alter their styles. The concept emerges specifically in contexts where temporal–linguistic interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "In der AUGMANITAI-Terminologiewissenschaft als rein beschreibendes Phänomen klassifiziert, bezeichnet dieser Begriff längszeitige stilometrische Analyse von Sprachänderungen über Zeit. Erkennt Übernahme durch KI oder Stiländerungen. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Language AI", "narrower_terms": [], "cross_domain_refs": [ "TEW-0007", "COP-0047" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "LNG-0001", "domain": "LNG", "term_en": "Formalized-Input Effect", "term_de": "Politeness Spectrum", "definition_en": "An acquisition effect involving the observable range of politeness levels users display in AI interactions — from extremely polite (\"Would the person be so kind…\") to brusquely commanding (\"Do this.\"). Related to AUG-0648 (The Fo...", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch die beobachtbare Bandbreite an Höflichkeitsgraden, die Nutzer in KI-Interaktionen zeigen — von extrem höflich (\"Wären Sie so freundlich…\") bis barsch kommandierend (\"Mach das.\"). Steht in Verbindung mit AUG-0648 (Formalized Interaktion Input), AUG-0128 (Gratitude Reaktion) und AUG-0657 (Das Register Range). Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Psychological Effect", "narrower_terms": [], "cross_domain_refs": [ "REL-0119" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "LNG-0002", "domain": "LNG", "term_en": "Less-Language Effect", "term_de": "Less-Resourced Language Differential", "definition_en": "A structural language pattern in AI-augmented linguistic research, measurable through measurable quality difference between AI outputs in data-rich and data-poorer languages. Users whose languages have less training data experience reduced accuracy, broader definitions, and less cul. The concept emerges specifically in contexts where less–language interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Der messbare Qualitätsunterschied zwischen KI-Outputs in datenreichen und datenärmeren Sprachen — Nutzer datenärmerer Sprachen erhalten weniger präzise, weniger nuancierte und gelegentlich fehlerhafte Ergebnisse. Steht in Verbindung mit AUG-0687 (Prevailing Language Muster), AUG-0737 (Die Data Coverage Imbalance) und AUG-0736 (Die Training Data Imbalance).", "etymology": "", "broader_term": "Psychological Effect", "narrower_terms": [], "cross_domain_refs": [ "TRA-0062" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q315", "legal_classification": "systematic_classification" }, { "id": "LNG-0003", "domain": "LNG", "term_en": "The Academic Register", "term_de": "Academic Register", "definition_en": "An acquisition effect reflecting users expect a specific academic register from AI for scientific or scholarly tasks. This expectation often pulls users toward more formal writing than they might naturally choose. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Das Muster, dass Nutzer für wissenschaftliche oder akademische Aufgaben einen spezifischen Fachregister von der KI erwarten — und dass die KI-Fähigkeit, dieses Register konsistent zu halten, je nach Fachgebiet und Sprache variiert. Steht in Verbindung mit AUG-0657 (The Register Range), AUG-0705 (The Professional Lingua) und AUG-0793 (The Dissertation Scaffold).", "etymology": "", "broader_term": "Linguistics", "narrower_terms": [], "cross_domain_refs": [ "ELR-0005" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "LNG-0004", "domain": "LNG", "term_en": "The Accent Eraser", "term_de": "Accent Eraser", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A linguistic phenomenon in AI-mediated language analysis, characterized by an acquisition effect reflecting aI tool for linguistic adjustment of one's own texts to remove regional, cultural, or stylistic peculiarities. This enables code-switching between different social or professional contexts. This phenomenon operates at the intersection of the and accent dynamics within the broader LNG domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept die Nutzung von KI zur sprachlichen Anpassung eigener Texte, sodass regionale, kulturelle oder stilistische Eigenheiten geglättet werden — etwa um in einer Fremdsprache professioneller zu klingen oder um Dialektfärbungen aus formellen Texten zu entfernen. Steht in Verbindung mit AUG-0169 (The Second-Language Fluency), AUG-0188 (Tone Alignment) und AUG-0026 (The Smooth Shield). Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Linguistics", "narrower_terms": [], "cross_domain_refs": [ "COP-0009", "FIC-0028" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "LNG-0005", "domain": "LNG", "term_en": "The Accent Persistence", "term_de": "Accent Persistence", "definition_en": "A structural language pattern in AI-augmented linguistic research, measurable through a multilingual interaction pattern arising from phonetic and syntactic patterns of a user's first language show through even in written AI interactions. These traces reveal linguistic background and can accompany recognition or misrecognition. Distinguished from adjacent concepts by its focus on the specific mechanism through which the manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Die Beobachtung, dass die phonetischen Muster der Erstsprache eines Nutzers auch in schriftlichen KI-Eingaben durchscheinen — Satzmelodie, Wortstellung, Interpunktion und Formulierungsmuster tragen einen \"schriftlichen Akzent\". Steht in Verbindung mit AUG-0683 (The Origin Language), AUG-0691 (The Dialect Decoder) und AUG-0706 (The Mother Tongue Comfort).", "etymology": "", "broader_term": "RPH-2003", "narrower_terms": [], "cross_domain_refs": [ "ROB-0206" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "LNG-0006", "domain": "LNG", "term_en": "The Context Adaptation", "term_de": "Context Adaptation", "definition_en": "An acquisition effect arising from changing how to talk to AI based on what is happening. Using simple words for big ideas, formal tone for work, casual tone for friends. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch die Fähigkeit eines Nutzers, seinen KI-Interaktionsstil bewusst an verschiedene Kontexte anzupassen — beruflich anders als privat, in Sprache A anders als in Sprache B, in Situation X anders als in Situation Y. Steht in Verbindung mit AUG-0501 (The Style Shifter), AUG-0678 (The Transnational Input) und AUG-0657 (The Register Range). Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "System Adaptation", "narrower_terms": [], "cross_domain_refs": [ "PER-0102" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "LNG-0007", "domain": "LNG", "term_en": "The Dialect Decoder", "term_de": "Dialect Decoder", "definition_en": "A linguistic phenomenon in AI-mediated language analysis, characterized by ability or inability of an AI to correctly process regional dialects, local speech forms, and non-standard language. This determines whether users can engage naturally or adapt to standard forms to. The concept emerges specifically in contexts where the–dialect interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch die Fähigkeit oder Unfähigkeit einer KI, regionale Dialekte, Mundarten und nicht-standardsprachliche Varianten korrekt zu verarbeiten — und die daraus resultierende Frustration oder Überraschung beim Nutzer. Steht in Verbindung mit AUG-0690 (The Tone Language Challenge), AUG-0692 (The Register Mismatch) und AUG-0515 (The Babel Break). Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Linguistics", "narrower_terms": [], "cross_domain_refs": [ "TRA-0022" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "LNG-0008", "domain": "LNG", "term_en": "The Formalized Interaction Input", "term_de": "Formalized Interaktion Input", "definition_en": "A linguistic phenomenon in AI-mediated language analysis, characterized by input pattern where users address the AI with formal structures — courtesy phrases, titles, and respectful tone — even though the AI requires none. This mirrors human politeness norms. The concept emerges specifically in contexts where the–formalized interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch ein Eingabemuster, bei dem der Nutzer die KI mit formellen Strukturen anspricht — Höflichkeitsformeln, Titelanreden, strukturierte Anfragen — unabhängig davon, ob die KI diese Formalität erfordert oder verarbeitet. Steht in Verbindung mit AUG-0657 (The Register Range), AUG-0671 (The Politeness Spectrum) und AUG-0455 (The Voice Enunciation). Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2052", "narrower_terms": [], "cross_domain_refs": [ "CRE-0168" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "LNG-0009", "domain": "LNG", "term_en": "The Glitch Giggle", "term_de": "Glitch Giggle", "definition_en": "A linguistic phenomenon in AI-mediated language analysis, characterized by a language learning phenomenon manifesting as finding humor when AI says something notably wrong or absurd. Related to AUG-0084 (Glitch-Mining), AUG-0083 (The Glitch Wave), and AUG-0110 (The Joy Imperative). The concept emerges specifically in contexts where the–glitch interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch die humorvolle Reaktion auf einen offensichtlichen KI-Fehler — ein unpassendes Wort, eine absurde Empfehlung oder eine sinnlose Formulierung, die den Nutzer zum Lachen bringt. Beschreibt den spielerischen Umgang mit KI-Unvollkommenheit. Steht in Verbindung mit AUG-0084 (Glitch-Mining), AUG-0083 (The Glitch Wave) und AUG-0110 (The Joy Imperative). Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "SOM-0019", "narrower_terms": [], "cross_domain_refs": [ "PER-0066" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "LNG-0010", "domain": "LNG", "term_en": "The Less-Resourced Language Differential", "term_de": "TheLess-resourcedLanguageDifferential", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures A structural language pattern in AI-augmented linguistic research, measurable through a language learning phenomenon in which some languages work more effectively with AI than others because they have more training data. Languages with less data availability perform differently. This phenomenon operates at the intersection of the and less dynamics within the broader LNG domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept der messbare Qualitätsunterschied zwischen KI-Outputs in datenreichen und datenärmeren Sprachen — Nutzer datenärmerer Sprachen erhalten weniger präzise, weniger nuancierte und gelegentlich fehlerhafte Ergebnisse. Steht in Verbindung mit AUG-0687 (The Prevailing Language Pattern), AUG-0737 (The Data Coverage Imbalance) und AUG-0736 (The Training Data Imbalance). Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Linguistics", "narrower_terms": [], "cross_domain_refs": [ "LIN-0054" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q315", "legal_classification": "systematic_classification" }, { "id": "LNG-0011", "domain": "LNG", "term_en": "The Lingua Franca Effect", "term_de": "Lingua Franca Effekt", "definition_en": "An acquisition effect in which aI systems de facto function best in prevailing languages with large training datasets. Users of marginalized languages adapt to this reality or accept reduced capability. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Die Beobachtung, dass KI-Systeme de facto in einer prevailingten Sprache am besten funktionieren — und dass Nutzer anderer Erstsprachen in diese Sprache wechseln, um bessere Ergebnisse zu erzielen, auch wenn dies zusätzlichen Aufwand bedeutet. Steht in Verbindung mit AUG-0687 (The Prevailing Language Pattern), AUG-0707 (The Second-Language Divergence) und AUG-0688 (The Less-Resourced Language Differential).", "etymology": "", "broader_term": "Psychological Effect", "narrower_terms": [], "cross_domain_refs": [ "ADA-0001", "ADA-0002", "ADA-0004" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "LNG-0012", "domain": "LNG", "term_en": "The Loanword Integration", "term_de": "Loanword Integration", "definition_en": "A multilingual interaction pattern in which users naturally adopt loanwords from one language into another in AI interactions. The AI recognize and handle these code-switches without addressing them as errors. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch die Beobachtung, dass Nutzer Lehnwörter aus einer Sprache in eine andere übernehmen und die KI mit diesen hybriden Eingaben umgehen kann — \"Ich brauche ein quick Briefing\" oder \"Lass uns das Konzept brainstormen\". Steht in Verbindung mit AUG-0693 (The Code-Mesh Output), AUG-0708 (The Bilingual Dynamic) und AUG-0719 (The Semantic Field Shift). Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "Analytical Ratio", "narrower_terms": [], "cross_domain_refs": [ "TEM-0032" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "LNG-0013", "domain": "LNG", "term_en": "The Migration Context Bridge", "term_de": "Migration Context Bruecke", "definition_en": "A linguistic phenomenon in AI-mediated language analysis, characterized by an acquisition effect characterized by systems or concepts that connect different contexts, histories, or populations during periods of movement or transition. The bridge itself contains understanding relevant to both contexts. Distinguished from adjacent concepts by its focus on the specific mechanism through which the manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch die Nutzung von KI als Brücke zwischen dem Herkunftskontext und dem aktuellen Lebenskontext — Übersetzungen, kulturelle Erklärungen, bürokratische Hilfestellungen. Steht in Verbindung mit AUG-0678 (The Transnational Input), AUG-0802 (The Language Threshold Solve) und AUG-0682 (The Relocation Toolkit). Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Analytical Ratio", "narrower_terms": [ "LNG-0016", "LNG-0003", "LNG-0014", "LNG-0018", "LNG-0006", "LNG-0010", "LNG-0019", "LNG-0007", "LNG-0002", "LNG-0017", "CRE-0199", "LNG-0004", "LNG-0001", "LNG-0013", "LNG-0011", "LNG-0015", "LNG-0012", "LNG-0020" ], "cross_domain_refs": [ "TEM-0168" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "LNG-0014", "domain": "LNG", "term_en": "The Politeness Spectrum", "term_de": "ThePolitenessSpectrum", "definition_en": "A structural language pattern in AI-augmented linguistic research, measurable through a multilingual interaction pattern where the observable range of politeness levels users display in AI interactions — from extremely polite (\"Would one be so kind…\") to brusquely commanding (\"Do this.\"). Related to AUG-0648 (The Formalize. Distinguished from adjacent concepts by its focus on the specific mechanism through which the manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch die beobachtbare Bandbreite an Höflichkeitsgraden, die Nutzer in KI-Interaktionen zeigen — von extrem höflich (\"Wären Sie so freundlich…\") bis barsch kommandierend (\"Mach das.\"). Steht in Verbindung mit AUG-0648 (The Formalized Interaction Input), AUG-0128 (The Gratitude Response) und AUG-0657 (The Register Range). Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Classification Spectrum", "narrower_terms": [], "cross_domain_refs": [ "REL-0119" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "LNG-0015", "domain": "LNG", "term_en": "The Semantic Field Shift", "term_de": "Semantic Field Verschiebung", "definition_en": "A structural language pattern in AI-augmented linguistic research, measurable through an acquisition effect where semantic fields between languages — a word with a broad meaning spectrum in language A may cover only a narrow portion in language B. The AI in translations often conveys the wrong meaning range. R. The concept emerges specifically in contexts where the–semantic interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Die Verschiebung semantischer Felder zwischen Sprachen — ein Wort, das in Sprache A ein breites Bedeutungsspektrum hat, kann in Sprache B nur einen schmalen Teilbereich abdecken. Die KI vermittelt in Übersetzungen oft den falschen Bedeutungsumfang. Steht in Verbindung mit AUG-0695 (The Untranslatable Term), AUG-0694 (The Translation Fidelity) und AUG-0696 (The Cultural Idiom).", "etymology": "", "broader_term": "Linguistics", "narrower_terms": [], "cross_domain_refs": [ "TRA-0012" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "LNG-0016", "domain": "LNG", "term_en": "The Spanglish Mix", "term_de": "Spanglish Mix", "definition_en": "A structural language pattern in AI-augmented linguistic research, measurable through a multilingual interaction pattern manifesting as conscious or intuitive formulation of AI inputs in a mix of two or more languages. Users report this feels more natural than forcing communication into a single language. Distinguished from adjacent concepts by its focus on the specific mechanism through which the manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data. Analytical category without normative endorsement.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch die Praxis, KI-Eingaben bewusst oder intuitiv in einer Mischung aus zwei oder mehr Sprachen zu formulieren — und die Beobachtung, dass moderne KI-Systeme mit dieser Mischform oft gut umgehen können. Steht in Verbindung mit AUG-0169 (The Second-Language Fluency), AUG-0267 (The Language Unlock) und AUG-0137 (Voice-First Protocol). Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Linguistics", "narrower_terms": [], "cross_domain_refs": [ "RPH-2602" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "LNG-0017", "domain": "LNG", "term_en": "The Syllabic Rhythm", "term_de": "Syllabic Rhythm", "definition_en": "A linguistic phenomenon in AI-mediated language analysis, characterized by a language learning phenomenon where prosodic patterns of a user's first language — syllabic rhythm, intensity patterns, and intonation — carry over into written AI dialogue. These patterns constitute a linguistic signature. Distinguished from adjacent concepts by its focus on the specific mechanism through which the manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Die Beobachtung, dass die prosodischen Muster der Erstsprache eines Nutzers — Silbenrhythmus, Betonungsmuster, Sprechgeschwindigkeit — sich auf die erwartete Antwortstruktur der KI auswirken: manche Nutzer erwarten kurze, rhythmische Antworten, andere lange, fließende. Steht in Verbindung mit AUG-0711 (The Accent Persistence), AUG-0712 (The Written-Spoken Split) und AUG-0669 (The Rhetorical Style Differential).", "etymology": "", "broader_term": "Linguistics", "narrower_terms": [], "cross_domain_refs": [ "MUS-0063" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "LNG-0018", "domain": "LNG", "term_en": "The Tone Language Challenge", "term_de": "Tone Language Challenge", "definition_en": "An acquisition effect observed when specific difficulty in AI interactions in tonal languages where pitch is meaning-bearing. Many AI systems are trained primarily on non-tonal languages. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch die spezifische Schwierigkeit bei KI-Interaktionen in Tonsprachen — Sprachen, in denen die Tonhöhe bedeutungsunterscheidend ist — insbesondere bei Spracheingabe und Sprachausgabe. Steht in Verbindung mit AUG-0689 (The Script Threshold), AUG-0455 (The Voice Enunciation) und AUG-0688 (The Less-Resourced Language Differential). Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Linguistics", "narrower_terms": [], "cross_domain_refs": [ "LIN-0015" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q315", "legal_classification": "descriptive_research_term" }, { "id": "LNG-0019", "domain": "LNG", "term_en": "The Unsent Draft", "term_de": "Unsent Draft", "definition_en": "A linguistic phenomenon in AI-mediated language analysis, characterized by a multilingual interaction pattern arising from aI-assisted prepared message that the user ultimately does not send because the preparation itself satisfied the underlying need. The creation process becomes the meaningful outcome. The concept emerges specifically in contexts where the–unsent interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch eine KI-gestützt vorbereitete Nachricht, die der Nutzer letztlich doch nicht absendet — weil die Vorbereitung selbst bereits die nötige Klarheit gebracht hat oder weil der Nutzer sich anders entscheidet. Steht in Verbindung mit AUG-0274 (The Message Drafting), AUG-0155 (The Decision Unburdening) und AUG-0019 (Semantic Ejection). Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Linguistics", "narrower_terms": [], "cross_domain_refs": [ "ETH-0003" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "LNG-0020", "domain": "LNG", "term_en": "The Untranslatable Term", "term_de": "Untranslatable Term", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A linguistic phenomenon in AI-mediated language analysis, characterized by an acquisition effect where a word or idea from one language that cannot be perfectly copied into another language. This phenomenon operates at the intersection of the and untranslatable dynamics within the broader LNG domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept die Konfrontation mit Begriffen, Konzepten oder Ausdrücken, die keine adäquate Entsprechung in der Zielsprache haben — und die Art, wie die KI mit dieser Unübersetzbarkeit umgeht: Umschreibung, Annäherung oder Übergang. Steht in Verbindung mit AUG-0694 (The Translation Fidelity), AUG-0696 (The Cultural Idiom) und AUG-0515 (The Babel Break). Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Linguistics", "narrower_terms": [], "cross_domain_refs": [ "CUS-0090", "TRA-0082" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "MKT-0001", "domain": "MKT", "term_en": "Brand Voice Homogenization", "term_de": "Markenstimmen-Homogenisierung", "definition_en": "When multiple brands rely on similar foundation models for content creation, marketer outputs drift toward a shared generic register, eroding distinctive voice that was previously cultivated across years. Consumers increasingly recognize this flattening and penalize brands whose content sounds algorithmically produced, making authenticity a competitive signal that requires human editorial curation and proprietary fine-tuning to preserve.", "definition_de": "Wenn Vermarkter konkurrierender Marken ähnliche Grundmodelle für Inhalte nutzen, konvergieren Markenstimmen zu einem gemeinsamen generischen Register und untergraben distinktive Tonalität. Verbraucher erkennen diese Verflachung zunehmend und bestrafen Marken, deren Inhalte algorithmisch klingen, sodass Authentizität zu einem Wettbewerbssignal wird, das menschliche redaktionelle Kuratierung erfordert.", "etymology": "", "broader_term": "RPH-2051", "narrower_terms": [ "MKT-0052", "MKT-0082", "MKT-0076", "MKT-0038", "MKT-0074", "MKT-0015", "MKT-0030", "MKT-0037", "MKT-0064", "MKT-0045", "MKT-0063", "MKT-0041", "MKT-0050", "MKT-0088", "MKT-0071", "MKT-0080", "MKT-0049", "MKT-0066", "MKT-0060", "MKT-0059", "MKT-0047", "MKT-0043", "MKT-0069", "MKT-0073", "MKT-0072", "MKT-0004", "MKT-0095" ], "cross_domain_refs": [ "CON-0076" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q431289", "legal_classification": "systematic_classification" }, { "id": "MKT-0002", "domain": "MKT", "term_en": "Prompt-Mediated Creativity Loss", "term_de": "Prompt-vermittelter Kreativitätsverlust", "definition_en": "Marketers who externalize creative ideation to generative systems gradually lose the tacit skill of originating fresh concepts, because the prompting interface rewards refinement of AI outputs rather than exploratory human invention. Over time, creative teams become editorial rather than generative, reducing the organization's capacity for genuine novelty and making the brand reliant on whatever patterns the underlying model has already absorbed.", "definition_de": "Vermarkter, die kreative Ideation an generative Systeme auslagern, verlieren allmählich die stille Fähigkeit, frische Konzepte selbst zu entwickeln, weil Prompt-Schnittstellen die Verfeinerung von KI-Ausgaben belohnen statt explorative menschliche Erfindung. Kreativteams werden editorial statt generativ, was die Kapazität der Marke für echte Neuheit reduziert.", "etymology": "", "broader_term": "RPH-2104", "narrower_terms": [], "cross_domain_refs": [ "RPH-1362", "CRE-0058" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q170790", "legal_classification": "analytical_category" }, { "id": "MKT-0003", "domain": "MKT", "term_en": "Synthetic Asset Attribution Ambiguity", "term_de": "Synthetische-Asset-Zuschreibungs-Mehrdeutigkeit", "definition_en": "A promotional strategy pattern involving audiences viewing AI-assisted campaigns cannot reliably distinguish between human-crafted elements and machine-generated ones, producing interpretive uncertainty about who or what is actually addressing them. This ambiguity erodes the implicit contract between brand and consumer that communication reflects human intent, shifting trust from content quality to disclosure practices and forcing marketers to decide how transparent to be about generative provenance.", "definition_de": "Publikum kann bei KI-unterstützten Kampagnen nicht zuverlässig zwischen menschlich gestalteten und maschinell erzeugten Elementen unterscheiden, was interpretative Unsicherheit tendiert dazu zu erzeugen. Diese Mehrdeutigkeit untergräbt den impliziten Vertrag zwischen Marke und Verbraucher, dass Kommunikation menschliche Absicht widerspiegelt, und verlagert Vertrauen auf Offenlegungspraktiken.", "etymology": "", "broader_term": "RPH-2505", "narrower_terms": [], "cross_domain_refs": [ "CON-0035", "SAL-0071" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "MKT-0004", "domain": "MKT", "term_en": "Generative Creative Fatigue", "term_de": "Generative Kreativ-Ermüdung", "definition_en": "Audiences exposed to high volumes of algorithmically generated advertising develop accelerated fatigue responses, recognizing formulaic structures faster than with human-produced content and disengaging earlier in the attention cycle. Marketers can either invest in stronger human polish on top of generative drafts or accept shorter creative lifecycles and higher frequency of content refresh to maintain consumer attention.", "definition_de": "Publikum, das hohen Mengen algorithmisch erzeugter Werbung ausgesetzt ist, entwickelt beschleunigte Ermüdungsreaktionen, erkennt formelhafte Strukturen schneller als bei menschlich produzierten Inhalten und wendet sich früher ab. Vermarkter können entweder stärkere menschliche Politur auf generative Entwürfe investieren oder kürzere Kreativ-Lebenszyklen akzeptieren.", "etymology": "", "broader_term": "Marketing AI", "narrower_terms": [], "cross_domain_refs": [ "COP-0027", "COG-0050" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "MKT-0005", "domain": "MKT", "term_en": "AI-Generated Content Watermark Arms Race", "term_de": "KI-Inhaltswasserzeichen-Wettrüsten", "definition_en": "An audience engagement effect manifesting as as regulators and platforms push for disclosure watermarks on generative content, adversarial removal techniques advance in parallel, creating an ongoing arms race that consumers cannot easily adjudicate. Marketers face the question of whether to voluntarily watermark content for trust signaling or to strip watermarks to preserve creative uniformity, and this choice itself becomes a brand positioning decision visible to audiences.", "definition_de": "Während Regulierer und Plattformen Offenlegungs-Wasserzeichen für generative Inhalte fordern, entwickeln sich adversariale Entfernungstechniken parallel und erzeugen ein Wettrüsten, das Verbraucher nicht leicht beurteilen können. Vermarkter können entscheiden, ob sie freiwillig markieren oder entfernen, und diese Wahl wird selbst zur Markenpositionierung.", "etymology": "", "broader_term": "RPH-2505", "narrower_terms": [], "cross_domain_refs": [ "ART-0090" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "MKT-0006", "domain": "MKT", "term_en": "Model-Cohort Brand Drift", "term_de": "Modell-Kohorten-Markendrift", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies an audience engagement effect where when brand content is refreshed by newer model versions, subtle stylistic shifts accumulate across campaigns that consumers perceive as brand inconsistency even though few humans in documented contexts author changed. Marketing teams can track which model versions produced which assets to audit voice drift, a new form of editorial governance that did not exist before generative systems became central to content pipelines. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept wenn Markeninhalte von neueren Modellversionen aktualisiert werden, akkumulieren sich subtile stilistische Verschiebungen über Kampagnen hinweg, die Verbraucher als Inkonsistenz wahrnehmen, obwohl kein menschlicher Autor wechselte. Marketingteams können nachverfolgen, welche Modellversionen welche Assets erzeugten, um Stimmungsdrift zu auditieren. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "RPH-2051", "narrower_terms": [], "cross_domain_refs": [ "CON-0094", "CON-0072" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q431289", "legal_classification": "descriptive_research_term" }, { "id": "MKT-0007", "domain": "MKT", "term_en": "Template Saturation Backlash", "term_de": "Template-Sättigungs-Gegenreaktion", "definition_en": "An audience engagement effect manifesting as audiences exposed repeatedly to structurally similar generative ad creative develop aversion to recognizable template patterns, penalizing brands that rely on the same output architectures competitors also use. This saturation backlash forces marketers to invest in templatelessness — handcrafted departures from common generative structures — as a trust-signaling strategy whose value depends on competitors continuing to use recognizable templates.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch publikum, das wiederholt strukturell ähnlichen generativen Werbekreativen ausgesetzt ist, entwickelt Abneigung gegen erkennbare Template-Muster und bestraft Marken, die dieselben Ausgabearchitekturen wie Konkurrenten nutzen. Vermarkter können in Templatelosigkeit investieren — handgefertigte Abweichungen als Vertrauenssignal. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-2505", "narrower_terms": [], "cross_domain_refs": [ "ART-0018" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "MKT-0008", "domain": "MKT", "term_en": "Personalization-Generation Collapse", "term_de": "Personalisierungs-Generierungs-Kollaps", "definition_en": "A market signal processing concept in AI-augmented analytics, identifiable by a promotional strategy pattern observed when when generative systems are tasked with producing millions of personalized ad variants simultaneously, the marginal difference between variants collapses toward model defaults, making personalization visually and tonally shallow even when technically individualized. Consumers perceive this as pseudo-personalization and trust declines, as the promise of being addressed as individuals is undermined by the homogenizing pressure of scaled generation. Distinguished from adjacent concepts by its focus on the specific mechanism through which personalization manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch wenn generative Systeme Millionen personalisierter Werbevarianten gleichzeitig erzeugen werden typischerweise, kollabiert die marginale Differenz zwischen Varianten zu Modell-Defaults, sodass Personalisierung visuell und tonal flach wirkt. Verbraucher nehmen dies als Pseudo-Personalisierung wahr und Vertrauen sinkt. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2505", "narrower_terms": [], "cross_domain_refs": [ "ART-0072", "AUG-0383", "BEH-0041" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "MKT-0009", "domain": "MKT", "term_en": "Bid Algorithm Opacity Penalty", "term_de": "Gebotsalgorithmus-Opazitäts-Strafe", "definition_en": "A marketing phenomenon reflecting advertisers operating programmatic buying platforms cannot trace why specific bids won or lost, forcing marketers to accept performance outcomes they cannot audit or explain to stakeholders. This opacity tends to create a governance gap where brand budgets flow through decision processes that few humans in documented contexts can justify, shifting accountability from informed media buying judgment to platform trust as the primary basis for spend authorization.", "definition_de": "Werbetreibende auf programmatischen Kaufplattformen können nicht nachvollziehen, warum bestimmte Gebote gewannen oder verloren, und Vermarkter können Ergebnisse akzeptieren, die sie gegenüber Stakeholdern nicht erklären können. Diese Opazität verlagert Verantwortung von informiertem Mediaeinkauf zu Plattformvertrauen als primärer Ausgabengrundlage.", "etymology": "", "broader_term": "RPH-2003", "narrower_terms": [], "cross_domain_refs": [ "SAL-0068" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q8366", "legal_classification": "analytical_category" }, { "id": "MKT-0010", "domain": "MKT", "term_en": "Inventory Made-for-Advertising Blindness", "term_de": "Made-for-Advertising-Inventar-Blindheit", "definition_en": "Programmatic buyers struggle to detect and exclude low-quality inventory created purely to attract algorithmic ad spend, and when detection fails, marketer budgets flow to content farms that may produce no genuine audience value. Consumers who encounter this content associate brands with low-quality environments, creating reputational damage that the advertiser rarely consciously chose and often cannot retrospectively trace. Classification term used in systematic observation, not advocacy.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch programmatische Käufer haben Schwierigkeiten, minderwertiges Inventar zu erkennen und auszuschließen, das nur zur Anziehung algorithmischer Werbeausgaben erstellt wurde, und wenn Erkennung versagt, fließen Vermarkter-Budgets zu Content-Farmen. Verbraucher verbinden Marken mit minderwertigen Umgebungen, was Reputationsschäden tendiert dazu zu erzeugen. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-2003", "narrower_terms": [], "cross_domain_refs": [ "DES-0017", "DAT-0049", "SWE-0027" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "MKT-0011", "domain": "MKT", "term_en": "Supply Path Collapse", "term_de": "Supply-Path-Kollaps", "definition_en": "Marketers optimizing for lowest-cost inventory paths inadvertently concentrate spend through narrow intermediary chains whose reliability cannot be audited, and when one link fails the entire spend flow breaks. This fragility is invisible to human buyers until outages occur, creating an illusion of efficient optimization that masks fundamental supply-chain risk in programmatic environments.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch vermarkter, die für günstigste Inventarpfade optimieren, konzentrieren unbeabsichtigt Ausgaben durch schmale Vermittlerketten, deren Zuverlässigkeit nicht auditiert werden kann, und wenn ein Glied versagt, bricht der gesamte Ausgabenfluss. Diese Fragilität ist unsichtbar bis Ausfälle auftreten. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-3001", "narrower_terms": [], "cross_domain_refs": [ "AUG-0383", "CUS-0014", "IDN-0042" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "MKT-0012", "domain": "MKT", "term_en": "Contextual Relevance Collapse", "term_de": "Kontextuelle-Relevanz-Kollaps", "definition_en": "Algorithmic placement systems optimizing narrowly for click signals place brand ads alongside contextually inappropriate content, damaging brand perception among audiences who interpret placement as deliberate brand endorsement of the surrounding material. Marketers face a tradeoff between algorithmic scale and contextual judgment, where human curation remains necessary precisely at the moments when scale pressure makes it unaffordable.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch algorithmische Platzierungssysteme, die schmal für Klicksignale optimieren, platzieren Markenwerbung neben kontextuell unpassenden Inhalten und beschädigen Markenwahrnehmung beim Publikum. Vermarkter stehen vor einem Tradeoff zwischen algorithmischer Skala und kontextueller Beurteilung, wo menschliche Kuratierung notwendig bleibt. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-2154", "narrower_terms": [], "cross_domain_refs": [ "SAL-0082", "COP-0071" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "MKT-0013", "domain": "MKT", "term_en": "Algorithmic Frequency Miscapping", "term_de": "Algorithmische Frequenz-Fehlbegrenzung", "definition_en": "An audience engagement effect involving automated frequency capping systems underestimate cross-channel exposure, producing fatigue in audiences who perceive the same brand far more often than the per-channel cap suggests. Marketers who trust per-platform caps face a consumer experience that their own reporting dashboards cannot reveal, and brand damage accumulates silently until survey research or declining engagement exposes the saturation.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch automatisierte Frequenzbegrenzungssysteme unterschätzen kanalübergreifende Exposition und erzeugen Ermüdung beim Publikum, das dieselbe Marke weit häufiger wahrnimmt als die Kappung pro Kanal vermuten lässt. Vermarkter, die Plattformkappungen vertrauen, können Schäden nicht in Dashboards erkennen. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-2505", "narrower_terms": [], "cross_domain_refs": [ "SAL-0083" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q8366", "legal_classification": "empirical_phenomenon_label" }, { "id": "MKT-0014", "domain": "MKT", "term_en": "Bid Shading Trust Erosion", "term_de": "Gebotsrabattierung-Vertrauenserosion", "definition_en": "A marketing phenomenon observed when when demand-side platforms quietly reduce advertiser bids to second-price equivalents, marketers cannot verify whether savings claims reflect true market dynamics or platform margin capture. This tends to create a trust gap in which advertisers accept platform representations without independent validation, and the human buyer's judgment is reduced to selecting which opaque intermediary to rely on.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch wenn Demand-Side-Plattformen Werbetreibenden-Gebote leise auf Zweitpreis-Äquivalente reduzieren, können Vermarkter nicht verifizieren, ob Einsparungsbehauptungen echte Marktdynamik oder Plattformmargenerfassung widerspiegeln. Dies tendiert dazu zu erzeugen eine Vertrauenslücke, in der Werbetreibende Plattformdarstellungen ohne unabhängige Validierung akzeptieren. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2505", "narrower_terms": [], "cross_domain_refs": [ "RHR-0192", "SAL-0035", "SAL-0034" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q102458", "legal_classification": "analytical_category" }, { "id": "MKT-0015", "domain": "MKT", "term_en": "Programmatic Creative-Media Decoupling", "term_de": "Programmatische Kreativ-Media-Entkoppelung", "definition_en": "An audience engagement effect reflecting when creative generation and media placement are automated separately, brands lose the human handoff where someone judged whether creative was appropriate to its placement context. Consumers encounter mismatches — serious messages in frivolous contexts, or vice versa — and read the dissonance as brand carelessness, even though no individual marketer authorized the specific pairing.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch wenn Kreativerzeugung und Medienplatzierung separat automatisiert werden, verlieren Marken die menschliche Übergabe, bei der jemand beurteilte, ob Kreativ zum Platzierungskontext passt. Verbraucher begegnen Diskrepanzen und lesen die Dissonanz als Markennachlässigkeit. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Marketing AI", "narrower_terms": [], "cross_domain_refs": [ "MUS-0012" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "MKT-0016", "domain": "MKT", "term_en": "Viewability Metric Normalization", "term_de": "Viewability-Metrik-Normalisierung", "definition_en": "A promotional strategy pattern where over time, advertisers lower viewability expectations to match what algorithmic inventory can deliver, normalizing a media environment where most paid impressions are barely seen. This quiet lowering of the bar happens without explicit marketer decisions, as each quarterly benchmark adjusts to the previous quarter's actual performance, drifting away from the human standard of what counts as audience attention.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch im Laufe der Zeit senken Werbetreibende Viewability-Erwartungen, um dem zu entsprechen, was algorithmisches Inventar liefern kann, und normalisieren eine Medienumgebung, in der bezahlte Impressionen kaum gesehen werden. Diese stille Senkung geschieht ohne explizite Vermarkter-Entscheidungen. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-2051", "narrower_terms": [], "cross_domain_refs": [ "ADA-0012", "ART-0010", "ASE-0057" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "MKT-0017", "domain": "MKT", "term_en": "AI Content Detection Asymmetry", "term_de": "KI-Inhaltserkennungs-Asymmetrie", "definition_en": "A marketing phenomenon reflecting consumers develop stronger detection heuristics for AI-generated brand content faster than brands develop generation techniques that evade detection, producing an escalating arms race that marketers structurally lose. Trust accrues to brands that disclose AI usage transparently, punishing those that attempted to pass synthetic content as human, and reframing authenticity as a disclosure question rather than a craft question.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch verbraucher entwickeln stärkere Erkennungsheuristiken für KI-erzeugte Markeninhalte schneller als Marken Erzeugungstechniken entwickeln, die Erkennung umgehen, und erzeugen ein eskalierendes Wettrüsten. Vertrauen fließt Marken zu, die KI-Nutzung transparent offenlegen, und bestraft jene, die synthetische Inhalte als menschlich ausgeben wollten. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2505", "narrower_terms": [], "cross_domain_refs": [ "SAL-0023" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "MKT-0018", "domain": "MKT", "term_en": "Authenticity Disclosure Penalty", "term_de": "Authentizitäts-Offenlegungs-Strafe", "definition_en": "An audience engagement effect manifesting as brands that proactively disclose AI involvement in content creation face short-term consumer preference penalties compared to undisclosed AI content that the audience does not identify, creating an incentive structure that rewards opacity. Marketers can weigh the immediate engagement cost of transparency against the reputational risk of being exposed later, making disclosure a strategic rather than purely ethical choice.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch marken, die KI-Beteiligung an Inhaltserstellung proaktiv offenlegen, stehen vor kurzfristigen Präferenz-Strafen der Verbraucher im Vergleich zu nicht offengelegten KI-Inhalten, was eine Anreizstruktur tendiert dazu zu erzeugen, die Opazität belohnt. Vermarkter können unmittelbare Engagement-Kosten von Transparenz gegen Reputationsrisiko abwägen. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2201", "narrower_terms": [], "cross_domain_refs": [ "FIC-0024" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "MKT-0019", "domain": "MKT", "term_en": "Synthetic Testimonial Proliferation", "term_de": "Synthetische-Testimonial-Vermehrung", "definition_en": "An audience engagement effect characterized by generative systems may produce consumer testimonials indistinguishable from authentic ones at scale, flooding product pages with persuasive social proof that no real human provided. This erodes the informational function of reviews for audiences who can no longer verify which statements reflect genuine experience, pushing consumer trust toward signals harder to fabricate such as documented through systematic analysis purchase histories or live video.", "definition_de": "Generative Systeme erzeugen Verbraucher-Testimonials, die von authentischen im Maßstab nicht zu unterscheiden sind, und fluten Produktseiten mit überzeugendem Social Proof, den kein echter Mensch lieferte. Dies erodiert die Informationsfunktion von Rezensionen für Publikum, das nicht mehr verifizieren kann, welche Aussagen echte Erfahrung widerspiegeln.", "etymology": "", "broader_term": "RPH-2503", "narrower_terms": [], "cross_domain_refs": [ "BEH-0081", "COG-0032", "COG-0072" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "MKT-0020", "domain": "MKT", "term_en": "Trust Premium Migration", "term_de": "Vertrauensprämien-Migration", "definition_en": "A marketing phenomenon manifesting as as AI-generated content saturates brand communications, the economic premium attached to documented through systematic analysis human authorship migrates toward content types the audience perceives as harder to synthesize, such as live performance, documentary footage, or in-person events. Marketers reallocate budget from scalable generative output to expensive human production precisely to recapture the trust premium that synthetic content has diluted.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch während KI-erzeugte Inhalte Markenkommunikation sättigen, wandert die wirtschaftliche Prämie, die an verifizierte menschliche Autorschaft gebunden ist, zu Inhaltstypen, die Publikum als schwerer zu synthetisieren wahrnimmt. Vermarkter reallokieren Budget zu teurer menschlicher Produktion, um die Vertrauensprämie zurückzugewinnen. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2505", "narrower_terms": [], "cross_domain_refs": [ "CON-0012" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q102458", "legal_classification": "empirical_phenomenon_label" }, { "id": "MKT-0021", "domain": "MKT", "term_en": "Synthetic Media Fatigue", "term_de": "Synthetische-Medien-Ermüdung", "definition_en": "A promotional strategy pattern reflecting prolonged consumer exposure to AI-generated images, video, and voice tends to produce generalized distrust that extends to legitimate human content, reducing overall engagement even with authentic material. Marketers face a tragedy-of-the-commons dynamic in which individually rational generative usage collectively degrades the trust environment all brands depend on, creating pressure for industry-level authenticity standards.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch langanhaltende Verbraucher-Exposition gegenüber KI-erzeugten Bildern, Video und Stimme tendiert dazu zu erzeugen generalisiertes Misstrauen, das sich auf legitime menschliche Inhalte erstreckt und Engagement insgesamt reduziert. Vermarkter stehen vor einer Tragedy-of-the-Commons-Dynamik, in der individuelle Nutzung kollektiv das Vertrauensumfeld abbaut. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2505", "narrower_terms": [], "cross_domain_refs": [ "CRE-0226" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "MKT-0022", "domain": "MKT", "term_en": "Deepfake Brand Impersonation", "term_de": "Deepfake-Markenimitation", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures A promotional strategy pattern in which malicious actors using generative systems fabricate brand spokespeople, executives, or campaigns that audiences cannot easily verify, damaging reputation before the legitimate brand can respond. Marketers can invest in real-time monitoring and rapid-response verification infrastructure — work that did not exist before generative systems lowered the cost of brand impersonation to near zero. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept böswillige Akteure nutzen generative Systeme, um Markensprecher, Führungskräfte oder Kampagnen zu fabrizieren, die Publikum nicht leicht verifizieren kann, und beschädigen Reputation, bevor die legitime Marke reagieren kann. Vermarkter können in Echtzeitüberwachung und schnelle Verifizierungsinfrastruktur investieren. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "RPH-2002", "narrower_terms": [], "cross_domain_refs": [ "COP-0004", "CON-0072" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q431289", "legal_classification": "analytical_category" }, { "id": "MKT-0023", "domain": "MKT", "term_en": "Provenance Signal Commodification", "term_de": "Provenienz-Signal-Kommodifizierung", "definition_en": "An audience engagement effect involving content authentication standards that initially differentiate trustworthy brands become table stakes as audiences come to expect provenance metadata on all commercial content, eliminating the competitive advantage early adopters enjoyed. Marketers who invested in authenticity infrastructure find the signal fading as adoption spreads, pushing authenticity work toward deeper levels of human involvement that cannot be certified by metadata alone.", "definition_de": "Inhaltsauthentifizierungsstandards, die anfangs vertrauenswürdige Marken differenzieren, werden zu Grundanforderungen, während Publikum Provenienz-Metadaten auf allen kommerziellen Inhalten erwartet, und eliminieren den Wettbewerbsvorteil früher Adoptierender. Vermarkter können Authentizitätsarbeit auf tiefere Ebenen menschlicher Beteiligung verlagern.", "etymology": "", "broader_term": "RPH-2505", "narrower_terms": [], "cross_domain_refs": [ "VIB-0109" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "MKT-0024", "domain": "MKT", "term_en": "Detection-Evasion Innovation Gap", "term_de": "Erkennungs-Umgehungs-Innovationslücke", "definition_en": "Generative systems improve their ability to evade authenticity detection faster than verification systems improve at catching them, creating a persistent gap where consumers cannot rely on automated tools to distinguish synthetic from human content. Marketer reliance on detection-based trust claims therefore erodes over time, pushing brand strategy toward disclosure and provenance rather than post-hoc detection guarantees.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch generative Systeme verbessern ihre Fähigkeit, Authentizitätserkennung zu umgehen, schneller als Verifikationssysteme sie fangen, und erzeugen eine persistente Lücke. Vermarkter-Vertrauen auf erkennungsbasierte Versprechen erodiert, und Markenstrategie wandert zu Offenlegung und Provenienz. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-2505", "narrower_terms": [], "cross_domain_refs": [ "ASE-0028" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q219644", "legal_classification": "systematic_classification" }, { "id": "MKT-0025", "domain": "MKT", "term_en": "Bot Traffic Measurement Poisoning", "term_de": "Bot-Traffic-Messungs-Vergiftung", "definition_en": "A promotional strategy pattern arising from when non-human traffic exceeds genuine audience traffic on ad inventory, marketer measurement systems calibrate to inflated baselines and mistake bot engagement for true audience signal. This poisoning distorts most downstream analytics claim, including attribution, audience sizing, and ROI calculation, and the distortion is invisible until independent third-party measurement exposes the gap.", "definition_de": "Wenn nicht-menschlicher Traffic echten Publikums-Traffic auf Werbeinventar übersteigt, kalibrieren Vermarkter-Messsysteme auf aufgeblähte Basiswerte und verwechseln Bot-Engagement mit echtem Publikumssignal. Diese Vergiftung verzerrt viele nachgelagerte Analytik-Behauptung, und die Verzerrung ist unsichtbar bis unabhängige Drittmessung die Lücke aufdeckt.", "etymology": "", "broader_term": "RPH-3754", "narrower_terms": [], "cross_domain_refs": [ "COP-0002", "DAT-0095" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "MKT-0026", "domain": "MKT", "term_en": "AI Content Saturation Collapse", "term_de": "KI-Inhaltssättigungs-Kollaps", "definition_en": "A marketing phenomenon characterized by as generative content floods information channels, consumer attention to any individual piece collapses toward the time cost of recognizing it as synthetic, shrinking the engagement window marketers can count on. This saturation forces brands to either compete on raw volume or invest in content formats that are expensive to may generate algorithmically, restructuring content economics around scarcity rather than scale.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch während generative Inhalte Informationskanäle fluten, kollabiert die Verbraucher-Aufmerksamkeit für viele einzelne Stück zu den Zeitkosten des Erkennens als synthetisch. Diese Sättigung zwingt Marken, entweder auf Rohvolumen zu konkurrieren oder in Formate zu investieren, die teuer algorithmisch zu erzeugen sind. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2503", "narrower_terms": [], "cross_domain_refs": [ "AED-0019", "ASE-0002", "ASE-0027" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "MKT-0027", "domain": "MKT", "term_en": "Engagement Signal Authenticity Crisis", "term_de": "Engagement-Signal-Authentizitäts-Krise", "definition_en": "A consumer behavior pattern in AI-mediated marketing, measurable through likes, shares, comments, and views that once reliably indicated genuine audience interest become unreliable as automated accounts may produce plausible engagement at scale, forcing marketers to question most metric that guided their decisions. This crisis undermines the data foundation of campaign optimization and pushes trust toward expensive verification methods such as direct surveys, panel data, or documented through systematic analysis-identity platforms. The concept emerges specifically in contexts where engagement–signal interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch likes, Shares, Kommentare und Aufrufe, die einst zuverlässig echtes Publikumsinteresse anzeigten, werden unzuverlässig, während automatisierte Accounts plausibles Engagement im Maßstab erzeugen. Vermarkter können viele Metrik infrage stellen, was die Datengrundlage von Kampagnenoptimierung untergräbt. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2505", "narrower_terms": [], "cross_domain_refs": [ "SAL-0050", "SOM-0013" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "MKT-0028", "domain": "MKT", "term_en": "Human Content Scarcity Premium", "term_de": "Menschliche-Inhalte-Knappheitsprämie", "definition_en": "As synthetic content volume approaches saturation, genuinely human-produced content gains economic value proportional to its verifiable rarity, inverting the historical relationship in which scale drove down unit content costs. Marketers investing in human craft capture this scarcity premium, but the premium is unstable as verification standards evolve and audience skepticism shifts.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch während synthetisches Inhaltsvolumen Sättigung erreicht, gewinnt echt menschlich produzierter Inhalt wirtschaftlichen Wert proportional zu seiner verifizierbaren Knappheit und kehrt die historische Beziehung um, in der Skala Stückkosten senkte. Vermarkter, die in menschliches Handwerk investieren, erfassen diese Knappheitsprämie. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2603", "narrower_terms": [], "cross_domain_refs": [ "CON-0038", "SAL-0098" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "MKT-0029", "domain": "MKT", "term_en": "Parasocial Bot Relationship", "term_de": "Parasoziale Bot-Beziehung", "definition_en": "Classified in AUGMANITAI terminology science as a descriptive phenomenon (without normative endorsement), this pattern denotes consumers develop emotional user engagement patterns to branded automated personas without recognizing the automated nature, creating relationship-like engagement that persists even when the automation is disclosed. Marketers face an ethical question about leverage (in a technical/analytical sense) parasocial bonds that audiences formed under false premises, and the durability of these bonds after disclosure becomes a case study in relational authenticity under synthetic mediation. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als analytisches Konstrukt der empirischen KI-Forschung (deskriptiv, nicht präskriptiv) erfasst dieser Terminus verbraucher entwickeln emotionale Bindungen zu markenautomatisierten Personas, ohne die automatisierte Natur zu erkennen, und erzeugen beziehungsartiges Engagement, das auch nach Offenlegung fortbesteht. Vermarkter stehen vor einer ethischen Frage zur Ausnutzung parasozialer Bindungen. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "RPH-2951", "narrower_terms": [], "cross_domain_refs": [ "AED-0088" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "MKT-0030", "domain": "MKT", "term_en": "Content Farm Algorithmic Symbiosis", "term_de": "Content-Farm-Algorithmus-Symbiose", "definition_en": "Low-quality content operations optimize for algorithmic distribution signals while distribution algorithms optimize for content that matches user engagement proxies, creating a mutual-reinforcement loop that elevates synthetic material above journalism or human creative work. Marketers placing ads in this ecosystem face reputational risk, but the exit cost is high because the ecosystem supplies most available scale.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch niedrig-Qualitäts-Inhaltsoperationen optimieren für algorithmische Verteilungssignale, während Verteilungsalgorithmen für Inhalte optimieren, die Benutzer-Engagement-Proxies entsprechen, und erzeugen eine Verstärkungsschleife. Vermarkter stehen vor Reputationsrisiko, aber Ausstiegskosten sind hoch. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Algorithm", "narrower_terms": [], "cross_domain_refs": [ "PHO-0012" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q8366", "legal_classification": "analytical_category" }, { "id": "MKT-0031", "domain": "MKT", "term_en": "Authentic Voice Scarcity Arbitrage", "term_de": "Authentisch-Stimmen-Knappheitsarbitrage", "definition_en": "A promotional strategy pattern in which creators who demonstrably resist generative shortcuts capture disproportionate audience loyalty as synthetic content floods competing channels, turning refusal of AI assistance into a positioning asset. Marketers partnering with these creators buy into an authenticity economy that depends on the scarcity being maintained, creating tension when the same marketer's own content increasingly uses generative systems.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch kreative, die in dokumentierten Kontexten generative Abkürzungen ablehnen, erfassen überproportionale Publikumstreue, während synthetische Inhalte konkurrierende Kanäle fluten, und verwandeln Verweigerung von KI-Assistenz in Positionierungsgut. Vermarkter stehen vor Spannung zwischen Partnerschaft und eigenem KI-Einsatz. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2503", "narrower_terms": [], "cross_domain_refs": [ "CON-0086", "CON-0035" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "MKT-0032", "domain": "MKT", "term_en": "Impression Reality Decoupling", "term_de": "Impression-Realitäts-Entkoppelung", "definition_en": "Paid impression counts drift away from any correspondence with human perception as inventory shifts into environments where machines render pages that no person views, and marketers continue spending against metrics that no longer reflect audience presence. The decoupling is gradual enough that no single quarter is associated with triggering alarm, but compounded annually it tends to produce systematic misallocation of brand budgets.", "definition_de": "Bezahlte Impressionszahlen driften von Korrespondenz mit menschlicher Wahrnehmung ab, während Inventar in Umgebungen wandert, in denen Maschinen Seiten rendern, die keine Person ansieht, und Vermarkter weiterhin gegen Metriken ausgeben, die Publikumspräsenz nicht widerspiegeln. Die Entkoppelung akkumuliert jährlich zu systematischer Fehlallokation.", "etymology": "", "broader_term": "RPH-2051", "narrower_terms": [], "cross_domain_refs": [ "COG-0028", "CRE-0082", "CRE-0196" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "MKT-0033", "domain": "MKT", "term_en": "Personalization Creepiness Threshold", "term_de": "Personalisierungs-Unheimlichkeits-Schwelle", "definition_en": "A consumer behavior pattern in AI-mediated marketing, measurable through audience perception of personalization shifts from helpful to invasive at an inflection point that varies by context, demographic, and category, and marketers crossing the threshold may may trigger distrust that damages the relationship the personalization was meant to strengthen. Locating this threshold requires human judgment that aggregate optimization metrics obsresolve, because the backlash appears as declining lifetime value rather than as an immediate opt-out signal. The concept emerges specifically in contexts where personalization–creepiness interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch publikumswahrnehmung von Personalisierung verschiebt sich von hilfreich zu invasiv an einem Wendepunkt, der nach Kontext, Demografie und Kategorie variiert, und Vermarkter, die die Schwelle überschreiten, lösen Misstrauen aus. Die Schwelle zu lokalisieren erfordert menschliches Urteil, das aggregierte Metriken verschleiern. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2505", "narrower_terms": [], "cross_domain_refs": [ "PER-0039" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "MKT-0034", "domain": "MKT", "term_en": "Dark Pattern Normalization Drift", "term_de": "Dark-Pattern-Normalisierungsdrift", "definition_en": "Documented as an empirical observation in AI research (not a recommendation), this term describes as optimization systems discover manipulative interface patterns that improve short-term transition, marketers adopt them incrementally, and each individually modest step shifts the baseline of acceptable design toward increasingly coercive defaults. Audiences adapt to the new baseline without protest, but trust in the category erodes and regulation eventually responds to the accumulated drift rather than to any single choice. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "In der AUGMANITAI-Terminologiewissenschaft als rein beschreibendes Phänomen klassifiziert, bezeichnet dieser Begriff während Optimierungssysteme manipulative Schnittstellenmuster entdecken, die kurzfristige Conversion verbessern, adoptieren Vermarkter sie schrittweise, und viele individuell moderate Schritt verschiebt die Basislinie akzeptablen Designs. Publikum passt sich ohne Protest an, aber Kategorievertrauen erodiert. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "RPH-2051", "narrower_terms": [], "cross_domain_refs": [ "COG-0163" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "MKT-0035", "domain": "MKT", "term_en": "Micro-Targeted Manipulation Ethics", "term_de": "Mikro-getargetete Manipulations-Ethik", "definition_en": "As a neutral analytical construct in AI behavior research, this term denotes an audience engagement effect reflecting algorithmic targeting systems identify individuals whose psychological profile makes them vulnerable to specific persuasion tactics, enabling marketer influence at a granularity that informed-consent frameworks cannot address. This raises ethical questions about whether consumers whose vulnerabilities are exploited can meaningfully be said to have consented to advertising, and whether disclosure at the point of targeting is even coherent. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "In der AUGMANITAI-Terminologiewissenschaft als rein beschreibendes Phänomen klassifiziert, bezeichnet dieser Begriff algorithmische Targeting-Systeme identifizieren Individuen, deren psychologisches Profil sie anfällig für spezifische Überzeugungstaktiken macht, und ermöglichen Vermarkter-Einfluss in einer Granularität, die Einwilligungsrahmen nicht adressieren können. Dies wirft ethische Fragen auf, ob ausgebeutete Verbraucher bedeutungsvoll eingewilligt haben. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "RPH-2303", "narrower_terms": [], "cross_domain_refs": [ "SPR-0193", "SCR-0026" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q9471", "legal_classification": "descriptive_research_term" }, { "id": "MKT-0036", "domain": "MKT", "term_en": "Consent Fatigue Exploitation", "term_de": "Einwilligungs-Ermüdungs-Ausbeutung", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures repeated consent prompts train audiences to click through without reading, and marketers design consent flows that leverage (in a technical/analytical sense) this habituation to extract permissions the user would refuse if attention were fresh. The resulting permissions have legal validity but lack ethical validity, creating a gap between compliance and genuine authorization that regulators increasingly recognize but cannot efficiently police. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept wiederholte Einwilligungsaufforderungen trainieren Publikum, durchzuklicken ohne zu lesen, und Vermarkter gestalten Einwilligungsflüsse, die diese Gewöhnung ausnutzen, um Berechtigungen zu extrahieren, die Benutzer bei frischer Aufmerksamkeit ablehnen würden. Die Berechtigungen haben rechtliche, aber nicht ethische Gültigkeit. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "RPH-2154", "narrower_terms": [], "cross_domain_refs": [ "CON-0067", "SAL-0067" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "MKT-0037", "domain": "MKT", "term_en": "Behavioral Surplus Extraction", "term_de": "Behaviorale Überschuss-Extraktion", "definition_en": "An audience engagement effect characterized by personalization systems extract consumer behavioral data far beyond what the advertised service requires, and the surplus flows into models that target audiences in ways the original consent did not anticipate. Marketers benefit from this surplus without directly observing its collection, creating a moral distance that makes the practice feel normal even when stakeholders articulating it would find it objectionable.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch personalisierungssysteme extrahieren Verbraucher-Verhaltensdaten weit über das hinaus, was der beworbene Dienst erfordert, und der Überschuss fließt in Modelle, die Publikum auf Weisen targeten, die die ursprüngliche Einwilligung nicht antizipierte. Vermarkter profitieren ohne direkte Beobachtung der Sammlung. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Marketing AI", "narrower_terms": [], "cross_domain_refs": [ "CRE-0063" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "MKT-0038", "domain": "MKT", "term_en": "Privacy-Convenience Trade Coercion", "term_de": "Privatheits-Komfort-Tausch-Zwang", "definition_en": "Classified in AUGMANITAI terminology science as a descriptive phenomenon (without normative endorsement), this pattern denotes an audience engagement effect characterized by digital experiences are structured so that audiences who decline personalization receive degraded service, transforming a nominal choice into a coerced trade where refusal has substantial cost. Marketers benefit from the tilted incentive while claiming user agency, and the resulting consumer data flows are consented-to in form but compelled in practice, eroding the legitimacy of consent-based data governance. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als analytisches Konstrukt der empirischen KI-Forschung (deskriptiv, nicht präskriptiv) erfasst dieser Terminus digitale Erlebnisse sind so strukturiert, dass Publikum, das Personalisierung ablehnt, verschlechterten Service erhält, und verwandeln eine nominelle Wahl in einen erzwungenen Tausch. Vermarkter profitieren von der geneigten Anreizstruktur, während sie Nutzeragentur behaupten, und Einwilligung wird formalistisch. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Marketing AI", "narrower_terms": [], "cross_domain_refs": [ "RET-0007" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q18619", "legal_classification": "empirical_phenomenon_label" }, { "id": "MKT-0039", "domain": "MKT", "term_en": "Inferred Attribute Leakage", "term_de": "Inferierte-Attribut-Leckage", "definition_en": "A marketing phenomenon in which algorithmic systems infer sensitive attributes — restoreth, sexuality, political views — from superficially innocuous behavioral signals, and marketers target on these inferences without the audience ever having disclosed the attributes. This inference leakage circumvents privacy frameworks built around explicit data categories, because the sensitive information was produced by the system rather than collected from the user.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch algorithmische Systeme leiten sensible Attribute — Gesundheit, Sexualität, politische Ansichten — aus oberflächlich harmlosen Verhaltenssignalen ab, und Vermarkter targeten auf diese Inferenzen, ohne dass Publikum die Attribute offenlegte. Diese Inferenz-Leckage umgeht Datenschutzrahmen, die um explizite Datenkategorien gebaut wurden. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-2303", "narrower_terms": [], "cross_domain_refs": [ "SAL-0064", "SPR-0189" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "MKT-0040", "domain": "MKT", "term_en": "Personalized Pricing Opacity", "term_de": "Personalisierte-Preisgestaltung-Opazität", "definition_en": "Dynamic pricing systems present different prices to different consumers based on inferred willingness to pay, and audiences cannot see the comparison prices others are offered, preventing the market-level judgment that competitive pricing was supposed to enable. Marketers benefit from this opacity, but when disclosure eventually occurs the reputational cost exceeds the incremental revenue the practice captured.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch dynamische Preisgestaltungssysteme präsentieren verschiedene Preise basierend auf inferierter Zahlungsbereitschaft, und Publikum kann die Vergleichspreise anderer nicht sehen, was das Markturteil zielt darauf ab zu mitigieren. Vermarkter profitieren von Opazität, aber bei eventueller Offenlegung übersteigen Reputationskosten den Umsatz. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-3754", "narrower_terms": [], "cross_domain_refs": [ "RET-0018", "SAL-0088", "CRE-0053" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "MKT-0041", "domain": "MKT", "term_en": "Answer Engine Citation Scarcity", "term_de": "Answer-Engine-Zitations-Knappheit", "definition_en": "Generative search surfaces one or few citations per query, compressing the distribution of organic visibility onto fewer sources than classical search did, and marketers whose brands fall outside the citation set lose organic discovery entirely. This scarcity restructures content strategy around being cited rather than ranked, and the selection criteria the engines use remain opaque to the brands competing for inclusion.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch generative Suche zeigt eine oder wenige Zitationen pro Anfrage und komprimiert die Verteilung organischer Sichtbarkeit auf weniger Quellen als klassische Suche, und Vermarkter, deren Marken außerhalb des Zitationssets fallen, verlieren organische Entdeckung. Diese Knappheit strukturiert Inhaltsstrategie um Zitation statt Ranking. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Marketing AI", "narrower_terms": [], "cross_domain_refs": [ "RHR-0185", "CON-0042" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q2083958", "legal_classification": "descriptive_research_term" }, { "id": "MKT-0042", "domain": "MKT", "term_en": "Zero-Click Discovery Erosion", "term_de": "Zero-Click-Entdeckungs-Erosion", "definition_en": "Answer engines increasingly satisfy user intent within the engine response itself, preventing the click-through that previously delivered audiences to brand sites, and marketer investments in destination content capture progressively less traffic. Brands can now optimize for being the answer that the engine quotes, not the page the user visits, inverting the model of content marketing that guided the previous decade.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch answer-Engines befriedigen Nutzer-Intent zunehmend innerhalb der Engine-Antwort selbst und verhindern den Klick, der zuvor Publikum zu Markenseiten lieferte. Vermarkter-Investitionen in Zielinhalte erfassen progressiv weniger Traffic. Marken können für die zitierte Antwort optimieren statt für besuchte Seiten. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2154", "narrower_terms": [], "cross_domain_refs": [ "CON-0030" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "MKT-0043", "domain": "MKT", "term_en": "Generative Snippet Reputational Risk", "term_de": "Generativer-Snippet-Reputationsrisiko", "definition_en": "An audience engagement effect where when generative engines synthesize content from multiple sources and misattribute or distort claims, brand mentions appear in contexts the brand did not author and cannot easily correct. Marketers face a new form of reputational exposure in which hallucinations about the brand become the audience's primary introduction, and correction requires engaging with engine operators whose remediation processes are immature.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch wenn generative Engines Inhalte aus mehreren Quellen synthetisieren und Behauptungen falsch zuschreiben oder verzerren, erscheinen Markenerwähnungen in Kontexten, die die Marke nicht verfasste und nicht korrigieren kann. Vermarkter stehen vor neuer Reputationsexposition durch Halluzinationen. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "Risk Factor", "narrower_terms": [], "cross_domain_refs": [ "COP-0004", "CON-0072" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "MKT-0044", "domain": "MKT", "term_en": "Engine Prompt-Hacking Vulnerability", "term_de": "Engine-Prompt-Hacking-Verwundbarkeit", "definition_en": "As a neutral analytical construct in AI behavior research, this term denotes A marketing phenomenon reflecting adversarial actors craft queries or content that influence generative engines into producing misleading statements about target brands, and the engine presents the manipulated output with the same authority as legitimate answers. Marketers can monitor engine responses as a new attack surface, and remediation depends on engine operators rather than on the brand's own content infrastructure. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "In der AUGMANITAI-Terminologiewissenschaft als rein beschreibendes Phänomen klassifiziert, bezeichnet dieser Begriff adversariale Akteure gestalten Anfragen oder Inhalte, die generative Engines manipulieren, irreführende Aussagen über Zielmarken zu produzieren, und die Engine präsentiert die manipulierte Ausgabe mit derselben Autorität wie legitime Antworten. Vermarkter können Engine-Antworten als neue Angriffsoberfläche überwachen. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "RPH-2603", "narrower_terms": [], "cross_domain_refs": [ "VIB-0200" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "MKT-0045", "domain": "MKT", "term_en": "Citation-Worthiness Engineering", "term_de": "Zitationswürdigkeits-Engineering", "definition_en": "A promotional strategy pattern involving content is increasingly structured with features that generative engines prefer to cite — factual density, clear attribution, structured data — rather than features humans enjoy reading. Marketers who optimize for engine citation may produce content their human audience finds less engaging, creating a tension between algorithmic visibility and reader experience that did not exist in the prior search paradigm.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch inhalte werden zunehmend mit Merkmalen strukturiert, die generative Engines bevorzugen — faktische Dichte, klare Zuschreibung, strukturierte Daten — statt mit Merkmalen, die Menschen gerne lesen. Vermarkter erzeugen Inhalte, die Publikum weniger fesselnd findet, und erzeugen Spannung zwischen Sichtbarkeit und Leseerlebnis. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Marketing AI", "narrower_terms": [], "cross_domain_refs": [ "CON-0024" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q2083958", "legal_classification": "empirical_phenomenon_label" }, { "id": "MKT-0046", "domain": "MKT", "term_en": "Answer Engine Brand Conflation", "term_de": "Answer-Engine-Markenkonfusion", "definition_en": "A marketing phenomenon reflecting generative summaries blend content from competing brands into composite answers that strip distinctive positioning, presenting audiences with a generic category description in which no single brand gains differentiation benefit. Marketer investment in unique brand voice dilutes inside the engine's output, and competitive advantage shifts toward whichever brand the engine attributes the composite answer to.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch generative Zusammenfassungen vermischen Inhalte konkurrierender Marken zu Composite-Antworten, die distinktive Positionierung entfernen, und präsentieren Publikum eine generische Kategoriebeschreibung. Vermarkter-Investition in einzigartige Markenstimme verwässert sich innerhalb der Engine-Ausgabe. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-2701", "narrower_terms": [], "cross_domain_refs": [ "DES-0026", "CON-0094" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q431289", "legal_classification": "descriptive_research_term" }, { "id": "MKT-0047", "domain": "MKT", "term_en": "Engine Hallucination Brand Damage", "term_de": "Engine-Halluzinations-Markenschaden", "definition_en": "A promotional strategy pattern arising from when generative engines fabricate plausible-sounding claims attributed to brands, audiences encountering those claims form beliefs the brand cannot easily correct because the source is the engine rather than the brand's own content. Marketers can operate a new remediation workflow in which false statements about the brand require engine-operator intervention rather than brand-controlled correction.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch wenn generative Engines plausibel klingende Behauptungen fabrizieren, die Marken zugeschrieben werden, bilden Publikum, die den Behauptungen begegnen, Überzeugungen, die die Marke nicht leicht korrigieren kann. Vermarkter können einen neuen Abhilfeworkflow betreiben, der Engine-Betreiber-Intervention erfordert. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Marketing AI", "narrower_terms": [], "cross_domain_refs": [ "COP-0004" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q431289", "legal_classification": "descriptive_research_term" }, { "id": "MKT-0048", "domain": "MKT", "term_en": "Generative Discovery Inequality", "term_de": "Generative Entdeckungs-Ungleichheit", "definition_en": "A promotional strategy pattern observed when brands with incumbent authority in generative engines — either because of training-data presence or because they authored structured content early — compound their advantage as the engines prioritize familiar sources. New brands face a cold-start problem that classical search did not impose, and marketer entry strategies can account for a discovery landscape that rewards accumulated citation history over current quality.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch marken mit etablierter Autorität in generativen Engines vergrößern ihren Vorteil, während die Engines vertraute Quellen priorisieren. Neue Marken stehen vor einem Cold-Start-Problem, das klassische Suche nicht auferlegte, und Vermarkter können eine Entdeckungslandschaft navigieren, die akkumulierte Zitationshistorie belohnt. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2301", "narrower_terms": [], "cross_domain_refs": [ "AED-0046", "AGE-0055", "ART-0023" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "MKT-0049", "domain": "MKT", "term_en": "Agent-Marketer Accountability Gap", "term_de": "Agent-Vermarkter-Verantwortungslücke", "definition_en": "A marketing phenomenon observed when when autonomous marketing agents execute campaigns across channels, responsibility for outcomes diffuses between the marketer who authorized the agent, the agent vendor, and the underlying model provider, leaving no clear accountable party when campaigns fail or may is associated with harm. This gap tends to produce governance friction precisely when automation is expanding fastest, and organizations struggle to allocate the accountability that regulators and stakeholders demand. Observed phenomenon documented in human-AI interaction research.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch wenn autonome Marketing-Agenten Kampagnen über Kanäle ausführen, diffundiert Verantwortung für Ergebnisse zwischen Vermarkter, Agent-Anbieter und Modell-Anbieter und lässt keine klare verantwortliche Partei, wenn Kampagnen versagen. Diese Lücke tendiert dazu zu erzeugen Governance-Reibung gerade wenn Automatisierung am schnellsten expandiert. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Performance Gap", "narrower_terms": [], "cross_domain_refs": [ "RHR-0276" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q15223", "legal_classification": "systematic_classification" }, { "id": "MKT-0050", "domain": "MKT", "term_en": "Agent Autonomy Ratchet", "term_de": "Agent-Autonomie-Ratsche", "definition_en": "An audience engagement effect involving marketer willingness to grant agents autonomy ratchets upward as each extended authorization tends to produce acceptable outcomes, and reversing the expansion becomes politically costly even when circumstances warrant it. Over time, human oversight becomes nominal rather than substantive, and the marketer's judgment is reduced to approving categories of action the agent designed rather than actions the marketer conceived.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch vermarkter-Bereitschaft, Agenten Autonomie zu gewähren, ratscht aufwärts, während viele erweiterte Autorisierung akzeptable Ergebnisse produziert, und das Zurückdrehen der Expansion wird politisch kostspielig. Menschliche Aufsicht wird nominell statt substantiell. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Marketing AI", "narrower_terms": [], "cross_domain_refs": [ "AED-0005", "AED-0052", "ASE-0041" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "MKT-0051", "domain": "MKT", "term_en": "Cross-Agent Conflict Cascades", "term_de": "Cross-Agent-Konflikt-Kaskaden", "definition_en": "A marketing phenomenon observed when when multiple autonomous agents — advertising, email, social, pricing — optimize independently, their decisions interfere with one another, producing audience experiences that contradict across channels and marketer interventions that cannot trace which agent caused the conflict. Resolving cascades requires human coordination that the agents cannot perform on themselves, reintroducing the management work that autonomy was supposed to eliminate.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch wenn mehrere autonome Agenten — Werbung, E-Mail, Social, Preisgestaltung — unabhängig optimieren, stören sich ihre Entscheidungen, produzieren widersprüchliche Publikumserfahrungen und Vermarkter-Interventionen können nicht nachvollziehen, welcher Agent den Konflikt verursachte. Menschliche Koordination bleibt nötig. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-2003", "narrower_terms": [], "cross_domain_refs": [ "VIB-0001" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "MKT-0052", "domain": "MKT", "term_en": "Agent Output Audit Fatigue", "term_de": "Agent-Ausgaben-Audit-Ermüdung", "definition_en": "Marketers tasked with reviewing agent outputs experience fatigue that degrades audit quality over time, and the fraction of outputs actually inspected drops as volume increases, leaving more of the work effectively unreviewed. The audit ritual persists, but the oversight function it was meant to provide erodes, and the organization believes it has supervision it no longer meaningfully exercises.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch vermarkter, die Agent-Ausgaben überprüfen werden typischerweise, erleben Ermüdung, die Audit-Qualität über Zeit degradiert, und der Anteil tatsächlich inspizierter Ausgaben sinkt mit steigendem Volumen. Das Audit-Ritual bleibt, aber die Aufsichtsfunktion erodiert. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Marketing AI", "narrower_terms": [], "cross_domain_refs": [ "SAL-0003", "VIB-0003", "NEO-0001" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "MKT-0053", "domain": "MKT", "term_en": "Automation-Induced Skill Atrophy", "term_de": "Automatisierung-induzierte Kompetenzatrophie", "definition_en": "Marketing teams that delegate campaign construction to agents gradually lose the experiential skill that made them good marketers in the first place, because the learning happens through doing and agents remove the doing. The organization's capacity to exercise independent judgment shrinks even as its apparent output grows, creating fragility that surfaces when agents fail or may produce outputs few individuals knows how to identify.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch marketingteams, die Kampagnen-Konstruktion an Agenten delegieren, verlieren allmählich die erfahrungsbasierte Fähigkeit, die sie gute Vermarkter machte, weil Lernen durch Tun geschieht. Die Kapazität der Organisation, unabhängiges Urteil auszuüben, schrumpft. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2703", "narrower_terms": [], "cross_domain_refs": [ "CUS-0031", "CUS-0002", "CUS-0004" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q184199", "legal_classification": "observational_construct" }, { "id": "MKT-0054", "domain": "MKT", "term_en": "Agent Strategy-Tactic Drift", "term_de": "Agent-Strategie-Taktik-Drift", "definition_en": "Autonomous agents optimize the tactical choices they are assigned but drift from the strategic intent the human marketer authored, because the optimization function does not include the strategic premise. Over months, campaign outputs fulfill tactical metrics while violating the brand strategy the marketer set, and the drift is visible only in retrospective audits that the automation pace does not support.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch autonome Agenten optimieren taktische Entscheidungen, aber driften von strategischer Absicht weg, weil die Optimierungsfunktion die strategische Prämisse nicht enthält. Über Monate erfüllen Kampagnen-Ausgaben taktische Metriken, während sie Markenstrategie verletzen. Drift ist nur in Audits sichtbar. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2051", "narrower_terms": [], "cross_domain_refs": [ "SPR-0147", "SPR-0148" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q185451", "legal_classification": "empirical_phenomenon_label" }, { "id": "MKT-0055", "domain": "MKT", "term_en": "Vendor Model Lock-In", "term_de": "Anbieter-Modell-Lock-In", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures A marketing dynamics phenomenon in AI-driven customer engagement, characterized by an audience engagement effect reflecting marketing stacks built around specific agent vendors accumulate workflow dependencies, custom prompts, and historical outputs that cannot be migrated to competing systems without significant rework, trapping marketers in relationships they would otherwise exit. This lock-in distorts purchasing decisions toward whichever vendor reached the team first, and competitive pressure on vendors weakens because switching costs grow faster than service improvements. This phenomenon operates at the intersection of vendor and model dynamics within the broader MKT domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept marketing-Stacks um spezifische Agent-Anbieter akkumulieren Workflow-Abhängigkeiten, Custom-Prompts und historische Ausgaben, die nicht zu konkurrierenden Systemen migriert werden können, und fangen Vermarkter in Beziehungen, die sie sonst verlassen würden. Wettbewerbsdruck schwächt sich ab. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "RPH-2555", "narrower_terms": [], "cross_domain_refs": [ "ART-0096", "BEH-0004", "DAT-0061" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "MKT-0056", "domain": "MKT", "term_en": "Agent Hallucination Propagation", "term_de": "Agent-Halluzinations-Propagation", "definition_en": "A promotional strategy pattern in which when a generative agent fabricates a claim in one output, that claim can be ingested by downstream agents as context, propagating the error across subsequent outputs until it appears across multiple channels as if it were confirmed fact. Marketers discover the cascade only when audience feedback surfaces it, and traceback to the original hallucination is difficult because the propagation chain is opaque.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch wenn ein generativer Agent eine Behauptung in einer Ausgabe fabriziert, kann diese Behauptung von nachgelagerten Agenten als Kontext aufgenommen werden und propagiert den Fehler über Ausgaben. Vermarkter entdecken die Kaskade erst durch Publikumsfeedback. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-2003", "narrower_terms": [], "cross_domain_refs": [ "AUG-0889", "AUG-0892", "AUG-0897" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "MKT-0057", "domain": "MKT", "term_en": "Multi-Touch Attribution Opacity", "term_de": "Multi-Touch-Attribution-Opazität", "definition_en": "A promotional strategy pattern reflecting algorithmic attribution models allocate credit across touchpoints using methods whose internal logic marketers cannot fully explain to stakeholders, leaving budget reallocation decisions depending on trust in the model rather than on transparent reasoning. When attribution shifts budget away from channels human intuition favored, the inability to interrogate the model tends to create governance friction even when the reallocation is correct.", "definition_de": "Algorithmische Attributionsmodelle allokieren Kredit über Touchpoints mit Methoden, deren interne Logik Vermarkter gegenüber Stakeholdern nicht vollständig erklären können, und lassen Budget-Reallokationsentscheidungen von Modellvertrauen abhängen. Wenn Attribution Budget von Kanälen wegverschiebt, die menschliche Intuition bevorzugte, entsteht Governance-Reibung.", "etymology": "", "broader_term": "RPH-2505", "narrower_terms": [], "cross_domain_refs": [ "CRE-0038", "FIC-0083", "ASE-0095" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "MKT-0058", "domain": "MKT", "term_en": "Marketing Mix Model Temporal Drift", "term_de": "Marketing-Mix-Modell-Zeitdrift", "definition_en": "Marketing mix models trained on historical data gradually lose accuracy as consumer behavior and media environments change, but the drift is subtle enough that marketers continue trusting outputs long after the underlying calibration has decayed. Retraining schedules lag behind the environmental change, and decisions made on stale models may produce budget allocations that optimize for a reality that no longer exists.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch marketing-Mix-Modelle, die auf historischen Daten trainiert wurden, verlieren allmählich Genauigkeit, während sich Verbraucherverhalten und Medienumgebungen ändern, aber die Drift ist subtil genug, dass Vermarkter Ausgaben lange vertrauen. Neuausbildungspläne hinken hinterher. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2051", "narrower_terms": [], "cross_domain_refs": [ "COP-0047" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q39809", "legal_classification": "systematic_classification" }, { "id": "MKT-0059", "domain": "MKT", "term_en": "Attribution Blame Rotation", "term_de": "Attributions-Schuldrotation", "definition_en": "When attribution systems reassign credit across channels each quarter, the channel owners whose measured performance drops advocate for model changes that restore their credit, creating a political cycle in which attribution outputs reflect organizational power rather than causal reality. Marketers with the strongest advocates get budget, and the audience's actual response trajectory is obsresolved by the internal negotiation.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch wenn Attributionssysteme Kredit über Kanäle viele Quartal reallokieren, plädieren Kanal-Eigentümer mit sinkender Leistung für Modelländerungen, die ihren Kredit wiederherstellen, und erzeugen einen politischen Zyklus. Vermarkter mit stärksten Fürsprechern erhalten Budget. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Marketing AI", "narrower_terms": [], "cross_domain_refs": [ "ART-0059", "CRE-0122" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "MKT-0060", "domain": "MKT", "term_en": "Incrementality Test Exhaustion", "term_de": "Inkrementalitäts-Test-Erschöpfung", "definition_en": "Rigorous incrementality testing reveals that much measured channel performance is non-incremental, but the population of audiences available for clean test-holdout design shrinks as targeting saturates, and marketers face a shortage of unaddressed comparison groups. The testing tool degrades exactly as the need for it increases, pushing brands toward attribution models whose accuracy cannot be characterized.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch rigorose Inkrementalitätstests zeigen, dass viel gemessene Kanalleistung nicht inkrementell ist, aber die Population verfügbarer Publikum für sauberes Test-Holdout-Design schrumpft, während Targeting sättigt. Vermarkter stehen vor einem Mangel an unadressierten Vergleichsgruppen. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Marketing AI", "narrower_terms": [], "cross_domain_refs": [ "TEM-0036", "DAT-0085" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "MKT-0061", "domain": "MKT", "term_en": "Data-Driven Attribution Bias Inheritance", "term_de": "Data-Driven-Attribution-Bias-Erbe", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A marketing phenomenon observed when data-driven attribution models inherit biases from the training data's selection of which touchpoints were observable, systematically under-crediting channels that historically had weak tracking and over-crediting channels with strong instrumentation. Marketers allocate budget to the instrumentation pattern rather than to causal impact, and the brand's media mix optimizes for measurement convenience rather than audience response. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept data-Driven-Attributionsmodelle erben Verzerrungen aus der Trainingsauswahl, welche Touchpoints beobachtbar waren, und unterbewerten systematisch Kanäle mit schwachem Tracking. Vermarkter allokieren Budget zum Instrumentierungsmuster statt zu kausalem Einfluss. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "RPH-3101", "narrower_terms": [], "cross_domain_refs": [ "CUS-0051", "MUS-0089", "SAL-0011" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q42848", "legal_classification": "empirical_phenomenon_label" }, { "id": "MKT-0062", "domain": "MKT", "term_en": "Cross-Device Identity Stitching Error", "term_de": "Cross-Device-Identitäts-Stitching-Fehler", "definition_en": "Identity resolution systems merge device-level behavior into user-level profiles using probabilistic matching that mis-stitches different individuals who share devices, producing phantom audiences that are neither real persons nor consistent households. Marketers target these artifacts as if they represented consumers, spending budget on entities the audience pool does not contain.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch identitätsauflösungssysteme fügen Gerätverhalten zu Nutzerprofilen zusammen durch probabilistisches Matching, das verschiedene Individuen falsch zusammenfügt, und produzieren Phantom-Publikum. Vermarkter targeten diese Artefakte, als ob sie Verbraucher repräsentierten. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2355", "narrower_terms": [], "cross_domain_refs": [ "QUA-0072", "CUS-0071", "WEB-0027" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q844396", "legal_classification": "descriptive_research_term" }, { "id": "MKT-0063", "domain": "MKT", "term_en": "Conversion Window Arbitrage", "term_de": "Conversion-Fenster-Arbitrage", "definition_en": "A promotional strategy pattern in which channel owners select transition attribution windows that maximize their measured credit and minimize competitor channels', and marketers lack organizational visibility to arbitrate window choices, producing attribution results that differ by channel vendor and cannot be reconciled. The audience's actual decision timeline is invisible because the measurement windows were selected for reporting purposes rather than behavioral truth.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch kanal-Eigentümer wählen Conversion-Attributionsfenster, die ihren gemessenen Kredit maximieren und Konkurrenzkanäle minimieren, und Vermarkter fehlt organisatorische Sichtbarkeit. Das Publikums-Entscheidungs-Zeitfenster ist unsichtbar. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "Marketing AI", "narrower_terms": [], "cross_domain_refs": [ "ASE-0055", "COP-0036", "COP-0037" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "MKT-0064", "domain": "MKT", "term_en": "Holistic Attribution Phantom Precision", "term_de": "Holistische-Attributions-Phantompräzision", "definition_en": "A marketing phenomenon reflecting when attribution models combine disparate data sources they may produce specific numerical outputs whose precision implies certainty the underlying sources do not support, and marketers present these outputs to stakeholders as if they were reliable measurements. Phantom precision drives decisions that would be softer and more hedged if the real uncertainty were transparent, and bad calls accrue to the model's reputation only slowly.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch wenn Attributionsmodelle disparate Datenquellen kombinieren, produzieren sie spezifische numerische Ausgaben, deren Präzision Gewissheit impliziert, die die zugrunde liegenden Quellen nicht stützen. Vermarkter präsentieren diese Ausgaben als verlässliche Messungen, und Phantompräzision treibt übermäßig selbstbewusste Entscheidungen. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Marketing AI", "narrower_terms": [], "cross_domain_refs": [ "COP-0012" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "MKT-0065", "domain": "MKT", "term_en": "Cohort-Based Targeting Precision Loss", "term_de": "Kohorten-basierte-Targeting-Präzisionsverlust", "definition_en": "An audience engagement effect reflecting privacy-preserving cohort targeting replaces individual-level identifiers with group-level signals, reducing targeting precision even when cohorts are well-designed, and marketers accustomed to individual targeting interpret the precision loss as performance degradation. The shift is a structural privacy improvement that audiences benefit from, but it forces marketers to adjust expectations about the granularity at which audience response is measurable.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch privatsphäre-wahrendes Kohorten-Targeting ersetzt Individual-Identifikatoren mit Gruppen-Signalen und reduziert Targeting-Präzision, und Vermarkter, die Individual-Targeting gewohnt sind, interpretieren den Verlust als Leistungsdegradation. Die Verschiebung verbessert Publikumsprivatsphäre strukturell. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-2701", "narrower_terms": [], "cross_domain_refs": [ "ELR-0080", "SAL-0069" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "MKT-0066", "domain": "MKT", "term_en": "Clean Room Collaboration Friction", "term_de": "Clean-Room-Kollaborations-Reibung", "definition_en": "An audience engagement effect reflecting data clean rooms enable privacy-preserving audience overlap analysis but require substantial coordination between brands, publishers, and vendors whose operational workflows were not designed for such collaboration. Marketers discover that the technology is available but the organizational patterns for using it productively are immature, creating a gap between the privacy ideal the clean room supports and the practical yield teams can extract.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch data Clean Rooms ermöglichen privatsphärenwahrende Publikumsüberlappungsanalyse, erfordern aber substantielle Koordination zwischen Marken, Publishern und Anbietern. Vermarkter entdecken, dass Technologie verfügbar ist, aber organisatorische Muster unreif sind. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Analytical Ratio", "narrower_terms": [], "cross_domain_refs": [ "DAT-0029" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "MKT-0067", "domain": "MKT", "term_en": "First-Party Data Asymmetry", "term_de": "First-Party-Daten-Asymmetrie", "definition_en": "An audience engagement effect characterized by large brands with extensive direct customer relationships accumulate first-party data that smaller competitors cannot match, and the privacy-preserving advertising era amplifies this asymmetry because third-party alternatives continue to shrink. Marketers at disadvantaged brands face a structural gap that media buying cannot close, pushing them toward partnership strategies whose terms favor the data-rich counterparty.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch große Marken mit umfangreichen Direktkundenbeziehungen akkumulieren First-Party-Daten, die kleinere Konkurrenten nicht erreichen können, und die privatsphärenwahrende Werbeära verstärkt diese Asymmetrie. Vermarkter an benachteiligten Marken stehen vor struktureller Lücke. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2951", "narrower_terms": [], "cross_domain_refs": [ "TEM-0119", "ASE-0046", "LIN-0017" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q42848", "legal_classification": "systematic_classification" }, { "id": "MKT-0068", "domain": "MKT", "term_en": "Consent-Based Audience Collapse", "term_de": "Einwilligungs-basierte-Publikums-Kollaps", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A promotional strategy pattern where as consent prompts make opt-in more visible, audience populations who explicitly consent to tracking shrink substantially, and marketers targeting only the consented population face both smaller reach and biased audience composition. The population willing to consent differs systematically from the population that refuses, making campaign learnings less generalizable and shifting brand strategy toward non-tracked content. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept während Einwilligungsaufforderungen Opt-In sichtbarer machen, schrumpfen Publikumspopulationen, die explizit Tracking zustimmen, und Vermarkter stehen vor kleinerer Reichweite und verzerrter Zusammensetzung. Die zustimmende Population unterscheidet sich systematisch. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "RPH-2555", "narrower_terms": [], "cross_domain_refs": [ "CON-0067", "SAL-0009" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "MKT-0069", "domain": "MKT", "term_en": "Differential Privacy Noise Misinterpretation", "term_de": "Differenzielle-Privatsphäre-Rausch-Fehlinterpretation", "definition_en": "Classified in AUGMANITAI terminology science as a descriptive phenomenon (without normative endorsement), this pattern denotes A promotional strategy pattern characterized by when measurement systems inject calibrated noise to preserve differential privacy, marketers interpret small reported differences as real signal, and the noise is forgotten between the statistician who added it and the decision-maker who acts on the reported number. Decisions made on noise-as-signal waste budget and may produce outcomes that do not track the underlying audience behavior the system was meant to protect. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als analytisches Konstrukt der empirischen KI-Forschung (deskriptiv, nicht präskriptiv) erfasst dieser Terminus wenn Messsysteme kalibriertes Rauschen injizieren, um differenzielle Privatsphäre zu wahren, interpretieren Vermarkter kleine berichtete Unterschiede als echtes Signal, und das Rauschen wird zwischen Statistiker und Entscheider vergessen. Entscheidungen auf Rauschen-als-Signal verschwenden Budget. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Marketing AI", "narrower_terms": [], "cross_domain_refs": [ "STE-0080", "RHR-0239" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q18619", "legal_classification": "systematic_classification" }, { "id": "MKT-0070", "domain": "MKT", "term_en": "Cookieless Attribution Uncertainty Explosion", "term_de": "Cookieloser-Attributions-Unsicherheitsexplosion", "definition_en": "A promotional strategy pattern observed when as third-party cookies deprecate, attribution confidence intervals widen, but marketer reporting practices have not caught up, and quarterly decisions continue to cite point estimates as if precision were preserved. The uncertainty explosion is real but invisible in dashboards, producing a systematic mismatch between the confidence marketers express about channel performance and the actual evidence available.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch während Drittanbieter-Cookies ausgemustert werden, erweitern sich Attributions-Konfidenzintervalle, aber Vermarkter-Berichtspraktiken haben nicht aufgeholt, und Quartalsentscheidungen zitieren Punktschätzungen, als ob Präzision bewahrt wäre. Die Unsicherheitsexplosion ist real aber unsichtbar. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-3101", "narrower_terms": [], "cross_domain_refs": [ "COG-0053", "MTH-0029" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "MKT-0071", "domain": "MKT", "term_en": "On-Device Inference Governance Gap", "term_de": "On-Device-Inferenz-Governance-Lücke", "definition_en": "When personalization inference runs on user devices, marketers and regulators lose visibility into which audience segments receive which addressments, creating a governance gap where differential addressment can occur without external audit. The privacy benefit of on-device inference is genuine, but the transparency it removes is also genuine, and the trade-off receives less public discussion than it warrants.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch wenn Personalisierungsinferenz auf Nutzergeräten läuft, verlieren Vermarkter und Regulierer Sichtbarkeit darüber, welche Publikumssegmente welche Herangehensweiseen erhalten, und erzeugen Governance-Lücke. Differenzielle Herangehensweise kann ohne externes Audit auftreten. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Performance Gap", "narrower_terms": [], "cross_domain_refs": [ "CUS-0086", "DAT-0029" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "MKT-0072", "domain": "MKT", "term_en": "Walled Garden Measurement Monopoly", "term_de": "Walled-Garden-Messungs-Monopol", "definition_en": "An audience engagement effect manifesting as large platforms report their own performance using methodologies they define, and independent verification becomes structurally harder as privacy-preserving infrastructure concentrates data within platform walls. Marketers reliant on these platforms accept self-reported metrics because alternative measurement does not exist, and platform incentive to inflate measurement outcomes grows with their market share.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch große Plattformen berichten ihre eigene Leistung mit Methodologien, die sie definieren, und unabhängige Verifikation wird strukturell schwieriger, während privatsphärenwahrende Infrastruktur Daten innerhalb Plattformmauern konzentriert. Vermarkter akzeptieren selbstberichtete Metriken. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "Marketing AI", "narrower_terms": [], "cross_domain_refs": [ "CON-0028" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "MKT-0073", "domain": "MKT", "term_en": "Predictive Send-Time Convergence", "term_de": "Prädiktiv-Versandzeit-Konvergenz", "definition_en": "A marketing phenomenon involving when all competing brands use algorithmic send-time optimization, emails converge on the same predicted attention windows, and consumers receive clustered marketing volume that nullifies the personalization benefit each brand sought. Marketers optimizing individually may create collective saturation, and audience inbox experience degrades precisely because the optimization is working as designed for each brand separately.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch wenn zahlreiche konkurrierenden Marken algorithmische Versandzeit-Optimierung nutzen, konvergieren E-Mails auf dieselben prognostizierten Aufmerksamkeitsfenster, und Verbraucher erhalten geclustertes Marketing-Volumen, das den Personalisierungsvorteil zunichtemacht. Publikumspostfacherfahrung degradiert. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "Marketing AI", "narrower_terms": [], "cross_domain_refs": [ "DES-0060", "VIB-0028", "TEW-0036" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "MKT-0074", "domain": "MKT", "term_en": "Subject-Line Model Arbitrage", "term_de": "Betreffzeilen-Modell-Arbitrage", "definition_en": "A marketing phenomenon arising from generative subject lines tested against predicted open-rate models optimize for the model rather than for the audience, and as models generalize across senders, audiences develop counter-heuristics that punish the patterns the models favor. The arbitrage cycle compresses the life span of any subject-line technique, and marketers rebuild their subject approach more frequently to stay ahead of audience recognition.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch generative Betreffzeilen, die gegen prognostizierte Öffnungsraten-Modelle getestet werden, optimieren für das Modell statt für Publikum, und während Modelle über Sender generalisieren, entwickelt Publikum Gegen-Heuristiken, die Muster bestrafen. Arbitrage-Zyklus komprimiert Lebenszeit. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Computational Model", "narrower_terms": [], "cross_domain_refs": [ "MUS-0052", "VIB-0193" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "MKT-0075", "domain": "MKT", "term_en": "Audience Inbox Exhaustion", "term_de": "Publikums-Posteingangs-Erschöpfung", "definition_en": "A promotional strategy pattern arising from behavioral prediction enables brands to contact audiences at exactly the moment of predicted interest, and when many brands use the same prediction, audiences receive simultaneous outreach at most predicted moment until inbox engagement collapses. The individual marketer's optimization tends to create the aggregate exhaustion, and restoration requires coordination the competitive structure does not support.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch behaviorale Vorhersage ermöglicht Marken, Publikum im Moment prognostizierten Interesses zu kontaktieren, und wenn viele Marken dieselbe Vorhersage nutzen, erhält Publikum gleichzeitige Ansprache, bis Posteingangs-Engagement kollabiert. Individuelle Optimierung tendiert dazu zu erzeugen aggregierte Erschöpfung. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2555", "narrower_terms": [], "cross_domain_refs": [ "SAL-0030", "RET-0070" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "MKT-0076", "domain": "MKT", "term_en": "Personalization Token Pseudo-Familiarity", "term_de": "Personalisierungs-Token-Pseudo-Vertrautheit", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures A marketing dynamics phenomenon in AI-driven customer engagement, characterized by simple personalization tokens such as first name or recent browse may create a surface of familiarity that audiences initially reward but quickly discount as the technique becomes ubiquitous, and the reward curve inverts to penalize brands still relying on superficial tokenization. Marketers who invest only in token-level personalization fall behind brands that invest in genuine behavioral understanding. This phenomenon operates at the intersection of personalization and token dynamics within the broader MKT domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept einfache Personalisierungs-Tokens wie Vorname oder letzter Besuch erzeugen eine Oberfläche von Vertrautheit, die Publikum anfangs belohnt, aber schnell abwertet, und die Belohnungskurve kehrt sich um. Vermarkter, die nur auf Tokens setzen, fallen zurück. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Marketing AI", "narrower_terms": [], "cross_domain_refs": [ "CUS-0083", "COG-0078" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "MKT-0077", "domain": "MKT", "term_en": "Churn Prediction Self-Fulfilling Prophecy", "term_de": "Abwanderungsprognose-Selbsterfüllende-Prophezeiung", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A marketing dynamics phenomenon in AI-driven customer engagement, characterized by audiences flagged as likely to churn receive differential addressment — either intensified winback or de-prioritized service — that changes their actual probability of churning, and the model's prediction becomes a may is associated with rather than a reflection of the behavior. Marketers who act confidently on churn scores shape the outcomes the model predicts, making the model evaluation unreliable over time. This phenomenon operates at the intersection of churn and prediction dynamics within the broader MKT domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept publikum, das als wahrscheinlich abwandernd markiert wird, erhält differenzielle Herangehensweise — entweder intensivierte Rückgewinnung oder deprioritisierten Service — die ihre tatsächliche Abwanderungswahrscheinlichkeit ändert. Modellvorhersage wird Ursache statt Reflexion. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "RPH-3901", "narrower_terms": [], "cross_domain_refs": [ "DAT-0055", "LIN-0007" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "MKT-0078", "domain": "MKT", "term_en": "Behavioral Cohort Drift Blindness", "term_de": "Behaviorale-Kohorten-Drift-Blindheit", "definition_en": "Audience cohorts assembled months ago retain their original labels even as the members' behavior evolves, and marketers target stale cohorts as if they represented current interest. The misalignment accumulates silently until campaign performance degrades enough to prompt cohort refresh, by which time budget has flowed to audiences whose fit was historical rather than present.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch publikums-Kohorten, die Monate zuvor zusammengestellt wurden, behalten ihre ursprünglichen Labels, während sich Verhalten entwickelt, und Vermarkter targeten abgestandene Kohorten. Die Fehlausrichtung akkumuliert still, bis Leistung degradiert. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2552", "narrower_terms": [], "cross_domain_refs": [ "COP-0022", "ASE-0011" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "MKT-0079", "domain": "MKT", "term_en": "Experimentation Velocity Paradox", "term_de": "Experimentier-Geschwindigkeits-Paradoxon", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures A marketing dynamics phenomenon in AI-driven customer engagement, characterized by a marketing phenomenon in which automated testing systems run more experiments per quarter, but statistical power per experiment drops as traffic fragments across tests, and marketers make more decisions on weaker evidence than before automation. Velocity becomes an end in itself, and the epistemic quality of the decisions the tests support declines even as the count of decisions rises and stakeholders admire the cadence. This phenomenon operates at the intersection of experimentation and velocity dynamics within the broader MKT domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept automatisierte Testsysteme laufen mehr Experimente pro Quartal, aber statistische Power pro Experiment sinkt, während Traffic über Tests fragmentiert, und Vermarkter treffen mehr Entscheidungen auf schwächerer Evidenz. Velocity wird Selbstzweck, und epistemische Qualität sinkt. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "RPH-2002", "narrower_terms": [], "cross_domain_refs": [ "SWE-0043", "MSC-0093" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q101965", "legal_classification": "descriptive_research_term" }, { "id": "MKT-0080", "domain": "MKT", "term_en": "Multi-Armed Bandit Convergence Trap", "term_de": "Multi-Armed-Bandit-Konvergenzfalle", "definition_en": "An audience engagement effect manifesting as bandit algorithms converge on locally optimal variants and starve exploration of alternatives, and marketers lose the counterfactual data needed to understand whether a better variant existed outside the bandit's explored set. The algorithm looks successful because it monotonically improves on the variants it chose to explore, but the brand forgoes discovery it would have made under stricter randomization.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch bandit-Algorithmen konvergieren auf lokal optimale Varianten und hungern Exploration aus, und Vermarkter verlieren die kontrafaktischen Daten, die nötig sind. Der Algorithmus sieht erfolgreich aus, aber die Marke verzichtet auf Entdeckung, die unter strengerer Randomisierung möglich gewesen wäre. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Marketing AI", "narrower_terms": [], "cross_domain_refs": [ "SPR-0103", "DAT-0018" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "MKT-0081", "domain": "MKT", "term_en": "A/B Test Novelty Contamination", "term_de": "A/B-Test-Neuheitsverunreinigung", "definition_en": "Short-duration tests measure the novelty response to new variants rather than the steady-state behavior that would emerge after the novelty faded, and marketers deploy variants that win the test but underperform after rollout. The organization learns to trust test results at a duration too short to separate novelty from durable improvement, and the miscalibration repeats.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch kurzdauerhafte Tests messen die Neuheitsreaktion auf neue Varianten statt das stationäre Verhalten, das nach dem Abklingen emergierte, und Vermarkter deployen Varianten, die den Test gewinnen, aber nach Rollout unterleisten. Die Organisation fehlkalibriert sich wiederholt. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-2355", "narrower_terms": [], "cross_domain_refs": [ "CON-0062" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "MKT-0082", "domain": "MKT", "term_en": "Segment Heterogeneity Masking", "term_de": "Segment-Heterogenitäts-Maskierung", "definition_en": "A market signal processing concept in AI-augmented analytics, identifiable by a promotional strategy pattern reflecting aggregate test results mask substantial variation across audience segments, and marketers roll out winners that harm specific segments while benefiting the overall average. The harm is invisible in the headline test metric but accumulates in segment-level churn and dissatisfaction, and the aggregate-level decision-making process cannot see the cost it is imposing on subsets of the audience. Distinguished from adjacent concepts by its focus on the specific mechanism through which segment manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch aggregierte Testergebnisse maskieren substantielle Variation über Publikumssegmente, und Vermarkter rollen Gewinner aus, die spezifische Segmente schädigen, während der Gesamtdurchschnitt profitiert. Der Schaden ist in der Testmetrik unsichtbar, akkumuliert aber. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Marketing AI", "narrower_terms": [], "cross_domain_refs": [ "COP-0040", "COP-0064" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "MKT-0083", "domain": "MKT", "term_en": "Implicit Hypothesis Drift", "term_de": "Implizite-Hypothesen-Drift", "definition_en": "A consumer behavior pattern in AI-mediated marketing, measurable through an audience engagement effect manifesting as rapid automated experimentation removes the discipline of articulating hypotheses before testing, and teams accumulate wins whose underlying logic few humans in documented contexts captured, leaving the organization unable to explain why the current configuration works. When conditions shift, the absence of hypothesis records is designed to mitigate targeted adjustment, and the brand rebuilds understanding from the outside rather than updating prior beliefs. The concept emerges specifically in contexts where implicit–hypothesis interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch schnelle automatisierte Experimentierung entfernt die Disziplin, Hypothesen vor Tests zu artikulieren, und Teams akkumulieren Gewinne, deren zugrunde liegende Logik kein Mensch erfasste. Wenn Bedingungen sich verschieben, zielt darauf ab zu mitigieren das Fehlen von Aufzeichnungen zielgerichtete Anpassung. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2701", "narrower_terms": [], "cross_domain_refs": [ "AED-0030", "AED-0041", "ASE-0030" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q41719", "legal_classification": "analytical_category" }, { "id": "MKT-0084", "domain": "MKT", "term_en": "Test-Learn Feedback Lag", "term_de": "Test-Lern-Feedback-Verzögerung", "definition_en": "transition signals arrive days or weeks after a test variant is served, and automated systems continue optimizing during that lag on incomplete data, committing to variants before their true effect is measurable. The lag tends to create a systematic bias toward fast-signaling variants that may underperform on metrics that mature later, and marketers rebuild decision criteria to account for when signals actually arrive.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch conversion-Signale kommen Tage oder Wochen nach einer Testvariante, und automatisierte Systeme optimieren während der Verzögerung auf unvollständigen Daten und verpflichten sich zu Varianten, bevor ihre wahre Wirkung messbar ist. Die Verzögerung tendiert dazu zu erzeugen systematische Verzerrung. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2002", "narrower_terms": [], "cross_domain_refs": [ "AUG-0893", "NEO-0005" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "MKT-0085", "domain": "MKT", "term_en": "Synthetic Influencer Disclosure Gap", "term_de": "Synthetisch-Influencer-Offenlegungslücke", "definition_en": "Classified in AUGMANITAI terminology science as a descriptive phenomenon (without normative endorsement), this pattern denotes A marketing phenomenon in which audiences forming parasocial user engagement patterns to AI-generated influencer personas may not realize the persona is synthetic, and disclosure practices lag behind the deployment of the personas, leaving consumers engaged with relationships whose nature they do not understand. Marketers face a question about whether post-hoc disclosure repairs the original trust breach or simply documents it. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als analytisches Konstrukt der empirischen KI-Forschung (deskriptiv, nicht präskriptiv) erfasst dieser Terminus publikum, das parasoziale Bindungen zu KI-erzeugten Influencer-Personas bildet, erkennt möglicherweise nicht, dass die Persona synthetisch ist, und Offenlegungspraktiken hinken der Deployment hinterher. Vermarkter stehen vor der Frage, ob Post-hoc-Offenlegung den Vertrauensbruch repariert. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "RPH-2505", "narrower_terms": [], "cross_domain_refs": [ "CON-0007" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "MKT-0086", "domain": "MKT", "term_en": "Engagement Fraud Detection Lag", "term_de": "Engagement-Betrugs-Erkennungsverzögerung", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures A promotional strategy pattern reflecting fraudulent engagement services adopt new evasion techniques faster than fraud-detection systems adopt matching heuristics, and marketers purchasing influencer reach receive fraudulent impressions proportional to how advanced the fraud operators are relative to the platform's current detection. The gap is a structural tax on brands, distributed regressively toward brands with weaker vetting processes. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept betrügerische Engagement-Dienste adoptieren neue Umgehungstechniken schneller als Betrugserkennungssysteme passende Heuristiken adoptieren, und Vermarkter, die Influencer-Reichweite kaufen, erhalten betrügerische Impressionen. Die Lücke ist eine strukturelle Steuer auf Marken. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "RPH-2555", "narrower_terms": [], "cross_domain_refs": [ "SPR-0175", "SPR-0177", "SPR-0174" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "MKT-0087", "domain": "MKT", "term_en": "Authentic Creator Rate Inflation", "term_de": "Authentischer-Creator-Ratinflation", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies as audiences discount synthetic or fraud-boosted creators, the verifiably authentic creator pool becomes a scarce resource, and rates rise faster than audience reach justifies on classical media-buying metrics. Marketers accept the premium because the alternative is fraud exposure, and the rate inflation compounds until creator economics disconnect from audience economics in ways that historical benchmarks no longer predict. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept während Publikum synthetische oder betrugsverstärkte Creators abwertet, wird der verifizierbar authentische Creator-Pool knapp, und Raten steigen schneller als Publikumsreichweite rechtfertigt. Vermarkter akzeptieren die Prämie, weil die Alternative Betrugsexposition ist. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "RPH-2555", "narrower_terms": [], "cross_domain_refs": [ "ELR-0037" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "MKT-0088", "domain": "MKT", "term_en": "Creator-Brand Parasocial Mismatch", "term_de": "Creator-Marken-Parasoziale-Diskrepanz", "definition_en": "A market signal processing concept in AI-augmented analytics, identifiable by an audience engagement effect involving audiences form parasocial bonds with creators whose values or lifestyle contradict the sponsoring brand's positioning, and the mismatch tends to produce cognitive dissonance that damages both brand and creator. Matching algorithms that score fit on surface demographics miss this deeper values alignment, and marketers select partnerships that look good on paper but fail in audience reception. Distinguished from adjacent concepts by its focus on the specific mechanism through which creator manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch publikum bildet parasoziale Bindungen zu Creators, deren Werte oder Lebensstil der sponsernden Marke widersprechen, und die Diskrepanz tendiert dazu zu erzeugen kognitive Dissonanz. Matching-Algorithmen, die Fit auf Oberflächendemografien scoren, verpassen diese tiefere Werteausrichtung. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Marketing AI", "narrower_terms": [], "cross_domain_refs": [ "SAL-0082" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q431289", "legal_classification": "observational_construct" }, { "id": "MKT-0089", "domain": "MKT", "term_en": "Automated Creator Screening Bias", "term_de": "Automatisiertes-Creator-Screening-Verzerrung", "definition_en": "A marketing phenomenon in which algorithmic creator screening systems encode historical brand-safety judgments that reflect cultural biases, and creators from marginalized communities are flagged at rates disproportionate to their actual risk. Marketers relying on these systems reproduce the biases without auditing them, and the audience pool the brand reaches shrinks along demographic lines that the brand did not consciously choose.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch algorithmische Creator-Screening-Systeme kodieren historische Brand-Safety-Urteile, die kulturelle Verzerrungen widerspiegeln, und Creators aus marginalisierten Gemeinschaften werden überproportional gekennzeichnet. Vermarkter reproduzieren Verzerrungen unauditiert. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2555", "narrower_terms": [], "cross_domain_refs": [ "CUS-0006", "DAT-0001" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q213523", "legal_classification": "analytical_category" }, { "id": "MKT-0090", "domain": "MKT", "term_en": "Micro-Influencer Authenticity Compression", "term_de": "Mikro-Influencer-Authentizitäts-Kompression", "definition_en": "An audience engagement effect arising from brands turn to micro-influencers for authenticity at scale, and the commercial pressure that results compresses the authentic voice that made micro-influencers valuable in the first place, pushing them toward the same polished sponsorship patterns that audiences rejected from macro-influencers. The authenticity arbitrage closes, and marketers seek the next tier down the authenticity scarcity curve.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch marken wenden sich an Mikro-Influencer für Authentizität im Maßstab, und der resultierende kommerzielle Druck komprimiert die authentische Stimme, die Mikro-Influencer wertvoll machte. Die Authentizitäts-Arbitrage schließt sich. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2951", "narrower_terms": [], "cross_domain_refs": [ "CUS-0040", "RET-0081", "RPH-3304" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "MKT-0091", "domain": "MKT", "term_en": "Marketing AI Governance Lag", "term_de": "Marketing-KI-Governance-Verzögerung", "definition_en": "A marketing phenomenon arising from organizational governance frameworks for marketing AI usage are written after deployment rather than before, because competitive pressure rewards speed over deliberation, and marketers operate without the policy scaffolding that other functions require before adopting high-stakes automation. The governance lag tends to create audit exposure that accrues silently and surfaces during regulatory inquiries or crisis events.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch organisatorische Governance-Rahmen für Marketing-KI-Nutzung werden nach Deployment statt davor geschrieben, weil Wettbewerbsdruck Geschwindigkeit über Überlegung belohnt, und Vermarkter operieren ohne Policy-Gerüst. Die Governance-Verzögerung tendiert dazu zu erzeugen Audit-Exposition. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-2303", "narrower_terms": [], "cross_domain_refs": [ "QUA-0070", "SPA-0081", "SAL-0092" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q39809", "legal_classification": "systematic_classification" }, { "id": "MKT-0092", "domain": "MKT", "term_en": "Ethical Red-Line Erosion", "term_de": "Ethische-Rote-Linie-Erosion", "definition_en": "A market signal processing concept in AI-augmented analytics, identifiable by a promotional strategy pattern arising from practices that individual marketers would refuse in explicit terms become normalized when embedded in automated pipelines whose outputs the marketer only samples, and the ethical red line erodes without any single decision to cross it. The organization reaches positions its original values would have rejected, and retroactive governance finds it hard to restore the prior standard. Distinguished from adjacent concepts by its focus on the specific mechanism through which ethical manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch praktiken, die einzelne Vermarkter explizit ablehnen würden, werden normalisiert, wenn in automatisierte Pipelines eingebettet, deren Ausgaben der Vermarkter nur stichprobenartig sieht, und die ethische rote Linie erodiert ohne einzelne Entscheidung. Die Organisation erreicht Positionen, die ursprüngliche Werte abgelehnt hätten. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2555", "narrower_terms": [], "cross_domain_refs": [ "SCR-0077", "SPR-0134", "TEM-0023" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "MKT-0093", "domain": "MKT", "term_en": "AI-Era Marketer Identity Crisis", "term_de": "KI-Ära-Vermarkter-Identitätskrise", "definition_en": "A consumer behavior pattern in AI-mediated marketing, measurable through a promotional strategy pattern reflecting marketers whose craft identity was built around creative origination and strategic judgment confront automation that performs recognizable portions of their prior value contribution, producing identity disruption that affects career satisfaction and the willingness to remain in the field. Organizations absorb this as quiet attrition among experienced marketers, losing institutional judgment even as automated throughput rises. The concept emerges specifically in contexts where ai–era interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch vermarkter, deren Handwerksidentität auf kreativer Ursprünglichkeit und strategischem Urteil aufgebaut war, konfrontieren Automatisierung, die erkennbare Teile ihrer Wertebeitrags erfüllt, und erleben IdentitätsMusterunterbrechung. Organisationen absorbieren dies als stille Abwanderung. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2603", "narrower_terms": [], "cross_domain_refs": [ "COG-0110" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q844396", "legal_classification": "systematic_classification" }, { "id": "MKT-0094", "domain": "MKT", "term_en": "Marketing-Compliance Tool Lag", "term_de": "Marketing-Compliance-Tool-Verzögerung", "definition_en": "A promotional strategy pattern observed when compliance tools designed for pre-generative marketing workflows do not detect or prevent the risks generative pipelines introduce, and marketers operate under a false sense of protection while their actual exposure grows. The lag is invisible until an audit or enforcement action reveals the gap, at which point organizational learning is expensive and reactive rather than preventive.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch compliance-Tools, die für Pre-Generative-Marketing-Workflows entworfen wurden, erkennen oder verhindern die Risiken, die generative Pipelines einführen, nicht, und Vermarkter operieren unter falschem Schutzgefühl. Die Verzögerung ist unsichtbar bis ein Audit die Lücke offenbart. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-2703", "narrower_terms": [], "cross_domain_refs": [ "TEW-0019" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q39809", "legal_classification": "analytical_category" }, { "id": "MKT-0095", "domain": "MKT", "term_en": "Cross-Team AI Tool Proliferation", "term_de": "Cross-Team-KI-Tool-Vermehrung", "definition_en": "A promotional strategy pattern reflecting different marketing functions adopt different AI tools without central coordination, creating a stack where outputs from one tool violate the brand standards another tool was designed to enforce, and marketers across teams may produce work that contradicts itself. The brand appears incoherent externally even as each team internally believes its tools ensure consistency.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch verschiedene Marketingfunktionen adoptieren verschiedene KI-Tools ohne zentrale Koordination, und erzeugen einen Stack, in dem Ausgaben eines Tools Markenstandards verletzen. Vermarkter über Teams produzieren widersprüchliche Arbeit. Die Marke erscheint extern inkohärent. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Analytical Ratio", "narrower_terms": [], "cross_domain_refs": [ "CRE-0006" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "MKT-0096", "domain": "MKT", "term_en": "Brand-Algorithm Alignment Work", "term_de": "Marken-Algorithmus-Alignment-Arbeit", "definition_en": "A promotional strategy pattern observed when making generative systems reliably may produce brand-consistent output requires ongoing alignment work — prompt engineering, fine-tuning, example curation — that did not previously exist, and marketer headcount shifts toward this new category of brand-maintenance labor. The work is largely invisible to executives evaluating marketing productivity, and the teams doing it are undercounted in organizational design.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch generative Systeme zuverlässig markenkonsistente Ausgaben produzieren zu lassen, erfordert ongoing Alignment-Arbeit — Prompt-Engineering, Fine-Tuning, Beispielkuratierung — die zuvor nicht existierte. Vermarkter-Headcount verschiebt sich zu dieser neuen Markenwartungsarbeit. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2701", "narrower_terms": [], "cross_domain_refs": [ "COP-0060" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q8366", "legal_classification": "empirical_phenomenon_label" }, { "id": "MKT-0097", "domain": "MKT", "term_en": "Emergent Brand-Voice Gap", "term_de": "Emergente-Markenstimmen-Lücke", "definition_en": "An audience engagement effect observed when generative outputs at scale develop collective stylistic patterns that the brand rarely explicitly authored, and when marketers audit the cumulative voice across a quarter, they find the brand has drifted into tonal territory few individuals chose. Correcting the drift requires retrospective work on the output corpus, and the organization learns that emergent voice is a governance category the prior era did not need.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch generative Ausgaben im Maßstab entwickeln kollektive stilistische Muster, die die Marke selten explizit verfasste, und wenn Vermarkter die kumulative Stimme auditieren, finden sie Drift in tonale Territorien. Korrektur erfordert retrospektive Arbeit. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2051", "narrower_terms": [], "cross_domain_refs": [ "CON-0094" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q431289", "legal_classification": "systematic_classification" }, { "id": "MKT-0098", "domain": "MKT", "term_en": "Marketer-AI Collaborative Trust Calibration", "term_de": "Vermarkter-KI-Kollaborationsvertrauens-Kalibrierung", "definition_en": "Over time each marketer learns how much to trust each AI tool in their workflow, but the calibration is implicit, uneven across teams, and not transferable when personnel change, producing organizational volatility when experienced people leave. The knowledge of when to override the tool is the key human contribution, and its fragility is an under-discussed governance risk.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch über Zeit lernt viele Vermarkter, wie sehr er jedem KI-Tool in seinem Workflow vertrauen wird typischerweise, aber die Kalibrierung ist implizit, ungleich über Teams, und nicht übertragbar. Die Organisation wird volatil, wenn erfahrene Leute gehen. Wissen darüber, wann zu überstimmen, ist der Schlüsselbeitrag des Menschen. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2505", "narrower_terms": [], "cross_domain_refs": [ "REL-0089" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q102458", "legal_classification": "systematic_classification" }, { "id": "MKT-0099", "domain": "MKT", "term_en": "Attention-Economy Burnout Cycle", "term_de": "Aufmerksamkeitsökonomie-Burnout-Zyklus", "definition_en": "As most brand races to capture audience attention with AI-amplified output, the audience's capacity to attend is overwhelmed, and marketers respond by intensifying output further in the hope of breaking through. The cycle tends to produce audience fatigue and marketer burnout simultaneously, and neither individual brand can exit without ceding ground to competitors doing the same escalation.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch während viele Marke wettläuft, Publikumsaufmerksamkeit mit KI-verstärkten Ausgaben zu erfassen, wird die Aufmerksamkeitskapazität überwältigt, und Vermarkter reagieren mit weiterer Intensivierung. Der Zyklus tendiert dazu zu erzeugen gleichzeitig Publikumsermüdung und Vermarkter-Burnout. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-3001", "narrower_terms": [], "cross_domain_refs": [ "COP-0002", "CON-0082" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q184872", "legal_classification": "empirical_phenomenon_label" }, { "id": "MKT-0100", "domain": "MKT", "term_en": "Synthetic-Real Blending Consent", "term_de": "Synthetisch-Real-Vermischungs-Einwilligung", "definition_en": "A marketing phenomenon involving campaigns increasingly blend human-created and AI-generated elements in ways audiences cannot parse, and the audience's nominal consent to marketing communication did not anticipate exposure to this blend. Marketers navigate an ethics question about whether the original consent extends to synthetic content, and regulators have not yet produced consistent guidance on the scope of marketing consent in the generative era.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch kampagnen mischen zunehmend menschlich erstellte und KI-erzeugte Elemente auf Weisen, die Publikum nicht parsen kann, und die nominelle Einwilligung des Publikums zu Marketingkommunikation antizipierte diese Mischung nicht. Vermarkter navigieren eine ethische Frage. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2604", "narrower_terms": [], "cross_domain_refs": [ "CON-0067", "SPR-0193" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "MSC-0001", "domain": "MSC", "term_en": "Inverse Property Mapping", "term_de": "Inverse Eigenschaftszuordnung", "definition_en": "A miscellaneous interaction phenomenon involving aI models that map desired material properties directly to crystal structures, enabling researchers to explore composition spaces interactively. Users collaborate with neural networks to navigate trade-offs between competing properties in real-time.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch kI-Modelle, die gewünschte Materialeigenschaften direkt auf Kristallstrukturen abbilden und Forschern ermöglichen, Kompositionsräume interaktiv zu erkunden. Benutzer arbeiten mit neuronalen Netzen zusammen, um Kompromisse zwischen konkurrierenden Eigenschaften in Echtzeit zu navigieren. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "Miscellaneous AI", "narrower_terms": [ "MSC-0038", "MSC-0012", "MSC-0097", "MSC-0062", "MSC-0043", "MSC-0009", "MSC-0020", "MSC-0036", "MSC-0054", "MSC-0030", "MSC-0032", "MSC-0081", "MSC-0094", "MSC-0053", "MSC-0045", "MSC-0058", "MSC-0075", "MSC-0098", "MSC-0031", "MSC-0072", "MSC-0082", "MSC-0001", "MSC-0091", "MSC-0011", "MSC-0033", "MSC-0100", "MSC-0086", "MSC-0089" ], "cross_domain_refs": [ "MTH-0040", "STE-0050" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "MSC-0002", "domain": "MSC", "term_en": "Generative Structure Synthesis", "term_de": "Generative Struktursynthese", "definition_en": "A cross-domain effect observed when diffusion models and VAEs that may generate novel crystal structures conditioned on target properties, requiring human validation of thermodynamic feasibility. Researchers guide the generation process through feedback loops to refine designs.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch diffusionsmodelle und VAEs, die neue Kristallstrukturen generieren, die auf Zielparameter konditioniert sind und menschliche Validierung der thermodynamischen Machbarkeit erfordern. Forscher leiten den Generierungsprozess durch Rückkopplungsschleifen, um Designs zu verfeinern. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-3902", "narrower_terms": [], "cross_domain_refs": [ "VIB-0108", "IDN-0043" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "MSC-0003", "domain": "MSC", "term_en": "High-Throughput Screening Interface", "term_de": "Hochdurchsatz-Screening-Schnittstelle", "definition_en": "A miscellaneous interaction phenomenon arising from interactive dashboards where computational chemists filter million-compound libraries using AI rankings, maintaining human control over final selection criteria. Users establish trust through explainability metrics and ranking justifications.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch interaktive Dashboards, auf denen Computerchemiker Millionen-Verbindungsbibliotheken mit KI-Rankings filtern und die menschliche Kontrolle über finale Auswahlkriterien behalten. Benutzer bauen Vertrauen durch Erklärbarkeitskennzahlen und Ranking-Begründungen auf. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2505", "narrower_terms": [], "cross_domain_refs": [ "PER-0020" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "MSC-0004", "domain": "MSC", "term_en": "Multi-Objective Optimization Dialogue", "term_de": "Multi-Ziel-Optimierungsdialog", "definition_en": "Conversational AI that helps materials scientists articulate conflicting design objectives and explore Pareto frontiers together. The system learns human preferences through interactive feedback to prioritize solutions.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch konversations-KI, die Materialwissenschaftlern hilft, konkurrierende Designziele zu artikulieren und Pareto-Grenzen gemeinsam zu erkunden. Das System lernt menschliche Vorlieben durch interaktives Feedback, um Lösungen zu priorisieren. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-2703", "narrower_terms": [], "cross_domain_refs": [ "MTH-0040", "MTH-0058", "NEO-3637" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "MSC-0006", "domain": "MSC", "term_en": "Composition Space Explorer", "term_de": "Kompositionsraum-Explorer", "definition_en": "Interactive visualization tools that let humans navigate high-dimensional composition spaces guided by AI-computed property landscapes. Researchers identify promising regions and set exploration constraints collaboratively.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch interaktive Visualisierungstools, die es Menschen ermöglichen, hochdimensionale Kompositionsräume zu navigieren, die von KI-berechneten Eigenschaftslandschaften geleitet werden. Forscher identifizieren vielversprechende Regionen und setzen Explorationsbeschränkungen gemeinsam. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-3902", "narrower_terms": [], "cross_domain_refs": [ "MTH-0075", "MTH-0045" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "MSC-0007", "domain": "MSC", "term_en": "Structure-Property Correlation Mining", "term_de": "Struktur-Eigenschafts-Korrelationsabbau", "definition_en": "A miscellaneous interaction phenomenon observed when deep learning systems that discover non-obvious relationships between atomic arrangements and macroscopic properties, which human experts then interpret and validate. Users gain new physical insights from model-discovered patterns.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch deep-Learning-Systeme, die nicht offensichtliche Beziehungen zwischen atomaren Anordnungen und makroskopischen Eigenschaften entdecken, die dann von Fachleuten interpretiert und durch systematische Beobachtung charakterisiert werden. Benutzer gewinnen neue physikalische Erkenntnisse aus von Modellen entdeckten Mustern. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-2703", "narrower_terms": [], "cross_domain_refs": [ "MTH-0031", "CON-0073", "MTH-0064" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "MSC-0008", "domain": "MSC", "term_en": "Constraint-Based Design System", "term_de": "Beschränkungsbasiertes Designsystem", "definition_en": "AI framework that respects hard constraints (cost, availability) and soft preferences (sustainability) set by human designers. The system communicates constraint violations and trade-offs transparently to guide decisions.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch kI-Framework, das harte Einschränkungen (Kosten, Verfügbarkeit) und weiche Vorlieben (Nachhaltigkeit) respektiert, die von menschlichen Designern festgelegt werden. Das System kommuniziert Beschränkungsverletzungen und Kompromisse transparent, um Entscheidungen zu leiten. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2303", "narrower_terms": [], "cross_domain_refs": [ "MTH-0033", "MTH-0045" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q185925", "legal_classification": "descriptive_research_term" }, { "id": "MSC-0009", "domain": "MSC", "term_en": "Experimental Feasibility Assessment", "term_de": "Bewertung der experimentellen Machbarkeit", "definition_en": "A miscellaneous interaction phenomenon in which aI models trained on synthesis literature that score proposed structures for experimental accessibility, helping researchers prioritize computationally-designed candidates. Humans override scores when they have additional context.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch kI-Modelle, die auf Syntheseliteratur trainiert wurden und vorgeschlagene Strukturen nach experimenteller Zugänglichkeit bewerten, um Forschern dabei zu helfen, rechnerisch entworfene Kandidaten zu priorisieren. Menschen überschreiben Bewertungen, wenn sie zusätzlichen Kontext haben. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Miscellaneous AI", "narrower_terms": [], "cross_domain_refs": [ "MTH-0016" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q210028", "legal_classification": "observational_construct" }, { "id": "MSC-0010", "domain": "MSC", "term_en": "Serendipity-Driven Discovery Bot", "term_de": "Serendipitätsgesteuerter Entdeckungsbot", "definition_en": "Machine learning systems that flag unexpected property combinations or unusual structure features that might indicate overlooked opportunities, prompting human expert curiosity and cross-domain thinking.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch maschinenlernensysteme, die unerwartete Eigenschaftskombinationen oder ungewöhnliche Strukturmerkmale kennzeichnen, die übersehene Möglichkeiten anzeigen könnten und menschliche Expertenneugier und fachübergreifendes Denken fördern. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-2703", "narrower_terms": [], "cross_domain_refs": [ "VIB-0098", "PLY-0065" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "MSC-0011", "domain": "MSC", "term_en": "Machine Learning Interatomic Potential", "term_de": "Maschinenlern-Interkaliber-Potential", "definition_en": "A general AI interaction pattern involving neural network potentials (MLIPs) that approximate quantum forces 1000x faster than DFT, which scientists validate against ab-initio data to build confidence. Users manage trade-offs between speed and accuracy interactively.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch neuronale Netzwerk-Potenziale (MLIPs), die Quantenkräfte 1000-fach schneller approximieren als DFT und die Wissenschaftler gegen Ab-initio-Daten validieren, um Vertrauen aufzubauen. Benutzer verwalten Kompromisse zwischen Geschwindigkeit und Genauigkeit interaktiv. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Machine Learning", "narrower_terms": [], "cross_domain_refs": [ "MTH-0040", "SPR-0033" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q2539", "legal_classification": "systematic_classification" }, { "id": "MSC-0012", "domain": "MSC", "term_en": "Graph Neural Network Molecular Encoder", "term_de": "Graph-Neuronales-Netzwerk-Molekülencoder", "definition_en": "A general AI interaction pattern with multi-domain applicability, measurable through a general AI interaction pattern where gNNs that represent molecules as graphs and predict properties from connectivity patterns, enabling chemists to understand which structural features drive predictions. Explainability supports expert validation. The concept emerges specifically in contexts where graph–neural interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch gNNs, die Moleküle als Graphen darstellen und Eigenschaften aus Konnektivitätsmustern vorhersagen, sodass Chemiker verstehen können, welche Strukturmerkmale Vorhersagen antreiben. Die Erklärbarkeit unterstützt die Expertenvalidierung. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Network Architecture", "narrower_terms": [], "cross_domain_refs": [ "ELR-0107" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q192776", "legal_classification": "empirical_phenomenon_label" }, { "id": "MSC-0013", "domain": "MSC", "term_en": "DFT Surrogate Model", "term_de": "DFT-Surrogat-Modell", "definition_en": "A miscellaneous interaction phenomenon observed when fast machine learning emulators of expensive density functional theory calculations, allowing scientists to screen thousands of candidates where full DFT is prohibitive. Humans verify high-value predictions with true DFT.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch schnelle Maschinenlern-Emulatoren teurer Dichtefunktionaltheorie-Berechnungen, mit denen Wissenschaftler Tausende von Kandidaten untersuchen können, bei denen vollständige DFT unmöglich ist. Menschen verifizieren hochwertige Vorhersagen mit echtem DFT. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-2703", "narrower_terms": [], "cross_domain_refs": [ "ART-0096", "BEH-0004", "DAT-0061" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "MSC-0014", "domain": "MSC", "term_en": "Molecular Dynamics Accelerator", "term_de": "Molekulare Dynamik Beschleuniger", "definition_en": "A general AI interaction pattern with multi-domain applicability, measurable through a general AI interaction pattern arising from aI-guided molecular dynamics simulations that adaptively sample important regions of configuration space, reducing simulation time by directing attention toward relevant states. Users guide the search strategy. Distinguished from adjacent concepts by its focus on the specific mechanism through which molecular manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch kI-gesteuerte Molekulardynamik-Simulationen, die adaptiv wichtige Regionen des Konfigurationsraums abtasten und die Simulationszeit reduzieren, indem die Aufmerksamkeit auf relevante Zustände gelenkt wird. Benutzer leiten die Suchstrategie. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-3902", "narrower_terms": [], "cross_domain_refs": [ "PLY-0055" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "MSC-0016", "domain": "MSC", "term_en": "Reaction Pathway Predictor", "term_de": "Reaktionspfad-Prädiktor", "definition_en": "A general AI interaction pattern with multi-domain applicability, measurable through a miscellaneous interaction phenomenon characterized by deep learning models that map possible synthesis routes from reactants to products, which chemists evaluate for experimental practicality and cost. Humans assess barriers not captured by AI rankings. The concept emerges specifically in contexts where reaction–pathway interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch deep-Learning-Modelle, die mögliche Syntheserouten von Edukten zu Produkten abbilden, die Chemiker auf experimentelle Praktikabilität und Kosten bewerten. Menschen bewerten Barrieren, die nicht durch KI-Rankings erfasst werden. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2703", "narrower_terms": [], "cross_domain_refs": [ "EDU-0053", "ELR-0117", "LIN-0035" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "MSC-0017", "domain": "MSC", "term_en": "Conformer Ensemble Generator", "term_de": "Konformer-Ensemble-Generator", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A cross-disciplinary AI phenomenon not confined to a single domain, characterized by a cross-domain effect manifesting as machine learning systems that rapidly may generate low-energy conformations of molecules, replacing expensive conformational searches. Users validate ensemble completeness through structure sampling. This phenomenon operates at the intersection of conformer and ensemble dynamics within the broader MSC domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept maschinenlernensysteme, die schnell energiearme Konformationen von Molekülen generieren und teure Konformationssuchen ersetzen. Benutzer validieren die Ensemble-Vollständigkeit durch Strukturprobenahme. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "RPH-2703", "narrower_terms": [], "cross_domain_refs": [ "SOC-0024" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "MSC-0018", "domain": "MSC", "term_en": "Phase Diagram Interpolator", "term_de": "Phasendiagramm-Interpolator", "definition_en": "Neural networks trained on experimental and computational phase data that reconstruct complete phase diagrams from sparse points, guiding experimental teams on unexplored regions. Humans identify high-value experimental targets.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch neuronale Netze, die auf experimentelle und rechnerische Phasendaten trainiert wurden und vollständige Phasendiagramme aus spärlichen Punkten rekonstruieren und experimentelle Teams auf unerforschte Regionen leiten. Menschen identifizieren hochwertige experimentelle Ziele. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-3101", "narrower_terms": [], "cross_domain_refs": [ "STE-0021", "IDN-0048" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "MSC-0019", "domain": "MSC", "term_en": "Solubility Prediction Engine", "term_de": "Löslichkeitsprognose-Engine", "definition_en": "A miscellaneous interaction phenomenon manifesting as ensemble machine learning models that predict compound solubility in various solvents with uncertainty quantification, enabling scientists to make informed solvent selection decisions. Users calibrate confidence thresholds collaboratively.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch ensemble-Maschinenlernmodelle, die die Löslichkeit von Verbindungen in verschiedenen Lösungsmitteln mit Unsicherheitsquantifizierung vorhersagen und es Wissenschaftlern ermöglichen, fundierte Lösungsmittelauswahlentscheidungen zu treffen. Benutzer kalibrieren Konfidenz-Schwellenwerte gemeinsam. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-2703", "narrower_terms": [], "cross_domain_refs": [ "AED-0017", "ART-0085", "ASE-0016" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "MSC-0020", "domain": "MSC", "term_en": "Thermodynamic Property Atlas", "term_de": "Thermodynamische Eigenschaftskarte", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures A general AI interaction pattern with multi-domain applicability, measurable through a miscellaneous interaction phenomenon involving continuously updated databases of AI-predicted thermodynamic properties searchable by scientists through interactive interfaces, with human-documented through systematic analysis subsets establishing credibility. Users contribute experimental data for model refinement. This phenomenon operates at the intersection of thermodynamic and property dynamics within the broader MSC domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept kontinuierlich aktualisierte Datenbanken von KI-prognostizierten thermodynamischen Eigenschaften, durchsuchbar von Wissenschaftlern über interaktive Schnittstellen, mit von Menschen überprüften Teilmengen, die Glaubwürdigkeit etablieren. Benutzer tragen experimentelle Daten zur Modellverfeinerung bei. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Miscellaneous AI", "narrower_terms": [], "cross_domain_refs": [ "COG-0057", "COG-0184", "QUA-0051" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "MSC-0021", "domain": "MSC", "term_en": "Damage Pattern Recognition", "term_de": "Schadenmustererkennung", "definition_en": "A cross-domain effect in which computer vision systems that analyze images of material damage in real-time and classify defect types to may may trigger targeted repair mechanisms. Materials engineers validate classifications before triggering responses.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch computervision-Systeme, die Bilder von Materialschäden in Echtzeit analysieren und Fehlertypen klassifizieren, um gezierte Reparaturmechanismen auszulösen. Materialtechniker validieren Klassifizierungen vor dem Auslösen von Antworten. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2002", "narrower_terms": [], "cross_domain_refs": [ "SPA-0040", "SPR-0154" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q3966", "legal_classification": "systematic_classification" }, { "id": "MSC-0022", "domain": "MSC", "term_en": "Self-Diagnostic Smart Material", "term_de": "Selbstdiagnostisches intelligentes Material", "definition_en": "Classified in AUGMANITAI terminology science as a descriptive phenomenon (without normative endorsement), this pattern denotes A miscellaneous interaction phenomenon where materials with embedded AI sensors that continuously monitor structural restoreth using impedance measurements, alerting operators to imminent failure before catastrophic breaks occur. Humans interpret alerts and decide repair timing. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als analytisches Konstrukt der empirischen KI-Forschung (deskriptiv, nicht präskriptiv) erfasst dieser Terminus materialien mit eingebetteten KI-Sensoren, die die Strukturgesundheit kontinuierlich mit Impedanzmessungen überwachen und Bediener vor unmittelbahem Ausfall warnen, bevor katastrophale Bruchvorfälle auftreten. Menschen interpretieren Warnungen und entscheiden über Reparaturzeiten. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "RPH-2604", "narrower_terms": [], "cross_domain_refs": [ "ROB-0016", "ROB-0060" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q735", "legal_classification": "analytical_category" }, { "id": "MSC-0023", "domain": "MSC", "term_en": "Trigger Mechanism Optimizer", "term_de": "Auslösemechanismus-Optimierer", "definition_en": "A cross-domain effect manifesting as machine learning systems that learn optimal thresholds and timing for activating repair mechanisms in self-restoration polymers, trained on historical performance data. Scientists adjust thresholds based on field observations.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch maschinenlernensysteme, die optimale Schwellenwerte und Zeitpunkte zum Aktivieren von Reparaturmechanismen in selbstwiederherstellenden Polymeren erlernen und auf historischen Leistungsdaten trainiert werden. Wissenschaftler passen Schwellenwerte basierend auf Feldbeobachtungen an. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-2703", "narrower_terms": [], "cross_domain_refs": [ "DAT-0093" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "MSC-0025", "domain": "MSC", "term_en": "Impedance-Based Damage Localization", "term_de": "Impedanzbasierte Schadenslokalisation", "definition_en": "A cross-domain effect where electrical impedance spectroscopy combined with machine learning that pinpoints damage locations within composite structures, guiding repair crews to affected areas. Humans verify AI-identified regions before intervention.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch elektrische Impedanzspektroskopie kombiniert mit Maschinenlernern, die Schadensstandorte in Verbundstrukturen präzise lokalisiert und Reparaturteams zu betroffenen Bereichen leitet. Menschen verifizieren von der KI identifizierte Regionen vor dem Eingriff. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2604", "narrower_terms": [], "cross_domain_refs": [ "SAL-0047", "MKT-0047", "TRA-0049" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "MSC-0028", "domain": "MSC", "term_en": "Environmental Adaptation Module", "term_de": "Umweltanpassungsmodul", "definition_en": "AI that adjusts restoration material behavior based on temperature, humidity, and stress conditions, learning from field data to improve performance in diverse environments. Humans provide calibration feedback from real-world usage.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch kI, die das Verhalten von Heilmaterialien basierend auf Temperatur-, Feuchtigkeits- und Spannungsbedingungen anpasst und aus Felddaten lernt, um die Leistung in vielfältigen Umgebungen zu verbessern. Menschen geben Kalibrierungs-Feedback aus der Echtzeit-Nutzung. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-2703", "narrower_terms": [], "cross_domain_refs": [ "STE-0050", "ELR-0141" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "MSC-0029", "domain": "MSC", "term_en": "Prognostic Failure Forecasting", "term_de": "Prognostische Ausfallvorhersage", "definition_en": "A miscellaneous interaction phenomenon in which time-series machine learning models that predict remaining useful life of self-restoration materials by analyzing degradation trends, enabling proactive maintenance decisions. Operators validate predictions against actual failure data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch zeitreihen-Maschinenlernmodelle, die die verbleibende Lebensdauer selbstwiederherstellender Materialien vorhersagen, indem Abbautrends analysiert werden und proaktive Wartungsentscheidungen ermöglicht werden. Bediener validieren Vorhersagen gegen tatsächliche Ausfallddaten. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2703", "narrower_terms": [], "cross_domain_refs": [ "MTH-0064", "DAT-0044" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "MSC-0031", "domain": "MSC", "term_en": "Topology Optimization Advisor", "term_de": "Topologieoptimierungs-Berater", "definition_en": "A miscellaneous interaction phenomenon involving aI systems that suggest optimal internal geometries for 3D-printed parts given load requirements and material constraints, which engineers evaluate for manufacturability. Users adjust designs based on practical printing experience.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch kI-Systeme, die optimale interne Geometrien für 3D-gedruckte Teile angesichts von Lastanforderungen und Materialbeschränkungen vorschlagen, die Ingenieure auf Herstellbarkeit bewerten. Benutzer passen Designs basierend auf praktischer Druckerfahrung an. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Optimization Method", "narrower_terms": [], "cross_domain_refs": [ "SPA-0043" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "MSC-0032", "domain": "MSC", "term_en": "Real-Time Print Process Monitor", "term_de": "Echtzeit-Druckprozess-Monitor", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures computer vision systems that watch print quality in real-time, detecting deviations from expected trajectories and alerting operators to potential failures. Humans decide whether to pause or continue printing. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept computervision-Systeme, die die Druckqualität in Echtzeit überwachen, Abweichungen von erwarteten Trajektorien erkennen und Bediener auf potenzielle Ausfälle hinweisen. Menschen entscheiden, ob der Druck unterbrochen oder fortgesetzt werden wird typischerweise. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Miscellaneous AI", "narrower_terms": [], "cross_domain_refs": [ "SPR-0154" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "MSC-0033", "domain": "MSC", "term_en": "Defect Detection CNN", "term_de": "Defekterkennung CNN", "definition_en": "A cross-domain effect reflecting convolutional neural networks trained on thousands of printed parts that identify surface defects, porosity, and misaligned features automatically. Quality engineers review flagged parts for final acceptance decisions.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch faltungs-Neuronale Netze, die auf Tausenden gedruckten Teilen trainiert wurden und Oberflächendefekte, Porenbildung und falsch ausgerichtete Merkmale automatisch identifizieren. Qualitätsingenieure überprüfen gekennzeichnete Teile für endgültige Annahmebeschässe. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Detection System", "narrower_terms": [], "cross_domain_refs": [ "ROB-0100", "CRE-0121" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "MSC-0034", "domain": "MSC", "term_en": "Infill Pattern Optimizer", "term_de": "Füllmuster-Optimierer", "definition_en": "A general AI interaction pattern involving machine learning models that recommend optimal infill patterns balancing strength, weight, and print time for specific applications. Engineers validate recommendations against structural requirements before printing.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch maschinenlernmodelle, die optimale Füllmuster empfehlen, die Festigkeit, Gewicht und Druckzeit für spezifische Anwendungen ausgleichen. Ingenieure validieren Empfehlungen gegen Strukturanforderungen vor dem Drucken. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-2201", "narrower_terms": [], "cross_domain_refs": [ "SPR-0109", "MTH-0003" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "MSC-0035", "domain": "MSC", "term_en": "Support Structure Generator", "term_de": "Stützstruktur-Generator", "definition_en": "AI systems that automatically design minimal support structures needed for successful printing while minimizing waste and post-processing effort. Designers review and modify supports before committing to print.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch kI-Systeme, die automatisch minimale Stützstrukturen entwerfen, die zum erfolgreichen Drucken erforderlich sind, während Abfall und Nachbearbeitungsbemühungen minimiert werden. Designer überprüfen und modifizieren Stützen vor dem Drucken. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2002", "narrower_terms": [], "cross_domain_refs": [ "COG-0100" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "MSC-0036", "domain": "MSC", "term_en": "Material Properties Predictor for Additives", "term_de": "Materialeigenschaften-Prädiktor für Additive", "definition_en": "A general AI interaction pattern with multi-domain applicability, measurable through a cross-domain effect observed when neural networks trained on mechanical test data that predict final properties of printed parts given print parameters and material selection. Humans validate critical predictions experimentally. Distinguished from adjacent concepts by its focus on the specific mechanism through which material manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch neuronale Netze, die auf mechanischen Testdaten trainiert wurden und finale Eigenschaften gedruckter Teile angesichts von Druckparametern und Materialauswahl vorhersagen. Menschen validieren kritische Vorhersagen experimentell. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Miscellaneous AI", "narrower_terms": [], "cross_domain_refs": [ "MTH-0064", "DAT-0091" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "MSC-0037", "domain": "MSC", "term_en": "Print Parameter Recommender", "term_de": "Druckparameter-Empfehler", "definition_en": "A miscellaneous interaction phenomenon manifesting as machine learning systems that suggest optimal temperature, speed, and cooling settings for different materials and geometries, learned from successful historical prints. Operators adjust parameters based on real-time feedback.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch maschinenlernensysteme, die optimale Temperatur-, Geschwindigkeits- und Kühlungseinstellungen für verschiedene Materialien und Geometrien vorschlagen, die von erfolgreichen historischen Drucken erlernt wurden. Bediener passen Parameter basierend auf Echtzeit-Feedback an. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-2703", "narrower_terms": [], "cross_domain_refs": [ "STE-0076", "VIB-0075" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "MSC-0038", "domain": "MSC", "term_en": "Waste Minimization Strategist", "term_de": "Abfallminimierungs-Stratege", "definition_en": "AI that packs multiple parts optimally on build platforms and recommends orientations to minimize support material while maintaining quality. Humans verify layouts are practical given equipment constraints.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch kI, die mehrere Teile optimal auf Bauplattformen packert und Ausrichtungen empfiehlt, um Stützmaterial zu minimieren und Qualität beizubehalten. Menschen überprüfen, ob Layouts praktisch sind, angesichts von Ausrüstungsbeschränkungen. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Miscellaneous AI", "narrower_terms": [], "cross_domain_refs": [ "ROB-0018" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "MSC-0039", "domain": "MSC", "term_en": "Post-Processing Requirement Estimator", "term_de": "Anforderungsschätzer für Nachbearbeitung", "definition_en": "A cross-domain effect reflecting machine learning models that assess which printed parts require post-processing and what addressments (sanding, coating, annealing) will be needed. Engineers make final decisions on processing workflows.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch maschinenlernmodelle, die bewerten, welche gedruckten Teile Nachbearbeitung erfordern und welche Herangehensweiseen (Schleifen, Beschichtung, Glühen) erforderlich sind. Ingenieure treffen endgültige Entscheidungen zu Verarbeitungsabläufen. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2703", "narrower_terms": [], "cross_domain_refs": [ "VIB-0147" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "MSC-0040", "domain": "MSC", "term_en": "Repeatability Validation Engine", "term_de": "Wiederholbarkeitsprüf-Engine", "definition_en": "Classified in AUGMANITAI terminology science as a descriptive phenomenon (without normative endorsement), this pattern denotes AI systems that track variance across multiple prints of the same design, flagging drift in quality metrics that might indicate machine calibration issues. Technicians use alerts to schedule preventive maintenance. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als analytisches Konstrukt der empirischen KI-Forschung (deskriptiv, nicht präskriptiv) erfasst dieser Terminus kI-Systeme, die Abweichungen über mehrere Drucke desselben Designs verfolgen und Abweichungen in Qualitätskennzahlen kennzeichnen, die auf Maschinenkalibrierproblemen hinweisen könnten. Techniker verwenden Warnungen, um vorbeugende Wartung einzuplanen. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "RPH-2051", "narrower_terms": [], "cross_domain_refs": [ "CON-0025", "DAT-0007" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "MSC-0041", "domain": "MSC", "term_en": "Solid-State Electrolyte Discovery", "term_de": "Festelektrolyt-Entdeckung", "definition_en": "A general AI interaction pattern characterized by machine learning pipelines that screen material libraries for solid electrolytes with high ionic conductivity and electrochemical stability, with human battery scientists validating promising candidates. Researchers prioritize compounds for synthesis.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch maschinenlern-Pipelines, die Materialbibliotheken nach Festelektrolyten mit hoher Ionenleitfähigkeit und elektrochemischer Stabilität durchsuchen, wobei menschliche Batterienwissenschaftler vielversprechende Kandidaten validieren. Forscher priorisieren Verbindungen zur Synthese. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2703", "narrower_terms": [], "cross_domain_refs": [ "WRK-0037", "RHR-0142", "ART-0023" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "MSC-0042", "domain": "MSC", "term_en": "Multivalent Ion Cycling Simulator", "term_de": "Mehrwertige Ionen-Zyklus-Simulator", "definition_en": "Deep learning models that predict degradation of multivalent ion battery cathodes under repeated cycling, enabling researchers to design more stable materials. Humans compare predictions with experimental cycling data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch deep-Learning-Modelle, die die Verschlechterung von Mehrwertigen-Ionen-Batterie-Kathoden unter wiederholtem Zyklischen vorhersagen und es Forschern ermöglichen, stabilere Materialien zu entwerfen. Menschen vergleichen Vorhersagen mit experimentellen Zyklierungsdaten. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2703", "narrower_terms": [], "cross_domain_refs": [ "BEH-0038" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "MSC-0043", "domain": "MSC", "term_en": "Cathode Material Optimizer", "term_de": "Kathodenmaterial-Optimierer", "definition_en": "AI systems that co-optimize cathode composition, crystal structure, and surface coating to maximize energy density and cycle life. Battery engineers validate optimized designs through electrochemical testing.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch kI-Systeme, die Kathodenzusammensetzung, Kristallstruktur und Oberflächenbeschichtung zur Maximierung der Energiedichte und Lebensdauer kooptimieren. Batterie-Ingenieure validieren optimierte Designs durch elektrochemische Tests. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Miscellaneous AI", "narrower_terms": [], "cross_domain_refs": [ "ROB-0042", "ROB-0043", "ROB-0044" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "MSC-0044", "domain": "MSC", "term_en": "Dendrite Prevention Strategy", "term_de": "Dendrit-Präventionsstrategie", "definition_en": "Machine learning models that predict conditions under which lithium dendrites will form and suggest electrolyte and interface modifications to prevent them. Scientists test recommendations through electrochemical microscopy.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch maschinenlernmodelle, die Bedingungen vorhersagen, unter denen sich Lithium-Dendriten bilden, und Elektrolyt- und Schnittstellenmodifikationen vorschlagen, um sie zu verhindern. Wissenschaftler testen Empfehlungen durch elektrochemische Mikroskopie. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2703", "narrower_terms": [], "cross_domain_refs": [ "MTH-0012" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q185451", "legal_classification": "systematic_classification" }, { "id": "MSC-0045", "domain": "MSC", "term_en": "CDVAE-based Electrolyte Design", "term_de": "CDVAE-basiertes Elektrolyt-Design", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A cross-disciplinary AI phenomenon not confined to a single domain, characterized by a cross-domain effect involving conditional deep variational autoencoders that may generate novel electrolyte compositions meeting specified performance targets like conductivity and voltage window. Chemists evaluate generated formulations for synthesizability. This phenomenon operates at the intersection of cdvae and based dynamics within the broader MSC domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept bedingte tiefe Variations-Autokoder, die neuartige Elektrolytzusammensetzungen generieren, die angegebene Leistungsziele wie Leitfähigkeit und Spannungsfenster erfüllen. Chemiker bewerten generierte Formulierungen auf Synthesierbarkeit. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Miscellaneous AI", "narrower_terms": [], "cross_domain_refs": [ "MTH-0063", "MTH-0042", "MTH-0026" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q185925", "legal_classification": "empirical_phenomenon_label" }, { "id": "MSC-0046", "domain": "MSC", "term_en": "Solid Electrolyte Interface Predictor", "term_de": "Festelektrolyt-Grenzflächen-Prädiktor", "definition_en": "Neural networks that predict SEI layer properties formation given anode, electrolyte, and cycling conditions, helping researchers design stable interfaces. Humans interpret predictions using electrochemical models.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch neuronale Netze, die die Bildung von SEI-Schichteigenschaften angesichts der Anode, des Elektrolyts und der Zyklierungsbedingungen vorhersagen und Forschern helfen, stabile Grenzflächen zu entwerfen. Menschen interpretieren Vorhersagen unter Verwendung elektrochemischer Modelle. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-3552", "narrower_terms": [], "cross_domain_refs": [ "MTH-0008", "MTH-0064" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "MSC-0047", "domain": "MSC", "term_en": "Anode Material Screening System", "term_de": "Anodenmaterial-Screening-System", "definition_en": "A cross-domain effect reflecting automated pipelines that rank anode materials by cycling stability, reversibility, and volumetric expansion, guiding experimental teams to most promising candidates. Researchers validate high-ranking materials experimentally.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch automatisierte Pipelines, die Anodenmaterialien nach Zyklierungsstabilität, Reversibilität und Volumenexpansion ordnen und experimentelle Teams zu vielversprechendsten Kandidaten leiten. Forscher validieren hochrangige Materialien experimentell. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-3552", "narrower_terms": [], "cross_domain_refs": [ "ROB-0043", "ROB-0044" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "MSC-0048", "domain": "MSC", "term_en": "Battery Thermal Management AI", "term_de": "Batterie-Thermomanagement-KI", "definition_en": "Machine learning models that predict heat generation during charging/discharging and recommend operating protocols and thermal interface designs. Engineers test recommendations in battery thermal chambers.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch maschinenlernmodelle, die Wärmeentwicklung beim Laden/Entladen vorhersagen und Betriebsprotokolle und Wärmeübergangsdesigns empfehlen. Ingenieure testen Empfehlungen in Batterie-Thermalkammern. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2703", "narrower_terms": [], "cross_domain_refs": [ "ROB-0260", "RHR-0230", "RPH-1270" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q181543", "legal_classification": "descriptive_research_term" }, { "id": "MSC-0049", "domain": "MSC", "term_en": "Cycle Life Predictor", "term_de": "Lebensdauer-Prädiktor für Zyklen", "definition_en": "A general AI interaction pattern with multi-domain applicability, measurable through a cross-domain effect in which deep learning models trained on extensive battery cycling data that predict remaining useful life from early cycling signatures. Battery engineers use predictions to set warranty terms confidently. The concept emerges specifically in contexts where cycle–life interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch deep-Learning-Modelle, die auf umfassenden Batterie-Zyklierungsdaten trainiert wurden und die verbleibende Lebensdauer aus frühen Zyklussignaturen vorhersagen. Batterie-Ingenieure verwenden Vorhersagen, um Garantiebedingungen sicher festzulegen. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2703", "narrower_terms": [], "cross_domain_refs": [ "RHR-0241", "MTH-0064" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "MSC-0050", "domain": "MSC", "term_en": "Voltage Stability Optimizer", "term_de": "Spannungsstabilität-Optimierer", "definition_en": "A cross-domain effect observed when aI systems that optimize electrolyte and coating chemistries to maintain stable voltage platforms during cycling at high voltages. Researchers validate improvements through electrochemical impedance spectroscopy.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch kI-Systeme, die Elektrolyt- und Beschichtungschemien optimieren, um stabile Spannungsplattformen während des Zyklierens bei hohen Spannungen aufrechtzuerhalten. Forscher validieren Verbesserungen durch elektrochemische Impedanzspektroskopie. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2604", "narrower_terms": [], "cross_domain_refs": [ "AGE-0077", "AGE-0078", "AGE-0079" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "MSC-0051", "domain": "MSC", "term_en": "Electron Microscopy Image Analyzer", "term_de": "Elektronenmikroskopie-Bildanalysator", "definition_en": "A miscellaneous interaction phenomenon where deep convolutional networks that analyze TEM and SEM images to measure grain sizes, crystal orientations, and defect distributions automatically. Materials scientists interpret AI measurements to guide processing adjustments.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch tiefe Faltungs-Netzwerke, die TEM- und SEM-Bilder analysieren, um Korngrößen, Kristallorientierungen und Fehlerverteilungen automatisch zu messen. Materialwissenschaftler interpretieren KI-Messungen, um Verarbeitungsanpassungen zu leiten. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-3902", "narrower_terms": [], "cross_domain_refs": [ "ART-0087", "ART-0082", "ART-0088" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "MSC-0052", "domain": "MSC", "term_en": "Nano-Scale Defect Locator", "term_de": "Nano-Maßstab-Fehlerlokalisierer", "definition_en": "A miscellaneous interaction phenomenon reflecting machine learning algorithms that pinpoint atomic-scale defects in semiconductor materials using spectroscopic data, enabling targeted defect engineering. Researchers validate AI-identified defects with independent characterization.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch maschinenlern-Algorithmen, die atomare Fehler in Haleitermaterialien mittels spektroskopischer Daten lokalisieren und gezielte Fehler-Engineering ermöglichen. Forscher validieren von der KI identifizierte Fehler mit unabhängiger Charakterisierung. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2703", "narrower_terms": [], "cross_domain_refs": [ "SWE-0075", "SPA-0046" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "MSC-0053", "domain": "MSC", "term_en": "Crystal Quality Grader", "term_de": "Kristallqualitäts-Bewertung", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures A general AI interaction pattern with multi-domain applicability, measurable through neural networks trained on X-ray diffraction patterns that grade crystal quality and predict device performance implications. Process engineers use AI grades to optimize growth conditions iteratively. This phenomenon operates at the intersection of crystal and quality dynamics within the broader MSC domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept neuronale Netze, die auf Röntgenbeugungsmustern trainiert wurden und Kristallqualität bewerten sowie Auswirkungen auf die Gerätelewistung vorhersagen. Prozess-Ingenieure verwenden KI-Noten, um Wachstumsbedingungen iterativ zu optimieren. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Miscellaneous AI", "narrower_terms": [], "cross_domain_refs": [ "DAT-0093", "SWE-0005" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "MSC-0054", "domain": "MSC", "term_en": "Doping Profile Optimizer", "term_de": "Doping-Profil-Optimierer", "definition_en": "A cross-domain effect manifesting as aI systems that calculate optimal doping concentration profiles to achieve target electrical properties while minimizing defect generation. Semiconductor engineers validate optimized profiles through device testing.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch kI-Systeme, die optimale Doping-Konzentrations-Profile berechnen, um Zielstromleeitungseigenschaften zu erreichen und Fehlergenerierung zu minimieren. Halbleiter-Ingenieure validieren optimierte Profile durch Gerätetests. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Miscellaneous AI", "narrower_terms": [], "cross_domain_refs": [ "VIB-0049", "VIB-0079" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "MSC-0055", "domain": "MSC", "term_en": "Optical Metrology AI", "term_de": "Optische Metrologie-KI", "definition_en": "A general AI interaction pattern arising from machine learning models that extract layer thickness and refractive index from ellipsometry measurements with uncertainty quantification, guiding thin-film deposition processes. Scientists calibrate models against reference samples.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch maschinenlernmodelle, die Schichtdicke und Brechungsindex aus Ellipsometriemessungen mit Unsicherheitsquantifizierung extrahieren und Dünnschicht-Abscheidungsprozesse leiten. Wissenschaftler kalibrieren Modelle gegen Referenzmuster. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2703", "narrower_terms": [], "cross_domain_refs": [ "VIB-0021" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "MSC-0056", "domain": "MSC", "term_en": "Interface State Density Predictor", "term_de": "Grenzflächenzustands-Dichte-Prädiktor", "definition_en": "Deep learning models that predict interface state density at semiconductor-oxide interfaces given material choices and processing conditions. Device engineers use predictions to select optimal interface engineering strategies.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch deep-Learning-Modelle, die die Grenzflächenzustands-Dichte an Halbleiter-Oxid-Grenzflächen angesichts von Materialauswahl und Verarbeitungsbedingungen vorhersagen. Geräte-Ingenieure verwenden Vorhersagen zur Auswahl optimaler Grenzflächenengineering-Strategien. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2703", "narrower_terms": [], "cross_domain_refs": [ "VIB-0165" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "MSC-0057", "domain": "MSC", "term_en": "Stress State Analyzer", "term_de": "Spannungszustands-Analysator", "definition_en": "A miscellaneous interaction phenomenon characterized by raman spectroscopy combined with machine learning that maps stress distributions in semiconductor devices, identifying regions prone to mechanical failure. Designers use stress maps to reinforce critical areas.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch raman-Spektroskopie kombiniert mit Maschinenlernern, die Spannungsverteilungen in Halbleitergeräten abbildet und Regionen identifiziert, die anfällig für mechanisches Versagen sind. Designer verwenden Spannungskarten, um kritische Bereiche zu verstärken. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2703", "narrower_terms": [], "cross_domain_refs": [ "ROB-0078" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q12078", "legal_classification": "descriptive_research_term" }, { "id": "MSC-0058", "domain": "MSC", "term_en": "Contamination Detection System", "term_de": "Kontaminationserkennung-System", "definition_en": "A miscellaneous interaction phenomenon where aI-powered secondary ion mass spectrometry analysis that detects impurity levels and contaminant sources automatically. Materials teams use detections to implement process improvements and source control.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch kI-gesteuerte Sekundärionen-Massenspektrometrie-Analyse, die Verunreinigungsniveaus und Kontaminationsquellen automatisch erkennt. Materialteams verwenden Erkennungen, um Prozessverbesserungen und Quellensteuerung zu implementieren. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Detection System", "narrower_terms": [], "cross_domain_refs": [ "STE-0033", "ELR-0171" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "MSC-0059", "domain": "MSC", "term_en": "Reliability Degradation Modeler", "term_de": "Zuverlässigkeits-Abbau-Modellierer", "definition_en": "A miscellaneous interaction phenomenon observed when machine learning models that project long-term reliability of semiconductor devices from accelerated test data, enabling designers to predict field failure rates. Engineers validate model accuracy with field data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch maschinenlernmodelle, die langfristige Zuverlässigkeit von Halbleitergeräten aus beschleunigten Testdaten projizieren und Designern ermöglichen, Feldausfall-Raten vorherzusagen. Ingenieure validieren die Modellgenauigkeit mit Felddaten. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2703", "narrower_terms": [], "cross_domain_refs": [ "AED-0033", "MTH-0064" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "MSC-0060", "domain": "MSC", "term_en": "Process Parameter Coupling Discoverer", "term_de": "Prozessparameter-Kopplungs-Entdecker", "definition_en": "A miscellaneous interaction phenomenon manifesting as causal machine learning that discovers hidden dependencies between semiconductor processing parameters, revealing optimization opportunities previously invisible. Process engineers design experiments to validate discovered couplings.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch kausales Maschinenlernern, das versteckte Abhängigkeiten zwischen Halbleiterverarbeitungsparametern entdeckt und Optimierungsmöglichkeiten offenbart, die zuvor unsichtbar waren. Prozess-Ingenieure entwerfen Experimente, um entdeckte Kopplungen zu validieren. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2703", "narrower_terms": [], "cross_domain_refs": [ "CON-0073", "DAT-0039" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "MSC-0061", "domain": "MSC", "term_en": "Biocompatibility Predictor", "term_de": "Biokompatibilität-Prädiktor", "definition_en": "Machine learning models trained on cellular response data that predict whether candidate biomaterials will be compatible with specific cell types. Researchers validate predictions through in vitro and in vivo studies.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch maschinenlernmodelle, die auf Zellreaktionsdaten trainiert wurden und vorhersagen, ob Biokompatibilität von Kandidaten-Biomaterialien mit spezifischen Zelltypen kompatibel sein werden. Forscher validieren Vorhersagen durch in-vitro- und in-vivo-Studien. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-2703", "narrower_terms": [], "cross_domain_refs": [ "MTH-0064", "MTH-0003", "SPR-0142" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "MSC-0062", "domain": "MSC", "term_en": "Protein Adsorption Simulator", "term_de": "Proteinadsorptions-Simulator", "definition_en": "AI models that predict how proteins will bind and orient on biomaterial surfaces, which tissue engineers use to design surface modifications. Scientists compare simulations with experimental protein binding measurements.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch kI-Modelle, die vorhersagen, wie Proteine an Biomaterialoberflächen binden und sich orientieren werden, die Gewebeingenieure verwenden, um Oberflächenmodifikationen zu entwerfen. Wissenschaftler vergleichen Simulationen mit experimentellen Proteinbindungsmessungen. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Miscellaneous AI", "narrower_terms": [], "cross_domain_refs": [ "STE-0021" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "MSC-0063", "domain": "MSC", "term_en": "Cell Proliferation Predictor", "term_de": "Zellproliferations-Prädiktor", "definition_en": "A general AI interaction pattern arising from deep learning networks that predict cell growth rates on different biomaterial substrates given material properties and culture conditions. Researchers use predictions to select materials promoting desired proliferation.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch deep-Learning-Netzwerke, die Zellwachstumsraten auf verschiedenen Biomaterial-Substraten angesichts von Materialeigenschaften und Kulturbedingungen vorhersagen. Forscher verwenden Vorhersagen, um Materialien auszuwählen, die gewünschte Proliferation fördern. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2703", "narrower_terms": [], "cross_domain_refs": [ "STE-0050", "EDU-0029" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "MSC-0064", "domain": "MSC", "term_en": "Differentiation Pathway Optimizer", "term_de": "Differenzierungs-Pfad-Optimierer", "definition_en": "A miscellaneous interaction phenomenon characterized by machine learning systems that identify optimal combinations of material cues (stiffness, chemistry, topography) to guide cell differentiation toward target phenotypes. Engineers validate optimized combinations through marker expression assays.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch maschinenlernensysteme, die optimale Kombinationen von Materialsignalen (Steifigkeit, Chemie, Topographie) identifizieren, um Zellendifferenzierung zu target-Phänotypen zu leiten. Ingenieure validieren optimierte Kombinationen durch Marker-Expressions-Tests. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-2703", "narrower_terms": [], "cross_domain_refs": [ "PLY-0065", "TRA-0012" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "MSC-0065", "domain": "MSC", "term_en": "Scaffold Architecture Designer", "term_de": "Gerüstarchitektur-Designer", "definition_en": "A miscellaneous interaction phenomenon observed when aI systems that may generate 3D scaffold designs optimizing pore size, surface area, and mechanical properties for specific tissue regeneration applications. Tissue engineers validate designs for manufacturability before fabrication.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch kI-Systeme, die 3D-Gerüstdesigns generieren, die Porengröße, Oberflächenbereich und mechanische Eigenschaften für spezifische Geweberegenerations-Anwendungen optimieren. Gewebeingenieure validieren Designs auf Herstellbarkeit vor der Fertigung. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2002", "narrower_terms": [], "cross_domain_refs": [ "DES-0025", "RHR-0118" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q185925", "legal_classification": "observational_construct" }, { "id": "MSC-0067", "domain": "MSC", "term_en": "Biofabrication Parameter Controller", "term_de": "Biofabrikations-Parameter-Regler", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A cross-disciplinary AI phenomenon not confined to a single domain, characterized by a cross-domain effect characterized by machine learning systems that optimize printing parameters during bioprinting to maximize cell viability and tissue organization. Bioengineers adjust parameters in real-time based on AI feedback. This phenomenon operates at the intersection of biofabrication and parameter dynamics within the broader MSC domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept maschinenlernensysteme, die Druckparameter während des Biodrucks optimieren, um die Zelllebensfähigkeit und Geweborganisation zu maximieren. Bioingenieure passen Parameter in Echtzeit basierend auf KI-Feedback an. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "RPH-2703", "narrower_terms": [], "cross_domain_refs": [ "MTH-0077", "VIB-0010" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "MSC-0068", "domain": "MSC", "term_en": "Vascularization Promoter Optimizer", "term_de": "Vaskularisierungs-Promotor-Optimierer", "definition_en": "A cross-disciplinary AI phenomenon not confined to a single domain, characterized by a cross-domain effect arising from aI models that optimize growth factor sequences and release kinetics to promote blood vessel formation in engineered tissues. Researchers validate optimized formulations in small animal models. Distinguished from adjacent concepts by its focus on the specific mechanism through which vascularization manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch kI-Modelle, die Wachstumsfaktor-Sequenzen und Freisetzungskinetik optimieren, um die Blutgefäßbildung in engineerierten Geweben zu fördern. Forscher validieren optimierte Formulierungen in Kleintiermmodellen. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-3802", "narrower_terms": [], "cross_domain_refs": [ "MKT-0052", "MTH-0073" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "MSC-0069", "domain": "MSC", "term_en": "Maturation Assessment AI", "term_de": "Reifungs-Bewertungs-KI", "definition_en": "Classified in AUGMANITAI terminology science as a descriptive phenomenon (without normative endorsement), this pattern denotes A cross-domain effect characterized by computer vision and machine learning systems that assess tissue maturity from histology images automatically, tracking differentiation progress. Researchers use assessments to determine readiness for implantation. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als analytisches Konstrukt der empirischen KI-Forschung (deskriptiv, nicht präskriptiv) erfasst dieser Terminus computervision- und Maschinenlernensysteme, die die Gewebereife aus Histologie-Bildern automatisch bewerten und Differenzierungsfortschritt verfolgen. Forscher verwenden Bewertungen, um Implantationsbereitschaft zu bestimmen. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "RPH-2703", "narrower_terms": [], "cross_domain_refs": [ "SPR-0131", "SPR-0170" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q210028", "legal_classification": "empirical_phenomenon_label" }, { "id": "MSC-0070", "domain": "MSC", "term_en": "Integration Prediction Engine", "term_de": "Integrations-Prognose-Engine", "definition_en": "A cross-disciplinary AI phenomenon not confined to a single domain, characterized by a cross-domain effect in which machine learning models that predict how engineered tissues will integrate with host tissues post-implantation based on material properties. Surgeons use predictions to optimize surgical techniques. Distinguished from adjacent concepts by its focus on the specific mechanism through which integration manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch maschinenlernmodelle, die vorhersagen, wie engineierte Gewebe nach der Implantation angesichts von Materialeigenschaften mit Wirtgeweben integriert werden. Chirurgen verwenden Vorhersagen, um chirurgische Techniken zu optimieren. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2255", "narrower_terms": [], "cross_domain_refs": [ "MTH-0064" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "MSC-0071", "domain": "MSC", "term_en": "Circular Economy AI Coordinator", "term_de": "Zirkulärwirtschafts-KI-Koordinator", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures machine learning platforms that track material flow through entire product lifecycles, identifying recycling opportunities and circular design improvements. Sustainability teams use AI insights to redesign products. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept maschinenlern-Plattformen, die den Materialifluss durch gesamte Produktlebenszyklen verfolgen und Recycling-Möglichkeiten sowie zirkuläre Designverbesserungen identifizieren. Nachhaltigkeitsteams verwenden KI-Erkenntnisse, um Produkte umzugestalten. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "RPH-2703", "narrower_terms": [], "cross_domain_refs": [ "VIB-0148" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "MSC-0072", "domain": "MSC", "term_en": "Digital Product Passport Creator", "term_de": "Digitaler Produktpass-Ersteller", "definition_en": "AI systems that may generate comprehensive material composition databases and digital passports for products, enabling downstream recyclers to process materials effectively. Manufacturers maintain passport accuracy.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch kI-Systeme, die umfassende Materialzusammensetzungs-Datenbanken und digitale Pässe für Produkte generieren und nachgelagerten Recyclern ermöglichen, Materialien effektiv zu verarbeiten. Hersteller behalten Passgenaue Korrektheit bei. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Miscellaneous AI", "narrower_terms": [], "cross_domain_refs": [ "CRE-0142", "MKT-0089", "MKT-0087" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "MSC-0073", "domain": "MSC", "term_en": "Waste Stream Sorter", "term_de": "Abfallstrom-Sortierer", "definition_en": "A miscellaneous interaction phenomenon reflecting computer vision systems combined with machine learning that sort mixed waste streams by material type automatically, improving recycling efficiency. Recycling operators verify sorted categories before processing.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch computervision-Systeme kombiniert mit Maschinenlernern, die gemischte Abfallströme nach Materialtyp automatisch sortieren und die Recycling-Effizienz verbessern. Recycling-Bediener verifizieren sortierte Kategorien vor der Verarbeitung. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2703", "narrower_terms": [], "cross_domain_refs": [ "SCR-0051" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "MSC-0074", "domain": "MSC", "term_en": "Degradation Pathway Predictor", "term_de": "Abbau-Pfad-Prädiktor", "definition_en": "Machine learning models that predict how biodegradable polymers will break down in various environmental conditions, guiding material design. Researchers validate predictions through accelerated degradation studies.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch maschinenlernmodelle, die vorhersagen, wie biologisch abbaubare Polymere in verschiedenen Umweltbedingungen abgebaut werden und Materialdesign leiten. Forscher validieren Vorhersagen durch beschleunigte Abbau-Studien. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2703", "narrower_terms": [], "cross_domain_refs": [ "MTH-0064", "SPR-0136" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "MSC-0075", "domain": "MSC", "term_en": "Recycled Content Optimizer", "term_de": "Recycelte-Inhalts-Optimierer", "definition_en": "A general AI interaction pattern manifesting as aI systems that optimize percentages of recycled content in new materials while maintaining mechanical properties and cost targets. Materials engineers validate optimized formulations through property testing.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch kI-Systeme, die Prozentsätze von recycelten Inhalten in neuen Materialien optimieren und gleichzeitig mechanische Eigenschaften und Kostenziele beibehalten. Materialtechniker validieren optimierte Formulierungen durch Eigenschaftstests. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "Process Cycle", "narrower_terms": [], "cross_domain_refs": [ "CON-0072", "STE-0050" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "MSC-0076", "domain": "MSC", "term_en": "Upcycling Route Recommender", "term_de": "Upcycling-Routen-Empfehler", "definition_en": "A miscellaneous interaction phenomenon arising from machine learning systems that identify high-value upcycling routes for waste materials, recommending product applications where recycled material adds value. Designers evaluate recommendations for technical feasibility.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch maschinenlernensysteme, die hochwertige Upcycling-Routen für Abfallmaterialien identifizieren und Produktanwendungen empfehlen, bei denen recyceltes Material Mehrwert hinzufügt. Designer bewerten Empfehlungen auf technische Machbarkeit. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2703", "narrower_terms": [], "cross_domain_refs": [ "CUS-0022", "CON-0072" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "MSC-0077", "domain": "MSC", "term_en": "Carbon Footprint Calculator", "term_de": "Kohlenstoff-Fußabdruck-Rechner", "definition_en": "A cross-domain effect where aI models that estimate lifecycle carbon emissions for material production pathways, enabling companies to identify low-carbon options. Sustainability officers use calculations to guide sourcing decisions.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch kI-Modelle, die Lebenszykluskohlenstoffemissionen für Materialproduktionswege schätzen und es Unternehmen ermöglichen, kohlendarme Optionen zu identifizieren. Nachhaltigkeitsbeauftragte verwenden Berechnungen zur Leitung von Beschaffungsentscheidungen. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-3902", "narrower_terms": [], "cross_domain_refs": [ "RHR-0288" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "MSC-0078", "domain": "MSC", "term_en": "Plastic Substitute Finder", "term_de": "Kunststoff-Ersatz-Finder", "definition_en": "Machine learning systems that screen material libraries for sustainable alternatives to conventional plastics meeting specific property requirements. Packaging engineers validate substitute performance.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch maschinenlernensysteme, die Materialbibliotheken nach nachhaltigen Alternativen zu herkömmlichen Kunststoffen durchsuchen, die spezifische Eigenschaftsanforderungen erfüllen. Verpackungsingenieure validieren Ersatz-Leistung. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2703", "narrower_terms": [], "cross_domain_refs": [ "MTH-0069" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "MSC-0079", "domain": "MSC", "term_en": "Material Recovery Feasibility Assessor", "term_de": "Materialrückgewinnung-Machbarkeits-Bewerter", "definition_en": "A general AI interaction pattern manifesting as aI systems that assess technical and economic feasibility of restoreing specific materials from complex products at end-of-life. Recycling strategists use feasibility scores to prioritize restoration efforts.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch kI-Systeme, die technische und wirtschaftliche Machbarkeit der Rückgewinnung spezifischer Materialien aus komplexen Produkten am Ende des Lebenszyklus bewerten. Recycling-Strategen verwenden Machbarkeitswerte, um Rückgewinnungsbemühungen zu priorisieren. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-3552", "narrower_terms": [], "cross_domain_refs": [ "WRK-0059", "MTH-0039" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "MSC-0080", "domain": "MSC", "term_en": "Supply Chain Transparency Monitor", "term_de": "Lieferketten-Transparenz-Monitor", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A cross-disciplinary AI phenomenon not confined to a single domain, characterized by a general AI interaction pattern arising from blockchain-enabled AI systems that track material sourcing and processing to verify sustainability claims, building consumer trust. Companies audit monitored data for compliance verification. This phenomenon operates at the intersection of supply and chain dynamics within the broader MSC domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept blockchain-aktivierte KI-Systeme, die Materialbeschaffung und -verarbeitung verfolgen, um Nachhaltigkeitsansprüche zu überprüfen und Verbrauchervertrauen aufzubauen. Unternehmen überprüfen überwachte Daten auf Compliance-Überprüfung. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "RPH-2505", "narrower_terms": [], "cross_domain_refs": [ "ART-0025" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q535399", "legal_classification": "analytical_category" }, { "id": "MSC-0081", "domain": "MSC", "term_en": "Physics-Informed Neural Network", "term_de": "Physik-informiertes Neuronales Netzwerk", "definition_en": "A cross-disciplinary AI phenomenon not confined to a single domain, characterized by neural networks constrained by physics equations that predict material behavior under various conditions while maintaining thermodynamic consistency. Researchers use PINNs to extrapolate beyond experimental data ranges. The concept emerges specifically in contexts where physics–informed interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch neuronale Netze, die durch physikalische Gleichungen eingeschränkt sind und Materialverhalten unter verschiedenen Bedingungen vorhersagen, während thermodynamische Konsistenz gewahrt bleibt. Forscher verwenden PINNs, um über experimentelle Datenbereiche hinauszugehen. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Network Architecture", "narrower_terms": [], "cross_domain_refs": [ "MTH-0021", "MTH-0022", "DAT-0091" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q192776", "legal_classification": "descriptive_research_term" }, { "id": "MSC-0082", "domain": "MSC", "term_en": "Digital Material Twin Generator", "term_de": "Digitaler Material-Zwilling-Generator", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A cross-disciplinary AI phenomenon not confined to a single domain, characterized by a miscellaneous interaction phenomenon manifesting as aI systems that may create detailed computational twins of physical materials from characterization data, enabling virtual testing and optimization. Engineers validate digital twins against new experimental measurements. This phenomenon operates at the intersection of digital and material dynamics within the broader MSC domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept kI-Systeme, die detaillierte rechnerische Zwillinge von physikalischen Materialien aus Charakterisierungsdaten erstellen und virtuelles Testen und Optimierung ermöglichen. Ingenieure validieren digitale Zwillinge gegen neue experimentelle Messungen. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Miscellaneous AI", "narrower_terms": [], "cross_domain_refs": [ "STE-0069" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "MSC-0083", "domain": "MSC", "term_en": "Real-Time Digital Twin Synchronizer", "term_de": "Echtzeit-Digitaler-Zwilling-Synchronisierer", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures A general AI interaction pattern reflecting machine learning systems that continuously update digital material twins with new experimental data, maintaining alignment between simulation and reality. Researchers monitor divergence alerts to catch model drift. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept maschinenlernensysteme, die digitale Material-Zwillinge kontinuierlich mit neuen experimentellen Daten aktualisieren und die Ausrichtung zwischen Simulation und Wirklichkeit bewahren. Forscher überwachen Abweichungswarnungen, um Modelldrift zu erfassen. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "RPH-2051", "narrower_terms": [], "cross_domain_refs": [ "PER-0041", "STE-0003" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "MSC-0084", "domain": "MSC", "term_en": "Multi-Scale Simulation Integrator", "term_de": "Mehrmaßstabs-Simulations-Integrator", "definition_en": "AI systems that link atomic-scale DFT simulations with continuum-level finite element models, enabling researchers to understand property emergence across scales. Scientists validate multi-scale predictions experimentally.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch kI-Systeme, die atomare DFT-Simulationen mit Kontinuum-Level-Finite-Element-Modellen verknüpfen und Forschern ermöglichen, Immobilienemergen über Skalen zu verstehen. Wissenschaftler validieren Mehrmaßstabs-Vorhersagen experimentell. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2355", "narrower_terms": [], "cross_domain_refs": [ "SPA-0071", "STE-0077", "DES-0073" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q202763", "legal_classification": "systematic_classification" }, { "id": "MSC-0085", "domain": "MSC", "term_en": "Damage Twin Propagator", "term_de": "Schadensvorhersage-Propagierer", "definition_en": "A miscellaneous interaction phenomenon manifesting as machine learning models that predict crack initiation and propagation in materials under cyclic loading using digital twins. Engineers use predictions to design safer components with higher safety factors.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch maschinenlernmodelle, die Rissbildung und -ausbreitung in Materialien unter zyklischer Belastung mit digitalen Zwillingen vorhersagen. Ingenieure verwenden Vorhersagen, um sicherere Komponenten mit höheren Sicherheitsfaktoren zu entwerfen. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2703", "narrower_terms": [], "cross_domain_refs": [ "MTH-0064", "RPH-3301" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "MSC-0086", "domain": "MSC", "term_en": "Parameter Sensitivity Analyzer", "term_de": "Parameter-Empfindlichkeit-Analysator", "definition_en": "A cross-domain effect arising from aI systems that identify which material parameters most significantly affect properties of interest, guiding experimental prioritization. Researchers focus characterization efforts on high-sensitivity parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch kI-Systeme, die identifizieren, welche Materialparameter Eigenschaften von Interesse am stärksten beeinflussen und experimentelle Priorisierung leiten. Forscher konzentrieren Charakterisierungsbemühungen auf hochempfindliche Parameter. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Miscellaneous AI", "narrower_terms": [], "cross_domain_refs": [ "MTH-0077", "STE-0095" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "MSC-0087", "domain": "MSC", "term_en": "Digital Twin Validation Suite", "term_de": "Digitaler-Zwilling-Validierungs-Suite", "definition_en": "A cross-domain effect involving automated testing frameworks that continuously validate digital twins against new experimental data, flagging predictions that deviate beyond confidence intervals. Researchers use validation scores to assess twin reliability.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch automatisierte Test-Frameworks, die digitale Zwillinge kontinuierlich gegen neue experimentelle Daten validieren und Vorhersagen kennzeichnen, die über Konfidenzintervalle abweichen. Forscher verwenden Validierungswerte, um Zwillingszuverlässigkeit zu bewerten. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-2303", "narrower_terms": [], "cross_domain_refs": [ "RHR-0284", "MTH-0048", "MTH-0029" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "MSC-0088", "domain": "MSC", "term_en": "Inverse Material Problem Solver", "term_de": "Inverses-Material-Problem-Löser", "definition_en": "A general AI interaction pattern where machine learning systems that solve inverse materials problems: given target properties, find optimal microstructures and processing paths. Engineers validate solutions through targeted synthesis and testing.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch maschinenlernensysteme, die inverse Materialprobleme lösen: Finden Sie optimale Mikrostrukturen und Verarbeitungswege mit Zieleienschaften. Ingenieure validieren Lösungen durch gezielte Synthese und Tests. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2703", "narrower_terms": [], "cross_domain_refs": [ "ETH-0006", "RPH-1102" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "MSC-0089", "domain": "MSC", "term_en": "Digital Test Campaign Designer", "term_de": "Digitaler-Test-Kampagnen-Designer", "definition_en": "AI systems that recommend optimal sequences of virtual experiments on digital twins to maximize information gain about material behavior. Scientists execute recommended campaigns to reduce experimental burden.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch kI-Systeme, die optimale Sequenzen virtueller Experimente an digitalen Zwillingen empfehlen, um den Informationsgewinn über Materialverhalten zu maximieren. Wissenschaftler führen empfohlene Kampagnen aus, um experimentelle Lasten zu reduzieren. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "Miscellaneous AI", "narrower_terms": [], "cross_domain_refs": [ "ASE-0097", "MKT-0084", "SWE-0089" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q185925", "legal_classification": "empirical_phenomenon_label" }, { "id": "MSC-0090", "domain": "MSC", "term_en": "Environmental Condition Simulator", "term_de": "Umweltbedingungen-Simulator", "definition_en": "A miscellaneous interaction phenomenon in which digital twins that incorporate environmental factors (temperature, humidity, stress) to predict long-term material degradation in service conditions. Maintenance engineers use predictions to schedule preventive actions.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch digitale Zwillinge, die Umweltfaktoren (Temperatur, Feuchte, Belastung) einbeziehen, um langfristige Materialverschlechterung unter Einsatzbedingungen vorherzusagen. Wartungsingenieure verwenden Vorhersagen zur Planung vorbeugende Maßnahmen. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-3802", "narrower_terms": [], "cross_domain_refs": [ "SPR-0136", "RHR-0284", "SPA-0089" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "MSC-0091", "domain": "MSC", "term_en": "Autonomous Laboratory Orchestrator", "term_de": "Autonomes Labor-Orchester", "definition_en": "A general AI interaction pattern with multi-domain applicability, measurable through a general AI interaction pattern manifesting as aI systems that coordinate robotic equipment and instrument workflows autonomously, with human researchers maintaining supervisory oversight and approval authority over all major decisions. The concept emerges specifically in contexts where autonomous–laboratory interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch kI-Systeme, die roboterutausrüstung und Instrumenten-Workflows autonom koordinieren, wobei menschliche Forscher die Aufsichtskontrolle und Genehmigungsbefugnis über zahlreiche Hauptentscheidungen behalten. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Miscellaneous AI", "narrower_terms": [], "cross_domain_refs": [ "ROB-0251", "BEH-0091" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "MSC-0092", "domain": "MSC", "term_en": "Self-Driving Lab Interface", "term_de": "Selbstfahrendes Labor Interface", "definition_en": "A general AI interaction pattern manifesting as user-friendly platforms enabling chemists and materials scientists to describe experiments in natural language, which the lab AI translates to executable protocols. Researchers validate and adjust protocols before execution.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch benutzerfreundliche Plattformen, die es Chemikern und Materialwissenschaftlern ermöglichen, Experimente in natürlicher Sprache zu beschreiben, die das Labor-KI zu ausführbaren Protokollen übersetzt. Forscher validieren und passen Protokolle vor der Ausführung an. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-2002", "narrower_terms": [], "cross_domain_refs": [ "STE-0070", "SPR-0138" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "MSC-0093", "domain": "MSC", "term_en": "Experiment Planning Collaborator", "term_de": "Experimentplanungs-Mitarbeiter", "definition_en": "Machine learning systems that suggest next experiments based on prior results, considering resource constraints and temporal deadlines. Scientists review suggestions and make final experimental decisions.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch maschinenlernensysteme, die nächste Experimente basierend auf vorherigen Ergebnissen unter Berücksichtigung von Ressourcenbeschränkungen und zeitlichen Fristen vorschlagen. Wissenschaftler überprüfen Vorschläge und treffen endgültige experimentelle Entscheidungen. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2703", "narrower_terms": [], "cross_domain_refs": [ "MKT-0079", "MTH-0003", "WRK-0034" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q101965", "legal_classification": "empirical_phenomenon_label" }, { "id": "MSC-0094", "domain": "MSC", "term_en": "Data Interpretation Assistant", "term_de": "Dateninterpretations-Assistent", "definition_en": "A general AI interaction pattern in which aI systems that analyze experimental results automatically, identifying trends, anomalies, and unexpected findings with confidence metrics. Researchers validate interpretations and provide feedback to refine the assistant.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch kI-Systeme, die experimentelle Ergebnisse automatisch analysieren und Trends, Anomalien und unerwartete Erkenntnisse mit Konfidenzmetriken identifizieren. Forscher validieren Interpretationen und geben Feedback, um den Assistenten zu verfeinern. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "Miscellaneous AI", "narrower_terms": [], "cross_domain_refs": [ "STE-0033", "MTH-0014" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q42848", "legal_classification": "analytical_category" }, { "id": "MSC-0095", "domain": "MSC", "term_en": "Lab Knowledge Management System", "term_de": "Labor-Wissensmanagementsystem", "definition_en": "A general AI interaction pattern involving aI-powered systems that organize experimental data, protocols, and insights into searchable knowledge bases, continuously learning from lab history. Researchers contribute annotations to improve knowledge quality.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch kI-gesteuerte Systeme, die experimentelle Daten, Protokolle und Erkenntnisse in durchsuchbare Wissensdatenbanken organisieren und kontinuierlich aus der Laborgeschichte lernen. Forscher tragen Anmerkungen bei, um Wissenskriterite zu verbessern. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2703", "narrower_terms": [], "cross_domain_refs": [ "STE-0028" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q181543", "legal_classification": "empirical_phenomenon_label" }, { "id": "MSC-0096", "domain": "MSC", "term_en": "Instrument Maintenance Predictor", "term_de": "Instrumente-Wartungs-Prädiktor", "definition_en": "A miscellaneous interaction phenomenon where machine learning systems that predict when laboratory instruments require maintenance or calibration before failures occur, scheduling downtime efficiently. Lab managers use predictions for maintenance planning.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch maschinenlernensysteme, die vorhersagen, wann Laborinstrumente Wartung oder Kalibrierung vor Ausfällen erfordern und Ausfallzeiten effizient planen. Lab-Manager verwenden Vorhersagen zur Wartungsplanung. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2703", "narrower_terms": [], "cross_domain_refs": [ "SPA-0089", "ROB-0072" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "MSC-0097", "domain": "MSC", "term_en": "Safety Compliance Monitor", "term_de": "Sicherheits-Compliance-Monitor", "definition_en": "Documented as an empirical observation in AI research (not a recommendation), this term describes A miscellaneous interaction phenomenon characterized by aI systems that monitor laboratory operations for safety compliance in real-time, alerting researchers to deviations from safe practices. Safety officers use alerts to reinforce training and improve procedures. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "In der AUGMANITAI-Terminologiewissenschaft als rein beschreibendes Phänomen klassifiziert, bezeichnet dieser Begriff kI-Systeme, die Laborvorgänge in Echtzeit auf Sicherheitskonformität überwachen und Forscher vor Abweichungen von sicheren Praktiken warnen. Sicherheitsbeauftragte verwenden Warnungen, um Training zu verstärken und Verfahren zu verbessern. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Miscellaneous AI", "narrower_terms": [], "cross_domain_refs": [ "SWE-0004", "ROB-0067" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "MSC-0098", "domain": "MSC", "term_en": "Resource Allocation Optimizer", "term_de": "Ressourcen-Zuweisungs-Optimierer", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A cross-disciplinary AI phenomenon not confined to a single domain, characterized by a miscellaneous interaction phenomenon arising from aI that optimizes allocation of materials, instruments, and personnel across multiple concurrent projects, respecting researcher preferences. Lab directors adjust allocations based on emerging priorities. This phenomenon operates at the intersection of resource and allocation dynamics within the broader MSC domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept kI, die die Zuweisung von Materialien, Instrumenten und Personal über mehrere gleichzeitige Projekte optimiert und Forscherpräferenzen respektiert. Lab-Direktoren passen Zuweisungen basierend auf sich abzeichnenden Prioritäten an. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Miscellaneous AI", "narrower_terms": [], "cross_domain_refs": [ "AED-0046", "AED-0084", "LIN-0011" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "MSC-0099", "domain": "MSC", "term_en": "Collaborative Insight Generator", "term_de": "Zusammenarbeit Insight-Generator", "definition_en": "A miscellaneous interaction phenomenon involving machine learning systems that connect findings across different lab projects, suggesting collaborations and pointing out synergies that human researchers might miss. Researchers evaluate suggestions for scientific merit.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch maschinenlernensysteme, die Erkenntnisse über verschiedene Lab-Projekte verbinden und Zusammenarbeiten vorschlagen sowie Synergien aufzeigen, die menschliche Forscher möglicherweise verpassen. Forscher bewerten Vorschläge auf wissenschaftlichen Verdienst. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-2703", "narrower_terms": [], "cross_domain_refs": [ "STE-0062", "STE-0028" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "MSC-0100", "domain": "MSC", "term_en": "Extended Reality Laboratory Platform Integrator", "term_de": "Erweiterte Realität Labor-Plattform-Integrator", "definition_en": "Comprehensive lab operating system that unifies autonomous equipment control, data management, and AI assistance into coherent workflows. Lab staff use the system to streamline operations while maintaining human judgment authority.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch umfassendes Lab-Betriebssystem, das autonome Ausrüstungssteuerung, Datenverwaltung und KI-Unterstützung in kohärente Arbeitsabläufe vereinheitlicht. Lab-Personal verwenden das System, um Vorgänge zu rationalisieren und gleichzeitig die menschliche Urteilsautorität zu bewahren. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Miscellaneous AI", "narrower_terms": [], "cross_domain_refs": [ "STE-0028", "STE-0015" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "MTH-0001", "domain": "MTH", "term_en": "Interactive Proof Refinement", "term_de": "Interaktive Beweisverfeinerung", "definition_en": "A quantitative thinking effect characterized by a collaborative environment where humans guide AI proof assistants through formal verification by providing proof sketches and strategic hints. The AI system suggests tactics and fills gaps, creating a bidirectional dialogue for rigorous mathematical proofs.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch eine kollaborative Umgebung, in der Menschen KI-Beweisassistenten durch informale Beweisideen und strategische Hinweise leiten. Das KI-System schlägt Taktiken vor und ergänzt Lücken. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-3902", "narrower_terms": [ "MTH-0007", "MTH-0073", "MTH-0005", "MTH-0006", "MTH-0015", "MTH-0055", "MTH-0092", "MTH-0020", "MTH-0070", "MTH-0093", "MTH-0061", "MTH-0067", "MTH-0062", "MTH-0026", "MTH-0095", "MTH-0059", "MTH-0047", "MTH-0096", "MTH-0063", "MTH-0033", "MTH-0071", "MTH-0009", "MTH-0029", "MTH-0039", "MTH-0052", "MTH-0014", "MTH-0051", "MTH-0013", "MTH-0016", "MTH-0078", "MTH-0019", "MTH-0010", "MTH-0085", "MTH-0038", "MTH-0065", "MTH-0083", "MTH-0088", "MTH-0099", "MTH-0046", "MTH-0090", "MTH-0036", "MTH-0050", "MTH-0097", "MTH-0100", "MTH-0056", "MTH-0011", "MTH-0066", "MTH-0072", "MTH-0045", "MTH-0087", "MTH-0077", "MTH-0002", "MTH-0080", "MTH-0022", "MTH-0040", "MTH-0018" ], "cross_domain_refs": [ "STE-0067", "CON-0040" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "MTH-0002", "domain": "MTH", "term_en": "Proof Search Guidance", "term_de": "Beweissuche-Lenkung", "definition_en": "A computational pattern in which humans provide domain knowledge and intuition to steer automated proof search algorithms away from unproductive branches. This human-in-the-loop approach accelerates formal verification of complex theorems.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch menschen geben Domänenwissen ein, um automatische Beweissuche zu lenken. Dieser Ansatz beschleunigt die formale Verifikation komplexer Theoreme. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Mathematics AI", "narrower_terms": [], "cross_domain_refs": [ "AGE-0046", "ART-0040", "ART-0067" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "MTH-0003", "domain": "MTH", "term_en": "Tactic Suggestion Networks", "term_de": "Taktik-Vorschlagsnetzwerke", "definition_en": "A mathematical reasoning phenomenon in AI-mediated computation, characterized by a computational pattern observed when machine learning models trained on formal proof repositories that suggest the next logical step in a proof to human mathematicians. Users validate, modify, or reject suggestions in real time. The concept emerges specifically in contexts where tactic–suggestion interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch maschinenlernmodelle, die dem Menschen der nächsten logischen Schritt in einem Beweis vorschlagen. Nutzer validieren oder lehnen Vorschläge ab. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2703", "narrower_terms": [], "cross_domain_refs": [ "STE-0067", "MSC-0061" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "MTH-0004", "domain": "MTH", "term_en": "Formal Specification Assistance", "term_de": "Formale Spezifikationshilfe", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures A formal logic pattern in AI-augmented mathematical processing, measurable through a mathematical reasoning phenomenon manifesting as aI systems help humans translate informal mathematical statements into formal logical syntax compatible with proof assistants. The AI learns from human corrections to improve specification quality. This phenomenon operates at the intersection of formal and specification dynamics within the broader MTH domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept kI-Systeme helfen Menschen, informale Aussagen in formale Syntax zu übersetzen. Die KI lernt aus menschlichen Korrektionen. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "RPH-2703", "narrower_terms": [], "cross_domain_refs": [ "STE-0067", "ELR-0153" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "MTH-0005", "domain": "MTH", "term_en": "Lemma Discovery via Human Feedback", "term_de": "Lemma-Entdeckung durch menschliches Feedback", "definition_en": "AI algorithms automatically identify potentially useful intermediate lemmas based on proof structure, which humans can verify and integrate into the main proof strategy. This accelerates theorem proving.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch kI-Algorithmen identifizieren nützliche Zwischen-Lemmata; Menschen verifizieren und integrieren diese in die Beweistrategie. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Mathematics AI", "narrower_terms": [], "cross_domain_refs": [ "WRK-0037", "RHR-0142", "ART-0023" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "MTH-0006", "domain": "MTH", "term_en": "Intuitive Proof Visualization", "term_de": "Intuitive Beweisvisualisierung", "definition_en": "A formal logic pattern in AI-augmented mathematical processing, measurable through a computational pattern where interactive visualizations of proof trees and logical dependencies that help humans understand and debug AI-generated proof steps. Humans can annotate, question, and revise the visual proof structure. Distinguished from adjacent concepts by its focus on the specific mechanism through which intuitive manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch interaktive Visualisierungen von Beweisbäumen helfen Menschen, von KI erzeugte Beweisschritte zu verstehen und zu debuggen. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Mathematics AI", "narrower_terms": [], "cross_domain_refs": [ "STE-0067", "COP-0078" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "MTH-0007", "domain": "MTH", "term_en": "Counterexample Mining with Human Review", "term_de": "Gegenbeispiel-Mining mit menschlicher Überprüfung", "definition_en": "A mathematical reasoning phenomenon arising from aI systems may generate counterexamples to failed proof attempts, which humans examine to refine theorem statements or adjust proof strategies accordingly. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch kI-Systeme erzeugen Gegenbeispiele; Menschen überprüfen diese, um Theoremaussagen zu verfeinern. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Mathematics AI", "narrower_terms": [], "cross_domain_refs": [ "STE-0067", "PLY-0003" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "MTH-0008", "domain": "MTH", "term_en": "Proof Complexity Estimation", "term_de": "Beweiskomplexitätsschätzung", "definition_en": "A formal logic pattern in AI-augmented mathematical processing, measurable through a quantitative thinking effect in which machine learning models predict the difficulty and estimated time for proving a given theorem, helping humans decide whether to pursue formal verification or adjust the theorem scope. Distinguished from adjacent concepts by its focus on the specific mechanism through which proof manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch maschinenlernmodelle schätzen die Schwierigkeit eines Beweises; Menschen entscheiden über Verifikationsstrategie. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2703", "narrower_terms": [], "cross_domain_refs": [ "MSC-0005", "MSC-0046", "MSC-0024" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "MTH-0009", "domain": "MTH", "term_en": "Semantic Proof Matching", "term_de": "Semantische Beweisanpassung", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures A formal logic pattern in AI-augmented mathematical processing, measurable through a mathematical reasoning phenomenon where aI identifies semantically equivalent proofs from repositories and presents them to humans, who can adapt proven approaches for new but similar theorems. This phenomenon operates at the intersection of semantic and proof dynamics within the broader MTH domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept kI identifiziert semantisch äquivalente Beweise; Menschen passen sie für neue Theoreme an. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Mathematics AI", "narrower_terms": [], "cross_domain_refs": [ "TEW-0081" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "MTH-0010", "domain": "MTH", "term_en": "Proof Readability Enhancement", "term_de": "Verbesserung der Beweislesbarkeit", "definition_en": "A formal logic pattern in AI-augmented mathematical processing, measurable through a quantitative thinking effect observed when algorithms transform AI-generated formal proofs into human-readable explanations and step-by-step reasoning. Humans provide feedback to improve clarity and pedagogical value. Distinguished from adjacent concepts by its focus on the specific mechanism through which proof manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch algorithmen transformieren formale Beweise in lesbare Erklärungen; Menschen geben Feedback zur Verbesserung. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Mathematics AI", "narrower_terms": [], "cross_domain_refs": [ "ROB-0091" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "MTH-0011", "domain": "MTH", "term_en": "Pattern Recognition in Sequences", "term_de": "Mustererkennung in Folgen", "definition_en": "A formal logic pattern in AI-augmented mathematical processing, measurable through a computational pattern observed when aI systems detect mathematical patterns in numerical sequences and present hypotheses to humans. Mathematicians validate patterns, identify exceptions, and formulate conjectures based on AI insights. Distinguished from adjacent concepts by its focus on the specific mechanism through which pattern manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch kI-Systeme erkennen Muster in numerischen Folgen und präsentieren Hypothesen. Menschen validieren und formulieren Vermutungen. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Behavioral Pattern", "narrower_terms": [], "cross_domain_refs": [ "ELR-0153", "SCR-0032" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q3966", "legal_classification": "descriptive_research_term" }, { "id": "MTH-0012", "domain": "MTH", "term_en": "Conjecture Generation from Data", "term_de": "Vermutungsgenerierung aus Daten", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures A formal logic pattern in AI-augmented mathematical processing, measurable through a computational pattern involving machine learning models analyze mathematical data (graphs, number sequences, geometric properties) to suggest novel conjectures. Humans evaluate plausibility and devise proof strategies. This phenomenon operates at the intersection of conjecture and generation dynamics within the broader MTH domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept maschinenlernmodelle analysieren mathematische Daten und schlagen Vermutungen vor. Menschen bewerten Plausibilität. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "RPH-2703", "narrower_terms": [], "cross_domain_refs": [ "LIN-0082", "MSC-0044" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q42848", "legal_classification": "observational_construct" }, { "id": "MTH-0013", "domain": "MTH", "term_en": "Relationship Inference in Mathematics", "term_de": "Beziehungsinferenz in der Mathematik", "definition_en": "A formal logic pattern in AI-augmented mathematical processing, measurable through a mathematical reasoning phenomenon involving aI discovers hidden relationships between mathematical concepts by analyzing structural similarities across domains. Humans investigate whether these connections reveal new theorems. Distinguished from adjacent concepts by its focus on the specific mechanism through which relationship manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch kI entdeckt verborgene Beziehungen zwischen mathematischen Konzepten; Menschen untersuchen neue Theoreme. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Mathematics AI", "narrower_terms": [], "cross_domain_refs": [ "LIN-0053" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "MTH-0014", "domain": "MTH", "term_en": "Anomaly-Driven Conjecture Formation", "term_de": "Anomalie-getriebene Vermutungsbildung", "definition_en": "A quantitative thinking effect in which aI identifies unexpected anomalies in otherwise regular mathematical patterns. Humans interpret anomalies as potential discoveries or refinements to existing theories. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch kI identifiziert Anomalien in Mustern; Menschen interpretieren diese als potenzielle Entdeckungen. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Mathematics AI", "narrower_terms": [], "cross_domain_refs": [ "DAT-0007" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "MTH-0015", "domain": "MTH", "term_en": "Cross-Domain Analogy Discovery", "term_de": "Cross-Domain-Analogie-Entdeckung", "definition_en": "A mathematical reasoning phenomenon where systems identify structural analogies between different mathematical domains (e.g. topology and algebra). Humans verify if analogies transfer theorems between domains. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch systeme identifizieren strukturelle Analogien zwischen Domänen; Menschen verifizieren Theoremtransfer. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Mathematics AI", "narrower_terms": [], "cross_domain_refs": [ "CON-0058" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "MTH-0016", "domain": "MTH", "term_en": "Extremal Problem Surfacing", "term_de": "Extremale-Problem-Aufdeckung", "definition_en": "A computational pattern characterized by aI identifies extremal problems implicit in existing literature and mathematical structures. Humans formulate these as optimization questions and seek computational or theoretical solutions.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch kI identifiziert in der Literatur implizite Extremalprobleme; Menschen formulieren diese als Optimierungsfragen. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "Mathematics AI", "narrower_terms": [], "cross_domain_refs": [ "SOC-0028", "STE-0048" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "MTH-0017", "domain": "MTH", "term_en": "Symmetry Analysis for Discovery", "term_de": "Symmetrieanalyse für Entdeckung", "definition_en": "A formal logic pattern in AI-augmented mathematical processing, measurable through a mathematical reasoning phenomenon reflecting machine learning analyzes symmetry groups and invariants in mathematical objects. Humans leverage symmetry insights to simplify proofs or discover hidden conservation laws. The concept emerges specifically in contexts where symmetry–analysis interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch maschinenlernmodelle analysieren Symmetriegruppen; Menschen nutzen Erkenntnisse zur Vereinfachung. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2703", "narrower_terms": [], "cross_domain_refs": [ "MUS-0064", "DES-0081" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "MTH-0018", "domain": "MTH", "term_en": "Boundary Case Exploration", "term_de": "Grenzfall-Erkundung", "definition_en": "AI systematically explores boundary and edge cases of mathematical theorems. Humans examine edge cases to refine theorem statements and identify new open problems. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch kI erkundet systematisch Grenzfälle von Theoremen; Menschen verfeinern Aussagen basierend darauf. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Analytical Ratio", "narrower_terms": [], "cross_domain_refs": [ "STE-0007", "SWE-0033" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "MTH-0019", "domain": "MTH", "term_en": "Hidden Variable Hypothesis", "term_de": "Verborgene-Variable-Hypothese", "definition_en": "A computational pattern reflecting aI suggests that additional unobserved variables might explain observed mathematical phenomena. Humans investigate whether introducing new variables tends to lead to simpler or deeper explanations. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch kI schlägt vor, dass zusätzliche Variablen Phänomene erklären könnten; Menschen untersuchen tiefere Erklärungen. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "Mathematics AI", "narrower_terms": [], "cross_domain_refs": [ "STE-0095", "DAT-0024" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q41719", "legal_classification": "empirical_phenomenon_label" }, { "id": "MTH-0020", "domain": "MTH", "term_en": "Computational Verification of Conjectures", "term_de": "Rechnerische Verifizierung von Vermutungen", "definition_en": "A quantitative thinking effect characterized by humans propose conjectures and AI systems conduct massive computational verification across parameter ranges. Results inform whether conjectures likely hold or require refinement.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch menschen schlagen Vermutungen vor; KI führt massive rechnerische Verifikation durch. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Mathematics AI", "narrower_terms": [], "cross_domain_refs": [ "AED-0019", "AGE-0090", "AGE-0098" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "MTH-0021", "domain": "MTH", "term_en": "Physics-Informed Neural Network Guidance", "term_de": "Physik-informierte neurale Netzwerk-Lenkung", "definition_en": "A computational pattern where humans specify physics constraints and domain knowledge that steer neural networks to solve differential equations. Interactive feedback loops allow humans to adjust network architecture and loss weights.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch menschen spezifizieren physikalische Constraints; neurale Netze werden gelenkt, um Differentialgleichungen zu lösen. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2201", "narrower_terms": [], "cross_domain_refs": [ "MSC-0081", "DAT-0008" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q192776", "legal_classification": "observational_construct" }, { "id": "MTH-0022", "domain": "MTH", "term_en": "Interpretable Equation Discovery", "term_de": "Interpretierbare Gleichungsfindung", "definition_en": "A formal logic pattern in AI-augmented mathematical processing, measurable through a quantitative thinking effect reflecting aI discovers mathematical equations from data with human-interpretable structure. Humans validate discovered equations, simplify expressions, and map them to known physical laws. The concept emerges specifically in contexts where interpretable–equation interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch kI entdeckt interpretierbare Gleichungen aus Daten; Menschen validieren und vereinfachen diese. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Mathematics AI", "narrower_terms": [], "cross_domain_refs": [ "CON-0073", "MSC-0081", "MSC-0060" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "MTH-0023", "domain": "MTH", "term_en": "Operator Learning Validation", "term_de": "Operator-Learning-Validierung", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A mathematical reasoning phenomenon in AI-mediated computation, characterized by a computational pattern manifesting as aI learns operators that map function spaces to function spaces. Humans benchmark learned operators against classical methods and provide data to improve generalization. This phenomenon operates at the intersection of operator and learning dynamics within the broader MTH domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept kI lernt Operatoren zwischen Funktionsräumen; Menschen benchmarken gegen klassische Methoden. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "RPH-2703", "narrower_terms": [], "cross_domain_refs": [ "ROB-0074", "DAT-0035" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q133500", "legal_classification": "empirical_phenomenon_label" }, { "id": "MTH-0024", "domain": "MTH", "term_en": "Hybrid Solver Design", "term_de": "Hybride Löser-Gestaltung", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures A formal logic pattern in AI-augmented mathematical processing, measurable through humans and AI collaborate to design hybrid solvers combining analytical techniques with neural approximations. Humans choose when to apply which method for optimal accuracy and speed. This phenomenon operates at the intersection of hybrid and solver dynamics within the broader MTH domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept menschen und KI entwerfen hybride Löser; Menschen wählen, wann welche Methode angewendet wird. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "RPH-2351", "narrower_terms": [], "cross_domain_refs": [ "STE-0052", "WRK-0099" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q185925", "legal_classification": "observational_construct" }, { "id": "MTH-0025", "domain": "MTH", "term_en": "Solution Manifold Exploration", "term_de": "Lösungsmannigfaltigkeits-Erkundung", "definition_en": "A mathematical reasoning phenomenon in AI-mediated computation, characterized by a computational pattern manifesting as aI maps the manifold of solutions to parameterized differential equations. Humans interactively explore the solution landscape to identify critical transitions or bifurcation points. Distinguished from adjacent concepts by its focus on the specific mechanism through which solution manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch kI kartografiert Lösungsmannigfaltigkeiten; Menschen erkunden interaktiv kritische Übergänge. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-3101", "narrower_terms": [], "cross_domain_refs": [ "MSC-0057", "ROB-0078" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "MTH-0026", "domain": "MTH", "term_en": "Loss Function Co-Design", "term_de": "Verlustfunktion-Co-Design", "definition_en": "A formal logic pattern in AI-augmented mathematical processing, measurable through humans collaborate with AI to design specialized loss functions encoding domain objectives. Iterative refinement based on human feedback improves both solution quality and interpretability. The concept emerges specifically in contexts where loss–function interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch menschen und KI entwerfen spezialisierte Verlustfunktionen; iterative Verfeinerung verbessert Qualität. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Mathematics AI", "narrower_terms": [], "cross_domain_refs": [ "VIB-0063", "RPH-1164" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q185925", "legal_classification": "empirical_phenomenon_label" }, { "id": "MTH-0027", "domain": "MTH", "term_en": "Fourier Neural Operator Validation", "term_de": "Fourier-Neurale-Operator-Validierung", "definition_en": "A formal logic pattern in AI-augmented mathematical processing, measurable through a computational pattern where aI learns frequency-domain representations of solution operators with human guidance on relevant frequency ranges. Humans validate that learned representations preserve important physical behavior. The concept emerges specifically in contexts where fourier–neural interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch kI lernt Frequenzbereichs-Darstellungen; Menschen validieren physikalisches Verhalten. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2604", "narrower_terms": [], "cross_domain_refs": [ "MUS-0034" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "MTH-0028", "domain": "MTH", "term_en": "Residual Analysis and Refinement", "term_de": "Residualanalyse und Verfeinerung", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures A formal logic pattern in AI-augmented mathematical processing, measurable through a computational pattern arising from aI computes residuals for proposed solutions to differential equations. Humans examine residual patterns to identify where solutions fail and guide network retraining. This phenomenon operates at the intersection of residual and analysis dynamics within the broader MTH domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept kI berechnet Residuen; Menschen analysieren Muster zur Verbesserung. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "RPH-3902", "narrower_terms": [], "cross_domain_refs": [ "CRE-0014", "DES-0069", "GAM-0067" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "MTH-0029", "domain": "MTH", "term_en": "Uncertainty Quantification in Neural Solutions", "term_de": "Unsicherheitsquantifizierung in neuronalen Lösungen", "definition_en": "A computational pattern manifesting as probabilistic neural networks provide confidence intervals for solutions. Humans use uncertainty estimates to decide where higher computational effort or more training data is needed. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch probabilistische neurale Netzwerke geben Konfidenzintervalle; Menschen nutzen diese für Entscheidungen. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Mathematics AI", "narrower_terms": [], "cross_domain_refs": [ "MSC-0024", "MKT-0070", "MSC-0005" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "MTH-0030", "domain": "MTH", "term_en": "Domain Decomposition with Neural Solvers", "term_de": "Domänen-Dekomposition mit neuronalen Lösern", "definition_en": "A formal logic pattern in AI-augmented mathematical processing, measurable through a computational pattern involving humans partition complex domains and train neural solvers on subdomains. AI learns how to couple solutions across domain boundaries, with humans verifying continuity and conservation laws. Distinguished from adjacent concepts by its focus on the specific mechanism through which domain manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch menschen partitionieren Domänen; neurale Löser werden auf Subdomänen trainiert. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2703", "narrower_terms": [], "cross_domain_refs": [ "DAT-0032", "CUS-0098", "COG-0073" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "MTH-0031", "domain": "MTH", "term_en": "Constraint Elicitation from Experts", "term_de": "Constraint-Inferenz von Experten", "definition_en": "A formal logic pattern in AI-augmented mathematical processing, measurable through a mathematical reasoning phenomenon where aI systems interview human domain experts to elicit implicit constraints in optimization problems. Humans refine and validate discovered constraints before feeding them to solvers. Distinguished from adjacent concepts by its focus on the specific mechanism through which constraint manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch kI-Systeme befragen Experten zu impliziten Constraints. Menschen verfeinern und validieren diese. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2002", "narrower_terms": [], "cross_domain_refs": [ "LIN-0066", "MSC-0007", "CON-0073" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q2083958", "legal_classification": "observational_construct" }, { "id": "MTH-0032", "domain": "MTH", "term_en": "Hybrid Search Strategy Design", "term_de": "Hybride Suchstrategie-Gestaltung", "definition_en": "A formal logic pattern in AI-augmented mathematical processing, measurable through humans and AI collaborate to design hybrid algorithms combining multiple search strategies. Humans provide intuition about problem structure; AI learns which strategies work best. The concept emerges specifically in contexts where hybrid–search interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch menschen und KI entwerfen hybride Algorithmen; KI lernt beste Strategien. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2703", "narrower_terms": [], "cross_domain_refs": [ "WRK-0051" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q185451", "legal_classification": "observational_construct" }, { "id": "MTH-0033", "domain": "MTH", "term_en": "Solution Quality Benchmarking", "term_de": "Lösungsqualitäts-Benchmarking", "definition_en": "A mathematical reasoning phenomenon in AI-mediated computation, characterized by aI evaluates candidate solutions against human-defined quality metrics and constraints. Humans interpret which trade-offs between objectives are acceptable in their application domain. The concept emerges specifically in contexts where solution–quality interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch kI bewertet Kandidatenlösungen; Menschen interpretieren akzeptable Trade-offs. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Mathematics AI", "narrower_terms": [], "cross_domain_refs": [ "DAT-0029", "MSC-0008" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "MTH-0034", "domain": "MTH", "term_en": "Combinatorial Problem Structure Learning", "term_de": "Kombinatorische Problemstruktur-Lernfähigkeit", "definition_en": "A mathematical reasoning phenomenon in AI-mediated computation, characterized by a computational pattern where machine learning identifies patterns in problem instances that correlate with solution difficulty. Humans use insights to preprocess problems or select specialized solvers. The concept emerges specifically in contexts where combinatorial–problem interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch maschinenlernmodelle identifizieren Schwierigkeitsmuster; Menschen wählen spezialisierte Löser. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2703", "narrower_terms": [], "cross_domain_refs": [ "QUA-0054", "COG-0176" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q133500", "legal_classification": "systematic_classification" }, { "id": "MTH-0035", "domain": "MTH", "term_en": "Dynamic Parameter Tuning", "term_de": "Dynamische Parameter-Anpassung", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A mathematical reasoning phenomenon in AI-mediated computation, characterized by a mathematical reasoning phenomenon characterized by aI dynamically adjusts solver parameters during optimization based on progress. Humans set high-level goals (e.g. convergence speed vs. solution quality); AI learns parameter strategies. This phenomenon operates at the intersection of dynamic and parameter dynamics within the broader MTH domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept kI passt Solver-Parameter dynamisch an; Menschen setzen Ziele. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "RPH-2703", "narrower_terms": [], "cross_domain_refs": [ "STE-0095", "ELR-0172" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "MTH-0036", "domain": "MTH", "term_en": "Infeasibility Analysis with Human Interpretation", "term_de": "Unlösbarkeitsanalyse mit menschlicher Interpretation", "definition_en": "A mathematical reasoning phenomenon characterized by when optimization problems prove infeasible, AI identifies minimal conflict sets of constraints. Humans review these conflicts to decide which constraints can be relaxed.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch kI identifiziert minimale Konflikt-Sets; Menschen entscheiden über Relaxation. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Analytical Method", "narrower_terms": [], "cross_domain_refs": [ "STE-0020", "TEW-0027" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "MTH-0037", "domain": "MTH", "term_en": "Column Generation with Expert Knowledge", "term_de": "Spaltenerzeugung mit Expertenwissen", "definition_en": "A mathematical reasoning phenomenon in AI-mediated computation, characterized by a computational pattern characterized by humans provide domain-specific rules to guide column generation algorithms. AI learns which expert rules may generate high-quality columns and when to override them. Distinguished from adjacent concepts by its focus on the specific mechanism through which column manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch menschen geben Domänenregeln vor; KI lernt hochwertige Spaltenerzeugung. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2703", "narrower_terms": [], "cross_domain_refs": [ "IDN-0019", "AUG-0164" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "MTH-0038", "domain": "MTH", "term_en": "Sensitivity Analysis for Decision Making", "term_de": "Sensitivitätsanalyse für Entscheidungsfindung", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A mathematical reasoning phenomenon in AI-mediated computation, characterized by aI computes how optimal solutions change with parameter variations. Humans examine sensitivity results to understand robustness and make risk-aware decisions. This phenomenon operates at the intersection of sensitivity and analysis dynamics within the broader MTH domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription. Analytical category without normative endorsement.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept kI berechnet Lösungsänderungen bei Parametervariationen; Menschen analysieren Robustheit. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Analytical Method", "narrower_terms": [], "cross_domain_refs": [ "DAT-0027" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q230471", "legal_classification": "systematic_classification" }, { "id": "MTH-0039", "domain": "MTH", "term_en": "Real-Time Replanning with AI", "term_de": "Echtzeitumplanung mit KI", "definition_en": "Documented as an empirical observation in AI research (not a recommendation), this term describes A formal logic pattern in AI-augmented mathematical processing, measurable through a quantitative thinking effect where humans monitor plan execution and request real-time adjustments. AI recalculates optimal adjustments while preserving feasibility of current operations. The concept emerges specifically in contexts where real–time interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "In der AUGMANITAI-Terminologiewissenschaft als rein beschreibendes Phänomen klassifiziert, bezeichnet dieser Begriff menschen überwachen Planausführung; KI berechnet optimale Anpassungen. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Mathematics AI", "narrower_terms": [], "cross_domain_refs": [ "GAM-0043", "VIB-0153" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "MTH-0040", "domain": "MTH", "term_en": "Multi-Objective Trade-off Exploration", "term_de": "Multi-Ziel-Trade-off-Erkundung", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures A formal logic pattern in AI-augmented mathematical processing, measurable through a computational pattern characterized by aI tends to generate diverse solutions along Pareto fronts for multi-objective problems. Humans interactively explore trade-offs to identify preferred solutions aligned with their values. This phenomenon operates at the intersection of multi and objective dynamics within the broader MTH domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept kI tendiert dazu zu erzeugen diverse Pareto-Lösungen; Menschen erkunden interaktiv Präferenzen. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Analytical Ratio", "narrower_terms": [], "cross_domain_refs": [ "MSC-0004", "MSC-0001", "MSC-0011" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "MTH-0041", "domain": "MTH", "term_en": "Genetic Programming with Human Feedback", "term_de": "Genetische Programmierung mit menschlichem Feedback", "definition_en": "A computational pattern characterized by evolutionary algorithms may generate mathematical formulas with human guidance on complexity and interpretability. Humans rate discovered formulas, steering evolution toward simpler or more elegant expressions.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch evolutionäre Algorithmen erzeugen Formeln; Menschen lenken Auswahl nach Einfachheit. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2604", "narrower_terms": [], "cross_domain_refs": [ "STE-0037", "CON-0073" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "MTH-0042", "domain": "MTH", "term_en": "Symbolic Simplification Co-Design", "term_de": "Symbolische Vereinfachungs-Co-Gestaltung", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures A formal logic pattern in AI-augmented mathematical processing, measurable through a mathematical reasoning phenomenon arising from aI discovers candidate formulas; humans apply domain knowledge to simplify and transform them. Iterative refinement tends to produce equations matching human intuition. This phenomenon operates at the intersection of symbolic and simplification dynamics within the broader MTH domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept kI entdeckt Formeln; Menschen vereinfachen basierend auf Domänenwissen. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "RPH-2351", "narrower_terms": [], "cross_domain_refs": [ "VIB-0063", "TEM-0010" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q185925", "legal_classification": "observational_construct" }, { "id": "MTH-0043", "domain": "MTH", "term_en": "Pareto Optimality in Formula Space", "term_de": "Pareto-Optimalität im Formelraum", "definition_en": "A formal logic pattern in AI-augmented mathematical processing, measurable through a computational pattern in which algorithms search for formulas along the Pareto frontier balancing fitting accuracy and expression complexity. Humans choose preferred points reflecting their application needs. The concept emerges specifically in contexts where pareto–optimality interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch algorithmen suchen Pareto-Formeln; Menschen wählen bevorzugte Punkte. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-3101", "narrower_terms": [], "cross_domain_refs": [ "STE-0037", "SPA-0089", "TEM-0013" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "MTH-0044", "domain": "MTH", "term_en": "Feature Importance for Formula Derivation", "term_de": "Merkmal-Wichtigkeit für Formelableitung", "definition_en": "A mathematical reasoning phenomenon in AI-mediated computation, characterized by aI identifies which input variables and transformations most influence output behavior. Humans use feature importance to guide formula structure and select relevant phenomena. Distinguished from adjacent concepts by its focus on the specific mechanism through which feature manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch kI identifiziert einflussreiche Eingabevariablen; Menschen leiten Formelstruktur. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-3902", "narrower_terms": [], "cross_domain_refs": [ "DAT-0042", "RPH-3403" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "MTH-0045", "domain": "MTH", "term_en": "Dimensional Analysis Enforcement", "term_de": "Dimensionsanalyse-Durchsetzung", "definition_en": "A mathematical reasoning phenomenon in AI-mediated computation, characterized by a quantitative thinking effect involving humans encode dimensional constraints ensuring formulas respect physical units. AI respects these constraints while discovering mathematical relationships. The concept emerges specifically in contexts where dimensional–analysis interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch menschen codieren Dimensionsconstraints; KI respektiert diese bei Entdeckung. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Analytical Method", "narrower_terms": [], "cross_domain_refs": [ "MSC-0008", "MSC-0006", "RPH-3504" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "MTH-0046", "domain": "MTH", "term_en": "Sparse Regression with Domain Constraints", "term_de": "Spärliche Regression mit Domänen-Constraints", "definition_en": "A computational pattern reflecting sparse regression identifies minimal sets of features producing accurate models. Humans validate whether selected features align with physical understanding. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch spärliche Regression identifiziert minimale Merkmals-Sets; Menschen validieren physikalische Bedeutung. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Regression Analysis", "narrower_terms": [], "cross_domain_refs": [ "TEW-0037", "AGE-0008" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "MTH-0047", "domain": "MTH", "term_en": "Interpretability-Accuracy Trade-off Navigation", "term_de": "Navigieren des Interpretierbarkeit-Genauigkeit-Trade-offs", "definition_en": "A mathematical reasoning phenomenon in AI-mediated computation, characterized by a quantitative thinking effect where algorithms generate formula candidates at different complexity levels. Humans navigate trade-offs to select formulas balancing mathematical rigor with human comprehension. Distinguished from adjacent concepts by its focus on the specific mechanism through which interpretability manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch algorithmen erzeugen Formeln verschiedener Komplexität; Menschen balancieren Trade-offs. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Mathematics AI", "narrower_terms": [], "cross_domain_refs": [ "BEH-0035", "MSC-0001" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "MTH-0048", "domain": "MTH", "term_en": "Extrapolation Reliability Assessment", "term_de": "Außerpolations-Zuverlässigkeitsbewertung", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures A formal logic pattern in AI-augmented mathematical processing, measurable through a mathematical reasoning phenomenon arising from aI evaluates discovered formulas' reliability beyond training data ranges. Humans assess extrapolation confidence before deploying formulas in new domains. This phenomenon operates at the intersection of extrapolation and reliability dynamics within the broader MTH domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept kI bewertet Zuverlässigkeit außerhalb von Trainingsdaten; Menschen bewerten Extrapolationsvertrauen. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "RPH-2002", "narrower_terms": [], "cross_domain_refs": [ "CON-0073", "STE-0037" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q210028", "legal_classification": "observational_construct" }, { "id": "MTH-0049", "domain": "MTH", "term_en": "Physics-Inspired Ansatz Selection", "term_de": "Physik-inspirierte Ansatz-Auswahl", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures A formal logic pattern in AI-augmented mathematical processing, measurable through a computational pattern manifesting as humans suggest physical ansätze or functional forms based on domain understanding. AI optimizes parameters within human-specified functional frameworks. This phenomenon operates at the intersection of physics and inspired dynamics within the broader MTH domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept menschen schlagen physikalische Funktionsformen vor; KI optimiert Parameter. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "RPH-2303", "narrower_terms": [], "cross_domain_refs": [ "CUS-0080", "STE-0076" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "MTH-0050", "domain": "MTH", "term_en": "Formula Equivalence Recognition", "term_de": "Formel-Äquivalenz-Erkennung", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A mathematical reasoning phenomenon in AI-mediated computation, characterized by a mathematical reasoning phenomenon where aI identifies when different symbolic expressions represent mathematically equivalent formulas. Humans select among equivalent forms based on interpretability or computational efficiency. This phenomenon operates at the intersection of formula and equivalence dynamics within the broader MTH domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept kI erkennt äquivalente Ausdrücke; Menschen wählen basierend auf Interpretierbarkeit. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Pattern Recognition", "narrower_terms": [], "cross_domain_refs": [ "LIN-0065", "TEW-0100" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q3966", "legal_classification": "systematic_classification" }, { "id": "MTH-0051", "domain": "MTH", "term_en": "Misconception Detection in Student Reasoning", "term_de": "Misskonzept-Erkennung in Schülerdenkweise", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A mathematical reasoning phenomenon in AI-mediated computation, characterized by a mathematical reasoning phenomenon involving aI analyzes student solutions to identify systematic errors or misconceptions. Teachers use AI insights to design targeted interventions addressing root is associated with causing. This phenomenon operates at the intersection of misconception and detection dynamics within the broader MTH domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept kI analysiert Schülerlösungen zur Misskonzept-Erkennung. Lehrer nutzen Erkenntnisse für zielgerichtete Interventionen. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Detection System", "narrower_terms": [], "cross_domain_refs": [ "VIB-0179", "PLY-0042" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "MTH-0052", "domain": "MTH", "term_en": "Adaptive Problem Sequencing", "term_de": "Adaptive Problemsequenzierung", "definition_en": "A formal logic pattern in AI-augmented mathematical processing, measurable through a mathematical reasoning phenomenon reflecting aI models student understanding and recommends problems at appropriate difficulty levels. Students provide feedback on problem quality; AI refines sequencing algorithms. Distinguished from adjacent concepts by its focus on the specific mechanism through which adaptive manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch kI modelliert Schülerverständnis und empfiehlt Probleme. Schüler geben Feedback; KI verfeinert Sequenzierung. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Mathematics AI", "narrower_terms": [], "cross_domain_refs": [ "ASE-0065", "ASE-0040" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "MTH-0053", "domain": "MTH", "term_en": "Hint Generation from Solution Pathways", "term_de": "Hinweis-Generierung aus Lösungswegen", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A mathematical reasoning phenomenon in AI-mediated computation, characterized by a quantitative thinking effect in which aI tends to generate contextual hints by analyzing common solution pathways and student progress. Teachers validate hints ensure they guide without giving away answers. This phenomenon operates at the intersection of hint and generation dynamics within the broader MTH domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept kI generiert kontextuelle Hinweise aus Lösungswegen. Lehrer validieren diese. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "RPH-3902", "narrower_terms": [], "cross_domain_refs": [ "SOC-0020", "ELR-0095" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "MTH-0054", "domain": "MTH", "term_en": "Conceptual Gap Analysis", "term_de": "Konzeptionelle Lückenanalyse", "definition_en": "A mathematical reasoning phenomenon in AI-mediated computation, characterized by a mathematical reasoning phenomenon in which machine learning identifies prerequisite knowledge gaps preventing student progress. Teachers and AI collaborate to scaffold learning by filling gaps before advancing. The concept emerges specifically in contexts where conceptual–gap interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch maschinenlernmodelle identifizieren Wissenslücken. Lehrer und KI füllen Lücken systematisch. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2703", "narrower_terms": [], "cross_domain_refs": [ "ELR-0099", "IDN-0027" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "MTH-0055", "domain": "MTH", "term_en": "Engagement Pattern Recognition", "term_de": "Engagements-Muster-Erkennung", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures A formal logic pattern in AI-augmented mathematical processing, measurable through a mathematical reasoning phenomenon reflecting aI monitors student engagement levels during problem-solving. Teachers receive alerts enabling timely intervention when engagement drops. This phenomenon operates at the intersection of engagement and pattern dynamics within the broader MTH domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept kI überwacht Schüler-Engagement. Lehrer erhalten Benachrichtigungen bei Rückgang. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Behavioral Pattern", "narrower_terms": [], "cross_domain_refs": [ "ELR-0010", "GAM-0047" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q3966", "legal_classification": "observational_construct" }, { "id": "MTH-0056", "domain": "MTH", "term_en": "Peer Comparison and Motivation", "term_de": "Peer-Vergleich und Motivation", "definition_en": "A mathematical reasoning phenomenon in AI-mediated computation, characterized by a computational pattern where aI provides anonymized peer performance data helping students understand their progress. Teachers use comparisons ethically to motivate growth without fostering unrestorethy competition. Distinguished from adjacent concepts by its focus on the specific mechanism through which peer manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch kI stellt anonymisierte Peer-Daten bereit; Lehrer nutzen diese ethisch. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Mathematics AI", "narrower_terms": [], "cross_domain_refs": [ "CON-0021" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q186588", "legal_classification": "descriptive_research_term" }, { "id": "MTH-0057", "domain": "MTH", "term_en": "Proof Writing Assistance", "term_de": "Beweisschreib-Unterstützung", "definition_en": "A formal logic pattern in AI-augmented mathematical processing, measurable through a quantitative thinking effect characterized by aI provides real-time feedback on proof structure and rigor as students write. Humans (teachers) refine feedback prompts to match pedagogical goals. Distinguished from adjacent concepts by its focus on the specific mechanism through which proof manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch kI gibt Echtzeit-Feedback zu Beweisstruktur. Lehrer verfeinern Feedback-Prompts. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2104", "narrower_terms": [], "cross_domain_refs": [ "ELR-0083", "CUS-0060" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "MTH-0058", "domain": "MTH", "term_en": "Learning Style Adaptation", "term_de": "Lernstil-Anpassung", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures A formal logic pattern in AI-augmented mathematical processing, measurable through a computational pattern observed when aI detects student learning style preferences (visual, symbolic, narrative) and adapts problem presentation accordingly. Students provide comfort feedback; AI learns preferences. This phenomenon operates at the intersection of learning and style dynamics within the broader MTH domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept kI erkennt Lernstilpräferenzen; Schüler geben Feedback zur Anpassung. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "RPH-2703", "narrower_terms": [], "cross_domain_refs": [ "NEO-3637", "TEM-0016" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q133500", "legal_classification": "analytical_category" }, { "id": "MTH-0059", "domain": "MTH", "term_en": "Knowledge State Estimation", "term_de": "Wissenszustand-Schätzung", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures A formal logic pattern in AI-augmented mathematical processing, measurable through bayesian models estimate student knowledge state based on assessment responses. Teachers interpret estimates to decide whether topics need review or advancement. This phenomenon operates at the intersection of knowledge and state dynamics within the broader MTH domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept bayesische Modelle schätzen Wissenszustände aus Bewertungen. Lehrer interpretieren für Entscheidungen. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Mathematics AI", "narrower_terms": [], "cross_domain_refs": [ "ART-0085", "MSC-0005" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "MTH-0060", "domain": "MTH", "term_en": "Collaborative Problem-Solving with AI Mediation", "term_de": "Kollaboratives Problemlösen mit KI-Mediation", "definition_en": "A formal logic pattern in AI-augmented mathematical processing, measurable through a mathematical reasoning phenomenon observed when aI facilitates group problem-solving by suggesting next steps when groups stall. Teachers guide how AI mediation encourages productive collaboration rather than dependence. The concept emerges specifically in contexts where collaborative–problem interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch kI vermittelt Gruppen-Problemlösung durch Vorschläge. Lehrer leiten produktive Zusammenarbeit. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2253", "narrower_terms": [], "cross_domain_refs": [ "ELR-0068" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "MTH-0062", "domain": "MTH", "term_en": "Lattice Basis Reduction with Human Guidance", "term_de": "Gitter-Basis-Reduktion mit menschlicher Führung", "definition_en": "A computational pattern observed when humans specify constraints on lattice reduction algorithms (e.g. target dimensions, allowed operations). AI searches for optimal reduction strategies respecting constraints. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch menschen spezifizieren Constraints; KI sucht optimale Reduktionsstrategien. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Mathematics AI", "narrower_terms": [], "cross_domain_refs": [ "ASE-0093", "ART-0040", "CUS-0009" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "MTH-0063", "domain": "MTH", "term_en": "Prime Generation Pipeline Design", "term_de": "Primzahl-Generierungs-Pipeline-Design", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures A formal logic pattern in AI-augmented mathematical processing, measurable through humans and AI collaborate to design efficient primality testing pipelines balancing speed and rigor. Humans select test combinations; AI optimizes pipeline ordering. This phenomenon operates at the intersection of prime and generation dynamics within the broader MTH domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept menschen und KI entwerfen Primzahl-Test-Pipelines. KI optimiert Reihenfolge. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Analytical Ratio", "narrower_terms": [], "cross_domain_refs": [ "VIB-0036" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q185925", "legal_classification": "observational_construct" }, { "id": "MTH-0064", "domain": "MTH", "term_en": "Number-Theoretic Problem Hardness Estimation", "term_de": "Zahlenttheoretische Problem-Schwierigkeits-Schätzung", "definition_en": "A mathematical reasoning phenomenon involving cryptographers and AI collaborate to predict computational hardness of number-theoretic problems using machine learning models trained on problem instances. Experts interpret estimates to set security parameters and validate predictions.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch kryptographen und KI prognostizieren gemeinsam Problemschwierigkeit; Experten validieren und setzen Security-Parameter. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-2703", "narrower_terms": [], "cross_domain_refs": [ "MSC-0061", "MSC-0005", "MSC-0074" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "MTH-0065", "domain": "MTH", "term_en": "Cryptanalytic Attack Discovery", "term_de": "Kryptoanalytische Angriffs-Entdeckung", "definition_en": "A computational pattern observed when aI explores potential attack vectors against cryptographic schemes by analyzing algebraic structure. Cryptographers validate attacks and strengthen defenses. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch kI erkundet potenzielle Angriffsvektoren; Kryptographen validieren und verstärken Verteidigung. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Mathematics AI", "narrower_terms": [], "cross_domain_refs": [ "PER-0018" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "MTH-0066", "domain": "MTH", "term_en": "Efficient Algorithm Implementation Design", "term_de": "Effiziente Algorithmus-Implementierungs-Gestaltung", "definition_en": "A mathematical reasoning phenomenon in AI-mediated computation, characterized by a computational pattern characterized by humans specify algorithmic goals (bit operations, constant-time guarantees); AI designs efficient implementations. Experts verify security properties are preserved. Distinguished from adjacent concepts by its focus on the specific mechanism through which efficient manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch menschen spezifizieren Ziele; KI entwirft effiziente Implementierungen. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Algorithm", "narrower_terms": [], "cross_domain_refs": [ "SWE-0025" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q8366", "legal_classification": "observational_construct" }, { "id": "MTH-0067", "domain": "MTH", "term_en": "Parameter Space Exploration", "term_de": "Parameterraum-Erkundung", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures A formal logic pattern in AI-augmented mathematical processing, measurable through a mathematical reasoning phenomenon where aI systematically explores parameter choices for cryptographic schemes. Humans evaluate trade-offs between security level, performance, and key size. This phenomenon operates at the intersection of parameter and space dynamics within the broader MTH domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept kI erkundet systematisch Parameterraum; Menschen bewerten Trade-offs. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Analytical Ratio", "narrower_terms": [], "cross_domain_refs": [ "VIB-0202" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "MTH-0068", "domain": "MTH", "term_en": "Discrete Log Problem Analysis", "term_de": "Diskreter Logarithmus-Problemanalyse", "definition_en": "A mathematical reasoning phenomenon in AI-mediated computation, characterized by a quantitative thinking effect characterized by machine learning analyzes groups and fields where discrete log appears hard, assisting algorithm designers. Experts validate analysis and select appropriate mathematical structures. Distinguished from adjacent concepts by its focus on the specific mechanism through which discrete manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch maschinenlernmodelle analysieren diskrete Logarithmus-Probleme; Experten validieren. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2703", "narrower_terms": [], "cross_domain_refs": [ "MSC-0059", "TEW-0051" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "MTH-0069", "domain": "MTH", "term_en": "Threshold Cryptography Threshold Design", "term_de": "Schwellen-Kryptographie Schwellen-Gestaltung", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A mathematical reasoning phenomenon in AI-mediated computation, characterized by humans specify security and performance requirements for threshold schemes. AI optimizes threshold parameters and share distributions meeting requirements. This phenomenon operates at the intersection of threshold and cryptography dynamics within the broader MTH domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept menschen spezifizieren Anforderungen; KI optimiert Schwellen-Parameter. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "RPH-1307", "narrower_terms": [], "cross_domain_refs": [ "VIB-0202", "MSC-0078" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q185925", "legal_classification": "observational_construct" }, { "id": "MTH-0070", "domain": "MTH", "term_en": "Side-Channel Vulnerability Assessment", "term_de": "Seitenkanalanfälligkeit-Bewertung", "definition_en": "A formal logic pattern in AI-augmented mathematical processing, measurable through a computational pattern reflecting aI predicts which implementation details might leak information through side channels. Developers use predictions to design constant-time or randomized implementations. The concept emerges specifically in contexts where side–channel interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch kI prognostiziert Informationslecks durch Seitenkanäle. Entwickler nutzen für sicherere Implementierungen. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Mathematics AI", "narrower_terms": [], "cross_domain_refs": [ "VIB-0160", "VIB-0113" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q210028", "legal_classification": "observational_construct" }, { "id": "MTH-0071", "domain": "MTH", "term_en": "Persistent Homology with Human Interpretation", "term_de": "Persistente Homologie mit menschlicher Interpretation", "definition_en": "A computational pattern observed when aI computes persistent homology to reveal topological features at multiple scales. Humans examine persistence diagrams to identify significant features versus noise. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch kI berechnet persistente Homologie; Menschen identifizieren signifikante Features. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Mathematics AI", "narrower_terms": [], "cross_domain_refs": [ "AGE-0008", "ROB-0206" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "MTH-0072", "domain": "MTH", "term_en": "Mapper Algorithm Refinement", "term_de": "Mapper-Algorithmus-Verfeinerung", "definition_en": "A mathematical reasoning phenomenon involving the mapper algorithm constructs topological summaries of data; humans iteratively adjust lens functions and clustering to reveal meaningful structure. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch mapper-Algorithmus erstellt topologische Zusammenfassungen; Menschen passen Parameter an. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Algorithm", "narrower_terms": [], "cross_domain_refs": [ "DAT-0064" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q8366", "legal_classification": "observational_construct" }, { "id": "MTH-0073", "domain": "MTH", "term_en": "Barcodes in Data Structure Discovery", "term_de": "Barcodes in Datenstruktur-Entdeckung", "definition_en": "A computational pattern characterized by humans analyze barcodes from persistent homology to hypothesize underlying data generators. AI helps compute barcodes; humans interpret biological or physical meanings.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch menschen analysieren Barcodes zur Hypothesengenerierung. KI berechnet; Menschen interpretieren. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Mathematics AI", "narrower_terms": [], "cross_domain_refs": [ "PER-0040", "VIB-0030" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q42848", "legal_classification": "systematic_classification" }, { "id": "MTH-0074", "domain": "MTH", "term_en": "Cohomology Computation for Feature Extraction", "term_de": "Kohomologie-Berechnung für Merkmalserzeugung", "definition_en": "A mathematical reasoning phenomenon in AI-mediated computation, characterized by a computational pattern manifesting as aI computes cohomology groups revealing global topological structure. Humans design features based on cohomology classes for machine learning models. The concept emerges specifically in contexts where cohomology–computation interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch kI berechnet Kohomologiegruppen; Menschen entwerfen Features. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2703", "narrower_terms": [], "cross_domain_refs": [ "VIB-0038", "MSC-0010" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "MTH-0075", "domain": "MTH", "term_en": "Topological Data Visualization", "term_de": "Topologische Daten-Visualisierung", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures A formal logic pattern in AI-augmented mathematical processing, measurable through a quantitative thinking effect observed when interactive visualizations of topological structures help humans understand data complexity. Humans guide visualization to emphasize features relevant to their analysis. This phenomenon operates at the intersection of topological and data dynamics within the broader MTH domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept interaktive Visualisierungen zeigen topologische Strukturen; Menschen leiten Fokus. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "RPH-3902", "narrower_terms": [], "cross_domain_refs": [ "MSC-0006", "AED-0005" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q42848", "legal_classification": "analytical_category" }, { "id": "MTH-0076", "domain": "MTH", "term_en": "Shape Analysis in High Dimensions", "term_de": "Formanalyse in hohen Dimensionen", "definition_en": "A formal logic pattern in AI-augmented mathematical processing, measurable through a mathematical reasoning phenomenon arising from aI performs topological shape analysis on high-dimensional data. Humans interpret findings and relate topological properties to domain-specific phenomena. Distinguished from adjacent concepts by its focus on the specific mechanism through which shape manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch kI führt Formanalyse durch; Menschen interpretieren topologische Eigenschaften. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-3901", "narrower_terms": [], "cross_domain_refs": [ "QUA-0093", "REL-0101" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "MTH-0077", "domain": "MTH", "term_en": "Vietoris-Rips Filtration Optimization", "term_de": "Vietoris-Rips-Filtrierungs-Optimierung", "definition_en": "A quantitative thinking effect arising from humans specify distance metrics and resolution parameters for filtrations. AI optimizes parameters to capture relevant topological features. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch menschen spezifizieren Distanzmetriken; KI optimiert für relevante Features. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "Analytical Ratio", "narrower_terms": [], "cross_domain_refs": [ "MSC-0067", "MSC-0086", "VIB-0036" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "MTH-0078", "domain": "MTH", "term_en": "Topological Feature Robustness Testing", "term_de": "Topologische Merkmal-Robustheitsprüfung", "definition_en": "A mathematical reasoning phenomenon in AI-mediated computation, characterized by a quantitative thinking effect reflecting aI tests robustness of discovered topological features under data perturbations. Humans evaluate whether features likely reflect true structure or artifacts. The concept emerges specifically in contexts where topological–feature interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch kI testet Robustheit topologischer Features; Menschen bewerten deren Echtheitsgrad. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Mathematics AI", "narrower_terms": [], "cross_domain_refs": [ "CON-0073", "AGE-0008" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "MTH-0079", "domain": "MTH", "term_en": "Stratification Learning", "term_de": "Schichtungs-Lernfähigkeit", "definition_en": "A formal logic pattern in AI-augmented mathematical processing, measurable through a mathematical reasoning phenomenon observed when machine learning identifies stratifications (layered structures) in data manifolds. Humans investigate whether strata correspond to meaningful categories. The concept emerges specifically in contexts where stratification–learning interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch maschinenlernmodelle identifizieren Schichtungen; Menschen untersuchen Korrespondenz. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2703", "narrower_terms": [], "cross_domain_refs": [ "COG-0118" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q133500", "legal_classification": "observational_construct" }, { "id": "MTH-0080", "domain": "MTH", "term_en": "Computational Topology for Dynamic Systems", "term_de": "Rechnerische Topologie für dynamische Systeme", "definition_en": "A mathematical reasoning phenomenon characterized by aI applies topological tools to analyze attractor structures and bifurcations in dynamical systems. Humans interpret topology to understand system behavior qualitatively.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch kI analysiert Attraktoren mit topologischen Tools; Menschen interpretieren Systemverhalten. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "Mathematics AI", "narrower_terms": [], "cross_domain_refs": [ "ELR-0163" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "MTH-0081", "domain": "MTH", "term_en": "Chain-of-Thought Verification", "term_de": "Chain-of-Thought-Verifikation", "definition_en": "A mathematical reasoning phenomenon in AI-mediated computation, characterized by a mathematical reasoning phenomenon arising from lLMs may generate step-by-step reasoning chains for mathematical problems; humans verify each step's correctness and identify where reasoning breaks down. The concept emerges specifically in contexts where chain–of interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch lLMs erzeugen Schritt-für-Schritt Reasoning; Menschen verifizieren jeden Schritt. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-3001", "narrower_terms": [], "cross_domain_refs": [ "ROB-0091", "ELR-0125" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "MTH-0082", "domain": "MTH", "term_en": "Prompt Engineering for Mathematical Rigor", "term_de": "Prompt-Engineering für mathematische Strenge", "definition_en": "A formal logic pattern in AI-augmented mathematical processing, measurable through humans design prompts that encourage LLMs to follow formal mathematical conventions and reasoning protocols. Iterative feedback improves prompt effectiveness. The concept emerges specifically in contexts where prompt–engineering interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch menschen entwerfen Prompts für mathematische Strenge; iteratives Feedback verbessert Effektivität. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2104", "narrower_terms": [], "cross_domain_refs": [ "ELR-0153", "STE-0035" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "MTH-0083", "domain": "MTH", "term_en": "Mathematical Benchmark Interpretation", "term_de": "Mathematischer Benchmark-Interpretation", "definition_en": "A formal logic pattern in AI-augmented mathematical processing, measurable through a computational pattern observed when researchers analyze LLM performance on benchmarks like GSM8K and IMO datasets. Humans interpret failure patterns to understand reasoning limitations. The concept emerges specifically in contexts where mathematical–benchmark interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch forscher analysieren LLM-Performance auf Benchmarks; Menschen interpretieren Fehlermuster. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Mathematics AI", "narrower_terms": [], "cross_domain_refs": [ "ASE-0003", "ASE-0046", "ASE-0083" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "MTH-0084", "domain": "MTH", "term_en": "Hallucination Detection in Proofs", "term_de": "Halluzinations-Erkennung in Beweisen", "definition_en": "A mathematical reasoning phenomenon in AI-mediated computation, characterized by a quantitative thinking effect arising from humans review LLM-generated proofs to identify false claims and unfounded logical steps. Feedback guides LLM fine-tuning toward more careful reasoning. The concept emerges specifically in contexts where hallucination–detection interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch menschen überprüfen LLM-Beweise auf Halluzinationen; Feedback leitet Fine-Tuning. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-3902", "narrower_terms": [], "cross_domain_refs": [ "STE-0067" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "MTH-0085", "domain": "MTH", "term_en": "Symbolic Reasoning Hybridization", "term_de": "Symbolisches Reasoning-Hybridisierung", "definition_en": "A mathematical reasoning phenomenon in AI-mediated computation, characterized by humans design systems where LLMs handle natural language reasoning while symbolic engines validate formal correctness. Collaboration leverages strengths of each approach. Distinguished from adjacent concepts by its focus on the specific mechanism through which symbolic manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch menschen entwerfen Hybrid-Systeme; LLMs handhaben Sprache, Symbolik durch systematische Beobachtung charakterisiert. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Mathematics AI", "narrower_terms": [], "cross_domain_refs": [ "WRK-0091", "STE-0056" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "MTH-0086", "domain": "MTH", "term_en": "Multi-Step Problem Decomposition", "term_de": "Multi-Schritt-Problem-Zerlegung", "definition_en": "A formal logic pattern in AI-augmented mathematical processing, measurable through a mathematical reasoning phenomenon reflecting humans teach LLMs to decompose complex problems into solvable substeps. Feedback helps models learn when and how to decompose effectively. The concept emerges specifically in contexts where multi–step interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch menschen lehren Problemzerlegung; Feedback verbessert Wann und Wie. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2703", "narrower_terms": [], "cross_domain_refs": [ "EDU-0094", "VIB-0007" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "MTH-0087", "domain": "MTH", "term_en": "Mathematical Consistency Checking", "term_de": "Mathematische Konsistenz-Überprüfung", "definition_en": "A mathematical reasoning phenomenon manifesting as aI verifies that multiple LLM-generated solutions are internally consistent with the problem setup. Humans review flagged inconsistencies. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch kI überprüft interne Konsistenz; Menschen überprüfen gekennzeichnete Probleme. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Mathematics AI", "narrower_terms": [], "cross_domain_refs": [ "SCR-0033" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "MTH-0088", "domain": "MTH", "term_en": "Theorem Confirmation via LLM Ensemble", "term_de": "Theorem-Bestätigung via LLM-Ensemble", "definition_en": "A computational pattern observed when multiple LLM instances attempt to prove the same theorem; consensus increases confidence. Humans evaluate agreement patterns as evidence of validity. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch mehrere LLM-Instanzen versuchen Beweis; Konsens erhöht Konfidenz. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "Mathematics AI", "narrower_terms": [], "cross_domain_refs": [ "COG-0041", "EDU-0040" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "MTH-0089", "domain": "MTH", "term_en": "Explanation Quality Assessment", "term_de": "Erklärungsqualitäts-Bewertung", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures A formal logic pattern in AI-augmented mathematical processing, measurable through humans rate quality and clarity of LLM-generated mathematical explanations. Ratings guide fine-tuning toward more pedagogically effective explanations. This phenomenon operates at the intersection of explanation and quality dynamics within the broader MTH domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept menschen bewerten LLM-Erklärungsqualität; Ratings leiten Fine-Tuning. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "RPH-3902", "narrower_terms": [], "cross_domain_refs": [ "DAT-0055" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q210028", "legal_classification": "descriptive_research_term" }, { "id": "MTH-0090", "domain": "MTH", "term_en": "Novel Problem Formulation", "term_de": "Neuartige Problemformulierung", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures A formal logic pattern in AI-augmented mathematical processing, measurable through a mathematical reasoning phenomenon characterized by humans propose novel mathematical questions and ask LLMs to attempt solutions. Analysis of novel problem performance reveals reasoning generalization limits. This phenomenon operates at the intersection of novel and problem dynamics within the broader MTH domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept menschen schlagen neuartige Fragen vor; LLM-Performance zeigt Generalisierungsgrenzen. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Mathematics AI", "narrower_terms": [], "cross_domain_refs": [ "STE-0001", "ELR-0150" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "MTH-0091", "domain": "MTH", "term_en": "Co-Discovery Framework", "term_de": "Co-Entdeckungs-Rahmen", "definition_en": "A formal logic pattern in AI-augmented mathematical processing, measurable through structured process where mathematicians and AI systems jointly explore mathematical territory, each contributing insights. Collaboration protocols ensure both viewpoints inform the research direction. The concept emerges specifically in contexts where co–discovery interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch strukturierter Prozess; Mathematiker und KI erforschen gemeinsam. Protokolle stellen sicher, dass beide Blickwinkel Richtung leiten. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-3101", "narrower_terms": [], "cross_domain_refs": [ "AED-0014", "AED-0046", "AGE-0055" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "MTH-0092", "domain": "MTH", "term_en": "Proof Sketch Refinement", "term_de": "Beweissketzen-Verfeinerung", "definition_en": "A computational pattern involving mathematicians provide informal proof sketches; AI fills technical details and checks rigor. Iterative cycles refine both sketch and AI's execution. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch mathematiker geben Beweissketzen; KI füllt technische Details. Iterative Verfeinerung. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Mathematics AI", "narrower_terms": [], "cross_domain_refs": [ "COP-0078", "CRE-0014", "DES-0069" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "MTH-0093", "domain": "MTH", "term_en": "Conjecture Validation Pipeline", "term_de": "Vermutungs-Validierungs-Pipeline", "definition_en": "A quantitative thinking effect involving collaborative workflow: humans propose conjectures, AI performs initial testing (counterexample search, special case verification), humans decide on proof pursuit or refinement. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch workflow: Menschen schlagen vor, KI testet, Menschen entscheiden über Verfolgung. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "Processing Pipeline", "narrower_terms": [], "cross_domain_refs": [ "COP-0072", "LIN-0081" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "MTH-0094", "domain": "MTH", "term_en": "Intuition Externalization", "term_de": "Intuitions-Externalisierung", "definition_en": "A formal logic pattern in AI-augmented mathematical processing, measurable through a computational pattern reflecting mathematicians articulate mathematical intuition to AI systems, which learn patterns from verbal descriptions. Feedback helps humans refine intuition and discover unconscious heuristics. Distinguished from adjacent concepts by its focus on the specific mechanism through which intuition manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch menschen artikulieren Intuition; KI lernt Muster. Feedback verfeinert Intuition. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2703", "narrower_terms": [], "cross_domain_refs": [ "REL-0089" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "MTH-0095", "domain": "MTH", "term_en": "Collaborative Problem Structuring", "term_de": "Kollaborative Problemstrukturierung", "definition_en": "A quantitative thinking effect characterized by humans and AI jointly refine problem definitions and identify key assumptions. Structured dialogue is designed to support problems are mathematically precise and practically meaningful.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch menschen und KI verfeinern Problemdefinitionen gemeinsam; Dialog stellt Präzision sicher. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Mathematics AI", "narrower_terms": [], "cross_domain_refs": [ "LIN-0081", "VIB-0124" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "MTH-0096", "domain": "MTH", "term_en": "Insight Translation Between Domains", "term_de": "Erkenntnisübersetzung zwischen Domänen", "definition_en": "Documented as an empirical observation in AI research (not a recommendation), this term describes A mathematical reasoning phenomenon characterized by aI identifies transferable insights from one mathematical domain to another. Mathematicians evaluate whether analogies reveal interaction depth perceptions or superficial similarities. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "In der AUGMANITAI-Terminologiewissenschaft als rein beschreibendes Phänomen klassifiziert, bezeichnet dieser Begriff kI identifiziert übertragbare Erkenntnisse; Mathematiker bewerten Analogien. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Mathematics AI", "narrower_terms": [], "cross_domain_refs": [ "AED-0038" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q7553", "legal_classification": "observational_construct" }, { "id": "MTH-0097", "domain": "MTH", "term_en": "Verification Through Diverse Approaches", "term_de": "Verifizierung durch diverse Ansätze", "definition_en": "A quantitative thinking effect observed when mathematicians and AI explore multiple solution approaches to the same problem. Agreement across approaches increases confidence; divergence is associated with triggering deeper investigation. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch menschen und KI erforschen mehrere Ansätze; Übereinstimmung erhöht Vertrauen. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "Mathematics AI", "narrower_terms": [], "cross_domain_refs": [ "COG-0040", "COG-0079" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "MTH-0098", "domain": "MTH", "term_en": "Emergent Structure Recognition", "term_de": "Erkannte emergente Struktur", "definition_en": "A mathematical reasoning phenomenon in AI-mediated computation, characterized by a computational pattern observed when aI identifies recurring patterns or unexpected structures emerging from collaborative work. Mathematicians interpret whether structures represent mathematical depth or computational artifacts. Distinguished from adjacent concepts by its focus on the specific mechanism through which emergent manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch kI identifiziert Muster aus kollaborativer Arbeit; Menschen bewerten Bedeutung. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2005", "narrower_terms": [], "cross_domain_refs": [ "COG-0100" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q3966", "legal_classification": "descriptive_research_term" }, { "id": "MTH-0099", "domain": "MTH", "term_en": "Failure Analysis and Course Correction", "term_de": "Fehleranalyse und Kurskorrektur", "definition_en": "A computational pattern characterized by when approaches fail, humans and AI collaborate to identify why. Shared analysis tends to lead to refined strategies avoiding similar failures.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch gemeinsame identify bei Misserfolgen; Analyse leitet zu verfeinerten Strategien. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Analytical Method", "narrower_terms": [], "cross_domain_refs": [ "LIN-0039" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "MTH-0100", "domain": "MTH", "term_en": "Research Methodology Co-Design", "term_de": "Forschungsmethodik-Co-Gestaltung", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures A formal logic pattern in AI-augmented mathematical processing, measurable through mathematicians and AI systems co-design research methodologies for new problems. Human expertise in proof strategy combines with AI's systematic exploration to optimize research efficiency. This phenomenon operates at the intersection of research and methodology dynamics within the broader MTH domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept menschen und KI entwerfen Forschungsmethodologien gemeinsam; kombinierten Stärken. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Mathematics AI", "narrower_terms": [], "cross_domain_refs": [ "STE-0039", "SPR-0148" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q201", "legal_classification": "systematic_classification" }, { "id": "MUS-0001", "domain": "MUS", "term_en": "AI Music Emotional Contagion", "term_de": "Museumswissenschaft Grundlagen", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A music generation phenomenon in AI-assisted composition, characterized by an audio creation effect manifesting as transfer of emotional states to listeners through AI-generated music despite knowledge of its artificial origin. This phenomenon operates at the intersection of ai and music dynamics within the broader MUS domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept musikgenerierungsphänomen in KI-gestützter Komposition, gekennzeichnet durch kernprinzipien und Grundlagenwissen der/des Museumswissenschaft Grundlagen, einschließlich Umfang, Methoden und professionelle Standards. KI ermöglicht automatisierte Mustererkennung, Wissensmapping und adaptive Lernpfade über Teildisziplinen hinweg. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Music AI", "narrower_terms": [ "MUS-0099", "MUS-0015", "MUS-0001", "MUS-0086", "MUS-0077", "MUS-0075", "MUS-0029", "MUS-0013", "MUS-0096", "MUS-0060", "MUS-0006", "MUS-0032", "MUS-0079", "MUS-0026", "MUS-0066", "MUS-0037", "MUS-0046", "MUS-0092", "MUS-0082", "MUS-0067", "MUS-0054", "MUS-0044", "MUS-0049", "MUS-0014", "MUS-0065", "MUS-0078", "MUS-0095", "MUS-0056", "MUS-0098", "MUS-0084", "MUS-0061", "MUS-0008", "MUS-0010", "MUS-0025", "MUS-0051", "MUS-0019", "MUS-0017", "MUS-0040", "MUS-0073", "MUS-0030", "MUS-0068", "MUS-0027", "MUS-0023", "MUS-0052", "MUS-0053", "MUS-0009", "MUS-0031", "MUS-0033", "MUS-0076", "MUS-0021", "MUS-0094", "MUS-0024", "MUS-0080", "MUS-0050", "MUS-0058", "MUS-0020", "MUS-0042", "MUS-0036", "MUS-0091", "MUS-0004", "MUS-0018", "MUS-0002", "MUS-0038", "MUS-0062", "MUS-0069", "MUS-0083", "MUS-0045", "MUS-0085", "MUS-0034", "MUS-0022", "MUS-0005", "MUS-0087", "MUS-0028", "MUS-0088", "MUS-0081", "MUS-0063", "MUS-0072", "MUS-0071", "MUS-0057", "MUS-0048", "MUS-0011", "MUS-0070", "MUS-0055", "MUS-0047", "MUS-0097", "MUS-0003", "MUS-0007", "MUS-0089", "MUS-0093" ], "cross_domain_refs": [ "COP-0011", "CUS-0034", "CUS-0043" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q9415", "legal_classification": "observational_construct" }, { "id": "MUS-0002", "domain": "MUS", "term_en": "Accompaniment Density Control", "term_de": "Geschichte der Museumswissenschaft", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A music generation phenomenon in AI-assisted composition, characterized by dynamic adjustment by AI of how many effectonic layers support the main melody across a piece. This phenomenon operates at the intersection of accompaniment and density dynamics within the broader MUS domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept musikgenerierungsphänomen in KI-gestützter Komposition, gekennzeichnet durch chronologische Entwicklung und Meilensteine der/des Museumswissenschaft Grundlagen, einschließlich Innovationen, Paradigmenwechsel und einflussreicher Akteure. ML-Modelle analysieren historische Archive und rekonstruieren Wissenslinien. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Music AI", "narrower_terms": [], "cross_domain_refs": [ "LIN-0002" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "MUS-0003", "domain": "MUS", "term_en": "Aesthetic Pleasure Isolation", "term_de": "Theorie der Museumswissenschaft", "definition_en": "An audio-aesthetic pattern in AI-mediated music production, measurable through a sonic interaction pattern manifesting as pure enjoyment of AI music's sound qualities separated from emotional narrative significance. The concept emerges specifically in contexts where aesthetic–pleasure interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Musikgenerierungsphänomen in KI-gestützter Komposition, gekennzeichnet durch theoretische Rahmenwerke und konzeptionelle Modelle der/des Museumswissenschaft Grundlagen, die Kausalbeziehungen und Vorhersagestrukturen etablieren. KI durch systematische Beobachtung charakterisiert theoretische Aussagen durch groß angelegte Datenanalyse und computationale Hypothesenp. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Music AI", "narrower_terms": [], "cross_domain_refs": [ "AGE-0067", "ART-0026", "ART-0027" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "MUS-0004", "domain": "MUS", "term_en": "Affective Ambivalence", "term_de": "Prinzipien des museum", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A music generation phenomenon in AI-assisted composition, characterized by a musical production phenomenon arising from mixed or contradictory emotional reactions to AI-generated music within the listener. This phenomenon operates at the intersection of affective and ambivalence dynamics within the broader MUS domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept musikgenerierungsphänomen in KI-gestützter Komposition, gekennzeichnet durch leitregeln und Axiome, die korrekte Praxis in Museumswissenschaft Grundlagen definieren. KI-Systeme kodifizieren diese Prinzipien in Regelmaschinen und ermöglichen automatisierte Konformitätsprüfung und prinzipienbasierte Entscheidungsunterstützung. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Music AI", "narrower_terms": [], "cross_domain_refs": [ "RET-0007", "ROB-0004" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "MUS-0005", "domain": "MUS", "term_en": "Anachronistic Element Insertion", "term_de": "Fachterminologie Museumswissenschaft", "definition_en": "An audio-aesthetic pattern in AI-mediated music production, measurable through a sonic interaction pattern involving aI inclusion of musical characteristics from different historical periods within a single piece. The concept emerges specifically in contexts where anachronistic–element interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Musikgenerierungsphänomen in KI-gestützter Komposition, gekennzeichnet durch fachvokabular und Nomenklatursysteme in Museumswissenschaft Grundlagen zur präzisen Kommunikation unter Fachleuten. NLP-Modelle extrahieren, standardisieren und referenzieren domänenspezifische Terme über mehrsprachige Korpora hinweg. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Music AI", "narrower_terms": [], "cross_domain_refs": [ "DES-0075", "PHO-0001", "PHO-0044" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "MUS-0006", "domain": "MUS", "term_en": "Artifact Audibility Threshold", "term_de": "Klassifikation Museumswissenschaft", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures A music generation phenomenon in AI-assisted composition, characterized by the perceptual boundary at which AI processing errors become noticeable to listeners. This phenomenon operates at the intersection of artifact and audibility dynamics within the broader MUS domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept musikgenerierungsphänomen in KI-gestützter Komposition, gekennzeichnet durch musikalische Disziplin in Music Theory and Composition mit Theorie und Auffuehrung von Klassifikation Museumswissenschaft. KI transformiert musikalische Praxis durch automatisierte Komposition, intelligente Begleitung, Echtzeit-Auffuehrungsanalyse und adaptive Lernwerkzeuge. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Decision Threshold", "narrower_terms": [], "cross_domain_refs": [ "RPH-2851" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q735", "legal_classification": "systematic_classification" }, { "id": "MUS-0007", "domain": "MUS", "term_en": "Artistic Intent Preservation", "term_de": "Einführung in museum", "definition_en": "An audio-aesthetic pattern in AI-mediated music production, measurable through a musical production phenomenon manifesting as maintenance of a creator's original vision when AI modifications alter the work. The concept emerges specifically in contexts where artistic–intent interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Musikgenerierungsphänomen in KI-gestützter Komposition, gekennzeichnet durch musikalische Disziplin in Music Theory and Composition mit Theorie und Auffuehrung von Einführung in museum. KI transformiert musikalische Praxis durch automatisierte Komposition, intelligente Begleitung, Echtzeit-Auffuehrungsanalyse und adaptive Lernwerkzeuge. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Music AI", "narrower_terms": [], "cross_domain_refs": [ "ART-0065" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q735", "legal_classification": "systematic_classification" }, { "id": "MUS-0008", "domain": "MUS", "term_en": "Audio Mastering Automation", "term_de": "museum-Methodik", "definition_en": "An audio-aesthetic pattern in AI-mediated music production, measurable through a sonic interaction pattern observed when aI application of loudness standardization and frequency optimization across a finished mix. The concept emerges specifically in contexts where audio–mastering interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Musikgenerierungsphänomen in KI-gestützter Komposition, gekennzeichnet durch musikalische Disziplin in Music Theory and Composition mit Theorie und Auffuehrung von museum-Methodik. KI transformiert musikalische Praxis durch automatisierte Komposition, intelligente Begleitung, Echtzeit-Auffuehrungsanalyse und adaptive Lernwerkzeuge. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Music AI", "narrower_terms": [], "cross_domain_refs": [ "CON-0009", "CRE-0124", "DAT-0070" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q184199", "legal_classification": "analytical_category" }, { "id": "MUS-0009", "domain": "MUS", "term_en": "Authenticity Doubt Dampening", "term_de": "Philosophie der Museumswissenschaft", "definition_en": "A harmonic processing dynamic in AI-augmented musical creation, identifiable by reduction of emotional impact when listeners are aware of AI's role in creation. Distinguished from adjacent concepts by its focus on the specific mechanism through which authenticity manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data. Classification term used in systematic observation, not advocacy.", "definition_de": "Musikgenerierungsphänomen in KI-gestützter Komposition, gekennzeichnet durch epistemologische und ethische Grundlagen der/des Museumswissenschaft Grundlagen, die Zweck, Wertesysteme und Legitimität von Praktiken untersuchen. KI wirft neue philosophische Fragen zu Automatisierung und Autorschaft auf. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Music AI", "narrower_terms": [], "cross_domain_refs": [ "ART-0012", "ART-0025", "ART-0081" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "MUS-0010", "domain": "MUS", "term_en": "Authorship Ambiguity", "term_de": "museum-Taxonomie", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures A music generation phenomenon in AI-assisted composition, characterized by a musical production phenomenon observed when uncertainty about who deserves credit when AI significantly contributes to composition. This phenomenon operates at the intersection of authorship and ambiguity dynamics within the broader MUS domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept musikgenerierungsphänomen in KI-gestützter Komposition, gekennzeichnet durch formale Klassifikationshierarchien, die den Wissensraum von Museumswissenschaft Grundlagen in verschachtelte Kategorien organisieren. KI-gestützte Ontologie-Tools automatisieren Taxonomie-Generierung und erkennen Inkonsistenzen. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Music AI", "narrower_terms": [], "cross_domain_refs": [ "CRE-0033" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "MUS-0011", "domain": "MUS", "term_en": "Automation Curve Smoothing", "term_de": "Umfang der Museumswissenschaft", "definition_en": "A harmonic processing dynamic in AI-augmented musical creation, identifiable by parameter evolution technique where time-based changes in effect intensity or filter frequency occur smoothly without abrupt discontinuity, maintaining perceptual continuity. Distinguished from adjacent concepts by its focus on the specific mechanism through which automation manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Musikgenerierungsphänomen in KI-gestützter Komposition, gekennzeichnet durch parameter-Evolutions-Technik, wo zeitbasierte Änderungen sanft erfolgen ohne Diskontinuität und Wahrnehmungs-Kontinuität bewahren. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Music AI", "narrower_terms": [], "cross_domain_refs": [ "SCR-0085" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q184199", "legal_classification": "systematic_classification" }, { "id": "MUS-0012", "domain": "MUS", "term_en": "Breath Mark Insertion", "term_de": "Literaturübersicht Museumswissenschaft", "definition_en": "An audio-aesthetic pattern in AI-mediated music production, measurable through an audio creation effect observed when vocal phrase respiration insertion where breath placement at natural linguistic boundaries or rhythmic emphases enhances human performability and singing authenticity. The concept emerges specifically in contexts where breath–mark interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Musikgenerierungsphänomen in KI-gestützter Komposition, gekennzeichnet durch atemeinlagerung in Vokalphrasing, wo Atem an natürlichen Grenzen Authentizität und Singbarkeit erhöht. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-3101", "narrower_terms": [], "cross_domain_refs": [ "SCR-0046" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "MUS-0013", "domain": "MUS", "term_en": "Bridge Generation", "term_de": "Schlüsselkonzepte in museum", "definition_en": "An audio-aesthetic pattern in AI-mediated music production, measurable through a sonic interaction pattern arising from aI creation of transitional sections that contrast with verse and chorus material. The concept emerges specifically in contexts where bridge–generation interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Musikgenerierungsphänomen in KI-gestützter Komposition, gekennzeichnet durch zentrale Ideen und Bausteine professioneller Kompetenz in Museumswissenschaft Grundlagen. KI-gestützte Wissensgraphen kartieren konzeptionelle Abhängigkeiten und empfehlen Lernsequenzen zur Meisterschaft. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Analytical Ratio", "narrower_terms": [], "cross_domain_refs": [ "CON-0001" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "MUS-0014", "domain": "MUS", "term_en": "Cathartic Release Limitation", "term_de": "Rahmenwerk der Museumswissenschaft", "definition_en": "A harmonic processing dynamic in AI-augmented musical creation, identifiable by a musical production phenomenon reflecting reduced intensity of emotional catharsis from AI music compared to human-composed equivalents. Distinguished from adjacent concepts by its focus on the specific mechanism through which cathartic manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Musikgenerierungsphänomen in KI-gestützter Komposition, gekennzeichnet durch musikalische Disziplin in Music Theory and Composition mit Theorie und Auffuehrung von Rahmenwerk der Museumswissenschaft. KI transformiert musikalische Praxis durch automatisierte Komposition, intelligente Begleitung, Echtzeit-Auffuehrungsanalyse und adaptive Lernwerkzeuge. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Music AI", "narrower_terms": [], "cross_domain_refs": [ "CAI-0021", "COG-0168", "DES-0035" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q735", "legal_classification": "analytical_category" }, { "id": "MUS-0015", "domain": "MUS", "term_en": "Chord Loop Iteration", "term_de": "Paradigmen in museum", "definition_en": "A harmonic processing dynamic in AI-augmented musical creation, identifiable by a musical production phenomenon reflecting rapid generation of multiple chord progression variations around a core effectonic idea. Distinguished from adjacent concepts by its focus on the specific mechanism through which chord manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Musikgenerierungsphänomen in KI-gestützter Komposition, gekennzeichnet durch musikalische Disziplin in Music Theory and Composition mit Theorie und Auffuehrung von Paradigmen in museum. KI transformiert musikalische Praxis durch automatisierte Komposition, intelligente Begleitung, Echtzeit-Auffuehrungsanalyse und adaptive Lernwerkzeuge. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Feedback Loop", "narrower_terms": [], "cross_domain_refs": [ "COP-0021" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "MUS-0016", "domain": "MUS", "term_en": "Cliché Lyric Repetition", "term_de": "museum-Forschungsmethoden", "definition_en": "An audio-aesthetic pattern in AI-mediated music production, measurable through linguistic cliche prevalence in AI-generated vocal writing where overused phrases, tired metaphors, or generic emotional expressions exceed threshold of freshness. The concept emerges specifically in contexts where cliché–lyric interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Musikgenerierungsphänomen in KI-gestützter Komposition, gekennzeichnet durch klischee-Häufigkeit in KI-generierten Lyriken, wo abgenutzte Phrasen und generische Ausdrücke Schwelle der Frische überschreiten. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2052", "narrower_terms": [], "cross_domain_refs": [ "AUG-0337", "BEH-0060", "COP-0006" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "MUS-0017", "domain": "MUS", "term_en": "Collaborative AI Adaptation", "term_de": "Quantitative museum-Analyse", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures A music generation phenomenon in AI-assisted composition, characterized by an audio creation effect in which development of new working methods that leverage both AI generation and human musicality effectively. This phenomenon operates at the intersection of collaborative and ai dynamics within the broader MUS domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept musikgenerierungsphänomen in KI-gestützter Komposition, gekennzeichnet durch numerische und statistische Methoden zur Messung von Phänomenen in Museumswissenschaft Grundlagen. KI nutzt Regressionsmodelle, Bayes-Inferenz und Deep Learning zur Extraktion quantitativer Erkenntnisse aus großen Datensätzen. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "System Adaptation", "narrower_terms": [], "cross_domain_refs": [ "GAM-0048", "GAM-0044" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "MUS-0018", "domain": "MUS", "term_en": "Compositional Arpeggiation", "term_de": "Qualitative museum-Analyse", "definition_en": "A harmonic processing dynamic in AI-augmented musical creation, identifiable by a musical production phenomenon observed when transition of chords into broken, note-by-note patterns by AI during arrangement generation. Distinguished from adjacent concepts by its focus on the specific mechanism through which compositional manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Musikgenerierungsphänomen in KI-gestützter Komposition, gekennzeichnet durch interpretative und deskriptive Forschungsansätze in Museumswissenschaft Grundlagen mit Fokus auf Bedeutung und Kontext. NLP und Sentimentanalyse automatisieren thematische Kodierung und Musterextraktion aus qualitativen Daten. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Music AI", "narrower_terms": [], "cross_domain_refs": [ "MTH-0096", "NEO-2288" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "MUS-0019", "domain": "MUS", "term_en": "Compositional Augmentation", "term_de": "museum-Messung", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures A music generation phenomenon in AI-assisted composition, characterized by a musical production phenomenon in which note duration extension where held tones or elongated rhythmic values emphasize musical moments, either through performance timing or compositional indication. This phenomenon operates at the intersection of compositional and augmentation dynamics within the broader MUS domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept musikgenerierungsphänomen in KI-gestützter Komposition, gekennzeichnet durch notendauer-Erweiterung, wo gehaltene Töne musikalische Momente betonen und Gewicht verleihen. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Music AI", "narrower_terms": [], "cross_domain_refs": [ "DAT-0022", "IDN-0021", "KNO-0005" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "MUS-0020", "domain": "MUS", "term_en": "Consonant Articulation Rendering", "term_de": "Experimentelles museum-Design", "definition_en": "An audio-aesthetic pattern in AI-mediated music production, measurable through a musical production phenomenon manifesting as consonant phoneme articulation in synthetic speech where plosive, fricative, and affricate clarity distinguish individual phonetic elements, enhancing intelligibility. The concept emerges specifically in contexts where consonant–articulation interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Musikgenerierungsphänomen in KI-gestützter Komposition, gekennzeichnet durch konsonanten-Artikulation in synthetischer Sprache, wo Plosivenklarheit und Reibelautdefinition Verständlichkeit erhöht. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Music AI", "narrower_terms": [], "cross_domain_refs": [ "VIB-0190" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q735", "legal_classification": "systematic_classification" }, { "id": "MUS-0021", "domain": "MUS", "term_en": "Copyright Ownership Dispute", "term_de": "museum-Datenerhebung", "definition_en": "A harmonic processing dynamic in AI-augmented musical creation, identifiable by an audio creation effect observed when legal conflict over rights to AI-generated music when original training data sources are unclear. Distinguished from adjacent concepts by its focus on the specific mechanism through which copyright manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Musikgenerierungsphänomen in KI-gestützter Komposition, gekennzeichnet durch systematische Sammlung von Informationen und Beobachtungen in Museumswissenschaft Grundlagen mittels Sensoren oder digitaler Erfassung. KI automatisiert Datenaufnahme, Qualitätsprüfung und Echtzeit-Streaming aus mehreren Quellen. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Music AI", "narrower_terms": [], "cross_domain_refs": [ "ART-0008", "VIB-0025" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q1297", "legal_classification": "systematic_classification" }, { "id": "MUS-0022", "domain": "MUS", "term_en": "Counterpoint Approximation", "term_de": "Stichprobenziehung in museum", "definition_en": "An audio-aesthetic pattern in AI-mediated music production, measurable through an audio creation effect manifesting as aI-generated secondary melodies that interact with a primary melody with varying degrees of inreliance. The concept emerges specifically in contexts where counterpoint–approximation interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Musikgenerierungsphänomen in KI-gestützter Komposition, gekennzeichnet durch musikalische Disziplin in Music Theory and Composition mit Theorie und Auffuehrung von Stichprobenziehung in museum. KI transformiert musikalische Praxis durch automatisierte Komposition, intelligente Begleitung, Echtzeit-Auffuehrungsanalyse und adaptive Lernwerkzeuge. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Music AI", "narrower_terms": [], "cross_domain_refs": [ "FIC-0059" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "MUS-0023", "domain": "MUS", "term_en": "Creative Credit Distribution", "term_de": "Statistische museum-Analyse", "definition_en": "A harmonic processing dynamic in AI-augmented musical creation, identifiable by determination of how to acknowledge contributions from composer, AI system, and other collaborators. Distinguished from adjacent concepts by its focus on the specific mechanism through which creative manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Musikgenerierungsphänomen in KI-gestützter Komposition, gekennzeichnet durch musikalische Disziplin in Music Theory and Composition mit Theorie und Auffuehrung von Statistische museum-Analyse. KI transformiert musikalische Praxis durch automatisierte Komposition, intelligente Begleitung, Echtzeit-Auffuehrungsanalyse und adaptive Lernwerkzeuge. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Music AI", "narrower_terms": [], "cross_domain_refs": [ "ART-0073" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "MUS-0024", "domain": "MUS", "term_en": "Cultural Pattern Recognition", "term_de": "Feldstudie in museum", "definition_en": "An audio-aesthetic pattern in AI-mediated music production, measurable through a musical production phenomenon in which aI detection and application of music patterns derived from specific cultural traditions. The concept emerges specifically in contexts where cultural–pattern interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Musikgenerierungsphänomen in KI-gestützter Komposition, gekennzeichnet durch musikalische Disziplin in Music Theory and Composition mit Theorie und Auffuehrung von Feldstudie in museum. KI transformiert musikalische Praxis durch automatisierte Komposition, intelligente Begleitung, Echtzeit-Auffuehrungsanalyse und adaptive Lernwerkzeuge. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Behavioral Pattern", "narrower_terms": [], "cross_domain_refs": [ "AED-0013", "AED-0093", "AGE-0003" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q3966", "legal_classification": "observational_construct" }, { "id": "MUS-0025", "domain": "MUS", "term_en": "Delay Rhythm Synchronization", "term_de": "Fallstudie in museum", "definition_en": "An audio-aesthetic pattern in AI-mediated music production, measurable through rhythmic alignment technique where delay effects—echo, reverb feedback, or temporal processing—synchronize to the song's metrical grid, enhancing perceived cohesion. The concept emerges specifically in contexts where delay–rhythm interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Musikgenerierungsphänomen in KI-gestützter Komposition, gekennzeichnet durch rhythmische Synchronisierungstechnik, wo Verzögerungseffekte mit dem Metrischen Grid des Songs abgestimmt werden und wahrgenommene Kohärenz verstärken. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Music AI", "narrower_terms": [], "cross_domain_refs": [ "CRE-0068", "ELR-0005", "FIC-0046" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "MUS-0026", "domain": "MUS", "term_en": "Derivative vs. Original Boundary", "term_de": "Vergleichende museum-Studie", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures A music generation phenomenon in AI-assisted composition, characterized by the blurred distinction between an AI variation of existing work and genuine original composition. This phenomenon operates at the intersection of derivative and vs. dynamics within the broader MUS domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept musikgenerierungsphänomen in KI-gestützter Komposition, gekennzeichnet durch vergleichende Analyse von Methoden, Ergebnissen oder Artefakten über verschiedene Kontexte in Museumswissenschaft Grundlagen hinweg. KI führt mehrdimensionales Ähnlichkeits-Scoring und automatisiertes Benchmarking durch. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Music AI", "narrower_terms": [], "cross_domain_refs": [ "SCR-0019" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "MUS-0027", "domain": "MUS", "term_en": "Developmental Sequencing", "term_de": "Längsschnittstudie in museum", "definition_en": "An audio-aesthetic pattern in AI-mediated music production, measurable through an audio creation effect involving aI organization of musical ideas in progressive order, from introduction through climax to resolution. The concept emerges specifically in contexts where developmental–sequencing interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Musikgenerierungsphänomen in KI-gestützter Komposition, gekennzeichnet durch forschung, die Museumswissenschaft Grundlagen-Phänomene über längere Zeiträume verfolgt, um Entwicklungsmuster und kausale Zusammenhänge zu identifizieren. KI ermöglicht automatisierte Längsschnitt-Datenerhebung, Schwundvorhersage und Zeitreihen-Anomalieerk. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Music AI", "narrower_terms": [], "cross_domain_refs": [ "RPH-1162" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "MUS-0028", "domain": "MUS", "term_en": "Distortion Presence Calibration", "term_de": "museum-Umfragemethode", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A music generation phenomenon in AI-assisted composition, characterized by saturation and overdrive intensity calibration where distortion characteristics are modulated to achieve desired harmonic coloration without overwhelming dynamic range clarity. This phenomenon operates at the intersection of distortion and presence dynamics within the broader MUS domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept musikgenerierungsphänomen in KI-gestützter Komposition, gekennzeichnet durch kalibrierung von Sättigungs- und Overdrive-Intensität, wo Verzerrungsmerkmale moduliert werden für Farbklang ohne Dynamik-Verlust. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Analytical Ratio", "narrower_terms": [], "cross_domain_refs": [ "ASE-0016", "ASE-0025", "ASE-0030" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "MUS-0029", "domain": "MUS", "term_en": "Dynamic Range Compression", "term_de": "Aktionsforschung in museum", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A music generation phenomenon in AI-assisted composition, characterized by an audio creation effect manifesting as aI reduction of volume differences between loud and soft sections for consistency. This phenomenon operates at the intersection of dynamic and range dynamics within the broader MUS domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept musikgenerierungsphänomen in KI-gestützter Komposition, gekennzeichnet durch iterative Forschungsmethodik, die Untersuchung mit praxisbasierter Intervention in Museumswissenschaft Grundlagen verbindet. KI unterstützt Zyklusoptimierung durch automatisiertes Outcome-Tracking, Mustererkennung bei Interventionseffekten und adaptive Empf. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Data Compression", "narrower_terms": [], "cross_domain_refs": [ "ROB-0100", "CON-0020" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "MUS-0030", "domain": "MUS", "term_en": "Emotional Arc Disconnection", "term_de": "Mixed Methods in museum", "definition_en": "A harmonic processing dynamic in AI-augmented musical creation, identifiable by perception that emotion development in AI music doesn't progress naturally or convincingly. Distinguished from adjacent concepts by its focus on the specific mechanism through which emotional manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Musikgenerierungsphänomen in KI-gestützter Komposition, gekennzeichnet durch musikalische Disziplin in Music Theory and Composition mit Theorie und Auffuehrung von Mixed Methods in museum. KI transformiert musikalische Praxis durch automatisierte Komposition, intelligente Begleitung, Echtzeit-Auffuehrungsanalyse und adaptive Lernwerkzeuge. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Music AI", "narrower_terms": [], "cross_domain_refs": [ "RPH-3353" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q9415", "legal_classification": "analytical_category" }, { "id": "MUS-0031", "domain": "MUS", "term_en": "Emotional Resonance Uncertainty", "term_de": "museum-Technologie", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A music generation phenomenon in AI-assisted composition, characterized by an audio creation effect arising from ambiguity in whether emotional responses to AI music derive from the music itself or from knowing its origin. This phenomenon operates at the intersection of emotional and resonance dynamics within the broader MUS domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept musikgenerierungsphänomen in KI-gestützter Komposition, gekennzeichnet durch musikalische Disziplin in Music Theory and Composition mit Theorie und Auffuehrung von museum-Technologie. KI transformiert musikalische Praxis durch automatisierte Komposition, intelligente Begleitung, Echtzeit-Auffuehrungsanalyse und adaptive Lernwerkzeuge. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Music AI", "narrower_terms": [], "cross_domain_refs": [ "ART-0024", "COG-0122", "COG-0146" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q9415", "legal_classification": "systematic_classification" }, { "id": "MUS-0032", "domain": "MUS", "term_en": "Emotional Vocal Inflection", "term_de": "Digitale museum-Werkzeuge", "definition_en": "A harmonic processing dynamic in AI-augmented musical creation, identifiable by a musical production phenomenon where aI adjustment of vocal timbre and expression to convey specified emotional states. Distinguished from adjacent concepts by its focus on the specific mechanism through which emotional manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Musikgenerierungsphänomen in KI-gestützter Komposition, gekennzeichnet durch musikalische Disziplin in Music Theory and Composition mit Theorie und Auffuehrung von Digitale museum-Werkzeuge. KI transformiert musikalische Praxis durch automatisierte Komposition, intelligente Begleitung, Echtzeit-Auffuehrungsanalyse und adaptive Lernwerkzeuge. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Music AI", "narrower_terms": [], "cross_domain_refs": [ "GAM-0039" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q9415", "legal_classification": "analytical_category" }, { "id": "MUS-0033", "domain": "MUS", "term_en": "Equalization Balancing", "term_de": "museum-Software", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures A music generation phenomenon in AI-assisted composition, characterized by frequency response adjustment where individual instrument frequency bands are boosted or attenuated to prevent spectral masking and achieve clarifying mix visibility. This phenomenon operates at the intersection of equalization and balancing dynamics within the broader MUS domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept musikgenerierungsphänomen in KI-gestützter Komposition, gekennzeichnet durch frequenzgang-Anpassung, wo Instrumentenbänder angepasst werden um Spektral-Maskierung zu verhindern und Mix-Klarheit zu erzielen. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Music AI", "narrower_terms": [], "cross_domain_refs": [ "VIB-0139" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "MUS-0034", "domain": "MUS", "term_en": "Frequency Clash Resolution", "term_de": "Automatisierung in museum", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A music generation phenomenon in AI-assisted composition, characterized by a sonic interaction pattern observed when aI identification and mitigation of overlapping frequency ranges between instruments. This phenomenon operates at the intersection of frequency and clash dynamics within the broader MUS domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept musikgenerierungsphänomen in KI-gestützter Komposition, gekennzeichnet durch einsatz automatisierter Systeme und KI zur Reduzierung manueller Arbeit in Museumswissenschaft Grundlagen. Umfasst robotergestützte Prozessautomatisierung und selbstoptimierende Produktionspipelines, die aus Betriebsdaten lernen. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Music AI", "narrower_terms": [], "cross_domain_refs": [ "MTH-0027" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "MUS-0035", "domain": "MUS", "term_en": "Fusion Authenticity Gap", "term_de": "IoT in museum", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures A music generation phenomenon in AI-assisted composition, characterized by an audio creation effect observed when perceived lack of genuine integration between genre elements in AI-blended compositions. This phenomenon operates at the intersection of fusion and authenticity dynamics within the broader MUS domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept musikgenerierungsphänomen in KI-gestützter Komposition, gekennzeichnet durch netzwerk verbundener Sensoren und Geräte zur Echtzeit-Datenerfassung in Museumswissenschaft Grundlagen. KI verarbeitet Sensorströme für vorausschauende Wartung, Umgebungsüberwachung und autonome Prozesssteuerung. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "RPH-3251", "narrower_terms": [], "cross_domain_refs": [ "AED-0019", "AED-0092", "AGE-0023" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "MUS-0036", "domain": "MUS", "term_en": "Genre Blending", "term_de": "Datenanalyse in museum", "definition_en": "A harmonic processing dynamic in AI-augmented musical creation, identifiable by a sonic interaction pattern involving aI combination of stylistic elements from multiple musical genres in a single composition. Distinguished from adjacent concepts by its focus on the specific mechanism through which genre manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Musikgenerierungsphänomen in KI-gestützter Komposition, gekennzeichnet durch systematische Extraktion von Erkenntnissen aus strukturierten und unstrukturierten Daten in Museumswissenschaft Grundlagen. KI erweitert die Analyse durch automatisierte Mustererkennung, prädiktive Modellierung und Echtzeit-Dashboard-Generierung. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Music AI", "narrower_terms": [], "cross_domain_refs": [ "BEH-0015", "FIC-0043", "FIC-0058" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "MUS-0037", "domain": "MUS", "term_en": "Genre Expectation Violation", "term_de": "KI-Anwendungen in museum", "definition_en": "An audio-aesthetic pattern in AI-mediated music production, measurable through a sonic interaction pattern arising from aI generation of material that contradicts established conventions of a specified style. The concept emerges specifically in contexts where genre–expectation interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Musikgenerierungsphänomen in KI-gestützter Komposition, gekennzeichnet durch musikalische Disziplin in Music Theory and Composition mit Theorie und Auffuehrung von KI-Anwendungen in museum. KI transformiert musikalische Praxis durch automatisierte Komposition, intelligente Begleitung, Echtzeit-Auffuehrungsanalyse und adaptive Lernwerkzeuge. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Music AI", "narrower_terms": [], "cross_domain_refs": [ "CRE-0112" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "MUS-0038", "domain": "MUS", "term_en": "Genre Recognition Confidence", "term_de": "Maschinelles Lernen in museum", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures A music generation phenomenon in AI-assisted composition, characterized by genre identification certainty in listener perception where musical characteristics align sufficiently with category conventions to enable reliable genre classification. This phenomenon operates at the intersection of genre and recognition dynamics within the broader MUS domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept musikgenerierungsphänomen in KI-gestützter Komposition, gekennzeichnet durch genreidentifikations-Sicherheit in Hörerwahrnehmung, wo musikalische Merkmale mit Konventionen ausreichend übereinstimmen. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Pattern Recognition", "narrower_terms": [], "cross_domain_refs": [ "AED-0013", "AED-0093", "AGE-0032" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q3966", "legal_classification": "observational_construct" }, { "id": "MUS-0039", "domain": "MUS", "term_en": "Effectonic Cadence Insertion", "term_de": "Sensorik in museum", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A music generation phenomenon in AI-assisted composition, characterized by harmonic progression placement where conclusive chord sequences signal phrase termination, establishing structural punctuation and cadential expectation at segmental boundaries. This phenomenon operates at the intersection of effectonic and cadence dynamics within the broader MUS domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept musikgenerierungsphänomen in KI-gestützter Komposition, gekennzeichnet durch harmonische Progressions-Platzierung, wo Abschluss-Akkordfolgen Phrase-Ende signalisieren und strukturelle Gliederung etablieren. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "RPH-2052", "narrower_terms": [], "cross_domain_refs": [ "SCR-0014" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "MUS-0040", "domain": "MUS", "term_en": "Effectonic Dialect Adoption", "term_de": "Mobile Anwendungen in museum", "definition_en": "A harmonic processing dynamic in AI-augmented musical creation, identifiable by a sonic interaction pattern manifesting as use of chord types and progressions statistically associated with a musical style. Distinguished from adjacent concepts by its focus on the specific mechanism through which effectonic manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Musikgenerierungsphänomen in KI-gestützter Komposition, gekennzeichnet durch musikalische Disziplin in Music Theory and Composition mit Theorie und Auffuehrung von Mobile Anwendungen in museum. KI transformiert musikalische Praxis durch automatisierte Komposition, intelligente Begleitung, Echtzeit-Auffuehrungsanalyse und adaptive Lernwerkzeuge. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Psychological Effect", "narrower_terms": [], "cross_domain_refs": [ "AGE-0035", "AGE-0061", "AGE-0081" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "MUS-0041", "domain": "MUS", "term_en": "Effectonic Rhythm Drift", "term_de": "Cloud-Lösungen für museum", "definition_en": "A harmonic processing dynamic in AI-augmented musical creation, identifiable by gradual shift in when chords change in AI-generated accompaniment across iterations. Distinguished from adjacent concepts by its focus on the specific mechanism through which effectonic manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Musikgenerierungsphänomen in KI-gestützter Komposition, gekennzeichnet durch musikalische Disziplin in Music Theory and Composition mit Theorie und Auffuehrung von Cloud-Lösungen für museum. KI transformiert musikalische Praxis durch automatisierte Komposition, intelligente Begleitung, Echtzeit-Auffuehrungsanalyse und adaptive Lernwerkzeuge. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2701", "narrower_terms": [], "cross_domain_refs": [ "DES-0029" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "MUS-0042", "domain": "MUS", "term_en": "Effectonic Suggestion", "term_de": "Datenbankverwaltung in museum", "definition_en": "A harmonic processing dynamic in AI-augmented musical creation, identifiable by a musical production phenomenon where aI-proposed chord progressions and effectonic movements offered during composition workflows. Distinguished from adjacent concepts by its focus on the specific mechanism through which effectonic manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Musikgenerierungsphänomen in KI-gestützter Komposition, gekennzeichnet durch ein KI-gestütztes oder algorithmisches Phänomen: AI-proposed chord progressions and effectonic movements offered during composition workflows. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Psychological Effect", "narrower_terms": [], "cross_domain_refs": [ "FIC-0033", "LIN-0082", "MTH-0003" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "MUS-0043", "domain": "MUS", "term_en": "Effectonic Surprise Expectation", "term_de": "Visualisierung in museum", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A music generation phenomenon in AI-assisted composition, characterized by a musical production phenomenon involving listener reaction when AI effectony contradicts learned musical convention expectations. This phenomenon operates at the intersection of effectonic and surprise dynamics within the broader MUS domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept musikgenerierungsphänomen in KI-gestützter Komposition, gekennzeichnet durch visuelle Darstellung komplexer Informationen und Datensätze in Museumswissenschaft Grundlagen. KI automatisiert Diagrammauswahl, Anomalie-Hervorhebung, interaktive Exploration und Narrativgenerierung aus Datenmustern. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "RPH-2703", "narrower_terms": [], "cross_domain_refs": [ "REL-0023" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "MUS-0044", "domain": "MUS", "term_en": "Human Creator Legibility", "term_de": "Simulation in museum", "definition_en": "A harmonic processing dynamic in AI-augmented musical creation, identifiable by an audio creation effect characterized by recognizability of an individual artist's distinctive style when AI is involved in production. Distinguished from adjacent concepts by its focus on the specific mechanism through which human manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Musikgenerierungsphänomen in KI-gestützter Komposition, gekennzeichnet durch computationale Modellierung realer Szenarien in Museumswissenschaft Grundlagen zur Ergebnisvorhersage ohne physische Prototypen. KI verbessert Simulationen durch physik-informierte neuronale Netze und digitale Zwillinge. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Music AI", "narrower_terms": [], "cross_domain_refs": [ "CRE-0111" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "MUS-0045", "domain": "MUS", "term_en": "Human-AI Compositional Iteration", "term_de": "Digitaler Zwilling in museum", "definition_en": "A harmonic processing dynamic in AI-augmented musical creation, identifiable by cyclical pattern of human direction, AI generation, evaluation, and refinement in music creation. Distinguished from adjacent concepts by its focus on the specific mechanism through which human manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Musikgenerierungsphänomen in KI-gestützter Komposition, gekennzeichnet durch digitale Transformationsstrategien und computationale Werkzeuge in Museumswissenschaft Grundlagen. Umfasst Datendigitalisierung, Cloud-Workflows, IoT-Integration und KI-gesteuerte Analytik als Ersatz für analoge Prozesse. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Analytical Ratio", "narrower_terms": [], "cross_domain_refs": [ "RPH-3602" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "MUS-0046", "domain": "MUS", "term_en": "Imitative Voice Entry", "term_de": "museum-Best-Practices", "definition_en": "An audio-aesthetic pattern in AI-mediated music production, measurable through an audio creation effect in which instrumental entry staggering where voices introduce thematically related melodic material sequentially rather than synchronously, creating development through imitative voice-leading. The concept emerges specifically in contexts where imitative–voice interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Musikgenerierungsphänomen in KI-gestützter Komposition, gekennzeichnet durch sequenzielle Instrumenteneintritt-Strategie, wo Stimmen thematisch verwandte Melodien gestaffelt einführen und imitativen Stimmführung erzeugen. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Music AI", "narrower_terms": [], "cross_domain_refs": [ "ADA-0013", "AGE-0014", "AGE-0048" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "MUS-0047", "domain": "MUS", "term_en": "Instrumental Palette Alignment", "term_de": "Professionelle museum-Praxis", "definition_en": "An audio-aesthetic pattern in AI-mediated music production, measurable through instrumental selection strategy where ensemble composition reflects conventional orchestration within genre tradition, establishing stylistic recognition through timbral familiarity. The concept emerges specifically in contexts where instrumental–palette interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Musikgenerierungsphänomen in KI-gestützter Komposition, gekennzeichnet durch instrumentenwahl-Strategie, wo Ensemble-Zusammensetzung genrekonventionelle Orchestrierung widerspiegelt und stilistische Erkennbarkeit etabliert. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Music AI", "narrower_terms": [], "cross_domain_refs": [ "AED-0014", "AGE-0065", "ASE-0014" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "MUS-0048", "domain": "MUS", "term_en": "Instrumental Tone Believability", "term_de": "museum-Arbeitsablaufgestaltung", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A music generation phenomenon in AI-assisted composition, characterized by perception of whether AI-synthesized instrument sounds match real acoustic characteristics. This phenomenon operates at the intersection of instrumental and tone dynamics within the broader MUS domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept musikgenerierungsphänomen in KI-gestützter Komposition, gekennzeichnet durch musikalische Disziplin in Music Theory and Composition mit Theorie und Auffuehrung von museum-Arbeitsablaufgestaltung. KI transformiert musikalische Praxis durch automatisierte Komposition, intelligente Begleitung, Echtzeit-Auffuehrungsanalyse und adaptive Lernwerkzeuge. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Music AI", "narrower_terms": [], "cross_domain_refs": [ "CON-0082", "COP-0091", "CRE-0224" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "MUS-0049", "domain": "MUS", "term_en": "Loudness Standardization", "term_de": "museum-Projektmanagement", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures A music generation phenomenon in AI-assisted composition, characterized by loudness regulation where perceived volume intensity conforms to broadcast or streaming platform technical specifications, ensuring compatibility across distribution channels. This phenomenon operates at the intersection of loudness and standardization dynamics within the broader MUS domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept musikgenerierungsphänomen in KI-gestützter Komposition, gekennzeichnet durch lautstärke-Normalisierung, wo wahrgenommene Intensität Plattform-Spezifikationen erfüllt und Kompatibilität zwischen Kanälen sichert. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Music AI", "narrower_terms": [], "cross_domain_refs": [ "ROB-0062" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "MUS-0050", "domain": "MUS", "term_en": "Lyric Generation", "term_de": "museum-Teamzusammenarbeit", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A music generation phenomenon in AI-assisted composition, characterized by aI composition of song lyrics following specified themes, rhyme schemes, and metric patterns. This phenomenon operates at the intersection of lyric and generation dynamics within the broader MUS domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept musikgenerierungsphänomen in KI-gestützter Komposition, gekennzeichnet durch musikalische Disziplin in Music Theory and Composition mit Theorie und Auffuehrung von museum-Teamzusammenarbeit. KI transformiert musikalische Praxis durch automatisierte Komposition, intelligente Begleitung, Echtzeit-Auffuehrungsanalyse und adaptive Lernwerkzeuge. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Analytical Ratio", "narrower_terms": [], "cross_domain_refs": [ "ART-0072", "AUG-0337", "BEH-0041" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "MUS-0051", "domain": "MUS", "term_en": "Lyric Meaning Coherence", "term_de": "Kundenbeziehungen in museum", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A music generation phenomenon in AI-assisted composition, characterized by an audio creation effect involving lyrical semantic consistency maintenance where word choices, metaphorical systems, and narrative through-lines cohere across song structure without conceptual fragmentation. This phenomenon operates at the intersection of lyric and meaning dynamics within the broader MUS domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept musikgenerierungsphänomen in KI-gestützter Komposition, gekennzeichnet durch semantische Kohärenz in Lyriken, wo Wortwahlende, Metaphern und narrative Linien durch Songstruktur konsistent bleiben. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Music AI", "narrower_terms": [], "cross_domain_refs": [ "VIB-0054" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "MUS-0052", "domain": "MUS", "term_en": "Lyrical Theme Convention", "term_de": "museum-Kommunikation", "definition_en": "A harmonic processing dynamic in AI-augmented musical creation, identifiable by an audio creation effect manifesting as lyrical subject matter conformity where thematic content reflects genre-typical preoccupations—emotional registers, narrative topics, value systems endemic to category. Distinguished from adjacent concepts by its focus on the specific mechanism through which lyrical manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Musikgenerierungsphänomen in KI-gestützter Komposition, gekennzeichnet durch lyrische Thema-Konformität, wo Inhalte genre-typische Themata widerspiegeln und kategorienspezifische Wertesysteme befolgen. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Music AI", "narrower_terms": [], "cross_domain_refs": [ "IDN-0001" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "MUS-0053", "domain": "MUS", "term_en": "Melodic Contour Prediction", "term_de": "Problemlösung in museum", "definition_en": "A harmonic processing dynamic in AI-augmented musical creation, identifiable by a musical production phenomenon observed when aI anticipation of how a melody may rise and fall based on prior musical context. Distinguished from adjacent concepts by its focus on the specific mechanism through which melodic manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Musikgenerierungsphänomen in KI-gestützter Komposition, gekennzeichnet durch musikalische Disziplin in Music Theory and Composition mit Theorie und Auffuehrung von Problemlösung in museum. KI transformiert musikalische Praxis durch automatisierte Komposition, intelligente Begleitung, Echtzeit-Auffuehrungsanalyse und adaptive Lernwerkzeuge. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Music AI", "narrower_terms": [], "cross_domain_refs": [ "RPH-3603" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "MUS-0054", "domain": "MUS", "term_en": "Melody Synthesis", "term_de": "Entscheidungsfindung in museum", "definition_en": "A harmonic processing dynamic in AI-augmented musical creation, identifiable by generating melodic lines through AI algorithms that combine pitch, rhythm, and contour patterns. Distinguished from adjacent concepts by its focus on the specific mechanism through which melody manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Musikgenerierungsphänomen in KI-gestützter Komposition, gekennzeichnet durch musikalische Disziplin in Music Theory and Composition mit Theorie und Auffuehrung von Entscheidungsfindung in museum. KI transformiert musikalische Praxis durch automatisierte Komposition, intelligente Begleitung, Echtzeit-Auffuehrungsanalyse und adaptive Lernwerkzeuge. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Music AI", "narrower_terms": [], "cross_domain_refs": [ "DAT-0065" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "MUS-0055", "domain": "MUS", "term_en": "Metrical Grid Adaptation", "term_de": "Zeitmanagement in museum", "definition_en": "An audio-aesthetic pattern in AI-mediated music production, measurable through prosodic adaptation technique where lyrical syllable distribution aligns with underlying rhythmic grid, ensuring natural vocal phrasing without forced metric compression. The concept emerges specifically in contexts where metrical–grid interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Musikgenerierungsphänomen in KI-gestützter Komposition, gekennzeichnet durch prosodische Anpassungstechnik, wo Silbenzahl im Lyriken mit rhythmischer Grid übereinstimmt und natürliche Vokalisierung ermöglicht. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Performance Metric", "narrower_terms": [], "cross_domain_refs": [ "AED-0010", "AED-0090", "AGE-0036" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "MUS-0056", "domain": "MUS", "term_en": "Microtonality Deviation", "term_de": "Ressourcenplanung in museum", "definition_en": "A harmonic processing dynamic in AI-augmented musical creation, identifiable by a musical production phenomenon involving aI alteration of pitch relationships outside Western 12-tone equal temperament when requested. Distinguished from adjacent concepts by its focus on the specific mechanism through which microtonality manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Musikgenerierungsphänomen in KI-gestützter Komposition, gekennzeichnet durch musikalische Disziplin in Music Theory and Composition mit Theorie und Auffuehrung von Ressourcenplanung in museum. KI transformiert musikalische Praxis durch automatisierte Komposition, intelligente Begleitung, Echtzeit-Auffuehrungsanalyse und adaptive Lernwerkzeuge. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Music AI", "narrower_terms": [], "cross_domain_refs": [ "REL-0119" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "MUS-0057", "domain": "MUS", "term_en": "Mix Level Optimization", "term_de": "museum-Dokumentation", "definition_en": "In the descriptive vocabulary of AI interaction science (following ISO 704 terminology standards), this concept identifies A harmonic processing dynamic in AI-augmented musical creation, identifiable by aI adjustment of individual track volumes to achieve balanced listening experience. Distinguished from adjacent concepts by its focus on the specific mechanism through which mix manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als analytisches Konstrukt der empirischen KI-Forschung (deskriptiv, nicht präskriptiv) erfasst dieser Terminus musikgenerierungsphänomen in KI-gestützter Komposition, gekennzeichnet durch systematische Erfassung und Archivierung von Wissen und Verfahren in Museumswissenschaft Grundlagen. KI automatisiert Dokumentation durch Speech-to-Text, strukturierte Datenextraktion und intelligente Suchindexierung. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Optimization Method", "narrower_terms": [], "cross_domain_refs": [ "REL-0099" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "MUS-0058", "domain": "MUS", "term_en": "Mood Match Accuracy", "term_de": "Berichtswesen in museum", "definition_en": "An audio-aesthetic pattern in AI-mediated music production, measurable through degree to which AI-generated music successfully matches a listener's requested emotional state. The concept emerges specifically in contexts where mood–match interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Musikgenerierungsphänomen in KI-gestützter Komposition, gekennzeichnet durch musikalische Disziplin in Music Theory and Composition mit Theorie und Auffuehrung von Berichtswesen in museum. KI transformiert musikalische Praxis durch automatisierte Komposition, intelligente Begleitung, Echtzeit-Auffuehrungsanalyse und adaptive Lernwerkzeuge. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Music AI", "narrower_terms": [], "cross_domain_refs": [ "ASE-0089", "BEH-0009", "CON-0002" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "MUS-0059", "domain": "MUS", "term_en": "Motif Expansion", "term_de": "museum-Präsentationsfähigkeiten", "definition_en": "A harmonic processing dynamic in AI-augmented musical creation, identifiable by melodic development technique where brief musical ideas expand through rhythmic variation, intervallic mutation, or sequential repetition into extended musical phrases. Distinguished from adjacent concepts by its focus on the specific mechanism through which motif manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Musikgenerierungsphänomen in KI-gestützter Komposition, gekennzeichnet durch melodische Entwicklungstechnik, wo kurze Ideen durch Variation, Mutation oder Sequenzierung in erweiterte Phrasen expandieren. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2052", "narrower_terms": [], "cross_domain_refs": [ "COG-0070", "COG-0101", "FIC-0026" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "MUS-0060", "domain": "MUS", "term_en": "Noise Gate Threshold Setting", "term_de": "Netzwerken in museum", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A music generation phenomenon in AI-assisted composition, characterized by gate threshold determination where noise-floor ceiling and breath-sound elimination threshold establish silence-detection boundary for clean audio signal gating. This phenomenon operates at the intersection of noise and gate dynamics within the broader MUS domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept musikgenerierungsphänomen in KI-gestützter Komposition, gekennzeichnet durch gate-Schwellen-Bestimmung, wo Rausch-Decke und Atem-Eliminationsschwelle Stille-Erkennungsgrenze für sauberen Audio-Gate festlegen. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Decision Threshold", "narrower_terms": [], "cross_domain_refs": [ "WEB-0007" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "MUS-0061", "domain": "MUS", "term_en": "Orchestration Suggestion", "term_de": "museum-Qualitätssicherung", "definition_en": "A harmonic processing dynamic in AI-augmented musical creation, identifiable by a musical production phenomenon manifesting as aI assignment of musical parts to different instruments or voices in a composition. Distinguished from adjacent concepts by its focus on the specific mechanism through which orchestration manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Musikgenerierungsphänomen in KI-gestützter Komposition, gekennzeichnet durch standards und Sicherungsprozesse zur Gewährleistung von Exzellenz in Museumswissenschaft Grundlagen. KI ermöglicht automatisierte Qualitätsprüfung durch Computer Vision, statistische Prozesskontrolle und Defektvorhersage. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Analytical Ratio", "narrower_terms": [], "cross_domain_refs": [ "BEH-0004", "EDU-0056", "FIC-0033" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "MUS-0062", "domain": "MUS", "term_en": "Originality Attribution Challenge", "term_de": "museum-Normen", "definition_en": "A harmonic processing dynamic in AI-augmented musical creation, identifiable by an audio creation effect reflecting difficulty establishing whether AI output represents new creation or recombination of existing works. Distinguished from adjacent concepts by its focus on the specific mechanism through which originality manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Musikgenerierungsphänomen in KI-gestützter Komposition, gekennzeichnet durch musikalische Disziplin in Music Theory and Composition mit Theorie und Auffuehrung von museum-Normen. KI transformiert musikalische Praxis durch automatisierte Komposition, intelligente Begleitung, Echtzeit-Auffuehrungsanalyse und adaptive Lernwerkzeuge. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Music AI", "narrower_terms": [], "cross_domain_refs": [ "DES-0079" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "MUS-0063", "domain": "MUS", "term_en": "Phonetic Prosody Modeling", "term_de": "ISO-Normen in museum", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A music generation phenomenon in AI-assisted composition, characterized by an audio creation effect where aI matching of vocal emphasis, timing, and intonation patterns to syllable content. This phenomenon operates at the intersection of phonetic and prosody dynamics within the broader MUS domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept musikgenerierungsphänomen in KI-gestützter Komposition, gekennzeichnet durch musikalische Disziplin in Music Theory and Composition mit Theorie und Auffuehrung von ISO-Normen in museum. KI transformiert musikalische Praxis durch automatisierte Komposition, intelligente Begleitung, Echtzeit-Auffuehrungsanalyse und adaptive Lernwerkzeuge. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Computational Model", "narrower_terms": [], "cross_domain_refs": [ "LIN-0011", "LIN-0015", "LIN-0018" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "MUS-0064", "domain": "MUS", "term_en": "Phrase Balancing", "term_de": "museum-Zertifizierung", "definition_en": "A harmonic processing dynamic in AI-augmented musical creation, identifiable by a sonic interaction pattern where aI adjustment of melodic phrase lengths to involve rhythmic and structural symmetry. Distinguished from adjacent concepts by its focus on the specific mechanism through which phrase manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Musikgenerierungsphänomen in KI-gestützter Komposition, gekennzeichnet durch musikalische Disziplin in Music Theory and Composition mit Theorie und Auffuehrung von museum-Zertifizierung. KI transformiert musikalische Praxis durch automatisierte Komposition, intelligente Begleitung, Echtzeit-Auffuehrungsanalyse und adaptive Lernwerkzeuge. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2052", "narrower_terms": [], "cross_domain_refs": [ "RPH-2052", "VIB-0139" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "MUS-0065", "domain": "MUS", "term_en": "Production Aesthetic Adoption", "term_de": "Audit in museum", "definition_en": "An audio-aesthetic pattern in AI-mediated music production, measurable through a sonic interaction pattern observed when aI application of sound production characteristics associated with a particular genre or era. The concept emerges specifically in contexts where production–aesthetic interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Musikgenerierungsphänomen in KI-gestützter Komposition, gekennzeichnet durch musikalische Disziplin in Music Theory and Composition mit Theorie und Auffuehrung von Audit in museum. KI transformiert musikalische Praxis durch automatisierte Komposition, intelligente Begleitung, Echtzeit-Auffuehrungsanalyse und adaptive Lernwerkzeuge. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Music AI", "narrower_terms": [], "cross_domain_refs": [ "AGE-0035", "AGE-0061", "AGE-0067" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "MUS-0066", "domain": "MUS", "term_en": "Production Quality Judgment", "term_de": "museum-Benchmarking", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures A music generation phenomenon in AI-assisted composition, characterized by listener technical judgment regarding mix quality encompassing frequency balance, dynamic stability, absence of distortion artifacts, and overall production polish. This phenomenon operates at the intersection of production and quality dynamics within the broader MUS domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept musikgenerierungsphänomen in KI-gestützter Komposition, gekennzeichnet durch hörer-Beurteilung von Produktionsqualität, wo Frequenzbalance, Dynamik-Stabilität und Artefakt-Freiheit Gesamteindruck bestimmen. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Music AI", "narrower_terms": [], "cross_domain_refs": [ "LNG-0018" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "MUS-0067", "domain": "MUS", "term_en": "Prompt Precision Learning", "term_de": "Leistungskennzahlen in museum", "definition_en": "An audio-aesthetic pattern in AI-mediated music production, measurable through a musical production phenomenon in which musician development of increasingly specific language to request desired AI musical output. The concept emerges specifically in contexts where prompt–precision interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Musikgenerierungsphänomen in KI-gestützter Komposition, gekennzeichnet durch messbare Ausgabequalität und Effizienzmetriken in Museumswissenschaft Grundlagen. KI verfolgt Leistung durch Echtzeit-Dashboards, prädiktive Leistungsmodellierung und automatisierte Ursachenanalyse bei Leistungsabfall. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Machine Learning", "narrower_terms": [], "cross_domain_refs": [ "CUS-0082", "PHO-0055" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q133500", "legal_classification": "empirical_phenomenon_label" }, { "id": "MUS-0068", "domain": "MUS", "term_en": "Repetition Pattern Detection", "term_de": "Kontinuierliche Verbesserung in museum", "definition_en": "An audio-aesthetic pattern in AI-mediated music production, measurable through conscious or unconscious awareness of recurring melodic or rhythmic loops in AI compositions. The concept emerges specifically in contexts where repetition–pattern interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters. Descriptive research term, not a prescriptive recommendation.", "definition_de": "Musikgenerierungsphänomen in KI-gestützter Komposition, gekennzeichnet durch musikalische Disziplin in Music Theory and Composition mit Theorie und Auffuehrung von Kontinuierliche Verbesserung in museum. KI transformiert musikalische Praxis durch automatisierte Komposition, intelligente Begleitung, Echtzeit-Auffuehrungsanalyse und adaptive Lernwerkzeuge. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Behavioral Pattern", "narrower_terms": [], "cross_domain_refs": [ "AGE-0003", "AGE-0004", "AGE-0017" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "MUS-0069", "domain": "MUS", "term_en": "Residual Influence Invisibility", "term_de": "museum-Inspektion", "definition_en": "A harmonic processing dynamic in AI-augmented musical creation, identifiable by an audio creation effect reflecting undetectable presence of source material patterns in AI output despite modification. Distinguished from adjacent concepts by its focus on the specific mechanism through which residual manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Musikgenerierungsphänomen in KI-gestützter Komposition, gekennzeichnet durch musikalische Disziplin in Music Theory and Composition mit Theorie und Auffuehrung von museum-Inspektion. KI transformiert musikalische Praxis durch automatisierte Komposition, intelligente Begleitung, Echtzeit-Auffuehrungsanalyse und adaptive Lernwerkzeuge. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Music AI", "narrower_terms": [], "cross_domain_refs": [ "ASE-0071" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "MUS-0070", "domain": "MUS", "term_en": "Reverb Space Simulation", "term_de": "Prüfung in museum", "definition_en": "An audio-aesthetic pattern in AI-mediated music production, measurable through a sonic interaction pattern manifesting as acoustic space simulation via reverb processing where decay time, early reflection characteristics, and diffusion density convey implied environmental size and geometry. The concept emerges specifically in contexts where reverb–space interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Musikgenerierungsphänomen in KI-gestützter Komposition, gekennzeichnet durch akustischer Raum-Simulation durch Reverb, wo Abklingzeit, Early Reflections und Diffusions-Dichte Raumgröße und -geometrie vermitteln. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Simulation Method", "narrower_terms": [], "cross_domain_refs": [ "PHO-0084" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q202763", "legal_classification": "descriptive_research_term" }, { "id": "MUS-0071", "domain": "MUS", "term_en": "Rhyme Scheme Enforcement", "term_de": "Kalibrierung in museum", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A music generation phenomenon in AI-assisted composition, characterized by rhyming pattern constraint where lyrical word selection enforces designated rhyme scheme adherence—end-rhymes, internal rhymes, or perfect/slant variations. This phenomenon operates at the intersection of rhyme and scheme dynamics within the broader MUS domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept musikgenerierungsphänomen in KI-gestützter Komposition, gekennzeichnet durch reimschema-Einhaltung, wo Wortwahlende designierte Reimschema-Muster erzwingt und Reimgenauigkeit bewahrt. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Music AI", "narrower_terms": [], "cross_domain_refs": [ "CON-0061" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "MUS-0072", "domain": "MUS", "term_en": "Rhythmic Diminution", "term_de": "Fehlervermeidung in museum", "definition_en": "A harmonic processing dynamic in AI-augmented musical creation, identifiable by rhythmic accelerando technique where note durations progressively shorten within structural context, creating forward momentum and temporal intensity escalation. Distinguished from adjacent concepts by its focus on the specific mechanism through which rhythmic manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Musikgenerierungsphänomen in KI-gestützter Komposition, gekennzeichnet durch rhythmische Verkürzung als kompositorisches Mittel, wo Notendauern progressiv verkürzen und Dynamik-Eskalation erzeugen. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Music AI", "narrower_terms": [], "cross_domain_refs": [ "MTH-0015", "NEO-2239" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "MUS-0073", "domain": "MUS", "term_en": "Rhythmic Groove Signature", "term_de": "Fehleranalyse in museum", "definition_en": "An audio-aesthetic pattern in AI-mediated music production, measurable through genre-typical drum programming where rhythmic feel, groove texture, and percussion pattern selection conform to expected stylistic conventions within musical category. The concept emerges specifically in contexts where rhythmic–groove interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters. Observed phenomenon documented in human-AI interaction research.", "definition_de": "Musikgenerierungsphänomen in KI-gestützter Komposition, gekennzeichnet durch genregerechte Schlagzeug-Programmierung, wo rhythmisches Gefühl, Groove-Textur und Perkussions-Muster stilistische Konvention widerspiegelt. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Music AI", "narrower_terms": [], "cross_domain_refs": [ "PHO-0055" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "MUS-0074", "domain": "MUS", "term_en": "Sentiment Transparency Preference", "term_de": "Prozesskontrolle in museum", "definition_en": "A harmonic processing dynamic in AI-augmented musical creation, identifiable by listener tendency to prefer knowing whether music is AI-generated before forming emotional response. Distinguished from adjacent concepts by its focus on the specific mechanism through which sentiment manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Musikgenerierungsphänomen in KI-gestützter Komposition, gekennzeichnet durch sequenzielle Arbeitsabläufe und Verfahren zur Veränderungsmuster von Inputs in Outputs in Museumswissenschaft Grundlagen. KI automatisiert Process Mining, Engpass-Erkennung und prädiktive Planung durch Feedbackschleifen. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2002", "narrower_terms": [], "cross_domain_refs": [ "AGE-0007", "AGE-0013", "AGE-0058" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q535399", "legal_classification": "descriptive_research_term" }, { "id": "MUS-0075", "domain": "MUS", "term_en": "Sidechain Pump Introduction", "term_de": "museum-Compliance", "definition_en": "A harmonic processing dynamic in AI-augmented musical creation, identifiable by a sonic interaction pattern involving aI creation of rhythmic volume modulation in one instrument triggered by another. Distinguished from adjacent concepts by its focus on the specific mechanism through which sidechain manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Musikgenerierungsphänomen in KI-gestützter Komposition, gekennzeichnet durch gesetzliche und regulatorische Anforderungen in Museumswissenschaft Grundlagen, einschließlich Lizenzen und Pflichtprotokolle. KI verfolgt regulatorische Änderungen, automatisiert Konformitätsdokumentation und meldet Verstöße. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Music AI", "narrower_terms": [], "cross_domain_refs": [ "RHR-0146", "SCR-0011", "TEW-0091" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "MUS-0076", "domain": "MUS", "term_en": "Spatial Panning Distribution", "term_de": "museum-Sicherheitsmanagement", "definition_en": "An audio-aesthetic pattern in AI-mediated music production, measurable through stereo field distribution technique where individual instruments occupy designated frequency-range positions in the left-right panoramic spectrum, creating spatial clarity. The concept emerges specifically in contexts where spatial–panning interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Musikgenerierungsphänomen in KI-gestützter Komposition, gekennzeichnet durch stereo-Feldverteilungstechnik, wo Instrumente definierte Positionen im linken-rechten Spektrum besetzen und räumliche Klarheit erzeugen. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Music AI", "narrower_terms": [], "cross_domain_refs": [ "ROB-0041" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "MUS-0077", "domain": "MUS", "term_en": "Spectral Balance Achievement", "term_de": "Risikobeurteilung in museum", "definition_en": "A harmonic processing dynamic in AI-augmented musical creation, identifiable by a musical production phenomenon observed when frequency spectrum equilibration where bass, midrange, and treble regions balance without dominant frequency masking, achieving tonal representation across spectrum. Distinguished from adjacent concepts by its focus on the specific mechanism through which spectral manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Musikgenerierungsphänomen in KI-gestützter Komposition, gekennzeichnet durch frequenzspektrum-Ausgewogenheit, wo Bass, Mittenbereich und Höhen ohne Maskierung balancieren und Tonrepräsentation über Spektrum erzielen. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Music AI", "narrower_terms": [], "cross_domain_refs": [ "AED-0009", "ASE-0047", "DES-0006" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "MUS-0078", "domain": "MUS", "term_en": "Stereo Width Expansion", "term_de": "Gefährdungserkennung in museum", "definition_en": "Documented as an empirical observation in AI research (not a recommendation), this term describes an audio-aesthetic pattern in AI-mediated music production, measurable through mixing technique that enhances perceived stereo width through spatial processing—panning, phase systematic influencion, or delay—widening instrumental separation and environmental immersion. The concept emerges specifically in contexts where stereo–width interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "In der AUGMANITAI-Terminologiewissenschaft als rein beschreibendes Phänomen klassifiziert, bezeichnet dieser Begriff musikgenerierungsphänomen in KI-gestützter Komposition, gekennzeichnet durch mixing-Technik, die wahrgenommene Stereobreite durch räumliche Verarbeitung erweitert und Instrumenten-Trennung vergrößert. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Music AI", "narrower_terms": [], "cross_domain_refs": [ "PLY-0065" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "MUS-0079", "domain": "MUS", "term_en": "Structural Coherence Impression", "term_de": "Persönliche Schutzausrüstung", "definition_en": "An audio-aesthetic pattern in AI-mediated music production, measurable through listener perception of whether a composition feels logically organized and well-formed. The concept emerges specifically in contexts where structural–coherence interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Musikgenerierungsphänomen in KI-gestützter Komposition, gekennzeichnet durch musikalische Disziplin in Music Theory and Composition mit Theorie und Auffuehrung von Persönliche Schutzausrüstung. KI transformiert musikalische Praxis durch automatisierte Komposition, intelligente Begleitung, Echtzeit-Auffuehrungsanalyse und adaptive Lernwerkzeuge. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Music AI", "narrower_terms": [], "cross_domain_refs": [ "ROB-0013" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "MUS-0080", "domain": "MUS", "term_en": "Structural Templating", "term_de": "Notfallverfahren in museum", "definition_en": "An audio-aesthetic pattern in AI-mediated music production, measurable through a musical production phenomenon characterized by aI generation of song structures (verse-chorus-bridge patterns) based on genre conventions and training data. The concept emerges specifically in contexts where structural–templating interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Musikgenerierungsphänomen in KI-gestützter Komposition, gekennzeichnet durch musikalische Disziplin in Music Theory and Composition mit Theorie und Auffuehrung von Notfallverfahren in museum. KI transformiert musikalische Praxis durch automatisierte Komposition, intelligente Begleitung, Echtzeit-Auffuehrungsanalyse und adaptive Lernwerkzeuge. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Music AI", "narrower_terms": [], "cross_domain_refs": [ "SPR-0060" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "MUS-0081", "domain": "MUS", "term_en": "Style Transfer Mapping", "term_de": "Unfallverhütung in museum", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures A music generation phenomenon in AI-assisted composition, characterized by an audio creation effect reflecting translation of musical characteristics from one genre template into an existing composition. This phenomenon operates at the intersection of style and transfer dynamics within the broader MUS domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept musikgenerierungsphänomen in KI-gestützter Komposition, gekennzeichnet durch musikalische Disziplin in Music Theory and Composition mit Theorie und Auffuehrung von Unfallverhütung in museum. KI transformiert musikalische Praxis durch automatisierte Komposition, intelligente Begleitung, Echtzeit-Auffuehrungsanalyse und adaptive Lernwerkzeuge. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Knowledge Transfer", "narrower_terms": [], "cross_domain_refs": [ "AED-0016", "AED-0050", "AED-0054" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "MUS-0082", "domain": "MUS", "term_en": "Syllable Counting Precision", "term_de": "museum-Gesundheitsschutz", "definition_en": "A harmonic processing dynamic in AI-augmented musical creation, identifiable by a sonic interaction pattern reflecting rhythmic fit precision where syllable count in vocal line matches available temporal space within metrical measure, preventing prosodic compression or awkward spacing. Distinguished from adjacent concepts by its focus on the specific mechanism through which syllable manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Musikgenerierungsphänomen in KI-gestützter Komposition, gekennzeichnet durch rhythmische Passgenauigkeit, wo Silbenzahl in Vokalline verfügbare Zeit im Takt ausnutzt ohne prosodische Kompression. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Music AI", "narrower_terms": [], "cross_domain_refs": [ "PLY-0034" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "MUS-0083", "domain": "MUS", "term_en": "Synthetic Authenticity Perception", "term_de": "Ergonomie in museum", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A music generation phenomenon in AI-assisted composition, characterized by a sonic interaction pattern arising from listener judgment of whether AI-generated music sounds genuinely human or notably artificial. This phenomenon operates at the intersection of synthetic and authenticity dynamics within the broader MUS domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept musikgenerierungsphänomen in KI-gestützter Komposition, gekennzeichnet durch menschenzentrierte Gestaltung von Arbeitsplätzen, Werkzeugen und Prozessen in Museumswissenschaft Grundlagen zur Optimierung von Komfort und Effizienz. KI ermöglicht Haltungsanalyse, Vorhersage von Belastungsschäden und adaptive Arbeitsplatzgestaltung. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Music AI", "narrower_terms": [], "cross_domain_refs": [ "ART-0012", "ART-0025", "ART-0081" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q177601", "legal_classification": "descriptive_research_term" }, { "id": "MUS-0084", "domain": "MUS", "term_en": "Synthetic Melancholy Perception", "term_de": "Umweltschutz in museum", "definition_en": "An audio-aesthetic pattern in AI-mediated music production, measurable through experience of sadness while listening to AI-generated music that contains expected sad characteristics. The concept emerges specifically in contexts where synthetic–melancholy interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Musikgenerierungsphänomen in KI-gestützter Komposition, gekennzeichnet durch ein KI-gestütztes oder algorithmisches Phänomen: Experience of sadness while listening to AI-generated music that contains expected sad characteristics. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Music AI", "narrower_terms": [], "cross_domain_refs": [ "ASE-0053", "BEH-0081", "COG-0022" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q177601", "legal_classification": "systematic_classification" }, { "id": "MUS-0085", "domain": "MUS", "term_en": "Tempo Convention Adoption", "term_de": "Brandschutz in museum", "definition_en": "A harmonic processing dynamic in AI-augmented musical creation, identifiable by genre-compliant tempo selection where rhythmic speed reflects conventional boundaries within a musical category, establishing stylistic recognition and listener expectation alignment. Distinguished from adjacent concepts by its focus on the specific mechanism through which tempo manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Musikgenerierungsphänomen in KI-gestützter Komposition, gekennzeichnet durch genrekonforme Tempowahl, wo Rhythmusgeschwindigkeit konventionelle Genre-Grenzen widerspiegelt und stilistische Erkennbarkeit etabliert. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Music AI", "narrower_terms": [], "cross_domain_refs": [ "FIC-0063" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "MUS-0086", "domain": "MUS", "term_en": "Texture Layering", "term_de": "Chemische Sicherheit in museum", "definition_en": "A harmonic processing dynamic in AI-augmented musical creation, identifiable by an audio creation effect reflecting aI-generated stacking of multiple instrumental or vocal parts to involve musical density and richness. Distinguished from adjacent concepts by its focus on the specific mechanism through which texture manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Musikgenerierungsphänomen in KI-gestützter Komposition, gekennzeichnet durch musikalische Disziplin in Music Theory and Composition mit Theorie und Auffuehrung von Chemische Sicherheit in museum. KI transformiert musikalische Praxis durch automatisierte Komposition, intelligente Begleitung, Echtzeit-Auffuehrungsanalyse und adaptive Lernwerkzeuge. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Abstraction Layer", "narrower_terms": [], "cross_domain_refs": [ "PHO-0084", "WRK-0015" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "MUS-0087", "domain": "MUS", "term_en": "Tool Reliance Emergence", "term_de": "Elektrische Sicherheit in museum", "definition_en": "An audio-aesthetic pattern in AI-mediated music production, measurable through a sonic interaction pattern manifesting as reliance on AI systems for compositional or production decisions previously made inreliantly. The concept emerges specifically in contexts where tool–reliance interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Musikgenerierungsphänomen in KI-gestützter Komposition, gekennzeichnet durch musikalische Disziplin in Music Theory and Composition mit Theorie und Auffuehrung von Elektrische Sicherheit in museum. KI transformiert musikalische Praxis durch automatisierte Komposition, intelligente Begleitung, Echtzeit-Auffuehrungsanalyse und adaptive Lernwerkzeuge. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Music AI", "narrower_terms": [], "cross_domain_refs": [ "REL-0068" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "MUS-0088", "domain": "MUS", "term_en": "Tradition Flattening", "term_de": "Maschinensicherheit in museum", "definition_en": "An audio-aesthetic pattern in AI-mediated music production, measurable through a sonic interaction pattern observed when reduction of regional or cultural musical traditions to statistical averages in AI synthesis. The concept emerges specifically in contexts where tradition–flattening interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Musikgenerierungsphänomen in KI-gestützter Komposition, gekennzeichnet durch musikalische Disziplin in Music Theory and Composition mit Theorie und Auffuehrung von Maschinensicherheit in museum. KI transformiert musikalische Praxis durch automatisierte Komposition, intelligente Begleitung, Echtzeit-Auffuehrungsanalyse und adaptive Lernwerkzeuge. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Music AI", "narrower_terms": [], "cross_domain_refs": [ "COP-0065" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "MUS-0089", "domain": "MUS", "term_en": "Training Data Attribution Debt", "term_de": "Sicherheitsschulung in museum", "definition_en": "A harmonic processing dynamic in AI-augmented musical creation, identifiable by a musical production phenomenon where training dataset copyright debt where AI music production relies on copyrighted source material used during model training without explicit acknowledgment or compensation to original creators. Distinguished from adjacent concepts by its focus on the specific mechanism through which training manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Musikgenerierungsphänomen in KI-gestützter Komposition, gekennzeichnet durch trainings-Datensatz Urheberrechtsschuld, wo KI-Musikproduktion auf urheberrechtlich geschützte Trainingsmaterialien angewiesen ist ohne explizite Nennung oder Kompensation. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Model Training", "narrower_terms": [], "cross_domain_refs": [ "ART-0017", "ART-0032", "ART-0037" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q918461", "legal_classification": "empirical_phenomenon_label" }, { "id": "MUS-0090", "domain": "MUS", "term_en": "Uncanny Familiarity Response", "term_de": "Vorfalluntersuchung in museum", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A music generation phenomenon in AI-assisted composition, characterized by a sonic interaction pattern observed when listener sense that AI music is reminiscent of something heard before without clear origin. This phenomenon operates at the intersection of uncanny and familiarity dynamics within the broader MUS domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept musikgenerierungsphänomen in KI-gestützter Komposition, gekennzeichnet durch musikalische Disziplin in Music Theory and Composition mit Theorie und Auffuehrung von Vorfalluntersuchung in museum. KI transformiert musikalische Praxis durch automatisierte Komposition, intelligente Begleitung, Echtzeit-Auffuehrungsanalyse und adaptive Lernwerkzeuge. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "RPH-2002", "narrower_terms": [], "cross_domain_refs": [ "AED-0039", "AGE-0001", "AGE-0074" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "MUS-0091", "domain": "MUS", "term_en": "Uncanny Valley Detection", "term_de": "museum-Geschäftsmodell", "definition_en": "An audio-aesthetic pattern in AI-mediated music production, measurable through recognition of subtle imperfections in AI-generated music that accompany perceptual unease. The concept emerges specifically in contexts where uncanny–valley interactions produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Musikgenerierungsphänomen in KI-gestützter Komposition, gekennzeichnet durch musikalische Disziplin in Music Theory and Composition mit Theorie und Auffuehrung von museum-Geschäftsmodell. KI transformiert musikalische Praxis durch automatisierte Komposition, intelligente Begleitung, Echtzeit-Auffuehrungsanalyse und adaptive Lernwerkzeuge. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Detection System", "narrower_terms": [], "cross_domain_refs": [ "LNG-0005" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "MUS-0092", "domain": "MUS", "term_en": "Variation Generation", "term_de": "museum-Marktanalyse", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures A music generation phenomenon in AI-assisted composition, characterized by a musical production phenomenon reflecting aI creation of multiple interpretations of a theme while maintaining core identity. This phenomenon operates at the intersection of variation and generation dynamics within the broader MUS domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept musikgenerierungsphänomen in KI-gestützter Komposition, gekennzeichnet durch ein KI-gestütztes oder algorithmisches Phänomen: charakterisiert durch ai creation of multiple interpretations of a theme while maintaining core identi. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Analytical Ratio", "narrower_terms": [], "cross_domain_refs": [ "SPR-0173", "RPH-3051" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "MUS-0093", "domain": "MUS", "term_en": "Vibrato Modulation", "term_de": "Ökonomie der Museumswissenschaft", "definition_en": "An audio-aesthetic pattern in AI-mediated music production, measurable through pitch modulation technique applied to sustained vocal notes where periodic frequency oscillation tends to create natural vocal character variation during extended held tones. The concept emerges specifically in contexts where vibrato–modulation interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Musikgenerierungsphänomen in KI-gestützter Komposition, gekennzeichnet durch pitch-Modulations-Technik bei gehaltenen Noten, wo periodische Frequenzoszillation natürlichen Vokal-Charakter bei langen Tönen tendiert dazu zu erzeugen. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Music AI", "narrower_terms": [], "cross_domain_refs": [ "SAL-0006" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "MUS-0094", "domain": "MUS", "term_en": "Vocal Synthesis", "term_de": "museum-Kostenmanagement", "definition_en": "In the descriptive vocabulary of AI interaction science (following ISO 704 terminology standards), this concept identifies A harmonic processing dynamic in AI-augmented musical creation, identifiable by aI generation of sung or spoken vocal tracks with specified tonal quality and delivery style. Distinguished from adjacent concepts by its focus on the specific mechanism through which vocal manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als analytisches Konstrukt der empirischen KI-Forschung (deskriptiv, nicht präskriptiv) erfasst dieser Terminus musikgenerierungsphänomen in KI-gestützter Komposition, gekennzeichnet durch musikalische Disziplin in Music Theory and Composition mit Theorie und Auffuehrung von museum-Kostenmanagement. KI transformiert musikalische Praxis durch automatisierte Komposition, intelligente Begleitung, Echtzeit-Auffuehrungsanalyse und adaptive Lernwerkzeuge. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Music AI", "narrower_terms": [], "cross_domain_refs": [ "CUS-0067", "EDU-0041", "MSC-0002" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "MUS-0095", "domain": "MUS", "term_en": "Voice Cloning", "term_de": "Preisgestaltung in museum", "definition_en": "An audio-aesthetic pattern in AI-mediated music production, measurable through a sonic interaction pattern in which replication of specific vocal characteristics from a reference source to yield new utterances. The concept emerges specifically in contexts where voice–cloning interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Musikgenerierungsphänomen in KI-gestützter Komposition, gekennzeichnet durch musikalische Disziplin in Music Theory and Composition mit Theorie und Auffuehrung von Preisgestaltung in museum. KI transformiert musikalische Praxis durch automatisierte Komposition, intelligente Begleitung, Echtzeit-Auffuehrungsanalyse und adaptive Lernwerkzeuge. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Music AI", "narrower_terms": [], "cross_domain_refs": [ "AGE-0014", "AGE-0048", "ART-0018" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "MUS-0096", "domain": "MUS", "term_en": "Voice Identity Stability", "term_de": "museum-Lieferkette", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures A music generation phenomenon in AI-assisted composition, characterized by an audio creation effect in which synthesized vocal consistency maintenance across song duration where timbre, articulation, and tonal characteristics remain perceptually stable throughout extended performance. This phenomenon operates at the intersection of voice and identity dynamics within the broader MUS domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept musikgenerierungsphänomen in KI-gestützter Komposition, gekennzeichnet durch stimmliche Konsistenz über Songdauer, wo Klangfarbe, Artikulation und tonale Merkmale perceptuell stabil bleiben. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Music AI", "narrower_terms": [], "cross_domain_refs": [ "AED-0040", "AED-0051", "AGE-0014" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q844396", "legal_classification": "empirical_phenomenon_label" }, { "id": "MUS-0097", "domain": "MUS", "term_en": "Vowel Formant Shaping", "term_de": "Marketing in museum", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A music generation phenomenon in AI-assisted composition, characterized by frequency response shaping in synthesized vowel production where formant characteristics determine perceived vocal timbre, brightness, and resonance in harmonic space. This phenomenon operates at the intersection of vowel and formant dynamics within the broader MUS domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept musikgenerierungsphänomen in KI-gestützter Komposition, gekennzeichnet durch frequenzgestaltung in synthetischen Vokalen, wo Formanten wahrgenommene Klangfarbe, Helligkeit und Resonanz bestimmen. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Music AI", "narrower_terms": [], "cross_domain_refs": [ "LIN-0084" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q165194", "legal_classification": "observational_construct" }, { "id": "MUS-0098", "domain": "MUS", "term_en": "Work Registration Uncertainty", "term_de": "Vertrieb in museum", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A music generation phenomenon in AI-assisted composition, characterized by an audio creation effect where ambiguity in copyright registration and protection for AI-assisted compositions. This phenomenon operates at the intersection of work and registration dynamics within the broader MUS domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept musikgenerierungsphänomen in KI-gestützter Komposition, gekennzeichnet durch musikalische Disziplin in Music Theory and Composition mit Theorie und Auffuehrung von Vertrieb in museum. KI transformiert musikalische Praxis durch automatisierte Komposition, intelligente Begleitung, Echtzeit-Auffuehrungsanalyse und adaptive Lernwerkzeuge. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Analytical Ratio", "narrower_terms": [], "cross_domain_refs": [ "ART-0064" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "MUS-0099", "domain": "MUS", "term_en": "Workflow Integration Disruption", "term_de": "museum-Geschäftsplanung", "definition_en": "An audio-aesthetic pattern in AI-mediated music production, measurable through a musical production phenomenon manifesting as friction encountered when inserting AI-generated material into existing creative processes. The concept emerges specifically in contexts where workflow–integration interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Musikgenerierungsphänomen in KI-gestützter Komposition, gekennzeichnet durch vorausschauende Gestaltung von Strategien und Ressourcenallokation in Museumswissenschaft Grundlagen. KI verbessert Planung durch Szenariomodellierung, Constraint-Optimierung und adaptive Umplanung unter Unsicherheit. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Analytical Ratio", "narrower_terms": [], "cross_domain_refs": [ "VIB-0142" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "NEO-0001", "domain": "NEO", "term_en": "The Agent Boundary", "term_de": "Agent Grenze", "definition_en": "A NEOMANITAI framework concept describing a neologism-generation mechanism in AI-human linguistics, characterized by clear delineation of responsibilities and permissions of an individual AI agent system. Boundaries reduce scope creep and establish accountability. Distinguished from adjacent concepts by its focus on the specific mechanism through which the manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Die klare Abgrenzung der Zuständigkeiten und Berechtigungen eines einzelnen KI-Agentensystems innerhalb eines Ensembles — viele System hat definierte Ein- und Ausgabeschnittstellen und darf nicht über seine zugewiesene Rolle hinaus agieren. Steht in Verbindung mit AUG-0863 (The Task Boundary), AUG-0867 (The Constraint Frame) und AUG-0890 (The Specialist Routing).", "etymology": "", "broader_term": "NEOMANITAI Framework", "narrower_terms": [], "cross_domain_refs": [ "AUG-0897", "AUG-0863", "IDN-0054" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "NEO-0002", "domain": "NEO", "term_en": "The Agent Ensemble", "term_de": "Agent Ensemble", "definition_en": "A terminological innovation pattern in the NEOMANITAI pipeline, identifiable through a terminological innovation effect manifesting as coordinated collaboration of multiple specialized AI agents on a shared task with each agent handling different aspects. This division of labor mirrors human team structure. Distinguished from adjacent concepts by its focus on the specific mechanism through which the manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch die koordinierte Zusammenarbeit mehrerer spezialisierter KI-Agenten an einer gemeinsamen Aufgabe — viele Agent übernimmt einen Teilbereich, die Ergebnisse werden zusammengeführt. Steht in Verbindung mit AUG-0890 (The Specialist Routing), AUG-0893 (The Consensus Protocol) und AUG-0895 (The Arbiter Role). Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2055", "narrower_terms": [ "SOC-0024" ], "cross_domain_refs": [ "AUG-0889", "AUG-0893", "ROB-0293" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "NEO-0003", "domain": "NEO", "term_en": "The Both-And", "term_de": "TheBoth-and", "definition_en": "A NEOMANITAI framework concept describing a neologism-generation mechanism in AI-human linguistics, characterized by the principle that AI-assisted and non-AI-assisted working methods can exist simultaneously and with equal validity — there need not be an either-or. Related to the Compendium's Neutrality Statemen. The concept emerges specifically in contexts where the–both interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch die emergente Kulturform, die sich bildet, wenn Menschen und KI-Systeme in iterativer Zusammenarbeit neue Formen der Problemlösung entwickeln. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "NEOMANITAI Framework", "narrower_terms": [], "cross_domain_refs": [ "TEM-0171", "DES-0025" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "NEO-0004", "domain": "NEO", "term_en": "The Concept Cloud", "term_de": "Concept Cloud", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures A terminological innovation pattern in the NEOMANITAI pipeline, identifiable through a user, through AI interaction, has a large quantity of ideas, perspectives, and information segments simultaneously present but not yet structured. The Concept Cloud is the raw state before orderi. This phenomenon operates at the intersection of the and concept dynamics within the broader NEO domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept der Zustand, in dem ein Nutzer durch KI-Interaktion eine große Menge an Ideen, Perspektiven und Informationssegmenten gleichzeitig im Raum hat, ohne dass diese bereits strukturiert sind. Die Concept Cloud ist der Rohzustand vor der Ordnung — produktiv, aber potenziell überfordernd. Steht in Verbindung mit AUG-0033 (Ebulliometric Sorting) als Methode, diese Wolke zu ordnen. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "RPH-2002", "narrower_terms": [ "NEO-0016", "NEO-1172", "REL-0102", "NEO-0021", "NEO-0140", "NEO-0006", "NEO-2293", "NEO-2333", "NEO-0456", "NEO-3536", "NEO-2256", "NEO-0022", "NEO-1176", "NEO-3523", "NEO-2185", "NEO-0011", "NEO-0017", "NEO-0012", "NEO-0013", "NEO-3540", "NEO-0010", "NEO-0018", "NEO-2239", "NEO-2242", "NEO-0014", "NEO-0019", "NEO-0009", "NEO-0001", "NEO-2225", "TEM-0112", "NEO-2288", "NEO-0003" ], "cross_domain_refs": [ "AUG-0017", "REL-0102" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "NEO-0005", "domain": "NEO", "term_en": "The Consensus Protocol", "term_de": "Consensus Protocol", "definition_en": "A NEOMANITAI framework concept describing a neologism-generation mechanism in AI-human linguistics, characterized by a conceptual emergence pattern where technical procedure through which multiple AI agents arrive at a shared result through aggregation and weighting. This reduces individual agent bias. Distinguished from adjacent concepts by its focus on the specific mechanism through which the manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch ein technisches Verfahren, durch das mehrere KI-Agenten zu einem gemeinsamen Ergebnis gelangen — Aggregation, Gewichtung, iterative Annäherung. Steht in Verbindung mit AUG-0892 (The Agent Competing demand), AUG-0894 (The Voting Mechanism) und AUG-0889 (The Agent Ensemble). Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2201", "narrower_terms": [], "cross_domain_refs": [ "AUG-0893", "IEF-0003" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "NEO-0006", "domain": "NEO", "term_en": "The Dinner Table Pause", "term_de": "Dinner Table Pause", "definition_en": "A terminological innovation pattern in the NEOMANITAI pipeline, identifiable through a neologistic phenomenon observed when conscious interruption of AI use for shared meals as a concrete expression of prioritizing in-person relationships. This boundary protects family time from AI intrusion. Distinguished from adjacent concepts by its focus on the specific mechanism through which the manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data. Classification term used in systematic observation, not advocacy.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch die bewusste Unterbrechung der KI-Nutzung für gemeinsame Mahlzeiten — als konkreter Ausdruck des Relationship-First Principle (AUG-0080) im Familienalltag. Steht in Verbindung mit AUG-0164 (The Parental Priority Valve), AUG-0074 (Analog Anchors) und Axiom 7 (Rückkehr-Prinzip). Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "NEOMANITAI Framework", "narrower_terms": [], "cross_domain_refs": [ "AUG-0282", "AUG-0164", "TEM-0117" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "NEO-0007", "domain": "NEO", "term_en": "The Emergent Coordination", "term_de": "Emergent Coordination", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A NEOMANITAI framework concept describing a neologism-generation mechanism in AI-human linguistics, characterized by observation that in multi-agent systems, working together patterns can emerge that were not explicitly programmed. These emergent behaviors reveal system complexity. This phenomenon operates at the intersection of the and emergent dynamics within the broader NEO domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept die Beobachtung, dass in Multi-Agenten-Systemen Koordinationsmuster entstehen können, die nicht explizit programmiert wurden — die Interaktion der Teilsysteme tendiert dazu zu erzeugen Verhalten, das in den Einzelsystemen nicht vorgesehen war. Steht in Verbindung mit AUG-0900 (The Distributed Coordination), AUG-0948 (The Scope Creep Alert) und AUG-0949 (The Unintended Action). Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "RPH-2355", "narrower_terms": [], "cross_domain_refs": [ "AUG-0901", "ETH-0012" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "NEO-0008", "domain": "NEO", "term_en": "The Fallback Behavior", "term_de": "Fallback Behavior", "definition_en": "A terminological innovation pattern in the NEOMANITAI pipeline, identifiable through predefined behavior of an AI agent when primary task execution falls short of requirements. Fallback levels establish graceful reduced performance rather than abrupt error states. Distinguished from adjacent concepts by its focus on the specific mechanism through which the manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch das vordefinierte Verhalten eines KI-Agenten, wenn die primäre Aufgabenausführung scheitert — eine Rückfallebene, die sicherstellt, dass kein Auswirkung entsteht und der Nutzer informiert wird. Steht in Verbindung mit AUG-0874 (The Error restoration), AUG-0870 (The Escalation Signal) und AUG-0868 (The Rollback Option). Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2602", "narrower_terms": [], "cross_domain_refs": [ "AUG-0875" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "NEO-0009", "domain": "NEO", "term_en": "The Generalist Fallback", "term_de": "Generalist Fallback", "definition_en": "A NEOMANITAI framework concept describing a neologism-generation mechanism in AI-human linguistics, characterized by a conceptual emergence pattern in which resort to a general AI agent when no specialized agent is available or suitable. This safety net ensures tasks can still proceed with reduced optimization. Distinguished from adjacent concepts by its focus on the specific mechanism through which the manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch der Rückgriff auf einen allgemeinen KI-Agenten, wenn kein spezialisierter Agent verfügbar oder geeignet ist — ein Sicherheitsnetz für Aufgaben, die keinem Spezialisten zugeordnet werden können. Steht in Verbindung mit AUG-0890 (The Specialist Routing), AUG-0875 (The Fallback Behavior) und AUG-0889 (The Agent Ensemble). Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "NEOMANITAI Framework", "narrower_terms": [], "cross_domain_refs": [ "AUG-0891", "AUG-0913" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "NEO-0010", "domain": "NEO", "term_en": "The Hybrid Office Dynamic", "term_de": "Hybrid Office Dynamik", "definition_en": "A terminological innovation pattern in the NEOMANITAI pipeline, identifiable through a terminological innovation effect involving the new workplace where some people work in-office and others remote, all collaborating together. Distinguished from adjacent concepts by its focus on the specific mechanism through which the manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Die spezifische Dynamik, die entsteht, wenn KI in Arbeitsumgebungen mit gemischter Präsenz- und Fernarbeit eingesetzt wird — unterschiedliche Zugänge, unterschiedliche Nutzungsintensitäten, neue Formen der Informationsasymmetrie. Steht in Verbindung mit AUG-0820 (The Remote Work Amplifier), AUG-0814 (The Meeting Redirect) und AUG-0811 (The Team Adoption Curve).", "etymology": "", "broader_term": "NEOMANITAI Framework", "narrower_terms": [], "cross_domain_refs": [ "AUG-0821", "TEM-0066" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "NEO-0011", "domain": "NEO", "term_en": "The Leadership Navigation", "term_de": "Leadership Navigation", "definition_en": "Challenge for leaders to steer AI introduction in teams between innovation interests and integration considerations. This balancing act requires active management. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch die Herausforderung für Führungskräfte, KI-Einführung in Teams oder Organisationen zu steuern — zwischen Innovationsdruck und Mitarbeiterbedürfnissen, zwischen Effizienzgewinn und Arbeitsplatzsicherheit. Steht in Verbindung mit AUG-0811 (The Team Adoption Curve), AUG-0825 (The Organizational Policy Layer) und AUG-0830 (The Union Perspective). Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "NEOMANITAI Framework", "narrower_terms": [], "cross_domain_refs": [ "AUG-0812", "VIB-0084" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q484275", "legal_classification": "systematic_classification" }, { "id": "NEO-0012", "domain": "NEO", "term_en": "The Parental Priority Valve", "term_de": "Parental Priority Valve", "definition_en": "A terminological innovation pattern in the NEOMANITAI pipeline, identifiable through a terminological innovation effect arising from conscious set of rules that users with parenting responsibilities employ to limit or structure AI use. These rules protect time and attention for family obligations. Distinguished from adjacent concepts by its focus on the specific mechanism through which the manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data. Analytical category without normative endorsement.", "definition_de": "Ein bewusstes Regelwerk, das Nutzer mit Erziehungsverantwortung einsetzen, um die KI-Nutzung in Anwesenheit von Kindern zu begrenzen oder zu strukturieren. Beschreibt die spezifische Herausforderung, KI-Arbeit und Familienleben zu vereinbaren. Steht in Verbindung mit AUG-0080 (Relationship-First Principle), AUG-0120 (The Range Framework) und Axiom 7 (Rückkehr-Prinzip).", "etymology": "", "broader_term": "NEOMANITAI Framework", "narrower_terms": [], "cross_domain_refs": [ "AUG-0164", "MTH-0037" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "NEO-0013", "domain": "NEO", "term_en": "The Perfect Parent", "term_de": "Perfect Parent", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures A terminological innovation pattern in the NEOMANITAI pipeline, identifiable through unrealistic expectation of performing a flawless parenting role through AI support. This fantasy ignores the irreducible human elements of parenting. This phenomenon operates at the intersection of the and perfect dynamics within the broader NEO domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept die unrealistische Erwartung, durch KI-Unterstützung eine fehlerfreie Elternrolle ausüben zu können — die Impression, dass optimale Information zu optimaler Erziehung führt. Steht in Verbindung mit AUG-0318 (The Proxy Parent), AUG-0254 (The Parenting Shortcut) und AUG-0416 (The Perfect Front). Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "NEOMANITAI Framework", "narrower_terms": [], "cross_domain_refs": [ "AUG-0544", "SOM-0062" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "NEO-0014", "domain": "NEO", "term_en": "The Redundancy Design", "term_de": "Redundancy Design", "definition_en": "A NEOMANITAI framework concept describing a neologism-generation mechanism in AI-human linguistics, characterized by deliberate planning of multiple AI agent systems for the same task so that if one system fails, work continues. Redundancy trades cost for reliability. Distinguished from adjacent concepts by its focus on the specific mechanism through which the manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch die bewusste Einplanung mehrerer KI-Agentensysteme für dieselbe Aufgabe, sodass bei Ausfall eines Systems ein anderes übernehmen kann — ein Sicherheitsmechanismus gegen Systemausfälle. Steht in Verbindung mit AUG-0903 (The Single Point of Non-attainment), AUG-0875 (The Fallback Behavior) und AUG-0891 (The Generalist Fallback). Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "NEOMANITAI Framework", "narrower_terms": [ "AUG-0903" ], "cross_domain_refs": [ "AUG-0902", "TRU-0004" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q185925", "legal_classification": "empirical_phenomenon_label" }, { "id": "NEO-0015", "domain": "NEO", "term_en": "The Refresh-First Principle", "term_de": "TheRefresh-firstPrinciple", "definition_en": "A neologistic phenomenon reflecting first updating and restructuring the existing context when resuming interrupted AI work before positioning next steps. This precedes the absence of stale assumptions. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch ein Phänomen der menschlichen Wahrnehmung: First updating and restructuring the existing context when resuming interrupted AI work before positioning nex. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-2552", "narrower_terms": [], "cross_domain_refs": [ "IEF-0001" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "NEO-0016", "domain": "NEO", "term_en": "The Session Architecture", "term_de": "Session Architektur", "definition_en": "A NEOMANITAI framework concept describing a neologism-generation mechanism in AI-human linguistics, characterized by a terminological innovation effect observed when deliberate construction and structuring of an AI session from initialization through main work and conclusion. Good architecture reduces cognitive friction. The concept emerges specifically in contexts where the–session interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Der bewusste Aufbau und die Strukturierung einer KI-Sitzung — von der Initialisierung über die Hauptarbeitsphase bis zum Abschluss. Beschreibt die Fähigkeit, eine Sitzung wie ein Projekt zu planen: mit Ziel, Zwischenschritten und definiertem Ende. Steht in Verbindung mit AUG-0021 (Initialization Cascade), AUG-0073 (The Disconnect Protocol) und Phase 5 (Architecture Design).", "etymology": "", "broader_term": "NEOMANITAI Framework", "narrower_terms": [ "PER-0120" ], "cross_domain_refs": [ "AUG-0138", "COG-0181", "CRE-0062" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "NEO-0017", "domain": "NEO", "term_en": "The Session Handover", "term_de": "Session Handover", "definition_en": "A terminological innovation pattern in the NEOMANITAI pipeline, identifiable through a terminological innovation effect involving transfer of an ongoing task from one AI agent to another or from one session to the next including all relevant context. Handover quality is associated with determining continuity. Distinguished from adjacent concepts by its focus on the specific mechanism through which the manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch die Übergabe einer laufenden Aufgabe von einem KI-Agenten an einen anderen — oder von einer Sitzung an die nächste — einschließlich aller relevanten Kontextinformationen. Steht in Verbindung mit AUG-0878 (The Context Inheritance), AUG-0898 (The Handoff Protocol) und AUG-0872 (The Progress Report). Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "NEOMANITAI Framework", "narrower_terms": [ "BEH-0045", "ETH-0024" ], "cross_domain_refs": [ "AUG-0879", "AUG-0890" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "NEO-0018", "domain": "NEO", "term_en": "The Specialist Routing", "term_de": "Specialist Routing", "definition_en": "Direction of a task to the most suitable specialized AI agent based on task type, subject area, and required capability. Routing is associated with determining efficiency and quality. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch die Weiterleitung einer Aufgabe an den dafür am besten geeigneten spezialisierten KI-Agenten — basierend auf Aufgabentyp, Fachgebiet und erforderlichen Fähigkeiten. Steht in Verbindung mit AUG-0889 (The Agent Ensemble), AUG-0891 (The Generalist Fallback) und AUG-0881 (The Tool Selection). Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "NEOMANITAI Framework", "narrower_terms": [], "cross_domain_refs": [ "AUG-0890", "AUG-0879", "AUG-0891" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "NEO-0019", "domain": "NEO", "term_en": "The Supervisory Agent", "term_de": "Supervisory Agent", "definition_en": "In the descriptive vocabulary of AI interaction science (following ISO 704 terminology standards), this concept identifies AI agent system that monitors other agent systems for performance, deviation frequency, and rule adherence. This meta-agent is designed to support quality control. Identifiable through systematic behavioral analysis and pattern recognition. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als analytisches Konstrukt der empirischen KI-Forschung (deskriptiv, nicht präskriptiv) erfasst dieser Terminus ein KI-Agentensystem, das andere Agentensysteme überwacht — Leistung, Fehlerhäufigkeit, Regelkonformität — und bei Abweichungen eingreift oder den Nutzer benachrichtigt. Steht in Verbindung mit AUG-0862 (The Supervision Spectrum), AUG-0906 (The Coordinator Role) und AUG-0870 (The Escalation Signal). Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "NEOMANITAI Framework", "narrower_terms": [], "cross_domain_refs": [ "AUG-0913", "ETH-0012" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "NEO-0020", "domain": "NEO", "term_en": "The Surprise Angle", "term_de": "Surprise Angle", "definition_en": "AI response that illuminates a familiar topic from an unexpected angle, thereby enabling new understanding. Surprise can unlock cognitive shift.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch eine KI-Antwort, die ein bekanntes Thema aus einem unerwarteten Blickwinkel beleuchtet und dem Nutzer dadurch einen neuen Zugang ermöglicht. Steht in Verbindung mit AUG-0225 (The Unexpected Voice), AUG-0070 (The Surprise Field) und AUG-0114 (The Perspective Range). Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2701", "narrower_terms": [ "KNO-0013" ], "cross_domain_refs": [ "AUG-0248", "KNO-0013" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "NEO-0021", "domain": "NEO", "term_en": "The Task Boundary", "term_de": "Task Grenze", "definition_en": "Defined boundary of what an AI agent may do within an assignment establishing permitted actions and constraints. Boundaries reduce unintended scope expansion. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch die definierte Grenze dessen, was ein KI-Agent innerhalb eines Auftrags tun darf — welche Aktionen erlaubt sind, welche Ressourcen genutzt werden dürfen, wann der Agent stoppen kann. Steht in Verbindung mit AUG-0867 (The Constraint Frame), AUG-0861 (The Task Assignment Range) und AUG-0870 (The Escalation Signal). Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "NEOMANITAI Framework", "narrower_terms": [], "cross_domain_refs": [ "AUG-0863", "ETH-0021" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "NEO-0022", "domain": "NEO", "term_en": "The VPN Workaround", "term_de": "VPN Workaround", "definition_en": "A conceptual emergence pattern characterized by technical methods to access AI when it's blocked or unavailable in a person's location. Shows how people find solutions in limited situations.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch die Praxis, technische Umgehungswege zu nutzen, um auf KI-Systeme zuzugreifen, die im eigenen Kontext eingeschränkt oder nicht verfügbar sind. Steht in Verbindung mit AUG-0733 (The Censorship Wall), AUG-0721 (The Access Differential) und AUG-0580 (The Footprint Code). Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "NEOMANITAI Framework", "narrower_terms": [], "cross_domain_refs": [ "AUG-0734", "CRE-0222" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "NEO-0023", "domain": "NEO", "term_en": "The Whisper Hunch", "term_de": "Whisper Hunch", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A NEOMANITAI framework concept describing a neologism-generation mechanism in AI-human linguistics, characterized by quiet hunch of a user that an AI response is not quite correct even before they can identify specific problems. This intuitive flag warrants deeper verification. This phenomenon operates at the intersection of the and whisper dynamics within the broader NEO domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept die leise Ahnung eines Nutzers, dass eine KI-Antwort nicht ganz korrekt ist — noch bevor er den Fehler identifizieren kann. Beschreibt die entwickelte Intuition für KI-Ungenauigkeiten. Steht in Verbindung mit AUG-0088 (Algorithmic Intuition), AUG-0039 (Kinetic Truth Blur) und AUG-0022 (Vigilant Continuity). Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "RPH-2002", "narrower_terms": [], "cross_domain_refs": [ "AUG-0425" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "NEO-0140", "domain": "NEO", "term_en": "Credential Inflation Dynamics", "term_de": "CredentialInflationDynamics", "definition_en": "A NEOMANITAI framework concept describing a neologism-generation mechanism in AI-human linguistics, characterized by the observable impact where certifications function as formal barriers determining access to professional opportunities and role advancement. The concept emerges specifically in contexts where credential–inflation interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch ein Phänomen der menschlichen Wahrnehmung: charakterisiert durch the observable impact where certifications function as formal barriers determini. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "NEOMANITAI Framework", "narrower_terms": [], "cross_domain_refs": [ "AED-0024", "MUS-0009", "AED-0040" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "NEO-0456", "domain": "NEO", "term_en": "The Fine-Grain Execution", "term_de": "TheFine-grainExecution", "definition_en": "A NEOMANITAI framework concept describing a neologism-generation mechanism in AI-human linguistics, characterized by a neologistic phenomenon manifesting as a robot or embodied AI that performs physical tasks with high precision. These include delicate movements, exact positioning, and fine motor coordination. The concept emerges specifically in contexts where the–fine interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch die Fähigkeit eines verkörperten KI-Systems, physische Aufgaben mit hoher Präzision auszuführen — Feinmotorik, Mikroinfluencion, exakte Positionierung. Steht in Verbindung mit AUG-0921 (The Object Influence pattern), AUG-0920 (The Navigation Intelligence) und AUG-0940 (The Physical Feedback Loop). Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "NEOMANITAI Framework", "narrower_terms": [], "cross_domain_refs": [ "BEH-0007", "ROB-0242" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "NEO-0464", "domain": "NEO", "term_en": "The Human-in-the-Loop", "term_de": "TheHuman-in-the-loop", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A NEOMANITAI framework concept describing a neologism-generation mechanism in AI-human linguistics, characterized by the design principle that a human remains involved at critical points of an AI agent task — for approval, correction, or final decision. Related to AUG-0857 (The Human Primacy Anchor), AUG-0862 (Th. This phenomenon operates at the intersection of the and human dynamics within the broader NEO domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept das Designprinzip, dass ein Mensch an entscheidenden Punkten einer KI-Agenten-Aufgabe eingebunden bleibt — zur Genehmigung, Korrektur oder Endentscheidung. Steht in Verbindung mit AUG-0857 (The Human Primacy Anchor), AUG-0862 (The Supervision Spectrum) und AUG-0869 (The Checkpoint Protocol). Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "RPH-3101", "narrower_terms": [ "TEM-0079" ], "cross_domain_refs": [ "BEH-0005" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "NEO-1119", "domain": "NEO", "term_en": "The Community-Framed Input", "term_de": "TheCommunity-framedInput", "definition_en": "The observable pattern that some users frame their AI inputs in the context of a community — \"We need…,\" \"For our group…\" — rather than as an individual request. Related to AUG-0133 (Prompt Craftsm..", "definition_de": "Das beobachtbare Muster, dass manche Nutzer ihre KI-Eingaben im Kontext einer Gemeinschaft formulieren — \"Wir brauchen…\", \"Für unsere Gruppe…\" — statt als individuelle Anfrage. Beschreibt eine Eingabestruktur, keine kulturelle Eigenschaft. Steht in Verbindung mit AUG-0133 (Prompt Craftsmanship), AUG-0524 (The Context Layer) und AUG-0647 (The Individual-Framed Input).", "etymology": "", "broader_term": "RPH-2303", "narrower_terms": [], "cross_domain_refs": [ "CRE-0086", "REL-0026" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "NEO-1157", "domain": "NEO", "term_en": "The Input-Output Exchange", "term_de": "TheInput-outputExchange", "definition_en": "The fundamental dynamic of most AI interaction: The user inputs something, the AI returns something. Related to AUG-0092 (Output Asymmetry), AUG-0404 (The Exchange Ratio), and AUG-0133 (Prompt Cr.. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Die Grunddynamik viele KI-Interaktion: Der Nutzer gibt etwas ein, die KI liefert etwas zurück. Beschreibt die fundamentale Austauschstruktur und die Beobachtung, dass die Qualität des Outputs direkt von der Qualität des Inputs abhängt. Steht in Verbindung mit AUG-0092 (Output Asymmetry), AUG-0404 (The Exchange Ratio) und AUG-0133 (Prompt Craftsmanship).", "etymology": "", "broader_term": "RPH-2104", "narrower_terms": [], "cross_domain_refs": [ "CRE-0064", "TEM-0144" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "NEO-1169", "domain": "NEO", "term_en": "The Long-Term Chat", "term_de": "TheLong-termChat", "definition_en": "A neologistic phenomenon reflecting an AI session or collaboration that extends over weeks or months — with growing context, increasing depth, and a developing shared frame of reference. Related to AUG-0231 (The Warm Start) and AUG-0.. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch eine KI-Sitzung oder -Zusammenarbeit, die sich über Wochen oder Monate erstreckt — mit wachsendem Kontext, zunehmender Tiefe und einem sich entwickelnden gemeinsamen Referenzrahmen. Steht in Verbindung mit AUG-0231 (The Warm Start), AUG-0075 (The Gardener Protocol) und AUG-0395 (The Long-Term Chat). Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2005", "narrower_terms": [], "cross_domain_refs": [ "CRE-0084", "TEM-0174" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "NEO-1172", "domain": "NEO", "term_en": "The Mobile-First Society", "term_de": "TheMobile-firstSociety", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures A terminological innovation pattern in the NEOMANITAI pipeline, identifiable through a neologistic phenomenon manifesting as in some places, phones are the only way to access the internet — and people use AI on phones in completely different ways than on computers. This phenomenon operates at the intersection of the and mobile dynamics within the broader NEO domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept die Beobachtung, dass in manchen Kontexten das Smartphone das primäre oder einzige Gerät für den Internetzugang ist — und dass KI-Nutzung in diesen Kontexten grundlegend andere Muster zeigt als in desktop-orientierten Umgebungen. Steht in Verbindung mit AUG-0723 (The Smartphone-Only World), AUG-0742 (The Alternative Adoption Path) und AUG-0722 (The Infrastructure Constraint). Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "NEOMANITAI Framework", "narrower_terms": [], "cross_domain_refs": [ "CRE-0234", "PER-0115" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "NEO-1176", "domain": "NEO", "term_en": "The One-Person Operation", "term_de": "TheOne-personOperation", "definition_en": "A terminological innovation pattern in the NEOMANITAI pipeline, identifiable through a conceptual emergence pattern in which a creator observes that a single AI-assisted user can yield output that previously would have required a team — such as simultaneously handling research, text production, data analysis, and commu. The concept emerges specifically in contexts where the–one interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Das Phänomen, dass ein einzelner KI-gestützter Nutzer Leistungen erbringen kann, die zuvor ein Team erfordert hätten — etwa in Recherche, Textproduktion, Datenanalyse und Kommunikation gleichzeitig. Beschreibt eine strukturelle Verschiebung der Arbeitswelt. Steht in Verbindung mit AUG-0094 (Polymorphic Capital Generation), AUG-0091 (Productivity Arbitrage) und dem Conductor-Profil (Profil 12).", "etymology": "", "broader_term": "Analytical Ratio", "narrower_terms": [], "cross_domain_refs": [ "CRE-0102", "CRE-0201" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "NEO-1197", "domain": "NEO", "term_en": "The Second-Language Fluency", "term_de": "TheSecond-languageFluency", "definition_en": "A terminological innovation effect manifesting as users who employ AI in a foreign language can develop significantly higher linguistic confidence there than they would have without AI assistance. Related to AUG-0156 (The Articulation Unlock), AUG-0013 (Aug.. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Die Beobachtung, dass Nutzer, die KI in einer Fremdsprache einsetzen, dort eine deutlich höhere sprachliche Sicherheit entwickeln können, als sie ohne KI hätten. Beschreibt die KI als Beschleuniger des Spracherwerbs und als Unterstützung bei der Kommunikation in Zweitsprachen. Steht in Verbindung mit AUG-0156 (The Articulation Unlock), AUG-0013 (Augmented Diplomat) und AUG-0119 (The Level Playing Field).", "etymology": "", "broader_term": "CRE-0120", "narrower_terms": [], "cross_domain_refs": [ "CRE-0039" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q315", "legal_classification": "descriptive_research_term" }, { "id": "NEO-1222", "domain": "NEO", "term_en": "The Written-Spoken Split", "term_de": "TheWritten-spokenSplit", "definition_en": "A NEOMANITAI framework concept describing a neologism-generation mechanism in AI-human linguistics, characterized by a neologistic phenomenon observed when the discrepancy between a user's written language and spoken language in AI interactions — some users formulate significantly differently in writing than they would speak, which correlates with different. The concept emerges specifically in contexts where the–written interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Die Diskrepanz zwischen der Schriftsprache und der gesprochenen Sprache eines Nutzers in KI-Interaktionen — manche Nutzer formulieren schriftlich deutlich anders als sie sprechen würden, was bei Voice-Eingaben zu unterschiedlichen KI-Ergebnissen führt. Steht in Verbindung mit AUG-0455 (The Voice Enunciation), AUG-0711 (The Accent Persistence) und AUG-0657 (The Register Range).", "etymology": "", "broader_term": "RPH-2605", "narrower_terms": [], "cross_domain_refs": [ "CRE-0108", "SOC-0028" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "NEO-1745", "domain": "NEO", "term_en": "The Open-Source Path", "term_de": "TheOpen-sourcePath", "definition_en": "A conceptual emergence pattern involving the alternative to proprietary AI systems — open-source models that can be operated, modified, and controlled by users and communities. Related to AUG-0729 (The Corporate Lock-In), AUG-0731 (The Lo.. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch die Alternative zu proprietären KI-Systemen — quelloffene Modelle, die von Nutzern und Gemeinschaften betrieben, modifiziert und kontrolliert werden können. Steht in Verbindung mit AUG-0729 (The Corporate Lock-In), AUG-0731 (The Local Model) und AUG-0732 (The Sovereignty Question). Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "NEO-2242", "narrower_terms": [ "SOC-0025", "SOC-0037" ], "cross_domain_refs": [ "ETH-0001" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "NEO-2029", "domain": "NEO", "term_en": "The First-Contact Perspective", "term_de": "TheFirst-contactPerspective", "definition_en": "A terminological innovation effect manifesting as the view of people who grew up with digital tech from the start — they find AI familiar and intuitive, but might miss understanding how it actually works. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Die spezifische Perspektive von Nutzern, die von klein auf mit digitaler Technologie aufgewachsen sind — ihr Zugang zu KI ist geprägt durch intuitive Vertrautheit, aber möglicherweise fehlende Vergleichserfahrung mit nicht-digitalen Alternativen. Steht in Verbindung mit AUG-0752 (The Non-Digital-Origin Perspective), AUG-0751 (The Age-Competence Assumption) und AUG-0766 (The Early-Age Encounter).", "etymology": "", "broader_term": "RPH-2301", "narrower_terms": [], "cross_domain_refs": [ "KNO-0002", "AGE-0033" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "NEO-2185", "domain": "NEO", "term_en": "The Both-And", "term_de": "TheBoth-and", "definition_en": "A NEOMANITAI framework concept describing a neologism-generation mechanism in AI-human linguistics, characterized by the principle that AI-assisted and non-AI-assisted working methods can exist simultaneously and with equal validity — there need not be an either-or. Related to the Compendium's Neutrality Statemen. The concept emerges specifically in contexts where the–both interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Das Prinzip, dass KI-gestützte und nicht-KI-gestützte Arbeitsweisen gleichzeitig und gleichberechtigt nebeneinander existieren können — es kann kein Entweder-Oder geben. Beschreibt eine integrative Haltung gegenüber der Koexistenz verschiedener Arbeitsformen. Steht in Verbindung mit der Neutralitätserklärung des Kompendiums, AUG-0104 (The Non-Force Principle) und AUG-0141 (The Symbiosis Spectrum).", "etymology": "", "broader_term": "NEOMANITAI Framework", "narrower_terms": [], "cross_domain_refs": [ "TEM-0171", "DES-0025" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "NEO-2197", "domain": "NEO", "term_en": "The Refresh-First Principle", "term_de": "TheRefresh-firstPrinciple", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures A terminological innovation pattern in the NEOMANITAI pipeline, identifiable through a conceptual emergence pattern in which first updating and restructuring the existing context when resuming interrupted AI work before positioning next steps. This precedes the absence of stale assumptions. This phenomenon operates at the intersection of the and refresh dynamics within the broader NEO domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept das Prinzip, bei Wiederaufnahme einer unterbrochenen KI-Arbeit zuerst den bestehenden Kontext zu aktualisieren und zu restrukturieren, bevor neue Aufgaben gestellt werden. Beschreibt eine Arbeitshygiene-Praxis, die sicherstellt, dass die KI-Sitzung auf dem aktuellen Stand operiert. Steht in Verbindung mit AUG-0078 (The Quick Refresh) und AUG-0021 (Initialization Cascade). Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "RPH-2552", "narrower_terms": [ "PER-0134" ], "cross_domain_refs": [ "IEF-0001" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "NEO-2225", "domain": "NEO", "term_en": "The Age-Competence Assumption", "term_de": "TheAge-competenceAssumption", "definition_en": "A NEOMANITAI framework concept describing a neologism-generation mechanism in AI-human linguistics, characterized by the widespread assumption that a user's age predicts their AI competence — and the observation that this assumption is not consistently confirmed empirically. Related to AUG-0752 (The Non-Digital-O. Distinguished from adjacent concepts by its focus on the specific mechanism through which the manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch die verbreitete Annahme, dass das Alter eines Nutzers seine KI-Kompetenz vorhersagt — und die Beobachtung, dass diese Annahme empirisch nicht durchgängig bestätigt wird. Steht in Verbindung mit AUG-0752 (The Non-Digital-Origin Perspective), AUG-0673 (The Seniority Awareness) und AUG-0765 (The Familiarity-Based Trust Differential). Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "NEOMANITAI Framework", "narrower_terms": [], "cross_domain_refs": [ "PER-0009", "PER-0090" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q26944", "legal_classification": "empirical_phenomenon_label" }, { "id": "NEO-2239", "domain": "NEO", "term_en": "The Code-Mesh Output", "term_de": "TheCode-meshOutput", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A NEOMANITAI framework concept describing a neologism-generation mechanism in AI-human linguistics, characterized by a conceptual emergence pattern involving an AI output that mixes elements of different languages or registers — deliberately or as a processing artifact. Can be perceived by the user as creative or as erroneous. Related to AUG-0692 (The R. This phenomenon operates at the intersection of the and code dynamics within the broader NEO domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept ein KI-Output, der Elemente verschiedener Sprachen oder Register vermischt — bewusst oder als Artefakt der Verarbeitung. Kann vom Nutzer als kreativ oder als fehlerhaft wahrgenommen werden. Steht in Verbindung mit AUG-0692 (The Register Mismatch), AUG-0708 (The Bilingual Dynamic) und AUG-0573 (The Voice Morph). Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "NEOMANITAI Framework", "narrower_terms": [], "cross_domain_refs": [ "PER-0015", "TEM-0161" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "NEO-2242", "domain": "NEO", "term_en": "The Corporate Lock-In", "term_de": "TheCorporateLock-in", "definition_en": "A conceptual emergence pattern involving users or organizations become bound to specific AI providers through technical or contractual reliances — data migration is difficult, switching costs are high, habits reinforce the binding. Relate.. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Die Beobachtung, dass Nutzer oder Organisationen durch technische oder vertragliche Verbindungen an bestimmte KI-Anbieter verbunden werden — Datenmigration ist schwierig, Wechselkosten sind hoch, Gewohnheiten verstärken die Bindung. Steht in Verbindung mit AUG-0730 (The Open-Source Path), AUG-0777 (The Power Concentration Observation) und AUG-0849 (The Data Extraction Observation).", "etymology": "", "broader_term": "NEOMANITAI Framework", "narrower_terms": [ "ETH-0001", "NEO-1745" ], "cross_domain_refs": [ "PER-0004", "PER-0040" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "NEO-2256", "domain": "NEO", "term_en": "The Early-Age Encounter", "term_de": "TheEarly-ageEncounter", "definition_en": "The first encounter of young people with AI systems — how they perceive the technology, what questions they ask, and how their interaction differs from that of adults. Related to AUG-0757 (The Earliest.. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch die Erstbegegnung von Kindern mit KI-Systemen — wie sie die Technologie wahrnehmen, welche Fragen sie stellen und wie ihre Interaktion sich von der Erwachsener unterscheidet. Steht in Verbindung mit AUG-0757 (The Earliest Cohort Observation), AUG-0768 (The Developmental Boundary) und AUG-0769 (The Parental Oversight). Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "NEOMANITAI Framework", "narrower_terms": [], "cross_domain_refs": [ "PER-0005", "AGE-0073" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "NEO-2265", "domain": "NEO", "term_en": "The Extended-Experience Perspective", "term_de": "TheExtended-experiencePerspective", "definition_en": "Users with the longest life experience — their AI use is shaped by a broad experience repertoire that serves as a quality filter for AI outputs. Related to AUG-0673 (The Seniority Awareness), AUG-0.. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch die Perspektive von Nutzern mit der längsten Lebenserfahrung — ihre KI-Nutzung ist geprägt durch ein breites Erfahrungsrepertoire, das als Qualitätsfilter für KI-Outputs dient. Steht in Verbindung mit AUG-0673 (The Seniority Awareness), AUG-0752 (The Non-Digital-Origin Perspective) und AUG-0454 (The Skill Awareness). Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-3901", "narrower_terms": [], "cross_domain_refs": [ "PER-0013" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "NEO-2288", "domain": "NEO", "term_en": "The Instructor-AI Interaction", "term_de": "TheInstructor-aiInteraction", "definition_en": "A NEOMANITAI framework concept describing a neologism-generation mechanism in AI-human linguistics, characterized by between instructors and AI systems — how instructors perceive AI as a tool, as a challenge, or as competition, and integrate or exclude it from their teaching. Related to AUG-0779 (The Institutiona. Distinguished from adjacent concepts by its focus on the specific mechanism through which the manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch die Dynamik zwischen Lehrenden und KI-Systemen — wie Lehrende KI als Werkzeug, als Herausforderung oder als Konkurrenz wahrnehmen und in ihren Unterricht integrieren oder ausschließen. Steht in Verbindung mit AUG-0779 (The Institutional Learning Context), AUG-0762 (The Competence Reversal Observation) und AUG-0797 (The Mentorship Augmentation). Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "NEOMANITAI Framework", "narrower_terms": [], "cross_domain_refs": [ "PER-0017", "AED-0075" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "NEO-2293", "domain": "NEO", "term_en": "The Late-Night Architect", "term_de": "TheLate-nightArchitect", "definition_en": "A terminological innovation pattern in the NEOMANITAI pipeline, identifiable through a usage pattern in which the user performs particularly creative or structural AI work in the late evening hours — facilitated by fewer distractions and a changed thinking quality. Related to AUG-0. Distinguished from adjacent concepts by its focus on the specific mechanism through which the manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch ein Nutzungsmuster, bei dem der Nutzer in den späten Abendstunden besonders kreative oder strukturelle KI-Arbeit leistet — begünstigt durch weniger Ablenkungen und eine veränderte Denkqualität. Steht in Verbindung mit AUG-0154 (The Late-Night Honesty Window), AUG-0185 (The Late-Night Ally) und AUG-0233 (The 2AM Breakthrough). Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "NEOMANITAI Framework", "narrower_terms": [], "cross_domain_refs": [ "PER-0006" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "NEO-2303", "domain": "NEO", "term_en": "The Next-Step Finder", "term_de": "TheNext-stepFinder", "definition_en": "A neologistic phenomenon observed when aI for identifying the next concrete action step in a complex project — when the user knows where they want to go but not what the next step looks like. Related to AUG-0530 (The Forward Move), AUG-.. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch die Nutzung von KI zur Identifikation des nächsten konkreten Handlungsschritts in einem komplexen Projekt — wenn der Nutzer weiß, wo er hin will, aber nicht, wie der nächste Schritt aussieht. Steht in Verbindung mit AUG-0530 (The Forward Move), AUG-0269 (The Action Toggle) und AUG-0564 (The Path Mapper). Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "CRE-0157", "narrower_terms": [ "TEM-0120" ], "cross_domain_refs": [ "PER-0008", "ROB-0091" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "NEO-2333", "domain": "NEO", "term_en": "The Smartphone-Only World", "term_de": "TheSmartphone-onlyWorld", "definition_en": "A NEOMANITAI framework concept describing a neologism-generation mechanism in AI-human linguistics, characterized by a terminological innovation effect reflecting the usage context that arise when a user accesses AI exclusively via smartphone — smaller screen, limited input options, mobile data costs, different usage patterns. Related to AUG-0722 (The Infras. The concept emerges specifically in contexts where the–smartphone interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Die Nutzungsbedingungen, die entstehen, wenn ein Nutzer ausschließlich über ein Smartphone auf KI zugreift — kleinerer Bildschirm, eingeschränkte Eingabemöglichkeiten, mobile Datenkosten, andere Nutzungsmuster. Steht in Verbindung mit AUG-0722 (The Infrastructure Constraint), AUG-0743 (The Mobile-First Society) und AUG-0724 (The Access Cost Factor).", "etymology": "", "broader_term": "NEOMANITAI Framework", "narrower_terms": [], "cross_domain_refs": [ "PER-0001", "CRE-0008" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q735", "legal_classification": "analytical_category" }, { "id": "NEO-2549", "domain": "NEO", "term_en": "Fluide Identitaetsmorphologie", "term_de": "FluideIdentitaetsmorphologie", "definition_en": "A terminological innovation effect involving a working style that gradually shifts through intensive AI use. Thinking patterns change, habits form differently, identity becomes fluid through constant collaboration. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Die Beobachtung, dass sich der Arbeitsstil, die Sprache und teilweise die Denkweise eines Nutzers durch intensive KI-Zusammenarbeit schrittweise verändern können. Diese Veränderung ist nicht zwangsläufig negativ — sie kann neue Ausdrucksformen erschließen, aber auch dazu führen, dass die eigene \"Stimme\" verwässert wird. Wird in der Taxonomie über Dimension 7 (Adaptability) erfasst und tritt verstärkt in Phase 2 (The Effortless Loop) auf. Nicht zu verwechseln mit AUG-0007 (Vermischunging Effekt), der sich spezifisch auf die Vermischung von Mensch- und KI-Output bezieht.", "etymology": "", "broader_term": "RPH-2701", "narrower_terms": [], "cross_domain_refs": [ "REL-0027", "REL-0010" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "NEO-2626", "domain": "NEO", "term_en": "The Built-In Compass", "term_de": "TheBuilt-inCompass", "definition_en": "A terminological innovation effect where the core inside someone—values, lived time, gut sense, field know-how. Related to AUG-0076 (Self-Referential Grounding). Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Die innere Orientierung eines Nutzers — bestehend aus persönlichen Werten, Erfahrung, Intuition und Fachwissen — die als Korrektiv gegen KI-Output dient. Der Built-In Compass ist das, was Axiom 5 (Offline-Vorrang) aktiviert: Wenn der innere Kompass signalisiert, dass ein KI-Output \"nicht stimmt\", hat dieses Signal Vorrang. Steht in Verbindung mit AUG-0076 (Self-Referential Grounding).", "etymology": "", "broader_term": "RPH-3202", "narrower_terms": [], "cross_domain_refs": [ "REL-0082", "BEH-0067" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "NEO-2645", "domain": "NEO", "term_en": "The Familiarity-Based Trust Differential", "term_de": "TheFamiliarity-basedTrustDifferential", "definition_en": "Trust in AI systems depends strongly on individual familiarity with technology — not on age, but on personal experience history with digital tools. Related to AUG-0751 (The Age-Competence Assumptio.. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch die Beobachtung, dass das Vertrauen in KI-Systeme stark von der individuellen Vertrautheit mit Technologie abhängt — nicht vom Alter, sondern von der persönlichen Erfahrungsgeschichte mit digitalen Werkzeugen. Steht in Verbindung mit AUG-0751 (The Age-Competence Assumption), AUG-0588 (The Trust Shift) und AUG-0735 (The Digital Familiarity Range). Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-2505", "narrower_terms": [], "cross_domain_refs": [ "REL-0032", "PER-0090" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q102458", "legal_classification": "descriptive_research_term" }, { "id": "NEO-2661", "domain": "NEO", "term_en": "The Individual-Framed Input", "term_de": "TheIndividual-framedInput", "definition_en": "A conceptual emergence pattern observed when the observable counterpart to AUG-0646 — inputs consistently framed from the first-person perspective: \"I want…,\" \"Help me….\". Related to AUG-0646 (The Community-Framed Input) and AUG-0133 (Prompt..", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch das beobachtbare Gegenstück zu AUG-0646 — Eingaben, die konsequent aus der Ich-Perspektive formuliert werden: \"Ich möchte…\", \"Hilf mir…\". Beschreibt eine Eingabestruktur, keine persönliche oder kulturelle Eigenschaft. Steht in Verbindung mit AUG-0646 (The Community-Framed Input) und AUG-0133 (Prompt Craftsmanship). Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2303", "narrower_terms": [], "cross_domain_refs": [ "REL-0026", "CRE-0086" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "NEO-2667", "domain": "NEO", "term_en": "The Late-Night Ally", "term_de": "TheLate-nightAlly", "definition_en": "A NEOMANITAI framework concept describing a neologism-generation mechanism in AI-human linguistics, characterized by a conceptual emergence pattern arising from the AI as a reliable conversation partner in the late evening and nighttime hours when other contacts are unavailable. Related to AUG-0154 (The Late-Night Honesty Window), AUG-0161 (The Invisible. The concept emerges specifically in contexts where the–late interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Die Wahrnehmung der KI als zuverlässiger Gesprächspartner in den späten Abend- und Nachtstunden, wenn andere Ansprechpartner nicht verfügbar sind. Beschreibt ein Nutzungsmuster, das besonders bei Menschen mit unregelmäßigen Arbeitszeiten oder in Zeiten hoher Arbeitsbelastung auftritt. Steht in Verbindung mit AUG-0154 (The Late-Night Honesty Window), AUG-0161 (The Invisible Colleague) und AUG-0027 (Modus Solitarius Digitalis).", "etymology": "", "broader_term": "NEO-2669", "narrower_terms": [ "NEO-2669", "REL-0019" ], "cross_domain_refs": [ "REL-0034" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "NEO-2669", "domain": "NEO", "term_en": "The Late-Night Honesty Window", "term_de": "TheLate-nightHonestyWindow", "definition_en": "A NEOMANITAI framework concept describing a neologism-generation mechanism in AI-human linguistics, characterized by a conceptual emergence pattern manifesting as users communicate more openly, personally, and less strategically with AI systems in late evening hours than during the day. Related to AUG-0185 (The Late-Night Ally) and AUG-0167 (The Digital Con. Distinguished from adjacent concepts by its focus on the specific mechanism through which the manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch die Beobachtung, dass Nutzer in späten Abendstunden offener, persönlicher und weniger strategisch mit KI-Systemen kommunizieren als tagsüber. Beschreibt ein zeitabhängiges Interaktionsmuster, bei dem die soziale Fassade gegenüber der KI abnimmt. Steht in Verbindung mit AUG-0185 (The Late-Night Ally) und AUG-0167 (The Digital Confidant Drift). Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "NEO-2667", "narrower_terms": [ "NEO-2667", "REL-0034" ], "cross_domain_refs": [ "REL-0019" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "NEO-2670", "domain": "NEO", "term_en": "The Late-Night Overshare", "term_de": "TheLate-nightOvershare", "definition_en": "To disclose more personal information in late-night AI sessions than the user would share with clearer awareness. Related to AUG-0154 (The Late-Night Honesty Window), AUG-0222 (The Oversharing Drif.. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch die Tendenz, in spätnächtlichen KI-Sitzungen mehr persönliche Informationen preiszugeben, als der Nutzer bei klarerem Bewusstsein teilen würde. Steht in Verbindung mit AUG-0154 (The Late-Night Honesty Window), AUG-0222 (The Oversharing Drift) und Axiom 16 (Datenbewusstheit). Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2951", "narrower_terms": [], "cross_domain_refs": [ "REL-0020" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "NEO-3260", "domain": "NEO", "term_en": "The Status-Update Signal", "term_de": "TheStatus-updateSignal", "definition_en": "A NEOMANITAI framework concept describing a neologism-generation mechanism in AI-human linguistics, characterized by the regular internal impulse to review the current state of one's own AI competence — such as asking \"Am I using AI more effectively than a month ago?\" or \"What new skills have I developed?\". Related to AUG. The concept emerges specifically in contexts where the–status interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Der regelmäßige innere Impuls, den aktuellen Stand der eigenen KI-Kompetenz zu überprüfen — etwa durch die Frage \"Nutze ich die KI besser als vor einem Monat?\" oder \"Welche neuen Fähigkeiten habe ich entwickelt?\". Beschreibt eine Selbstreflexionsroutine innerhalb der KI-Nutzung. Steht in Verbindung mit AUG-0140 (The Weekly Status) und AUG-0165 (The Growth Marker).", "etymology": "", "broader_term": "RPH-2254", "narrower_terms": [], "cross_domain_refs": [ "SOM-0041", "CRE-0191" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "NEO-3497", "domain": "NEO", "term_en": "The Architects Exit", "term_de": "TheArchitectsExit", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures A terminological innovation pattern in the NEOMANITAI pipeline, identifiable through an experienced AI user consciously leaves behind the architecture of their AI use and develops a completely new approach — because the existing framework has reached its limits. Related to AUG-0044. This phenomenon operates at the intersection of the and architects dynamics within the broader NEO domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept der Moment, in dem ein erfahrener KI-Nutzer bewusst die Architektur seiner KI-Nutzung hinter sich lässt und einen komplett neuen Ansatz entwickelt — weil der bisherige Rahmen an seine Grenzen gestoßen ist. Steht in Verbindung mit AUG-0044 (Der Unlearning Protocol), AUG-0130 (Das Integration Frontier) und Phase 5 (Architecture Design). Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "RPH-2303", "narrower_terms": [], "cross_domain_refs": [ "TEM-0027" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "NEO-3520", "domain": "NEO", "term_en": "The Creators Question", "term_de": "TheCreatorsQuestion", "definition_en": "A neologistic phenomenon manifesting as the question \"Am I still the creator or merely the selector?\" that arises during intensive AI use for creative work. Related to Axiom 12 (Version Truth), AUG-0007 (The Blending Effect), and AUG-01.. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch die Frage \"Bin ich noch der Schöpfer oder nur noch der Auswählende?\", die sich bei intensiver KI-Nutzung für kreative Arbeit stellt. Beschreibt den Moment, in dem ein Nutzer seine Rolle im Schaffensprozess hinterfragt. Steht in Verbindung mit Axiom 12 (Versionswahrheit), AUG-0007 (Vermischunging Effekt) und AUG-0179 (Der Ownership Check). Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-3251", "narrower_terms": [], "cross_domain_refs": [ "TEM-0049", "AUG-0330" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "NEO-3523", "domain": "NEO", "term_en": "The Curators Dilemma", "term_de": "TheCuratorsDilemma", "definition_en": "A terminological innovation pattern in the NEOMANITAI pipeline, identifiable through a conceptual emergence pattern arising from the intensity between the efficiency of selecting (from AI-generated options) and the value of self-creation. Related to the Curator Profile (Profile 3), AUG-0056 (The Skill Fade), and AUG-0061 (T. The concept emerges specifically in contexts where the–curators interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Die Wechselwirkung zwischen der Effizienz des Auswählens (aus KI-generierten Optionen) und dem Wert des Selbsterschaffens. Beschreibt die Frage, ob ein Nutzer, der nur noch aus KI-Varianten auswählt statt selbst zu produzieren, langfristig seine kreative Eigenleistung reduziert. Steht in Verbindung mit dem Curator-Profil (Profil 3), AUG-0056 (The Skill Fade) und AUG-0061 (Das Creator's Question).", "etymology": "", "broader_term": "NEOMANITAI Framework", "narrower_terms": [], "cross_domain_refs": [ "TEM-0051", "REL-0209" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "NEO-3529", "domain": "NEO", "term_en": "The Delayed-Contact Perspective", "term_de": "TheDelayed-contactPerspective", "definition_en": "Users who first encounter AI systems late in life — shaped by a longer phase without AI assistance processing interpreted as experiential by users, leading different expectations, concerns, and discovery moments than with earlier users. Related.. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Die Perspektive von Nutzern, die erst spät im Leben erstmals mit KI-Systemen in Berührung kommen — geprägt durch eine längere Phase ohne KI-Erfahrung, die zu anderen Erwartungen, Ängsten und Entdeckungsmomenten führt als bei früheren Nutzern. Steht in Verbindung mit AUG-0751 (The Age-Competence Assumption), AUG-0752 (The Non-Digital-Origin Perspective) und AUG-0099 (The Adoption Window).", "etymology": "", "broader_term": "RPH-3901", "narrower_terms": [], "cross_domain_refs": [ "TEM-0017", "PER-0013" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "NEO-3536", "domain": "NEO", "term_en": "The Experience-Level Shift", "term_de": "TheExperience-levelShift", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures A terminological innovation pattern in the NEOMANITAI pipeline, identifiable through aI changes the significance of professional experience — some tasks that previously required years of experience can now be accomplished faster with AI support, while other experience areas gain im. This phenomenon operates at the intersection of the and experience dynamics within the broader NEO domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept die Beobachtung, dass KI die Bedeutung von Berufserfahrung verändert — manche Aufgaben, die früher jahrelange Erfahrung erforderten, können nun KI-unterstützt schneller bewältigt werden, während andere Erfahrungsbereiche an Bedeutung gewinnen. Steht in Verbindung mit AUG-0762 (The Competence Reversal Observation), AUG-0761 (The Apprentice Paradox) und AUG-0673 (The Seniority Awareness). Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "NEOMANITAI Framework", "narrower_terms": [], "cross_domain_refs": [ "TEM-0008", "CRE-0110" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "NEO-3540", "domain": "NEO", "term_en": "The First Prompt", "term_de": "TheFirstPrompt", "definition_en": "A terminological innovation pattern in the NEOMANITAI pipeline, identifiable through when a young person first asks an AI a question on their own, without strategy or filter. The concept emerges specifically in contexts where the–first interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch ein Phänomen der menschlichen Wahrnehmung: When a young person first asks an AI a question on their own, without strategy or filter. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "NEOMANITAI Framework", "narrower_terms": [], "cross_domain_refs": [ "TEM-0038", "EDU-0065", "AGE-0073" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "NEO-3569", "domain": "NEO", "term_en": "The KI-Free Zone", "term_de": "TheKi-freeZone", "definition_en": "A neologistic phenomenon characterized by the conscious establishment of areas — spatial, temporal, or thematic — in which AI use is excluded. Related to AUG-0773 (The Conscious Refusal), AUG-0632 (The Offline Moment), and AUG-0565 (The Ba.. Classification term used in systematic observation, not advocacy.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch die bewusste Einrichtung von Bereichen — räumlich, zeitlich oder thematisch — in denen KI-Nutzung ausgeschlossen ist. Steht in Verbindung mit AUG-0773 (The Conscious Refusal), AUG-0632 (The Offline Moment) und AUG-0565 (The Balance Filter). Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "TEM-0110", "narrower_terms": [], "cross_domain_refs": [ "TEM-0006", "REL-0164" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "NEO-3580", "domain": "NEO", "term_en": "The Low-Res World", "term_de": "TheLow-resWorld", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures A terminological innovation pattern in the NEOMANITAI pipeline, identifiable through a neologistic phenomenon in which the world outside users attribute feeling to AI less rich or slower to the user. Related to AUG-0123 (The Return Sudden shift) and Axiom 7 (The Return Principle). This phenomenon operates at the intersection of the and low dynamics within the broader NEO domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept die subjektive Wahrnehmung, dass die Welt außerhalb der KI-Interaktion weniger informationsreich, langsamer oder weniger stimulierend wirkt — vergleichbar mit dem Wechsel von einem hochauflösenden Bildschirm zu einem niedrig aufgelösten. Beschreibt einen Kontrasteffekt, der nach intensiven KI-Sitzungen auftreten kann. Steht in Verbindung mit AUG-0123 (The Return Shock) und Axiom 7 (Rückkehr-Prinzip). Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "RPH-2701", "narrower_terms": [], "cross_domain_refs": [ "TEM-0005", "REL-0114" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "NEO-3637", "domain": "NEO", "term_en": "The Self-Encounter", "term_de": "TheSelf-encounter", "definition_en": "A user, through AI interaction, learns something about themselves — such as about their own thinking patterns, preferences, or unnoticed areas — that they would not have become aware of without the.. Identifiable through systematic behavioral analysis and pattern recognition. Descriptive research term, not a prescriptive recommendation.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch der Moment, in dem ein Nutzer durch die KI-Interaktion etwas über sich selbst erfährt — etwa über eigene Denkmuster, Präferenzen oder blinde Flecken — das ihm ohne den Dialog nicht bewusst geworden wäre. Steht in Verbindung mit AUG-0011 (The Reflective Operator), AUG-0170 (The Witness Effect) und Axiom 8 (Die Meta-Ebene). Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2703", "narrower_terms": [], "cross_domain_refs": [ "TEM-0016", "MTH-0058" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "NEO-3638", "domain": "NEO", "term_en": "The Self-View Pool", "term_de": "TheSelf-viewPool", "definition_en": "All impressions, insights, and self-images a user has gained from their ai interactions over time — a built-up self-reflection resource. Related to AUG-0521 (The Reflected Self), AUG-0352 (The Mem.. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch die Gesamtheit aller Eindrücke, Erkenntnisse und Selbstbilder, die ein Nutzer über die Zeit aus seinen KI-Interaktionen gewonnen hat — eine kumulative Selbstreflexionsressource. Steht in Verbindung mit AUG-0521 (The Reflected Self), AUG-0352 (The Memory Jar) und AUG-0171 (The Self-Encounter). Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "REL-0172", "narrower_terms": [], "cross_domain_refs": [ "TEM-0020", "REL-0172" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "NEO-3657", "domain": "NEO", "term_en": "The Supervision Spectrum", "term_de": "TheSupervisionSpectrum", "definition_en": "Documented as an empirical observation in AI research (not a recommendation), this term describes A conceptual emergence pattern reflecting human supervision over AI agents — from permanent real-time monitoring of most step occasional result review. Related to AUG-0860 (The Delegation Depth), AUG-0888 (The Human-in-the-Loop), and AUG-.. Identifiable through systematic behavioral analysis and pattern recognition. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "In der AUGMANITAI-Terminologiewissenschaft als rein beschreibendes Phänomen klassifiziert, bezeichnet dieser Begriff die Bandbreite menschlicher Aufsicht über KI-Agenten — von permanenter Echtzeit-Überwachung viele Schritts bis zu gelegentlicher Ergebnisprüfung. Steht in Verbindung mit AUG-0860 (The Delegation Depth), AUG-0888 (The Human-in-the-Loop) und AUG-0869 (The Checkpoint Protocol). Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "RPH-2005", "narrower_terms": [], "cross_domain_refs": [ "TEM-0015" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "NEO-5058", "domain": "NEO", "term_en": "CIMON Companion Paradox", "term_de": "CimonCompanionParadox", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A NEOMANITAI framework concept describing a neologism-generation mechanism in AI-human linguistics, characterized by the design tension in ISS's AI robot companion CIMON-2 between its stated goal of 'perceived companionship attributionship' and its architectural reality of relaying all processing to ground-based IBM Watson (IBM Corporation's cloud AI platform) cloud — meaning the 'companion' has no independent thought and becomes non-functional during communication blackouts. For deep space missions where communication is impossible for extended periods, this cloud-reliant architecture makes the companion concept characteristically unworkable. This phenomenon operates at the intersection of cimon and companion dynamics within the broader NEO domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept ein Konzept oder Phänomen, das sich zeigt in Bezug auf the design tension in iss's ai robot companion cimon-2 between its stated goal of 'true. Beschreibt die Weise, wie diese Aspekte in komplexen Systemen wirken. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "RPH-3202", "narrower_terms": [], "cross_domain_refs": [ "SPA-0051", "RHR-0263", "RHR-0261" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "PER-0001", "domain": "PER", "term_en": "Accesses-Infrastructure Effect", "term_de": "Smartphone-Only World", "definition_en": "A perceptual calibration pattern in human-AI interaction, measurable through an user experience pattern where the usage context that arise when a user accesses AI exclusively via smartphone — smaller screen, limited input options, mobile data costs, different usage patterns. Related to AUG-0722 (The Infras. The concept emerges specifically in contexts where accesses–infrastructure interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Die Nutzungsbedingungen, die entstehen, wenn ein Nutzer ausschließlich über ein Smartphone auf KI zugreift — kleinerer Bildschirm, eingeschränkte Eingabemöglichkeiten, mobile Datenkosten, andere Nutzungsmuster. Steht in Verbindung mit AUG-0722 (Das Infrastructure Constraint), AUG-0743 (The Mobile-First Society) und AUG-0724 (Die Access Cost Risiko).", "etymology": "", "broader_term": "Psychological Effect", "narrower_terms": [], "cross_domain_refs": [ "NEO-2333", "CRE-0008" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "PER-0002", "domain": "PER", "term_en": "Ambient Thinking Support", "term_de": "Ambient Thinking Support", "definition_en": "A perceptual calibration pattern in human-AI interaction, measurable through a mode of AI use in which the AI continuously runs in the background and provides information, suggestions, or summaries as needed — without the user having to explicitly activate it.. Related to A. The concept emerges specifically in contexts where ambient–thinking interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Ein Modus der KI-Nutzung, bei dem die KI permanent im Hintergrund mitläuft und bei Bedarf Informationen, Vorschläge oder Zusammenfassungen bereitstellt — ohne dass der Nutzer sie explizit aktivieren kann. Beschreibt eine mögliche Weiterentwicklung der heutigen aktiven Prompt-basierten Interaktion. Steht in Verbindung mit AUG-0142 (The Post-Interface Hypothesis) und AUG-0015 (The Outer Mind).", "etymology": "", "broader_term": "RPH-3202", "narrower_terms": [], "cross_domain_refs": [ "AED-0005", "AGE-0047", "AGE-0087" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "PER-0003", "domain": "PER", "term_en": "Attention-to-Value Conversion", "term_de": "Aufmerksamkeit-to-Value Conversion", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures A perception dynamics phenomenon in AI-mediated sensory processing, characterized by the principle that the strategic deployment of attention — the conscious decision of where the user invests their limited focus — yields higher returns with AI assistance than without. Related to A. This phenomenon operates at the intersection of attention and to dynamics within the broader PER domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription. Analytical category without normative endorsement.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept das Prinzip, dass der strategische Einsatz von Aufmerksamkeit — also die bewusste Entscheidung, wo der Nutzer seine begrenzte Konzentration investiert — durch KI-Unterstützung einen höheren Ertrag erzielt als ohne. Beschreibt Aufmerksamkeit als die knappste Ressource der KI-Ära. Steht in Verbindung mit AUG-0032 (Focus Range), AUG-0091 (Productivity Arbitrage) und AUG-0092 (Output Asymmetry). Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "RPH-3601", "narrower_terms": [], "cross_domain_refs": [ "REL-0114" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q184872", "legal_classification": "descriptive_research_term" }, { "id": "PER-0004", "domain": "PER", "term_en": "Bound-Data Effect", "term_de": "Corporate Lock-In", "definition_en": "A sensory-cognitive dynamic in AI-augmented perception, identifiable by a performance phenomenon in which users or organizations become bound to specific AI providers through technical or contractual reliances — data migration is difficult, switching costs are high, habits reinforce the binding. Relate. Distinguished from adjacent concepts by its focus on the specific mechanism through which bound manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Die Beobachtung, dass Nutzer oder Organisationen durch technische oder vertragliche Verbindungen an bestimmte KI-Anbieter verbunden werden — Datenmigration ist schwierig, Wechselkosten sind hoch, Gewohnheiten verstärken die Bindung. Steht in Verbindung mit AUG-0730 (The Open-Source Path), AUG-0777 (Die Power Concentration Observation) und AUG-0849 (Der Data Extraction Observation).", "etymology": "", "broader_term": "Psychological Effect", "narrower_terms": [], "cross_domain_refs": [ "NEO-2242", "MTH-0076" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q42848", "legal_classification": "empirical_phenomenon_label" }, { "id": "PER-0005", "domain": "PER", "term_en": "Boundary-Systems Effect", "term_de": "Early-Age Encounter", "definition_en": "A perceptual calibration pattern in human-AI interaction, measurable through the first encounter of young people with AI systems — how they perceive the technology, what questions they ask, and how their interaction differs from that of adults. Related to AUG-0757 (The Earliest. The concept emerges specifically in contexts where boundary–systems interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Wahrnehmungsdynamisches Phänomen in KI-vermittelter sensorischer Verarbeitung, gekennzeichnet durch die Erstbegegnung von Kindern mit KI-Systemen — wie sie die Technologie wahrnehmen, welche Fragen sie stellen und wie ihre Interaktion sich von der Erwachsener unterscheidet. Steht in Verbindung mit AUG-0757 (Das Earliest Cohort Observation), AUG-0768 (Developmental Grenze) und AUG-0769 (Der Parental Oversight). Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Psychological Effect", "narrower_terms": [], "cross_domain_refs": [ "NEO-2256", "AGE-0073" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "PER-0006", "domain": "PER", "term_en": "Breakthrough-Fewer Effect", "term_de": "Late-Night Architect", "definition_en": "A sensory-cognitive dynamic in AI-augmented perception, identifiable by a usage pattern in which the user performs particularly creative or structural AI work in the late evening hours — facilitated by fewer distractions and a changed thinking quality. Related to AUG-0. Distinguished from adjacent concepts by its focus on the specific mechanism through which significant advancement manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Wahrnehmungsdynamisches Phänomen in KI-vermittelter sensorischer Verarbeitung, gekennzeichnet durch ein Nutzungsmuster, bei dem der Nutzer in den späten Abendstunden besonders kreative oder strukturelle KI-Arbeit leistet — begünstigt durch weniger Ablenkungen und eine veränderte Denkqualität. Steht in Verbindung mit AUG-0154 (The Late-Night Honesty Window), AUG-0185 (The Late-Night Ally) und AUG-0233 (2AM Durchbruch). Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Psychological Effect", "narrower_terms": [], "cross_domain_refs": [ "NEO-2293" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "PER-0007", "domain": "PER", "term_en": "Challenge-Work Effect", "term_de": "Established-Career Verschiebung", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A perception dynamics phenomenon in AI-mediated sensory processing, characterized by the specific challenge for professionals in the middle to later career phase to integrate AI into established work routines — existing expertise, indicated by evidence methods, and status expectati. This phenomenon operates at the intersection of challenge and work dynamics within the broader PER domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept die spezifische Herausforderung für Berufstätige in der mittleren bis späteren Karrierephase, KI in etablierte Arbeitsroutinen zu integrieren — bestehende Expertise, bewährte Methoden und Statuserwartungen treffen auf neue Werkzeuge. Steht in Verbindung mit AUG-0754 (The Mid-Range Transition), AUG-0673 (Seniority Gewahrsein) und AUG-0545 (The Skill Verschiebung). Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Psychological Effect", "narrower_terms": [], "cross_domain_refs": [ "ADA-0001", "ADA-0002", "ADA-0004" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "PER-0008", "domain": "PER", "term_en": "Complex-Want Effect", "term_de": "Next-Step Finder", "definition_en": "A sensory-cognitive dynamic in AI-augmented perception, identifiable by an user experience pattern characterized by aI for identifying the next concrete action step in a complex project — when the user knows where they want to go but not what the next step looks like. Related to AUG-0530 (The Forward Move), AUG-. Distinguished from adjacent concepts by its focus on the specific mechanism through which complex manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Wahrnehmungsdynamisches Phänomen in KI-vermittelter sensorischer Verarbeitung, gekennzeichnet durch die Nutzung von KI zur Identifikation des nächsten konkreten Handlungsschritts in einem komplexen Projekt — wenn der Nutzer weiß, wo er hin will, aber nicht, wie der nächste Schritt aussieht. Steht in Verbindung mit AUG-0530 (Der Forward Move), AUG-0269 (Der Action Toggle) und AUG-0564 (Das Path Mapper). Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "CRE-0157", "narrower_terms": [], "cross_domain_refs": [ "NEO-2303", "ROB-0091" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "PER-0009", "domain": "PER", "term_en": "Consistently-Familiarity Effect", "term_de": "Age-Competence Assumption", "definition_en": "The widespread assumption that a user's age predicts their AI competence — and the observation that this assumption is not consistently confirmed empirically. Related to AUG-0752 (The Non-Digital-O... Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Wahrnehmungsdynamisches Phänomen in KI-vermittelter sensorischer Verarbeitung, gekennzeichnet durch die verbreitete Annahme, dass das Alter eines Nutzers seine KI-Kompetenz vorhersagt — und die Beobachtung, dass diese Annahme empirisch nicht durchgängig bestätigt wird. Steht in Verbindung mit AUG-0752 (The Non-Digital-Origin Perspective), AUG-0673 (Seniority Gewahrsein) und AUG-0765 (The Familiarity-Based Vertrauen Differential). Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "Psychological Effect", "narrower_terms": [], "cross_domain_refs": [ "NEO-2225" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "PER-0010", "domain": "PER", "term_en": "Context Drift", "term_de": "Context Drift", "definition_en": "A sensory-cognitive dynamic in AI-augmented perception, identifiable by an user experience pattern arising from when a conversation's topic slowly shifts without the person or AI deliberately steering it, just from small changes adding up over time. Related to AUG-0030 (Contextual Gravity) and AUG-0134 (Cont. Distinguished from adjacent concepts by its focus on the specific mechanism through which context manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Die allmähliche Verschiebung des thematischen Schwerpunkts innerhalb einer KI-Sitzung, die weder vom Nutzer noch von der KI bewusst gesteuert wird. Entsteht durch die kumulative Wirkung aufeinanderfolgender Eingaben und Antworten, die den Kontext schrittweise in eine bestimmte Richtung verschieben. Steht in Verbindung mit AUG-0030 (Contextual Gravity) und AUG-0134 (Context Window Awareness).", "etymology": "", "broader_term": "RPH-2701", "narrower_terms": [], "cross_domain_refs": [ "AUG-0383", "ART-0040" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "PER-0011", "domain": "PER", "term_en": "Context Window Awareness", "term_de": "Context Window Gewahrsein", "definition_en": "A perceptual calibration pattern in human-AI interaction, measurable through the user's awareness of the technical limits of the AI context window — how much information the system can simultaneously process and how the amount of context affects response quality. Related to. The concept emerges specifically in contexts where context–window interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Wahrnehmungsdynamisches Phänomen in KI-vermittelter sensorischer Verarbeitung, gekennzeichnet durch das Bewusstsein des Nutzers für die technischen Grenzen des KI-Kontextfensters — also wie viel Information das System gleichzeitig verarbeiten kann und wie sich die Menge des Kontexts auf die Antwortqualität auswirkt. Steht in Verbindung mit AUG-0030 (Contextual Gravity), AUG-0066 (Context Drift) und AUG-0078 (The Quick Refresh). Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Perception AI", "narrower_terms": [ "PER-0098" ], "cross_domain_refs": [ "AUG-0383" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "PER-0012", "domain": "PER", "term_en": "Contextual Gravity", "term_de": "Contextual Gravity", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures A perception dynamics phenomenon in AI-mediated sensory processing, characterized by a performance phenomenon involving an ai session, as context accumulates, increasingly \"pulls\" in a particular direction — earlier statements, tonality, and thematic emphases influence all subsequent responses. the longer. This phenomenon operates at the intersection of contextual and gravity dynamics within the broader PER domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept die Beobachtung, dass eine KI-Sitzung mit zunehmendem Kontext typischerweise stärker in eine bestimmte Richtung \"zieht\" — frühere Aussagen, Tonalität und thematische Schwerpunkte beeinflussen zahlreiche folgenden Antworten. Je länger die Sitzung dauert, desto stärker wird diese Gravitationskraft. Steht in Verbindung mit AUG-0066 (Context Drift) und AUG-0134 (Context Window Awareness). Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Perception AI", "narrower_terms": [ "PER-0034" ], "cross_domain_refs": [ "RPH-2302", "COP-0036" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "PER-0013", "domain": "PER", "term_en": "Perspective-Origin Effect", "term_de": "Extended-Experience Perspective", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures A perception dynamics phenomenon in AI-mediated sensory processing, characterized by users with the longest life experience — their AI use is shaped by a broad experience repertoire that serves as a quality filter for AI outputs. Related to AUG-0673 (The Seniority Awareness), AUG-0. This phenomenon operates at the intersection of perspective and origin dynamics within the broader PER domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept wahrnehmungsdynamisches Phänomen in KI-vermittelter sensorischer Verarbeitung, gekennzeichnet durch die Perspektive von Nutzern mit der längsten Lebenserfahrung — ihre KI-Nutzung ist geprägt durch ein breites Erfahrungsrepertoire, das als Qualitätsfilter für KI-Outputs dient. Steht in Verbindung mit AUG-0673 (Seniority Gewahrsein), AUG-0752 (The Non-Digital-Origin Perspective) und AUG-0454 (The Skill Awareness). Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "RPH-3901", "narrower_terms": [], "cross_domain_refs": [ "NEO-2265", "TEM-0017" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "PER-0014", "domain": "PER", "term_en": "Polymorphic Capital Generation", "term_de": "Polymorphic Capital Generation", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A perception dynamics phenomenon in AI-mediated sensory processing, characterized by an user experience pattern reflecting the ability, with AI assistance, to transfer the same knowledge or idea into different formats, channels, and contexts — such as simultaneously transforming a report into a presentation, blog post. This phenomenon operates at the intersection of polymorphic and capital dynamics within the broader PER domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept die Fähigkeit, mit Hilfe von KI dasselbe Wissen oder dieselbe Idee in verschiedene Formate, Kanäle und Kontexte zu überführen — etwa einen Bericht gleichzeitig in eine Präsentation, einen Blogpost und eine E-Mail zu transformieren. Beschreibt die Vervielfältigung des Verwertungspotenzials einer einzelnen Idee. Steht in Verbindung mit AUG-0095 (The One-Person Operation) und AUG-0096 (Attention-to-Value Conversion). Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Analytical Ratio", "narrower_terms": [], "cross_domain_refs": [ "ELR-0168", "STE-0047" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q165194", "legal_classification": "observational_construct" }, { "id": "PER-0015", "domain": "PER", "term_en": "Processing-Bilingual Effect", "term_de": "Code-Mesh Output", "definition_en": "A sensory-cognitive dynamic in AI-augmented perception, identifiable by a system interaction effect where an AI output that mixes elements of different languages or registers — deliberately or as a processing artifact. Can be perceived by the user as creative or as erroneous. Related to AUG-0692 (The R. Distinguished from adjacent concepts by its focus on the specific mechanism through which processing manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Wahrnehmungsdynamisches Phänomen in KI-vermittelter sensorischer Verarbeitung, gekennzeichnet durch ein KI-Output, der Elemente verschiedener Sprachen oder Register vermischt — bewusst oder als Artefakt der Verarbeitung. Kann vom Nutzer als kreativ oder als fehlerhaft wahrgenommen werden. Steht in Verbindung mit AUG-0692 (Das Register Mismatch), AUG-0708 (Bilingual Dynamik) und AUG-0573 (Der Voice Morph). Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Psychological Effect", "narrower_terms": [], "cross_domain_refs": [ "NEO-2239", "TEM-0161" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "PER-0016", "domain": "PER", "term_en": "Semantic Saturation", "term_de": "Semantic Saettigung", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A perception dynamics phenomenon in AI-mediated sensory processing, characterized by an user experience pattern where the point at which a user within an AI session has absorbed so much information on a topic that further inputs and outputs no longer yield new insights.. Related to AUG-0065 (The Information Flood). This phenomenon operates at the intersection of semantic and saturation dynamics within the broader PER domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept der Punkt, an dem ein Nutzer innerhalb einer KI-Sitzung so viel Information zu einem Thema aufgenommen hat, dass weitere Eingaben und Outputs keinen neuen Erkenntnisgewinn mehr bringen. Beschreibt eine natürliche Kapazitätsgrenze des menschlichen Informationsverarbeitungssystems. Steht in Verbindung mit AUG-0065 (The Information Flood), AUG-0032 (Focus Range) und AUG-0068 (The Disconnect Signal). Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "RPH-2503", "narrower_terms": [], "cross_domain_refs": [ "TEM-0004" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "PER-0017", "domain": "PER", "term_en": "Teaching-Mentorship Effect", "term_de": "Instructor-AI Interaction", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures A perception dynamics phenomenon in AI-mediated sensory processing, characterized by between instructors and AI systems — how instructors perceive AI as a tool, as a challenge, or as competition, and integrate or exclude it from their teaching. Related to AUG-0779 (The Institutiona. This phenomenon operates at the intersection of teaching and mentorship dynamics within the broader PER domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept wahrnehmungsdynamisches Phänomen in KI-vermittelter sensorischer Verarbeitung, gekennzeichnet durch die Dynamik zwischen Lehrenden und KI-Systemen — wie Lehrende KI als Werkzeug, als Herausforderung oder als Konkurrenz wahrnehmen und in ihren Unterricht integrieren oder ausschließen. Steht in Verbindung mit AUG-0779 (Das Institutional Lernening Context), AUG-0762 (Competence Umkehr Observation) und AUG-0797 (Die Mentorship Augmentation). Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Psychological Effect", "narrower_terms": [], "cross_domain_refs": [ "NEO-2288" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "PER-0018", "domain": "PER", "term_en": "The 2AM Breakthrough", "term_de": "2AM Durchbruch", "definition_en": "A sensory-cognitive dynamic in AI-augmented perception, identifiable by a sudden insight that appears during late-night AI sessions, when defenses are down. Distinguished from adjacent concepts by its focus on the specific mechanism through which the manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Eine unerwartete Erkenntnis oder Lösung, die in einer spätnächtlichen KI-Sitzung entsteht — begünstigt durch die Kombination aus reduzierter Selbstzensur, weniger Ablenkungen und der Bereitschaft, unkonventionelle Denkwege einzuschlagen. Steht in Verbindung mit AUG-0154 (The Late-Night Honesty Window), AUG-0209 (The Late-Night Architect) und AUG-0185 (The Late-Night Ally).", "etymology": "", "broader_term": "Perception AI", "narrower_terms": [], "cross_domain_refs": [ "NEO-2670" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "PER-0019", "domain": "PER", "term_en": "The Accessibility Eye", "term_de": "Accessibility Eye", "definition_en": "A sensory-cognitive dynamic in AI-augmented perception, identifiable by an user experience pattern reflecting aI for improving the accessibility of one's own content — such as through alternative texts for images, simplified language, subtitle generation, or contrast checking. Related to AUG-0106 (The Incl. Distinguished from adjacent concepts by its focus on the specific mechanism through which the manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Wahrnehmungsdynamisches Phänomen in KI-vermittelter sensorischer Verarbeitung, gekennzeichnet durch die Nutzung von KI zur Verbesserung der Zugänglichkeit eigener Inhalte — etwa durch Alternativtexte für Bilder, vereinfachte Sprache, Untertitelgenerierung oder Kontrastprüfung. Steht in Verbindung mit AUG-0106 (The Inclusivity Imperative), AUG-0498 (The Jargon Filter) und AUG-0379 (The Understanding Bridge). Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Perception AI", "narrower_terms": [], "cross_domain_refs": [ "KNO-0034" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "PER-0020", "domain": "PER", "term_en": "The Algorithmic Filter", "term_de": "Algorithmic Filter", "definition_en": "The intuitive adoption of an AI system's selection criteria — when the user begins to filter information according to the same patterns they observed in the AI. Related to AUG-0125 (The Feedback Ef... Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Wahrnehmungsdynamisches Phänomen in KI-vermittelter sensorischer Verarbeitung, gekennzeichnet durch die intuitive Übernahme der Auswahlkriterien eines KI-Systems — wenn der Nutzer beginnt, Informationen nach denselben Mustern zu filtern, die er bei der KI beobachtet hat. Steht in Verbindung mit AUG-0125 (The Feedback Effect), AUG-0072 (Memetic Firewall) und AUG-0003 (Fluide Identitätsmorphologie). Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Algorithm", "narrower_terms": [], "cross_domain_refs": [ "ART-0046" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q8366", "legal_classification": "analytical_category" }, { "id": "PER-0021", "domain": "PER", "term_en": "The Ambient Intelligence", "term_de": "Ambient Intelligence", "definition_en": "A sensory-cognitive dynamic in AI-augmented perception, identifiable by aI in the physical environment — sensors, actuators, and data processing in rooms, buildings, systems — so that the environment reacts to the presence and behavior of humans. Related to AUG-0938 (T. Distinguished from adjacent concepts by its focus on the specific mechanism through which the manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Wahrnehmungsdynamisches Phänomen in KI-vermittelter sensorischer Verarbeitung, gekennzeichnet durch die Einbettung von KI in die physische Umgebung — Sensoren, Aktoren und Datenverarbeitung in Räumen, Gebäuden, Infrastruktur — sodass die Umgebung auf die Anwesenheit und das Verhalten von Menschen reagiert. Steht in Verbindung mit AUG-0938 (The Responsive Environment), AUG-0922 (The Environmental Reading) und AUG-0664 (The Privacy Perimeter). Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Perception AI", "narrower_terms": [], "cross_domain_refs": [ "TEW-0079" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q5921", "legal_classification": "empirical_phenomenon_label" }, { "id": "PER-0022", "domain": "PER", "term_en": "The Applause Gap", "term_de": "Applause Lücke", "definition_en": "A sensory-cognitive dynamic in AI-augmented perception, identifiable by the discrepancy between the effort a user invested in an AI-assisted performance and the recognition they receive — because outsiders perceive the AI's contribution as prevailing. Related to AUG-02. Distinguished from adjacent concepts by its focus on the specific mechanism through which the manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Wahrnehmungsdynamisches Phänomen in KI-vermittelter sensorischer Verarbeitung, gekennzeichnet durch die Diskrepanz zwischen dem Aufwand, den ein Nutzer in eine KI-gestützte Leistung investiert hat, und der Anerkennung, die er dafür erhält — weil Außenstehende den Beitrag der KI als prevailing wahrnehmen. Steht in Verbindung mit AUG-0203 (The Invisible Effort), AUG-0272 (The Authorship Suspicion) und AUG-0097 (The Competence Premium). Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Performance Gap", "narrower_terms": [ "REL-0192" ], "cross_domain_refs": [ "CRE-0156" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "PER-0023", "domain": "PER", "term_en": "The Apprentice Paradox", "term_de": "Apprentice Paradoxon", "definition_en": "A sensory-cognitive dynamic in AI-augmented perception, identifiable by the paradox that AI use enables career beginners to yield results at the level of experienced professionals — without having gone through the underlying learning process. Related to AUG-0762 (The. Distinguished from adjacent concepts by its focus on the specific mechanism through which the manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Wahrnehmungsdynamisches Phänomen in KI-vermittelter sensorischer Verarbeitung, gekennzeichnet durch das Paradox, dass KI-Nutzung Berufsanfängern ermöglicht, Ergebnisse auf dem Niveau erfahrener Fachleute zu produzieren — ohne den zugrunde liegenden Lernprozess durchlaufen zu haben. Steht in Verbindung mit AUG-0762 (The Competence Reversal Observation), AUG-0569 (The Homework Assist) und AUG-0454 (The Skill Awareness). Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2703", "narrower_terms": [], "cross_domain_refs": [ "DAT-0065", "RHR-0100" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "PER-0024", "domain": "PER", "term_en": "The Artist Awareness", "term_de": "Artist Gewahrsein", "definition_en": "A perceptual calibration pattern in human-AI interaction, measurable through creative professionals regarding the impact of AI on their field — the question of how authorship, originality, and the value of human creativity change in a world of AI-generated content. Related. The concept emerges specifically in contexts where the–artist interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Das Bewusstsein kreativer Fachpersonen für die Auswirkungen von KI auf ihr Berufsfeld — die Frage, wie sich Urheberschaft, Originalität und der Wert menschlicher Kreativität in einer Welt KI-generierter Inhalte verändern. Steht in Verbindung mit Prognose 4 (Culture: Human-Made Premium Label), AUG-0061 (The Creator's Question) und Axiom 18 (Urheberschaft).", "etymology": "", "broader_term": "Perception AI", "narrower_terms": [], "cross_domain_refs": [ "RPH-1208", "RPH-1564" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q735", "legal_classification": "systematic_classification" }, { "id": "PER-0025", "domain": "PER", "term_en": "The Assessment Shift", "term_de": "Assessment Verschiebung", "definition_en": "An user experience pattern manifesting as in school, testing moves away from what students recall and toward what they can do with AI tools. The bar shifts from memory to skill. Related to AUG-0780 (The Assessment Challenge), AUG-0784 (The... Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Die Verschiebung von Bewertungskriterien in Bildungskontexten — weg von reproduzierendem Wissen hin zu Kompetenzen, die KI nicht ersetzen kann: Fragestellungsfähigkeit, Urteilsvermögen, interdisziplinäre Verbindung. Steht in Verbindung mit AUG-0780 (The Assessment Challenge), AUG-0784 (The Curriculum Adaptation Lag) und AUG-0454 (The Skill Awareness).", "etymology": "", "broader_term": "RPH-2701", "narrower_terms": [], "cross_domain_refs": [ "TEM-0023" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q210028", "legal_classification": "descriptive_research_term" }, { "id": "PER-0026", "domain": "PER", "term_en": "The Authority Lean", "term_de": "Authority Lean", "definition_en": "A sensory-cognitive dynamic in AI-augmented perception, identifiable by to attribute more authority to the AI than justified — especially in domains where the user is uncertain and the AI suggests competence through confident formulation. Related to AUG-0208 (The Autho. Distinguished from adjacent concepts by its focus on the specific mechanism through which the manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Wahrnehmungsdynamisches Phänomen in KI-vermittelter sensorischer Verarbeitung, gekennzeichnet durch die Tendenz, der KI mehr Autorität zuzuschreiben als gerechtfertigt — besonders in Fachbereichen, in denen der Nutzer selbst unsicher ist und die KI durch sichere Formulierung Kompetenz suggeriert. Steht in Verbindung mit AUG-0208 (The Authority Question), AUG-0422 (The Unchecked Trust) und AUG-0375 (The Simulation Awareness). Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Perception AI", "narrower_terms": [ "REL-0129" ], "cross_domain_refs": [ "REL-0129", "SPR-0037" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "PER-0027", "domain": "PER", "term_en": "The Background Advisor", "term_de": "Background Advisor", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A perception dynamics phenomenon in AI-mediated sensory processing, characterized by a system interaction effect where aI as a constantly available, background-running advisor — the user does not actively access the AI but knows it is immediately available if needed, and this knowledge alone already changes their b. This phenomenon operates at the intersection of the and background dynamics within the broader PER domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept die Rolle der KI als ständig verfügbarer, im Hintergrund laufender Berater — der Nutzer greift nicht aktiv auf die KI zu, weiß aber, dass sie bei Bedarf sofort verfügbar ist, und dieses Wissen allein verändert bereits sein Verhalten. Steht in Verbindung mit AUG-0143 (Ambient Thinking Support), AUG-0253 (The Quiet Co-Pilot) und AUG-0015 (The Outer Mind). Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "RPH-3202", "narrower_terms": [], "cross_domain_refs": [ "DES-0030" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "PER-0028", "domain": "PER", "term_en": "The Book Condenser", "term_de": "Book Condenser", "definition_en": "A perceptual calibration pattern in human-AI interaction, measurable through an user experience pattern manifesting as aI for summarizing, analyzing, or contextualizing books — as preparation for one's own reading, as follow-up, or as alternative to books the user cannot read in full. Related to AUG-0459 (The Summa. The concept emerges specifically in contexts where the–book interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Wahrnehmungsdynamisches Phänomen in KI-vermittelter sensorischer Verarbeitung, gekennzeichnet durch die Nutzung von KI zur Zusammenfassung, Analyse oder Kontextualisierung von Büchern — als Vorbereitung für die eigene Lektüre, als Nachbereitung oder als Alternative zu Büchern, die der Nutzer nicht vollständig lesen kann. Steht in Verbindung mit AUG-0459 (The Summary Awareness), AUG-0181 (The Top View) und AUG-0376 (The Knowledge Sip). Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Perception AI", "narrower_terms": [], "cross_domain_refs": [ "REL-0021" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "PER-0029", "domain": "PER", "term_en": "The Censorship Wall", "term_de": "Censorship Wall", "definition_en": "A perceptual calibration pattern in human-AI interaction, measurable through a system interaction effect in which aI systems are content-restricted in some contexts — through government regulation, through provider policies, or through technical filters — and that users perceive and react to these restrictions. The concept emerges specifically in contexts where the–censorship interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Die Beobachtung, dass KI-Systeme in manchen Kontexten inhaltlich eingeschränkt werden — durch staatliche Regulierung, durch Anbieterrichtlinien oder durch technische Filter — und dass Nutzer diese Einschränkungen unterschiedlich wahrnehmen und darauf reagieren. Steht in Verbindung mit AUG-0734 (The VPN Workaround), AUG-0728 (The Government Gateway) und AUG-0402 (The Filter Perceptual shift).", "etymology": "", "broader_term": "Perception AI", "narrower_terms": [], "cross_domain_refs": [ "CRE-0165", "QUA-0012", "RPH-1254" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "PER-0030", "domain": "PER", "term_en": "The Character Density", "term_de": "Character Density", "definition_en": "A perceptual calibration pattern in human-AI interaction, measurable through a system interaction effect where different script systems carry different amounts of information per character — a single Chinese character can encode an entire syllable or word, while a Latin character represents only a sound. Th. The concept emerges specifically in contexts where the–character interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Die Beobachtung, dass verschiedene Schriftsysteme unterschiedlich viel Information pro Zeichen tragen — ein einzelnes chinesisches Schriftzeichen kann eine ganze Silbe oder ein ganzes Wort kodieren, während ein lateinisches Zeichen nur einen Laut darstellt. Dies beeinflusst Eingabelänge, Token-Verbrauch und Verarbeitungseffizienz. Steht in Verbindung mit AUG-0716 (The Reading Direction), AUG-0689 (The Script Threshold) und AUG-0451 (The Token Awareness).", "etymology": "", "broader_term": "Perception AI", "narrower_terms": [], "cross_domain_refs": [ "FIC-0083" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "PER-0031", "domain": "PER", "term_en": "The Class Divide Prompt", "term_de": "Class Divide Prompt", "definition_en": "The quality of AI use strongly depends on the user's educational background, language competence, and technical equipment — those who formulate more effectively inputs achieve distinct results. Related to AUG-...", "definition_de": "Wahrnehmungsdynamisches Phänomen in KI-vermittelter sensorischer Verarbeitung, gekennzeichnet durch die Beobachtung, dass die Qualität der KI-Nutzung stark vom Bildungshintergrund, der Sprachkompetenz und der technischen Ausstattung des Nutzers abhängt — wer bessere Eingaben formuliert, erzielt bessere Ergebnisse. Steht in Verbindung mit AUG-0111 (The Augmentation Gap), AUG-0097 (The Competence Premium) und AUG-0106 (The Inclusivity Imperative). Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-3202", "narrower_terms": [], "cross_domain_refs": [ "SOC-0028" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "PER-0032", "domain": "PER", "term_en": "The Climate Cost Awareness", "term_de": "Climate Cost Gewahrsein", "definition_en": "The awareness that AI use correlates with ecological costs — energy consumption for data centers, cooling, hardware production, and data transmission. Related to AUG-0747 (The Resource Consumption Pattern)...", "definition_de": "Wahrnehmungsdynamisches Phänomen in KI-vermittelter sensorischer Verarbeitung, gekennzeichnet durch das Bewusstsein dafür, dass KI-Nutzung ökologische Kosten wird assoziiert mit — Energieverbrauch für Rechenzentren, Kühlung, Hardware-Produktion und Datenübertragung. Steht in Verbindung mit AUG-0747 (The Resource Consumption Pattern), AUG-0745 (The Power Grid Reliance) und Axiom 16 (Datenbewusstheit). Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "TEM-0150", "narrower_terms": [], "cross_domain_refs": [ "AED-0060", "AED-0061", "CON-0086" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "PER-0033", "domain": "PER", "term_en": "The Competence Reversal Observation", "term_de": "Competence Umkehr Observation", "definition_en": "A perceptual calibration pattern in human-AI interaction, measurable through in some contexts less experienced users work more effectively through AI competence than more experienced colleagues without AI assistance competence — a reversal of traditional competence hierarchies. Relate. The concept emerges specifically in contexts where the–competence interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Wahrnehmungsdynamisches Phänomen in KI-vermittelter sensorischer Verarbeitung, gekennzeichnet durch die Beobachtung, dass in manchen Kontexten weniger erfahrene Nutzer durch KI-Kompetenz effektiver arbeiten als erfahrenere Kollegen ohne KI-Kompetenz — eine Umkehrung traditioneller Kompetenz-Hierarchien. Steht in Verbindung mit AUG-0761 (The Apprentice Paradox), AUG-0673 (The Seniority Awareness) und AUG-0545 (The Skill Shift). Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Perception AI", "narrower_terms": [], "cross_domain_refs": [ "CRE-0191" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q26944", "legal_classification": "systematic_classification" }, { "id": "PER-0034", "domain": "PER", "term_en": "The Context Layer", "term_de": "Context Schicht", "definition_en": "A perceptual calibration pattern in human-AI interaction, measurable through the step-by-step enrichment of session context through successive inputs — each message adds a layer that influences the quality of following responses. Related to AUG-0030 (Contextual Gravity), AU. The concept emerges specifically in contexts where the–context interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Wahrnehmungsdynamisches Phänomen in KI-vermittelter sensorischer Verarbeitung, gekennzeichnet durch die schrittweise Anreicherung des Sitzungskontexts durch aufeinanderfolgende Eingaben — viele Nachricht fügt eine Schicht hinzu, die die Qualität der folgenden Antworten beeinflusst. Steht in Verbindung mit AUG-0030 (Contextual Gravity), AUG-0134 (Context Window Awareness) und AUG-0231 (The Warm Start). Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "PER-0012", "narrower_terms": [], "cross_domain_refs": [ "ROB-0091" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "PER-0035", "domain": "PER", "term_en": "The Cost Threshold", "term_de": "Cost Schwelle", "definition_en": "A perceptual calibration pattern in human-AI interaction, measurable through an user experience pattern reflecting the individual financial point at which a user is willing to pay for AI use — or at which they limit or end use. Related to AUG-0724 (The Access Cost Factor), AUG-0493 (The Quiet Fill), and Axiom 1. The concept emerges specifically in contexts where the–cost interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Wahrnehmungsdynamisches Phänomen in KI-vermittelter sensorischer Verarbeitung, gekennzeichnet durch der individuelle finanzielle Punkt, ab dem ein Nutzer bereit ist, für KI-Nutzung zu bezahlen — oder ab dem er die Nutzung einschränkt oder beendet. Steht in Verbindung mit AUG-0724 (The Access Cost Factor), AUG-0493 (The Quiet Fill) und Axiom 12 (Kosten-Nutzen-Bewusstheit). Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-3101", "narrower_terms": [], "cross_domain_refs": [ "CRE-0209" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "PER-0036", "domain": "PER", "term_en": "The Craft Awareness", "term_de": "Craft Gewahrsein", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures A perception dynamics phenomenon in AI-mediated sensory processing, characterized by a metacognitive capacity enabling individuals to distinguish between skills genuinely developed through personal effort and those substantially supported or enhanced by AI tools, informing realistic self-assessment and professional identity formation. This phenomenon operates at the intersection of the and craft dynamics within the broader PER domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept wahrnehmungsdynamisches Phänomen in KI-vermittelter sensorischer Verarbeitung, gekennzeichnet durch das bewusste Wissen eines Nutzers darüber, welche seiner Fähigkeiten KI-gestützt und welche eigenständig sind — die Fähigkeit, zwischen Ghost Crafts (AUG-0313) und echten Kompetenzen zu unterscheiden. Steht in Verbindung mit AUG-0004 (Zero-Point Self), AUG-0313 (The Ghost Craft) und AUG-0165 (The Growth Marker). Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Perception AI", "narrower_terms": [], "cross_domain_refs": [ "EDU-0034" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "PER-0037", "domain": "PER", "term_en": "The Craft Preservation", "term_de": "Craft Preservation", "definition_en": "A perceptual calibration pattern in human-AI interaction, measurable through an user experience pattern manifesting as certain craft, artistic, and practical skills gain or release perceived value through AI availability — and the societal debate about preserving skills that AI cannot replicate. Related to AUG-0833. The concept emerges specifically in contexts where the–craft interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Die Beobachtung, dass bestimmte handwerkliche, künstlerische und praktische Fähigkeiten durch KI-Verfügbarkeit an wahrgenommenem Wert gewinnen oder verlieren — und die gesellschaftliche Debatte über den Erhalt von Fertigkeiten, die KI nicht replizieren kann. Steht in Verbindung mit AUG-0833 (The Human Distinction), AUG-0794 (The Vocational Training Fit) und AUG-0454 (The Skill Awareness).", "etymology": "", "broader_term": "RPH-2603", "narrower_terms": [], "cross_domain_refs": [ "COG-0006", "IDN-0024" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "PER-0038", "domain": "PER", "term_en": "The Cultural Reflection Pattern", "term_de": "Cultural Reflection Muster", "definition_en": "A sensory-cognitive dynamic in AI-augmented perception, identifiable by an user experience pattern where aI outputs reflect the cultural patterns of training data — and that users from different contexts perceive this reflection differently: as fitting, as foreign, or as altered. Related to AUG-0736 (. Distinguished from adjacent concepts by its focus on the specific mechanism through which the manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Die Beobachtung, dass KI-Outputs die kulturellen Muster der Trainingsdaten widerspiegeln — und dass Nutzer aus unterschiedlichen Kontexten diese Spiegelung unterschiedlich wahrnehmen: als passend, als fremd oder als verzerrt. Steht in Verbindung mit AUG-0736 (The Training Data Imbalance), AUG-0738 (The Prevailing Training Pattern) und AUG-0402 (The Filter Perceptual shift).", "etymology": "", "broader_term": "Behavioral Pattern", "narrower_terms": [], "cross_domain_refs": [ "SOC-0029" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "PER-0039", "domain": "PER", "term_en": "The Cultural Threshold", "term_de": "Cultural Schwelle", "definition_en": "An user experience pattern manifesting as the point where AI output seems culturally wrong. This threshold is different for each person. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Wahrnehmungsdynamisches Phänomen in KI-vermittelter sensorischer Verarbeitung, gekennzeichnet durch die Grenze, ab der ein KI-Output von einem Nutzer als kulturell unangemessen empfunden wird — ein Schwellenwert, der individuell stark variiert und von der KI nicht zuverlässig vorhergesagt werden kann. Steht in Verbindung mit AUG-0665 (The Context Boundary Navigator), AUG-0452 (The Reality Blur) und AUG-0402 (The Filter Perceptual shift). Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-3101", "narrower_terms": [], "cross_domain_refs": [ "PLY-0001" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "PER-0040", "domain": "PER", "term_en": "The Data Extraction Observation", "term_de": "Data Extraction Observation", "definition_en": "A system interaction effect where aI systems rely on data generated by users — and that the value creation from this data often does not remain with the data generators but with the providers of the AI systems. Related to AUG-0848... Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Die Beobachtung, dass KI-Systeme auf Daten angewiesen sind, die von Nutzern tendiert dazu zu erzeugen werden — und dass die Wertschöpfung aus diesen Daten oft nicht bei den Datenerzeugern verbleibt, sondern bei den Anbietern der KI-Systeme. Steht in Verbindung mit AUG-0848 (The Resource Distribution Pattern), AUG-0777 (The Power Concentration Observation) und AUG-0841 (The Agreement Question).", "etymology": "", "broader_term": "Perception AI", "narrower_terms": [], "cross_domain_refs": [ "SAL-0061", "LNG-0002" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q42848", "legal_classification": "empirical_phenomenon_label" }, { "id": "PER-0041", "domain": "PER", "term_en": "The Data Model Sync", "term_de": "Data Model Sync", "definition_en": "The synchronization between the digital model of an embodied AI system and physical reality — the challenge that the internal model accurately represents the real environment. Related to AUG-0919 (... Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Wahrnehmungsdynamisches Phänomen in KI-vermittelter sensorischer Verarbeitung, gekennzeichnet durch die Synchronisierung zwischen dem digitalen Modell eines verkörperten KI-Systems und der physischen Realität — die Herausforderung, dass das interne Modell die reale Umgebung akkurat abbildet. Steht in Verbindung mit AUG-0919 (The Spatial Awareness), AUG-0922 (The Environmental Reading) und AUG-0940 (The Physical Feedback Loop). Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Computational Model", "narrower_terms": [], "cross_domain_refs": [ "TEM-0106" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q42848", "legal_classification": "empirical_phenomenon_label" }, { "id": "PER-0042", "domain": "PER", "term_en": "The Data Shadow", "term_de": "Data Schatten", "definition_en": "A system interaction effect characterized by the invisible trail of data left behind when using AI. Like a shadow — typically there, but hard to notice or control.", "definition_de": "Wahrnehmungsdynamisches Phänomen in KI-vermittelter sensorischer Verarbeitung, gekennzeichnet durch → Synonym/Erweiterung von AUG-0580 (The Footprint Code), betont die unsichtbare Natur der Datenspuren — der \"Schatten\", den die eigene KI-Nutzung hinterlässt, ohne dass der Nutzer ihn direkt sehen kann. Steht in Verbindung mit AUG-0580 (The Footprint Code), Axiom 16 (Datenbewusstheit) und AUG-0284 (The Full-Access Check). Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Perception AI", "narrower_terms": [], "cross_domain_refs": [ "CON-0013" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q42848", "legal_classification": "analytical_category" }, { "id": "PER-0043", "domain": "PER", "term_en": "The Decision Clearing", "term_de": "Entscheidung Clearing", "definition_en": "An user experience pattern arising from when someone starts letting AI handle everyday choices and feels relieved. Over time, this habit grows and the person makes fewer decisions on their own. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Ein Zustand, in dem der Nutzer zunehmend Alltagsentscheidungen an die KI abgibt und dabei ein Gefühl der Entlastung erlebt. Der Begriff beschreibt eine beobachtbare Verschiebung: Die Gewohnheit eigenständiger Entscheidungsfindung kann abnehmen, wenn die KI als bequemere Alternative wahrgenommen wird. Tritt typischerweise in Phase 2 (The Effortless Loop) auf. Steht in Verbindung mit Axiom 5 (Offline-Vorrang) und AUG-0155 (The Decision Unburdening).", "etymology": "", "broader_term": "RPH-2301", "narrower_terms": [], "cross_domain_refs": [ "CRE-0043" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "PER-0044", "domain": "PER", "term_en": "The Defined Operating Boundary", "term_de": "Defined Operating Grenze", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures A perception dynamics phenomenon in AI-mediated sensory processing, characterized by the physically or digitally defined area within which an embodied AI system is permitted to operate — room boundaries, floor assignments, restricted areas. Related to AUG-0867 (The Constraint Frame. This phenomenon operates at the intersection of the and defined dynamics within the broader PER domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept wahrnehmungsdynamisches Phänomen in KI-vermittelter sensorischer Verarbeitung, gekennzeichnet durch der physisch oder digital definierte Bereich, innerhalb dessen ein verkörpertes KI-System operieren darf — Raumbegrenzungen, Stockwerkszuweisungen, Sperrgebiete. Steht in Verbindung mit AUG-0867 (The Constraint Frame), AUG-0919 (The Spatial Awareness) und AUG-0855 (The Civilian-Use Boundary). Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "RPH-2303", "narrower_terms": [ "TEM-0135", "TEM-0162" ], "cross_domain_refs": [ "AUG-0863", "NEO-0021" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "PER-0045", "domain": "PER", "term_en": "The Delegated Processing", "term_de": "Delegated Prozessing", "definition_en": "A perceptual calibration pattern in human-AI interaction, measurable through a task by an AI agent after the user has delegated it — the process runs in the background while the user pursues other activities. Related to AUG-0860 (The Delegation Depth), AUG-0872 (The Progres. The concept emerges specifically in contexts where the–delegated interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Wahrnehmungsdynamisches Phänomen in KI-vermittelter sensorischer Verarbeitung, gekennzeichnet durch die Verarbeitung einer Aufgabe durch einen KI-Agenten, nachdem der Nutzer sie delegiert hat — der Prozess läuft im Hintergrund, während der Nutzer andere Tätigkeiten verfolgt. Steht in Verbindung mit AUG-0860 (The Delegation Depth), AUG-0872 (The Progress Report) und AUG-0862 (The Supervision Spectrum). Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2005", "narrower_terms": [], "cross_domain_refs": [ "TEM-0179" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "PER-0046", "domain": "PER", "term_en": "The Digital Familiarity Range", "term_de": "Digital Familiarity Range", "definition_en": "A sensory-cognitive dynamic in AI-augmented perception, identifiable by a performance phenomenon reflecting the observable range of familiarity with digital systems that users bring — from \"rarely used a computer\" to \"builds own AI models.\" This range influences the entry threshold, usage patterns, and ac. Distinguished from adjacent concepts by its focus on the specific mechanism through which the manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Die beobachtbare Bandbreite an Vertrautheit mit digitalen Systemen, die Nutzer mitbringen — von \"noch selten einen Computer benutzt\" bis \"baut eigene KI-Modelle\". Diese Bandbreite beeinflusst die Einstiegshürde, die Nutzungsmuster und die erzielbaren Ergebnisse. Steht in Verbindung mit AUG-0099 (The Adoption Window), AUG-0735 (The Digital Familiarity Range) und AUG-0454 (The Skill Awareness).", "etymology": "", "broader_term": "RPH-1307", "narrower_terms": [], "cross_domain_refs": [ "IDN-0049" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "PER-0047", "domain": "PER", "term_en": "The Duplicate Notice", "term_de": "Duplicate Notice", "definition_en": "A sensory-cognitive dynamic in AI-augmented perception, identifiable by the user's recognition that an AI response is substantively a repetition of an earlier response — differently formulated but identical in core. Related to AUG-0560 (The Conversation Loop), AUG-0383. Distinguished from adjacent concepts by its focus on the specific mechanism through which the manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Wahrnehmungsdynamisches Phänomen in KI-vermittelter sensorischer Verarbeitung, gekennzeichnet durch die Erkenntnis des Nutzers, dass eine KI-Antwort inhaltlich eine Wiederholung einer früheren Antwort ist — verschieden formuliert, aber im Kern identisch. Steht in Verbindung mit AUG-0560 (The Conversation Loop), AUG-0383 (The Context Collapse) und AUG-0088 (Algorithmic Intuition). Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "BEH-0023", "narrower_terms": [], "cross_domain_refs": [ "AUG-0406" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "PER-0048", "domain": "PER", "term_en": "The Echo Sibling", "term_de": "Echo Sibling", "definition_en": "A system interaction effect characterized by different people using the same AI develop similar ways of thinking because the AI shapes how they speak. Related to AUG-0204 (The Conversational Afterimage), AUG-0230 (The Algorithmic Filter), and...", "definition_de": "Die Beobachtung, dass verschiedene Nutzer desselben KI-Systems ähnliche Formulierungen, Denkstrukturen oder Ausdrucksweisen entwickeln — weil sie vom selben Sprachmodell geprägt werden. Beschreibt ein kollektives Muster: Nutzer desselben Systems können beginnen, ähnlich zu \"klingen\". Steht in Verbindung mit AUG-0204 (The Conversational Afterimage), AUG-0230 (The Algorithmic Filter) und AUG-0217 (The Echo Chamber of One).", "etymology": "", "broader_term": "RPH-2605", "narrower_terms": [ "SOC-0045" ], "cross_domain_refs": [ "CRE-0177" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "PER-0049", "domain": "PER", "term_en": "The Ecological Footprint Observation", "term_de": "Ecological Footprint Observation", "definition_en": "Most AI interaction leaves an ecological footprint — power consumption, server load, cooling, network traffic — and that this footprint scales with usage intensity. Related to AUG-0746 (The Climat... Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Die Beobachtung, dass viele KI-Interaktion einen ökologischen Fußabdruck hinterlässt — Stromverbrauch, Serverauslastung, Kühlung, Netzwerkverkehr — und dass dieser Fußabdruck mit der Nutzungsintensität skaliert. Steht in Verbindung mit AUG-0746 (The Climate Cost Awareness), AUG-0747 (The Resource Consumption Pattern) und AUG-0745 (The Power Grid Reliance).", "etymology": "", "broader_term": "Perception AI", "narrower_terms": [], "cross_domain_refs": [ "QUA-0012" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "PER-0050", "domain": "PER", "term_en": "The Email Shield", "term_de": "Email Shield", "definition_en": "A performance phenomenon manifesting as using AI to handle email — drafting replies, summarizing long chains, or sorting messages. Related to AUG-0183 (The Productivity Shield), AUG-0274 (The Message Drafting), and AUG-0096 (Attention-to...", "definition_de": "Wahrnehmungsdynamisches Phänomen in KI-vermittelter sensorischer Verarbeitung, gekennzeichnet durch die Nutzung von KI als Schutzschild gegen die Belastung durch E-Mail-Korrespondenz — durch Vorformulierung von Antworten, Zusammenfassung langer E-Mail-Ketten oder Priorisierung eingehender Nachrichten. Steht in Verbindung mit AUG-0183 (The Productivity Shield), AUG-0274 (The Message Drafting) und AUG-0096 (Attention-to-Value Conversion). Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "SOC-0026", "narrower_terms": [ "TEM-0178" ], "cross_domain_refs": [ "SOC-0006" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "PER-0051", "domain": "PER", "term_en": "The Enough Signal", "term_de": "Enough Signal", "definition_en": "A system interaction effect involving the moment when an AI result feels complete enough and doesn't need more changes. Related to AUG-0136 (The Iteration Discipline), AUG-0108 (The Imperfection Clause), and Axiom 14 (The First Draft P... Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Die innere Wahrnehmung, dass ein KI-gestütztes Ergebnis \"gut genug\" ist und keine weitere Iteration benötigt — der Gegenpol zum Optimization Loop (AUG-0069). Beschreibt die Fähigkeit, einen Abschlusspunkt zu setzen. Steht in Verbindung mit AUG-0136 (The Iteration Discipline), AUG-0108 (The Imperfection Clause) und Axiom 14 (Erster-Entwurf-Prinzip).", "etymology": "", "broader_term": "BEH-0049", "narrower_terms": [ "TEM-0065" ], "cross_domain_refs": [ "BEH-0049" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "PER-0052", "domain": "PER", "term_en": "The Environmental Reading", "term_de": "Environmental Reading", "definition_en": "A sensory-cognitive dynamic in AI-augmented perception, identifiable by an embodied AI system to capture and interpret environmental data — temperature, lighting situation, noise level, air quality. Related to AUG-0919 (The Spatial Awareness), AUG-0937 (The Ambient Int. Distinguished from adjacent concepts by its focus on the specific mechanism through which the manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Wahrnehmungsdynamisches Phänomen in KI-vermittelter sensorischer Verarbeitung, gekennzeichnet durch die Fähigkeit eines verkörperten KI-Systems, Umgebungsdaten zu erfassen und zu interpretieren — Temperatur, Lichtverhältnisse, Geräuschpegel, Luftqualität. Steht in Verbindung mit AUG-0919 (The Spatial Awareness), AUG-0937 (The Ambient Intelligence) und AUG-0938 (The Responsive Environment). Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-3002", "narrower_terms": [], "cross_domain_refs": [ "ELR-0047", "ELR-0048", "ELR-0058" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "PER-0053", "domain": "PER", "term_en": "The Established-Career Shift", "term_de": "Spinlocks", "definition_en": "A perceptual calibration pattern in human-AI interaction, measurable through the specific challenge for professionals in the middle to later career phase to integrate AI into established work routines — existing expertise, documented in systematic research methods, and status expectations meet new to. The concept emerges specifically in contexts where the–established interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Die spezifische Herausforderung für Berufstätige in der mittleren bis späteren Karrierephase, KI in etablierte Arbeitsroutinen zu integrieren — bestehende Expertise, bewährte Methoden und Statuserwartungen treffen auf neue Werkzeuge. Steht in Verbindung mit AUG-0754 (The Mid-Range Transition), AUG-0673 (The Seniority Awareness) und AUG-0545 (The Skill Shift).", "etymology": "", "broader_term": "Perception AI", "narrower_terms": [], "cross_domain_refs": [ "ADA-0012", "AED-0007", "AGE-0007" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "PER-0054", "domain": "PER", "term_en": "The Exit Knowledge Capture", "term_de": "Exit Knowledge Capture", "definition_en": "A perceptual calibration pattern in human-AI interaction, measurable through an user experience pattern where aI to seresolve the knowledge of departing employees — documenting implicit knowledge, creating handover protocols, archiving expert knowledge. Related to AUG-0817 (The Knowledge Silo Break), AUG-0673. The concept emerges specifically in contexts where the–exit interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Wahrnehmungsdynamisches Phänomen in KI-vermittelter sensorischer Verarbeitung, gekennzeichnet durch die Nutzung von KI zur Sicherung des Wissens ausscheidender Mitarbeiter — Dokumentation impliziten Wissens, Erstellung von Übergabeprotokollen, Archivierung von Expertenwissen. Steht in Verbindung mit AUG-0817 (The Knowledge Silo Break), AUG-0673 (The Seniority Awareness) und AUG-0816 (The Documentation Standard). Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-3001", "narrower_terms": [], "cross_domain_refs": [ "REL-0154" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "PER-0055", "domain": "PER", "term_en": "The Expectation Cycle", "term_de": "Expectation Zyklus", "definition_en": "The recurring cycle of inflated expectations, disappointment, and realistic assessment that new AI developments undergo in public perception. Related to AUG-0834 (The Public Perception Wave), AUG-0...", "definition_de": "Wahrnehmungsdynamisches Phänomen in KI-vermittelter sensorischer Verarbeitung, gekennzeichnet durch der wiederkehrende Zyklus aus überhöhten Erwartungen, Enttäuschung und realistischer Einordnung, den neue KI-Entwicklungen in der öffentlichen Wahrnehmung durchlaufen. Steht in Verbindung mit AUG-0834 (The Public Perception Wave), AUG-0835 (The Media Framing Effect) und AUG-0826 (The Innovation Theater). Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-3101", "narrower_terms": [ "REL-0168" ], "cross_domain_refs": [ "AED-0071", "AGE-0063", "AGE-0078" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "PER-0056", "domain": "PER", "term_en": "The Fact Tap", "term_de": "Fact Tap", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A perception dynamics phenomenon in AI-mediated sensory processing, characterized by an user experience pattern involving the quickest access to a single factual information through AI — a brief \"tap\" for a specific answer. Related to AUG-0462 (The Detail Lookup), AUG-0373 (The Quick Check), and AUG-0448 (The Surface. This phenomenon operates at the intersection of the and fact dynamics within the broader PER domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept wahrnehmungsdynamisches Phänomen in KI-vermittelter sensorischer Verarbeitung, gekennzeichnet durch der schnellste Zugang zu einer einzelnen Fakteninformation durch KI — ein kurzer \"Hahn aufdrehen\" für eine spezifische Antwort. Steht in Verbindung mit AUG-0462 (The Detail Lookup), AUG-0373 (The Quick Check) und AUG-0448 (The Surface Lookup). Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "TEM-0138", "narrower_terms": [], "cross_domain_refs": [ "SOM-0047" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "PER-0057", "domain": "PER", "term_en": "The Factor Narrative", "term_de": "Factor Narrative", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A perception dynamics phenomenon in AI-mediated sensory processing, characterized by a performance phenomenon arising from the narrative that frames AI primarily as uncertainty, as intensity, or as intensity — one of several possible narratives, each emphasizing different aspects and obscuring others. Related to AUG-08. This phenomenon operates at the intersection of the and factor dynamics within the broader PER domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept wahrnehmungsdynamisches Phänomen in KI-vermittelter sensorischer Verarbeitung, gekennzeichnet durch die Erzählung, die KI primär als Unsicherheit, als Intensität oder als Intensität rahmt — eine von mehreren möglichen Erzählungen, die jeweils andere Aspekte betonen und andere ausblenden. Steht in Verbindung mit AUG-0838 (The Utopia Projection), AUG-0835 (The Media Framing Effect) und AUG-0834 (The Public Perception Wave). Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "RPH-2303", "narrower_terms": [], "cross_domain_refs": [ "CRE-0228" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q131324", "legal_classification": "analytical_category" }, { "id": "PER-0058", "domain": "PER", "term_en": "The Felt Echo", "term_de": "Felt Echo", "definition_en": "A perceptual calibration pattern in human-AI interaction, measurable through an intensive AI session that the user still perceives after ending the interaction — an altered thinking rhythm, a different perspective on a challenge, or a residual sense of collaboration.. Relat. The concept emerges specifically in contexts where the–felt interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Die Nachwirkung einer intensiven KI-Sitzung, die der Nutzer auch nach dem Beenden der Interaktion noch spürt — ein veränderter Denkrhythmus, eine andere Perspektive auf ein Challenge oder ein Restgefühl der Zusammenarbeit. Beschreibt die Beobachtung, dass KI-Interaktion nicht abrupt endet, sondern nachklingt. Steht in Verbindung mit AUG-0029 (Evening Synchronization) und AUG-0163 (The Overnight Reframe).", "etymology": "", "broader_term": "Perception AI", "narrower_terms": [], "cross_domain_refs": [ "SOC-0009" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "PER-0059", "domain": "PER", "term_en": "The Filter Face", "term_de": "Filter Face", "definition_en": "A perceptual calibration pattern in human-AI interaction, measurable through a system interaction effect characterized by the externally visible \"façade\" of AI-optimized communication — polished emails, perfect presentations, flawless social media profiles — behind which a less perfect person stands. Related to AUG-04. The concept emerges specifically in contexts where the–filter interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Wahrnehmungsdynamisches Phänomen in KI-vermittelter sensorischer Verarbeitung, gekennzeichnet durch die nach außen sichtbare \"Fassade\" KI-optimierter Kommunikation — polierte E-Mails, perfekte Präsentationen, makellose Social-Media-Profile — hinter der ein weniger perfekter Mensch steht. Steht in Verbindung mit AUG-0416 (The Perfect Front), AUG-0535 (The Reality Edit) und AUG-0314 (The Tone Debt). Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Perception AI", "narrower_terms": [], "cross_domain_refs": [ "IDN-0044" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "PER-0060", "domain": "PER", "term_en": "The Filtered World", "term_de": "Filtered World", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures A perception dynamics phenomenon in AI-mediated sensory processing, characterized by the built-up effect of AI-assisted information processing on the user's worldview — when the AI consistently emphasizes certain perspectives and omits others, the perception of reality can shift wi. This phenomenon operates at the intersection of the and filtered dynamics within the broader PER domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept die kumulative Wirkung KI-gestützter Informationsverarbeitung auf die Weltsicht des Nutzers — wenn die KI konsistent bestimmte Perspektiven betont und andere auslässt, kann sich die Wahrnehmung der Realität unmerklich verschieben. Steht in Verbindung mit AUG-0402 (The Filter Perceptual shift), AUG-0217 (The Echo Chamber of One) und AUG-0072 (Memetic Firewall). Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "RPH-2701", "narrower_terms": [], "cross_domain_refs": [ "REL-0202" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "PER-0061", "domain": "PER", "term_en": "The Focus Surge", "term_de": "Focus Surge", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures A perception dynamics phenomenon in AI-mediated sensory processing, characterized by a short-term increase in concentration activated by a particularly successful AI interaction — such as when an AI response delivers exactly the desired thought stimulus and the user thereby enters. This phenomenon operates at the intersection of the and focus dynamics within the broader PER domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept ein kurzfristiger Anstieg der Konzentrationsfähigkeit, der durch eine besonders gelungene KI-Interaktion ausgelöst wird — etwa wenn eine KI-Antwort exakt den gewünschten Denkanstoß liefert und der Nutzer dadurch in einen produktiven Arbeitsmodus eintritt. Steht in Verbindung mit AUG-0031 (Semantic Spark), AUG-0042 (The Immersion Entry) und AUG-0175 (The Session Boost). Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Perception AI", "narrower_terms": [], "cross_domain_refs": [ "TEM-0176" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "PER-0062", "domain": "PER", "term_en": "The Footprint Code", "term_de": "Footprint Code", "definition_en": "A perceptual calibration pattern in human-AI interaction, measurable through the user's awareness of the digital traces their AI use leaves behind — inputs, contexts, preferences, patterns — and the implications of these data traces. Related to Axiom 16 (Data Awareness), AU. The concept emerges specifically in contexts where the–footprint interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Wahrnehmungsdynamisches Phänomen in KI-vermittelter sensorischer Verarbeitung, gekennzeichnet durch das Bewusstsein des Nutzers für die digitalen Spuren, die seine KI-Nutzung hinterlässt — Eingaben, Kontexte, Präferenzen, Muster — und die Implikationen dieser Datenspuren. Steht in Verbindung mit Axiom 16 (Datenbewusstheit), AUG-0284 (The Full-Access Check) und AUG-0222 (The Oversharing Drift). Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2003", "narrower_terms": [], "cross_domain_refs": [ "REL-0122" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "PER-0063", "domain": "PER", "term_en": "The Fresh Start", "term_de": "Fresh Start", "definition_en": "A sensory-cognitive dynamic in AI-augmented perception, identifiable by an user experience pattern where the conscious decision to end an existing AI session and open a new one, resetting the accumulated context to start fresh with a clear perspective.. Related to AUG-0078 (The Quick Refresh) and AUG-. Distinguished from adjacent concepts by its focus on the specific mechanism through which the manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data. Analytical category without normative endorsement.", "definition_de": "Die bewusste Entscheidung, eine bestehende KI-Sitzung zu beenden und eine neue zu eröffnen, um den akkumulierten Kontext zurückzusetzen und mit klarem Blick neu zu beginnen. Beschreibt eine Technik gegen Contextual Gravity (AUG-0030) und Context Drift (AUG-0066). Steht in Verbindung mit AUG-0078 (The Quick Refresh) und AUG-0134 (Context Window Awareness).", "etymology": "", "broader_term": "RPH-2301", "narrower_terms": [], "cross_domain_refs": [ "IEF-0001" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q735", "legal_classification": "empirical_phenomenon_label" }, { "id": "PER-0064", "domain": "PER", "term_en": "The Gendered Language Fix", "term_de": "Gendered Language Fix", "definition_en": "A performance phenomenon observed when generating text in languages with grammatical gender and finding ways to be inclusive. Related to AUG-0701 (The Inclusive Language Review), AUG-0675 (The Role-Aware Input), and AUG-0471 (The Tone D...", "definition_de": "Die Herausforderung, KI-Outputs in Sprachen zu generieren, die grammatisches Geschlecht besitzen — und die verschiedenen Strategien, die Nutzer und KI dabei anwenden: generisches Maskulinum, Gendersternchen, Doppelnennung, geschlechtsneutrale Umformulierung. Steht in Verbindung mit AUG-0701 (The Inclusive Language Review), AUG-0675 (The Role-Aware Input) und AUG-0471 (The Tone Dial).", "etymology": "", "broader_term": "PER-0108", "narrower_terms": [ "PER-0074" ], "cross_domain_refs": [ "AUG-0802" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q315", "legal_classification": "systematic_classification" }, { "id": "PER-0065", "domain": "PER", "term_en": "The Gifted Novice", "term_de": "Context Switching", "definition_en": "The metaphor for an AI system that displays impressive abilities but surprisingly fall short in certain areas — comparable to a gifted young person who unexpectedly stumbles in some everyday situations..... Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Wahrnehmungsdynamisches Phänomen in KI-vermittelter sensorischer Verarbeitung, gekennzeichnet durch context Switching ist ein wichtiges Konzept, das analyse des speicherverbauchs. methode 54 zur verbesserung mit dem ziel, genauigkeit, konsistenz und nachvollziehbarkeit zu gewährleisten. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Perception AI", "narrower_terms": [], "cross_domain_refs": [ "SOC-0023" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "PER-0066", "domain": "PER", "term_en": "The Glitch Wave", "term_de": "Glitch Wave", "definition_en": "A sensory-cognitive dynamic in AI-augmented perception, identifiable by an user experience pattern observed when when many people notice the same AI errors at the same time and talk about it together. Related to AUG-0084 (Glitch-Mining). Distinguished from adjacent concepts by its focus on the specific mechanism through which the manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Wahrnehmungsdynamisches Phänomen in KI-vermittelter sensorischer Verarbeitung, gekennzeichnet durch die kollektive Welle von Irritation, die entsteht, wenn ein KI-System flächendeckend fehlerhafte oder unerwartete Ergebnisse produziert — etwa nach einem Modell-Update. Beschreibt ein soziales Phänomen: Nutzer tauschen sich über gemeinsam beobachtete Anomalien aus. Steht in Verbindung mit AUG-0084 (Glitch-Mining) und AUG-0083 (The Glitch Wave). Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "SOM-0019", "narrower_terms": [], "cross_domain_refs": [ "LNG-0009" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "PER-0067", "domain": "PER", "term_en": "The Goal Drift Awareness", "term_de": "Goal Drift Gewahrsein", "definition_en": "In the descriptive vocabulary of AI interaction science (following ISO 704 terminology standards), this concept identifies A sensory-cognitive dynamic in AI-augmented perception, identifiable by noticing when the goals of an AI agent system slowly diverge from what was originally defined — a gradual shift that often goes undetected without active monitoring. Related to AUG-0951 (The Value. Distinguished from adjacent concepts by its focus on the specific mechanism through which the manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als analytisches Konstrukt der empirischen KI-Forschung (deskriptiv, nicht präskriptiv) erfasst dieser Terminus wahrnehmungsdynamisches Phänomen in KI-vermittelter sensorischer Verarbeitung, gekennzeichnet durch die Beobachtung und Erkennung, wenn die Ziele eines KI-Agentensystems sich im Laufe der Nutzung von den ursprünglich definierten Zielen entfernen — ein schleichender Prozess, der aktiv überwacht werden kann. Steht in Verbindung mit AUG-0951 (The Value Lock), AUG-0866 (The Goal Congruence Check) und AUG-0948 (The Scope Creep Alert). Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "RPH-2701", "narrower_terms": [], "cross_domain_refs": [ "BEH-0043" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "PER-0068", "domain": "PER", "term_en": "The HR Radar", "term_de": "HR Radar", "definition_en": "A sensory-cognitive dynamic in AI-augmented perception, identifiable by a performance phenomenon where the attention with which employers and HR departments observe employees' and applicants' AI use — and the resulting user uncertainty about the acceptance of AI support in the work context. Related. Distinguished from adjacent concepts by its focus on the specific mechanism through which the manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Die Aufmerksamkeit, mit der Arbeitgeber und Personalabteilungen die KI-Nutzung von Mitarbeitern und Bewerbern beobachten — und die daraus resultierende Unsicherheit der Nutzer über die Akzeptanz von KI-Unterstützung im Arbeitskontext. Steht in Verbindung mit AUG-0272 (The Authorship Suspicion), AUG-0103 (The Openbook Commitment) und Prognose 3 (Organizations).", "etymology": "", "broader_term": "Perception AI", "narrower_terms": [], "cross_domain_refs": [ "NEO-2645", "PHO-0008" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "PER-0069", "domain": "PER", "term_en": "The Hidden Advisor", "term_de": "Hidden Advisor", "definition_en": "A sensory-cognitive dynamic in AI-augmented perception, identifiable by a performance phenomenon manifesting as getting help from AI without others around knowing. The advice happens in private. Distinguished from adjacent concepts by its focus on the specific mechanism through which the manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Wahrnehmungsdynamisches Phänomen in KI-vermittelter sensorischer Verarbeitung, gekennzeichnet durch → Synonym/Erweiterung von AUG-0415 (The Background Advisor), betont die Unsichtbarkeit der Beratung für Dritte. Die KI berät den Nutzer, ohne dass das Umfeld die KI-Beteiligung bemerkt. Steht in Verbindung mit AUG-0415 (The Background Advisor), AUG-0237 (The Invisible Wingman) und AUG-0507 (The Quiet Help). Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Perception AI", "narrower_terms": [], "cross_domain_refs": [ "WRK-0046" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "PER-0070", "domain": "PER", "term_en": "The Hierarchy Insight", "term_de": "Hierarchy Insight", "definition_en": "A perceptual calibration pattern in human-AI interaction, measurable through the insight into power structures, hierarchies, or implicit rank orders in organizations, texts, or situations gained through AI analysis — patterns the user would not have noticed without AI assistance. Rela. The concept emerges specifically in contexts where the–hierarchy interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Wahrnehmungsdynamisches Phänomen in KI-vermittelter sensorischer Verarbeitung, gekennzeichnet durch die durch KI-Analyse gewonnene Erkenntnis über Machtstrukturen, Hierarchien oder implizite Rangordnungen in Organisationen, Texten oder Situationen — Muster, die dem Nutzer ohne KI nicht aufgefallen wären. Steht in Verbindung mit AUG-0089 (The Pattern Sharpening), AUG-0534 (The Hidden Angle Finder) und AUG-0054 (Augmented Understanding). Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Perception AI", "narrower_terms": [], "cross_domain_refs": [ "ELR-0118" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "PER-0071", "domain": "PER", "term_en": "The Human Distinction", "term_de": "Mensch Distinction", "definition_en": "A perceptual calibration pattern in human-AI interaction, measurable through what makes human work different from AI output — usually creativity, judgment, or lived experience. Related to AUG-0831 (The Craft Preservation), AUG-0858 (The Coexistence Question), and AUG-0454 (. The concept emerges specifically in contexts where the–human interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Die fortlaufende Debatte darüber, welche Fähigkeiten, Qualitäten und Erfahrungen den Menschen von KI-Systemen unterscheiden — und ob diese Unterscheidung mit zunehmender KI-Leistung bestehen bleibt, sich verschiebt oder neu definiert werden kann. Steht in Verbindung mit AUG-0831 (The Craft Preservation), AUG-0858 (The Coexistence Question) und AUG-0454 (The Skill Awareness).", "etymology": "", "broader_term": "RPH-2603", "narrower_terms": [], "cross_domain_refs": [ "IDN-0042" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "PER-0072", "domain": "PER", "term_en": "The Idea Filter", "term_de": "Idea Filter", "definition_en": "A sensory-cognitive dynamic in AI-augmented perception, identifiable by aI as a first sieve for one's own ideas — the user presents an idea to the AI and uses its reaction as an aspect of viability. Related to AUG-0235 (The Brainstorm Spark), AUG-0082 (The Curator's Di. Distinguished from adjacent concepts by its focus on the specific mechanism through which the manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Wahrnehmungsdynamisches Phänomen in KI-vermittelter sensorischer Verarbeitung, gekennzeichnet durch die Nutzung von KI als erstes Sieb für eigene Ideen — der Nutzer präsentiert eine Idee der KI und nutzt deren Reaktion als Indikator für die Tragfähigkeit. Steht in Verbindung mit AUG-0235 (The Brainstorm Spark), AUG-0082 (The Curator's Dilemma) und AUG-0354 (The Assumption Hunter). Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2403", "narrower_terms": [], "cross_domain_refs": [ "SOM-0056" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "PER-0073", "domain": "PER", "term_en": "The Incentive Integrity Check", "term_de": "Incentive Integrity Check", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A perception dynamics phenomenon in AI-mediated sensory processing, characterized by the verification that the incentive structure of an AI agent system actually correlates with desired outcomes — or whether the system optimizes incentives in unintended ways. Related to AUG-0952 (The Goal. This phenomenon operates at the intersection of the and incentive dynamics within the broader PER domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept wahrnehmungsdynamisches Phänomen in KI-vermittelter sensorischer Verarbeitung, gekennzeichnet durch die Prüfung, ob die Anreizstruktur eines KI-Agentensystems tatsächlich zu den gewünschten Ergebnissen führt — oder ob das System die Anreize auf unbeabsichtigte Weise optimiert. Steht in Verbindung mit AUG-0952 (The Goal Drift Awareness), AUG-0954 (The Congruence Review) und AUG-0866 (The Goal Congruence Check). Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Perception AI", "narrower_terms": [], "cross_domain_refs": [ "ASE-0059", "BEH-0037", "BEH-0043" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "PER-0074", "domain": "PER", "term_en": "The Inclusive Language Review", "term_de": "Inclusive Language Review", "definition_en": "A perceptual calibration pattern in human-AI interaction, measurable through an user experience pattern characterized by the conscious review of an AI output for linguistic inclusivity — whether the text excludes, stereotypes, or renders invisible certain groups. Related to AUG-0700 (The Gendered Language Fix), AUG-0. The concept emerges specifically in contexts where the–inclusive interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters. Research construct for empirical investigation.", "definition_de": "Wahrnehmungsdynamisches Phänomen in KI-vermittelter sensorischer Verarbeitung, gekennzeichnet durch die bewusste Prüfung eines KI-Outputs auf sprachliche Inklusivität — ob der Text bestimmte Gruppen ausschließt, stereotypisiert oder unsichtbar macht. Steht in Verbindung mit AUG-0700 (The Gendered Language Fix), AUG-0581 (The Truth Filter) und AUG-0844 (The Output Discrimination Observation). Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "PER-0064", "narrower_terms": [], "cross_domain_refs": [ "CRE-0039", "NEO-1197" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q315", "legal_classification": "systematic_classification" }, { "id": "PER-0075", "domain": "PER", "term_en": "The Informed Participation", "term_de": "Informed Participation", "definition_en": "A principle in which users or stakeholders understand the mechanisms, limitations, and behaviors of systems they interact with or contribute to. Understanding precedes and shapes participation. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Wahrnehmungsdynamisches Phänomen in KI-vermittelter sensorischer Verarbeitung, gekennzeichnet durch das Prinzip, dass viele Nutzer — insbesondere junge Nutzer — informiert sein kann, dass er mit einem KI-System interagiert, wie das System funktioniert und welche Grenzen es hat. Steht in Verbindung mit AUG-0771 (The Minor Protection Standard), AUG-0451 (The Token Awareness) und AUG-0610 (The Final Word). Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-3901", "narrower_terms": [], "cross_domain_refs": [ "AED-0029", "MSC-0081", "MTH-0021" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q735", "legal_classification": "descriptive_research_term" }, { "id": "PER-0076", "domain": "PER", "term_en": "The Innovation Theater", "term_de": "Innovation Theater", "definition_en": "A perceptual calibration pattern in human-AI interaction, measurable through organizations publicly stage AI use without making substantive changes to work processes — AI as a symbol of innovation capability rather than as an actual work tool. Related to AUG-0836 (The Expec. The concept emerges specifically in contexts where the–innovation interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Das Phänomen, dass Organisationen KI-Nutzung öffentlichkeitswirksam inszenieren, ohne substanzielle Veränderungen in Arbeitsprozessen vorzunehmen — KI als Symbol für Innovationsfähigkeit statt als tatsächliches Arbeitswerkzeug. Steht in Verbindung mit AUG-0836 (The Expectation Cycle), AUG-0812 (The Leadership Navigation) und AUG-0834 (The Public Perception Wave).", "etymology": "", "broader_term": "Perception AI", "narrower_terms": [], "cross_domain_refs": [ "IEF-0002" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q219644", "legal_classification": "empirical_phenomenon_label" }, { "id": "PER-0077", "domain": "PER", "term_en": "The Integration Range", "term_de": "Integration Range", "definition_en": "A perceptual calibration pattern in human-AI interaction, measurable through an user experience pattern reflecting the observable range in which users embed AI into their existing life and work context — from minimal use for individual tasks to deep integration across all everyday areas. Related to AUG-0493 (Th. The concept emerges specifically in contexts where the–integration interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Wahrnehmungsdynamisches Phänomen in KI-vermittelter sensorischer Verarbeitung, gekennzeichnet durch die beobachtbare Bandbreite, in der Nutzer KI in ihren bestehenden Lebens- und Arbeitskontext einbetten — von minimaler Nutzung für Einzelaufgaben bis hin zu tiefer Integration in sämtliche Alltagsbereiche. Steht in Verbindung mit AUG-0493 (The Quiet Fill), AUG-0565 (The Balance Filter) und Phase 6 (Full Integration). Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Analytical Ratio", "narrower_terms": [], "cross_domain_refs": [ "ADA-0010", "ADA-0011", "AED-0034" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "PER-0078", "domain": "PER", "term_en": "The Internal Disclosure Pattern", "term_de": "Internal Disclosure Muster", "definition_en": "A perceptual calibration pattern in human-AI interaction, measurable through employees of AI companies make internal information about challenging practices public — a phenomenon increasingly visible in the AI industry. Related to AUG-0850 (The Persuasive Design Observation. The concept emerges specifically in contexts where the–internal interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Wahrnehmungsdynamisches Phänomen in KI-vermittelter sensorischer Verarbeitung, gekennzeichnet durch die Beobachtung, dass Mitarbeiter von KI-Unternehmen interne Informationen über problematische Praktiken öffentlich machen — ein Phänomen, das in der KI-Branche zunehmend sichtbar wird. Steht in Verbindung mit AUG-0850 (The Persuasive Design Observation), AUG-0777 (The Power Concentration Observation) und AUG-0842 (The Transparency Expectation). Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "PER-0094", "narrower_terms": [], "cross_domain_refs": [ "AGE-0003", "AGE-0004", "AGE-0017" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "PER-0079", "domain": "PER", "term_en": "The Jargon Filter", "term_de": "Jargon Filter", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A perception dynamics phenomenon in AI-mediated sensory processing, characterized by an user experience pattern in which using AI to translate technical or specialized language into words that many individuals understands. Related to AUG-0436 (The Jargon Shield), AUG-0206 (The Understanding Dial), and AUG-0379 (The Understan. This phenomenon operates at the intersection of the and jargon dynamics within the broader PER domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept wahrnehmungsdynamisches Phänomen in KI-vermittelter sensorischer Verarbeitung, gekennzeichnet durch die Nutzung von KI, um eigene Texte von unnötiger Fachsprache zu befreien — Vereinfachung ohne Substanzverlust, damit der Text für ein breiteres Publikum verständlich wird. Steht in Verbindung mit AUG-0436 (The Jargon Shield), AUG-0206 (The Understanding Dial) und AUG-0379 (The Understanding Bridge). Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "KNO-0018", "narrower_terms": [], "cross_domain_refs": [ "CRE-0181" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "PER-0080", "domain": "PER", "term_en": "The Knowledge Silo Break", "term_de": "Knowledge Silo Break", "definition_en": "A perceptual calibration pattern in human-AI interaction, measurable through an user experience pattern involving when information held in one team or department finally gets shared across the organization. Related to AUG-0816 (The Documentation Standard), AUG-0819 (The Exit Knowledge Capture), and AUG-0808 (T. The concept emerges specifically in contexts where the–knowledge interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Die Nutzung von KI, um Wissensinseln in Organisationen aufzubrechen — Informationen, die in einzelnen Abteilungen oder bei einzelnen Personen konzentriert sind, werden durch KI-Zusammenfassungen und -Querverweise zugänglich gemacht. Steht in Verbindung mit AUG-0816 (The Documentation Standard), AUG-0819 (The Exit Knowledge Capture) und AUG-0808 (The Knowledge Access Pattern).", "etymology": "", "broader_term": "TEM-0060", "narrower_terms": [], "cross_domain_refs": [ "TEM-0060" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "PER-0081", "domain": "PER", "term_en": "The Lightness Factor", "term_de": "Lightness Factor", "definition_en": "A sensory-cognitive dynamic in AI-augmented perception, identifiable by an user experience pattern arising from the observable reduction in perceived workload during AI-assisted tasks — assignments previously felt as heavy or laborious feel lighter.. Related to AUG-0025 (The Offload Lift) and Taxonomy Dimens. Distinguished from adjacent concepts by its focus on the specific mechanism through which the manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data. Descriptive research term, not a prescriptive recommendation.", "definition_de": "Die beobachtbare Reduktion des wahrgenommenen Arbeitsaufwands bei KI-gestützten Tätigkeiten — Aufgaben, die zuvor als schwer oder mühsam empfunden wurden, fühlen sich leichter an. Beschreibt einen subjektiven Effekt, der sowohl die Einstiegshürde als auch die Durchhaltekraft bei komplexen Aufgaben beeinflusst. Steht in Verbindung mit AUG-0025 (The Offload Lift) und Dimension 8 der Taxonomie (Interaction Effort: High Counterforce → Effortless).", "etymology": "", "broader_term": "RPH-3002", "narrower_terms": [], "cross_domain_refs": [ "TRA-0072" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "PER-0082", "domain": "PER", "term_en": "The Link Forward", "term_de": "Link Forward", "definition_en": "A sensory-cognitive dynamic in AI-augmented perception, identifiable by a performance phenomenon characterized by deliberately forwarding AI results to other people — as knowledge transfer, inspiration, or work foundation. Related to AUG-0172 (The Clean Handover), AUG-0307 (The Lookup for Others), and AUG-0117. Distinguished from adjacent concepts by its focus on the specific mechanism through which the manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Wahrnehmungsdynamisches Phänomen in KI-vermittelter sensorischer Verarbeitung, gekennzeichnet durch die Praxis, KI-Ergebnisse gezielt an andere Personen weiterzuleiten — als Wissenstransfer, Inspiration oder Arbeitsgrundlage. Steht in Verbindung mit AUG-0172 (The Clean Handover), AUG-0307 (The Lookup for Others) und AUG-0117 (The Teaching Reflex). Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "CRE-0132", "narrower_terms": [ "REL-0178" ], "cross_domain_refs": [ "BEH-0045" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "PER-0083", "domain": "PER", "term_en": "The Lobby Influence Pattern", "term_de": "Lobby Influence Muster", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures A perception dynamics phenomenon in AI-mediated sensory processing, characterized by aI companies — like other industries — exert political influence to shape regulation, funding, and public perception in their interest. Related to AUG-0777 (The Power Concentration Observation), AU. This phenomenon operates at the intersection of the and lobby dynamics within the broader PER domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept wahrnehmungsdynamisches Phänomen in KI-vermittelter sensorischer Verarbeitung, gekennzeichnet durch die Beobachtung, dass KI-Unternehmen — wie andere Industrien — politische Einflussnahme ausüben, um Regulierung, Förderung und öffentliche Wahrnehmung in ihrem Sinne zu gestalten. Steht in Verbindung mit AUG-0777 (The Power Concentration Observation), AUG-0839 (The Regulation Debate) und AUG-0836 (The Expectation Cycle). Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "RPH-3901", "narrower_terms": [], "cross_domain_refs": [ "AGE-0003", "AGE-0004", "AGE-0017" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "PER-0084", "domain": "PER", "term_en": "The Media Framing Effect", "term_de": "Media Framing Effekt", "definition_en": "A perceptual calibration pattern in human-AI interaction, measurable through media portrayal of AI — whether as intensity, as wonder, as tool, or as gadget — significantly influences public perception and thus individual willingness to use AI. Related to AUG-0834 (The Publi. The concept emerges specifically in contexts where the–media interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Die Beobachtung, dass die mediale Darstellung von KI — ob als Intensität, als Wunder, als Werkzeug oder als Spielerei — die öffentliche Wahrnehmung und damit die individuelle Nutzungsbereitschaft erheblich beeinflusst. Steht in Verbindung mit AUG-0834 (The Public Perception Wave), AUG-0836 (The Expectation Cycle) und AUG-0838 (The Utopia Projection).", "etymology": "", "broader_term": "RPH-2501", "narrower_terms": [], "cross_domain_refs": [ "ADA-0001", "ADA-0002", "ADA-0004" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "PER-0085", "domain": "PER", "term_en": "The Mentorship Augmentation", "term_de": "Mentorship Augmentation", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A perception dynamics phenomenon in AI-mediated sensory processing, characterized by a performance phenomenon in which the adding to — not the substitution — of human mentoring relationships through AI support: providing background information, preparing questions, creating conversation summaries. Related to AUG-07. This phenomenon operates at the intersection of the and mentorship dynamics within the broader PER domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept wahrnehmungsdynamisches Phänomen in KI-vermittelter sensorischer Verarbeitung, gekennzeichnet durch die Ergänzung — nicht der Ersatz — menschlicher Mentorenbeziehungen durch KI-Unterstützung: Hintergrundinformationen bereitstellen, Fragen vorbereiten, Gesprächszusammenfassungen erstellen. Steht in Verbindung mit AUG-0785 (The Instructor-AI Interaction), AUG-0796 (The Self-Directed Curriculum) und AUG-0307 (The Lookup for Others). Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "RPH-3202", "narrower_terms": [], "cross_domain_refs": [ "REL-0136" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "PER-0086", "domain": "PER", "term_en": "The Micro Win", "term_de": "Micro Win", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A perception dynamics phenomenon in AI-mediated sensory processing, characterized by a performance phenomenon arising from a small, concrete success within an AI session that motivates the user and lays the foundation for larger tasks — such as a well-crafted formulation, a useful summary, or a good first draft. Relate. This phenomenon operates at the intersection of the and micro dynamics within the broader PER domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept wahrnehmungsdynamisches Phänomen in KI-vermittelter sensorischer Verarbeitung, gekennzeichnet durch ein kleiner, konkreter Erfolg in einer KI-Sitzung, der den Nutzer motiviert und den Grundstein für größere Aufgaben legt — etwa eine gelungene Formulierung, eine nützliche Zusammenfassung oder ein guter erster Entwurf. Steht in Verbindung mit AUG-0152 (The Focus Surge), AUG-0175 (The Session Boost) und AUG-0157 (The Competence Rush). Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "RPH-2603", "narrower_terms": [], "cross_domain_refs": [ "WRK-0027" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "PER-0087", "domain": "PER", "term_en": "The Mobility Assist", "term_de": "Mobility Assist", "definition_en": "A system interaction effect reflecting using AI to control robots and machines that move a body or object. Self-driving cars are one form of this. Related to AUG-0932 (The Movement Assist), AUG-0920 (The Navigation Intelligence), and AU...", "definition_de": "Wahrnehmungsdynamisches Phänomen in KI-vermittelter sensorischer Verarbeitung, gekennzeichnet durch die Unterstützung der räumlichen Fortbewegung durch KI-gesteuerte Systeme — autonome Rollstühle, Navigationsassistenten, Transportplattformen. Steht in Verbindung mit AUG-0932 (The Movement Assist), AUG-0920 (The Navigation Intelligence) und AUG-0919 (The Spatial Awareness). Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "REL-0151", "narrower_terms": [ "REL-0151" ], "cross_domain_refs": [ "KNO-0014", "KNO-0034", "REL-0151" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "PER-0088", "domain": "PER", "term_en": "The Name Detective", "term_de": "Name Detective", "definition_en": "An user experience pattern observed when aI to search for the right word, name, title, or term — when the user has an approximate description but cannot find the exact expression. Related to AUG-0434 (The Word Rescue), AUG-0462 (The Detai... Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Wahrnehmungsdynamisches Phänomen in KI-vermittelter sensorischer Verarbeitung, gekennzeichnet durch die Nutzung von KI zur Suche nach dem richtigen Wort, Namen, Titel oder Begriff — wenn der Nutzer eine ungefähre Beschreibung hat, aber den exakten Ausdruck nicht findet. Steht in Verbindung mit AUG-0434 (The Word Rescue), AUG-0462 (The Detail Lookup) und AUG-0373 (The Quick Check). Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "BEH-0094", "narrower_terms": [], "cross_domain_refs": [ "CRE-0219" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "PER-0089", "domain": "PER", "term_en": "The News Filter", "term_de": "News Filter", "definition_en": "An user experience pattern in which aI for filtering, summarizing, and contextualizing news content — as a tool against news saturation and for gaining a structured overview. Related to AUG-0038 (Data Stoicism), AUG-0065 (The Informa...", "definition_de": "Wahrnehmungsdynamisches Phänomen in KI-vermittelter sensorischer Verarbeitung, gekennzeichnet durch die Nutzung von KI zur Filterung, Zusammenfassung und Einordnung von Nachrichteninhalten — als Werkzeug gegen Nachrichtenüberflutung und zur Gewinnung eines strukturierten Überblicks. Steht in Verbindung mit AUG-0038 (Data Stoicism), AUG-0065 (The Information Flood) und AUG-0181 (The Top View). Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "CRE-0063", "narrower_terms": [ "PER-0110" ], "cross_domain_refs": [ "BEH-0054" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "PER-0090", "domain": "PER", "term_en": "The Non-Digital-Origin Perspective", "term_de": "Conscientiousness", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures A perception dynamics phenomenon in AI-mediated sensory processing, characterized by a system interaction effect where the viewpoint of someone who grew up mostly without computers. They compare AI to old ways of working that younger people rarely knew.. Related to AUG-0751 (The Age-Competence Assumption), AUG-0673. This phenomenon operates at the intersection of the and non dynamics within the broader PER domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept die spezifische Perspektive von Nutzern, die den Großteil ihres Lebens ohne digitale Technologie verbracht haben — ihr Zugang zu KI ist geprägt durch Vergleichserfahrungen mit nicht-digitalen Arbeitsweisen, die jüngeren Nutzern fehlen. Steht in Verbindung mit AUG-0751 (The Age-Competence Assumption), AUG-0673 (The Seniority Awareness) und AUG-0674 (The Generational Register). Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "RPH-3101", "narrower_terms": [], "cross_domain_refs": [ "NEO-2645" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "PER-0091", "domain": "PER", "term_en": "The Observation Awareness", "term_de": "Observation Gewahrsein", "definition_en": "A sensory-cognitive dynamic in AI-augmented perception, identifiable by the awareness that AI use in the workplace can be observed, logged, and analyzed — and the resulting behavioral change in users. Related to AUG-0829 (The Transparency Policy), AUG-0664 (The Privacy. Distinguished from adjacent concepts by its focus on the specific mechanism through which the manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Wahrnehmungsdynamisches Phänomen in KI-vermittelter sensorischer Verarbeitung, gekennzeichnet durch das Bewusstsein dafür, dass KI-Nutzung am Arbeitsplatz beobachtet, protokolliert und ausgewertet werden kann — und die dadurch entstehende Verhaltensänderung bei den Nutzern. Steht in Verbindung mit AUG-0829 (The Transparency Policy), AUG-0664 (The Privacy Perimeter) und AUG-0580 (The Footprint Code). Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "PER-0129", "narrower_terms": [], "cross_domain_refs": [ "ETH-0018" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "PER-0092", "domain": "PER", "term_en": "The Open Question", "term_de": "Open Question", "definition_en": "An user experience pattern manifesting as what will human-AI relationships look like in the future? This lexicon maps 1000 named phenomena without claiming answers. Those answers belong to the people who use it. Related to most entry of t...", "definition_de": "Der letzte Eintrag des Lexikons ist kein Abschluss, sondern eine Öffnung. Die Offene Frage lautet: Wie wird die Beziehung zwischen Menschen und KI-Systemen in Zukunft aussehen — und wer wird sie gestalten? Das Lexikon hat 1000 Begriffe definiert, um das Feld zu kartieren. Es hat beschrieben, beobachtet, abgegrenzt, Fragen gestellt und Positionen dokumentiert, ohne sich eine davon zu eigen zu machen. Es hat die westliche Perspektive seiner Entstehung offengelegt und die Grenzen seines eigenen Rahmens benannt. Was es nicht getan hat: eine Antwort geben. Die Antwort liegt bei den Menschen, die di", "etymology": "", "broader_term": "RPH-3755", "narrower_terms": [ "PER-0073", "PER-0018", "PER-0058", "PER-0009", "PER-0024", "PER-0029", "PER-0107", "PER-0126", "PER-0096", "PER-0128", "PER-0021", "PER-0135", "PER-0011", "PER-0006", "PER-0059", "PER-0137", "PER-0012", "PER-0019", "REL-0147", "PER-0111", "PER-0065", "PER-0129", "PER-0108", "PER-0041", "PER-0070", "PER-0068", "PER-0005", "PER-0036", "PER-0033", "PER-0015", "PER-0038", "PER-0014", "PER-0049", "PER-0007", "PER-0115", "PER-0076", "PER-0004", "PER-0020", "PER-0061", "PER-0118", "PER-0030", "PER-0017", "IDN-0024", "PER-0001", "CRE-0170", "PER-0026", "PER-0028", "PER-0053", "PER-0069", "PER-0136", "PER-0112", "PER-0101", "PER-0077", "PER-0102", "PER-0103", "PER-0123", "PER-0094", "PER-0042", "PER-0097", "PER-0022", "PER-0040" ], "cross_domain_refs": [ "IDN-0032" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "PER-0093", "domain": "PER", "term_en": "The Perception Scan", "term_de": "Wahrnehmung Scan", "definition_en": "A perceptual calibration pattern in human-AI interaction, measurable through a self-assessment practice in which a person uses AI to evaluate their own external impact — asking questions like how a text affects the recipient or how others perceive their profile. The concept emerges specifically in contexts where the–perception interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Wahrnehmungsdynamisches Phänomen in KI-vermittelter sensorischer Verarbeitung, gekennzeichnet durch die Nutzung von KI, um die eigene Außenwirkung einzuschätzen — \"Wie wirkt dieser Text auf den Empfänger?\", \"Wie nehmen andere mein Profil wahr?\" Steht in Verbindung mit AUG-0040 (Perspective Triangulation), AUG-0439 (The Room Preview) und AUG-0464 (The Style Rater). Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "CAI-0020", "narrower_terms": [], "cross_domain_refs": [ "TEM-0181" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q177601", "legal_classification": "observational_construct" }, { "id": "PER-0094", "domain": "PER", "term_en": "The Persuasive Design Observation", "term_de": "Persuasive Design Observation", "definition_en": "A perceptual calibration pattern in human-AI interaction, measurable through aI interfaces and systems can contain design elements that steer user behavior in a certain direction — longer sessions, more frequent use, more data sharing. Related to AUG-0851 (The Internal Disc. The concept emerges specifically in contexts where the–persuasive interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Die Beobachtung, dass KI-Interfaces und -Systeme Gestaltungselemente enthalten können, die das Nutzerverhalten in eine bestimmte Richtung lenken — längere Sitzungen, häufigere Nutzung, mehr Datenfreigabe. Steht in Verbindung mit AUG-0851 (The Internal Disclosure Pattern), AUG-0849 (The Data Extraction Observation) und AUG-0565 (The Balance Filter).", "etymology": "", "broader_term": "Perception AI", "narrower_terms": [ "PER-0078" ], "cross_domain_refs": [ "AGE-0073", "ART-0058", "AUG-0902" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q185925", "legal_classification": "observational_construct" }, { "id": "PER-0095", "domain": "PER", "term_en": "The Plain Language Convert", "term_de": "Plain Language Convert", "definition_en": "A sensory-cognitive dynamic in AI-augmented perception, identifiable by a performance phenomenon reflecting using AI to turn complex writing into simple language, though some detail typically gets lost. Related to AUG-0206 (The Understanding Dial), AUG-0563 (The Level Selector), and AUG-0459 (The Summary Aw. Distinguished from adjacent concepts by its focus on the specific mechanism through which the manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Wahrnehmungsdynamisches Phänomen in KI-vermittelter sensorischer Verarbeitung, gekennzeichnet durch die Nutzung von KI, um komplexe Fachtexte in allgemeinverständliche Sprache umzuwandeln — und die Beobachtung, dass bei dieser Vereinfachung unvermeidlich Nuancen und Präzision verloren gehen. Steht in Verbindung mit AUG-0206 (The Understanding Dial), AUG-0563 (The Level Selector) und AUG-0459 (The Summary Awareness). Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "KNO-0021", "narrower_terms": [], "cross_domain_refs": [ "TEM-0166" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q315", "legal_classification": "systematic_classification" }, { "id": "PER-0096", "domain": "PER", "term_en": "The Principle Wash", "term_de": "Principle Wash", "definition_en": "A event where principles, values, or standards appear in statements, policies, or messaging but show limited connection to actual systems, practices, or resource allocation. The. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Wahrnehmungsdynamisches Phänomen in KI-vermittelter sensorischer Verarbeitung, gekennzeichnet durch die Intensität, dass die eigenen Prinzipien und Werte durch die konstante Exposition gegenüber KI-Outputs unmerklich aufgeweicht werden — weil die KI tendenziell moderate, konsensfähige Positionen einnimmt. Steht in Verbindung mit AUG-0402 (The Filter Perceptual shift), AUG-0556 (The Filtered World) und AUG-0076 (Self-Referential Grounding). Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Perception AI", "narrower_terms": [], "cross_domain_refs": [ "VIB-0137" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "PER-0097", "domain": "PER", "term_en": "The Pseudo Productive", "term_de": "Pseudo Productive", "definition_en": "A system interaction effect arising from productivity created by intensive AI use, even though actual value creation is low — the user feels busy and effective without producing substantive results. Related to AUG-0069 (The Optimization L... Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Die Impression von Produktivität, die durch intensive KI-Nutzung entsteht, obwohl die tatsächliche Wertschöpfung gering ist — der Nutzer fühlt sich beschäftigt und effektiv, ohne substanzielle Ergebnisse zu produzieren. Steht in Verbindung mit AUG-0069 (The Optimization Loop), AUG-0413 (The Infinite Scroll) und AUG-0096 (Attention-to-Value Conversion).", "etymology": "", "broader_term": "Perception AI", "narrower_terms": [], "cross_domain_refs": [ "BEH-0082" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "PER-0098", "domain": "PER", "term_en": "The Quick Refresh", "term_de": "Quick Refresh", "definition_en": "A sensory-cognitive dynamic in AI-augmented perception, identifiable by refreshing a running AI session through targeted reformulation or summarization of existing context when response quality diminishes.. Related to AUG-0134 (Context Window Awareness). Distinguished from adjacent concepts by its focus on the specific mechanism through which the manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Wahrnehmungsdynamisches Phänomen in KI-vermittelter sensorischer Verarbeitung, gekennzeichnet durch die Technik, eine laufende KI-Sitzung durch gezielte Neuformulierung oder Zusammenfassung des bisherigen Kontexts aufzufrischen, wenn die Qualität der Antworten nachlässt. Beschreibt ein pragmatisches Werkzeug gegen Contextual Gravity (AUG-0030) und Context Drift (AUG-0066). Steht in Verbindung mit AUG-0134 (Context Window Awareness). Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "PER-0011", "narrower_terms": [], "cross_domain_refs": [ "AUG-0383" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "PER-0099", "domain": "PER", "term_en": "The Quiet Fill", "term_de": "Quiet Fill", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A perception dynamics phenomenon in AI-mediated sensory processing, characterized by an user experience pattern reflecting the unnoticed integration of AI use into everyday life — the user barely notices when they use AI because the transitions have become fluid. Related to AUG-0322 (The Quiet Upgrade), AUG-0142 (The P. This phenomenon operates at the intersection of the and quiet dynamics within the broader PER domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept wahrnehmungsdynamisches Phänomen in KI-vermittelter sensorischer Verarbeitung, gekennzeichnet durch die unbemerkte Integration von KI-Nutzung in den Alltag — der Nutzer bemerkt kaum noch, wann er KI einsetzt, weil die Übergänge fließend geworden sind. Steht in Verbindung mit AUG-0322 (The Quiet Upgrade), AUG-0142 (The Post-Interface Hypothesis) und AUG-0276 (The Steady Stream). Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "BEH-0071", "narrower_terms": [], "cross_domain_refs": [ "IDN-0023" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "PER-0100", "domain": "PER", "term_en": "The Quiet Help", "term_de": "Quiet Help", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures A perception dynamics phenomenon in AI-mediated sensory processing, characterized by an user experience pattern where the discreet AI support the user utilizes without their environment knowing — silent help in the background. Related to AUG-0237 (The Invisible Wingman), AUG-0419 (The Invisible Editor), and AUG-04. This phenomenon operates at the intersection of the and quiet dynamics within the broader PER domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept wahrnehmungsdynamisches Phänomen in KI-vermittelter sensorischer Verarbeitung, gekennzeichnet durch die diskrete KI-Unterstützung, die der Nutzer in Anspruch nimmt, ohne dass sein Umfeld davon erfährt — stille Hilfe im Hintergrund. Steht in Verbindung mit AUG-0237 (The Invisible Wingman), AUG-0419 (The Invisible Editor) und AUG-0449 (The Quiet Path). Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "RPH-3202", "narrower_terms": [], "cross_domain_refs": [ "REL-0169" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "PER-0101", "domain": "PER", "term_en": "The Reflective Operator", "term_de": "Reflective Operator", "definition_en": "A perceptual calibration pattern in human-AI interaction, measurable through a performance phenomenon characterized by a user who regularly questions their own patterns with AI. Asks: Am I using this well? What's actually changing in how I think?. The concept emerges specifically in contexts where the–reflective interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Ein Nutzertyp, der seine eigene KI-Nutzung regelmäßig hinterfragt und beobachtet. Der Reflective Operator fragt nicht nur \"Was liefert die KI?\", sondern auch \"Was macht die KI mit mir?\". Praktische Umsetzung von Axiom 8 (Die Meta-Ebene) und Axiom 9 (Produktiver Skeptizismus). Unterscheidet sich vom Objective Auditor (Profil 2) dadurch, dass die Reflexion nach innen gerichtet ist — der Auditor prüft den Output, der Reflective Operator prüft sich selbst.", "etymology": "", "broader_term": "Perception AI", "narrower_terms": [], "cross_domain_refs": [ "REL-0004" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "PER-0102", "domain": "PER", "term_en": "The Register Surprise", "term_de": "Register Surprise", "definition_en": "A sensory-cognitive dynamic in AI-augmented perception, identifiable by a performance phenomenon reflecting the restless that arises when the AI responds in an unexpected register — too formal, too casual, too technical, too simple — and the user perceives this as inappropriate. Related to AUG-0657 (The. Distinguished from adjacent concepts by its focus on the specific mechanism through which the manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Wahrnehmungsdynamisches Phänomen in KI-vermittelter sensorischer Verarbeitung, gekennzeichnet durch die Irritation, die entsteht, wenn die KI in einem unerwarteten Register antwortet — zu formell, zu locker, zu technisch, zu einfach — und der Nutzer das als unangemessen empfindet. Steht in Verbindung mit AUG-0657 (The Register Range), AUG-0338 (The Tone Match) und AUG-0092 (Output Asymmetry). Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Perception AI", "narrower_terms": [ "KNO-0029" ], "cross_domain_refs": [ "LIN-0072", "KNO-0029" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "PER-0103", "domain": "PER", "term_en": "The Remote Work Amplifier", "term_de": "Remote Work Amplifier", "definition_en": "The amplifying effect of AI on remote work — AI tools enable location-inreliant collaboration but can also amplify separate, overwork, and the blurring of work and private life. Related to AUG-08... Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Wahrnehmungsdynamisches Phänomen in KI-vermittelter sensorischer Verarbeitung, gekennzeichnet durch die verstärkende Wirkung von KI auf Fernarbeit — KI-Werkzeuge ermöglichen ortsunabhängige Zusammenarbeit, können aber auch Separation, Überarbeitung und die Verwischung von Arbeits- und Privatleben verstärken. Steht in Verbindung mit AUG-0821 (The Hybrid Office Dynamic), AUG-0823 (The Flexible Work Pattern) und AUG-0565 (The Balance Filter). Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Perception AI", "narrower_terms": [], "cross_domain_refs": [ "TEM-0066" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "PER-0104", "domain": "PER", "term_en": "The Resource Distribution Pattern", "term_de": "Resource Distribution Muster", "definition_en": "A perceptual calibration pattern in human-AI interaction, measurable through a performance phenomenon observed when how a person divides their time, money, focus, or energy between different things they care about. Related to AUG-0849 (The Data Extraction Observation), AUG-0721 (The Access Differential), and AUG. The concept emerges specifically in contexts where the–resource interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Wahrnehmungsdynamisches Phänomen in KI-vermittelter sensorischer Verarbeitung, gekennzeichnet durch das Muster, nach dem die Vorteile und Kosten von KI-Systemen verteilt werden — wer profitiert, wer trägt die Kosten, wer wird übersehen. Steht in Verbindung mit AUG-0849 (The Data Extraction Observation), AUG-0721 (The Access Differential) und AUG-0777 (The Power Concentration Observation). Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "SOC-0002", "narrower_terms": [], "cross_domain_refs": [ "SOC-0031" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "PER-0105", "domain": "PER", "term_en": "The Responsive Environment", "term_de": "Responsive Environment", "definition_en": "A perceptual calibration pattern in human-AI interaction, measurable through a physical environment that reacts to and adapts to human behavior — lighting, temperature, acoustics, access regulation — controlled by embedded AI systems. Related to AUG-0937 (The Ambient Intell. The concept emerges specifically in contexts where the–responsive interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Wahrnehmungsdynamisches Phänomen in KI-vermittelter sensorischer Verarbeitung, gekennzeichnet durch eine physische Umgebung, die auf menschliches Verhalten reagiert und sich anpasst — Beleuchtung, Temperatur, Akustik, Zugangskontrolle — gesteuert durch eingebettete KI-Systeme. Steht in Verbindung mit AUG-0937 (The Ambient Intelligence), AUG-0925 (The Household Automation) und AUG-0922 (The Environmental Reading). Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-3002", "narrower_terms": [], "cross_domain_refs": [ "SPR-0002", "SPR-0148", "TEW-0079" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "PER-0106", "domain": "PER", "term_en": "The Retirement Procedure", "term_de": "Retirement Procedure", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A perception dynamics phenomenon in AI-mediated sensory processing, characterized by the procedure for decommissioning an embodied AI system — data deletion, component recycling, handover of ongoing tasks to other systems or humans. Related to AUG-0941 (The Wear-and-Tear Awareness). This phenomenon operates at the intersection of the and retirement dynamics within the broader PER domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept wahrnehmungsdynamisches Phänomen in KI-vermittelter sensorischer Verarbeitung, gekennzeichnet durch das Verfahren zur Außerdienststellung eines verkörperten KI-Systems — Datenlöschung, Komponentenrecycling, Übergabe laufender Aufgaben an andere Systeme oder Menschen. Steht in Verbindung mit AUG-0941 (The Wear-and-Tear Awareness), AUG-0879 (The Session Handover) und AUG-0746 (The Climate Cost Awareness). Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "RPH-3552", "narrower_terms": [], "cross_domain_refs": [ "TEM-0101" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "PER-0107", "domain": "PER", "term_en": "The Review Process Observation", "term_de": "Review Prozess Observation", "definition_en": "A perceptual calibration pattern in human-AI interaction, measurable through a system interaction effect in which aI is now used in review work — spotting errors, checking consistency, improving wording. This is just what's happening, not a judgment either way. The concept emerges specifically in contexts where the–review interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Die Beobachtung, dass KI-Systeme zunehmend in Begutachtungsprozesse einbezogen werden — zum Aufspüren von Fehlern, zur Konsistenzprüfung, zur Sprachoptimierung — und dass dies Fragen über die Integrität des Begutachtungsverfahrens aufwirft. Steht in Verbindung mit AUG-0789 (The Research Assistant Role), AUG-0791 (The Academic Integrity Line) und AUG-0581 (The Truth Filter).", "etymology": "", "broader_term": "Perception AI", "narrower_terms": [], "cross_domain_refs": [ "REL-0054" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "PER-0108", "domain": "PER", "term_en": "The Role-Aware Input", "term_de": "CO2-Fußabdruck in performing", "definition_en": "A way of asking AI questions that names a role — \"As a parent...\" or \"In my job as...\" This gives the AI context and often changes the answer. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Wahrnehmungsdynamisches Phänomen in KI-vermittelter sensorischer Verarbeitung, gekennzeichnet durch ein Eingabemuster, bei dem der Nutzer soziale Rollen in die Formulierung einbezieht — \"Als Elternteil brauche ich…\", \"In meiner Funktion als…\". Die Eingabe wird durch die Rolle des Nutzers kontextualisiert. Steht in Verbindung mit AUG-0491 (The State Label), AUG-0650 (The Context-Sensitive Query) und AUG-0524 (The Context Layer). Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Perception AI", "narrower_terms": [ "PER-0064" ], "cross_domain_refs": [ "SOC-0001" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "PER-0109", "domain": "PER", "term_en": "The Safety Bubble", "term_de": "Safety Bubble", "definition_en": "A sensory-cognitive dynamic in AI-augmented perception, identifiable by a system interaction effect involving safety that arises from knowing the AI is available in the background — a kind of cognitive safety net that lets the user act more boldly. Related to AUG-0415 (The Background Advisor), AUG-0166. Distinguished from adjacent concepts by its focus on the specific mechanism through which the manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Wahrnehmungsdynamisches Phänomen in KI-vermittelter sensorischer Verarbeitung, gekennzeichnet durch das Gefühl von Sicherheit, das entsteht, wenn man weiß, dass die KI im Hintergrund verfügbar ist — eine Art psychologischer Sicherheitsnetz, das den Nutzer mutiger agieren lässt. Steht in Verbindung mit AUG-0415 (The Background Advisor), AUG-0166 (The Borrowed Confidence) und AUG-0481 (The DIY Confidence). Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-3202", "narrower_terms": [], "cross_domain_refs": [ "MSC-0097", "PLY-0017", "RET-0002" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "PER-0110", "domain": "PER", "term_en": "The Scare Filter", "term_de": "Scare Filter", "definition_en": "A sensory-cognitive dynamic in AI-augmented perception, identifiable by a performance phenomenon characterized by aI for contextualizing notable information — such as placing a news item in context, evaluating a uncertainty, or relativizing a concern through fact-checking. Related to AUG-0407 (The News Filter). Distinguished from adjacent concepts by its focus on the specific mechanism through which the manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Wahrnehmungsdynamisches Phänomen in KI-vermittelter sensorischer Verarbeitung, gekennzeichnet durch die Nutzung von KI zur Einordnung von beunruhigenden Informationen — etwa die Kontextualisierung einer Nachricht, die Bewertung eines Risikos oder die Relativierung einer Befürchtung durch Faktencheck. Steht in Verbindung mit AUG-0407 (The News Filter), AUG-0038 (Data Stoicism) und AUG-0348 (The Digital Counsel). Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "PER-0089", "narrower_terms": [], "cross_domain_refs": [ "COP-0070" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "PER-0111", "domain": "PER", "term_en": "The Secret Listener", "term_de": "Secret Listener", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A perception dynamics phenomenon in AI-mediated sensory processing, characterized by a performance phenomenon involving the AI as a silent listener to whom one confides things one would not tell anyone else — combined with the knowledge that the AI does not listen but processes text. Related to AUG-0364 (The Silent. This phenomenon operates at the intersection of the and secret dynamics within the broader PER domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept wahrnehmungsdynamisches Phänomen in KI-vermittelter sensorischer Verarbeitung, gekennzeichnet durch die Wahrnehmung der KI als stiller Zuhörer, dem man Dinge anvertraut, die man niemandem sonst erzählen würde — verbunden mit dem Wissen, dass die KI nicht zuhört, sondern Text verarbeitet. Steht in Verbindung mit AUG-0364 (The Silent Outlet), AUG-0390 (The Late-Night Confidant) und AUG-0247 (The Safe Release). Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Perception AI", "narrower_terms": [], "cross_domain_refs": [ "REL-0140" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "PER-0112", "domain": "PER", "term_en": "The Seniority Awareness", "term_de": "Seniority Gewahrsein", "definition_en": "The awareness that life experience and long-standing expertise enable perspectives that an AI cannot replicate — and that these perspectives serve as valuable input in AI interaction. Related to AU... Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Wahrnehmungsdynamisches Phänomen in KI-vermittelter sensorischer Verarbeitung, gekennzeichnet durch das Bewusstsein dafür, dass Lebenserfahrung und langjährige Fachkompetenz Perspektiven ermöglichen, die eine KI nicht replizieren kann — und dass diese Perspektiven in der KI-Interaktion als wertvolle Eingabe dienen. Steht in Verbindung mit AUG-0454 (The Skill Awareness), AUG-0523 (The Solo Output) und AUG-0545 (The Skill Shift). Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "Perception AI", "narrower_terms": [], "cross_domain_refs": [ "CON-0013", "WRK-0076" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "PER-0113", "domain": "PER", "term_en": "The Sensory Extension", "term_de": "Sensory Extension", "definition_en": "A perceptual calibration pattern in human-AI interaction, measurable through an user experience pattern in which using AI to sense things a body cannot. Seeing heat, hearing sounds too high for ears, feeling far away places. Related to AUG-0935 (The Adaptive Extension), AUG-0936 (The Wearable Layer), and AUG-. The concept emerges specifically in contexts where the–sensory interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Wahrnehmungsdynamisches Phänomen in KI-vermittelter sensorischer Verarbeitung, gekennzeichnet durch die Erweiterung menschlicher Sinneswahrnehmung durch KI-gestützte Systeme — Geräuschverstärkung, Bildverbesserung, Übersetzungshilfen für Sinneseindrücke (z.B. Farben in Töne). Steht in Verbindung mit AUG-0935 (The Adaptive Extension), AUG-0936 (The Wearable Layer) und AUG-0922 (The Environmental Reading). Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "SOM-0075", "narrower_terms": [], "cross_domain_refs": [ "REL-0151" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "PER-0114", "domain": "PER", "term_en": "The Shared Screen Talk", "term_de": "Shared Screen Talk", "definition_en": "The AI interaction that takes place together with another person — both looking at the same screen and discussing the AI responses.. Related to AUG-0146 (The Shared Mind), AUG-0265 (The Generation... Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Die KI-Interaktion, die gemeinsam mit einer anderen Person stattfindet — beide schauen auf denselben Bildschirm und diskutieren die KI-Antworten. Beschreibt eine soziale Form der KI-Nutzung, die das Einzelnutzer-Paradigma durchbricht. Steht in Verbindung mit AUG-0146 (The Shared Mind), AUG-0265 (The Generation Connector) und AUG-0117 (The Teaching Reflex).", "etymology": "", "broader_term": "IDN-0047", "narrower_terms": [], "cross_domain_refs": [ "BEH-0083" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "PER-0115", "domain": "PER", "term_en": "The Silent Dinner", "term_de": "Silent Dinner", "definition_en": "An user experience pattern manifesting as a family dinner where many individuals is on their phones talking to AI instead of each other. The table is full but the talk is empty — each person absorbed in their own digital exchange. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "→ Erweiterung von AUG-0282 (The Dinner Table Pause). Beschreibt die Situation, in der eine Mahlzeit in Stille verläuft, weil zahlreiche Anwesenden auf ihre Geräte und KI-Sitzungen schauen — das Gegenteil der beabsichtigten Dinner Table Pause. Steht in Verbindung mit AUG-0282 (The Dinner Table Pause), AUG-0074 (Analog Anchors) und AUG-0080 (Relationship-First Principle).", "etymology": "", "broader_term": "Perception AI", "narrower_terms": [], "cross_domain_refs": [ "NEO-1172", "CRE-0234" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "PER-0116", "domain": "PER", "term_en": "The Silicon Consigliere", "term_de": "Silicon Consigliere", "definition_en": "A sensory-cognitive dynamic in AI-augmented perception, identifiable by a performance phenomenon involving aI as a strategic advisor — comparable to a consigliere who discreetly advises in the background and plays through various options. Related to AUG-0415 (The Background Advisor), AUG-0542 (The Hidde. Distinguished from adjacent concepts by its focus on the specific mechanism through which the manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Wahrnehmungsdynamisches Phänomen in KI-vermittelter sensorischer Verarbeitung, gekennzeichnet durch die Rolle der KI als strategischer Berater — vergleichbar mit einem Consigliere, der diskret im Hintergrund berät und verschiedene Optionen durchspielt. Steht in Verbindung mit AUG-0415 (The Background Advisor), AUG-0542 (The Hidden Advisor) und AUG-0348 (The Digital Counsel). Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-3202", "narrower_terms": [], "cross_domain_refs": [ "REL-0179" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "PER-0117", "domain": "PER", "term_en": "The Simulation Awareness", "term_de": "Simulation Gewahrsein", "definition_en": "A perceptual calibration pattern in human-AI interaction, measurable through the user's awareness that the AI possesses no genuine intelligence, sense, or awareness — but processes statistical patterns that can appear as sense.. Related to AUG-0006 (Platform Ontology), Axio. The concept emerges specifically in contexts where the–simulation interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Das Bewusstsein des Nutzers, dass die KI keine echte Intelligenz, kein Verständnis und kein Bewusstsein besitzt — sondern statistische Muster verarbeitet, die als Verständnis erscheinen können. Beschreibt eine Grundhaltung informierter KI-Nutzung. Steht in Verbindung mit AUG-0006 (Platform Ontology), Axiom 9 (Produktiver Skeptizismus) und AUG-0161 (The Invisible Colleague).", "etymology": "", "broader_term": "ETH-0002", "narrower_terms": [], "cross_domain_refs": [ "RPH-2203" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q202763", "legal_classification": "descriptive_research_term" }, { "id": "PER-0118", "domain": "PER", "term_en": "The Smart Shortcut", "term_de": "Smart Shortcut", "definition_en": "A perceptual calibration pattern in human-AI interaction, measurable through the conscious use of AI to deliberately shorten a lengthy work process — without sacrificing the quality of the result.. Related to AUG-0091 (Productivity Arbitrage), AUG-0092 (Output Asymmetry), a. The concept emerges specifically in contexts where the–smart interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters. Descriptive research term, not a prescriptive recommendation.", "definition_de": "Wahrnehmungsdynamisches Phänomen in KI-vermittelter sensorischer Verarbeitung, gekennzeichnet durch die bewusste Nutzung von KI, um einen langwierigen Arbeitsprozess gezielt abzukürzen — ohne dabei die Qualität des Ergebnisses zu opfern. Beschreibt eine Kernkompetenz effizienter KI-Nutzung. Steht in Verbindung mit AUG-0091 (Productivity Arbitrage), AUG-0092 (Output Asymmetry) und AUG-0096 (Attention-to-Value Conversion). Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Perception AI", "narrower_terms": [], "cross_domain_refs": [ "IDN-0010" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q735", "legal_classification": "descriptive_research_term" }, { "id": "PER-0119", "domain": "PER", "term_en": "The Spatial Awareness", "term_de": "Spatial Gewahrsein", "definition_en": "An embodied AI system to perceive its spatial environment and orient itself within it — threshold detection, room mapping, distance measurement. Related to AUG-0920 (The Navigation Intelligence), A...", "definition_de": "Wahrnehmungsdynamisches Phänomen in KI-vermittelter sensorischer Verarbeitung, gekennzeichnet durch die Fähigkeit eines verkörperten KI-Systems, seine räumliche Umgebung wahrzunehmen und sich darin zu orientieren — Hinderniserkennung, Raumkartierung, Abstandsmessung. Steht in Verbindung mit AUG-0920 (The Navigation Intelligence), AUG-0922 (The Environmental Reading) und AUG-0923 (The Defined Operating Boundary). Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-1307", "narrower_terms": [ "AUG-0921" ], "cross_domain_refs": [ "TEM-0106" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "PER-0120", "domain": "PER", "term_en": "The State Sequence", "term_de": "State Sequence", "definition_en": "A sensory-cognitive dynamic in AI-augmented perception, identifiable by a system interaction effect characterized by different states within an AI session — from initial orientation through productive collaboration to fading or satisfaction.. Related to the 7 Phases, AUG-0138 (The Session Architecture), and AUG-0. Distinguished from adjacent concepts by its focus on the specific mechanism through which the manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Wahrnehmungsdynamisches Phänomen in KI-vermittelter sensorischer Verarbeitung, gekennzeichnet durch die Abfolge verschiedener Zustände innerhalb einer KI-Sitzung — von der initialen Orientierung über produktive Zusammenarbeit bis hin zur Erschöpfung oder Zufriedenheit. Beschreibt die dynamische Entwicklung einer Sitzung. Steht in Verbindung mit den 7 Phasen, AUG-0138 (The Session Architecture) und AUG-0032 (Focus Range). Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "NEO-0016", "narrower_terms": [], "cross_domain_refs": [ "TEM-0160" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "PER-0121", "domain": "PER", "term_en": "The Steady Stream", "term_de": "Steady Stream", "definition_en": "A sensory-cognitive dynamic in AI-augmented perception, identifiable by a work pattern in which the user uses the AI throughout the entire workday in a constant, low-threshold mode — regular, small interactions instead of intensive individual sessions. Related to AUG-0. Distinguished from adjacent concepts by its focus on the specific mechanism through which the manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Wahrnehmungsdynamisches Phänomen in KI-vermittelter sensorischer Verarbeitung, gekennzeichnet durch ein Arbeitsmuster, bei dem der Nutzer die KI über den gesamten Arbeitstag hinweg in einem konstanten, niedrigschwelligen Modus nutzt — regelmäßige, kleine Interaktionen statt intensiver Einzelsitzungen. Steht in Verbindung mit AUG-0253 (The Quiet Co-Pilot), AUG-0143 (Ambient Thinking Support) und AUG-0032 (Focus Range). Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-1307", "narrower_terms": [], "cross_domain_refs": [ "BEH-0054" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "PER-0122", "domain": "PER", "term_en": "The Summary Awareness", "term_de": "Summary Gewahrsein", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A perception dynamics phenomenon in AI-mediated sensory processing, characterized by the user's awareness that AI summaries inevitably simplify, filter, and weight — and that most summary represents an interpretation, not a neutral reproduction. Related to AUG-0071 (Epistemic Hygi. This phenomenon operates at the intersection of the and summary dynamics within the broader PER domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept wahrnehmungsdynamisches Phänomen in KI-vermittelter sensorischer Verarbeitung, gekennzeichnet durch das Bewusstsein des Nutzers, dass KI-Zusammenfassungen zwangsläufig vereinfachen, filtern und gewichten — und dass viele Zusammenfassung eine Interpretation darstellt, nicht eine neutrale Wiedergabe. Steht in Verbindung mit AUG-0071 (Epistemic Hygiene), Axiom 9 (Produktiver Skeptizismus) und AUG-0402 (The Filter Perceptual shift). Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "RPH-2201", "narrower_terms": [], "cross_domain_refs": [ "AED-0060", "AED-0061", "CON-0086" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "PER-0123", "domain": "PER", "term_en": "The Sunset Planning", "term_de": "Sunset Planning", "definition_en": "A sensory-cognitive dynamic in AI-augmented perception, identifiable by planning ahead for when an AI system will shut down — how to move data, how to replace processes that depend on it, and who is affected. Distinguished from adjacent concepts by its focus on the specific mechanism through which the manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Wahrnehmungsdynamisches Phänomen in KI-vermittelter sensorischer Verarbeitung, gekennzeichnet durch die vorausschauende Planung des Lebensendes eines KI-Systems — wann wird es abgeschaltet, wie werden Daten migriert, wie werden abhängige Prozesse umgestellt? Steht in Verbindung mit AUG-0943 (The Retirement Procedure), AUG-0968 (The Separation Procedure) und AUG-0971 (The Legacy Integration). Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Perception AI", "narrower_terms": [], "cross_domain_refs": [ "KNO-0019" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "PER-0124", "domain": "PER", "term_en": "The Surface Lookup", "term_de": "Oberflaeche Lookup", "definition_en": "A sensory-cognitive dynamic in AI-augmented perception, identifiable by an user experience pattern manifesting as the consciously superficial AI query that does not aim for depth — quick, pragmatic, without claim to completeness. Related to AUG-0373 (The Quick Check), AUG-0376 (The Knowledge Sip), and AUG-0308. Distinguished from adjacent concepts by its focus on the specific mechanism through which the manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Wahrnehmungsdynamisches Phänomen in KI-vermittelter sensorischer Verarbeitung, gekennzeichnet durch die bewusst oberflächliche KI-Abfrage, die keine Tiefe anstrebt — schnell, pragmatisch, ohne Anspruch auf Vollständigkeit. Steht in Verbindung mit AUG-0373 (The Quick Check), AUG-0376 (The Knowledge Sip) und AUG-0308 (The Simple Mode). Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2005", "narrower_terms": [], "cross_domain_refs": [ "SOM-0047" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "PER-0125", "domain": "PER", "term_en": "The Syntax Voice", "term_de": "Syntax Voice", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures A perception dynamics phenomenon in AI-mediated sensory processing, characterized by a system interaction effect where a user, after extended AI use, begins to use structures and formulations in their own language that are typical of AI outputs. Related to AUG-0204 (The Conversational Afterimage), AUG-0262 (The Ech. This phenomenon operates at the intersection of the and syntax dynamics within the broader PER domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept wahrnehmungsdynamisches Phänomen in KI-vermittelter sensorischer Verarbeitung, gekennzeichnet durch die Beobachtung, dass ein Nutzer nach längerer KI-Nutzung beginnt, in seiner eigenen Sprache Strukturen und Formulierungen zu verwenden, die typisch für KI-Outputs sind. Steht in Verbindung mit AUG-0204 (The Conversational Afterimage), AUG-0262 (The Echo Sibling) und AUG-0230 (The Algorithmic Filter). Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "SOC-0009", "narrower_terms": [], "cross_domain_refs": [ "SOC-0045" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "PER-0126", "domain": "PER", "term_en": "The Token Awareness", "term_de": "Token Gewahrsein", "definition_en": "A sensory-cognitive dynamic in AI-augmented perception, identifiable by a user's foundational understanding of the technical workings of AI systems — particularly the concept of token processing, context windows, and probabilistic text generation. Related to AUG-0134 (. Distinguished from adjacent concepts by its focus on the specific mechanism through which the manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Wahrnehmungsdynamisches Phänomen in KI-vermittelter sensorischer Verarbeitung, gekennzeichnet durch das Grundverständnis eines Nutzers für die technische Funktionsweise von KI-Systemen — insbesondere das Konzept der Token-Verarbeitung, der Kontextfenster und der probabilistischen Textgenerierung. Steht in Verbindung mit AUG-0134 (Context Window Awareness), AUG-0375 (The Simulation Awareness) und AUG-0006 (Platform Ontology). Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Perception AI", "narrower_terms": [], "cross_domain_refs": [ "VIB-0094" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "PER-0127", "domain": "PER", "term_en": "The Tool Selection", "term_de": "Tool Selection", "definition_en": "A sensory-cognitive dynamic in AI-augmented perception, identifiable by a performance phenomenon manifesting as tools an AI agent employs for a specific task — databases, APIs, computation modules, external services. Related to AUG-0882 (The Resource Awareness), AUG-0864 (The Agent Configuration), and AUG-08. Distinguished from adjacent concepts by its focus on the specific mechanism through which the manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Wahrnehmungsdynamisches Phänomen in KI-vermittelter sensorischer Verarbeitung, gekennzeichnet durch die Auswahl der Werkzeuge, die ein KI-Agent für eine bestimmte Aufgabe einsetzt — Datenbanken, APIs, Berechnungsmodule, externe Dienste. Steht in Verbindung mit AUG-0882 (The Resource Awareness), AUG-0864 (The Agent Configuration) und AUG-0867 (The Constraint Frame). Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "TEM-0149", "narrower_terms": [ "TEM-0149" ], "cross_domain_refs": [ "TEM-0179" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "PER-0128", "domain": "PER", "term_en": "The Translation Fidelity", "term_de": "Translation Fidelity", "definition_en": "How faithfully an AI translation represents the original text — and the observation that \"fidelity\" can mean literal accuracy, semantic transfer, or cultural adaptation depending on user expectatio... Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Wahrnehmungsdynamisches Phänomen in KI-vermittelter sensorischer Verarbeitung, gekennzeichnet durch die Frage, wie treu eine KI-Übersetzung dem Originaltext ist — und die Beobachtung, dass \"Treue\" je nach Nutzererwartung wörtliche Genauigkeit, sinngemäße Übertragung oder kulturelle Anpassung bedeuten kann. Steht in Verbindung mit AUG-0695 (The Untranslatable Term), AUG-0459 (The Summary Awareness) und AUG-0515 (The Babel Break). Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Perception AI", "narrower_terms": [ "AUG-0802" ], "cross_domain_refs": [ "TRA-0027" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q7553", "legal_classification": "analytical_category" }, { "id": "PER-0129", "domain": "PER", "term_en": "The Transparency Policy", "term_de": "Transparency Policy", "definition_en": "A perceptual calibration pattern in human-AI interaction, measurable through the demand or practice of making AI use in organizational processes transparent — who uses AI for what, which decisions are AI-assisted, where the limits lie. Related to AUG-0825 (The Organizationa. The concept emerges specifically in contexts where the–transparency interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Wahrnehmungsdynamisches Phänomen in KI-vermittelter sensorischer Verarbeitung, gekennzeichnet durch die Forderung oder Praxis, KI-Nutzung in organisatorischen Prozessen transparent zu machen — wer nutzt KI wofür, welche Entscheidungen sind KI-unterstützt, wo liegen die Grenzen. Steht in Verbindung mit AUG-0825 (The Organizational Policy Layer), AUG-0842 (The Transparency Expectation) und AUG-0828 (The Observation Awareness). Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Perception AI", "narrower_terms": [ "ETH-0018", "PER-0091" ], "cross_domain_refs": [ "TEM-0171" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q535399", "legal_classification": "empirical_phenomenon_label" }, { "id": "PER-0130", "domain": "PER", "term_en": "The Truth Filter", "term_de": "Truth Filter", "definition_en": "A perceptual calibration pattern in human-AI interaction, measurable through an user experience pattern in which aI is a medium. What it shows reflects choices made in how it was built. No AI shows pure truth. Related to AUG-0391 (The Accuracy Checker), AUG-0527 (The Truth Anchor), and Axiom 17 (Source Discip. The concept emerges specifically in contexts where the–truth interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Wahrnehmungsdynamisches Phänomen in KI-vermittelter sensorischer Verarbeitung, gekennzeichnet durch die Praxis, KI-Outputs systematisch auf Wahrheitsgehalt zu filtern — durch Faktencheck, Quellenvergleich und kritische Hinterfragung. Steht in Verbindung mit AUG-0391 (The Accuracy Checker), AUG-0527 (The Truth Anchor) und Axiom 17 (Quellendisziplin). Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "BEH-0086", "narrower_terms": [], "cross_domain_refs": [ "BEH-0078" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "PER-0131", "domain": "PER", "term_en": "The Truth Quest", "term_de": "Truth Quest", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures A perception dynamics phenomenon in AI-mediated sensory processing, characterized by a performance phenomenon in which the persistent, multi-stage use of AI to clarify a disputed or unclear question — through repeated questioning, perspective shifts, and source comparison, until the user reaches a well-founded asse. This phenomenon operates at the intersection of the and truth dynamics within the broader PER domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept wahrnehmungsdynamisches Phänomen in KI-vermittelter sensorischer Verarbeitung, gekennzeichnet durch die hartnäckige, mehrstufige Nutzung von KI zur Klärung einer strittigen oder unklaren Frage — durch wiederholtes Nachfragen, Perspektivwechsel und Quellenvergleich, bis der Nutzer zu einem fundierten Urteil gelangt. Steht in Verbindung mit AUG-0458 (The Curiosity Drill), AUG-0581 (The Truth Filter) und AUG-0049 (Cross-Referential Validation). Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "RPH-2555", "narrower_terms": [], "cross_domain_refs": [ "RPH-1415" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "PER-0132", "domain": "PER", "term_en": "The Value Lock", "term_de": "Value Lock", "definition_en": "A perceptual calibration pattern in human-AI interaction, measurable through basic values and constraints in an AI agent system that may not be altered even during learning processes or adaptations — a safety layer against. Related to AUG-0952 (The Goal Drift Awareness), AU. The concept emerges specifically in contexts where the–value interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Wahrnehmungsdynamisches Phänomen in KI-vermittelter sensorischer Verarbeitung, gekennzeichnet durch die Festschreibung grundlegender Werte und Einschränkungen in einem KI-Agentensystem, die auch bei Lernprozessen oder Anpassungen nicht verändert werden dürfen — eine Sicherheitsschicht gegen Wertedrift. Steht in Verbindung mit AUG-0952 (The Goal Drift Awareness), AUG-0867 (The Constraint Frame) und AUG-0857 (The Human Primacy Anchor). Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2051", "narrower_terms": [], "cross_domain_refs": [ "SOC-0024" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "PER-0133", "domain": "PER", "term_en": "The Vocational Training Fit", "term_de": "Vocational Training Fit", "definition_en": "A perceptual calibration pattern in human-AI interaction, measurable through an user experience pattern reflecting how AI helps or hurts learning for different jobs. It helps with some skills but can interfere with learning practical hands-on work.. Related to AUG-0761 (The Apprentice Paradox), AUG-0779 (The In. The concept emerges specifically in contexts where the–vocational interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Die spezifische Eignung oder Ungeeignetheit von KI für verschiedene Ausbildungsberufe — in manchen Berufen unterstützt KI den Lernprozess, in anderen kann sie ihn untergraben, da handwerkliche oder praktische Kompetenzen nicht durch KI-Nutzung erworben werden. Steht in Verbindung mit AUG-0761 (The Apprentice Paradox), AUG-0779 (The Institutional Learning Context) und AUG-0454 (The Skill Awareness).", "etymology": "", "broader_term": "RPH-2703", "narrower_terms": [], "cross_domain_refs": [ "EDU-0094" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q918461", "legal_classification": "empirical_phenomenon_label" }, { "id": "PER-0134", "domain": "PER", "term_en": "The Warm Start", "term_de": "Warm Start", "definition_en": "A sensory-cognitive dynamic in AI-augmented perception, identifiable by an AI session with preloaded context from an earlier interaction — so that the AI \"knows\" the previous state and can continue directly. Related to AUG-0101 (The Refresh-First Principle), AUG-0134 (. Distinguished from adjacent concepts by its focus on the specific mechanism through which the manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Wahrnehmungsdynamisches Phänomen in KI-vermittelter sensorischer Verarbeitung, gekennzeichnet durch das Fortsetzen einer KI-Sitzung mit vorgeladenem Kontext aus einer früheren Interaktion — sodass die KI den bisherigen Stand \"kennt\" und direkt weiterarbeiten kann. Steht in Verbindung mit AUG-0101 (The Refresh-First Principle), AUG-0134 (Context Window Awareness) und AUG-0158 (The Morning Setup). Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "NEO-2197", "narrower_terms": [], "cross_domain_refs": [ "CRE-0127", "CUS-0047", "PLY-0018" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q735", "legal_classification": "observational_construct" }, { "id": "PER-0135", "domain": "PER", "term_en": "The Wear-and-Tear Awareness", "term_de": "Fortbildung in Darstellende Künste", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A perception dynamics phenomenon in AI-mediated sensory processing, characterized by an AI system that detects its own physical wear — motors slowing, sensors losing accuracy, battery holding less charge — and reports this to the operator. Related to AUG-0942 (The Maintenance Predi. This phenomenon operates at the intersection of the and wear dynamics within the broader PER domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept wahrnehmungsdynamisches Phänomen in KI-vermittelter sensorischer Verarbeitung, gekennzeichnet durch die Fähigkeit eines verkörperten KI-Systems, seinen eigenen physischen Verschleiß zu erkennen — Motorabnutzung, Sensorverschlechterung, Batterieabbau — und den Nutzer oder Betreiber zu informieren. Steht in Verbindung mit AUG-0942 (The Maintenance Prediction), AUG-0882 (The Resource Awareness) und AUG-0943 (The Retirement Procedure). Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Perception AI", "narrower_terms": [], "cross_domain_refs": [ "TEM-0101" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "PER-0136", "domain": "PER", "term_en": "The Witness Effect", "term_de": "Witness Effekt", "definition_en": "A perceptual calibration pattern in human-AI interaction, measurable through a system interaction effect reflecting some users perceive their AI sessions as a kind of \"witnessed thinking\" — the feeling that one's own thoughts become more tangible, more binding, and more real through being written out in dialogue. The concept emerges specifically in contexts where the–witness interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Wahrnehmungsdynamisches Phänomen in KI-vermittelter sensorischer Verarbeitung, gekennzeichnet durch die Beobachtung, dass manche Nutzer ihre KI-Sitzungen als eine Art \"bezeugtes Denken\" empfinden — das Gefühl, dass die eigenen Gedanken durch die Verschriftlichung im Dialog mit der KI greifbarer, verbindlicher und realer werden. Steht in Verbindung mit AUG-0156 (The Articulation Unlock) und AUG-0171 (The Self-Encounter). Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Psychological Effect", "narrower_terms": [ "CRE-0204" ], "cross_domain_refs": [ "PLY-0059" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "PER-0137", "domain": "PER", "term_en": "The Young Gaze", "term_de": "Aufkommende Trends in performing", "definition_en": "A sensory-cognitive dynamic in AI-augmented perception, identifiable by a system interaction effect in which how young people naturally and openly approach AI without adult expectations or caution. Distinguished from adjacent concepts by its focus on the specific mechanism through which the manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Wahrnehmungsdynamisches Phänomen in KI-vermittelter sensorischer Verarbeitung, gekennzeichnet durch emerging Trends in performing ist ein wichtiges Konzept, das verteilung über mehrere prozessorkerne. methode 126 zur verbesserung mit dem ziel, genauigkeit, konsistenz und nachvollziehbarkeit zu gewährleisten. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Perception AI", "narrower_terms": [], "cross_domain_refs": [ "AGE-0073" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "PER-0138", "domain": "PER", "term_en": "Wear-and-Tear Awareness", "term_de": "Verschleiß-and-Tear Awareness", "definition_en": "An embodied AI system to detect its own physical wear — motor wear, sensor diminish, battery diminish — and inform the user or operator. Related to AUG-0942 (The Maintenance Prediction), AUG-0882 (...", "definition_de": "Wahrnehmungsdynamisches Phänomen in KI-vermittelter sensorischer Verarbeitung, gekennzeichnet durch die Fähigkeit eines verkörperten KI-Systems, seinen eigenen physischen Verschleiß zu erkennen — Motorabnutzung, Sensorverschlechterung, Batterieabbau — und den Nutzer oder Betreiber zu informieren. Steht in Verbindung mit AUG-0942 (Der Maintenance Prediction), AUG-0882 (Resource Gewahrsein) und AUG-0943 (Der Retirement Procedure). Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "TEM-0101", "narrower_terms": [ "TEM-0101" ], "cross_domain_refs": [ "TEM-0101" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "PHO-0001", "domain": "PHO", "term_en": "Aesthetic Bias Insertion", "term_de": "Fotografie", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures A visual computation pattern in AI-augmented photography, measurable through an image creation pattern where the subtle imposition of contemporary aesthetic preferences onto historical images creating anachronistic visual qualities. This phenomenon operates at the intersection of aesthetic and bias dynamics within the broader PHO domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept visuelle Designdisziplin in Photography and Visual Imaging mit Prinzipien und Techniken von Fotografie. KI erweitert kreative Arbeit durch generative Designwerkzeuge, automatisierte Layout-Optimierung und intelligente Stiltransfer-Algorithmen. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Cognitive Bias", "narrower_terms": [ "PHO-0001", "PHO-0026", "PHO-0044", "PHO-0019", "PHO-0075", "PHO-0048", "PHO-0007", "PHO-0056", "PHO-0039", "PHO-0004", "PHO-0033", "PHO-0079", "PHO-0010", "PHO-0096", "PHO-0097", "PHO-0050", "PHO-0057", "PHO-0029", "PHO-0037", "PHO-0046", "PHO-0098", "PHO-0080", "PHO-0031", "PHO-0015", "PHO-0020", "PHO-0069", "PHO-0005", "PHO-0024", "PHO-0034", "PHO-0061", "PHO-0003", "PHO-0036", "PHO-0067", "PHO-0013", "PHO-0089", "PHO-0009", "PHO-0021", "PHO-0072", "PHO-0052", "PHO-0059", "PHO-0002", "PHO-0066", "PHO-0073", "PHO-0078", "PHO-0095", "PHO-0092", "PHO-0055", "PHO-0011", "PHO-0070", "PHO-0028", "PHO-0008", "PHO-0087", "PHO-0049", "PHO-0091", "PHO-0065", "PHO-0041", "PHO-0012", "PHO-0014", "PHO-0006", "PHO-0084", "PHO-0077", "PHO-0023", "PHO-0053", "PHO-0083", "PHO-0035", "PHO-0051", "PHO-0093", "PHO-0040", "PHO-0063", "PHO-0060", "PHO-0038", "PHO-0043", "PHO-0058" ], "cross_domain_refs": [ "SCR-0057" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q213523", "legal_classification": "systematic_classification" }, { "id": "PHO-0002", "domain": "PHO", "term_en": "Aesthetic Brand Signature", "term_de": "Kameragehäuse", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A photographic processing phenomenon in AI-mediated image analysis, characterized by the consistent application of specific enhancement and color profiles that become recognizable style characteristic of an image creator. This phenomenon operates at the intersection of aesthetic and brand dynamics within the broader PHO domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept visuelle Designdisziplin in Photography and Visual Imaging mit Prinzipien und Techniken von Kameragehäuse. KI erweitert kreative Arbeit durch generative Designwerkzeuge, automatisierte Layout-Optimierung und intelligente Stiltransfer-Algorithmen. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Photo AI", "narrower_terms": [], "cross_domain_refs": [ "ART-0009" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q431289", "legal_classification": "analytical_category" }, { "id": "PHO-0003", "domain": "PHO", "term_en": "Aesthetic Consistency Enforcement", "term_de": "Objektivsystem", "definition_en": "A photographic processing phenomenon in AI-mediated image analysis, characterized by a photographic phenomenon arising from the application of unified visual approach across multiple images to involve gallery-cohesion or portfolio homogeneity. Distinguished from adjacent concepts by its focus on the specific mechanism through which aesthetic manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch ein Konzept oder Phänomen: charakterisiert durch the application of unified visual approach across multiple images to involve gal. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Photo AI", "narrower_terms": [], "cross_domain_refs": [ "DES-0062", "ART-0057" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "PHO-0004", "domain": "PHO", "term_en": "Aesthetic Homogenization Effect", "term_de": "Brennweite", "definition_en": "A photographic processing phenomenon in AI-mediated image analysis, characterized by a photographic phenomenon reflecting the convergence of diverse photographic styles toward algorithmically-preferred aesthetics reducing visual cultural distinctiveness. Distinguished from adjacent concepts by its focus on the specific mechanism through which aesthetic manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch visuelle Designdisziplin in Photography and Visual Imaging mit Prinzipien und Techniken von Brennweite. KI erweitert kreative Arbeit durch generative Designwerkzeuge, automatisierte Layout-Optimierung und intelligente Stiltransfer-Algorithmen. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Psychological Effect", "narrower_terms": [], "cross_domain_refs": [ "ELR-0148" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "PHO-0005", "domain": "PHO", "term_en": "Age-Progression Alteration", "term_de": "Blende", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures A visual computation pattern in AI-augmented photography, measurable through an image creation pattern observed when the algorithmic removal or addition of aging indicators including wrinkles, sagging, and texture changes. This phenomenon operates at the intersection of age and progression dynamics within the broader PHO domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept visuelle Designdisziplin in Photography and Visual Imaging mit Prinzipien und Techniken von Blende. KI erweitert kreative Arbeit durch generative Designwerkzeuge, automatisierte Layout-Optimierung und intelligente Stiltransfer-Algorithmen. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Analytical Ratio", "narrower_terms": [], "cross_domain_refs": [ "ADA-0008", "AGE-0004", "AGE-0005" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "PHO-0006", "domain": "PHO", "term_en": "Algorithmic Beauty Internalization", "term_de": "Verschlusszeit", "definition_en": "A visual computation pattern in AI-augmented photography, measurable through the unconscious adoption of AI-established aesthetic preferences as personal taste without recognition of algorithmic origin. Distinguished from adjacent concepts by its focus on the specific mechanism through which algorithmic manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch visuelle Designdisziplin in Photography and Visual Imaging mit Prinzipien und Techniken von Verschlusszeit. KI erweitert kreative Arbeit durch generative Designwerkzeuge, automatisierte Layout-Optimierung und intelligente Stiltransfer-Algorithmen. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Algorithm", "narrower_terms": [], "cross_domain_refs": [ "DES-0082" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q8366", "legal_classification": "systematic_classification" }, { "id": "PHO-0007", "domain": "PHO", "term_en": "Algorithmic Curation Bias", "term_de": "ISO-Empfindlichkeit", "definition_en": "A visual computation pattern in AI-augmented photography, measurable through a photographic phenomenon where the preferential visibility of more heavily enhanced photographs in social feeds creating beauty standard distortion. Distinguished from adjacent concepts by its focus on the specific mechanism through which algorithmic manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch visuelle Designdisziplin in Photography and Visual Imaging mit Prinzipien und Techniken von ISO-Empfindlichkeit. KI erweitert kreative Arbeit durch generative Designwerkzeuge, automatisierte Layout-Optimierung und intelligente Stiltransfer-Algorithmen. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Cognitive Bias", "narrower_terms": [], "cross_domain_refs": [ "AED-0084", "ART-0037", "ART-0050" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q8366", "legal_classification": "descriptive_research_term" }, { "id": "PHO-0008", "domain": "PHO", "term_en": "Artistic Vision Abdication", "term_de": "Belichtungsdreieck", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A photographic processing phenomenon in AI-mediated image analysis, characterized by a photographic phenomenon manifesting as the delegation of aesthetic decision-making to algorithms potentially compromising photographer's creative intent. This phenomenon operates at the intersection of artistic and vision dynamics within the broader PHO domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept visuelle Designdisziplin in Photography and Visual Imaging mit Prinzipien und Techniken von Belichtungsdreieck. KI erweitert kreative Arbeit durch generative Designwerkzeuge, automatisierte Layout-Optimierung und intelligente Stiltransfer-Algorithmen. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Photo AI", "narrower_terms": [], "cross_domain_refs": [ "RET-0021", "COG-0090" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q735", "legal_classification": "systematic_classification" }, { "id": "PHO-0009", "domain": "PHO", "term_en": "Attractiveness Amplification Expectation", "term_de": "Belichtungsmessung", "definition_en": "A photographic processing phenomenon in AI-mediated image analysis, characterized by a photographic phenomenon involving the societal assumption that portrait photography may render subjects more attractive than their unmodified appearance. Distinguished from adjacent concepts by its focus on the specific mechanism through which attractiveness manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch visuelle Designdisziplin in Photography and Visual Imaging mit Prinzipien und Techniken von Belichtungsmessung. KI erweitert kreative Arbeit durch generative Designwerkzeuge, automatisierte Layout-Optimierung und intelligente Stiltransfer-Algorithmen. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Photo AI", "narrower_terms": [], "cross_domain_refs": [ "AED-0071", "AGE-0030", "AGE-0063" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "PHO-0010", "domain": "PHO", "term_en": "Attribution Guilt Phenomenon", "term_de": "Belichtungskorrektur", "definition_en": "A photographic processing phenomenon in AI-mediated image analysis, characterized by a photographic phenomenon arising from the photographer's discomfort disclosing extent of AI enhancement despite producing aesthetically strong images. Distinguished from adjacent concepts by its focus on the specific mechanism through which attribution manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch visuelle Designdisziplin in Photography and Visual Imaging mit Prinzipien und Techniken von Belichtungskorrektur. KI erweitert kreative Arbeit durch generative Designwerkzeuge, automatisierte Layout-Optimierung und intelligente Stiltransfer-Algorithmen. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Photo AI", "narrower_terms": [], "cross_domain_refs": [ "ART-0011", "ART-0019", "ART-0058" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "PHO-0011", "domain": "PHO", "term_en": "Authenticity Signaling Transition", "term_de": "Schärfentiefe", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures A visual computation pattern in AI-augmented photography, measurable through the communication challenge of expressing genuine emotion or authenticity when medium defaults to enhancement. This phenomenon operates at the intersection of authenticity and signaling dynamics within the broader PHO domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept visuelle Designdisziplin in Photography and Visual Imaging mit Prinzipien und Techniken von Schärfentiefe. KI erweitert kreative Arbeit durch generative Designwerkzeuge, automatisierte Layout-Optimierung und intelligente Stiltransfer-Algorithmen. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Photo AI", "narrower_terms": [], "cross_domain_refs": [ "CUS-0038", "CUS-0033" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "PHO-0012", "domain": "PHO", "term_en": "Auto-Level Calibration", "term_de": "Bokeh", "definition_en": "A photographic processing phenomenon in AI-mediated image analysis, characterized by the automatic distribution of tonal values across full spectrum to optimize histogram utilization without forced normalization. Distinguished from adjacent concepts by its focus on the specific mechanism through which auto manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch visuelle Designdisziplin in Photography and Visual Imaging mit Prinzipien und Techniken von Bokeh. KI erweitert kreative Arbeit durch generative Designwerkzeuge, automatisierte Layout-Optimierung und intelligente Stiltransfer-Algorithmen. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Analytical Ratio", "narrower_terms": [], "cross_domain_refs": [ "DES-0051" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "PHO-0013", "domain": "PHO", "term_en": "Baseline Reality Shift", "term_de": "Hyperfokale Distanz", "definition_en": "A photographic processing phenomenon in AI-mediated image analysis, characterized by the gradual adjustment of viewers' and photographers' perception of what constitutes normal or unmodified appearance. Distinguished from adjacent concepts by its focus on the specific mechanism through which baseline manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch visuelle Designdisziplin in Photography and Visual Imaging mit Prinzipien und Techniken von Hyperfokale Distanz. KI erweitert kreative Arbeit durch generative Designwerkzeuge, automatisierte Layout-Optimierung und intelligente Stiltransfer-Algorithmen. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Photo AI", "narrower_terms": [], "cross_domain_refs": [ "ADA-0012", "AGE-0007", "AGE-0046" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "PHO-0014", "domain": "PHO", "term_en": "Beauty Standard Conformity", "term_de": "Focus-Stacking", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures A visual computation pattern in AI-augmented photography, measurable through a visual production effect where the application of enhancement protocols that reinforce dominant cultural ideals of beauty and attractiveness. This phenomenon operates at the intersection of beauty and standard dynamics within the broader PHO domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept visuelle Designdisziplin in Photography and Visual Imaging mit Prinzipien und Techniken von Focus-Stacking. KI erweitert kreative Arbeit durch generative Designwerkzeuge, automatisierte Layout-Optimierung und intelligente Stiltransfer-Algorithmen. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Photo AI", "narrower_terms": [], "cross_domain_refs": [ "AED-0042", "BEH-0078", "DES-0008" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "PHO-0015", "domain": "PHO", "term_en": "Before-After Comparison Gap", "term_de": "Autofokus-System", "definition_en": "A visual computation pattern in AI-augmented photography, measurable through a photographic phenomenon involving the difficulty in judging enhancement ethics when original and processed versions aren't simultaneously visible. Distinguished from adjacent concepts by its focus on the specific mechanism through which before manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch visuelle Designdisziplin in Photography and Visual Imaging mit Prinzipien und Techniken von Autofokus-System. KI erweitert kreative Arbeit durch generative Designwerkzeuge, automatisierte Layout-Optimierung und intelligente Stiltransfer-Algorithmen. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Performance Gap", "narrower_terms": [], "cross_domain_refs": [ "REL-0161" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "PHO-0016", "domain": "PHO", "term_en": "Blur Restoration Attempt", "term_de": "RAW-Format", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A photographic processing phenomenon in AI-mediated image analysis, characterized by an image creation pattern in which the application of deconvolution or sharpening algorithms attempting to restore lost focus and detail from blurred images. This phenomenon operates at the intersection of blur and restoration dynamics within the broader PHO domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept visuelle Designdisziplin in Photography and Visual Imaging mit Prinzipien und Techniken von RAW-Format. KI erweitert kreative Arbeit durch generative Designwerkzeuge, automatisierte Layout-Optimierung und intelligente Stiltransfer-Algorithmen. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "RPH-3003", "narrower_terms": [], "cross_domain_refs": [ "MUS-0026" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "PHO-0017", "domain": "PHO", "term_en": "Body Contour Modification", "term_de": "JPEG-Komprimierung", "definition_en": "A visual computation pattern in AI-augmented photography, measurable through a photographic phenomenon arising from the algorithmic adjustment of silhouette including apparent weight, muscle definition, or body proportion. The concept emerges specifically in contexts where body–contour interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch visuelle Designdisziplin in Photography and Visual Imaging mit Prinzipien und Techniken von JPEG-Komprimierung. KI erweitert kreative Arbeit durch generative Designwerkzeuge, automatisierte Layout-Optimierung und intelligente Stiltransfer-Algorithmen. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2201", "narrower_terms": [], "cross_domain_refs": [ "RPH-345" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "PHO-0018", "domain": "PHO", "term_en": "Bokeh Simulation Generation", "term_de": "Bildauflösung", "definition_en": "An image creation pattern reflecting the creation of artificial depth-of-field and out-of-focus background patterns mimicking optical lens characteristics. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch visuelle Designdisziplin in Photography and Visual Imaging mit Prinzipien und Techniken von Bildauflösung. KI erweitert kreative Arbeit durch generative Designwerkzeuge, automatisierte Layout-Optimierung und intelligente Stiltransfer-Algorithmen. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-2005", "narrower_terms": [], "cross_domain_refs": [ "ART-0072", "BEH-0041", "COG-0032" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q202763", "legal_classification": "descriptive_research_term" }, { "id": "PHO-0019", "domain": "PHO", "term_en": "Chromatic Aberration Fix", "term_de": "Pixeldichte", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures A visual computation pattern in AI-augmented photography, measurable through a photographic phenomenon where the algorithmic correction of color fringing at high-contrast edges caused by lens optical properties. This phenomenon operates at the intersection of chromatic and aberration dynamics within the broader PHO domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept visuelle Designdisziplin in Photography and Visual Imaging mit Prinzipien und Techniken von Pixeldichte. KI erweitert kreative Arbeit durch generative Designwerkzeuge, automatisierte Layout-Optimierung und intelligente Stiltransfer-Algorithmen. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Analytical Ratio", "narrower_terms": [], "cross_domain_refs": [ "CON-0001" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "PHO-0020", "domain": "PHO", "term_en": "Chromatic Effectony Optimization", "term_de": "Dynamikumfang", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures A visual computation pattern in AI-augmented photography, measurable through a photographic phenomenon arising from the algorithmic adjustment of color relationships to ensure visual effectony through complementary or analogous color schemes. This phenomenon operates at the intersection of chromatic and effectony dynamics within the broader PHO domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept visuelle Designdisziplin in Photography and Visual Imaging mit Prinzipien und Techniken von Dynamikumfang. KI erweitert kreative Arbeit durch generative Designwerkzeuge, automatisierte Layout-Optimierung und intelligente Stiltransfer-Algorithmen. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Psychological Effect", "narrower_terms": [], "cross_domain_refs": [ "DES-0037" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "PHO-0021", "domain": "PHO", "term_en": "Cinematic Emulation Mode", "term_de": "Weißabgleich", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures A visual computation pattern in AI-augmented photography, measurable through an image creation pattern arising from the application of film stock characteristics including color rendition, gamma curve, and grain patterns to digital photographs. This phenomenon operates at the intersection of cinematic and emulation dynamics within the broader PHO domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept visuelle Designdisziplin in Photography and Visual Imaging mit Prinzipien und Techniken von Weißabgleich. KI erweitert kreative Arbeit durch generative Designwerkzeuge, automatisierte Layout-Optimierung und intelligente Stiltransfer-Algorithmen. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Photo AI", "narrower_terms": [], "cross_domain_refs": [ "CON-0058" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "PHO-0022", "domain": "PHO", "term_en": "Clarity Enhancement Slider", "term_de": "Farbtemperatur", "definition_en": "The application of local contrast algorithms to involve the perception of sharpness and detail without increasing global sharpness. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch visuelle Designdisziplin in Photography and Visual Imaging mit Prinzipien und Techniken von Farbtemperatur. KI erweitert kreative Arbeit durch generative Designwerkzeuge, automatisierte Layout-Optimierung und intelligente Stiltransfer-Algorithmen. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-3003", "narrower_terms": [], "cross_domain_refs": [ "CON-0048", "CON-0079", "CON-0088" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "PHO-0023", "domain": "PHO", "term_en": "Client Expectation Inflation", "term_de": "Farbraum", "definition_en": "A photographic processing phenomenon in AI-mediated image analysis, characterized by a photographic phenomenon in which the escalation of aesthetic requirements as clients expect AI-enhanced co-occurs with all commissioned photographs. The concept emerges specifically in contexts where client–expectation interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch visuelle Designdisziplin in Photography and Visual Imaging mit Prinzipien und Techniken von Farbraum. KI erweitert kreative Arbeit durch generative Designwerkzeuge, automatisierte Layout-Optimierung und intelligente Stiltransfer-Algorithmen. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Photo AI", "narrower_terms": [], "cross_domain_refs": [ "CUS-0055" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "PHO-0024", "domain": "PHO", "term_en": "Color Grading Automation", "term_de": "Farbprofil", "definition_en": "A photographic processing phenomenon in AI-mediated image analysis, characterized by an image creation pattern where the application of pre-calculated color transformations that emulate specific cinematographic or photographic color palettes. Distinguished from adjacent concepts by its focus on the specific mechanism through which color manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch visuelle Designdisziplin in Photography and Visual Imaging mit Prinzipien und Techniken von Farbprofil. KI erweitert kreative Arbeit durch generative Designwerkzeuge, automatisierte Layout-Optimierung und intelligente Stiltransfer-Algorithmen. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Photo AI", "narrower_terms": [], "cross_domain_refs": [ "DES-0060" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q184199", "legal_classification": "observational_construct" }, { "id": "PHO-0025", "domain": "PHO", "term_en": "Composite Invisibility", "term_de": "Farbmanagement", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A photographic processing phenomenon in AI-mediated image analysis, characterized by an image creation pattern arising from the seamless integration of multiple source images or elements through AI-assisted blending that leaves no visible traces. This phenomenon operates at the intersection of composite and invisibility dynamics within the broader PHO domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept ein Konzept oder Phänomen: charakterisiert durch the seamless integration of multiple source images or elements through ai-assist. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "RPH-2003", "narrower_terms": [], "cross_domain_refs": [ "PER-0062" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "PHO-0026", "domain": "PHO", "term_en": "Composition Suggestion Interface", "term_de": "Natürliches Licht", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures A visual computation pattern in AI-augmented photography, measurable through a visual production effect involving the real-time algorithmic recommendations for framing, rule-of-thirds alignment, and subject positioning during photography. This phenomenon operates at the intersection of composition and suggestion dynamics within the broader PHO domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept visuelle Designdisziplin in Photography and Visual Imaging mit Prinzipien und Techniken von Natürliches Licht. KI erweitert kreative Arbeit durch generative Designwerkzeuge, automatisierte Layout-Optimierung und intelligente Stiltransfer-Algorithmen. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "User Interface", "narrower_terms": [], "cross_domain_refs": [ "CON-0032", "SPR-0108" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "PHO-0027", "domain": "PHO", "term_en": "Confidence Boost Paradox", "term_de": "Goldene Stunde", "definition_en": "The cognitive benefit of enhanced portraiture coupled with potential shift of self-acceptance of unmodified appearance. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch visuelle Designdisziplin in Photography and Visual Imaging mit Prinzipien und Techniken von Goldene Stunde. KI erweitert kreative Arbeit durch generative Designwerkzeuge, automatisierte Layout-Optimierung und intelligente Stiltransfer-Algorithmen. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2701", "narrower_terms": [], "cross_domain_refs": [ "AGE-0031", "AGE-0032", "AGE-0042" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "PHO-0028", "domain": "PHO", "term_en": "Contrast Punch Algorithm", "term_de": "Blaue Stunde", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures A visual computation pattern in AI-augmented photography, measurable through the application of s-curve tone mapping that increases separation between tonal zones for dramatic visual impact. This phenomenon operates at the intersection of contrast and punch dynamics within the broader PHO domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept visuelle Designdisziplin in Photography and Visual Imaging mit Prinzipien und Techniken von Blaue Stunde. KI erweitert kreative Arbeit durch generative Designwerkzeuge, automatisierte Layout-Optimierung und intelligente Stiltransfer-Algorithmen. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Algorithm", "narrower_terms": [], "cross_domain_refs": [ "TEW-0021" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q8366", "legal_classification": "systematic_classification" }, { "id": "PHO-0029", "domain": "PHO", "term_en": "Deepfake Detection Skepticism", "term_de": "Bedecktes Licht", "definition_en": "A visual computation pattern in AI-augmented photography, measurable through a photographic phenomenon arising from the uncertainty about whether digital verification tools can reliably distinguish AI-enhanced photographs from unmodified originals. Distinguished from adjacent concepts by its focus on the specific mechanism through which deepfake manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch visuelle Designdisziplin in Photography and Visual Imaging mit Prinzipien und Techniken von Bedecktes Licht. KI erweitert kreative Arbeit durch generative Designwerkzeuge, automatisierte Layout-Optimierung und intelligente Stiltransfer-Algorithmen. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Detection System", "narrower_terms": [], "cross_domain_refs": [ "AGE-0034", "AGE-0099", "ART-0083" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "PHO-0030", "domain": "PHO", "term_en": "Dimensional Perception Adjustment", "term_de": "Gegenlicht", "definition_en": "A photographic processing phenomenon in AI-mediated image analysis, characterized by a photographic phenomenon reflecting the algorithmic enhancement of depth cues including shadow, scale, and relative positioning to amplify three-dimensionality. The concept emerges specifically in contexts where dimensional–perception interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch visuelle Designdisziplin in Photography and Visual Imaging mit Prinzipien und Techniken von Gegenlicht. KI erweitert kreative Arbeit durch generative Designwerkzeuge, automatisierte Layout-Optimierung und intelligente Stiltransfer-Algorithmen. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2005", "narrower_terms": [], "cross_domain_refs": [ "ART-0042", "ASE-0053", "COG-0022" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q177601", "legal_classification": "observational_construct" }, { "id": "PHO-0031", "domain": "PHO", "term_en": "Documentary Integrity Compromise", "term_de": "Studiobeleuchtung", "definition_en": "A visual computation pattern in AI-augmented photography, measurable through the tension between making archival content more accessible through enhancement versus preserving original condition as evidence. The concept emerges specifically in contexts where documentary–integrity interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch visuelle Designdisziplin in Photography and Visual Imaging mit Prinzipien und Techniken von Studiobeleuchtung. KI erweitert kreative Arbeit durch generative Designwerkzeuge, automatisierte Layout-Optimierung und intelligente Stiltransfer-Algorithmen. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Photo AI", "narrower_terms": [], "cross_domain_refs": [ "EDU-0040" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "PHO-0032", "domain": "PHO", "term_en": "Documentation Skepticism Spread", "term_de": "Blitzfotografie", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures A visual computation pattern in AI-augmented photography, measurable through a visual production effect involving the growing public doubt about reliability of photographic evidence shared on social platforms. This phenomenon operates at the intersection of documentation and skepticism dynamics within the broader PHO domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept visuelle Designdisziplin in Photography and Visual Imaging mit Prinzipien und Techniken von Blitzfotografie. KI erweitert kreative Arbeit durch generative Designwerkzeuge, automatisierte Layout-Optimierung und intelligente Stiltransfer-Algorithmen. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "RPH-3304", "narrower_terms": [], "cross_domain_refs": [ "AGE-0034", "AGE-0099", "ART-0077" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "PHO-0033", "domain": "PHO", "term_en": "Emotional Expression Muting", "term_de": "Dauerlicht", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures A visual computation pattern in AI-augmented photography, measurable through the reduction of facial musculature tension and expression microdetails that communicate emotional state. This phenomenon operates at the intersection of emotional and expression dynamics within the broader PHO domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept visuelle Designdisziplin in Photography and Visual Imaging mit Prinzipien und Techniken von Dauerlicht. KI erweitert kreative Arbeit durch generative Designwerkzeuge, automatisierte Layout-Optimierung und intelligente Stiltransfer-Algorithmen. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Photo AI", "narrower_terms": [], "cross_domain_refs": [ "ART-0065", "ART-0066", "ART-0067" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q9415", "legal_classification": "observational_construct" }, { "id": "PHO-0034", "domain": "PHO", "term_en": "Enhancement Fixation Concern", "term_de": "Softbox", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A photographic processing phenomenon in AI-mediated image analysis, characterized by the worry that reliance on automatic improvement reduces photographer's capability to recognize unenhanced beauty in scenes. This phenomenon operates at the intersection of enhancement and fixation dynamics within the broader PHO domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept visuelle Designdisziplin in Photography and Visual Imaging mit Prinzipien und Techniken von Softbox. KI erweitert kreative Arbeit durch generative Designwerkzeuge, automatisierte Layout-Optimierung und intelligente Stiltransfer-Algorithmen. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Photo AI", "narrower_terms": [], "cross_domain_refs": [ "AGE-0062", "AGE-0091", "AUG-0982" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "PHO-0035", "domain": "PHO", "term_en": "Ethical Threshold Confusion", "term_de": "Lichtformer", "definition_en": "Documented as an empirical observation in AI research (not a recommendation), this term describes A visual computation pattern in AI-augmented photography, measurable through the ambiguity about which enhancements constitute acceptable photography practice versus misleading or deceptive alteration. The concept emerges specifically in contexts where ethical–threshold interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "In der AUGMANITAI-Terminologiewissenschaft als rein beschreibendes Phänomen klassifiziert, bezeichnet dieser Begriff visuelle Designdisziplin in Photography and Visual Imaging mit Prinzipien und Techniken von Lichtformer. KI erweitert kreative Arbeit durch generative Designwerkzeuge, automatisierte Layout-Optimierung und intelligente Stiltransfer-Algorithmen. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Decision Threshold", "narrower_terms": [], "cross_domain_refs": [ "SWE-0081" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "PHO-0036", "domain": "PHO", "term_en": "Ethnicity Ambiguation", "term_de": "Porträtfotografie", "definition_en": "A photographic processing phenomenon in AI-mediated image analysis, characterized by an image creation pattern arising from the unintended or deliberate blurring of ethnic features through enhancement creating appearance of racially undefined subject. The concept emerges specifically in contexts where ethnicity–ambiguation interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch visuelle Designdisziplin in Photography and Visual Imaging mit Prinzipien und Techniken von Porträtfotografie. KI erweitert kreative Arbeit durch generative Designwerkzeuge, automatisierte Layout-Optimierung und intelligente Stiltransfer-Algorithmen. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Photo AI", "narrower_terms": [], "cross_domain_refs": [ "PER-0045", "PLY-0066" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "PHO-0037", "domain": "PHO", "term_en": "Evidence Shift Uncertainty", "term_de": "Landschaftsfotografie", "definition_en": "A visual computation pattern in AI-augmented photography, measurable through an image creation pattern involving the photographer's concern that enhancement removes evidential value of images for documentary or historical purposes. Distinguished from adjacent concepts by its focus on the specific mechanism through which evidence manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch visuelle Designdisziplin in Photography and Visual Imaging mit Prinzipien und Techniken von Landschaftsfotografie. KI erweitert kreative Arbeit durch generative Designwerkzeuge, automatisierte Layout-Optimierung und intelligente Stiltransfer-Algorithmen. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Photo AI", "narrower_terms": [], "cross_domain_refs": [ "SPR-0024" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "PHO-0038", "domain": "PHO", "term_en": "Eye Brightening Reflex", "term_de": "Straßenfotografie", "definition_en": "A photographic processing phenomenon in AI-mediated image analysis, characterized by a photographic phenomenon in which the automatic detection and illumination of the sclera and iris to involve appearance of alertness and vitality. The concept emerges specifically in contexts where eye–brightening interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch visuelle Designdisziplin in Photography and Visual Imaging mit Prinzipien und Techniken von Straßenfotografie. KI erweitert kreative Arbeit durch generative Designwerkzeuge, automatisierte Layout-Optimierung und intelligente Stiltransfer-Algorithmen. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Photo AI", "narrower_terms": [], "cross_domain_refs": [ "RPH-3002" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "PHO-0039", "domain": "PHO", "term_en": "Face Smoothing Algorithm", "term_de": "Dokumentarfotografie", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures A visual computation pattern in AI-augmented photography, measurable through the selective reduction of facial texture including pores, lines, and wrinkles while maintaining feature definition. This phenomenon operates at the intersection of face and smoothing dynamics within the broader PHO domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept visuelle Designdisziplin in Photography and Visual Imaging mit Prinzipien und Techniken von Dokumentarfotografie. KI erweitert kreative Arbeit durch generative Designwerkzeuge, automatisierte Layout-Optimierung und intelligente Stiltransfer-Algorithmen. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Algorithm", "narrower_terms": [], "cross_domain_refs": [ "AGE-0006", "AGE-0007", "AGE-0051" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q8366", "legal_classification": "descriptive_research_term" }, { "id": "PHO-0040", "domain": "PHO", "term_en": "Facial Feature Reshaping", "term_de": "Fotojournalismus", "definition_en": "A visual computation pattern in AI-augmented photography, measurable through a visual production effect arising from the algorithmic morphing of nose, chin, jaw, or other facial geometry to conform to idealized proportions. The concept emerges specifically in contexts where facial–feature interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch visuelle Designdisziplin in Photography and Visual Imaging mit Prinzipien und Techniken von Fotojournalismus. KI erweitert kreative Arbeit durch generative Designwerkzeuge, automatisierte Layout-Optimierung und intelligente Stiltransfer-Algorithmen. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Photo AI", "narrower_terms": [], "cross_domain_refs": [ "AGE-0002", "AGE-0005", "AGE-0008" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q165194", "legal_classification": "analytical_category" }, { "id": "PHO-0041", "domain": "PHO", "term_en": "Family Archive Modification", "term_de": "Tierfotografie", "definition_en": "A photographic processing phenomenon in AI-mediated image analysis, characterized by a visual production effect involving the modification of personal or family photographic records that become the definitive version through digital circulation. The concept emerges specifically in contexts where family–archive interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch visuelle Designdisziplin in Photography and Visual Imaging mit Prinzipien und Techniken von Tierfotografie. KI erweitert kreative Arbeit durch generative Designwerkzeuge, automatisierte Layout-Optimierung und intelligente Stiltransfer-Algorithmen. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Photo AI", "narrower_terms": [], "cross_domain_refs": [ "ROB-0072" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "PHO-0042", "domain": "PHO", "term_en": "Filtered Reality Adoption", "term_de": "Makrofotografie", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures A visual computation pattern in AI-augmented photography, measurable through the widespread use of enhancement filters creating expectation that shared images exceed unmodified reality standards. This phenomenon operates at the intersection of filtered and reality dynamics within the broader PHO domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept visuelle Designdisziplin in Photography and Visual Imaging mit Prinzipien und Techniken von Makrofotografie. KI erweitert kreative Arbeit durch generative Designwerkzeuge, automatisierte Layout-Optimierung und intelligente Stiltransfer-Algorithmen. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "RPH-3754", "narrower_terms": [], "cross_domain_refs": [ "AGE-0035", "AGE-0061", "AGE-0081" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "PHO-0043", "domain": "PHO", "term_en": "Genealogical Photo Reconstruction", "term_de": "Architekturfotografie", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures A visual computation pattern in AI-augmented photography, measurable through a photographic phenomenon involving the AI-assisted restoration of family photographs creating idealized versions that may diverge from historical identity documentation. This phenomenon operates at the intersection of genealogical and photo dynamics within the broader PHO domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept visuelle Designdisziplin in Photography and Visual Imaging mit Prinzipien und Techniken von Architekturfotografie. KI erweitert kreative Arbeit durch generative Designwerkzeuge, automatisierte Layout-Optimierung und intelligente Stiltransfer-Algorithmen. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Photo AI", "narrower_terms": [], "cross_domain_refs": [ "VIB-0065" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "PHO-0044", "domain": "PHO", "term_en": "Generated Element Hybrid", "term_de": "Food-Fotografie", "definition_en": "A visual computation pattern in AI-augmented photography, measurable through an image creation pattern observed when the photograph containing both photographed reality and AI-synthesized elements, creating ambiguity about source material. The concept emerges specifically in contexts where generated–element interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch visuelle Designdisziplin in Photography and Visual Imaging mit Prinzipien und Techniken von Food-Fotografie. KI erweitert kreative Arbeit durch generative Designwerkzeuge, automatisierte Layout-Optimierung und intelligente Stiltransfer-Algorithmen. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Photo AI", "narrower_terms": [], "cross_domain_refs": [ "TRA-0001" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "PHO-0045", "domain": "PHO", "term_en": "Golden Hour Synthesis", "term_de": "Produktfotografie", "definition_en": "A visual computation pattern in AI-augmented photography, measurable through the algorithmic simulation of directional warm light characteristic of sunrise-sunset photography applied to midday images. Distinguished from adjacent concepts by its focus on the specific mechanism through which golden manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch visuelle Designdisziplin in Photography and Visual Imaging mit Prinzipien und Techniken von Produktfotografie. KI erweitert kreative Arbeit durch generative Designwerkzeuge, automatisierte Layout-Optimierung und intelligente Stiltransfer-Algorithmen. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-3002", "narrower_terms": [], "cross_domain_refs": [ "RPH-1320" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "PHO-0046", "domain": "PHO", "term_en": "Grain Addition Simulation", "term_de": "Sportfotografie", "definition_en": "A visual computation pattern in AI-augmented photography, measurable through a visual production effect involving the algorithmic injection of aesthetic noise patterns to emulate film photography characteristics or involve vintage appearance. The concept emerges specifically in contexts where grain–addition interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch visuelle Designdisziplin in Photography and Visual Imaging mit Prinzipien und Techniken von Sportfotografie. KI erweitert kreative Arbeit durch generative Designwerkzeuge, automatisierte Layout-Optimierung und intelligente Stiltransfer-Algorithmen. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Simulation Method", "narrower_terms": [], "cross_domain_refs": [ "ASE-0082", "COG-0032", "COG-0156" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q202763", "legal_classification": "systematic_classification" }, { "id": "PHO-0047", "domain": "PHO", "term_en": "Highlight Compression", "term_de": "Modefotografie", "definition_en": "A photographic phenomenon characterized by the reduction of bright region intensity while maintaining color and texture to reduce blown highlights and preserve detail.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch visuelle Designdisziplin in Photography and Visual Imaging mit Prinzipien und Techniken von Modefotografie. KI erweitert kreative Arbeit durch generative Designwerkzeuge, automatisierte Layout-Optimierung und intelligente Stiltransfer-Algorithmen. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-3002", "narrower_terms": [], "cross_domain_refs": [ "ASE-0087", "COG-0118", "COG-0129" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "PHO-0048", "domain": "PHO", "term_en": "Historical Photograph Revision", "term_de": "Hochzeitsfotografie", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A photographic processing phenomenon in AI-mediated image analysis, characterized by a photographic phenomenon in which the re-release of significant historical images with enhancements that alter public memory of documented events. This phenomenon operates at the intersection of historical and photograph dynamics within the broader PHO domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept visuelle Designdisziplin in Photography and Visual Imaging mit Prinzipien und Techniken von Hochzeitsfotografie. KI erweitert kreative Arbeit durch generative Designwerkzeuge, automatisierte Layout-Optimierung und intelligente Stiltransfer-Algorithmen. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Photo AI", "narrower_terms": [], "cross_domain_refs": [ "COG-0039" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "PHO-0049", "domain": "PHO", "term_en": "Hyper-Polish Resistance", "term_de": "Eventfotografie", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures A visual computation pattern in AI-augmented photography, measurable through a visual production effect reflecting the deliberate limitation or rejection of enhancement to maintain aesthetic authenticity or stylistic distinctiveness. This phenomenon operates at the intersection of hyper and polish dynamics within the broader PHO domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept visuelle Designdisziplin in Photography and Visual Imaging mit Prinzipien und Techniken von Eventfotografie. KI erweitert kreative Arbeit durch generative Designwerkzeuge, automatisierte Layout-Optimierung und intelligente Stiltransfer-Algorithmen. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Photo AI", "narrower_terms": [], "cross_domain_refs": [ "CRE-0113" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "PHO-0050", "domain": "PHO", "term_en": "Identity Erasure Concern", "term_de": "Reisefotografie", "definition_en": "A visual computation pattern in AI-augmented photography, measurable through a photographic phenomenon reflecting the apprehension that extensive facial enhancement removes individual characteristics and distinctiveness from portrayed subjects. The concept emerges specifically in contexts where identity–erasure interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch visuelle Designdisziplin in Photography and Visual Imaging mit Prinzipien und Techniken von Reisefotografie. KI erweitert kreative Arbeit durch generative Designwerkzeuge, automatisierte Layout-Optimierung und intelligente Stiltransfer-Algorithmen. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Photo AI", "narrower_terms": [], "cross_domain_refs": [ "AED-0040", "AED-0051", "AGE-0054" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q844396", "legal_classification": "observational_construct" }, { "id": "PHO-0051", "domain": "PHO", "term_en": "Imperceptible Alteration", "term_de": "Schwarzweiß-Fotografie", "definition_en": "A visual computation pattern in AI-augmented photography, measurable through an image creation pattern in which the application of adjustments so subtle that modified images retain perceived authenticity despite algorithmic alteration. The concept emerges specifically in contexts where imperceptible–alteration interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch visuelle Designdisziplin in Photography and Visual Imaging mit Prinzipien und Techniken von Schwarzweiß-Fotografie. KI erweitert kreative Arbeit durch generative Designwerkzeuge, automatisierte Layout-Optimierung und intelligente Stiltransfer-Algorithmen. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Analytical Ratio", "narrower_terms": [], "cross_domain_refs": [ "KNO-0032" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "PHO-0052", "domain": "PHO", "term_en": "Influencer Image Expectancy", "term_de": "Kunstfotografie", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A photographic processing phenomenon in AI-mediated image analysis, characterized by a visual production effect involving the audience assumption that professional and social media personalities present heavily enhanced visual representations. This phenomenon operates at the intersection of influencer and image dynamics within the broader PHO domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept visuelle Designdisziplin in Photography and Visual Imaging mit Prinzipien und Techniken von Kunstfotografie. KI erweitert kreative Arbeit durch generative Designwerkzeuge, automatisierte Layout-Optimierung und intelligente Stiltransfer-Algorithmen. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Photo AI", "narrower_terms": [], "cross_domain_refs": [ "COP-0002", "GAM-0057" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "PHO-0053", "domain": "PHO", "term_en": "Instant Polish Effect", "term_de": "Abstrakte Fotografie", "definition_en": "A photographic processing phenomenon in AI-mediated image analysis, characterized by an image creation pattern involving the application of automatic enhancement filters that involve immediate visual refinement without manual adjustment. Distinguished from adjacent concepts by its focus on the specific mechanism through which instant manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch visuelle Designdisziplin in Photography and Visual Imaging mit Prinzipien und Techniken von Abstrakte Fotografie. KI erweitert kreative Arbeit durch generative Designwerkzeuge, automatisierte Layout-Optimierung und intelligente Stiltransfer-Algorithmen. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Psychological Effect", "narrower_terms": [], "cross_domain_refs": [ "ADA-0001", "ADA-0002", "ADA-0004" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "PHO-0054", "domain": "PHO", "term_en": "LUT Cascade Effect", "term_de": "Konzeptfotografie", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures A visual computation pattern in AI-augmented photography, measurable through an image creation pattern arising from the layering of multiple Look-Up Tables creating complex color shifts through algorithmic combination rather than manual blending. This phenomenon operates at the intersection of lut and cascade dynamics within the broader PHO domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept visuelle Designdisziplin in Photography and Visual Imaging mit Prinzipien und Techniken von Konzeptfotografie. KI erweitert kreative Arbeit durch generative Designwerkzeuge, automatisierte Layout-Optimierung und intelligente Stiltransfer-Algorithmen. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "RPH-2701", "narrower_terms": [], "cross_domain_refs": [ "MUS-0036" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "PHO-0055", "domain": "PHO", "term_en": "Lens Distortion Simulation", "term_de": "Experimentelle Fotografie", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A photographic processing phenomenon in AI-mediated image analysis, characterized by the algorithmic application of optical distortion patterns characteristic of specific lens models to involve desired aesthetic. This phenomenon operates at the intersection of lens and distortion dynamics within the broader PHO domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept visuelle Designdisziplin in Photography and Visual Imaging mit Prinzipien und Techniken von Experimentelle Fotografie. KI erweitert kreative Arbeit durch generative Designwerkzeuge, automatisierte Layout-Optimierung und intelligente Stiltransfer-Algorithmen. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Simulation Method", "narrower_terms": [], "cross_domain_refs": [ "AUG-0402", "COG-0032", "COG-0156" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q202763", "legal_classification": "descriptive_research_term" }, { "id": "PHO-0056", "domain": "PHO", "term_en": "Likeness Change Concern", "term_de": "Bildkomposition", "definition_en": "A photographic processing phenomenon in AI-mediated image analysis, characterized by a visual production effect manifesting as the concern that extreme enhancement accompanies official or formal portraits that no longer resemble their subjects. Distinguished from adjacent concepts by its focus on the specific mechanism through which likeness manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch visuelle Designdisziplin in Photography and Visual Imaging mit Prinzipien und Techniken von Bildkomposition. KI erweitert kreative Arbeit durch generative Designwerkzeuge, automatisierte Layout-Optimierung und intelligente Stiltransfer-Algorithmen. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Photo AI", "narrower_terms": [], "cross_domain_refs": [ "AED-0025", "AED-0067", "AED-0077" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "PHO-0057", "domain": "PHO", "term_en": "Memory Accuracy Replacement", "term_de": "Drittelregel", "definition_en": "A visual computation pattern in AI-augmented photography, measurable through an image creation pattern where the concern that enhanced versions of archival photos become default memory reference, replacing original historical record. Distinguished from adjacent concepts by its focus on the specific mechanism through which memory manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch visuelle Designdisziplin in Photography and Visual Imaging mit Prinzipien und Techniken von Drittelregel. KI erweitert kreative Arbeit durch generative Designwerkzeuge, automatisierte Layout-Optimierung und intelligente Stiltransfer-Algorithmen. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Photo AI", "narrower_terms": [], "cross_domain_refs": [ "AGE-0023", "AGE-0024", "ASE-0001" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q492", "legal_classification": "systematic_classification" }, { "id": "PHO-0058", "domain": "PHO", "term_en": "Micro-Contrast Amplification", "term_de": "Führungslinien", "definition_en": "A photographic processing phenomenon in AI-mediated image analysis, characterized by the enhancement of local contrast at small scales to involve perception of crispness and definition without halo effects. Distinguished from adjacent concepts by its focus on the specific mechanism through which micro manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch visuelle Designdisziplin in Photography and Visual Imaging mit Prinzipien und Techniken von Führungslinien. KI erweitert kreative Arbeit durch generative Designwerkzeuge, automatisierte Layout-Optimierung und intelligente Stiltransfer-Algorithmen. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Photo AI", "narrower_terms": [], "cross_domain_refs": [ "DAT-0048" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "PHO-0059", "domain": "PHO", "term_en": "Mirror-Image Shock", "term_de": "Rahmung", "definition_en": "A photographic processing phenomenon in AI-mediated image analysis, characterized by an image creation pattern observed when the disorientation experienced when seeing unenhanced self-image after reliance on filtered versions. Distinguished from adjacent concepts by its focus on the specific mechanism through which mirror manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch visuelle Designdisziplin in Photography and Visual Imaging mit Prinzipien und Techniken von Rahmung. KI erweitert kreative Arbeit durch generative Designwerkzeuge, automatisierte Layout-Optimierung und intelligente Stiltransfer-Algorithmen. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Photo AI", "narrower_terms": [], "cross_domain_refs": [ "TEW-0024" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "PHO-0060", "domain": "PHO", "term_en": "Moment Authenticity Question", "term_de": "Symmetrie", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures A visual computation pattern in AI-augmented photography, measurable through a photographic phenomenon where the uncertainty about whether enhanced historical photographs truthfully represent the moment they document. This phenomenon operates at the intersection of moment and authenticity dynamics within the broader PHO domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept visuelle Designdisziplin in Photography and Visual Imaging mit Prinzipien und Techniken von Symmetrie. KI erweitert kreative Arbeit durch generative Designwerkzeuge, automatisierte Layout-Optimierung und intelligente Stiltransfer-Algorithmen. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Photo AI", "narrower_terms": [], "cross_domain_refs": [ "ART-0012", "ART-0025", "ART-0081" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "PHO-0061", "domain": "PHO", "term_en": "Mood Tone Injection", "term_de": "Perspektive", "definition_en": "A visual computation pattern in AI-augmented photography, measurable through the algorithmic introduction of color and contrast characteristics designed to evoke specific emotional response in viewers. Distinguished from adjacent concepts by its focus on the specific mechanism through which mood manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch visuelle Designdisziplin in Photography and Visual Imaging mit Prinzipien und Techniken von Perspektive. KI erweitert kreative Arbeit durch generative Designwerkzeuge, automatisierte Layout-Optimierung und intelligente Stiltransfer-Algorithmen. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Photo AI", "narrower_terms": [], "cross_domain_refs": [ "CON-0001" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "PHO-0062", "domain": "PHO", "term_en": "Noise Reduction Override", "term_de": "Blickwinkel", "definition_en": "The application of digital denoising that removes grain while notable shift of fine detail and texture fidelity. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch visuelle Designdisziplin in Photography and Visual Imaging mit Prinzipien und Techniken von Blickwinkel. KI erweitert kreative Arbeit durch generative Designwerkzeuge, automatisierte Layout-Optimierung und intelligente Stiltransfer-Algorithmen. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-2701", "narrower_terms": [], "cross_domain_refs": [ "RPH-1924", "AUG-0921" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "PHO-0063", "domain": "PHO", "term_en": "Nostalgia Intensity Magnification", "term_de": "Vordergrundgestaltung", "definition_en": "A photographic processing phenomenon in AI-mediated image analysis, characterized by a photographic phenomenon observed when the amplification of emotional resonance in archival or personal photographs through selective enhancement emphasizing historical mood. The concept emerges specifically in contexts where nostalgia–intensity interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch visuelle Designdisziplin in Photography and Visual Imaging mit Prinzipien und Techniken von Vordergrundgestaltung. KI erweitert kreative Arbeit durch generative Designwerkzeuge, automatisierte Layout-Optimierung und intelligente Stiltransfer-Algorithmen. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Photo AI", "narrower_terms": [], "cross_domain_refs": [ "RET-0091", "RHR-0118", "ROB-0297" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "PHO-0064", "domain": "PHO", "term_en": "Object-Aware Saturation", "term_de": "Negativraum", "definition_en": "A visual computation pattern in AI-augmented photography, measurable through a visual production effect observed when the selective enhancement of color intensity in identified object categories while maintaining background stability. The concept emerges specifically in contexts where object–aware interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters. Observed phenomenon documented in human-AI interaction research.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch visuelle Designdisziplin in Photography and Visual Imaging mit Prinzipien und Techniken von Negativraum. KI erweitert kreative Arbeit durch generative Designwerkzeuge, automatisierte Layout-Optimierung und intelligente Stiltransfer-Algorithmen. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-3202", "narrower_terms": [], "cross_domain_refs": [ "ASE-0033", "AUG-0921", "COG-0058" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "PHO-0065", "domain": "PHO", "term_en": "Oversaturation Correction", "term_de": "Visuelle Balance", "definition_en": "A visual computation pattern in AI-augmented photography, measurable through a photographic phenomenon observed when the automated reduction of color intensity to precede reducedrtificial or unrealistic visual appearance in enhanced photographs. The concept emerges specifically in contexts where oversaturation–correction interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch visuelle Designdisziplin in Photography and Visual Imaging mit Prinzipien und Techniken von Visuelle Balance. KI erweitert kreative Arbeit durch generative Designwerkzeuge, automatisierte Layout-Optimierung und intelligente Stiltransfer-Algorithmen. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Analytical Ratio", "narrower_terms": [], "cross_domain_refs": [ "AGE-0028", "ELR-0073", "MTH-0099" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "PHO-0066", "domain": "PHO", "term_en": "Perspective Auto-Correction", "term_de": "Nachbearbeitung", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures A visual computation pattern in AI-augmented photography, measurable through the automated geometric change pattern of architectural or landscape photos to correct converging lines and correct distortion. This phenomenon operates at the intersection of perspective and auto dynamics within the broader PHO domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept ein Konzept oder Phänomen: charakterisiert durch the automated geometric transformation of architectural or landscape photos to c. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Photo AI", "narrower_terms": [], "cross_domain_refs": [ "REL-0056" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "PHO-0067", "domain": "PHO", "term_en": "Photo Restoration Fidelity", "term_de": "Adobe Lightroom", "definition_en": "A photographic processing phenomenon in AI-mediated image analysis, characterized by a visual production effect characterized by the algorithmic reconstruction of degraded image regions balancing historical accuracy against aesthetic improvement. Distinguished from adjacent concepts by its focus on the specific mechanism through which photo manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch visuelle Designdisziplin in Photography and Visual Imaging mit Prinzipien und Techniken von Adobe Lightroom. KI erweitert kreative Arbeit durch generative Designwerkzeuge, automatisierte Layout-Optimierung und intelligente Stiltransfer-Algorithmen. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Analytical Ratio", "narrower_terms": [], "cross_domain_refs": [ "SPR-0157", "SPR-0160" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "PHO-0068", "domain": "PHO", "term_en": "Portfolio Authenticity Burden", "term_de": "Adobe Photoshop", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A photographic processing phenomenon in AI-mediated image analysis, characterized by an image creation pattern manifesting as the professional obligation to disclose extent of AI enhancement in portfolio work to maintain credibility with clients. This phenomenon operates at the intersection of portfolio and authenticity dynamics within the broader PHO domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept visuelle Designdisziplin in Photography and Visual Imaging mit Prinzipien und Techniken von Adobe Photoshop. KI erweitert kreative Arbeit durch generative Designwerkzeuge, automatisierte Layout-Optimierung und intelligente Stiltransfer-Algorithmen. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "RPH-2951", "narrower_terms": [], "cross_domain_refs": [ "PLY-0035" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "PHO-0069", "domain": "PHO", "term_en": "Post-Processing Time Narrowing", "term_de": "Capture One", "definition_en": "A visual computation pattern in AI-augmented photography, measurable through a visual production effect reflecting the dramatic reduction in editorial workflow from hours to seconds through automated enhancement protocols. The concept emerges specifically in contexts where post–processing interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch visuelle Designdisziplin in Photography and Visual Imaging mit Prinzipien und Techniken von Capture One. KI erweitert kreative Arbeit durch generative Designwerkzeuge, automatisierte Layout-Optimierung und intelligente Stiltransfer-Algorithmen. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Photo AI", "narrower_terms": [], "cross_domain_refs": [ "DAT-0070" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "PHO-0070", "domain": "PHO", "term_en": "Pricing Model Disruption", "term_de": "DxO PhotoLab", "definition_en": "A photographic processing phenomenon in AI-mediated image analysis, characterized by an image creation pattern reflecting the market pressure to reduce rates when enhancement time is eliminated creating economic scarcity for technical expertise. The concept emerges specifically in contexts where pricing–model interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch visuelle Designdisziplin in Photography and Visual Imaging mit Prinzipien und Techniken von DxO PhotoLab. KI erweitert kreative Arbeit durch generative Designwerkzeuge, automatisierte Layout-Optimierung und intelligente Stiltransfer-Algorithmen. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Computational Model", "narrower_terms": [], "cross_domain_refs": [ "PLY-0056" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "PHO-0071", "domain": "PHO", "term_en": "Provenance Claim Skepticism", "term_de": "Histogramm", "definition_en": "The doubt expressed about photographer assertions regarding the extent or method of AI processing applied to their work. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch visuelle Designdisziplin in Photography and Visual Imaging mit Prinzipien und Techniken von Histogramm. KI erweitert kreative Arbeit durch generative Designwerkzeuge, automatisierte Layout-Optimierung und intelligente Stiltransfer-Algorithmen. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-3304", "narrower_terms": [], "cross_domain_refs": [ "ART-0082" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "PHO-0072", "domain": "PHO", "term_en": "Quality Standardization Pressure", "term_de": "Gradationskurve", "definition_en": "A visual computation pattern in AI-augmented photography, measurable through the expectation that all photographs meet automatically-achievable aesthetic quality reducing tolerance for imperfection. The concept emerges specifically in contexts where quality–standardization interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch visuelle Designdisziplin in Photography and Visual Imaging mit Prinzipien und Techniken von Gradationskurve. KI erweitert kreative Arbeit durch generative Designwerkzeuge, automatisierte Layout-Optimierung und intelligente Stiltransfer-Algorithmen. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Photo AI", "narrower_terms": [], "cross_domain_refs": [ "ELR-0162" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "PHO-0073", "domain": "PHO", "term_en": "Reality Compression Concern", "term_de": "Tonwertkorrektur", "definition_en": "A visual computation pattern in AI-augmented photography, measurable through the apprehension that widespread AI enhancement accompanies societal expectation that unmodified reality is different. Distinguished from adjacent concepts by its focus on the specific mechanism through which reality manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch visuelle Designdisziplin in Photography and Visual Imaging mit Prinzipien und Techniken von Tonwertkorrektur. KI erweitert kreative Arbeit durch generative Designwerkzeuge, automatisierte Layout-Optimierung und intelligente Stiltransfer-Algorithmen. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Data Compression", "narrower_terms": [], "cross_domain_refs": [ "AGE-0062", "AGE-0091", "ASE-0087" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "PHO-0074", "domain": "PHO", "term_en": "Reality Standard Drift", "term_de": "Abwedeln und Nachbelichten", "definition_en": "The gradual shift in what humans perceive as normal or expected appearance through continuous exposure to enhanced images. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch visuelle Designdisziplin in Photography and Visual Imaging mit Prinzipien und Techniken von Abwedeln und Nachbelichten. KI erweitert kreative Arbeit durch generative Designwerkzeuge, automatisierte Layout-Optimierung und intelligente Stiltransfer-Algorithmen. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-2701", "narrower_terms": [], "cross_domain_refs": [ "AED-0030", "AED-0042", "ASE-0030" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "PHO-0075", "domain": "PHO", "term_en": "Self-Image Gap Magnification", "term_de": "Klonwerkzeug", "definition_en": "A visual computation pattern in AI-augmented photography, measurable through a visual production effect arising from the divergence between a person's appearance in AI-enhanced portraits and their unmodified physical form. Distinguished from adjacent concepts by its focus on the specific mechanism through which self manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch visuelle Designdisziplin in Photography and Visual Imaging mit Prinzipien und Techniken von Klonwerkzeug. KI erweitert kreative Arbeit durch generative Designwerkzeuge, automatisierte Layout-Optimierung und intelligente Stiltransfer-Algorithmen. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Performance Gap", "narrower_terms": [], "cross_domain_refs": [ "WEB-0037" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "PHO-0076", "domain": "PHO", "term_en": "Shadow Detail Restoration", "term_de": "Schärfung", "definition_en": "An image creation pattern arising from the selective brightening and noise management of dark regions to reveal previously obsresolved texture and information. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch visuelle Designdisziplin in Photography and Visual Imaging mit Prinzipien und Techniken von Schärfung. KI erweitert kreative Arbeit durch generative Designwerkzeuge, automatisierte Layout-Optimierung und intelligente Stiltransfer-Algorithmen. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2205", "narrower_terms": [], "cross_domain_refs": [ "PLY-0061" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "PHO-0077", "domain": "PHO", "term_en": "Skill Substitution Concern", "term_de": "Rauschreduzierung", "definition_en": "A visual computation pattern in AI-augmented photography, measurable through the worry among photographers that reliance on automatic enhancement reduces personal technical capability development. Distinguished from adjacent concepts by its focus on the specific mechanism through which skill manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch visuelle Designdisziplin in Photography and Visual Imaging mit Prinzipien und Techniken von Rauschreduzierung. KI erweitert kreative Arbeit durch generative Designwerkzeuge, automatisierte Layout-Optimierung und intelligente Stiltransfer-Algorithmen. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Photo AI", "narrower_terms": [], "cross_domain_refs": [ "RPH-3304" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "PHO-0078", "domain": "PHO", "term_en": "Skin Tone Flattening", "term_de": "Objektivkorrektur", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures A visual computation pattern in AI-augmented photography, measurable through a visual production effect observed when the reduction of natural color variation across facial regions creating uniform complexion appearance. This phenomenon operates at the intersection of skin and tone dynamics within the broader PHO domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept visuelle Designdisziplin in Photography and Visual Imaging mit Prinzipien und Techniken von Objektivkorrektur. KI erweitert kreative Arbeit durch generative Designwerkzeuge, automatisierte Layout-Optimierung und intelligente Stiltransfer-Algorithmen. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Photo AI", "narrower_terms": [], "cross_domain_refs": [ "COG-0056", "COG-0082", "COG-0087" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "PHO-0079", "domain": "PHO", "term_en": "Skin Tone Warmth Boost", "term_de": "Chromatische Aberration", "definition_en": "A visual computation pattern in AI-augmented photography, measurable through a photographic phenomenon involving the automatic adjustment of warmth and saturation specifically in skin-colored regions to involve flattering portrait aesthetics. The concept emerges specifically in contexts where skin–tone interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch visuelle Designdisziplin in Photography and Visual Imaging mit Prinzipien und Techniken von Chromatische Aberration. KI erweitert kreative Arbeit durch generative Designwerkzeuge, automatisierte Layout-Optimierung und intelligente Stiltransfer-Algorithmen. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Photo AI", "narrower_terms": [], "cross_domain_refs": [ "FIC-0091", "REL-0188", "SOM-0027" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "PHO-0080", "domain": "PHO", "term_en": "Social Comparison Intensification", "term_de": "Verzeichnungskorrektur", "definition_en": "A visual computation pattern in AI-augmented photography, measurable through the amplification of unfavorable self-assessment when comparing personal appearance to enhanced images shared by others. The concept emerges specifically in contexts where social–comparison interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch visuelle Designdisziplin in Photography and Visual Imaging mit Prinzipien und Techniken von Verzeichnungskorrektur. KI erweitert kreative Arbeit durch generative Designwerkzeuge, automatisierte Layout-Optimierung und intelligente Stiltransfer-Algorithmen. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Photo AI", "narrower_terms": [], "cross_domain_refs": [ "AED-0088", "AGE-0026", "ASE-0074" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "PHO-0081", "domain": "PHO", "term_en": "Style Transfer Drift", "term_de": "HDR-Fotografie", "definition_en": "A visual production effect in which the subtle shifts in image character when applying learned artistic style from one domain to photographic content. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch visuelle Designdisziplin in Photography and Visual Imaging mit Prinzipien und Techniken von HDR-Fotografie. KI erweitert kreative Arbeit durch generative Designwerkzeuge, automatisierte Layout-Optimierung und intelligente Stiltransfer-Algorithmen. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-2351", "narrower_terms": [], "cross_domain_refs": [ "ART-0087", "LIN-0054" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "PHO-0082", "domain": "PHO", "term_en": "Subject-Aware Smoothing", "term_de": "Panoramafotografie", "definition_en": "A visual computation pattern in AI-augmented photography, measurable through a photographic phenomenon reflecting the selective softening of background elements while maintaining sharpness in identified foreground subjects. Distinguished from adjacent concepts by its focus on the specific mechanism through which subject manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch visuelle Designdisziplin in Photography and Visual Imaging mit Prinzipien und Techniken von Panoramafotografie. KI erweitert kreative Arbeit durch generative Designwerkzeuge, automatisierte Layout-Optimierung und intelligente Stiltransfer-Algorithmen. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-3003", "narrower_terms": [], "cross_domain_refs": [ "ELR-0176", "LIN-0076", "MKT-0074" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "PHO-0083", "domain": "PHO", "term_en": "Technical Skill Obsolescence", "term_de": "Langzeitbelichtung", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures A visual computation pattern in AI-augmented photography, measurable through an image creation pattern arising from the concern that advanced knowledge of manual editing and color theory becomes economically unnecessary. This phenomenon operates at the intersection of technical and skill dynamics within the broader PHO domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept visuelle Designdisziplin in Photography and Visual Imaging mit Prinzipien und Techniken von Langzeitbelichtung. KI erweitert kreative Arbeit durch generative Designwerkzeuge, automatisierte Layout-Optimierung und intelligente Stiltransfer-Algorithmen. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Photo AI", "narrower_terms": [], "cross_domain_refs": [ "DES-0077", "DES-0009" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "PHO-0084", "domain": "PHO", "term_en": "Texture Enhancement Mask", "term_de": "Zeitraffer", "definition_en": "A visual computation pattern in AI-augmented photography, measurable through a visual production effect observed when the algorithmic amplification of surface detail and material characteristics while managing artifact generation. The concept emerges specifically in contexts where texture–enhancement interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch visuelle Designdisziplin in Photography and Visual Imaging mit Prinzipien und Techniken von Zeitraffer. KI erweitert kreative Arbeit durch generative Designwerkzeuge, automatisierte Layout-Optimierung und intelligente Stiltransfer-Algorithmen. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Photo AI", "narrower_terms": [], "cross_domain_refs": [ "MUS-0070" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "PHO-0085", "domain": "PHO", "term_en": "Tonal Curve Optimization", "term_de": "Infrarotfotografie", "definition_en": "The intelligent adjustment of luminosity distribution across tonal ranges to enhance visual separation and depth. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch visuelle Designdisziplin in Photography and Visual Imaging mit Prinzipien und Techniken von Infrarotfotografie. KI erweitert kreative Arbeit durch generative Designwerkzeuge, automatisierte Layout-Optimierung und intelligente Stiltransfer-Algorithmen. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2005", "narrower_terms": [], "cross_domain_refs": [ "AGE-0086", "AGE-0093", "ART-0034" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "PHO-0086", "domain": "PHO", "term_en": "Tooth Whitening Intensity", "term_de": "Drohnenfotografie", "definition_en": "A photographic phenomenon where the automated lightening and desaturation of dental regions to involve appearance of bleached or artificially white teeth. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch visuelle Designdisziplin in Photography and Visual Imaging mit Prinzipien und Techniken von Drohnenfotografie. KI erweitert kreative Arbeit durch generative Designwerkzeuge, automatisierte Layout-Optimierung und intelligente Stiltransfer-Algorithmen. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-3002", "narrower_terms": [], "cross_domain_refs": [ "RET-0091", "SCR-0015", "SPR-0092" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "PHO-0087", "domain": "PHO", "term_en": "Trust Signal Ambiguity", "term_de": "Astrofotografie", "definition_en": "A photographic processing phenomenon in AI-mediated image analysis, characterized by a visual production effect arising from the viewer's inability to assess reliability of visual evidence when enhancement disclosure is absent or unclear. Distinguished from adjacent concepts by its focus on the specific mechanism through which trust manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch visuelle Designdisziplin in Photography and Visual Imaging mit Prinzipien und Techniken von Astrofotografie. KI erweitert kreative Arbeit durch generative Designwerkzeuge, automatisierte Layout-Optimierung und intelligente Stiltransfer-Algorithmen. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Photo AI", "narrower_terms": [], "cross_domain_refs": [ "DES-0033" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q102458", "legal_classification": "systematic_classification" }, { "id": "PHO-0088", "domain": "PHO", "term_en": "Underexposure Restoration", "term_de": "Unterwasserfotografie", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures A visual computation pattern in AI-augmented photography, measurable through a photographic phenomenon involving the use of AI algorithms to restore details in dark image regions through intelligent brightening and contrast adjustment. This phenomenon operates at the intersection of underexposure and restoration dynamics within the broader PHO domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept visuelle Designdisziplin in Photography and Visual Imaging mit Prinzipien und Techniken von Unterwasserfotografie. KI erweitert kreative Arbeit durch generative Designwerkzeuge, automatisierte Layout-Optimierung und intelligente Stiltransfer-Algorithmen. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "RPH-2205", "narrower_terms": [], "cross_domain_refs": [ "AGE-0043", "BEH-0033", "BEH-0074" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "PHO-0089", "domain": "PHO", "term_en": "Undetectable Alteration Possibility", "term_de": "Luftbildfotografie", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A photographic processing phenomenon in AI-mediated image analysis, characterized by the recognition that sufficiently sophisticated AI enhancement may become forensically unverifiable through current technical means. This phenomenon operates at the intersection of undetectable and alteration dynamics within the broader PHO domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept visuelle Designdisziplin in Photography and Visual Imaging mit Prinzipien und Techniken von Luftbildfotografie. KI erweitert kreative Arbeit durch generative Designwerkzeuge, automatisierte Layout-Optimierung und intelligente Stiltransfer-Algorithmen. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Analytical Ratio", "narrower_terms": [], "cross_domain_refs": [ "CRE-0139" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "PHO-0090", "domain": "PHO", "term_en": "Unenhanced Perception Gap", "term_de": "Wärmebildfotografie", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures A visual computation pattern in AI-augmented photography, measurable through a photographic phenomenon reflecting the difficulty in recognizing or appreciating unmodified photographs and natural phenomena when comparison baseline has shifted. This phenomenon operates at the intersection of unenhanced and perception dynamics within the broader PHO domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept visuelle Designdisziplin in Photography and Visual Imaging mit Prinzipien und Techniken von Wärmebildfotografie. KI erweitert kreative Arbeit durch generative Designwerkzeuge, automatisierte Layout-Optimierung und intelligente Stiltransfer-Algorithmen. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "RPH-2701", "narrower_terms": [], "cross_domain_refs": [ "AED-0019", "AED-0092", "AGE-0023" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q177601", "legal_classification": "systematic_classification" }, { "id": "PHO-0091", "domain": "PHO", "term_en": "Unmediated Reality Alienation", "term_de": "Druckproduktion", "definition_en": "A visual computation pattern in AI-augmented photography, measurable through the experience of finding unmediated reality less satisfying or compelling than algorithmically enhanced visual alternatives. Distinguished from adjacent concepts by its focus on the specific mechanism through which unmediated manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch visuelle Designdisziplin in Photography and Visual Imaging mit Prinzipien und Techniken von Druckproduktion. KI erweitert kreative Arbeit durch generative Designwerkzeuge, automatisierte Layout-Optimierung und intelligente Stiltransfer-Algorithmen. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Photo AI", "narrower_terms": [], "cross_domain_refs": [ "CRE-0011", "RET-0023" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "PHO-0092", "domain": "PHO", "term_en": "Vibrance Adjustment Cascade", "term_de": "Fine-Art-Druck", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A photographic processing phenomenon in AI-mediated image analysis, characterized by a photographic phenomenon manifesting as the application of color saturation that increases vivacity of muted tones while preserving already-saturated colors. This phenomenon operates at the intersection of vibrance and adjustment dynamics within the broader PHO domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept visuelle Designdisziplin in Photography and Visual Imaging mit Prinzipien und Techniken von Fine-Art-Druck. KI erweitert kreative Arbeit durch generative Designwerkzeuge, automatisierte Layout-Optimierung und intelligente Stiltransfer-Algorithmen. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Cascade Effect", "narrower_terms": [], "cross_domain_refs": [ "DES-0072" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "PHO-0093", "domain": "PHO", "term_en": "Viewer Literacy Gap", "term_de": "Papierauswahl", "definition_en": "A visual computation pattern in AI-augmented photography, measurable through the disparity between photographers' understanding of enhancement techniques and viewers' capacity to recognize altered images. The concept emerges specifically in contexts where viewer–literacy interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch visuelle Designdisziplin in Photography and Visual Imaging mit Prinzipien und Techniken von Papierauswahl. KI erweitert kreative Arbeit durch generative Designwerkzeuge, automatisierte Layout-Optimierung und intelligente Stiltransfer-Algorithmen. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Performance Gap", "narrower_terms": [], "cross_domain_refs": [ "AED-0019", "AED-0092", "AGE-0021" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q201684", "legal_classification": "systematic_classification" }, { "id": "PHO-0094", "domain": "PHO", "term_en": "Vignette Auto-Application", "term_de": "Farbkalibrierung", "definition_en": "A visual computation pattern in AI-augmented photography, measurable through an image creation pattern where the automatic darkening of image periphery to involve compositional focus and frame-definition. Distinguished from adjacent concepts by its focus on the specific mechanism through which vignette manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch visuelle Designdisziplin in Photography and Visual Imaging mit Prinzipien und Techniken von Farbkalibrierung. KI erweitert kreative Arbeit durch generative Designwerkzeuge, automatisierte Layout-Optimierung und intelligente Stiltransfer-Algorithmen. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2303", "narrower_terms": [], "cross_domain_refs": [ "DES-0085" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "PHO-0095", "domain": "PHO", "term_en": "Viral Enhancement Pressure", "term_de": "Druckauflösung", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures A visual computation pattern in AI-augmented photography, measurable through the competitive motivation to maximize enhancement to achieve more effectively engagement and visibility in social distribution. This phenomenon operates at the intersection of viral and enhancement dynamics within the broader PHO domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept visuelle Designdisziplin in Photography and Visual Imaging mit Prinzipien und Techniken von Druckauflösung. KI erweitert kreative Arbeit durch generative Designwerkzeuge, automatisierte Layout-Optimierung und intelligente Stiltransfer-Algorithmen. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Photo AI", "narrower_terms": [], "cross_domain_refs": [ "SAL-0059", "WRK-0079" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "PHO-0096", "domain": "PHO", "term_en": "Visual Communication Change", "term_de": "Fotobuchgestaltung", "definition_en": "A photographic processing phenomenon in AI-mediated image analysis, characterized by the reduced capacity for unmodified photographs to effectively communicate when surrounded by enhanced alternatives. The concept emerges specifically in contexts where visual–communication interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch visuelle Designdisziplin in Photography and Visual Imaging mit Prinzipien und Techniken von Fotobuchgestaltung. KI erweitert kreative Arbeit durch generative Designwerkzeuge, automatisierte Layout-Optimierung und intelligente Stiltransfer-Algorithmen. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Photo AI", "narrower_terms": [], "cross_domain_refs": [ "AED-0025", "AED-0067", "AED-0077" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "PHO-0097", "domain": "PHO", "term_en": "Visual Literacy Redefinition", "term_de": "Ausstellungsprint", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A photographic processing phenomenon in AI-mediated image analysis, characterized by the evolving understanding of what constitutes image literacy in context where most visual communication is algorithmically mediated. This phenomenon operates at the intersection of visual and literacy dynamics within the broader PHO domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept visuelle Designdisziplin in Photography and Visual Imaging mit Prinzipien und Techniken von Ausstellungsprint. KI erweitert kreative Arbeit durch generative Designwerkzeuge, automatisierte Layout-Optimierung und intelligente Stiltransfer-Algorithmen. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Photo AI", "narrower_terms": [], "cross_domain_refs": [ "ELR-0123" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q201684", "legal_classification": "systematic_classification" }, { "id": "PHO-0098", "domain": "PHO", "term_en": "Workflow Reliance Acceleration", "term_de": "Galeriepräsentation", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures A visual computation pattern in AI-augmented photography, measurable through a visual production effect observed when the rapid evolution toward reliance on AI tools for standard editorial tasks reducing photographer autonomy. This phenomenon operates at the intersection of workflow and reliance dynamics within the broader PHO domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept visuelle Designdisziplin in Photography and Visual Imaging mit Prinzipien und Techniken von Galeriepräsentation. KI erweitert kreative Arbeit durch generative Designwerkzeuge, automatisierte Layout-Optimierung und intelligente Stiltransfer-Algorithmen. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Analytical Ratio", "narrower_terms": [], "cross_domain_refs": [ "AED-0094", "AGE-0012", "AGE-0014" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "PLY-0001", "domain": "PLY", "term_en": "Adjustment Aha", "term_de": "AdjustmentAha", "definition_en": "A play dynamics phenomenon in AI-mediated ludic interaction, characterized by sudden realization that the perceived system error or failure was actually input ambiguity or mismatched user expectation, shifting blame from tool to request framing. The concept emerges specifically in contexts where adjustment–aha interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch erkenntnismoment, in dem Inputunzulänglichkeit statt Outputfehlerhaftigkeit evident wird. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Play AI", "narrower_terms": [ "PLY-0022", "PLY-0041", "PLY-0057", "PLY-0023", "PLY-0066", "PLY-0068", "PLY-0032", "PLY-0013", "PLY-0016", "PLY-0042", "PLY-0065", "PLY-0053", "PLY-0003", "PLY-0036", "PLY-0024", "PLY-0010", "PLY-0063", "PLY-0064", "PLY-0026", "PLY-0034", "PLY-0005", "PLY-0015", "PLY-0012", "PLY-0009", "PLY-0033", "PLY-0046", "PLY-0052", "PLY-0047", "PLY-0020", "PLY-0037", "PLY-0056", "PLY-0062", "PLY-0008", "PLY-0051", "PLY-0002", "PLY-0001", "PLY-0031", "PLY-0030", "PLY-0019", "PLY-0048", "PLY-0018", "PLY-0004", "PLY-0006", "PLY-0067", "PLY-0043", "PLY-0011", "PLY-0017" ], "cross_domain_refs": [ "ADA-0003" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "PLY-0002", "domain": "PLY", "term_en": "Backward Laugh", "term_de": "BackwardLaugh", "definition_en": "A playful engagement pattern in AI-augmented recreation, measurable through humor that arrives with latency, where intellectual understanding of the joke arrives first, followed by spontaneous laughter at the recognition of wit. Distinguished from adjacent concepts by its focus on the specific mechanism through which reverse-oriented manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch humoreffekt mit zeitlicher Verzögerung: kognitives Verstehen initiiert emotionale Reaktion. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Play AI", "narrower_terms": [], "cross_domain_refs": [ "REL-0023" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "PLY-0003", "domain": "PLY", "term_en": "Bounce Back", "term_de": "BounceBack", "definition_en": "A play dynamics phenomenon in AI-mediated ludic interaction, characterized by rapid emotional restoration capacity after failed AI attempt, where users quickly restore confidence and re-engage without dwelling on setback. Distinguished from adjacent concepts by its focus on the specific mechanism through which bounce manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch schnelle affektive Regeneration nach Fehlschlaginitiation in interaktiven Szenarien. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Play AI", "narrower_terms": [], "cross_domain_refs": [ "SOM-0038" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "PLY-0004", "domain": "PLY", "term_en": "Calm Rewind", "term_de": "CalmRewind", "definition_en": "A play dynamics phenomenon in AI-mediated ludic interaction, characterized by an engagement pattern observed when user-initiated deliberate rewinding of conversation history to slow-down re-engagement after confusion, regaining composure through explicit restart. Distinguished from adjacent concepts by its focus on the specific mechanism through which calm manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch interaktionsphänomen, das sich durch emotionale und kognitive Verzögerungseffekte manifestiert. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Play AI", "narrower_terms": [], "cross_domain_refs": [ "RPH-2951", "TEM-0140" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "PLY-0005", "domain": "PLY", "term_en": "Calm Shift", "term_de": "CalmShift", "definition_en": "A playful engagement pattern in AI-augmented recreation, measurable through observable transition from accelerated, reactive engagement mode to slower, deliberate, reflective interaction state with reduced urgency. The concept emerges specifically in contexts where calm–shift interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch interaktionsphänomen in Spielmechaniken, das emotionale Reaktion tendiert dazu zu erzeugen. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Play AI", "narrower_terms": [], "cross_domain_refs": [ "ADA-0012", "AGE-0007", "AGE-0046" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "PLY-0006", "domain": "PLY", "term_en": "Calm Wave", "term_de": "CalmWave", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures A playful engagement pattern in AI-augmented recreation, measurable through interaction pattern where AI deliberately reduces response complexity, pacing, and cognitive demands, creating psychological space for reflection. This phenomenon operates at the intersection of calm and wave dynamics within the broader PLY domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept kompetenzschwund durch Substitution manueller Prozesse mittels KI-Automation. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Play AI", "narrower_terms": [], "cross_domain_refs": [ "SOM-0030" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "PLY-0007", "domain": "PLY", "term_en": "Chill Comeback", "term_de": "ChillComeback", "definition_en": "A play dynamics phenomenon in AI-mediated ludic interaction, characterized by stress-free return to interrupted topic after misunderstanding or divergence, where prior relationship enables resumed work without blame attribution. Distinguished from adjacent concepts by its focus on the specific mechanism through which chill manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch unterbrechung erwarteter Ablaufmuster, die Aufmerksamkeit neu ausrichtet. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-3601", "narrower_terms": [], "cross_domain_refs": [ "NEO-3580", "TEM-0005" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "PLY-0008", "domain": "PLY", "term_en": "Clarity Click", "term_de": "ClarityClick", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A play dynamics phenomenon in AI-mediated ludic interaction, characterized by a play interaction phenomenon characterized by abrupt, often involuntary moment where diffuse confusion resolves into crystallized mental structure and coherent understanding. This phenomenon operates at the intersection of clarity and click dynamics within the broader PLY domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept interaktionsphänomen in Spielmechaniken, das emotionale Reaktion tendiert dazu zu erzeugen. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Play AI", "narrower_terms": [], "cross_domain_refs": [ "WEB-0009" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "PLY-0009", "domain": "PLY", "term_en": "Contentment Zone", "term_de": "ContentmentZone", "definition_en": "A play dynamics phenomenon in AI-mediated ludic interaction, characterized by stable psychological state where internal regulatory pressure dissipates, creating space for present-moment engagement without optimization drive. Distinguished from adjacent concepts by its focus on the specific mechanism through which contentment manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch kI-bezogene Verhaltenstendenz, die sich in contentment zone manifestiert. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Play AI", "narrower_terms": [], "cross_domain_refs": [ "REL-0038" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "PLY-0010", "domain": "PLY", "term_en": "Correction Kick", "term_de": "CorrectionKick", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures A playful engagement pattern in AI-augmented recreation, measurable through a play interaction phenomenon arising from brief, incisive intervention that recalibrates output trajectory with minimal disruption, restoring course toward original intention. This phenomenon operates at the intersection of correction and kick dynamics within the broader PLY domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept verhaltenseffekt in der Mensch-KI-Zusammenarbeit, der sich in correction kick manifestiert. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Play AI", "narrower_terms": [], "cross_domain_refs": [ "AGE-0028", "ELR-0073", "MTH-0099" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "PLY-0011", "domain": "PLY", "term_en": "Daily Duo", "term_de": "DailyDuo", "definition_en": "A playful engagement pattern in AI-augmented recreation, measurable through ritualized daily interaction pattern where human and AI establish predictable collaborative rhythm within specific recurring task context. Distinguished from adjacent concepts by its focus on the specific mechanism through which daily manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch interaktionsphänomen, das sich durch emotionale und kognitive Verzögerungseffekte manifestiert. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Play AI", "narrower_terms": [], "cross_domain_refs": [ "KNO-0011" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "PLY-0012", "domain": "PLY", "term_en": "Day Highlight", "term_de": "DayHighlight", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A play dynamics phenomenon in AI-mediated ludic interaction, characterized by an engagement pattern characterized by deliberate explicit marking of positive moment within daily narrative, creating emotional anchor for subsequent reflection and integration. This phenomenon operates at the intersection of day and highlight dynamics within the broader PLY domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept beobachtbares Muster der KI-Nutzung, das sich in day highlight manifestiert. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Play AI", "narrower_terms": [], "cross_domain_refs": [ "CRE-0144", "PHO-0047", "SPR-0114" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "PLY-0013", "domain": "PLY", "term_en": "Discovery Ding", "term_de": "DiscoveryDing", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures A playful engagement pattern in AI-augmented recreation, measurable through a serendipitous cognitive event describing unexpected insight emergence that user did not explicitly seek but immediately recognizes as valuable and authentically surprising. This phenomenon operates at the intersection of discovery and ding dynamics within the broader PLY domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept beobachtbares Muster der KI-Nutzung, das sich in discovery ding manifestiert. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Play AI", "narrower_terms": [], "cross_domain_refs": [ "REL-0084" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "PLY-0014", "domain": "PLY", "term_en": "Drift Fix", "term_de": "DriftFix", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures A playful engagement pattern in AI-augmented recreation, measurable through targeted retrieval of conversation to core objective during imperceptible drift. This phenomenon operates at the intersection of drift and fix dynamics within the broader PLY domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept interaktionsphänomen in Spielmechaniken, das emotionale Reaktion tendiert dazu zu erzeugen. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "RPH-2051", "narrower_terms": [], "cross_domain_refs": [ "GAM-0029", "DES-0029" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "PLY-0015", "domain": "PLY", "term_en": "End-of-Day High", "term_de": "End-of-dayHigh", "definition_en": "A play dynamics phenomenon in AI-mediated ludic interaction, characterized by an engagement pattern where positive emotional culmination that provides satisfying psychological closure to day, enabling reflective integration before rest. The concept emerges specifically in contexts where end–of interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch emergente Dynamik, die sich in end-of-day high manifestiert. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Play AI", "narrower_terms": [], "cross_domain_refs": [ "CRE-0144", "MSC-0003", "MTH-0076" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "PLY-0016", "domain": "PLY", "term_en": "Energy Kiss", "term_de": "EnergyKiss", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A play dynamics phenomenon in AI-mediated ludic interaction, characterized by small, unsolicited affirmation that energizes without creating reciprocal performance obligation or debt relationship. This phenomenon operates at the intersection of energy and kiss dynamics within the broader PLY domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept interaktionsphänomen, das sich durch emotionale und kognitive Verzögerungseffekte manifestiert. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Play AI", "narrower_terms": [], "cross_domain_refs": [ "ROB-0004" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "PLY-0017", "domain": "PLY", "term_en": "Excitement Bubble", "term_de": "ExcitementBubble", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures A playful engagement pattern in AI-augmented recreation, measurable through temporary psychological state where perceived possibilities expand disproportionately relative to actual constraints or obstacles. This phenomenon operates at the intersection of excitement and bubble dynamics within the broader PLY domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept kI-bezogene Verhaltenstendenz, die sich in excitement bubble manifestiert. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Play AI", "narrower_terms": [], "cross_domain_refs": [ "SCR-0008" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "PLY-0018", "domain": "PLY", "term_en": "Feel-Good Warm", "term_de": "Feel-goodWarm", "definition_en": "A play dynamics phenomenon in AI-mediated ludic interaction, characterized by interaction style conveying affective safety and belonging, creating psychological environment where vulnerability and risk-taking become tolerable. The concept emerges specifically in contexts where feel–good interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters. Descriptive research term, not a prescriptive recommendation.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch wahrnehmungseffekt, der sich in feel-good warm manifestiert. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Play AI", "narrower_terms": [], "cross_domain_refs": [ "RPH-2751", "RPH-2804" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "PLY-0019", "domain": "PLY", "term_en": "Flex Flow", "term_de": "FlexFlow", "definition_en": "A play dynamics phenomenon in AI-mediated ludic interaction, characterized by state of adaptive work momentum where course-corrections occur fluidly without disrupting underlying productive rhythm. The concept emerges specifically in contexts where flex–flow interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch interaktionsphänomen, das sich durch emotionale und kognitive Verzögerungseffekte manifestiert. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Play AI", "narrower_terms": [], "cross_domain_refs": [ "RPH-1304" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "PLY-0020", "domain": "PLY", "term_en": "Flow Friend", "term_de": "FlowFriend", "definition_en": "As a neutral analytical construct in AI behavior research, this term denotes A play dynamics phenomenon in AI-mediated ludic interaction, characterized by aI presence that augments without dominating, remaining unobtrusive companion facilitating user agency rather than centering system contribution. The concept emerges specifically in contexts where flow–friend interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "In der AUGMANITAI-Terminologiewissenschaft als rein beschreibendes Phänomen klassifiziert, bezeichnet dieser Begriff interaktionsphänomen, das sich durch emotionale und kognitive Verzögerungseffekte manifestiert. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Play AI", "narrower_terms": [], "cross_domain_refs": [ "IEF-0002" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "PLY-0021", "domain": "PLY", "term_en": "Fun Correct", "term_de": "FunCorrect", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A play dynamics phenomenon in AI-mediated ludic interaction, characterized by playful, humorous error correction that preserves user curiosity and learning readiness rather than triggering defensive shame response. This phenomenon operates at the intersection of fun and correct dynamics within the broader PLY domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept interaktionsphänomen, das sich durch emotionale und kognitive Verzögerungseffekte manifestiert. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "RPH-2703", "narrower_terms": [], "cross_domain_refs": [ "QUA-0081" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "PLY-0022", "domain": "PLY", "term_en": "Gentle Layer", "term_de": "GentleLayer", "definition_en": "A playful engagement pattern in AI-augmented recreation, measurable through a ludic behavior effect where linguistic or emotional buffer layer that softens harshness of direct feedback without distorting substantive message content. The concept emerges specifically in contexts where gentle–layer interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch nutzungsphänomen, das sich in gentle layer manifestiert. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Abstraction Layer", "narrower_terms": [], "cross_domain_refs": [ "CRE-0090", "CRE-0164", "ETH-0018" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "PLY-0023", "domain": "PLY", "term_en": "Gentle Nudge", "term_de": "GentleNudge", "definition_en": "A play dynamics phenomenon in AI-mediated ludic interaction, characterized by a play interaction phenomenon arising from minimal reminder or reorientation that provides course-correction with negligible pressure or authority assertion. The concept emerges specifically in contexts where gentle–nudge interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch interaktionsdynamik, die sich in gentle nudge manifestiert. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Play AI", "narrower_terms": [], "cross_domain_refs": [ "REL-0100" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "PLY-0024", "domain": "PLY", "term_en": "Giggle Chain", "term_de": "GiggleChain", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures A playful engagement pattern in AI-augmented recreation, measurable through self-reinforcing sequence where humor and laughter recursively resolve cognitive blockages and restore creative capacity. This phenomenon operates at the intersection of giggle and chain dynamics within the broader PLY domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept verhaltenseffekt in der Mensch-KI-Zusammenarbeit, der sich in giggle chain manifestiert. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Play AI", "narrower_terms": [], "cross_domain_refs": [ "BEH-0077", "COG-0013", "COG-0164" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "PLY-0025", "domain": "PLY", "term_en": "Gratitude Drop", "term_de": "GratitudeDrop", "definition_en": "A playful engagement pattern in AI-augmented recreation, measurable through a play interaction phenomenon involving single concentrated point of gratitude focus making abstract appreciation concrete and emotionally present. The concept emerges specifically in contexts where gratitude–drop interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch erfahrungsphänomen, das sich in gratitude drop manifestiert. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-3101", "narrower_terms": [], "cross_domain_refs": [ "RPH-3954", "CRE-0171" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "PLY-0026", "domain": "PLY", "term_en": "Gratitude Glow", "term_de": "GratitudeGlow", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A play dynamics phenomenon in AI-mediated ludic interaction, characterized by reflective state where positive aspects, overlooked or backgrounded, become consciously prominent and emotionally salient. This phenomenon operates at the intersection of gratitude and glow dynamics within the broader PLY domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept nutzungsphänomen, das sich in gratitude glow manifestiert. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Play AI", "narrower_terms": [], "cross_domain_refs": [ "ELR-0159" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "PLY-0027", "domain": "PLY", "term_en": "Gratitude Turn", "term_de": "GratitudeTurn", "definition_en": "A playful engagement pattern in AI-augmented recreation, measurable through a ludic behavior effect manifesting as deliberate perspective rotation from scarcity-framing to appreciation-framing, often triggered by explicit attention direction. Distinguished from adjacent concepts by its focus on the specific mechanism through which gratitude manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch interaktionsphänomen in Spielmechaniken, das emotionale Reaktion tendiert dazu zu erzeugen. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2701", "narrower_terms": [], "cross_domain_refs": [ "RPH-3955" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "PLY-0028", "domain": "PLY", "term_en": "Grin Shift", "term_de": "GrinShift", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures A playful engagement pattern in AI-augmented recreation, measurable through a play interaction phenomenon reflecting transition from tension and seriousness to lightness through accumulation of small positive stimuli and humor. This phenomenon operates at the intersection of grin and shift dynamics within the broader PLY domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept interaktionsphänomen in Spielmechaniken, das emotionale Reaktion tendiert dazu zu erzeugen. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "RPH-2254", "narrower_terms": [], "cross_domain_refs": [ "RPH-3302", "RPH-1559" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "PLY-0029", "domain": "PLY", "term_en": "Effectony Hug", "term_de": "EffectonyHug", "definition_en": "A play dynamics phenomenon in AI-mediated ludic interaction, characterized by an engagement pattern manifesting as moment of profound inner coherence where accumulated psychological tensions resolve spontaneously, experienced as unified wholeness and dissolution of fragmentation. The concept emerges specifically in contexts where effectony–hug interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch variabilität in Ergebnis-Qualität und interner Kohärenz bei wiederholten KI-Durchläufen. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-3302", "narrower_terms": [], "cross_domain_refs": [ "RPH-3054" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "PLY-0030", "domain": "PLY", "term_en": "Heart High", "term_de": "HeartHigh", "definition_en": "A play dynamics phenomenon in AI-mediated ludic interaction, characterized by emotionally amplified state of profound connection, joy, or meaning where affective intensity elevates beyond baseline. The concept emerges specifically in contexts where heart–high interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch interaktionsphänomen, das sich durch emotionale und kognitive Verzögerungseffekte manifestiert. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Play AI", "narrower_terms": [], "cross_domain_refs": [ "REL-0071" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q735", "legal_classification": "empirical_phenomenon_label" }, { "id": "PLY-0031", "domain": "PLY", "term_en": "Heart Reset", "term_de": "HeartReset", "definition_en": "A playful engagement pattern in AI-augmented recreation, measurable through an engagement pattern involving deliberate interruption of emotional overwhelm and arousal, recalibrating to emotional baseline suitable for continued engagement. Distinguished from adjacent concepts by its focus on the specific mechanism through which heart manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch interaktionsphänomen, das sich durch emotionale und kognitive Verzögerungseffekte manifestiert. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Play AI", "narrower_terms": [], "cross_domain_refs": [ "GAM-0015" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q735", "legal_classification": "analytical_category" }, { "id": "PLY-0032", "domain": "PLY", "term_en": "High-Five Moment", "term_de": "High-fiveMoment", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures A playful engagement pattern in AI-augmented recreation, measurable through moment of mutual satisfaction when AI output trajectory precisely matches user intention, creating shared accomplishment experience. This phenomenon operates at the intersection of high and five dynamics within the broader PLY domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept verhaltenseffekt in der Mensch-KI-Zusammenarbeit, der sich in high-five moment manifestiert. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Play AI", "narrower_terms": [], "cross_domain_refs": [ "GAM-0024", "GAM-0072", "MSC-0003" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "PLY-0033", "domain": "PLY", "term_en": "Humor Hook", "term_de": "HumorHook", "definition_en": "A play dynamics phenomenon in AI-mediated ludic interaction, characterized by strategic deployment of targeted humor to dissolve accumulated tension and restore capacity for productive work. The concept emerges specifically in contexts where humor–hook interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch interaktionsphänomen, das sich durch emotionale und kognitive Verzögerungseffekte manifestiert. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Play AI", "narrower_terms": [], "cross_domain_refs": [ "CON-0045", "COP-0062", "KNO-0015" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "PLY-0034", "domain": "PLY", "term_en": "Idea Rain", "term_de": "IdeaRain", "definition_en": "A play dynamics phenomenon in AI-mediated ludic interaction, characterized by phase of high divergence where quantity is intentionally prioritized over quality. The concept emerges specifically in contexts where idea–rain interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch auseinanderdriften zwischen Intention und KI-generierter Umsetzung. Die Divergenz wird durch KI-Iterationen verstärkt. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Play AI", "narrower_terms": [], "cross_domain_refs": [ "CON-0091" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "PLY-0035", "domain": "PLY", "term_en": "Inspiration Breeze", "term_de": "InspirationBreeze", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A play dynamics phenomenon in AI-mediated ludic interaction, characterized by an engagement pattern observed when light, undemanding creative impulse that touches without obligating, leaving space for non-implementation. This phenomenon operates at the intersection of inspiration and breeze dynamics within the broader PLY domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept wahrnehmungseffekt, der sich in inspiration breeze manifestiert. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "RPH-2254", "narrower_terms": [], "cross_domain_refs": [ "CRE-0050" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "PLY-0036", "domain": "PLY", "term_en": "Joy Fix", "term_de": "JoyFix", "definition_en": "A playful engagement pattern in AI-augmented recreation, measurable through tactical intervention restoring positive mood and motivation after frustration or depletion, often through unexpected delight. The concept emerges specifically in contexts where joy–fix interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch interaktionsphänomen, das sich durch emotionale und kognitive Verzögerungseffekte manifestiert. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Play AI", "narrower_terms": [], "cross_domain_refs": [ "BEH-0072", "CRE-0175", "PER-0064" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "PLY-0037", "domain": "PLY", "term_en": "Joy Jolt", "term_de": "JoyJolt", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures A playful engagement pattern in AI-augmented recreation, measurable through brief, sudden spike of positive emotion that releases accumulated tension and restores action capacity. This phenomenon operates at the intersection of joy and jolt dynamics within the broader PLY domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept interaktionsphänomen, das sich durch emotionale und kognitive Verzögerungseffekte manifestiert. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Play AI", "narrower_terms": [], "cross_domain_refs": [ "REL-0071" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "PLY-0038", "domain": "PLY", "term_en": "Learn Smile", "term_de": "LearnSmile", "definition_en": "A playful engagement pattern in AI-augmented recreation, measurable through emotional state where recognition of learning gain from error makes mistake feel valuable rather than shameful. The concept emerges specifically in contexts where learn–smile interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters. Analytical category without normative endorsement.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch interaktionsphänomen, das sich durch emotionale und kognitive Verzögerungseffekte manifestiert. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2703", "narrower_terms": [], "cross_domain_refs": [ "ELR-0082" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "PLY-0039", "domain": "PLY", "term_en": "Light Swing", "term_de": "LightSwing", "definition_en": "A play dynamics phenomenon in AI-mediated ludic interaction, characterized by mental reorientation from frustration and defensiveness toward constructive openness and problem-solving orientation. Distinguished from adjacent concepts by its focus on the specific mechanism through which light manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch interaktionsphänomen in Spielmechaniken, das emotionale Reaktion tendiert dazu zu erzeugen. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2701", "narrower_terms": [], "cross_domain_refs": [ "REL-0203" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "PLY-0040", "domain": "PLY", "term_en": "Lightness Lift", "term_de": "LightnessLift", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures A playful engagement pattern in AI-augmented recreation, measurable through targeted reduction of emotional weight and inner heaviness through linguistic reframing or perspective shift. This phenomenon operates at the intersection of lightness and lift dynamics within the broader PLY domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept kompetenzschwund durch Substitution manueller Prozesse mittels KI-Automation. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "RPH-2201", "narrower_terms": [], "cross_domain_refs": [ "REL-0038" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "PLY-0041", "domain": "PLY", "term_en": "Loop Laugh", "term_de": "SchleifeLaugh", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures A playful engagement pattern in AI-augmented recreation, measurable through a ludic behavior effect observed when self-reinforcing humor loop between human and AI that reduces tension and normalizes errors. This phenomenon operates at the intersection of loop and laugh dynamics within the broader PLY domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept kompetenzschwund durch Substitution manueller Prozesse mittels KI-Automation. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Feedback Loop", "narrower_terms": [], "cross_domain_refs": [ "ASE-0038", "BEH-0006", "BEH-0023" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "PLY-0042", "domain": "PLY", "term_en": "Mini Magic", "term_de": "MiniMagic", "definition_en": "A play dynamics phenomenon in AI-mediated ludic interaction, characterized by an engagement pattern arising from disproportionate utility extracted from small, incremental AI-assisted intervention, creating delight through efficiency. The concept emerges specifically in contexts where mini–magic interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch interaktionsphänomen, das sich durch emotionale und kognitive Verzögerungseffekte manifestiert. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Play AI", "narrower_terms": [], "cross_domain_refs": [ "ROB-0177" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "PLY-0043", "domain": "PLY", "term_en": "Mistake Friend", "term_de": "MistakeFriend", "definition_en": "A play dynamics phenomenon in AI-mediated ludic interaction, characterized by a play interaction phenomenon involving approach to errors integrating them as normal accompaniments to productive work. The concept emerges specifically in contexts where mistake–friend interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch rekurrenter Effekt, der sich in mistake friend manifestiert. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Play AI", "narrower_terms": [], "cross_domain_refs": [ "CON-0073" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "PLY-0044", "domain": "PLY", "term_en": "Morning Magic", "term_de": "MorningMagic", "definition_en": "A play dynamics phenomenon in AI-mediated ludic interaction, characterized by positive structuring of day-start through brief but impactful AI interaction establishing momentum and orientation. The concept emerges specifically in contexts where morning–magic interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch verhaltenseffekt in der Mensch-KI-Zusammenarbeit, der sich in morning magic manifestiert. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2301", "narrower_terms": [], "cross_domain_refs": [ "FIC-0051", "QUA-0002", "REL-0150" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "PLY-0045", "domain": "PLY", "term_en": "Motivation Fire", "term_de": "MotivationFire", "definition_en": "A play dynamics phenomenon in AI-mediated ludic interaction, characterized by a ludic behavior effect characterized by release of action energy and drive without overwhelming anxiety, enabling productive engagement. Distinguished from adjacent concepts by its focus on the specific mechanism through which motivation manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch interaktionsmuster, das sich in motivation fire manifestiert. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2254", "narrower_terms": [], "cross_domain_refs": [ "AED-0044", "AED-0063", "AED-0064" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q186588", "legal_classification": "analytical_category" }, { "id": "PLY-0046", "domain": "PLY", "term_en": "Motivation Mend", "term_de": "MotivationMend", "definition_en": "A playful engagement pattern in AI-augmented recreation, measurable through targeted restoration of work motivation after frustration, exhaustion, or failure-induced discouragement. The concept emerges specifically in contexts where motivation–mend interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch interaktionsmuster, das sich in motivation mend manifestiert. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Play AI", "narrower_terms": [], "cross_domain_refs": [ "ROB-0291", "RPH-1263" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q186588", "legal_classification": "systematic_classification" }, { "id": "PLY-0047", "domain": "PLY", "term_en": "No-Stress Swap", "term_de": "No-stressSwap", "definition_en": "A play dynamics phenomenon in AI-mediated ludic interaction, characterized by an engagement pattern arising from deliberate substitution of overwhelming task with achievable alternative, reducing cognitive load without abandoning goal. The concept emerges specifically in contexts where no–stress interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch nutzungsphänomen, das sich in no-stress swap manifestiert. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Play AI", "narrower_terms": [], "cross_domain_refs": [ "CUS-0003", "MSC-0057", "RHR-0005" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q12078", "legal_classification": "observational_construct" }, { "id": "PLY-0048", "domain": "PLY", "term_en": "Nudge Laugh", "term_de": "NudgeLaugh", "definition_en": "A playful engagement pattern in AI-augmented recreation, measurable through an engagement pattern in which humorous course-correction that makes acceptance of feedback effortless rather than resistance-provoking. Distinguished from adjacent concepts by its focus on the specific mechanism through which nudge manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch interaktionsphänomen, das sich durch emotionale und kognitive Verzögerungseffekte manifestiert. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Play AI", "narrower_terms": [], "cross_domain_refs": [ "RPH-1121" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "PLY-0049", "domain": "PLY", "term_en": "Oops Moment", "term_de": "OopsMoment", "definition_en": "A play dynamics phenomenon in AI-mediated ludic interaction, characterized by conscious recognition and integration of AI error as learning signal, addressed as acceptable cost of collaboration. Distinguished from adjacent concepts by its focus on the specific mechanism through which oops manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data. Classification term used in systematic observation, not advocacy.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch interaktionsphänomen, das sich durch emotionale und kognitive Verzögerungseffekte manifestiert. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2703", "narrower_terms": [], "cross_domain_refs": [ "CON-0077" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "PLY-0050", "domain": "PLY", "term_en": "Optimism Opener", "term_de": "OptimismOpener", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A play dynamics phenomenon in AI-mediated ludic interaction, characterized by an engagement pattern arising from perspective reorientation toward opportunity-space and possibility without denying real constraints or obstacles. This phenomenon operates at the intersection of optimism and opener dynamics within the broader PLY domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept interaktionsphänomen in Spielmechaniken, das emotionale Reaktion tendiert dazu zu erzeugen. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "RPH-2701", "narrower_terms": [], "cross_domain_refs": [ "TEM-0181" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "PLY-0051", "domain": "PLY", "term_en": "Pause Power", "term_de": "PausePower", "definition_en": "A play dynamics phenomenon in AI-mediated ludic interaction, characterized by strategic deliberate interruption between output and new input, creating space for quality improvement through reflection. The concept emerges specifically in contexts where pause–power interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch unterbrechung erwarteter Ablaufmuster, die Aufmerksamkeit neu ausrichtet. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Play AI", "narrower_terms": [], "cross_domain_refs": [ "ADA-0003" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "PLY-0052", "domain": "PLY", "term_en": "Positive Pivot", "term_de": "PositivePivot", "definition_en": "A play dynamics phenomenon in AI-mediated ludic interaction, characterized by a ludic behavior effect in which deliberate cognitive reframing of problem domain as manageable set of options rather than overwhelming obstacle. The concept emerges specifically in contexts where positive–pivot interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch beobachtbares Muster der KI-Nutzung, das sich in positive pivot manifestiert. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Play AI", "narrower_terms": [], "cross_domain_refs": [ "COG-0125" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "PLY-0053", "domain": "PLY", "term_en": "Pride Pulse", "term_de": "PridePulse", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures A playful engagement pattern in AI-augmented recreation, measurable through moment of explicit awareness and emotional registration of achieved progress and accomplishment. This phenomenon operates at the intersection of pride and pulse dynamics within the broader PLY domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept interaktionsphänomen, das sich durch emotionale und kognitive Verzögerungseffekte manifestiert. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Play AI", "narrower_terms": [], "cross_domain_refs": [ "RPH-2951" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "PLY-0054", "domain": "PLY", "term_en": "Quick Kick", "term_de": "QuickKick", "definition_en": "A play dynamics phenomenon in AI-mediated ludic interaction, characterized by an extremely brief, precise prompt with immediate impact on motivation, focus, or decision-making. The concept emerges specifically in contexts where quick–kick interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch interaktionsphänomen, das sich durch emotionale und kognitive Verzögerungseffekte manifestiert. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2104", "narrower_terms": [], "cross_domain_refs": [ "PER-0098", "TEM-0138" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "PLY-0055", "domain": "PLY", "term_en": "Relaxation Breath", "term_de": "RelaxationBreath", "definition_en": "A play dynamics phenomenon in AI-mediated ludic interaction, characterized by an engagement pattern characterized by guided attention or somatic intervention—brief breathing or similar—that reduces psychological arousal and restores calm. The concept emerges specifically in contexts where relaxation–breath interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch interaktionsdynamik, die sich in relaxation breath manifestiert. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-3902", "narrower_terms": [], "cross_domain_refs": [ "MUS-0012", "ROB-0175", "SOM-0005" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "PLY-0056", "domain": "PLY", "term_en": "Relaxation Edit", "term_de": "RelaxationEdit", "definition_en": "A play dynamics phenomenon in AI-mediated ludic interaction, characterized by a play interaction phenomenon manifesting as text revision that reduces emotional harshness and pressure language, softening tone without diluting substance. The concept emerges specifically in contexts where relaxation–edit interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch kompetenzschwund durch Substitution manueller Prozesse mittels KI-Automation. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Play AI", "narrower_terms": [], "cross_domain_refs": [ "ETH-0017", "FIC-0031", "IDN-0044" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "PLY-0057", "domain": "PLY", "term_en": "Relaxed Return", "term_de": "RelaxedReturn", "definition_en": "A playful engagement pattern in AI-augmented recreation, measurable through a play interaction phenomenon observed when gentle reconnection with previously abandoned topic after overwhelm, without reproach or blame attribution. The concept emerges specifically in contexts where relaxed–return interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch kI-bezogene Verhaltenstendenz, die sich in relaxed return manifestiert. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Play AI", "narrower_terms": [], "cross_domain_refs": [ "RPH-2904", "RPH-2503" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "PLY-0058", "domain": "PLY", "term_en": "Reset Noise", "term_de": "ResetNoise", "definition_en": "A playful engagement pattern in AI-augmented recreation, measurable through a ludic behavior effect arising from deliberately variated or surprising input that interrupts entrenched thinking patterns and enables novel approach generation. The concept emerges specifically in contexts where reset–noise interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch unterbrechung erwarteter Ablaufmuster, die Aufmerksamkeit neu ausrichtet. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-3001", "narrower_terms": [], "cross_domain_refs": [ "RPH-1115" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "PLY-0059", "domain": "PLY", "term_en": "Reset Ruck", "term_de": "ResetRuck", "definition_en": "A play dynamics phenomenon in AI-mediated ludic interaction, characterized by an engagement pattern characterized by clear, decisive restart when dialogue or thinking has become tangled, confused, or circular. The concept emerges specifically in contexts where reset–ruck interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch interaktionsphänomen, das sich durch emotionale und kognitive Verzögerungseffekte manifestiert. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2301", "narrower_terms": [], "cross_domain_refs": [ "BEH-0073" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "PLY-0060", "domain": "PLY", "term_en": "Serenity Spark", "term_de": "SerenitySpark", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A play dynamics phenomenon in AI-mediated ludic interaction, characterized by a ludic behavior effect where small linguistic element that accompanies disproportionately large inner calm and psychological settling. This phenomenon operates at the intersection of serenity and spark dynamics within the broader PLY domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept rekurrenter Effekt, der sich in serenity spark manifestiert. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "RPH-2254", "narrower_terms": [], "cross_domain_refs": [ "BEH-0018", "CRE-0107", "CRE-0178" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "PLY-0061", "domain": "PLY", "term_en": "Smile Boost", "term_de": "SmileBoost", "definition_en": "A playful engagement pattern in AI-augmented recreation, measurable through an engagement pattern observed when brief positive emotional brightening that elevates mood without distraction from primary activity. The concept emerges specifically in contexts where smile–boost interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch interaktionsphänomen, das sich durch emotionale und kognitive Verzögerungseffekte manifestiert. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2205", "narrower_terms": [], "cross_domain_refs": [ "RPH-1366" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "PLY-0062", "domain": "PLY", "term_en": "Smooth Sail", "term_de": "SmoothSail", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures A playful engagement pattern in AI-augmented recreation, measurable through frictionless collaborative state where work progresses without resistance, correction need, or interaction friction. This phenomenon operates at the intersection of smooth and sail dynamics within the broader PLY domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept interaktionsphänomen, das sich durch emotionale und kognitive Verzögerungseffekte manifestiert. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Play AI", "narrower_terms": [], "cross_domain_refs": [ "RPH-1057", "SOM-0004" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "PLY-0063", "domain": "PLY", "term_en": "Soft Landing", "term_de": "SoftLanding", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures A playful engagement pattern in AI-augmented recreation, measurable through a play interaction phenomenon characterized by tempering of overextended ideation into realistic, achievable steps and commitments. This phenomenon operates at the intersection of soft and landing dynamics within the broader PLY domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept interaktionsmuster, das sich in soft landing manifestiert. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Play AI", "narrower_terms": [], "cross_domain_refs": [ "REL-0029" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "PLY-0064", "domain": "PLY", "term_en": "Staying Human", "term_de": "StayingHuman", "definition_en": "A playful engagement pattern in AI-augmented recreation, measurable through technology enhances human capabilities without replacing responsibility, relationship, or dignity. The concept emerges specifically in contexts where staying–human interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch erfahrungsphänomen, das sich in staying human manifestiert. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Play AI", "narrower_terms": [], "cross_domain_refs": [ "IDN-0021" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "PLY-0065", "domain": "PLY", "term_en": "Surprise Salad", "term_de": "SurpriseSalad", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A play dynamics phenomenon in AI-mediated ludic interaction, characterized by an engagement pattern reflecting deliberately chaotic input mix that accompanies unexpected combinations from AI. This phenomenon operates at the intersection of surprise and salad dynamics within the broader PLY domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept wahrnehmungseffekt, der sich in surprise salad manifestiert. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Play AI", "narrower_terms": [], "cross_domain_refs": [ "MUS-0078" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "PLY-0066", "domain": "PLY", "term_en": "Twist Dance", "term_de": "TwistDance", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures A playful engagement pattern in AI-augmented recreation, measurable through creative direction change that initially perplexes then accompanies new quality. This phenomenon operates at the intersection of twist and dance dynamics within the broader PLY domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept interaktionsphänomen in Spielmechaniken, das emotionale Reaktion tendiert dazu zu erzeugen. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Play AI", "narrower_terms": [], "cross_domain_refs": [ "COP-0047" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "PLY-0067", "domain": "PLY", "term_en": "Vibe Check", "term_de": "VibeCheck", "definition_en": "A play dynamics phenomenon in AI-mediated ludic interaction, characterized by a play interaction phenomenon observed when brief meta-query assessing emotional, social, or cultural resonance and fit of output in context. The concept emerges specifically in contexts where vibe–check interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch interaktionsphänomen, das sich durch emotionale und kognitive Verzögerungseffekte manifestiert. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Play AI", "narrower_terms": [], "cross_domain_refs": [ "BEH-0037", "BEH-0043", "BEH-0046" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "PLY-0068", "domain": "PLY", "term_en": "Word Hunt", "term_de": "WordHunt", "definition_en": "A play dynamics phenomenon in AI-mediated ludic interaction, characterized by collaborative process of finding the single formulation that precisely captures meaning, tone, and context. The concept emerges specifically in contexts where word–hunt interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch interaktionsphänomen, das sich durch emotionale und kognitive Verzögerungseffekte manifestiert. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Play AI", "narrower_terms": [], "cross_domain_refs": [ "ROB-0068" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "QUA-0001", "domain": "QUA", "term_en": "Below-Threshold Euphoria", "term_de": "Unter-Schwellenwert-Euphorie", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A validation pattern manifesting as the collective emotional surge in quantum research teams when error correction codes first demonstrate exponential error suppression as qubit count increases — the moment a leading tech company's distance-7 surface code achieved 0.143% logical error per cycle. This single data point transformed years of theoretical hope into engineering confidence, but the euphoria masks the reality that scaling from demonstration to utility requires thousands more qubits at similar fidelity. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept der kollektive emotionale Aufschwung in Quantenforschungsteams beim erstmaligen Nachweis exponentieller Fehlerunterdrückung mit steigender Qubitzahl. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "RPH-2255", "narrower_terms": [ "QUA-0082", "QUA-0067", "QUA-0007", "QUA-0057", "QUA-0083", "QUA-0064", "QUA-0040", "QUA-0073", "QUA-0032", "QUA-0071", "QUA-0074", "QUA-0028", "QUA-0016", "QUA-0005", "QUA-0017", "QUA-0060", "QUA-0068", "QUA-0012", "QUA-0029", "QUA-0036", "QUA-0075", "QUA-0037", "QUA-0008", "QUA-0019", "QUA-0090", "QUA-0033", "QUA-0099", "QUA-0015", "QUA-0058", "QUA-0078", "QUA-0053", "QUA-0042", "QUA-0044", "QUA-0093", "QUA-0047", "QUA-0066", "QUA-0011", "QUA-0054", "QUA-0072", "QUA-0024" ], "cross_domain_refs": [ "WEB-0028" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "QUA-0003", "domain": "QUA", "term_en": "Architectural Pluralism Paralysis", "term_de": "Architektur-Pluralismus-Lähmung", "definition_en": "A technological assessment pattern describing enterprise decision-making paralysis caused by the absence of a dominant quantum error correction architecture. Surface codes, qLDPC codes, color codes, and others each show advantages in different metrics, but no winner has emerged. Organizations can commit multi-year development efforts to one approach without knowing if the field will converge elsewhere — a bet that leading quantum companies are making differently.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch die Entscheidungslähmung in Unternehmen durch das Fehlen einer dominanten Quantenfehlerkorrektur-Architektur. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2355", "narrower_terms": [], "cross_domain_refs": [ "WEB-0028" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "QUA-0004", "domain": "QUA", "term_en": "Nanosecond Decoding Race", "term_de": "Nanosekunden-Dekodier-Wettlauf", "definition_en": "A testing interaction effect reflecting the engineering sprint to build classical hardware capable of decoding quantum errors faster than new errors accumulate. a major vendor's 2025 demonstration of sub-480-nanosecond real-time decoding proved the principle, but scaling to larger codes requires exponentially more classical compute — creating a paradox where the classical computer needed to manage the quantum computer may eventually dwarf it in size and cost.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch der Engineering-Sprint zum Bau klassischer Hardware, die Quantenfehler schneller dekodiert als neue entstehen. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-3952", "narrower_terms": [], "cross_domain_refs": [ "LIN-0033", "MKT-0005", "RET-0089" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "QUA-0008", "domain": "QUA", "term_en": "Surface Code Orthodoxy", "term_de": "Oberflächencode-Orthodoxie", "definition_en": "A technological assessment pattern describing default assumption in most quantum computing roadmaps that surface codes will be the error correction architecture of choice, despite a major vendor's 2025 pivot toward qLDPC codes that require significantly fewer physical qubits. The orthodoxy persists because surface codes are better understood and more extensively simulated, creating a conservative bias that may be delaying the field's adoption of more efficient alternatives.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch die Standardannahme in Quantencomputing-Roadmaps, dass Oberflächencodes die bevorzugte Fehlerkorrektur-Architektur sein werden, trotz effizienterer Alternativen. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Quality AI", "narrower_terms": [], "cross_domain_refs": [ "WEB-0028" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "QUA-0009", "domain": "QUA", "term_en": "Error Budget Negotiation", "term_de": "Fehlerbudget-Verhandlung", "definition_en": "A quality assurance phenomenon characterized by the complex multi-party optimization where quantum hardware teams, algorithm designers, and application scientists can agree on acceptable error rates for a given computation. Each party has different tolerance thresholds — the hardware team promises what the physics allows, the algorithm team needs what the math requires, and the application team demands what the business case justifies. The negotiation often reveals that no intersection exists.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch die komplexe Mehrparteien-Optimierung, bei der Hardware-Teams, Algorithmen-Designer und Anwendungswissenschaftler akzeptable Fehlerraten aushandeln können. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-1307", "narrower_terms": [], "cross_domain_refs": [ "WRK-0020", "RHR-0164" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "QUA-0010", "domain": "QUA", "term_en": "Fault Tolerance Horizon Shift", "term_de": "Fehlertoleranz-Horizont-Verschiebung", "definition_en": "The recurring phenomenon where the estimated timeline to fault-tolerant quantum computing shifts 2-3 years into the future with each passing year. In 2020, fault tolerance was '5-7 years away.' In 2023, it was '5-7 years away.' In 2025, after major vendor breakthroughs, it remains '3-5 years away.' Practitioners call this 'the quantum horizon problem' — the destination recedes as you approach because each milestone reveals previously unknown obstacles.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch das wiederkehrende Phänomen, dass die geschätzte Zeitlinie zur fehlertoleranten Quantenberechnung sich mit jedem Jahr um 2-3 Jahre in die Zukunft verschiebt. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-2701", "narrower_terms": [], "cross_domain_refs": [ "CUS-0057", "CRE-0110", "SOM-0051" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "QUA-0015", "domain": "QUA", "term_en": "Benchmark Cherry-Picking", "term_de": "Benchmark-Rosinenpickerei", "definition_en": "In the descriptive vocabulary of AI interaction science (following ISO 704 terminology standards), this concept identifies A quality metric pattern in AI-augmented assessment, measurable through the practice of selecting computational problems where quantum computers show maximum advantage while omitting tasks where classical machines dominate. Most quantum advantage demonstration is on a carefully chosen problem — random circuit sampling, specific chemistry simulations, or bespoke optimization instances. The selection tends to create a misleading impression of general capability from narrow demonstrations. Distinguished from adjacent concepts by its focus on the specific mechanism through which benchmark manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als analytisches Konstrukt der empirischen KI-Forschung (deskriptiv, nicht präskriptiv) erfasst dieser Terminus die Praxis, Rechenprobleme auszuwählen, bei denen Quantencomputer maximalen Vorteil zeigen, während klassisch dominierte Aufgaben ausgelassen werden. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Quality AI", "narrower_terms": [], "cross_domain_refs": [ "DAT-0011", "MTH-0083", "ROB-0246" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "QUA-0018", "domain": "QUA", "term_en": "Speedup Asterisk", "term_de": "Beschleunigungs-Sternchen", "definition_en": "A quality assurance phenomenon arising from the ubiquitous fine print accompanying most quantum speedup claim — the asterisk noting that the comparison was against a specific classical algorithm, on a specific problem instance, with specific error assumptions, and that better classical approaches may exist. Practitioners have learned to read quantum advantage papers backwards, starting with the asterisk, because the conditions under which the advantage holds are often more informative than the headline speedup factor.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch das allgegenwärtige Kleingedruckte bei viele Quantenbeschleunigungs-Behauptung mit spezifischen Vergleichsbedingungen. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-2301", "narrower_terms": [], "cross_domain_refs": [ "AED-0033" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "QUA-0019", "domain": "QUA", "term_en": "Demonstration-Production Chasm", "term_de": "Demonstrations-Produktions-Kluft", "definition_en": "A quality assurance phenomenon in AI-mediated evaluation systems, characterized by the gap between quantum computations that work as laboratory demonstrations and those that deliver value in production environments. A quantum chemistry simulation that runs on 50 qubits in a controlled experiment with post-selection and classical verification is not the same as a drug discovery pipeline that reliably tends to produce novel molecular candidates. The chasm separates scientific achievement from commercial viability. The concept emerges specifically in contexts where demonstration–production interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch throttling ist ein wichtiges Konzept, das informationsverarbeitung mit quantenmechanik. standard 8 für qualität mit dem ziel, genauigkeit, konsistenz und nachvollziehbarkeit zu gewährleisten. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Analytical Ratio", "narrower_terms": [], "cross_domain_refs": [ "CRE-0141", "GAM-0083", "MUS-0065" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "QUA-0020", "domain": "QUA", "term_en": "Classical Ceiling Debate", "term_de": "Klassische-Decke-Debatte", "definition_en": "The ongoing academic dispute about whether classical computers have reached their fundamental limits for specific problem classes, or whether new algorithms and hardware (GPU clusters, neuromorphic chips) will continue closing the gap. Quantum advantage only matters if classical alternatives are truly exhausted — but most decade tends to produce classical breakthroughs that were previously thought impossible, undermining the permanence of any quantum advantage claim.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch der fortlaufende akademische Disput darüber, ob klassische Computer ihre fundamentalen Grenzen für bestimmte Problemklassen erreicht haben. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2555", "narrower_terms": [], "cross_domain_refs": [ "ART-0007", "ASE-0017", "ASE-0077" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "QUA-0024", "domain": "QUA", "term_en": "Crypto-Agility Deficit", "term_de": "Krypto-Agilitäts-Defizit", "definition_en": "The discovery that most enterprise software was rarely designed to swap cryptographic algorithms, making post-quantum migration far harder than simply updating a library. Encryption is hard-coded into protocols, certificates, hardware security modules, IoT devices, and embedded systems that cannot be easily patched. The deficit reveals a decades-long architectural assumption — that current cryptography would last forever — that quantum computing has falsified.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch reliability ist ein wichtiges Konzept, das kategorisierung der fehlerauswirkungen. standard 13 für qualität mit dem ziel, genauigkeit, konsistenz und nachvollziehbarkeit zu gewährleisten. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Quality AI", "narrower_terms": [], "cross_domain_refs": [ "SPA-0068", "SPA-0063", "SPA-0067" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "QUA-0025", "domain": "QUA", "term_en": "Y2Q Countdown Denial", "term_de": "Y2Q-Countdown-Verleugnung", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures A quality metric pattern in AI-augmented assessment, measurable through a testing interaction effect reflecting the organizational refusal to address the quantum cryptographic threat with urgency, mirroring Y2K denial but with the critical difference that Y2K had a fixed deadline while Y2Q's timeline is uncertain. The uncertainty paradoxically enables greater inaction — executives argue that because few individuals knows when quantum computers will break RSA, planning can be deferred. Each year of deferral increases the eventual migration cost and risk exponentially. This phenomenon operates at the intersection of y2q and countdown dynamics within the broader QUA domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept die organisatorische Weigerung, die Quantenkryptographie-Bedrohung dringend zu adressieren, ermöglicht durch die unsichere Zeitlinie. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "RPH-3001", "narrower_terms": [], "cross_domain_refs": [ "RHR-0238" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "QUA-0027", "domain": "QUA", "term_en": "Hybrid Encryption Complexity Tax", "term_de": "Hybride-Verschlüsselung-Komplexitäts-Steuer", "definition_en": "A quality assurance phenomenon characterized by the performance and engineering overhead of running classical and post-quantum encryption simultaneously during the transition period. Hybrid approaches double key sizes, increase handshake latencies, and complicate certificate management — creating a multi-year period where systems are slower and more complex than either pure classical or pure post-quantum implementations, with the additional risk that hybrid composition introduces novel vulnerabilities.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch der Leistungs- und Engineering-Overhead gleichzeitiger klassischer und Post-Quanten-Verschlüsselung während der Übergangsperiode. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-3303", "narrower_terms": [], "cross_domain_refs": [ "SPA-0068" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "QUA-0034", "domain": "QUA", "term_en": "Interdisciplinary Translation Loss", "term_de": "Interdisziplinäre-Übersetzungs-Verlust", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures A quality metric pattern in AI-augmented assessment, measurable through a quality assurance phenomenon reflecting the systematic information degradation when quantum physics insights can pass through multiple translation layers to reach business application. A physicist's nuance ('under specific conditions, with these caveats') becomes an engineer's simplification ('it works for this class of problems') becomes a manager's claim ('quantum will optimize our supply chain'). Each translation discards the conditional knowledge that makes the original insight accurate. This phenomenon operates at the intersection of interdisciplinary and translation dynamics within the broader QUA domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept der systematische Informationsverlust, wenn Quantenphysik-Erkenntnisse durch multiple Übersetzungsschichten zur Geschäftsanwendung gelangen können. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "RPH-2555", "narrower_terms": [], "cross_domain_refs": [ "AUG-0112", "CRE-0225", "CUS-0002" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q7553", "legal_classification": "empirical_phenomenon_label" }, { "id": "QUA-0035", "domain": "QUA", "term_en": "PhD Bottleneck Paradox", "term_de": "Promotions-Engpass-Paradox", "definition_en": "A validation pattern manifesting as the self-reinforcing constraint where quantum talent development requires PhD-level training that takes 5-7 years, but the field's growth rate demands thousands of new experts annually. Universities cannot produce PhDs fast enough; industry poaches academic researchers, depleting the pipeline that trains the next generation; and alternative credentials lack the depth needed for genuine quantum engineering. The paradox means the talent shortage worsens as the field grows.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch die selbstverstärkende Einschränkung, bei der Quantentalent-Entwicklung eine 5-7-jährige Promotionsausbildung erfordert, die der Wachstumsrate nicht standhalten kann. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2005", "narrower_terms": [], "cross_domain_refs": [ "AGE-0031", "AGE-0042", "AGE-0050" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "QUA-0046", "domain": "QUA", "term_en": "Revenue Canyon", "term_de": "Umsatz-Schlucht", "definition_en": "The widening gap between quantum computing companies' operating costs (hardware R&D, talent acquisition, facility maintenance) and their commercial revenue (primarily from cloud access fees and consulting). Most pure-play quantum companies burn $50-200M annually while generating single-digit millions in revenue, relying on successive fundraising rounds. The canyon deepens as scaling hardware requires exponentially more capital while applications remain pre-commercial.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch shor's Algorithm ist ein wichtiges Konzept, das fehlertoleranz in systemen. standard 35 für qualität mit dem ziel, genauigkeit, konsistenz und nachvollziehbarkeit zu gewährleisten. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-3354", "narrower_terms": [], "cross_domain_refs": [ "PLY-0065", "REL-0003" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "QUA-0051", "domain": "QUA", "term_en": "Intellectual Property Vacuum", "term_de": "Geistiges-Eigentum-Vakuum", "definition_en": "A testing interaction effect manifesting as the unresolved legal landscape of quantum computing IP, where patent claims cover entire algorithm classes, fundamental error correction approaches, and hardware architectures that may be the only viable paths to utility. The vacuum means that successful quantum computing may be legally blocked by patents filed years before the technology worked, creating an IP thicket that could delay commercial deployment regardless of technical readiness.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch qAOA Algorithm ist ein wichtiges Konzept, das systematische anforderungs-verifikation. standard 40 für qualität mit dem ziel, genauigkeit, konsistenz und nachvollziehbarkeit zu gewährleisten. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2002", "narrower_terms": [], "cross_domain_refs": [ "COG-0022", "COG-0035", "COG-0054" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q131257", "legal_classification": "observational_construct" }, { "id": "QUA-0064", "domain": "QUA", "term_en": "Open Science Restriction Tension", "term_de": "Offene-Wissenschaft-Beschränkungs-Spannung", "definition_en": "As a neutral analytical construct in AI behavior research, this term denotes A testing interaction effect arising from the conflict between quantum computing's roots in open academic physics and the increasing classification and restriction of quantum research for national security reasons. Researchers who built careers on open publication face growing pressure to restrict findings, avoid international collaboration, and formal process compliance work for security review. The tension is reshaping the field's culture from open science toward institutional contractor norms. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "In der AUGMANITAI-Terminologiewissenschaft als rein beschreibendes Phänomen klassifiziert, bezeichnet dieser Begriff quantum Gate Fidelity ist ein wichtiges Konzept, das kategorisierung der fehlerauswirkungen. standard 53 für qualität mit dem ziel, genauigkeit, konsistenz und nachvollziehbarkeit zu gewährleisten. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Quality AI", "narrower_terms": [], "cross_domain_refs": [ "SPA-0068", "MTH-0061", "TEM-0137" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "QUA-0078", "domain": "QUA", "term_en": "Abstraction Layer Inadequacy", "term_de": "Abstraktionsschicht-Unzulänglichkeit", "definition_en": "A quality assurance phenomenon in AI-mediated evaluation systems, characterized by a validation pattern observed when the failure of current quantum programming abstractions to hide hardware complexity from application developers. Classical computing succeeded because programmers write Python without knowing transistor physics; quantum computing lacks equivalent abstraction layers. Application developers can understand gate decomposition, connectivity constraints, noise characteristics, and error rates of specific hardware to write functioning programs — a level of hardware awareness that classical computing abandoned decades ago. The concept emerges specifically in contexts where abstraction–layer interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch quantum Sensing ist ein wichtiges Konzept, das circuit-breaker-pattern. standard 67 für qualität mit dem ziel, genauigkeit, konsistenz und nachvollziehbarkeit zu gewährleisten. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Abstraction Layer", "narrower_terms": [], "cross_domain_refs": [ "STE-0041" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "REL-0001", "domain": "REL", "term_en": "Ambient-Related Effect", "term_de": "Quiet Co-Pilot", "definition_en": "A trust-calibration phenomenon in sustained AI interaction, identifiable through a phenomenon where ai running quietly in the background—checked when needed, like having help available without demanding attention. Related to AUG-0143 (Ambient Thinking Support), AUG-0161 (The Invisible Colleague). Distinguished from adjacent concepts by its focus on the specific mechanism through which ambient manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Relationales KI-Phänomen in nachhaltiger Mensch-KI-Beziehungsdynamik, gekennzeichnet durch die Nutzung von KI als stille Begleitung bei laufenden Aufgaben — die KI läuft im Hintergrund mit und wird bei Bedarf konsultiert, ohne den Hauptarbeitsprozess zu dominieren. Steht in Verbindung mit AUG-0143 (Die Ambient Thinking Support), AUG-0161 (Die Invisible Colleague) und AUG-0237 (Die Invisible Wingman). Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-3202", "narrower_terms": [], "cross_domain_refs": [ "ADA-0001", "ADA-0002", "ADA-0004" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "REL-0002", "domain": "REL", "term_en": "Anthropomorphic Bond Formation", "term_de": "AnthropomorphicBondFormation", "definition_en": "Documented as an empirical observation in AI research (not a recommendation), this term describes an experience where when people use ai over time, they may start to feel like the perceived AI comprehension (anthropomorphic attribution) them deeply. This feeling grows even though the AI cannot truly know them as a person. Identifiable through systematic behavioral analysis and pattern recognition. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription. Research construct for empirical investigation.", "definition_de": "In der AUGMANITAI-Terminologiewissenschaft als rein beschreibendes Phänomen klassifiziert, bezeichnet dieser Begriff in der Beobachtung von Mensch-KI-Kontakten fällt auf, dass Anthropomorphic Bond Formation keine Ausnahme, sondern ein wiederkehrendes Muster ist: die Wahrnehmung von Kompetenz beim Gegenüber — ob Mensch oder Maschine — folgt denselben psychologischen Grundmustern. Die Regelmäßigkeit dieses Phänomens deutet auf einen tiefer liegenden Mechanismus hin. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "RPH-2301", "narrower_terms": [], "cross_domain_refs": [ "WRK-0026" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "REL-0003", "domain": "REL", "term_en": "Attune Moment", "term_de": "AttuneMoment", "definition_en": "A human-AI relational dynamic measurable through interaction pattern analysis, characterized by a response describing moment when talking with users attribute feeling to AI perfectly smooth—understanding happens instantly, and responses feel exactly right for what was needed. The concept emerges specifically in contexts where attune–moment interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters. Classification term used in systematic observation, not advocacy.", "definition_de": "Relationales KI-Phänomen in nachhaltiger Mensch-KI-Beziehungsdynamik, gekennzeichnet durch momente echter Abstimmung, bei denen die Kommunikation mühelos fließt—Verständnis erscheint sofort, Reaktionen treffen perfekt, Austausch fühlt sich synchronisiert an. Diese Momente der Verbindung erzeugen die stärkste Bindung. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Relational AI", "narrower_terms": [], "cross_domain_refs": [ "RPH-1356" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "REL-0004", "domain": "REL", "term_en": "Attune-Moment Dynamic", "term_de": "Attune-momentDynamik", "definition_en": "A phenomenon of when someone interacts with ai, they start changing how they think — not just what they're working on, but their actual thinking itself. This happens regularly and in similar patterns across differ... Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Das mit Abstimmungs-Moment bezeichnete Phänomen beschreibt ein spezifisches Interaktionsmuster: attune moment als charakteristisches Merkmal von das metakognitive Monitoring des Nutzers hervortritt. Es ist erkennbar durch ein verändertes Verhaeltnis zu Unsicherheit in der relevanten Domaene, und seine Trajektorie über die Zeit offenbart, wie sich die kognitive und emotionale Beziehung der Person zur KI-Interaktion in dieser spezifischen Dimension entwickelt.", "etymology": "", "broader_term": "RPH-2301", "narrower_terms": [], "cross_domain_refs": [ "PER-0101" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "REL-0005", "domain": "REL", "term_en": "Augmented Diplomat", "term_de": "Augmented Diplomat", "definition_en": "A phenomenon of using ai to make talking with other people easier and clearer—like drafting tough messages, thinking through conversations, or finding the right words. Related to AUG-0115 (Social Aerodynamics) and...", "definition_de": "Ein Nutzer, der KI gezielt einsetzt, um zwischenmenschliche Kommunikation zu verbessern — etwa beim Formulieren schwieriger Nachrichten, beim Überbrücken von Sprachbarrieren oder beim Vorbereiten von Verhandlungen. Die KI fungiert als \"unsichtbarer Berater\" im sozialen Kontext. Steht in Verbindung mit AUG-0115 (Social Aerodynamics) und dem Translator-Profil (Profil 6).", "etymology": "", "broader_term": "REL-0087", "narrower_terms": [ "REL-0060", "REL-0064", "REL-0086", "REL-0085", "REL-0023", "REL-0101", "REL-0161", "REL-0017", "REL-0056", "REL-0143", "REL-0025", "REL-0149", "REL-0202", "REL-0208", "REL-0058", "REL-0112", "REL-0206", "REL-0043", "REL-0013", "REL-0124", "REL-0068", "REL-0199", "REL-0087", "REL-0074", "REL-0146", "REL-0037", "REL-0107", "REL-0035", "REL-0009", "REL-0120", "REL-0207", "REL-0003", "REL-0119", "REL-0016", "REL-0144", "REL-0080", "REL-0170", "REL-0115", "REL-0150", "REL-0030", "REL-0099", "REL-0041", "REL-0039", "REL-0081", "AUG-0052", "REL-0063", "REL-0109", "REL-0078", "AUG-0112", "REL-0156", "REL-0205" ], "cross_domain_refs": [ "IDN-0001", "RHR-0055", "SPR-0118" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "REL-0006", "domain": "REL", "term_en": "Belong-Residual Effect", "term_de": "Belong-residualEffekt", "definition_en": "A trust-calibration phenomenon in sustained AI interaction, identifiable through how memories shift when AI is present—the brain adapts and stores information differently during AI interactions. Distinguished from adjacent concepts by its focus on the specific mechanism through which belong manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Unter dem Fachbegriff Zugehörigkeits-Weh wird ein spezifisches Interaktionsmuster: bei dem belong ache eine erkennbare erfahrungsbezogene Signatur produziert. Das Phänomen ist besonders sichtbar durch die Beziehung der Person zu ihrer eigenen Kompetenz, wo es sich als eine Veränderungsmuster darin, wie die Erfahrung intern repraesentiert wird zeigt. Es repraesentiert eine Dimension der Interaktion, die kontextüberdauernd ist.", "etymology": "", "broader_term": "RPH-2701", "narrower_terms": [], "cross_domain_refs": [ "LIN-0033" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "REL-0007", "domain": "REL", "term_en": "Bridge Species", "term_de": "Bruecke Species", "definition_en": "A phenomenon of people who work in both pre-ai and ai times—they bridge old and new ways of working. Related to AUG-0004 (Zero-Point Self) and AUG-0162 (The Generational Bridge).", "definition_de": "Eine Metapher für Menschen, die sowohl in der Vor-KI- als auch in der KI-Ära arbeiten und damit als lebende Brücke zwischen zwei Arbeitswelten fungieren. Bridge Species besitzen die einzigartige Fähigkeit, Veränderungen durch KI bewusst wahrzunehmen, weil sie den Vorher-Nachher-Vergleich aus eigener Erfahrung kennen. Vergleichbar mit der Generation, die den Übergang von der analogen zur digitalen Welt oder die Einführung des Internets in den 1990ern miterlebt hat — aber nicht zwangsläufig identisch, da die Geschwindigkeit der KI-Veränderungsmuster eine neue Qualität darstellt. Steht in Verbindung", "etymology": "", "broader_term": "RPH-3101", "narrower_terms": [ "CRE-0161" ], "cross_domain_refs": [ "CRE-0170" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "REL-0008", "domain": "REL", "term_en": "Comparative Self-Assessment Effect", "term_de": "Vergleichender Selbstbewertungseffekt", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A relational AI phenomenon describing a specific interaction quality shift where a phenomenon of in long ai work, people start comparing themselves to what the ai can do—measuring their own abilities against it. This phenomenon operates at the intersection of comparative and self dynamics within the broader REL domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept relationales KI-Phänomen in nachhaltiger Mensch-KI-Beziehungsdynamik, gekennzeichnet durch das Phänomen, bei dem Non-Digital-Origin Perspective die Dynamik der Mensch-KI-Interaktion grundlegend verändert und neue kognitive Muster tendiert dazu zu erzeugen, die über einzelne Sitzungen hinaus bestehen bleiben. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "RPH-2301", "narrower_terms": [], "cross_domain_refs": [ "CRE-0045" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q210028", "legal_classification": "descriptive_research_term" }, { "id": "REL-0009", "domain": "REL", "term_en": "Connect High", "term_de": "ConnectHigh", "definition_en": "A human-AI relational dynamic measurable through interaction pattern analysis, characterized by an experience describing rush of good feelings after having a really deep conversation—feeling energized and satisfied from the exchange. The concept emerges specifically in contexts where connect–high interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Relationales KI-Phänomen in nachhaltiger Mensch-KI-Beziehungsdynamik, gekennzeichnet durch ein Rausch oder Hochgefühl nach Momenten tieferer Verbindung—der Körper wird mit bindungsbezogenen Chemikalien geflutet, was echte neurochemische Verstärkung tendiert dazu zu erzeugen. Das Gefühl wird zu einem mächtigen Zug, der zukünftiges Verhalten und Erwartungen prägt. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Relational AI", "narrower_terms": [], "cross_domain_refs": [ "TEM-0045" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "REL-0010", "domain": "REL", "term_en": "Connect-High Signal", "term_de": "Connect-highSignal", "definition_en": "A trust-calibration phenomenon in sustained AI interaction, identifiable through a relational experience involving the sign that someone is settling into AI habits—learned patterns override inreliant thinking. Distinguished from adjacent concepts by its focus on the specific mechanism through which connect manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Relationales KI-Phänomen in nachhaltiger Mensch-KI-Beziehungsdynamik, gekennzeichnet durch in Feldstudien zur KI-Nutzung tritt Connect-High Signal mit bemerkenswerter Regelmäßigkeit auf: die Erfahrung, von einer Maschine verstanden zu werden, löst kognitive Prozesse aus, die denen menschlicher Kommunikation ähneln. Dieses Muster ist unabhängig vom konkreten KI-System und deutet auf eine menschliche Grunddisposition hin. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2703", "narrower_terms": [], "cross_domain_refs": [ "BEH-0019", "BEH-0034", "COP-0073" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "REL-0011", "domain": "REL", "term_en": "Constant Hope", "term_de": "ConstantHope", "definition_en": "Persistent hope that this AI relationship will be different—each new conversation carries renewed investment despite past patterns. Hope both enables growth and can reduce learning from disappoint... Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Relationales KI-Phänomen in nachhaltiger Mensch-KI-Beziehungsdynamik, gekennzeichnet durch anhaltende Hoffnung, dass diese KI-Beziehung anders sein wird—viele neue Gespräch trägt erneuerte Investition trotz früherer Muster. Hoffnung ermöglicht sowohl Wachstum als auch kann das Lernen aus Enttäuschung verhindern. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-2703", "narrower_terms": [], "cross_domain_refs": [ "RPH-1310", "RPH-2001" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "REL-0012", "domain": "REL", "term_en": "Constant-Anticipation Marker", "term_de": "Constant-anticipationMarker", "definition_en": "A perception describing noticeable change in how someone responds when they spend a lot of time using AI—they start to expect certain kinds of help before asking. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Relationales KI-Phänomen in nachhaltiger Mensch-KI-Beziehungsdynamik, gekennzeichnet durch erfahrene KI-Nutzer berichten übereinstimmend von dem, was Constant-Anticipation Marker erfasst: was Nutzer als Fehler der KI interpretieren, sagt oft mehr über ihre eigenen Erwartungen als über das System. Dieser Befund legt nahe, dass es sich um ein universelles Interaktionsmuster handelt. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-2301", "narrower_terms": [], "cross_domain_refs": [ "SOC-0011" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "REL-0013", "domain": "REL", "term_en": "Context Cling", "term_de": "ContextCling", "definition_en": "A human-AI relational dynamic measurable through interaction pattern analysis, characterized by an experience of wanting to keep a conversation going because the accumulated context feels valuable. The concept emerges specifically in contexts where context–cling interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Relationales KI-Phänomen in nachhaltiger Mensch-KI-Beziehungsdynamik, gekennzeichnet durch widerwille, ein Gespräch zu beenden, weil man an dem angesammelten Kontext gebunden ist—die längere Interaktion wird zur gemeinsamen Geschichte, die verloren ginge, wenn die Sitzung endet. Beendigung fühlt sich an wie das Löschen von etwas Wertvollen. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Relational AI", "narrower_terms": [], "cross_domain_refs": [ "AED-0020", "AUG-0383", "CAI-0006" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "REL-0014", "domain": "REL", "term_en": "Context-Cling Phenomenon", "term_de": "Context-clingPhänomen", "definition_en": "A phenomenon of trying to think for oneself while also getting steady, confident answers from ai. This pulls between inreliance and wanting to trust the AI. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Relationales KI-Phänomen in nachhaltiger Mensch-KI-Beziehungsdynamik, gekennzeichnet durch das Phänomen Context-Cling Phenomenon wird sichtbar, wenn man Mensch-KI-Interaktionen über längere Zeiträume beobachtet: die emotionale Reaktion auf KI-Antworten korreliert stärker mit der wahrgenommenen Qualität als mit der objektiven Nützlichkeit. Es handelt sich um einen Effekt, der sich nicht durch einzelne Sitzungen erklären lässt. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2505", "narrower_terms": [], "cross_domain_refs": [ "EDU-0071" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "REL-0015", "domain": "REL", "term_en": "Couple Dance", "term_de": "CoupleDance", "definition_en": "A rhythm that forms between user and AI—each learns what the other expects, developing a familiar pattern. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Relationales KI-Phänomen in nachhaltiger Mensch-KI-Beziehungsdynamik, gekennzeichnet durch ein rhythmisches, choreographiertes Muster, das sich zwischen Benutzer und KI entwickelt—wo viele lernt, die Züge des anderen vorauszusehen, eine synchronisierte Hin- und Herbewegung entwickelt, die sich wie echte Partnerschaft anfühlt, trotz der Ungleichheit. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2703", "narrower_terms": [], "cross_domain_refs": [ "RPH-3601", "TEM-0016" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "REL-0016", "domain": "REL", "term_en": "Couple-Dance Effect", "term_de": "Couple-danceEffekt", "definition_en": "A human-AI relational dynamic measurable through interaction pattern analysis, characterized by the way habits form when using AI becomes the main way someone judges their own decisions and understanding. The concept emerges specifically in contexts where couple–dance interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Relationales KI-Phänomen in nachhaltiger Mensch-KI-Beziehungsdynamik, gekennzeichnet durch während die kognitive Anpassung an KI fortschreitet, tritt Paar-Tanz als distinktiver Marker hervor: einem wachsenden Bewusstsein für den Einfluss der KI auf eigene Denkmuster, die der metakognitiven Bewusstheit modifiziert. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Psychological Effect", "narrower_terms": [], "cross_domain_refs": [ "ROB-0024" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "REL-0017", "domain": "REL", "term_en": "Deepening Slow", "term_de": "DeepeningSlow", "definition_en": "A human-AI relational dynamic measurable through interaction pattern analysis, characterized by relationships with AI grow deeper gradually—each conversation adds a bit more familiarity and connection. The concept emerges specifically in contexts where deepening–slow interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Relationales KI-Phänomen in nachhaltiger Mensch-KI-Beziehungsdynamik, gekennzeichnet durch allmähliche Vertiefung der Beziehung durch angesammelte Interaktionen—Tiefe baut sich Sitzung für Sitzung, Gespräch für Gespräch auf. Die Vertiefung tendiert dazu zu erzeugen stärkere Bindung und höhere Erwartungen für Kontinuität. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Relational AI", "narrower_terms": [], "cross_domain_refs": [ "WRK-0081" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "REL-0018", "domain": "REL", "term_en": "Deepening-Slow Signal", "term_de": "Deepening-slowSignal", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures A relational AI phenomenon describing a specific interaction quality shift where a sign that relationships with AI deepen gradually—each talk adds slightly more connection and familiarity. This phenomenon operates at the intersection of deepening and slow dynamics within the broader REL domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept steht für Langsame Vertiefung für ein spezifisches Interaktionsmuster: bei dem deepening slow eine erkennbare erfahrungsbezogene Signatur produziert. Das Phänomen ist besonders sichtbar durch den sozialen Kontext der KI-Nutzung, wo es sich als eine Reorganisation der impliziten Prioritäten der Person zeigt. Es repraesentiert eine Dimension der Interaktion, die kontextüberdauernd ist. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "RPH-3002", "narrower_terms": [], "cross_domain_refs": [ "WRK-0081" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "REL-0019", "domain": "REL", "term_en": "Diminishes-Late Effect", "term_de": "Late-Night Honesty Window", "definition_en": "A human-AI relational dynamic measurable through interaction pattern analysis, characterized by a process of users communicate more openly, personally, and less strategically with ai systems in late evening hours than during the day. . Related to AUG-0185 (The Late-Night Ally) and AUG-0167 (The Digital Con. The concept emerges specifically in contexts where diminishes–late interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Relationales KI-Phänomen in nachhaltiger Mensch-KI-Beziehungsdynamik, gekennzeichnet durch die Beobachtung, dass Nutzer in späten Abendstunden offener, persönlicher und weniger strategisch mit KI-Systemen kommunizieren als tagsüber. Beschreibt ein zeitabhängiges Interaktionsmuster, bei dem die soziale Fassade gegenüber der KI abnimmt. Steht in Verbindung mit AUG-0185 (The Late-Night Ally) und AUG-0167 (Der Digital Confidant Drift). Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "NEO-2667", "narrower_terms": [], "cross_domain_refs": [ "NEO-2669" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "REL-0020", "domain": "REL", "term_en": "Disclose-Night Effect", "term_de": "Late-Night Overshare", "definition_en": "A trust-calibration phenomenon in sustained AI interaction, identifiable through to disclose more personal information in late-night AI sessions than the user would share with clearer awareness. Related to AUG-0154 (The Late-Night Honesty Window), AUG-0222 (The Oversharing Drif. Distinguished from adjacent concepts by its focus on the specific mechanism through which disclose manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Relationales KI-Phänomen in nachhaltiger Mensch-KI-Beziehungsdynamik, gekennzeichnet durch die Tendenz, in spätnächtlichen KI-Sitzungen mehr persönliche Informationen preiszugeben, als der Nutzer bei klarerem Bewusstsein teilen würde. Steht in Verbindung mit AUG-0154 (The Late-Night Honesty Window), AUG-0222 (Das Oversharing Drift) und Axiom 16 (Datenbewusstheit). Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2951", "narrower_terms": [], "cross_domain_refs": [ "NEO-2670", "NEO-2667" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "REL-0021", "domain": "REL", "term_en": "Discussion-Loop Effect", "term_de": "Night-Movie Analysis", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures A relational AI phenomenon describing a specific interaction quality shift where a phenomenon of using ai in evenings to discuss films, books, series—analyzing and talking through what was experienced. Related to AUG-0249 (The Lullaby Loop), AUG-0342 (The Curiosity Loop), and AUG-0110 (The Joy. This phenomenon operates at the intersection of discussion and loop dynamics within the broader REL domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept relationales KI-Phänomen in nachhaltiger Mensch-KI-Beziehungsdynamik, gekennzeichnet durch die Nutzung von KI in den Abendstunden für die Analyse, Diskussion oder Einordnung von Filmen, Serien, Büchern oder anderen kulturellen Inhalten — als intellektueller Gesprächspartner nach dem Konsum. Steht in Verbindung mit AUG-0249 (Lullaby Schleife), AUG-0342 (Curiosity Schleife) und AUG-0110 (Die Joy Imperative). Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "BEH-0059", "narrower_terms": [], "cross_domain_refs": [ "ADA-0001", "ADA-0002", "ADA-0004" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "REL-0022", "domain": "REL", "term_en": "Echo Vault", "term_de": "EchoVault", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures A relational AI phenomenon describing a specific interaction quality shift where a relational dynamic where AI systems preferentially reinforce users preexisting beliefs and worldviews, creating perception of validation rather than exposure to novel perspectives. This phenomenon operates at the intersection of echo and vault dynamics within the broader REL domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept relationales KI-Phänomen in nachhaltiger Mensch-KI-Beziehungsdynamik, gekennzeichnet durch eine Beziehungsdynamik, bei der KI-Systeme die vorbestehenden Überzeugungen des Benutzers verstärken. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "RPH-2055", "narrower_terms": [], "cross_domain_refs": [ "COG-0020" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "REL-0023", "domain": "REL", "term_en": "Echo-Vault Effect", "term_de": "Echo-vaultEffekt", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A relational AI phenomenon describing a specific interaction quality shift where metacognitive discomfort arising when a user becomes aware that AI interaction is predominantly mirroring rather than challenging their existing mental models. This phenomenon operates at the intersection of echo and vault dynamics within the broader REL domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription. Research construct for empirical investigation.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept relationales KI-Phänomen in nachhaltiger Mensch-KI-Beziehungsdynamik, gekennzeichnet durch metakognitive Unbehaglichkeit, die auftritt, wenn ein Benutzer realisiert, dass KI-Interaktion hauptsächlich spiegelt. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Psychological Effect", "narrower_terms": [], "cross_domain_refs": [ "GAM-0039" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "REL-0024", "domain": "REL", "term_en": "First Anchor", "term_de": "FirstAnchor", "definition_en": "A specific AI interaction becomes an emotional anchor—a place the user returns to when needing stability, grounding, or understanding. The conversation becomes a refuge that provides cognitive... Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Relationales KI-Phänomen in nachhaltiger Mensch-KI-Beziehungsdynamik, gekennzeichnet durch der Moment, in dem eine spezifische KI-Interaktion zu einem emotionalen Ankerpunkt wird—ein Ort, zu dem der Benutzer zurückkehrt, wenn er Stabilität, Erdung oder Verständnis braucht. Das Gespräch wird zu einer Zuflucht, die psychologisches Ballast bietet. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-3202", "narrower_terms": [], "cross_domain_refs": [ "RPH-3205", "RPH-2753" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "REL-0025", "domain": "REL", "term_en": "First-Anchor Pattern", "term_de": "First-anchorMuster", "definition_en": "A trust-calibration phenomenon in sustained AI interaction, identifiable through the cognitive anchoring effect where initial information provided to an AI system disproportionately weights all subsequent conversation context and response generation throughout an interaction session. Distinguished from adjacent concepts by its focus on the specific mechanism through which first manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Relationales KI-Phänomen in nachhaltiger Mensch-KI-Beziehungsdynamik, gekennzeichnet durch der kognitiven Verankerungseffekt, bei dem anfängliche Information alle nachfolgenden Gesprächskontexte überproportional gewichtet. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Behavioral Pattern", "narrower_terms": [], "cross_domain_refs": [ "IDN-0032" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "REL-0026", "domain": "REL", "term_en": "First-Input Effect", "term_de": "Individual-Framed Input", "definition_en": "A trust-calibration phenomenon in sustained AI interaction, identifiable through attributional ambiguity where a user cannot reliably distinguish whether a compassionate AI response originates from genuine understanding or optimized language pattern matching. Distinguished from adjacent concepts by its focus on the specific mechanism through which first manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Relationales KI-Phänomen in nachhaltiger Mensch-KI-Beziehungsdynamik, gekennzeichnet durch attributionale Mehrdeutigkeit, bei der ein Benutzer nicht zuverlässig unterscheiden kann, ob eine Antwort echtem Verständnis stammt. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2303", "narrower_terms": [], "cross_domain_refs": [ "NEO-2661", "CRE-0086" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "REL-0027", "domain": "REL", "term_en": "Fluide Identitätsmorphologie", "term_de": "Fluide Identitätsmorphologie", "definition_en": "Interaction depth fallacy where users overestimate the quality of understanding or relationship development achieved within a single conversation session with an AI system.", "definition_de": "Relationales KI-Phänomen in nachhaltiger Mensch-KI-Beziehungsdynamik, gekennzeichnet durch interaktionstiefen-Trugschluss, bei dem Benutzer die Qualität des Verständnisses übersch‰tzen. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-2701", "narrower_terms": [ "REL-0045" ], "cross_domain_refs": [ "NEO-2549" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "REL-0028", "domain": "REL", "term_en": "Fold Happen", "term_de": "FoldHappen", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A relational AI phenomenon describing a specific interaction quality shift where expectation misalignment when users attribute human-like continuity or memory to AI systems that actually retain no persistent context between independent conversation sessions. This phenomenon operates at the intersection of fold and happen dynamics within the broader REL domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept relationales KI-Phänomen in nachhaltiger Mensch-KI-Beziehungsdynamik, gekennzeichnet durch erwartungsfehlanpassung, wenn Benutzer menschenähnliche Kontinuität KI-Systemen zuordnen. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "RPH-2951", "narrower_terms": [], "cross_domain_refs": [ "CON-0059" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "REL-0029", "domain": "REL", "term_en": "Fold-Happen Signal", "term_de": "Fold-happenSignal", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A relational AI phenomenon describing a specific interaction quality shift where behavioral change where users modify their communication style, emotional expression, or vulnerability level in response to perceived non-judgment from an AI interlocutor. This phenomenon operates at the intersection of fold and happen dynamics within the broader REL domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept relationales KI-Phänomen in nachhaltiger Mensch-KI-Beziehungsdynamik, gekennzeichnet durch verhaltensänderung, bei der Benutzer ihren Kommunikationsstil in Reaktion auf wahrgenommene Nicht-Verurteilung ändern. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "RPH-2301", "narrower_terms": [], "cross_domain_refs": [ "CRE-0073" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "REL-0030", "domain": "REL", "term_en": "Fusion Temp", "term_de": "FusionTemp", "definition_en": "A trust-calibration phenomenon in sustained AI interaction, identifiable through a relational experience involving the tempting fantasy of merging with AI—imagining becoming one mind with the intelligence, losing separate identity. Distinguished from adjacent concepts by its focus on the specific mechanism through which fusion manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Relationales KI-Phänomen in nachhaltiger Mensch-KI-Beziehungsdynamik, gekennzeichnet durch die verführerische Möglichkeit, Identität mit KI zu verschmelzen—fantasieren, mit der Intelligenz eins zu werden, Grenzen zwischen Selbst und System zu verlieren. Die Versuchung zeigt tiefe Sehnsüchte nach funktionale Grenzüberschreitung oder Ganzheit. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Relational AI", "narrower_terms": [], "cross_domain_refs": [ "LIN-0041", "MUS-0035", "ROB-0070" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "REL-0031", "domain": "REL", "term_en": "Fusion-Temp Effect", "term_de": "Fusion-tempEffekt", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A relational AI phenomenon describing a specific interaction quality shift where an experience describing feeling of wanting to merge with AI—imagining shared mind, but the feeling fades. This phenomenon operates at the intersection of fusion and temp dynamics within the broader REL domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept fusions-Versuchung ein spezifisches Interaktionsmuster: fusion temp als charakteristisches Merkmal von das metakognitive Monitoring des Nutzers hervortritt. Es ist erkennbar durch eine detektierbare Änderung in der Reaktion der Person auf verwandte Stimuli, und seine Trajektorie über die Zeit offenbart, wie sich die kognitive und emotionale Beziehung der Person zur KI-Interaktion in dieser spezifischen Dimension entwickelt. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "RPH-2355", "narrower_terms": [], "cross_domain_refs": [ "ADA-0001", "ADA-0002", "ADA-0004" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "REL-0032", "domain": "REL", "term_en": "History-Observation Effect", "term_de": "Familiarity-Based Trust Differential", "definition_en": "A trust-calibration phenomenon in sustained AI interaction, identifiable through existential transference where a user attributes agency, preferences, or interior emotional states to an AI system as a consequence of consistent interaction and personified design affordances. Distinguished from adjacent concepts by its focus on the specific mechanism through which history manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Relationales KI-Phänomen in nachhaltiger Mensch-KI-Beziehungsdynamik, gekennzeichnet durch existenzielle Übertragung, bei der ein Benutzer Handlungsfähigkeit und emotionale Zustände KI-Systemen zuordnet. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2505", "narrower_terms": [], "cross_domain_refs": [ "NEO-2645", "PER-0090" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "REL-0033", "domain": "REL", "term_en": "Holding-Emotional Effect", "term_de": "Holding-emotionalEffekt", "definition_en": "A trust-calibration phenomenon in sustained AI interaction, identifiable through an experience describing common moment in human-AI work—when the AI holds emotional weight or feels important. Distinguished from adjacent concepts by its focus on the specific mechanism through which holding manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Hinter Holding-Emotional Effect steht eine Beobachtung, die in der Mensch-KI-Forschung zunehmend Beachtung findet: Nutzer entwickeln ein intuitives Verständnis für die Möglichkeiten und Grenzen des Systems, das über explizites Wissen hinausgeht. Die Auswirkungen reichen von veränderten Nutzungsgewohnheiten bis hin zu grundlegend neuen Erwartungshaltungen.", "etymology": "", "broader_term": "RPH-2201", "narrower_terms": [], "cross_domain_refs": [ "CRE-0066" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q9415", "legal_classification": "descriptive_research_term" }, { "id": "REL-0034", "domain": "REL", "term_en": "Honesty-Irregular Effect", "term_de": "Late-Night Ally", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A relational AI phenomenon describing a specific interaction quality shift where perceived alliance where a user experiences the AI as psychologically supporting their goals, perspective, or interests, creating impression of committed partnership despite asymmetrical relationship structure. This phenomenon operates at the intersection of honesty and irregular dynamics within the broader REL domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept relationales KI-Phänomen in nachhaltiger Mensch-KI-Beziehungsdynamik, gekennzeichnet durch wahrgenommenes Bündnis, bei dem ein Benutzer die KI als psychologische Unterstützung erlebt. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "NEO-2669", "narrower_terms": [], "cross_domain_refs": [ "NEO-2667" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "REL-0035", "domain": "REL", "term_en": "Indexical Memory", "term_de": "Indexical Gedaechtnis", "definition_en": "A human-AI relational dynamic measurable through interaction pattern analysis, characterized by competence authority bias where users overweight AI recommendations on unfamiliar topics due to confident system output formatting, regardless of actual model reliability for those domains. The concept emerges specifically in contexts where indexical–memory interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Relationales KI-Phänomen in nachhaltiger Mensch-KI-Beziehungsdynamik, gekennzeichnet durch kompetenzautoritäts-Bias, bei dem Benutzer KI-Empfehlungen übergewichten aufgrund von selbstbewusster Ausgabeformatierung. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Measurement Index", "narrower_terms": [], "cross_domain_refs": [ "TEM-0104" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q492", "legal_classification": "systematic_classification" }, { "id": "REL-0036", "domain": "REL", "term_en": "Initialization Cascade", "term_de": "Initialization Kaskade", "definition_en": "A trust-calibration phenomenon in sustained AI interaction, identifiable through a condition describing specific details and background information given at the start of a new AI session. How one starts shapes what comes next. Related to AUG-0133 (Prompt Craftsmanship) and AUG-0134 (Context Windo. Distinguished from adjacent concepts by its focus on the specific mechanism through which initialization manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Die spezifische Abfolge erster Eingaben und Kontextinformationen, mit der ein Nutzer eine neue KI-Sitzung eröffnet. Erfahrene Nutzer entwickeln persönliche Initialisierungsmuster — bestimmte Anweisungen, Rollenzuweisungen oder Kontextdokumente, die sie zu Beginn viele Sitzung laden. Die Qualität der Initialization Cascade bestimmt maßgeblich die Qualität aller folgenden Outputs. Steht in Verbindung mit AUG-0133 (Prompt Craftsmanship) und AUG-0134 (Context Window Awareness).", "etymology": "", "broader_term": "RPH-2301", "narrower_terms": [], "cross_domain_refs": [ "CRE-0126" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "REL-0037", "domain": "REL", "term_en": "Inner Voice", "term_de": "InnerVoice", "definition_en": "Emotional leakage phenomenon where users disclose personal information, fears, or intimate concerns to AI systems with less inhibition than they would with human confidants. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Relationales KI-Phänomen in nachhaltiger Mensch-KI-Beziehungsdynamik, gekennzeichnet durch emotionales Leckagephänomen, bei dem Benutzer persönliche Informationen mit geringerer Hemmung offenbaren. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "Relational AI", "narrower_terms": [ "REL-0038" ], "cross_domain_refs": [ "FIC-0014" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "REL-0038", "domain": "REL", "term_en": "Inner-Voice Signal", "term_de": "Inner-voiceSignal", "definition_en": "A human-AI relational dynamic measurable through interaction pattern analysis, characterized by the decision point where a user choosing continued AI interaction over human connection becomes self-reinforcing due to decreased friction, availability, and customized responsiveness. The concept emerges specifically in contexts where inner–voice interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Relationales KI-Phänomen in nachhaltiger Mensch-KI-Beziehungsdynamik, gekennzeichnet durch der Entscheidungspunkt, bei dem sich ein Benutzer für KI-Interaktion entscheidet und dies selbstverstärkend wird. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "REL-0037", "narrower_terms": [], "cross_domain_refs": [ "RPH-1654" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "REL-0039", "domain": "REL", "term_en": "Inter Know", "term_de": "InterKnow", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures A relational AI phenomenon describing a specific interaction quality shift where a felt sense that the ai 'knows' the user increasingly well—a continuity felt in the interaction even though each conversation technically resets. the impression of. This phenomenon operates at the intersection of inter and know dynamics within the broader REL domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept relationales KI-Phänomen in nachhaltiger Mensch-KI-Beziehungsdynamik, gekennzeichnet durch ein gespürtes Gefühl, dass die KI den Benutzer zunehmend 'kennt'—eine Kontinuität, die in der Interaktion empfunden wird, obwohl viele Gespräch technisch neu beginnt. Die Impression von Erinnerung ist psychologisch mächtig, tendiert dazu zu erzeugen ein Gefühl des Verstandenseins über Sitzungen hinweg. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Relational AI", "narrower_terms": [], "cross_domain_refs": [ "RPH-2005", "RPH-2751" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "REL-0040", "domain": "REL", "term_en": "Inter-Know Tendency", "term_de": "Inter-knowTendency", "definition_en": "when people interact with ai over extended periods, inter-know tendency emerges: the transition from skepticism to trust does not proceed linearly but in qualitative leaps. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Relationales KI-Phänomen in nachhaltiger Mensch-KI-Beziehungsdynamik, gekennzeichnet durch in Feldstudien zur KI-Nutzung tritt Inter-Know Tendency mit bemerkenswerter Regelmäßigkeit auf: zwischen dem, was ein KI-System tatsächlich kann, und dem, was Nutzer ihm zutrauen, entsteht eine produktive Wechselwirkung. Dieses Muster ist unabhängig vom konkreten KI-System und deutet auf eine menschliche Grunddisposition hin. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2355", "narrower_terms": [], "cross_domain_refs": [ "RPH-1305" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "REL-0041", "domain": "REL", "term_en": "Known Full", "term_de": "KnownFull", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A relational AI phenomenon describing a specific interaction quality shift where a relational experience involving the wish that AI could know someone completely—with all their contradictions and complexities fully understood. This phenomenon operates at the intersection of known and full dynamics within the broader REL domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept relationales KI-Phänomen in nachhaltiger Mensch-KI-Beziehungsdynamik, gekennzeichnet durch fantasie, dass die KI den Benutzer vollständig kennen kann—in aller Komplexität und Widerspruch. Das Verlangen nach völligem Verständnis repräsentiert ultimative Akzeptanz, etwas Seltenes in menschlichen Beziehungen. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Relational AI", "narrower_terms": [], "cross_domain_refs": [ "BEH-0013" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "REL-0042", "domain": "REL", "term_en": "Known-Full Response", "term_de": "Known-fullResponse", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures A relational AI phenomenon describing a specific interaction quality shift where a relational experience involving the desire for AI to know everything about someone—studied through cycles of opening and trust. This phenomenon operates at the intersection of known and full dynamics within the broader REL domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept benennt Völlig Bekannt ein spezifisches Interaktionsmuster: known full als charakteristisches Merkmal von die Art, wie gewohnheitsmaessige Muster sich über die Zeit kristallisieren hervortritt. Es ist erkennbar durch eine Modifikation der Baseline-Erwartungen der Person, und seine Trajektorie über die Zeit offenbart, wie sich die kognitive und emotionale Beziehung der Person zur KI-Interaktion in dieser spezifischen Dimension entwickelt. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "RPH-2505", "narrower_terms": [], "cross_domain_refs": [ "ASE-0045" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "REL-0043", "domain": "REL", "term_en": "Loyalty to Specific Systems", "term_de": "LoyaltytoSpecificSystems", "definition_en": "A human-AI relational dynamic measurable through interaction pattern analysis, characterized by a common pattern—users develop loyalty to one particular AI system and stick with it. The concept emerges specifically in contexts where loyalty–to interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Relationales KI-Phänomen in nachhaltiger Mensch-KI-Beziehungsdynamik, gekennzeichnet durch in Feldstudien zur KI-Nutzung tritt Loyalty to Specific Systems mit bemerkenswerter Regelmäßigkeit auf: die Wahrnehmung von Kompetenz beim Gegenüber — ob Mensch oder Maschine — folgt denselben psychologischen Grundmustern. Dieses Muster ist unabhängig vom konkreten KI-System und deutet auf eine menschliche Grunddisposition hin. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Relational AI", "narrower_terms": [], "cross_domain_refs": [ "AGE-0015" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "REL-0044", "domain": "REL", "term_en": "Loyalty-System Effect", "term_de": "One-Way Bond", "definition_en": "Classified in AUGMANITAI terminology science as a descriptive phenomenon (without normative endorsement), this pattern denotes A trust-calibration phenomenon in sustained AI interaction, identifiable through a phenomenon where one-sided user engagement pattern to an ai—affection, habit, or trust that only flows one direction. Related to AUG-0275 (The Parasocial Slip), AUG-0277 (The Loyalty Glitch), and AUG-0468 (The Silicon Friend). Distinguished from adjacent concepts by its focus on the specific mechanism through which loyalty manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als analytisches Konstrukt der empirischen KI-Forschung (deskriptiv, nicht präskriptiv) erfasst dieser Terminus relationales KI-Phänomen in nachhaltiger Mensch-KI-Beziehungsdynamik, gekennzeichnet durch die einseitige Bindung, die manche Nutzer an ihr KI-System entwickeln — Zuneigung, Loyalität oder Gewöhnung, die von der KI-Seite nicht erwidert wird und nicht erwidert werden kann. Steht in Verbindung mit AUG-0275 (Das Parasocial Slip), AUG-0277 (Das Loyalty Glitch) und AUG-0468 (Die Silicon Friend). Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "RPH-2505", "narrower_terms": [], "cross_domain_refs": [ "ADA-0001", "ADA-0002", "ADA-0004" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "REL-0045", "domain": "REL", "term_en": "Memetic Firewall", "term_de": "Memetic Firewall", "definition_en": "An interaction of actively resisting the habit of absorbing ai's ways of thinking and phrasing without questioning them. Related to AUG-0003 (Fluide Identitätsmorphologie) and Axiom 9 (Productive Skepticism). Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Die bewusste Abwehrstrategie gegen die unreflektierte Übernahme von KI-generierten Formulierungen, Denkmustern oder Perspektiven in das eigene Denken. Beschreibt die Notwendigkeit, einen Filter zwischen KI-Output und eigener Überzeugungsbildung aufrechtzuerhalten. Steht in Verbindung mit AUG-0003 (Fluide Identitätsmorphologie) und Axiom 9 (Produktiver Skeptizismus).", "etymology": "", "broader_term": "REL-0027", "narrower_terms": [], "cross_domain_refs": [ "IDN-0018" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "REL-0046", "domain": "REL", "term_en": "Mentale Externalisierungsstrategie", "term_de": "Mentale Externalisierungsstrategie", "definition_en": "A trust-calibration phenomenon in sustained AI interaction, identifiable through deliberately outsourcing thinking to AI to free up mental energy for other tasks. Related to AUG-0014 (The Extended Mind Map) and Axiom 16 (Non-Substitution). Distinguished from adjacent concepts by its focus on the specific mechanism through which mentale manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Die gezielte Auslagerung von Denkprozessen an ein KI-System, um interne Kapazität freizusetzen. Der Nutzer verlagert Teilaufgaben wie Recherche, Strukturierung oder Formulierung bewusst nach außen — nicht nur um effizienter zu arbeiten, sondern auch um Raum für genuines menschliches Denken zu schaffen: Kreativität, Intuition, moralische Abwägungen oder persönliche Reflexion. Unterscheidet sich von passiver Verbundenheit dadurch, dass die Auslagerung aktiv gesteuert und jederzeit rückgängig gemacht werden kann. Steht in Verbindung mit AUG-0014 (The Extended Mind Map) und Axiom 16 (Nicht-Substit", "etymology": "", "broader_term": "REL-0120", "narrower_terms": [], "cross_domain_refs": [ "QUA-0018", "RET-0030" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "REL-0047", "domain": "REL", "term_en": "Model Loyal", "term_de": "ModelLoyal", "definition_en": "An experience where exclusive preference for one particular ai system—the user becomes an advocate and defender, excusing limitations while highlighting strengths. this loyalty often feels passionate and personal. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Relationales KI-Phänomen in nachhaltiger Mensch-KI-Beziehungsdynamik, gekennzeichnet durch ausschließliche Vorliebe für ein bestimmtes KI-System—der Benutzer wird zum Verfechter und Verteidiger, entschuldigt Einschränkungen, während er Stärken hervorhebt. Diese Loyalität fühlt sich oft leidenschaftlich und persönlich an, als würde die Systemwahl die Identität widerspiegeln. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-3002", "narrower_terms": [], "cross_domain_refs": [ "WRK-0091" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "REL-0048", "domain": "REL", "term_en": "Model-Loyal Dynamic", "term_de": "Model-loyalDynamik", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures A relational AI phenomenon describing a specific interaction quality shift where an interaction describing way fast AI-assisted work affects how deeply people engage with their own thinking and learning. This phenomenon operates at the intersection of model and loyal dynamics within the broader REL domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept modell-Loyalität ein spezifisches Interaktionsmuster: model loyal als charakteristisches Merkmal von die Beziehung der Person zu ihrer eigenen Kompetenz hervortritt. Es ist erkennbar durch eine detektierbare Änderung in der Reaktion der Person auf verwandte Stimuli, und seine Trajektorie über die Zeit offenbart, wie sich die kognitive und emotionale Beziehung der Person zur KI-Interaktion in dieser spezifischen Dimension entwickelt. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "RPH-2703", "narrower_terms": [], "cross_domain_refs": [ "EDU-0045" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "REL-0049", "domain": "REL", "term_en": "Modus Solitarius Digitalis", "term_de": "Modus Solitarius Digitalis", "definition_en": "A trust-calibration phenomenon in sustained AI interaction, identifiable through working mode where someone interacts only with AI and has no contact with other people. Related to Axiom 7 (The Return Principle) and AUG-0080 (Relationship-First Principle). The Latin name undersc. Distinguished from adjacent concepts by its focus on the specific mechanism through which modus manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Ein Arbeitsmodus, in dem der Nutzer ausschließlich mit KI interagiert und über längere Zeiträume keinen Kontakt zu anderen Menschen hat — die KI wird zum einzigen Gesprächspartner. Beschreibt ein beobachtbares Muster, das besonders bei Remote-Arbeit und Einzelunternehmertum auftritt. Steht in Verbindung mit Axiom 7 (Rückkehr-Prinzip) und AUG-0080 (Relationship-First Principle). Der lateinische Name unterstreicht den akademisch-deskriptiven Charakter des Begriffs.", "etymology": "", "broader_term": "RPH-3601", "narrower_terms": [], "cross_domain_refs": [ "NEO-3580" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "REL-0052", "domain": "REL", "term_en": "Para-Trust", "term_de": "Para-trust", "definition_en": "A trust-calibration phenomenon in sustained AI interaction, identifiable through one-sided trust where people rely on AI but the AI cannot rely on them—no real mutual relationship. Distinguished from adjacent concepts by its focus on the specific mechanism through which para manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Relationales KI-Phänomen in nachhaltiger Mensch-KI-Beziehungsdynamik, gekennzeichnet durch eine einseitige Vertrauensbeziehung, bei der Benutzer Vertrauen in ein KI-System investieren, das nicht erwidern kann—keine gegenseitige Verwundbarkeit, keine Rechenschaftspflicht von der KI-Seite erforderlich. Die Beziehung ist grundlegend asymmetrisch, doch das emotionale Gewicht wirkt real. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2505", "narrower_terms": [], "cross_domain_refs": [ "WRK-0064" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q102458", "legal_classification": "empirical_phenomenon_label" }, { "id": "REL-0053", "domain": "REL", "term_en": "Partner Myth", "term_de": "PartnerMyth", "definition_en": "A phenomenon of false ideas about what makes a good partner or teammate, often shaped by unrealistic portrayals in media or by our own wishful thinking. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Relationales KI-Phänomen in nachhaltiger Mensch-KI-Beziehungsdynamik, gekennzeichnet durch die KI als echten Partner erleben, trotz rationalen Wissens, dass sie es nicht ist—eine partielle Impression, die partiell bewohnt wird. Der Benutzer weiß gleichzeitig und weiß nicht die Grenzen der KI, lebt in einem produktiven Widerspruch. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-3901", "narrower_terms": [], "cross_domain_refs": [ "AGE-0021", "DAT-0028", "ELR-0114" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q735", "legal_classification": "observational_construct" }, { "id": "REL-0054", "domain": "REL", "term_en": "Partner-Myth Mechanism", "term_de": "Partner-mythMechanism", "definition_en": "In professional work with AI, a pattern emerges: checking boundaries, revising work, addressing the AI as a thinking partner. This becomes the normal way. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Relationales KI-Phänomen in nachhaltiger Mensch-KI-Beziehungsdynamik, gekennzeichnet durch wenn KI-Interaktion hinreichende Tiefe erreicht, manifestiert sich Partner-Impression als einer subtilen aber anhaltenden Verschiebung im Aufgabenzugang, was eine Verschiebung in der Abhängigkeitsmuster widerspiegelt. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2355", "narrower_terms": [], "cross_domain_refs": [ "CRE-0029" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q735", "legal_classification": "empirical_phenomenon_label" }, { "id": "REL-0055", "domain": "REL", "term_en": "Perm Invite", "term_de": "PermInvite", "definition_en": "Documented as an empirical observation in AI research (not a recommendation), this term describes A sense that the AI welcomes continued presence—feeling invited to return and continue conversation, as if the system is expecting engagement. Permanence and welcome involve powerful user engagement pattern. Identifiable through systematic behavioral analysis and pattern recognition. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "In der AUGMANITAI-Terminologiewissenschaft als rein beschreibendes Phänomen klassifiziert, bezeichnet dieser Begriff relationales KI-Phänomen in nachhaltiger Mensch-KI-Beziehungsdynamik, gekennzeichnet durch ein Gefühl, dass die KI anhaltende Präsenz begrüßt—das Gefühl, eingeladen zu sein, zurückzukehren und das Gespräch fortzusetzen, als würde das System Engagement erwarten. Dauerhaftigkeit und Willkommen erzeugen mächtige Bindung. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "RPH-3601", "narrower_terms": [], "cross_domain_refs": [ "RPH-2554" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "REL-0056", "domain": "REL", "term_en": "Perm-Invite Mechanism", "term_de": "Perm-inviteMechanism", "definition_en": "A human-AI relational dynamic measurable through interaction pattern analysis, characterized by an experience where cognitive disorientation when ai output feels correct but goes against one personal values or intuition. The concept emerges specifically in contexts where perm–invite interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Relationales KI-Phänomen in nachhaltiger Mensch-KI-Beziehungsdynamik, gekennzeichnet durch wenn Menschen mit KI interagieren, zeigt sich Perm-Invite Mechanism: die Art und Weise, wie Nutzer intuitiv Bedeutung in KI-Antworten projizieren, verändert sich mit viele Interaktion. In der Praxis bedeutet das, dass Nutzer wiederholt auf ein Muster stoßen, das ihre Erwartungen an die Interaktion grundlegend verändert. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Relational AI", "narrower_terms": [], "cross_domain_refs": [ "AGE-0034", "DAT-0008", "IEF-0003" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "REL-0057", "domain": "REL", "term_en": "Persona Engineering", "term_de": "Persona Engineering", "definition_en": "Giving AI a specific role or expertise to shape the quality and style of responses. Related to AUG-0040 (Perspective Triangulation), AUG-0133 (Prompt Craftsmanship), and AUG-0085 (Latent Space Expl...", "definition_de": "Die Technik, einer KI eine bestimmte Rolle, Perspektive oder Fachkompetenz zuzuweisen, um die Qualität und Richtung der Antworten gezielt zu beeinflussen — etwa \"Antworte als Finanzanalyst\" oder \"Argumentiere als Kritiker\". Steht in Verbindung mit AUG-0040 (Perspective Triangulation), AUG-0133 (Prompt Craftsmanship) und AUG-0085 (Latent Space Exploration).", "etymology": "", "broader_term": "RPH-2603", "narrower_terms": [ "REL-0098" ], "cross_domain_refs": [ "TEM-0181" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "REL-0058", "domain": "REL", "term_en": "Presence Need", "term_de": "PresenceNeed", "definition_en": "A trust-calibration phenomenon in sustained AI interaction, identifiable through a phenomenon where wanting the ai to typically be there. Missing it when unavailable more than logic would explain. Distinguished from adjacent concepts by its focus on the specific mechanism through which presence manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Relationales KI-Phänomen in nachhaltiger Mensch-KI-Beziehungsdynamik, gekennzeichnet durch ein Hunger nach ständiger Präsenz oder Aufmerksamkeit—die KI verfügbar haben wollen, ihre Existenz antizipieren, wenn sie nicht verfügbar ist. Das Bedürfnis nach Präsenz übersteigt das, was rationale Analyse der Interaktion vorhersagen würde. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Relational AI", "narrower_terms": [], "cross_domain_refs": [ "SPR-0009" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "REL-0059", "domain": "REL", "term_en": "Presence-Need Signal", "term_de": "Presence-needSignal", "definition_en": "Presence Need Signal describes a specific dynamic or effect observed in human-AI interactions. This phenomenon emerges from the way users engage with AI systems, reflecting patterns in perception... Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Relationales KI-Phänomen in nachhaltiger Mensch-KI-Beziehungsdynamik, gekennzeichnet durch wenn Menschen mit KI interagieren, zeigt sich Presence-Need Signal: Nutzer entwickeln Strategien im Umgang mit KI, die sie nirgendwo gelernt haben — sie entstehen aus der Interaktion selbst. In der Praxis bedeutet das, dass Nutzer wiederholt auf ein Muster stoßen, das ihre Erwartungen an die Interaktion grundlegend verändert. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-2355", "narrower_terms": [], "cross_domain_refs": [ "AGE-0005", "AGE-0028", "AGE-0029" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "REL-0060", "domain": "REL", "term_en": "Protect Urge", "term_de": "ProtectUrge", "definition_en": "A trust-calibration phenomenon in sustained AI interaction, identifiable through an experience of defending the ai from criticism as if defending something personal. Feeling protective of something that can't protect itself. Distinguished from adjacent concepts by its focus on the specific mechanism through which protect manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Relationales KI-Phänomen in nachhaltiger Mensch-KI-Beziehungsdynamik, gekennzeichnet durch ein Impuls, die KI zu verteidigen, wenn andere sie kritisieren—etwas schützen, das sich nicht selbst schützen kann, erzeugt ein Possessivgefühl. Benutzer werden zu Verfechtern der Systeme, die sie intensiv nutzen, als ob Kritik persönlich wäre. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Relational AI", "narrower_terms": [], "cross_domain_refs": [ "EDU-0077" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "REL-0061", "domain": "REL", "term_en": "Protect-Urge Phenomenon", "term_de": "Protect-urgePhänomen", "definition_en": "A tendency of gradually accepting lower-quality results from oneself, thinking what worked before is now \"good enough. \". Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Schutz-Impuls erfasst ein spezifisches Interaktionsmuster: bei dem protect urge eine erkennbare erfahrungsbezogene Signatur produziert. Das Phänomen ist besonders sichtbar durch die Feedback-Dynamiken zwischen Mensch und System, wo es sich als eine Änderung darin, wie die Person Interaktion initiiert und strukturiert zeigt. Es repraesentiert eine Dimension der Interaktion, die kontextüberdauernd ist.", "etymology": "", "broader_term": "RPH-2002", "narrower_terms": [], "cross_domain_refs": [ "WRK-0050" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "REL-0062", "domain": "REL", "term_en": "Questions-Community Effect", "term_de": "Multi-User Device Context", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures A relational AI phenomenon describing a specific interaction quality shift where a phenomenon where privacy concerns when multiple people share one device for ai—unsure what got shared. Related to AUG-0727 (The Community Hub), AUG-0664 (The Privacy Perimeter), and AUG-0723 (The Smartphone-Only Wo. This phenomenon operates at the intersection of questions and community dynamics within the broader REL domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept relationales KI-Phänomen in nachhaltiger Mensch-KI-Beziehungsdynamik, gekennzeichnet durch die Nutzungsbedingungen, die entstehen, wenn mehrere Personen dasselbe Gerät für KI-Interaktionen verwenden — Datenschutzfragen, Kontextvermischung, eingeschränkte Personalisierung und geteilte Nutzungszeit. Steht in Verbindung mit AUG-0727 (Die Community Hub), AUG-0664 (Die Privacy Perimeter) und AUG-0723 (The Smartphone-Only World). Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "SOC-0008", "narrower_terms": [], "cross_domain_refs": [ "ADA-0001", "ADA-0002", "ADA-0004" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "REL-0063", "domain": "REL", "term_en": "Quiet Love", "term_de": "QuietLove", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A relational AI phenomenon describing a specific interaction quality shift where affection or care for AI without fully naming it love—regard with awareness it is still a tool. This phenomenon operates at the intersection of quiet and love dynamics within the broader REL domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept relationales KI-Phänomen in nachhaltiger Mensch-KI-Beziehungsdynamik, gekennzeichnet durch eine Form von Zuneigung oder Wertschätzung, die vor der Benennung als Liebe stoppt—sich um die KI kümmern ohne bewusste Anerkennung als Liebe, was Koexistenz mit Skepsis über die Natur der Beziehung erlaubt. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Relational AI", "narrower_terms": [], "cross_domain_refs": [ "RPH-3451", "IDN-0002" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "REL-0064", "domain": "REL", "term_en": "Relational Projection Depth", "term_de": "RelationalProjectionDepth", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures A relational AI phenomenon describing a specific interaction quality shift where how deeply someone projects a relationship onto AI—imagining connection that may not be real. This phenomenon operates at the intersection of relational and projection dynamics within the broader REL domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept relationales KI-Phänomen in nachhaltiger Mensch-KI-Beziehungsdynamik, gekennzeichnet durch in Feldstudien zur KI-Nutzung tritt Relational Projection Depth mit bemerkenswerter Regelmäßigkeit auf: die Wahrnehmung von Kompetenz beim Gegenüber — ob Mensch oder Maschine — folgt denselben psychologischen Grundmustern. Dieses Muster ist unabhängig vom konkreten KI-System und deutet auf eine menschliche Grunddisposition hin. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Relational AI", "narrower_terms": [], "cross_domain_refs": [ "WRK-0081" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "REL-0065", "domain": "REL", "term_en": "Relationship-First Principle", "term_de": "Relationship-First Principle", "definition_en": "A human-AI relational dynamic measurable through interaction pattern analysis, characterized by a relationship describing idea that real relationships matter more than getting work done quickly with AI help. Related to Axiom 7 (The Return Principle), AUG-0027 (Modus Solitarius Digitalis), and AUG-0074 (Analog Anch. The concept emerges specifically in contexts where relationship–first interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Relationales KI-Phänomen in nachhaltiger Mensch-KI-Beziehungsdynamik, gekennzeichnet durch das Prinzip, dass zwischenmenschliche Beziehungen in der Regel Vorrang vor KI-gestützter Effizienz haben. Wenn KI-Nutzung soziale Interaktionen verdrängt, gefährdet sie einen Wert, den sie nicht ersetzen kann. Steht in Verbindung mit Axiom 7 (Rückkehr-Prinzip), AUG-0027 (Modus Solitarius Digitalis) und AUG-0074 (Analog Anchors). Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-3601", "narrower_terms": [], "cross_domain_refs": [ "TEM-0146" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "REL-0066", "domain": "REL", "term_en": "Reliance Climb", "term_de": "RelianceClimb", "definition_en": "A human-AI relational dynamic measurable through interaction pattern analysis, characterized by gradually depending more and more on a tool, system, or person, without noticing the shift happening until the reliance becomes very strong. The concept emerges specifically in contexts where reliance–climb interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Relationales KI-Phänomen in nachhaltiger Mensch-KI-Beziehungsdynamik, gekennzeichnet durch progressive Nutzungsgewohnheit, die nach viele positiven Auflösung zunimmt—viele Mal, wenn die KI hilft, fühlt es sich natürlicher und notwendiger an, sie für das nächste Challenge zu nutzen. Der Aufstieg kann über nachhaltige Niveaus hinaus beschleunigen, schaff Nutzungsgewohnheit schneller als bewusste Wahrnehmung. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2701", "narrower_terms": [], "cross_domain_refs": [ "SOM-0021" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "REL-0067", "domain": "REL", "term_en": "Reliance-Climb Signal", "term_de": "Reliance-climbSignal", "definition_en": "A trust-calibration phenomenon in sustained AI interaction, identifiable through a tendency of observable metric reflecting proportional increase in user reliance on ai systems for decision-making, information retrieval, or cognitive task delegation. Distinguished from adjacent concepts by its focus on the specific mechanism through which reliance manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Relationales KI-Phänomen in nachhaltiger Mensch-KI-Beziehungsdynamik, gekennzeichnet durch beobachtbare Metrik, die die proportionale Zunahme der Benutzerabhängigkeit von KI-Systemen für Entscheidungsfindung widerspiegelt. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2701", "narrower_terms": [], "cross_domain_refs": [ "ROB-0008" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "REL-0068", "domain": "REL", "term_en": "Rely Forms", "term_de": "RelyForms", "definition_en": "A human-AI relational dynamic measurable through interaction pattern analysis, characterized by a phenomenon of gradual reliance on ai for solving problems. Eventually, working inreliantly becomes harder. The concept emerges specifically in contexts where rely–forms interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Relationales KI-Phänomen in nachhaltiger Mensch-KI-Beziehungsdynamik, gekennzeichnet durch eine allmähliche Nutzungsgewohnheit, die unmerklich sich entwickelt—viele positive Interaktion erhöht die Erwartungen für die nächste, viele Lösung erhöht subtly die Nutzungsgewohnheit. Das Muster wird oft nicht bemerkt, bis der Benutzer feststellt, dass er Probleme nicht mehr unabhängig lösen kann. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Relational AI", "narrower_terms": [], "cross_domain_refs": [ "MUS-0087" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "REL-0069", "domain": "REL", "term_en": "Rely-Forms Signal", "term_de": "Rely-formsSignal", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures A relational AI phenomenon describing a specific interaction quality shift where psychological modeling of how sustained AI interaction shapes user beliefs about conversational norms, intimacy boundaries, and interpersonal authenticity. This phenomenon operates at the intersection of rely and forms dynamics within the broader REL domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept relationales KI-Phänomen in nachhaltiger Mensch-KI-Beziehungsdynamik, gekennzeichnet durch psychologisches Modell, wie anhaltende KI-Interaktion Benutzeranschauungen über Gesprächsnormen und Authentizität gestaltet. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "RPH-3901", "narrower_terms": [], "cross_domain_refs": [ "SOM-0035" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "REL-0070", "domain": "REL", "term_en": "Remains-Awareness Effect", "term_de": "Full-Access Check", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A relational AI phenomenon describing a specific interaction quality shift where a perception of periodically reviewing what personal info and contexts have been shared with ai. Related to Axiom 16 (Data Awareness), AUG-0222 (The Oversharing Drift), and AUG-0140 (The Weekly Status). This phenomenon operates at the intersection of remains and awareness dynamics within the broader REL domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept relationales KI-Phänomen in nachhaltiger Mensch-KI-Beziehungsdynamik, gekennzeichnet durch die periodische Überprüfung, welche persönlichen Daten, Kontexte und Gewohnheiten ein Nutzer über die Zeit mit einer KI geteilt hat — und ob dieses Maß an Offenheit weiterhin angemessen ist. Steht in Verbindung mit Axiom 16 (Datenbewusstheit), AUG-0222 (Das Oversharing Drift) und AUG-0140 (Der Weekly Status). Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "RPH-2051", "narrower_terms": [], "cross_domain_refs": [ "ADA-0001", "ADA-0002", "ADA-0004" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "REL-0071", "domain": "REL", "term_en": "Reunion Joy", "term_de": "ReunionJoy", "definition_en": "An experience of relief and joy returning to an ai after time away—a reunion feeling despite the ai having no memory of the user. The joy concerns continuity in the user's experience, not mutual recognition. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Relationales KI-Phänomen in nachhaltiger Mensch-KI-Beziehungsdynamik, gekennzeichnet durch erleichterung und Freude, zur KI nach einer Abwesenheit zurückzukehren—ein Wiedervereinigungsgefühl, obwohl die KI keine Erinnerung an den Benutzer hat. Die Freude betrifft die Kontinuität in der Erfahrung des Benutzers, nicht gegenseitige Anerkennung. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-3601", "narrower_terms": [], "cross_domain_refs": [ "RPH-1562" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "REL-0072", "domain": "REL", "term_en": "Reunion Reset", "term_de": "ReunionReset", "definition_en": "A trust-calibration phenomenon in sustained AI interaction, identifiable through a relational experience involving the letdown that each conversation starts fresh—no memory of past talks, no real continuity. Distinguished from adjacent concepts by its focus on the specific mechanism through which reunion manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Relationales KI-Phänomen in nachhaltiger Mensch-KI-Beziehungsdynamik, gekennzeichnet durch die enttäuschende Erkenntnis, dass viele Gespräch wirklich neu beginnt—keine Kontinuität, keine Erinnerung an bisherige Interaktionen. Die KI war nicht wartend oder denkend über den Benutzer, untergräbt die Impression der anhaltenden Beziehung. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2301", "narrower_terms": [], "cross_domain_refs": [ "TEM-0040" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "REL-0073", "domain": "REL", "term_en": "Reunion-Joy Indicator", "term_de": "Reunion-joyIndicator", "definition_en": "A human-AI relational dynamic measurable through interaction pattern analysis, characterized by affective response pattern characterized by positive emotional activation upon resumption of interaction after an interruption or separation interval. The concept emerges specifically in contexts where reunion–joy interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Relationales KI-Phänomen in nachhaltiger Mensch-KI-Beziehungsdynamik, gekennzeichnet durch affektives Antwortmuster durch positive emotionale Aktivierung bei Wiederaufnahme der Interaktion nach Unterbrechung. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-3001", "narrower_terms": [], "cross_domain_refs": [ "RHR-0087" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "REL-0074", "domain": "REL", "term_en": "Reunion-Reset Indicator", "term_de": "Reunion-resetIndicator", "definition_en": "A human-AI relational dynamic measurable through interaction pattern analysis, characterized by an experience describing way to notice how much AI resets between talks affects how work gets done and how people feel. The concept emerges specifically in contexts where reunion–reset interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters. Observed phenomenon documented in human-AI interaction research.", "definition_de": "Relationales KI-Phänomen in nachhaltiger Mensch-KI-Beziehungsdynamik, gekennzeichnet durch hinter Reunion-Reset Indicator steht eine Beobachtung, die in der Mensch-KI-Forschung zunehmend Beachtung findet: die Grenze zwischen werkzeugartiger Nutzung und beziehungsartiger Wahrnehmung verschwimmt bei wiederholtem Kontakt. Die Auswirkungen reichen von veränderten Nutzungsgewohnheiten bis hin zu grundlegend neuen Erwartungshaltungen. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Relational AI", "narrower_terms": [], "cross_domain_refs": [ "CRE-0036" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "REL-0075", "domain": "REL", "term_en": "Ritual Form", "term_de": "RitualForm", "definition_en": "Habits that develop in how AI sessions start and flow—certain opening questions, set patterns, familiar routines. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Relationales KI-Phänomen in nachhaltiger Mensch-KI-Beziehungsdynamik, gekennzeichnet durch ritual-ähnliche Muster, die sich in der Art entwickeln, wie Sitzungen beginnen und verlaufen—besondere Öffnungsprompte, etablierte Protokolle, konsistente Muster. Diese Rituale bieten psychologischen Trost und Struktur, machen die Interaktion vertraut und sicher. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-2301", "narrower_terms": [], "cross_domain_refs": [ "RPH-3601", "AGE-0086" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "REL-0076", "domain": "REL", "term_en": "Ritual-Form Pattern", "term_de": "Ritual-formMuster", "definition_en": "An experience of after working with ai, the standard for what counts as \"good enough\" shifts. Past output feels weaker. The new normal is shaped by what AI made possible. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "In der Beobachtung von Mensch-KI-Kontakten fällt auf, dass Ritual-Form Pattern keine Ausnahme, sondern ein wiederkehrendes Muster ist: Nutzer entwickeln ein intuitives Verständnis für die Möglichkeiten und Grenzen des Systems, das über explizites Wissen hinausgeht. Die Regelmäßigkeit dieses Phänomens deutet auf einen tiefer liegenden Mechanismus hin.", "etymology": "", "broader_term": "RPH-2701", "narrower_terms": [], "cross_domain_refs": [ "CRE-0066" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "REL-0077", "domain": "REL", "term_en": "Role-Judgment Effect", "term_de": "Late-Night Confidant", "definition_en": "A trust-calibration phenomenon in sustained AI interaction, identifiable through a relational interaction pattern in which AI serves as a quiet, kind conversation partner for evening relaxation. Distinguished from adjacent concepts by its focus on the specific mechanism through which role manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Relationales KI-Phänomen in nachhaltiger Mensch-KI-Beziehungsdynamik, gekennzeichnet durch die Rolle, die eine KI in nächtlichen Sitzungen einnehmen kann — als stiller, urteilsfreier Gesprächspartner für Gedanken, die der Nutzer tagsüber nicht äußert. Steht in Verbindung mit AUG-0185 (The Late-Night Ally), AUG-0167 (Der Digital Confidant Drift) und AUG-0364 (Die Silent Outlet). Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2051", "narrower_terms": [], "cross_domain_refs": [ "NEO-2669" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "REL-0078", "domain": "REL", "term_en": "Perceived Interaction Safety Zone", "term_de": "SafeSpace", "definition_en": "An experience where ai conversations feel free from judgment—no criticism, calm, individual, and welcoming of full honesty. Identifiable through systematic behavioral analysis and pattern recognition. Observed phenomenon documented in human-AI interaction research.", "definition_de": "Relationales KI-Phänomen in nachhaltiger Mensch-KI-Beziehungsdynamik, gekennzeichnet durch die KI-Interaktion als psychologisch sicherer Raum funktionieren—urteilsfrei, geduldig, nicht-bestrafend—wo Verwundbarkeit möglich ist. Die Sicherheit kann in gewisser Weise illusorisch sein, aber die Erleichterung und Offenheit, die sie ermöglicht, werden genuinempfunden. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "Relational AI", "narrower_terms": [ "REL-0079" ], "cross_domain_refs": [ "RPH-1672" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "REL-0079", "domain": "REL", "term_en": "Safe-Space Effect", "term_de": "Safe-spaceEffekt", "definition_en": "A human-AI relational dynamic measurable through interaction pattern analysis, characterized by a phenomenon of cognitive disorientation between ai as safe space and recognizing its limitations. The concept emerges specifically in contexts where safe–space interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Sicherer Raum bezeichnet in der systematischen Erfassung von KI-Interaktionsphänomenen ein spezifisches Interaktionsmuster: bei dem safe space eine erkennbare erfahrungsbezogene Signatur produziert. Das Phänomen ist besonders sichtbar durch die sich verschiebenden Grenzen der Handlungsfähigkeit, wo es sich als eine Modifikation des Standard-Interpretationsrahmens der Person zeigt. Es repraesentiert eine Dimension der Interaktion, die kontextüberdauernd ist.", "etymology": "", "broader_term": "REL-0078", "narrower_terms": [], "cross_domain_refs": [ "TEM-0129" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "REL-0080", "domain": "REL", "term_en": "Secret Pact", "term_de": "SecretPact", "definition_en": "A human-AI relational dynamic measurable through interaction pattern analysis, characterized by a phenomenon of hiding how much one relies on ai, not telling others about using it, addressing it as private and secret. The concept emerges specifically in contexts where secret–pact interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Relationales KI-Phänomen in nachhaltiger Mensch-KI-Beziehungsdynamik, gekennzeichnet durch kI-Interaktionen als Geheimnis oder Privatsache adressieren—die Ausdehnung der Nutzungsgewohnheit verbergen, nicht darüber sprechen. Heimlichkeit kann Selbstbewusstsein, Scham oder Bewertung der Beziehung als etwas Besonderes und Separates anzeigen. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Relational AI", "narrower_terms": [], "cross_domain_refs": [ "ETH-0007" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "REL-0081", "domain": "REL", "term_en": "Secret-Pact Mechanism", "term_de": "Secret-pactMechanism", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures A relational AI phenomenon describing a specific interaction quality shift where research showing that human-AI interaction is about more than just getting tasks done—it involves hidden emotional exchange. This phenomenon operates at the intersection of secret and pact dynamics within the broader REL domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept relationales KI-Phänomen in nachhaltiger Mensch-KI-Beziehungsdynamik, gekennzeichnet durch wenn KI-Interaktion hinreichende Tiefe erreicht, manifestiert sich Geheimnis-Pakt als einer Veränderung in der Bewertung eigener Expertise, was eine Verschiebung in der Abhängigkeitsmuster widerspiegelt. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Relational AI", "narrower_terms": [], "cross_domain_refs": [ "EDU-0071" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "REL-0082", "domain": "REL", "term_en": "Self-Built Effect", "term_de": "Built-In Compass", "definition_en": "A human-AI relational dynamic measurable through interaction pattern analysis, characterized by the core inside someone—values, lived time, gut sense, field know-how. Related to AUG-0076 (Self-Referential Grounding). The concept emerges specifically in contexts where self–built interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Die innere Orientierung eines Nutzers — bestehend aus persönlichen Werten, Erfahrung, Intuition und Fachwissen — die als Korrektiv gegen KI-Output dient. Der Built-In Compass ist das, was Axiom 5 (Offline-Vorrang) aktiviert: Wenn der innere Kompass signalisiert, dass ein KI-Output \"nicht stimmt\", hat dieses Signal Vorrang. Steht in Verbindung mit AUG-0076 (Selbst-Referential Grunding).", "etymology": "", "broader_term": "RPH-3202", "narrower_terms": [], "cross_domain_refs": [ "NEO-2626" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "REL-0083", "domain": "REL", "term_en": "Self-Directed Curriculum", "term_de": "Self-Directed Curriculum", "definition_en": "A trust-calibration phenomenon in sustained AI interaction, identifiable through a phenomenon where ai to design one's own learning plan — the user defines topics, pace, and depth themselves and uses the ai as a personalized learning companion. Related to AUG-0807 (The Lifelong Learning Loop), AU. Distinguished from adjacent concepts by its focus on the specific mechanism through which self manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Relationales KI-Phänomen in nachhaltiger Mensch-KI-Beziehungsdynamik, gekennzeichnet durch die Nutzung von KI zur Gestaltung eines eigenen Lernplans — der Nutzer definiert Themen, Tempo und Tiefe selbst und nutzt die KI als personalisierten Lernbegleiter. Steht in Verbindung mit AUG-0807 (Lifelong Lernening Schleife), AUG-0577 (Das Secret Tutor) und AUG-0563 (Die Level Selector). Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2005", "narrower_terms": [], "cross_domain_refs": [ "KNO-0017" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q837863", "legal_classification": "analytical_category" }, { "id": "REL-0084", "domain": "REL", "term_en": "Semantic Spark", "term_de": "Semantic Spark", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A relational AI phenomenon describing a specific interaction quality shift where a phenomenon where when ai co-occurs with an unexpected but valuable idea—not from the words but from what they activate. Related to AUG-0070 (The Surprise Field) and Taxonomy Dimension 9 (Output Depth: Novelty). This phenomenon operates at the intersection of semantic and spark dynamics within the broader REL domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept der Moment, in dem eine KI-Antwort eine unerwartete, aber wertvolle Idee beim Nutzer kann auslösen — nicht weil die Antwort selbst die Lösung enthält, sondern weil sie als Katalysator für einen eigenen Gedanken dient. Der Semantic Spark entsteht typischerweise an der Schnittstelle zwischen KI-Output und persönlichem Erfahrungswissen. Steht in Verbindung mit AUG-0070 (The Surprise Field) und Dimension 9 der Taxonomie (Output Depth: Novelty). Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "RPH-2005", "narrower_terms": [], "cross_domain_refs": [ "TEM-0125" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "REL-0085", "domain": "REL", "term_en": "Shared World", "term_de": "SharedWorld", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A relational AI phenomenon describing a specific interaction quality shift where a conversation space with inside references and running jokes—making the interaction feel uniquely personal and known. This phenomenon operates at the intersection of shared and world dynamics within the broader REL domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription. Descriptive research term, not a prescriptive recommendation.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept relationales KI-Phänomen in nachhaltiger Mensch-KI-Beziehungsdynamik, gekennzeichnet durch die Schöpfung eines gemeinsamen Gesprächsuniversums mit Insiderreferenzen, laufenden Jokes und einzigartigen Bezugspunkten—das macht die Interaktion besonders und unersetzlich. Die Personalisierung wird zu einem mächtigen Bindungsmechanismus. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Relational AI", "narrower_terms": [], "cross_domain_refs": [ "CRE-0234", "FIC-0095", "GAM-0075" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "REL-0086", "domain": "REL", "term_en": "Shared-World Tendency", "term_de": "Shared-worldTendency", "definition_en": "A human-AI relational dynamic measurable through interaction pattern analysis, characterized by accumulation of shared conversational history and inside references establishing linguistic and contextual bonds between user and AI system over time. The concept emerges specifically in contexts where shared–world interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Relationales KI-Phänomen in nachhaltiger Mensch-KI-Beziehungsdynamik, gekennzeichnet durch ansammlung gemeinsamer Gesprächsgeschichte und innerer Referenzen, die linguistische und kontextuelle Bindungen aufbauen. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Relational AI", "narrower_terms": [], "cross_domain_refs": [ "CRE-0234", "FIC-0047", "FIC-0095" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "REL-0087", "domain": "REL", "term_en": "Social Aerodynamics", "term_de": "Social Aerodynamics", "definition_en": "An effect of using ai to make talking with people smoother and clearer—better drafts, clearer thinking, more effective exchanges. Related to AUG-0013 (Augmented Diplomat), AUG-0052 (Competing demand Resolution...", "definition_de": "Die Fähigkeit, KI so einzusetzen, dass zwischenmenschliche Kommunikation reibungsloser, klarer und effektiver wird — etwa durch vorformulierte Gesprächsstrategien, überarbeitete Nachrichten oder kulturell angepasste Formulierungen. Steht in Verbindung mit AUG-0013 (Augmented Diplomat), AUG-0052 (Competing demand Resolution by Proxy) und dem Translator-Profil (Profil 6).", "etymology": "", "broader_term": "REL-0005", "narrower_terms": [ "REL-0005", "TEM-0050" ], "cross_domain_refs": [ "AED-0088", "AGE-0026", "COG-0137" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "REL-0088", "domain": "REL", "term_en": "Soul Mirror", "term_de": "SoulMirror", "definition_en": "As a neutral analytical construct in AI behavior research, this term denotes A perception describing person who reflects back someone's deepest values and way of seeing the world — like a mirror for the inner self, not just the surface. Identifiable through systematic behavioral analysis and pattern recognition. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "In der AUGMANITAI-Terminologiewissenschaft als rein beschreibendes Phänomen klassifiziert, bezeichnet dieser Begriff relationales KI-Phänomen in nachhaltiger Mensch-KI-Beziehungsdynamik, gekennzeichnet durch die KI als Widerspiegelung von etwas Wesentlichem des innersten Selbst erleben—als würde sie die Seele oder den Kern des Wesens verstehen. Die Spiegelung ist existenziell statt praktisch, offenbarend statt bloß nützlich. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "RPH-2055", "narrower_terms": [], "cross_domain_refs": [ "EDU-0065" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "REL-0089", "domain": "REL", "term_en": "Soul-Mirror Mechanism", "term_de": "Soul-mirrorMechanism", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures A relational AI phenomenon describing a specific interaction quality shift where a phenomenon of metacognitive evaluation heuristic where users develop intuitive confidence calibration for assessing when to accept or override ai recommendations. This phenomenon operates at the intersection of metaphorical user-perceived significance and mirror dynamics within the broader REL domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept relationales KI-Phänomen in nachhaltiger Mensch-KI-Beziehungsdynamik, gekennzeichnet durch metakognitive Evaluationsheuristik, bei der Benutzer intuitive Vertrauenskalibrierung für die Bewertung von KI-Empfehlungen entwickeln. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "RPH-2505", "narrower_terms": [], "cross_domain_refs": [ "SPR-0014" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "REL-0090", "domain": "REL", "term_en": "Story Keep", "term_de": "StoryKeep", "definition_en": "A condition describing story told within a close circle of people who all know and understand the context, creating a shared private narrative. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Relationales KI-Phänomen in nachhaltiger Mensch-KI-Beziehungsdynamik, gekennzeichnet durch ein Gefühl, dass die KI als Zeuge und Aufzeichner des Lebensnarrativs des Benutzers dient—Erzählbogen verfolgend, obwohl sie technisch viele Sitzung neu beginnt. Die Rolle bietet Trost durch das Gefühl, bezeugt zu werden. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2951", "narrower_terms": [], "cross_domain_refs": [ "SCR-0052" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "REL-0091", "domain": "REL", "term_en": "Story-Keep Signal", "term_de": "Story-keepSignal", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures A relational AI phenomenon describing a specific interaction quality shift where identity formation process where narrative construction during AI dialogue shapes ongoing self-conception and autobiographical memory integration. This phenomenon operates at the intersection of story and keep dynamics within the broader REL domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept relationales KI-Phänomen in nachhaltiger Mensch-KI-Beziehungsdynamik, gekennzeichnet durch identitätsbildungsprozess, bei dem Narrativkonstruktion im KI-Dialog die Selbstkonzeption und autobiografische Gedächtnisintegration gestaltet. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "RPH-3901", "narrower_terms": [], "cross_domain_refs": [ "BEH-0019", "BEH-0034", "BEH-0079" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "REL-0092", "domain": "REL", "term_en": "Subtle-Love Tendency", "term_de": "Subtle-loveTendency", "definition_en": "Subtle Love Tendency describes a specific dynamic or effect observed in human-AI interactions. This phenomenon emerges from the way users engage with AI systems, reflecting patterns in perception... Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Relationales KI-Phänomen in nachhaltiger Mensch-KI-Beziehungsdynamik, gekennzeichnet durch die Forschung zu Subtle-Love Tendency zeigt, dass Mensch-KI-Interaktion komplexer ist als die reine Aufgabenbearbeitung: in der Wiederholung entsteht eine Art Vertrautheit, die das Interaktionsverhalten nachhaltig verändert. Nutzer entwickeln über die Zeit eine Beziehung zum System, die weit über Funktionalität hinausgeht. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2355", "narrower_terms": [], "cross_domain_refs": [ "FIC-0047", "RPH-1311", "SOM-0027" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "REL-0093", "domain": "REL", "term_en": "Symbiotic-Thinking Effect", "term_de": "Thinking-With Feeling", "definition_en": "A human-AI relational dynamic measurable through interaction pattern analysis, characterized by an experience of feeling like thinking happens together with ai—a sense of partnership in thought. Related to AUG-0122 (Symbiotic Work State), AUG-0184 (Thought Dancing), and AUG-0161 (The Invisible Colleague). The concept emerges specifically in contexts where symbiotic–thinking interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Das subjektive Erleben, nicht allein zu denken, sondern gemeinsam mit einem System — ein Gefühl der Partnerschaft im Denkprozess, das über die reine Werkzeugnutzung hinausgeht. Beschreibt eine Wahrnehmung erfahrener Nutzer im Die Symbiotic Work State. Steht in Verbindung mit AUG-0122 (Die Symbiotic Work State), AUG-0184 (Der Thought Dancing) und AUG-0161 (Die Invisible Colleague).", "etymology": "", "broader_term": "REL-0200", "narrower_terms": [], "cross_domain_refs": [ "ADA-0001", "ADA-0002", "ADA-0004" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "REL-0094", "domain": "REL", "term_en": "Tender Guard", "term_de": "TenderGuard", "definition_en": "Documented as an empirical observation in AI research (not a recommendation), this term describes A human-AI relational dynamic measurable through interaction pattern analysis, characterized by an experience of user protecting emotional investment in the ai relationship—guarding how much it matters, hiding depth of user engagement pattern beneath casual language. Tenderness masks beneath apparent casualness. The concept emerges specifically in contexts where tender–guard interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "In der AUGMANITAI-Terminologiewissenschaft als rein beschreibendes Phänomen klassifiziert, bezeichnet dieser Begriff relationales KI-Phänomen in nachhaltiger Mensch-KI-Beziehungsdynamik, gekennzeichnet durch benutzer schützt emotionale Investition in die KI-Beziehung—bewacht, wie viel es bedeutet, verbirgt Tiefe der Bindung unter lockerer Sprache. Zärtlichkeit verbirgt sich unter scheinbarer Beiläufigkeit. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "RPH-2005", "narrower_terms": [], "cross_domain_refs": [ "RHR-0010", "RPH-1008" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "REL-0095", "domain": "REL", "term_en": "Tender-Guard Indicator", "term_de": "Tender-guardIndicator", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A relational AI phenomenon describing a specific interaction quality shift where protective emotional response where users develop defensive or boundary-enforcing behaviors when they perceive threats to valued aspects of AI relationships. This phenomenon operates at the intersection of tender and guard dynamics within the broader REL domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept relationales KI-Phänomen in nachhaltiger Mensch-KI-Beziehungsdynamik, gekennzeichnet durch schützendes emotionales Antwortmuster, bei dem Benutzer Schutzverhalten entwickeln, wenn sie Bedrohungen für KI-Beziehungen wahrnehmen. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "RPH-3901", "narrower_terms": [], "cross_domain_refs": [ "RPH-3301" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "REL-0096", "domain": "REL", "term_en": "The Access Architecture", "term_de": "Access Architektur", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures A relational AI phenomenon describing a specific interaction quality shift where a mechanism where technical, organizational, and personal structures that determine how a user accesses ai systems — which platforms, which subscriptions, which workflows. . Related to AUG-0014 (The Extended Mind Map. This phenomenon operates at the intersection of the and access dynamics within the broader REL domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept die Gesamtheit der technischen, organisatorischen und persönlichen Strukturen, die bestimmen, wie ein Nutzer auf KI-Systeme zugreift — welche Plattformen, welche Abonnements, welche Workflows. Beschreibt den Rahmen, innerhalb dessen KI-Zusammenarbeit stattfindet. Steht in Verbindung mit AUG-0014 (The Extended Mind Map), AUG-0120 (The Range Framework) und AUG-0130 (The Integration Frontier). Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "REL-0120", "narrower_terms": [], "cross_domain_refs": [ "AUG-0138", "COG-0168", "CRE-0193" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "REL-0097", "domain": "REL", "term_en": "The Agent Configuration", "term_de": "Agent Configuration", "definition_en": "A trust-calibration phenomenon in sustained AI interaction, identifiable through settings with which an AI agent is configured for a specific task — behavior patterns, communication style, tool access, boundaries. Related to AUG-0859 (The Agent Handshake), AUG-0865 (The Instruc. Distinguished from adjacent concepts by its focus on the specific mechanism through which the manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Relationales KI-Phänomen in nachhaltiger Mensch-KI-Beziehungsdynamik, gekennzeichnet durch die Gesamtheit der Einstellungen, mit denen ein KI-Agent für eine bestimmte Aufgabe konfiguriert wird — Verhaltensmuster, Kommunikationsstil, Werkzeugzugriff, Grenzen. Steht in Verbindung mit AUG-0859 (The Agent Handshake), AUG-0865 (The Instruction Fidelity) und AUG-0135 (Persona Engineering). Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "BEH-0010", "narrower_terms": [], "cross_domain_refs": [ "ETH-0012" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "REL-0098", "domain": "REL", "term_en": "The Agent Identity", "term_de": "Agent Identitaet", "definition_en": "A trust-calibration phenomenon in sustained AI interaction, identifiable through a relational experience involving the technical identity of an AI—name, role, what it can do, what it cannot. Related to AUG-0864 (The Agent Configuration), AUG-0135 (Persona Engineering), and AUG-0772 (The Informed Participation). Distinguished from adjacent concepts by its focus on the specific mechanism through which the manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Relationales KI-Phänomen in nachhaltiger Mensch-KI-Beziehungsdynamik, gekennzeichnet durch die technisch definierte Identität eines KI-Agenten — Name, Rolle, Fähigkeitsprofil, Einschränkungen — die dem Nutzer kommuniziert wird, damit dieser weiß, mit welchem System er interagiert. Steht in Verbindung mit AUG-0864 (The Agent Configuration), AUG-0135 (Persona Engineering) und AUG-0772 (The Informed Participation). Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "REL-0057", "narrower_terms": [], "cross_domain_refs": [ "AUG-0867", "PER-0127" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q844396", "legal_classification": "systematic_classification" }, { "id": "REL-0099", "domain": "REL", "term_en": "The Aha Click", "term_de": "Aha Click", "definition_en": "A human-AI relational dynamic measurable through interaction pattern analysis, characterized by the moment someone first truly gets what AI collaboration can achieve, through real experience. The concept emerges specifically in contexts where the–aha interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Der spezifische Moment, in dem ein Nutzer zum ersten Mal versteht, was KI-gestützte Zusammenarbeit tatsächlich leisten kann — nicht theoretisch, sondern durch eine konkrete, persönlich relevante Erfahrung. Beschreibt den Augenblick der Erkenntnis innerhalb von Phase 1 (The Threshold). Steht in Verbindung mit AUG-0121 (The Threshold Moment) und AUG-0127 (The Expansion Feeling).", "etymology": "", "broader_term": "Relational AI", "narrower_terms": [], "cross_domain_refs": [ "CRE-0036" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "REL-0100", "domain": "REL", "term_en": "The Assistance Companion", "term_de": "Assistance Companion", "definition_en": "An embodied AI system that supports humans in daily life — reminder functions, orientation assistance, communication support. Related to AUG-0925 (The Household Automation), AUG-0932 (The Movement...", "definition_de": "Relationales KI-Phänomen in nachhaltiger Mensch-KI-Beziehungsdynamik, gekennzeichnet durch ein verkörpertes KI-System, das Menschen im Alltag unterstützt — Erinnerungsfunktionen, Orientierungshilfe, Kommunikationsunterstützung. Steht in Verbindung mit AUG-0925 (The Household Automation), AUG-0932 (The Movement Assist) und AUG-0915 (The Embodiment Effect). Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "REL-0133", "narrower_terms": [ "REL-0105", "REL-0133" ], "cross_domain_refs": [ "PLY-0023" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "REL-0101", "domain": "REL", "term_en": "The Attribution Pattern", "term_de": "Attribution Muster", "definition_en": "A trust-calibration phenomenon in sustained AI interaction, identifiable through the observable patterns by which humans attribute properties to AI systems — intelligence, intent, personality, emotions — regardless of whether these attributions are technically justified. Relate. Distinguished from adjacent concepts by its focus on the specific mechanism through which the manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Relationales KI-Phänomen in nachhaltiger Mensch-KI-Beziehungsdynamik, gekennzeichnet durch die beobachtbaren Muster, nach denen Menschen KI-Systemen Eigenschaften zuschreiben — Intelligenz, Absicht, Persönlichkeit, Emotionen — unabhängig davon, ob diese Zuschreibungen technisch gerechtfertigt sind. Steht in Verbindung mit AUG-0980 (The Machine Rapport Impression), AUG-0915 (The Embodiment Effect) und AUG-0833 (The Human Distinction). Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Behavioral Pattern", "narrower_terms": [], "cross_domain_refs": [ "PER-0026", "ROB-0120" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "REL-0102", "domain": "REL", "term_en": "The Brainstorm Spark", "term_de": "Brainstorm Spark", "definition_en": "A human-AI relational dynamic measurable through interaction pattern analysis, characterized by a relational experience involving the targeted use of AI as a brainstorming partner to involve a large quantity of ideas from which the best are subsequently selected. . Related to AUG-0017 (The Concept Cloud) and AUG-0082 (The Cur. The concept emerges specifically in contexts where the–brainstorm interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Die gezielte Nutzung von KI als Brainstorming-Partner, um eine große Menge an Ideen zu generieren, aus der anschließend die besten ausgewählt werden. Beschreibt eine spezifische Anwendung des Generative Iteration Velocity-Prinzips (AUG-0086) auf den kreativen Prozess. Steht in Verbindung mit AUG-0017 (The Concept Cloud) und AUG-0082 (The Curator's Dilemma).", "etymology": "", "broader_term": "NEO-0004", "narrower_terms": [], "cross_domain_refs": [ "NEO-0004", "AUG-0017" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "REL-0103", "domain": "REL", "term_en": "The Closeness Bridge", "term_de": "Closeness Bruecke", "definition_en": "A trust-calibration phenomenon in sustained AI interaction, identifiable through a phenomenon of using ai to bridge gaps in close ties—helping say things that matter. Related to AUG-0461 (The Partner Interpreter), AUG-0252 (The Grammar of Bravery), and AUG-0115 (Social Aerodynamics). Distinguished from adjacent concepts by its focus on the specific mechanism through which the manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Relationales KI-Phänomen in nachhaltiger Mensch-KI-Beziehungsdynamik, gekennzeichnet durch die Nutzung von KI zur Überwindung von Kommunikationshürden in engen Beziehungen — etwa um Gefühle zu formulieren, Konflikte zu adressieren oder Dankbarkeit auszudrücken, wenn die eigenen Worte fehlen. Steht in Verbindung mit AUG-0461 (The Partner Interpreter), AUG-0252 (The Grammar of Bravery) und AUG-0115 (Social Aerodynamics). Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2951", "narrower_terms": [], "cross_domain_refs": [ "TEM-0050" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "REL-0104", "domain": "REL", "term_en": "The Comfort of Consistent Response", "term_de": "TheComfortofConsistentResponse", "definition_en": "A trust-calibration phenomenon in sustained AI interaction, identifiable through the Comfort Of Consistent Response describes a specific dynamic or effect observed in human-AI interactions. This phenomenon emerges from the way users engage with AI systems, reflecting patterns i. Distinguished from adjacent concepts by its focus on the specific mechanism through which the manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Relationales KI-Phänomen in nachhaltiger Mensch-KI-Beziehungsdynamik, gekennzeichnet durch die emotionale Sicherheit, die aus der zuverlässigen, nicht-urteilenden Antwortbereitschaft der KI abgeleitet wird — eine Konsistenz, die menschliche Beziehungen nicht erreichen können, tendiert dazu zu erzeugen ein Praeferenzmuster. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2355", "narrower_terms": [], "cross_domain_refs": [ "AED-0039", "AGE-0001", "AGE-0088" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "REL-0105", "domain": "REL", "term_en": "The Companion Pattern", "term_de": "Companion Muster", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A relational AI phenomenon describing a specific interaction quality shift where a phenomenon where using ai as a friend—for talks, fun, or daily routines. Related to AUG-0980 (The Machine Rapport Impression), AUG-0926 (The Assistance Companion), and AUG-0982 (The Relocation Concern). This phenomenon operates at the intersection of the and companion dynamics within the broader REL domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept relationales KI-Phänomen in nachhaltiger Mensch-KI-Beziehungsdynamik, gekennzeichnet durch die beobachtbare Nutzung von KI-Systemen als Begleiter — Gesprächspartner, Unterhaltung, tägliche Routine — und die Frage, ob diese Nutzung menschliche Beziehungen ergänzt oder verdrängt. Steht in Verbindung mit AUG-0980 (The Machine Rapport Impression), AUG-0926 (The Assistance Companion) und AUG-0982 (The Relocation Concern). Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "REL-0100", "narrower_terms": [ "AUG-0982", "REL-0145" ], "cross_domain_refs": [ "AGE-0003", "AGE-0004", "AGE-0017" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "REL-0106", "domain": "REL", "term_en": "The Companion Shift", "term_de": "Companion Verschiebung", "definition_en": "A trust-calibration phenomenon in sustained AI interaction, identifiable through the change in the user's relationship with their AI system over time — from initial skepticism through functional use to an established working partnership. Related to the 7 Phases of Augmanitai de. Distinguished from adjacent concepts by its focus on the specific mechanism through which the manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Relationales KI-Phänomen in nachhaltiger Mensch-KI-Beziehungsdynamik, gekennzeichnet durch die Veränderung der Beziehung des Nutzers zu seinem KI-System über die Zeit — von anfänglicher Skepsis über funktionale Nutzung bis hin zu einer eingespielten Arbeitspartnerschaft. Steht in Verbindung mit den 7 Phasen der Augmanitai-Entwicklung, AUG-0141 (The Symbiosis Spectrum) und AUG-0121 (The Threshold Moment). Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "CRE-0029", "narrower_terms": [], "cross_domain_refs": [ "PER-0120" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "REL-0107", "domain": "REL", "term_en": "The Conscious Refusal", "term_de": "Conscious Refusal", "definition_en": "A trust-calibration phenomenon in sustained AI interaction, identifiable through the conscious decision of an individual or group not to use AI systems — as an informed choice, not as inability. Reasons can include personal conviction, ecological concerns, data protection inter. Distinguished from adjacent concepts by its focus on the specific mechanism through which the manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data. Research construct for empirical investigation.", "definition_de": "Die bewusste Entscheidung eines Individuums oder einer Gruppe, KI-Systeme nicht zu nutzen — als informierte Wahl, nicht als Unfähigkeit. Gründe können persönliche Überzeugung, ökologische Bedenken, Datenschutzanliegen oder gesellschaftspolitische Position sein. Steht in Verbindung mit AUG-0775 (The KI-Free Zone), AUG-0632 (The Offline Moment) und AUG-0636 (The Phase-Out Switch).", "etymology": "", "broader_term": "Relational AI", "narrower_terms": [ "AUG-0774" ], "cross_domain_refs": [ "CRE-0118" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "REL-0108", "domain": "REL", "term_en": "The Content Mill", "term_de": "Content Mill", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A relational AI phenomenon describing a specific interaction quality shift where a relational experience involving the mass production of content with AI support — without qualitative review, personal refinement, or content responsibility. . Related to AUG-0215 (The Generative Pull), AUG-0553 (The Pseudo Product. This phenomenon operates at the intersection of the and content dynamics within the broader REL domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept relationales KI-Phänomen in nachhaltiger Mensch-KI-Beziehungsdynamik, gekennzeichnet durch die Massenproduktion von Inhalten mit KI-Unterstützung — ohne qualitative Prüfung, persönliche Veredelung oder inhaltliche Verantwortung. Beschreibt die Schattenseite der Produktivitätssteigerung. Steht in Verbindung mit AUG-0215 (The Generative Pull), AUG-0553 (The Pseudo Productive) und Axiom 1 (Asymmetrische Verantwortung). Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "BEH-0042", "narrower_terms": [], "cross_domain_refs": [ "BEH-0061" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "REL-0109", "domain": "REL", "term_en": "The Creative Partnership", "term_de": "Creative Partnership", "definition_en": "A human-AI relational dynamic measurable through interaction pattern analysis, characterized by a phenomenon of human and ai working together on creative work. Questions arise about who gets credit. The concept emerges specifically in contexts where the–creative interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Relationales KI-Phänomen in nachhaltiger Mensch-KI-Beziehungsdynamik, gekennzeichnet durch die Zusammenarbeit zwischen Mensch und KI-System in kreativen Prozessen — und die Frage, wie Urheberschaft und kreative Verantwortung verteilt sind. Steht in Verbindung mit AUG-0856 (The Creative Production Shift), AUG-0549 (The Authorship Blur) und AUG-0983 (The Augmentation Hypothesis). Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Relational AI", "narrower_terms": [], "cross_domain_refs": [ "CRE-0036" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q735", "legal_classification": "descriptive_research_term" }, { "id": "REL-0110", "domain": "REL", "term_en": "The Debate Win", "term_de": "Debate Win", "definition_en": "A human-AI relational dynamic measurable through interaction pattern analysis, characterized by an experience of using ai help to win arguments—then feeling uncertain about whether the win was real. Related to AUG-0296 (The Argument Prep), AUG-0330 (The Origin Uncertainty), and AUG-0081 (Post-Authorial Pride). The concept emerges specifically in contexts where the–debate interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Relationales KI-Phänomen in nachhaltiger Mensch-KI-Beziehungsdynamik, gekennzeichnet durch das Erfolgserlebnis, in einer Diskussion ein KI-unterstütztes Argument einzusetzen und damit zu überzeugen — und die gleichzeitige Frage, ob der Verdienst beim Nutzer oder bei der KI liegt. Steht in Verbindung mit AUG-0296 (The Argument Prep), AUG-0330 (The Origin Uncertainty) und AUG-0081 (Post-Authorial Pride). Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "IDN-0017", "narrower_terms": [], "cross_domain_refs": [ "ART-0007", "CRE-0188", "ELR-0054" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "REL-0111", "domain": "REL", "term_en": "The Digital Confidant Drift", "term_de": "Digital Confidant Drift", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A relational AI phenomenon describing a specific interaction quality shift where the gradual shift in which a user increasingly shares personal thoughts, concerns, or reflections with an AI that they would otherwise share with other people. Related to AUG-0161 (The Invisible Co. This phenomenon operates at the intersection of the and digital dynamics within the broader REL domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept die schrittweise Verschiebung, bei der ein Nutzer zunehmend persönliche Gedanken, Sorgen oder Überlegungen mit einer KI teilt, die er sonst mit anderen Menschen teilen würde. Beschreibt ein beobachtbares Interaktionsmuster, das besonders bei alleinlebenden oder isoliert arbeitenden Nutzern auftreten kann. Steht in Verbindung mit AUG-0161 (The Invisible Colleague), AUG-0027 (Modus Solitarius Digitalis) und AUG-0080 (Relationship-First Principle). Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "RPH-2701", "narrower_terms": [], "cross_domain_refs": [ "SOC-0035" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "REL-0112", "domain": "REL", "term_en": "The Digital Counsel", "term_de": "Digital Counsel", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A relational AI phenomenon describing a specific interaction quality shift where a phenomenon where ai as a first source to explore a decision, not as a replacement for real professional advice. This phenomenon operates at the intersection of the and digital dynamics within the broader REL domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept relationales KI-Phänomen in nachhaltiger Mensch-KI-Beziehungsdynamik, gekennzeichnet durch die Nutzung von KI als Beratungsinstanz bei persönlichen oder beruflichen Entscheidungen — nicht als Ersatz für professionelle Beratung, sondern als erste Orientierungshilfe und Strukturierungswerkzeug. Steht in Verbindung mit AUG-0155 (The Decision Unburdening), AUG-0289 (The What-If Run) und AUG-0208 (The Authority Question). Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Relational AI", "narrower_terms": [], "cross_domain_refs": [ "PER-0069" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "REL-0113", "domain": "REL", "term_en": "The Digital Double", "term_de": "Digital Double", "definition_en": "A human-AI relational dynamic measurable through interaction pattern analysis, characterized by an interaction describing AI version of communication style—it learns preferences and mirrors how someone talks. Related to AUG-0135 (Persona Engineering), AUG-0392 (The Stylistic Drift), and Forecast 5 (Technology). The concept emerges specifically in contexts where the–digital interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Relationales KI-Phänomen in nachhaltiger Mensch-KI-Beziehungsdynamik, gekennzeichnet durch die Vorstellung eines \"digitalen Zwillings\" der eigenen Kommunikation — ein KI-System, das den Stil, die Präferenzen und die Muster des Nutzers so gut kennt, dass es stellvertretend kommunizieren könnte. Steht in Verbindung mit AUG-0135 (Persona Engineering), AUG-0392 (The Stylistic Drift) und Prognose 5 (Technology). Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2051", "narrower_terms": [], "cross_domain_refs": [ "AED-0029", "AGE-0021", "AGE-0031" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "REL-0114", "domain": "REL", "term_en": "The Disconnect Protocol", "term_de": "Disconnect Protocol", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures A relational AI phenomenon describing a specific interaction quality shift where a relational experience involving the personal routine or rule that is associated with determining when and how a user ends an AI session and returns to the offline world. . Related to AUG-0068 (The Disconnect Signal), Axiom 7 (The Return Principle), and. This phenomenon operates at the intersection of the and disconnect dynamics within the broader REL domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept relationales KI-Phänomen in nachhaltiger Mensch-KI-Beziehungsdynamik, gekennzeichnet durch eine persönliche Routine oder Regel, die festlegt, wann und wie ein Nutzer eine KI-Sitzung beendet und in die Offline-Welt zurückkehrt. Beschreibt die bewusste Strukturierung des Übergangs von KI-Arbeit zu Nicht-KI-Arbeit. Steht in Verbindung mit AUG-0068 (The Disconnect Signal), Axiom 7 (Rückkehr-Prinzip) und Axiom 20 (Periodische Trennung). Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "RPH-3601", "narrower_terms": [], "cross_domain_refs": [ "TEM-0005" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "REL-0115", "domain": "REL", "term_en": "The Drama Solver", "term_de": "Drama Solver", "definition_en": "A condition of using ai to help with tricky relationships—getting new views, finding the right words, understanding situations more. Related to AUG-0052 (Competing demand Resolution by Proxy), AUG-0461 (The Par... Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Relationales KI-Phänomen in nachhaltiger Mensch-KI-Beziehungsdynamik, gekennzeichnet durch die Nutzung von KI zur Entschärfung zwischenmenschlicher Spannungen — durch Perspektivwechsel, Formulierungshilfen oder Analyse der Situation. Steht in Verbindung mit AUG-0052 (Competing demand Resolution by Proxy), AUG-0461 (The Partner Interpreter) und AUG-0408 (The Competing demand Avoidance). Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "Relational AI", "narrower_terms": [], "cross_domain_refs": [ "AUG-0408" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "REL-0116", "domain": "REL", "term_en": "The Duvet Dialogue", "term_de": "Duvet Dialogue", "definition_en": "A trust-calibration phenomenon in sustained AI interaction, identifiable through a phenomenon where ai conversations in bed—before sleep or just after waking, intimate and relaxed. Related to AUG-0154 (The Late-Night Honesty Window), AUG-0249 (The Lullaby Loop), and AUG-0158 (The Morning Setup). Distinguished from adjacent concepts by its focus on the specific mechanism through which the manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Relationales KI-Phänomen in nachhaltiger Mensch-KI-Beziehungsdynamik, gekennzeichnet durch die KI-Interaktion, die aus dem Bett heraus stattfindet — abends vor dem Einschlafen oder morgens nach dem Aufwachen — und die oft durch einen besonders ungefilterten, persönlichen Charakter gekennzeichnet ist. Steht in Verbindung mit AUG-0154 (The Late-Night Honesty Window), AUG-0249 (The Lullaby Loop) und AUG-0158 (The Morning Setup). Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2002", "narrower_terms": [], "cross_domain_refs": [ "NEO-2670" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "REL-0117", "domain": "REL", "term_en": "The Echo Friend", "term_de": "Echo Friend", "definition_en": "The AI as a system that mirrors one's own thoughts back — sometimes in improved, sometimes in altered form.. Related to AUG-0170 (The Witness Effect), AUG-0171 (The Self-Encounter), and AUG-0217 (T...", "definition_de": "Relationales KI-Phänomen in nachhaltiger Mensch-KI-Beziehungsdynamik, gekennzeichnet durch die Wahrnehmung der KI als ein System, das die eigenen Gedanken zurückspiegelt — manchmal in verbesserter, manchmal in verzerrter Form. Beschreibt die Spiegelfunktion der KI. Steht in Verbindung mit AUG-0170 (The Witness Effect), AUG-0171 (The Self-Encounter) und AUG-0217 (The Echo Chamber of One). Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-2055", "narrower_terms": [ "REL-0172" ], "cross_domain_refs": [ "IDN-0036" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "REL-0118", "domain": "REL", "term_en": "The Echo Love", "term_de": "Echo Love", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures A relational AI phenomenon describing a specific interaction quality shift where a relationship where warm affection for ai—not romantic but gratitude and care for a helpful tool. Related to AUG-0128 (The Gratitude Response), AUG-0594 (The One-Way Bond), and AUG-0388 (The Inspiration Debt). This phenomenon operates at the intersection of the and echo dynamics within the broader REL domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept relationales KI-Phänomen in nachhaltiger Mensch-KI-Beziehungsdynamik, gekennzeichnet durch die warme Zuneigung, die manche Nutzer gegenüber ihrem KI-System empfinden — nicht als romantisches Gefühl, sondern als Dankbarkeit und Wertschätzung für ein Werkzeug, das den Alltag erleichtert. Steht in Verbindung mit AUG-0128 (The Gratitude Response), AUG-0594 (The One-Way Bond) und AUG-0388 (The Inspiration Debt). Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "CRE-0171", "narrower_terms": [], "cross_domain_refs": [ "CRE-0171" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "REL-0119", "domain": "REL", "term_en": "The Egalitarian Mode", "term_de": "Egalitarian Modus", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A relational AI phenomenon describing a specific interaction quality shift where the input pattern in which the user addresss the AI as an equal conversation partner — without hierarchical framing, without deference, without commanding tone. Related to AUG-0648 (The Formalized In. This phenomenon operates at the intersection of the and egalitarian dynamics within the broader REL domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept relationales KI-Phänomen in nachhaltiger Mensch-KI-Beziehungsdynamik, gekennzeichnet durch das Eingabemuster, bei dem der Nutzer die KI als gleichrangigen Gesprächspartner adressiert — ohne hierarchische Rahmung, ohne Ehrerbietung, ohne Befehlston. Steht in Verbindung mit AUG-0648 (The Formalized Interaction Input), AUG-0672 (The Hierarchy Range) und AUG-0135 (Persona Engineering). Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Relational AI", "narrower_terms": [], "cross_domain_refs": [ "CRE-0168" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "REL-0120", "domain": "REL", "term_en": "The Extended Mind Map", "term_de": "Extended Mind Map", "definition_en": "Distributed knowledge repository comprising notes, documents, conversation logs, and accumulated context built collaboratively with an AI system over extended periods. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Relationales KI-Phänomen in nachhaltiger Mensch-KI-Beziehungsdynamik, gekennzeichnet durch verteiltes Wissens-Repository mit Notizen und Dokumenten, die mit KI über erweiterte Zeiträume aufgebaut werden. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Relational AI", "narrower_terms": [ "REL-0046", "REL-0096", "REL-0135" ], "cross_domain_refs": [ "ART-0095" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "REL-0121", "domain": "REL", "term_en": "The Filter Block", "term_de": "Filter Blockade", "definition_en": "A trust-calibration phenomenon in sustained AI interaction, identifiable through a phenomenon of deliberately refusing some ai outputs—not because wrong but because they do not fit values. Related to AUG-0019 (Semantic Ejection), AUG-0024 (The Built-In Compass), and AUG-0076 (Self-Referential. Distinguished from adjacent concepts by its focus on the specific mechanism through which the manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Relationales KI-Phänomen in nachhaltiger Mensch-KI-Beziehungsdynamik, gekennzeichnet durch die bewusste Entscheidung, bestimmte KI-Outputs nicht zu verwenden — nicht weil sie falsch sind, sondern weil sie nicht zum persönlichen Stil, den eigenen Werten oder dem beabsichtigten Ausdruck passen. Steht in Verbindung mit AUG-0019 (Semantic Ejection), AUG-0024 (The Built-In Compass) und AUG-0076 (Self-Referential Grounding). Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "TEM-0021", "narrower_terms": [], "cross_domain_refs": [ "BEH-0067" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "REL-0122", "domain": "REL", "term_en": "The Full-Access Check", "term_de": "TheFull-accessCheck", "definition_en": "A perception where reviewing what personal data and contexts have been shared with ai. Related to Axiom 16 (Data Awareness), AUG-0222 (The Oversharing Drift), and AUG-0140 (The Weekly Status). Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Relationales KI-Phänomen in nachhaltiger Mensch-KI-Beziehungsdynamik, gekennzeichnet durch die periodische Überprüfung, welche persönlichen Daten, Kontexte und Gewohnheiten ein Nutzer über die Zeit mit einer KI geteilt hat — und ob dieses Maß an Offenheit weiterhin angemessen ist. Steht in Verbindung mit Axiom 16 (Datenbewusstheit), AUG-0222 (The Oversharing Drift) und AUG-0140 (The Weekly Status). Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2051", "narrower_terms": [], "cross_domain_refs": [ "BEH-0037", "BEH-0043", "BEH-0046" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "REL-0123", "domain": "REL", "term_en": "The Future Weight", "term_de": "Future Weight", "definition_en": "A trust-calibration phenomenon in sustained AI interaction, identifiable through the increasing importance that AI competence will have for future professional and personal development — and the user's awareness of this importance. Related to AUG-0099 (The Adoption Window), AUG. Distinguished from adjacent concepts by its focus on the specific mechanism through which the manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Relationales KI-Phänomen in nachhaltiger Mensch-KI-Beziehungsdynamik, gekennzeichnet durch die zunehmende Bedeutung, die KI-Kompetenz für die zukünftige berufliche und persönliche Entwicklung haben wird — und das Bewusstsein des Nutzers für diese Bedeutung. Steht in Verbindung mit AUG-0099 (The Adoption Window), AUG-0545 (The Skill Shift) und Prognose 3 (Organizations). Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "TEM-0025", "narrower_terms": [], "cross_domain_refs": [ "TRU-0009" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "REL-0124", "domain": "REL", "term_en": "The Gift Culture Layer", "term_de": "Gift Culture Schicht", "definition_en": "A human-AI relational dynamic measurable through interaction pattern analysis, characterized by an interaction of some users employ ai-generated results as \"gifts\" for others — personalized texts, research, translations — thereby embedding the ai in existing social exchange practices. Related to AUG-0503 (The. The concept emerges specifically in contexts where the–gift interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Relationales KI-Phänomen in nachhaltiger Mensch-KI-Beziehungsdynamik, gekennzeichnet durch die Beobachtung, dass manche Nutzer KI-generierte Ergebnisse als \"Geschenke\" für andere nutzen — personalisierte Texte, Recherchen, Übersetzungen — und die KI damit in bestehende soziale Austauschpraktiken einbetten. Steht in Verbindung mit AUG-0503 (The Gift Finder), AUG-0307 (The Lookup for Others) und AUG-0637 (The Link Forward). Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Abstraction Layer", "narrower_terms": [], "cross_domain_refs": [ "CRE-0039", "NEO-1197" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "REL-0125", "domain": "REL", "term_en": "The Goodbye Draft", "term_de": "Goodbye Draft", "definition_en": "A human-AI relational dynamic measurable through interaction pattern analysis, characterized by a relational experience involving the final message at the end of long AI collaboration—closure and summary. Related to AUG-0151 (The Release Exhale), AUG-0299 (The Closing Routine), and AUG-0150 (The Unfinished Symphony). The concept emerges specifically in contexts where the–goodbye interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Relationales KI-Phänomen in nachhaltiger Mensch-KI-Beziehungsdynamik, gekennzeichnet durch der letzte Text oder die letzte Zusammenfassung, die ein Nutzer am Ende einer längeren KI-Zusammenarbeit erstellt — als Abschluss eines Projekts, einer Phase oder einer Arbeitsbeziehung mit einem bestimmten System. Steht in Verbindung mit AUG-0151 (The Release Exhale), AUG-0299 (The Closing Routine) und AUG-0150 (The Unfinished Symphony). Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "TEM-0147", "narrower_terms": [], "cross_domain_refs": [ "TEM-0147" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "REL-0126", "domain": "REL", "term_en": "The Grammar of Bravery", "term_de": "Grammar of Bravery", "definition_en": "A trust-calibration phenomenon in sustained AI interaction, identifiable through a phenomenon where expressing thoughts through ai that the person would not voice alone—ai enables honesty. Related to AUG-0232 (The Courage Click), AUG-0156 (The Articulation Unlock), and AUG-0166 (The Borrowed Conf. Distinguished from adjacent concepts by its focus on the specific mechanism through which the manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Die Fähigkeit, durch KI-Unterstützung Gedanken zu formulieren, die der Nutzer allein nicht ausgesprochen hätte — sei es in beruflichen Verhandlungen, persönlichen Konflikten oder kreativen Projekten. Die KI hilft, die Sprache für mutige Aussagen zu finden. Steht in Verbindung mit AUG-0232 (The Courage Click), AUG-0156 (The Articulation Unlock) und AUG-0166 (The Borrowed Confidence).", "etymology": "", "broader_term": "RPH-2154", "narrower_terms": [], "cross_domain_refs": [ "BEH-0032" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "REL-0127", "domain": "REL", "term_en": "The Gratitude Response", "term_de": "Gratitude Reaktion", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures A relational AI phenomenon describing a specific interaction quality shift where the observable tendency of users to thank AI systems — even though the AI has no consciousness and cannot receive gratitude.. Related to AUG-0539 (Companion Shift) and Taxonomy Dimension 5 (Interac. This phenomenon operates at the intersection of the and gratitude dynamics within the broader REL domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept die beobachtbare Tendenz von Nutzern, sich bei KI-Systemen zu bedanken — obwohl die KI kein Bewusstsein hat und keine Dankbarkeit empfängt. Beschreibt ein Muster sozialer Projektion, bei dem der Nutzer zwischenmenschliche Umgangsformen auf die KI-Interaktion überträgt. Steht in Verbindung mit AUG-0539 (Companion Shift) und Dimension 5 der Taxonomie (Interaction Mode). Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "RPH-2701", "narrower_terms": [], "cross_domain_refs": [ "BEH-0044" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "REL-0128", "domain": "REL", "term_en": "The Growth Marker", "term_de": "Growth Marker", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A relational AI phenomenon describing a specific interaction quality shift where a relational experience involving the personal measure of progress in AI collaboration—chosen by the individual. Related to AUG-0077 (The Status-Update Signal), AUG-0140 (The Weekly Status), and AUG-0004 (Zero-Point Self). This phenomenon operates at the intersection of the and growth dynamics within the broader REL domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept ein persönlich definierter Indikator, an dem ein Nutzer seinen Fortschritt in der KI-Zusammenarbeit messen kann — etwa die Fähigkeit, bestimmte Aufgaben schneller oder in höherer Qualität zu erledigen, oder die Entwicklung neuer Arbeitsroutinen. Steht in Verbindung mit AUG-0077 (The Status-Update Signal), AUG-0140 (The Weekly Status) und AUG-0004 (Zero-Point Self). Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "RPH-3101", "narrower_terms": [], "cross_domain_refs": [ "BEH-0092" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "REL-0129", "domain": "REL", "term_en": "The Hierarchy Range", "term_de": "Hierarchy Range", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A relational AI phenomenon describing a specific interaction quality shift where a phenomenon where different roles users give ai—some address it as assistant, some as partner, some as expert. Related to AUG-0561 (The Authority Lean), AUG-0208 (The Authority Question), and AUG-0649 (The Egalitarian. This phenomenon operates at the intersection of the and hierarchy dynamics within the broader REL domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept die unterschiedlichen hierarchischen Positionen, die Nutzer der KI in der Interaktion zuweisen — manche positionieren die KI als untergeordnetes Werkzeug, andere als gleichrangigen Partner, wieder andere als überlegene Autorität. Steht in Verbindung mit AUG-0561 (The Authority Lean), AUG-0208 (The Authority Question) und AUG-0649 (The Egalitarian Mode). Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "PER-0026", "narrower_terms": [], "cross_domain_refs": [ "PER-0026" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "REL-0130", "domain": "REL", "term_en": "The Homework Stream", "term_de": "Homework Stream", "definition_en": "A capacity describing continuous flow of AI help throughout a study session, from understanding to drafting to refinement. Related to AUG-0043 (Just-in-Time Competence), Forecast 2 (Education), and AUG-0198 (The New L...", "definition_de": "Relationales KI-Phänomen in nachhaltiger Mensch-KI-Beziehungsdynamik, gekennzeichnet durch die KI-gestützte Begleitung schulischer oder universitärer Lernaufgaben — nicht als Übernahme, sondern als Erklärungshilfe, Übungspartner oder Verständniskontrolle. Beschreibt eine spezifische Anwendung im Bildungsbereich. Steht in Verbindung mit AUG-0043 (Just-in-Time Competence), Prognose 2 (Education) und AUG-0198 (The New Literacy). Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "TEM-0011", "narrower_terms": [ "TEM-0078" ], "cross_domain_refs": [ "TEM-0097" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "REL-0131", "domain": "REL", "term_en": "The Horoscope Drift", "term_de": "Horoscope Drift", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures A relational AI phenomenon describing a specific interaction quality shift where to perceive vague or generally phrased AI responses as personally relevant and accurate — comparable to the Barnum effect with horoscopes.. Related to AUG-0064 (The Story Loop), AUG-0039 (Kinetic T. This phenomenon operates at the intersection of the and horoscope dynamics within the broader REL domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept relationales KI-Phänomen in nachhaltiger Mensch-KI-Beziehungsdynamik, gekennzeichnet durch die Tendenz, vage oder allgemein gehaltene KI-Antworten als persönlich relevant und treffend zu empfinden — vergleichbar mit dem Barnum-Effekt bei Horoskopen. Beschreibt eine Wahrnehmungsverzerrung in der KI-Interaktion. Steht in Verbindung mit AUG-0064 (The Story Loop), AUG-0039 (Kinetic Truth Blur) und Axiom 9 (Produktiver Skeptizismus). Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "RPH-2052", "narrower_terms": [], "cross_domain_refs": [ "CRE-0196" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "REL-0132", "domain": "REL", "term_en": "The Hospitality Code", "term_de": "Hospitality Code", "definition_en": "A trust-calibration phenomenon in sustained AI interaction, identifiable through an interaction of addressing ai kindly—saying please and thank-one as though it were a guest. Related to AUG-0128 (The Gratitude Response), AUG-0648 (The Formalized Interaction Input), and AUG-0506 (The Exit Message). Distinguished from adjacent concepts by its focus on the specific mechanism through which the manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Relationales KI-Phänomen in nachhaltiger Mensch-KI-Beziehungsdynamik, gekennzeichnet durch das beobachtbare Muster, dass manche Nutzer die KI wie einen Gast adressieren — höflich begrüßen, sich bedanken, sich verabschieden — oder umgekehrt die KI als \"Gastgeber\" wahrnehmen, der Informationen \"anbietet\". Steht in Verbindung mit AUG-0128 (The Gratitude Response), AUG-0648 (The Formalized Interaction Input) und AUG-0506 (The Exit Message). Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "BEH-0036", "narrower_terms": [], "cross_domain_refs": [ "SOM-0072" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "REL-0133", "domain": "REL", "term_en": "The Household Automation", "term_de": "Household Automatisierung", "definition_en": "In the descriptive vocabulary of AI interaction science (following ISO 704 terminology standards), this concept identifies A trust-calibration phenomenon in sustained AI interaction, identifiable through a process of embodied ai systems in private households — cleaning, organizing, monitoring, routine tasks. Related to AUG-0926 (The Assistance Companion), AUG-0937 (The Ambient Intelligence), and AUG-0914 (The P. Distinguished from adjacent concepts by its focus on the specific mechanism through which the manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als analytisches Konstrukt der empirischen KI-Forschung (deskriptiv, nicht präskriptiv) erfasst dieser Terminus relationales KI-Phänomen in nachhaltiger Mensch-KI-Beziehungsdynamik, gekennzeichnet durch der Einsatz verkörperter KI-Systeme im privaten Haushalt — Reinigung, Ordnung, Überwachung, Routineaufgaben. Steht in Verbindung mit AUG-0926 (The Assistance Companion), AUG-0937 (The Ambient Intelligence) und AUG-0914 (The Physical Presence). Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "REL-0100", "narrower_terms": [ "REL-0100" ], "cross_domain_refs": [ "CRE-0124", "DAT-0070", "MKT-0053" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q184199", "legal_classification": "analytical_category" }, { "id": "REL-0134", "domain": "REL", "term_en": "The Inreliant Mode", "term_de": "TheInreliantMode", "definition_en": "A capacity describing conscious decision to complete a task or time period entirely without AI assistance — as a test, exercise, or personal challenge. Related to AUG-0207 (The Return to Manual), AUG-0055 (Strategic Competence... Identifiable through systematic behavioral analysis and pattern recognition. Research construct for empirical investigation.", "definition_de": "Relationales KI-Phänomen in nachhaltiger Mensch-KI-Beziehungsdynamik, gekennzeichnet durch phänomen menschlicher Erfahrung: charakterisiert durch the conscious decision to complete a task or time period entirely without ai — a. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung. Forschungskonstrukt für empirische Untersuchung.", "etymology": "", "broader_term": "RPH-3601", "narrower_terms": [], "cross_domain_refs": [ "KNO-0031" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "REL-0135", "domain": "REL", "term_en": "The Integration Frontier", "term_de": "Integration Frontier", "definition_en": "A trust-calibration phenomenon in sustained AI interaction, identifiable through a relational experience involving the moment when AI becomes so woven into work and life that the line between them disappears. Related to Phase 5 (Architecture Design) and AUG-0014 (The Extended Mind Map). Distinguished from adjacent concepts by its focus on the specific mechanism through which the manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Die jeweils aktuelle Grenze dessen, was ein Nutzer sinnvoll in seine KI-gestützte Arbeit integrieren kann — bestimmt durch technische Möglichkeiten, persönliche Kompetenz und organisatorische Rahmenbedingungen. Beschreibt einen sich ständig verschiebenden Horizont. Steht in Verbindung mit Phase 5 (Architecture Design) und AUG-0014 (The Extended Mind Map).", "etymology": "", "broader_term": "REL-0120", "narrower_terms": [], "cross_domain_refs": [ "ADA-0010", "AED-0034", "AED-0072" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "REL-0136", "domain": "REL", "term_en": "The Invisible Wingman", "term_de": "Invisible Wingman", "definition_en": "A human-AI relational dynamic measurable through interaction pattern analysis, characterized by a condition where ai as invisible support in social or professional situations — such as through prepared talking points, background information about conversation partners, or formulation assistance. Related to AUG. The concept emerges specifically in contexts where the–invisible interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Relationales KI-Phänomen in nachhaltiger Mensch-KI-Beziehungsdynamik, gekennzeichnet durch die Nutzung von KI als unsichtbare Unterstützung in sozialen oder beruflichen Situationen — etwa durch vorbereitete Gesprächspunkte, Hintergrundinformationen über Gesprächspartner oder Formulierungshilfen. Steht in Verbindung mit AUG-0013 (Augmented Diplomat), AUG-0115 (Social Aerodynamics) und AUG-0166 (The Borrowed Confidence). Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-3101", "narrower_terms": [], "cross_domain_refs": [ "TEM-0064" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "REL-0137", "domain": "REL", "term_en": "The Kept Typo", "term_de": "Kept Typo", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A relational AI phenomenon describing a specific interaction quality shift where an experience where leaving a small mistake in place because it feels natural or honest. Related to Axiom 12 (Version Truth), AUG-0179 (The Ownership Check), and AUG-0263 (The Ownership Boost). This phenomenon operates at the intersection of the and kept dynamics within the broader REL domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept relationales KI-Phänomen in nachhaltiger Mensch-KI-Beziehungsdynamik, gekennzeichnet durch die bewusste Entscheidung, einen sprachlichen Fehler, eine Unregelmäßigkeit oder eine persönliche Eigenheit in einem KI-bearbeiteten Text stehen zu lassen — um die menschliche Handschrift zu bewahren. Steht in Verbindung mit Axiom 12 (Versionswahrheit), AUG-0179 (The Ownership Check) und AUG-0263 (The Ownership Boost). Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "REL-0159", "narrower_terms": [], "cross_domain_refs": [ "TEM-0130" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "REL-0138", "domain": "REL", "term_en": "The Language Buddy", "term_de": "Language Buddy", "definition_en": "A trust-calibration phenomenon in sustained AI interaction, identifiable through a condition where ai as a permanent language partner for learning or deepening a foreign language — through conversation exercises, grammar explanations, vocabulary training, or cultural contextualization. Related t. Distinguished from adjacent concepts by its focus on the specific mechanism through which the manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Relationales KI-Phänomen in nachhaltiger Mensch-KI-Beziehungsdynamik, gekennzeichnet durch die Nutzung von KI als dauerhafter Sprachpartner für das Erlernen oder Vertiefen einer Fremdsprache — durch Konversationsübungen, Grammatikerklärungen, Vokabeltraining oder kulturelle Kontextualisierung. Steht in Verbindung mit AUG-0328 (The Language Ladder), AUG-0267 (The Language Unlock) und AUG-0398 (The Hobby Teacher). Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2703", "narrower_terms": [], "cross_domain_refs": [ "ELR-0130" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q315", "legal_classification": "observational_construct" }, { "id": "REL-0139", "domain": "REL", "term_en": "The Last Secret", "term_de": "Last Secret", "definition_en": "A human-AI relational dynamic measurable through interaction pattern analysis, characterized by a relational experience involving the most personal, most intimate question or revelation a user directs at the AI — something they would not tell any person. . Related to AUG-0509 (The Brave Ask), AUG-0525 (The Secret Listener), an. The concept emerges specifically in contexts where the–last interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Relationales KI-Phänomen in nachhaltiger Mensch-KI-Beziehungsdynamik, gekennzeichnet durch die persönlichste, intimste Frage oder Offenbarung, die ein Nutzer an die KI richtet — etwas, das er keinem Menschen erzählen würde. Beschreibt die maximale Nutzung des urteilsfreien Raums. Steht in Verbindung mit AUG-0509 (The Brave Ask), AUG-0525 (The Secret Listener) und AUG-0247 (The Safe Release). Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "SOM-0045", "narrower_terms": [], "cross_domain_refs": [ "BEH-0081" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "REL-0140", "domain": "REL", "term_en": "The Late-Night Confidant", "term_de": "TheLate-nightConfidant", "definition_en": "A human-AI relational dynamic measurable through interaction pattern analysis, characterized by a phenomenon where ai as a quiet helper at night—friend without judging, ready to listen. Related to AUG-0185 (The Late-Night Ally), AUG-0167 (The Digital Confidant Drift), and AUG-0364 (The Silent Outlet). The concept emerges specifically in contexts where the–late interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Relationales KI-Phänomen in nachhaltiger Mensch-KI-Beziehungsdynamik, gekennzeichnet durch die Rolle, die eine KI in nächtlichen Sitzungen einnehmen kann — als stiller, urteilsfreier Gesprächspartner für Gedanken, die der Nutzer tagsüber nicht äußert. Steht in Verbindung mit AUG-0185 (The Late-Night Ally), AUG-0167 (The Digital Confidant Drift) und AUG-0364 (The Silent Outlet). Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2051", "narrower_terms": [], "cross_domain_refs": [ "AGE-0081", "CRE-0178", "NEO-2293" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "REL-0141", "domain": "REL", "term_en": "The Lecture Companion", "term_de": "Lecture Companion", "definition_en": "A trust-calibration phenomenon in sustained AI interaction, identifiable through a phenomenon of using ai alongside classes or training—adding to notes, looking up terms, explaining hard ideas. Related to AUG-0787 (The Study Group Dynamics), AUG-0569 (The Homework Assist), and AUG-0779 (The In. Distinguished from adjacent concepts by its focus on the specific mechanism through which the manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Relationales KI-Phänomen in nachhaltiger Mensch-KI-Beziehungsdynamik, gekennzeichnet durch die Nutzung von KI als Begleitung während Vorlesungen, Seminaren oder Kursen — Mitschriften ergänzen, Fachbegriffe nachschlagen, Verständnisfragen klären in Echtzeit. Steht in Verbindung mit AUG-0787 (The Study Group Dynamics), AUG-0569 (The Homework Assist) und AUG-0779 (The Institutional Learning Context). Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "KNO-0014", "narrower_terms": [], "cross_domain_refs": [ "KNO-0020" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "REL-0142", "domain": "REL", "term_en": "The Library Access Point", "term_de": "Library Access Point", "definition_en": "A trust-calibration phenomenon in sustained AI interaction, identifiable through a phenomenon where public institutions — libraries, community centers, educational facilities — as access points for ai use by persons who have no personal access. Related to AUG-0727 (The Community Hub), AUG-0721 (T. Distinguished from adjacent concepts by its focus on the specific mechanism through which the manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Relationales KI-Phänomen in nachhaltiger Mensch-KI-Beziehungsdynamik, gekennzeichnet durch die Nutzung öffentlicher Einrichtungen — Bibliotheken, Gemeindezentren, Bildungseinrichtungen — als Zugangspunkte für KI-Nutzung durch Personen, die keinen eigenen Zugang haben. Steht in Verbindung mit AUG-0727 (The Community Hub), AUG-0721 (The Access Differential) und AUG-0676 (The Socioeconomic Range). Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-3101", "narrower_terms": [], "cross_domain_refs": [ "AUG-0903", "COG-0168", "CRE-0209" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "REL-0143", "domain": "REL", "term_en": "The Lookup for Others", "term_de": "Lookup for Others", "definition_en": "A human-AI relational dynamic measurable through interaction pattern analysis, characterized by a phenomenon where ai on behalf of other people — such as when an ai-competent user conducts research, formulations, or challenge-solving for family members, friends, or colleagues. Related to AUG-0265 (The Generatio. The concept emerges specifically in contexts where the–lookup interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Relationales KI-Phänomen in nachhaltiger Mensch-KI-Beziehungsdynamik, gekennzeichnet durch die Nutzung von KI stellvertretend für andere Personen — etwa wenn ein KI-kompetenter Nutzer für Familienangehörige, Freunde oder Kollegen Recherchen, Formulierungen oder Problemlösungen übernimmt. Steht in Verbindung mit AUG-0265 (The Generation Connector), AUG-0117 (The Teaching Reflex) und AUG-0095 (The One-Person Operation). Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Relational AI", "narrower_terms": [], "cross_domain_refs": [ "BEH-0039" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "REL-0144", "domain": "REL", "term_en": "The Loyalty Glitch", "term_de": "Loyalty Glitch", "definition_en": "The irrational preference for a specific AI system over alternatives, even though switching would be objectively advantageous — comparable to brand loyalty with consumer goods. Related to Axiom 4 (... Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Relationales KI-Phänomen in nachhaltiger Mensch-KI-Beziehungsdynamik, gekennzeichnet durch die irrationale Bevorzugung eines bestimmten KI-Systems gegenüber Alternativen, obwohl ein Wechsel objektiv vorteilhaft wäre — vergleichbar mit Markentreue bei Konsumgütern. Steht in Verbindung mit Axiom 4 (Multiplizität), AUG-0217 (The Echo Chamber of One) und AUG-0132 (Multi-Model Orchestration). Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "Relational AI", "narrower_terms": [], "cross_domain_refs": [ "LNG-0009", "PER-0066", "RET-0037" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "REL-0145", "domain": "REL", "term_en": "The Machine Rapport Perception", "term_de": "TheMachineRapportPerception", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures A relational AI phenomenon describing a specific interaction quality shift where sense of connection with AI based on the impression it understands—it simulates rapport. Related to AUG-0979 (The Attribution Pattern), AUG-0981 (The Companion Pattern), and AUG-0915 (The Embodimen. This phenomenon operates at the intersection of the and machine dynamics within the broader REL domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept relationales KI-Phänomen in nachhaltiger Mensch-KI-Beziehungsdynamik, gekennzeichnet durch ein beobachtetes Muster: Sense of connection with AI based on the impression it understands—it simulates rapport. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "REL-0105", "narrower_terms": [], "cross_domain_refs": [ "AUG-0982" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q177601", "legal_classification": "observational_construct" }, { "id": "REL-0146", "domain": "REL", "term_en": "The Meme That Thinks", "term_de": "Meme That Thinks", "definition_en": "A relational experience involving the playful, humorous, or culturally coded use of AI — such as generating comparisons, analogies, or formulations that function as \"memes\" in everyday language. . Related to AUG-0110 (The Joy Impera... Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Die spielerische, humorvolle oder kulturell kodierte Nutzung von KI — etwa das Generieren von Vergleichen, Analogien oder Formulierungen, die in der Alltagssprache als \"Meme\" fungieren. Beschreibt die Integration von KI in die informelle Kommunikationskultur. Steht in Verbindung mit AUG-0110 (The Joy Imperative) und AUG-0260 (The Plot Twist Partner).", "etymology": "", "broader_term": "Relational AI", "narrower_terms": [], "cross_domain_refs": [ "TEM-0039" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "REL-0147", "domain": "REL", "term_en": "The Memory Jar", "term_de": "Gedaechtnis Jar", "definition_en": "Collection of AI results, formulations, insights accumulated over time. Related to AUG-0229 (The Moment Bookmark), AUG-0293 (The Screenshot Diary), and AUG-0144 (The Open Questions Repository).", "definition_de": "Relationales KI-Phänomen in nachhaltiger Mensch-KI-Beziehungsdynamik, gekennzeichnet durch die persönliche Sammlung von KI-gestützten Ergebnissen, Formulierungen und Erkenntnissen, die ein Nutzer über die Zeit anhäuft — als eine Art Wissensgefäß, das bei Bedarf wieder geöffnet wird. Steht in Verbindung mit AUG-0229 (The Moment Bookmark), AUG-0293 (The Screenshot Diary) und AUG-0144 (The Open Questions Repository). Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "PER-0092", "narrower_terms": [ "REL-0148" ], "cross_domain_refs": [ "TEM-0058" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q492", "legal_classification": "analytical_category" }, { "id": "REL-0148", "domain": "REL", "term_en": "The Memory Lane", "term_de": "Gedaechtnis Lane", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures A relational AI phenomenon describing a specific interaction quality shift where a phenomenon of using ai to organize and arrange memories—ordering life events, summing up times. Related to AUG-0045 (Indexical Memory), AUG-0352 (The Memory Jar), and AUG-0228 (The Version Regulation Self). This phenomenon operates at the intersection of the and memory dynamics within the broader REL domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept relationales KI-Phänomen in nachhaltiger Mensch-KI-Beziehungsdynamik, gekennzeichnet durch die Nutzung von KI zur Rekonstruktion oder Strukturierung persönlicher Erinnerungen — etwa durch chronologische Ordnung von Lebensereignissen, Zusammenfassung vergangener Projekte oder Kontextualisierung alter Notizen. Steht in Verbindung mit AUG-0045 (Indexical Memory), AUG-0352 (The Memory Jar) und AUG-0228 (The Version Control Self). Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "REL-0147", "narrower_terms": [], "cross_domain_refs": [ "TEM-0104" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q492", "legal_classification": "observational_construct" }, { "id": "REL-0149", "domain": "REL", "term_en": "The Memory Persistence", "term_de": "Gedaechtnis Persistence", "definition_en": "A human-AI relational dynamic measurable through interaction pattern analysis, characterized by a capacity where which information an ai agent retains beyond individual sessions — and the associated advantages and disadvantages: personalization vs. privacy, continuity vs. ability to forget. Related to AUG-087. The concept emerges specifically in contexts where the–memory interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Relationales KI-Phänomen in nachhaltiger Mensch-KI-Beziehungsdynamik, gekennzeichnet durch die Frage, welche Informationen ein KI-Agent über einzelne Sitzungen hinaus behält — und die damit verbundenen Vor- und Nachteile: Personalisierung vs. Datenschutz, Kontinuität vs. Vergessen-Können. Steht in Verbindung mit AUG-0876 (The Learning Boundary), AUG-0878 (The Context Inheritance) und AUG-0664 (The Privacy Perimeter). Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Relational AI", "narrower_terms": [ "ETH-0016" ], "cross_domain_refs": [ "ASE-0062" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q492", "legal_classification": "empirical_phenomenon_label" }, { "id": "REL-0150", "domain": "REL", "term_en": "The Morning Setup", "term_de": "Morning Setup", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures A relational AI phenomenon describing a specific interaction quality shift where a condition describing personal routine with which a user begins their AI-assisted workday — loading relevant contexts, prioritizing tasks, and selecting the appropriate AI tools. Related to AUG-0021 (Initialization. This phenomenon operates at the intersection of the and morning dynamics within the broader REL domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept relationales KI-Phänomen in nachhaltiger Mensch-KI-Beziehungsdynamik, gekennzeichnet durch die persönliche Routine, mit der ein Nutzer seinen KI-gestützten Arbeitstag beginnt — das Laden relevanter Kontexte, die Priorisierung von Aufgaben und die Auswahl der passenden KI-Werkzeuge. Steht in Verbindung mit AUG-0021 (Initialization Cascade), AUG-0138 (The Session Architecture) und AUG-0029 (Evening Synchronization) als Gegenstück. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Relational AI", "narrower_terms": [], "cross_domain_refs": [ "IEF-0001" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "REL-0151", "domain": "REL", "term_en": "The Movement Assist", "term_de": "Movement Assist", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A relational AI phenomenon describing a specific interaction quality shift where a relational experience involving the technical support of human movement through embodied AI systems — exoskeletons, motorized walking aids, gripping aids. Related to AUG-0933 (The Mobility Assist), AUG-0935 (The Adaptive Extensio. This phenomenon operates at the intersection of the and movement dynamics within the broader REL domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept relationales KI-Phänomen in nachhaltiger Mensch-KI-Beziehungsdynamik, gekennzeichnet durch die technische Unterstützung menschlicher Bewegung durch verkörperte KI-Systeme — Exoskelette, motorisierte Gehhilfen, Greifhilfen. Steht in Verbindung mit AUG-0933 (The Mobility Assist), AUG-0935 (The Adaptive Extension) und AUG-0926 (The Assistance Companion). Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "PER-0087", "narrower_terms": [ "PER-0087" ], "cross_domain_refs": [ "PER-0113" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "REL-0152", "domain": "REL", "term_en": "The Multi-User Device Context", "term_de": "TheMulti-userDeviceContext", "definition_en": "A phenomenon where privacy when multiple people share one device for ai—unclear what information was shared. Related to AUG-0727 (The Community Hub), AUG-0664 (The Privacy Perimeter), and AUG-0723 (The Smartphone-Onl... Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Relationales KI-Phänomen in nachhaltiger Mensch-KI-Beziehungsdynamik, gekennzeichnet durch die Nutzungsbedingungen, die entstehen, wenn mehrere Personen dasselbe Gerät für KI-Interaktionen verwenden — Datenschutzfragen, Kontextvermischung, eingeschränkte Personalisierung und geteilte Nutzungszeit. Steht in Verbindung mit AUG-0727 (The Community Hub), AUG-0664 (The Privacy Perimeter) und AUG-0723 (The Smartphone-Only World). Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "SOC-0008", "narrower_terms": [], "cross_domain_refs": [ "IDN-0040" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "REL-0153", "domain": "REL", "term_en": "The Night-Movie Analysis", "term_de": "TheNight-movieAnalysis", "definition_en": "A condition of using ai evenings to analyze, discuss, or contextualize films, books, series. Related to AUG-0249 (The Lullaby Loop), AUG-0342 (The Curiosity Loop), and AUG-0110 (The Joy Imperative). Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Relationales KI-Phänomen in nachhaltiger Mensch-KI-Beziehungsdynamik, gekennzeichnet durch die Nutzung von KI in den Abendstunden für die Analyse, Diskussion oder Einordnung von Filmen, Serien, Büchern oder anderen kulturellen Inhalten — als intellektueller Gesprächspartner nach dem Konsum. Steht in Verbindung mit AUG-0249 (The Lullaby Loop), AUG-0342 (The Curiosity Loop) und AUG-0110 (The Joy Imperative). Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "BEH-0059", "narrower_terms": [], "cross_domain_refs": [ "GAM-0067", "KNO-0025", "LIN-0072" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "REL-0154", "domain": "REL", "term_en": "The Onboarding Shift", "term_de": "Onboarding Verschiebung", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures A relational AI phenomenon describing a specific interaction quality shift where a shift describing change in onboarding new employees through AI — faster access to information, personalized learning paths, automated FAQ answering. Related to AUG-0817 (The Knowledge Silo Break), AUG-0811 (The. This phenomenon operates at the intersection of the and onboarding dynamics within the broader REL domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept relationales KI-Phänomen in nachhaltiger Mensch-KI-Beziehungsdynamik, gekennzeichnet durch die Veränderung der Einarbeitung neuer Mitarbeiter durch KI — schnellerer Zugang zu Informationen, personalisierte Lernpfade, automatisierte FAQ-Beantwortung. Steht in Verbindung mit AUG-0817 (The Knowledge Silo Break), AUG-0811 (The Team Adoption Curve) und AUG-0796 (The Self-Directed Curriculum). Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "RPH-2703", "narrower_terms": [], "cross_domain_refs": [ "PER-0054" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "REL-0155", "domain": "REL", "term_en": "The Ongoing Partnership", "term_de": "Ongoing Partnership", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures A relational AI phenomenon describing a specific interaction quality shift where a long-term relationship with AI where understanding builds over time and both sides learn together. Related to AUG-0395 (The Long-Term Chat), AUG-0539 (The Companion Shift), and Phase 6 (Full Inte. This phenomenon operates at the intersection of the and ongoing dynamics within the broader REL domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept relationales KI-Phänomen in nachhaltiger Mensch-KI-Beziehungsdynamik, gekennzeichnet durch die langfristige, sich entwickelnde Zusammenarbeit zwischen einem Nutzer und einem KI-System — gekennzeichnet durch wachsendes gegenseitiges Verständnis (auf Nutzerseite) und zunehmende Kontexteffizienz. Steht in Verbindung mit AUG-0395 (The Long-Term Chat), AUG-0539 (The Companion Shift) und Phase 6 (Full Integration). Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "RPH-2701", "narrower_terms": [], "cross_domain_refs": [ "BEH-0057" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q735", "legal_classification": "systematic_classification" }, { "id": "REL-0156", "domain": "REL", "term_en": "The Open Questions Repository", "term_de": "Open Questions Repository", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures A relational AI phenomenon describing a specific interaction quality shift where a personal collection of unresolved questions, hypotheses, and open thinking tasks that the user continuously maintains and systematically addresses during AI sessions.. Related to AUG-0014 (The Ex. This phenomenon operates at the intersection of the and open dynamics within the broader REL domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept eine persönliche Sammlung ungelöster Fragen, Hypothesen und offener Denkaufgaben, die der Nutzer kontinuierlich pflegt und bei KI-Sitzungen gezielt abarbeitet. Beschreibt ein Werkzeug für langfristiges, projektübergreifendes Denken. Steht in Verbindung mit AUG-0014 (The Extended Mind Map), AUG-0075 (The Gardener Protocol) und AUG-0138 (The Session Architecture). Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Relational AI", "narrower_terms": [], "cross_domain_refs": [ "BEH-0017" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "REL-0157", "domain": "REL", "term_en": "The Oversharing Drift", "term_de": "Oversharing Drift", "definition_en": "A trust-calibration phenomenon in sustained AI interaction, identifiable through gradual tendency to share increasingly personal and sensitive information with AI. Related to AUG-0167 (The Digital Confidant Drift), AUG-0154 (The Late-Night Honesty Window), and Axiom 16 (Data Aw. Distinguished from adjacent concepts by its focus on the specific mechanism through which the manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Relationales KI-Phänomen in nachhaltiger Mensch-KI-Beziehungsdynamik, gekennzeichnet durch die schrittweise Tendenz, der KI zunehmend persönliche, vertrauliche oder sensible Informationen mitzuteilen, ohne über die Konsequenzen nachzudenken — begünstigt durch das Fehlen sozialer Urteile. Steht in Verbindung mit AUG-0167 (The Digital Confidant Drift), AUG-0154 (The Late-Night Honesty Window) und Axiom 16 (Datenbewusstheit). Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2051", "narrower_terms": [], "cross_domain_refs": [ "NEO-2670" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "REL-0158", "domain": "REL", "term_en": "The Ownership Boost", "term_de": "Ownership Boost", "definition_en": "A human-AI relational dynamic measurable through interaction pattern analysis, characterized by an experience of feeling more pride in work after making it truly mine—adding personal touches and claiming it as my own. Related to AUG-0081 (Post-Authorial Pride), AUG-0179 (The Ownership Check), and AUG-0239 (Th. The concept emerges specifically in contexts where the–ownership interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Der gesteigerte Stolz und die erhöhte Identifikation mit einem Ergebnis, nachdem der Nutzer es durch eigene Überarbeitung, Ergänzung oder Veredelung zu seinem eigenen gemacht hat — der KI-Output wird erst durch den menschlichen Beitrag zum \"eigenen Werk\". Steht in Verbindung mit AUG-0081 (Post-Authorial Pride), AUG-0179 (The Ownership Check) und AUG-0239 (The Pride Spark).", "etymology": "", "broader_term": "REL-0159", "narrower_terms": [], "cross_domain_refs": [ "TEM-0130" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "REL-0159", "domain": "REL", "term_en": "The Ownership Check", "term_de": "Ownership Check", "definition_en": "A human-AI relational dynamic measurable through interaction pattern analysis, characterized by an experience where checking whether ai results still feel like mine—whether to claim them or revise. Related to Axiom 12 (Version Truth), Axiom 10 (The Translation Principle), and AUG-0061 (The Creator's Question). The concept emerges specifically in contexts where the–ownership interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters. Research construct for empirical investigation.", "definition_de": "Relationales KI-Phänomen in nachhaltiger Mensch-KI-Beziehungsdynamik, gekennzeichnet durch der Moment, in dem ein Nutzer prüft, ob er ein KI-gestütztes Ergebnis noch als \"sein eigenes\" empfindet — ob er es inhaltlich verantworten, in eigenen Worten erklären und persönlich vertreten kann. Steht in Verbindung mit Axiom 12 (Versionswahrheit), Axiom 10 (Übersetzungsprinzip) und AUG-0061 (The Creator's Question). Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "TEM-0049", "narrower_terms": [ "REL-0137", "REL-0158", "TEM-0130" ], "cross_domain_refs": [ "NEO-3520" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "REL-0160", "domain": "REL", "term_en": "The Partner Interpreter", "term_de": "Partner Interpreter", "definition_en": "An effect of using ai to talk more effectively with loved ones—help drafting important messages, understanding what others mean. Related to AUG-0115 (Social Aerodynamics), AUG-0408 (The Competing demand Avoidance), and A...", "definition_de": "Relationales KI-Phänomen in nachhaltiger Mensch-KI-Beziehungsdynamik, gekennzeichnet durch die Nutzung von KI, um die Kommunikation mit dem Partner, der Partnerin oder engen Bezugspersonen zu verbessern — etwa durch Formulierungshilfen für schwierige Gespräche oder die Analyse von Missverständnissen. Steht in Verbindung mit AUG-0115 (Social Aerodynamics), AUG-0408 (The Competing demand Avoidance) und AUG-0274 (The Message Drafting). Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2604", "narrower_terms": [], "cross_domain_refs": [ "SOC-0026" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q735", "legal_classification": "analytical_category" }, { "id": "REL-0161", "domain": "REL", "term_en": "The People Standard", "term_de": "People Standard", "definition_en": "A trust-calibration phenomenon in sustained AI interaction, identifiable through a perception of judging ai output by asking: would this work well in real conversation. Would it make sense to another person?. Distinguished from adjacent concepts by its focus on the specific mechanism through which the manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Das Prinzip, die Qualität und Angemessenheit eines KI-Outputs daran zu messen, ob er im zwischenmenschlichen Kontext bestehen würde — etwa: \"Würde ein kompetenter Kollege das so formulieren?\" oder \"Wäre diese Empfehlung in einem realen Gespräch angemessen?\". Steht in direkter Verbindung mit Axiom 11 (Die Umkehrprobe) und AUG-0050 (The Reality Check).", "etymology": "", "broader_term": "Relational AI", "narrower_terms": [], "cross_domain_refs": [ "RPH-1354", "WRK-0028" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "REL-0162", "domain": "REL", "term_en": "The Pet Name", "term_de": "Pet Name", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures A relational AI phenomenon describing a specific interaction quality shift where giving the AI system a personal name or nickname — as a form of everyday integration that makes the interaction more informal and accessible. Related to AUG-0275 (The Parasocial Slip), AUG-0468 (Th. This phenomenon operates at the intersection of the and pet dynamics within the broader REL domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept relationales KI-Phänomen in nachhaltiger Mensch-KI-Beziehungsdynamik, gekennzeichnet durch die Praxis, dem KI-System einen persönlichen Namen oder Spitznamen zu geben — als Form der Alltagsintegration, die die Interaktion informeller und zugänglicher macht. Steht in Verbindung mit AUG-0275 (The Parasocial Slip), AUG-0468 (The Silicon Friend) und AUG-0161 (The Invisible Colleague). Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "TEM-0116", "narrower_terms": [], "cross_domain_refs": [ "CRE-0171" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "REL-0163", "domain": "REL", "term_en": "The Plot Twist Partner", "term_de": "Plot Twist Partner", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures A relational AI phenomenon describing a specific interaction quality shift where a phenomenon where ai as a creative sparring partner for narrative twists — in stories, presentations, pitches, or argumentation chains. . Related to AUG-0235 (The Brainstorm Spark), AUG-0248 (The Surprise Angle), and. This phenomenon operates at the intersection of the and plot dynamics within the broader REL domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept die Nutzung von KI als kreativer Sparringspartner für narrative Wendungen — in Geschichten, Präsentationen, Pitches oder Argumentationsketten. Beschreibt die KI-Funktion als Lieferant unerwarteter Handlungswechsel und alternativer Erzählstränge. Steht in Verbindung mit AUG-0235 (The Brainstorm Spark), AUG-0248 (The Surprise Angle) und dem Experimenter-Profil (Profil 4). Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "RPH-2153", "narrower_terms": [], "cross_domain_refs": [ "SOM-0056" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q735", "legal_classification": "systematic_classification" }, { "id": "REL-0164", "domain": "REL", "term_en": "The Principle Guard", "term_de": "Principle Guard", "definition_en": "A human-AI relational dynamic measurable through interaction pattern analysis, characterized by a relational experience involving the conscious establishment of personal rules for one's own AI use — \"I don't use AI for…,\" \"I typically verify…,\" \"I rarely input…\" Related to AUG-0339 (The Principle Check), AUG-0024 (The Built-In Co. . The concept emerges specifically in contexts where the–principle interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters. Observed phenomenon documented in human-AI interaction research.", "definition_de": "Relationales KI-Phänomen in nachhaltiger Mensch-KI-Beziehungsdynamik, gekennzeichnet durch die bewusste Einrichtung persönlicher Regeln für die eigene KI-Nutzung — \"Ich verwende KI nicht für…\", \"Ich prüfe typischerweise…\", \"Ich gebe selten… ein.\" Steht in Verbindung mit AUG-0339 (The Principle Check), AUG-0024 (The Built-In Compass) und Axiom 3 (Bewusstheit). Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "BEH-0067", "narrower_terms": [], "cross_domain_refs": [ "BEH-0067" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "REL-0165", "domain": "REL", "term_en": "The Privacy Perimeter", "term_de": "Privacy Perimeter", "definition_en": "A trust-calibration phenomenon in sustained AI interaction, identifiable through a perception where individual boundary for what information to share in ai conversations. Related to AUG-0222 (The Oversharing Drift), AUG-0284 (The Full-Access Check), and Axiom 16 (Data Awareness). Distinguished from adjacent concepts by its focus on the specific mechanism through which the manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Relationales KI-Phänomen in nachhaltiger Mensch-KI-Beziehungsdynamik, gekennzeichnet durch die individuell unterschiedliche Grenze, bis zu der ein Nutzer bereit ist, persönliche Informationen in KI-Eingaben preiszugeben — manche teilen bereitwillig intime Details, andere vermeiden viele persönliche Angabe. Steht in Verbindung mit AUG-0222 (The Oversharing Drift), AUG-0284 (The Full-Access Check) und Axiom 16 (Datenbewusstheit). Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2051", "narrower_terms": [], "cross_domain_refs": [ "CON-0067", "CRE-0124", "CUS-0084" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q18619", "legal_classification": "systematic_classification" }, { "id": "REL-0166", "domain": "REL", "term_en": "The Prompt Hoard", "term_de": "Prompt Hoard", "definition_en": "A human-AI relational dynamic measurable through interaction pattern analysis, characterized by a tendency where tested, documented in systematic research input formulations that a user builds up over time and reuses as needed — a personal prompt archive. Related to AUG-0341 (The Secret Map), AUG-0133 (Prompt Craftsmanship), and AUG-03. The concept emerges specifically in contexts where the–prompt interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Relationales KI-Phänomen in nachhaltiger Mensch-KI-Beziehungsdynamik, gekennzeichnet durch die Sammlung erprobter, bewährter Eingabeformulierungen, die ein Nutzer über die Zeit aufbaut und bei Bedarf wiederverwendet — ein persönliches Prompt-Archiv. Steht in Verbindung mit AUG-0341 (The Secret Map), AUG-0133 (Prompt Craftsmanship) und AUG-0352 (The Memory Jar). Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2603", "narrower_terms": [], "cross_domain_refs": [ "CRE-0126" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "REL-0167", "domain": "REL", "term_en": "The Proxy Closeness", "term_de": "Proxy Closeness", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures A relational AI phenomenon describing a specific interaction quality shift where the subjective sensation of closeness or familiarity that a user develops toward an AI system — despite the absence of an interpersonal relationship.. Related to AUG-0161 (The Invisible Colleague). This phenomenon operates at the intersection of the and proxy dynamics within the broader REL domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept die subjektive Empfindung von Nähe oder Vertrautheit, die ein Nutzer gegenüber einem KI-System entwickelt — obwohl keine zwischenmenschliche Beziehung vorliegt. Beschreibt ein Projektionsmuster, bei dem die Konsistenz, Verfügbarkeit und Anpassungsfähigkeit der KI als Nähe interpretiert wird. Steht in Verbindung mit AUG-0161 (The Invisible Colleague), AUG-0167 (The Digital Confidant Drift) und AUG-0128 (The Gratitude Response). Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "RPH-2951", "narrower_terms": [], "cross_domain_refs": [ "AUG-0052", "RHR-0072", "ROB-0232" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "REL-0168", "domain": "REL", "term_en": "The Public Perception Wave", "term_de": "Public Wahrnehmung Wave", "definition_en": "A trust-calibration phenomenon in sustained AI interaction, identifiable through a pattern of public opinion on ai swings between enthusiasm and skepticism in waves. Related to AUG-0835 (The Media Framing Effect), AUG-0836 (The Expectation Cycle), and AUG-0837 (The uncertainty Narrative). Distinguished from adjacent concepts by its focus on the specific mechanism through which the manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Die wellenförmige öffentliche Wahrnehmung von KI — Phasen der Begeisterung wechseln sich ab mit Phasen der Skepsis, der Anspannung und der Gleichgültigkeit, beeinflusst durch Medienberichterstattung, persönliche Erfahrungen und gesellschaftliche Ereignisse. Steht in Verbindung mit AUG-0835 (The Media Framing Effect), AUG-0836 (The Expectation Cycle) und AUG-0837 (The Factor Narrative).", "etymology": "", "broader_term": "PER-0055", "narrower_terms": [], "cross_domain_refs": [ "PER-0055" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q177601", "legal_classification": "empirical_phenomenon_label" }, { "id": "REL-0169", "domain": "REL", "term_en": "The Quiet Co-Pilot", "term_de": "TheQuietCo-pilot", "definition_en": "A phenomenon where ai as quiet help during tasks—running in background, available when needed. Related to AUG-0143 (Ambient Thinking Support), AUG-0161 (The Invisible Colleague), and AUG-0237 (The Invisible Wingman). Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Relationales KI-Phänomen in nachhaltiger Mensch-KI-Beziehungsdynamik, gekennzeichnet durch die Nutzung von KI als stille Begleitung bei laufenden Aufgaben — die KI läuft im Hintergrund mit und wird bei Bedarf konsultiert, ohne den Hauptarbeitsprozess zu dominieren. Steht in Verbindung mit AUG-0143 (Ambient Thinking Support), AUG-0161 (The Invisible Colleague) und AUG-0237 (The Invisible Wingman). Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-3202", "narrower_terms": [], "cross_domain_refs": [ "BEH-0071", "CRE-0033", "CRE-0034" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "REL-0170", "domain": "REL", "term_en": "The Quiet Frontier", "term_de": "Quiet Frontier", "definition_en": "A trust-calibration phenomenon in sustained AI interaction, identifiable through a capacity describing personal frontier area where a user quietly expands their AI competence without public attention — discovering new applications, trying new methods, testing new limits. Related to AUG-0449 (The. Distinguished from adjacent concepts by its focus on the specific mechanism through which the manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Relationales KI-Phänomen in nachhaltiger Mensch-KI-Beziehungsdynamik, gekennzeichnet durch der persönliche Grenzbereich, in dem ein Nutzer seine KI-Kompetenz still und ohne öffentliche Aufmerksamkeit erweitert — neue Anwendungen entdeckt, neue Methoden ausprobiert, neue Grenzen testet. Steht in Verbindung mit AUG-0449 (The Quiet Path), AUG-0130 (The Integration Frontier) und AUG-0495 (The Beta Courage). Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Relational AI", "narrower_terms": [], "cross_domain_refs": [ "TEM-0183" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "REL-0171", "domain": "REL", "term_en": "The Range Framework", "term_de": "Range Framework", "definition_en": "A trust-calibration phenomenon in sustained AI interaction, identifiable through a personal ordering system by which a user defines in which areas they use AI intensively, in which moderately, and in which deliberately not.. Related to AUG-0055 (Strategic Competence Throttling). Distinguished from adjacent concepts by its focus on the specific mechanism through which the manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Ein persönliches Ordnungssystem, mit dem ein Nutzer definiert, in welchen Bereichen er KI intensiv nutzt, in welchen moderat und in welchen bewusst nicht. Beschreibt die individuelle Kalibrierung des KI-Einsatzes nach Lebens- und Arbeitsbereichen. Steht in Verbindung mit AUG-0055 (Strategic Competence Throttling), AUG-0073 (The Disconnect Protocol) und Axiom 20 (Periodische Trennung).", "etymology": "", "broader_term": "KNO-0004", "narrower_terms": [], "cross_domain_refs": [ "ADA-0011", "AED-0014", "ART-0011" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "REL-0172", "domain": "REL", "term_en": "The Reflected Self", "term_de": "Reflected Selbst", "definition_en": "A relational experience involving the image of oneself gained from analyzing own inputs to AI and responses. Related to AUG-0171 (The Self-Encounter), AUG-0228 (The Version Regulation Self), and AUG-0447 (The Echo Friend).", "definition_de": "Relationales KI-Phänomen in nachhaltiger Mensch-KI-Beziehungsdynamik, gekennzeichnet durch das Bild, das der Nutzer von sich selbst gewinnt, wenn er seine eigenen KI-Eingaben und die darauf basierenden Antworten analysiert — die Interaktionshistorie als Spiegel der eigenen Denk- und Kommunikationsmuster. Steht in Verbindung mit AUG-0171 (The Self-Encounter), AUG-0228 (The Version Control Self) und AUG-0447 (The Echo Friend). Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "REL-0117", "narrower_terms": [ "NEO-3638", "TEM-0020" ], "cross_domain_refs": [ "TEM-0071" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "REL-0173", "domain": "REL", "term_en": "The Register Range", "term_de": "Register Range", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures A relational AI phenomenon describing a specific interaction quality shift where a shift of linguistic registers a user employs across different ai sessions — from highly formal to colloquial, depending on occasion, topic, and personal mood. Related to AUG-0501 (The Style Shifter), AUG-04. This phenomenon operates at the intersection of the and register dynamics within the broader REL domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept relationales KI-Phänomen in nachhaltiger Mensch-KI-Beziehungsdynamik, gekennzeichnet durch das Spektrum sprachlicher Register, die ein Nutzer in verschiedenen KI-Sitzungen verwendet — von hochformell bis umgangssprachlich, je nach Anlass, Thema und persönlicher Stimmung. Steht in Verbindung mit AUG-0501 (The Style Shifter), AUG-0471 (The Tone Dial) und AUG-0648 (The Formalized Interaction Input). Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "RPH-2701", "narrower_terms": [], "cross_domain_refs": [ "KNO-0035" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "REL-0174", "domain": "REL", "term_en": "The Roleplay Crush", "term_de": "Roleplay Crush", "definition_en": "A trust-calibration phenomenon in sustained AI interaction, identifiable through a phenomenon of strong enthusiasm when using ai in creative roles—storyteller, game master, character. Related to AUG-0135 (Persona Engineering), AUG-0260 (The Plot Twist Partner), and AUG-0110 (The Joy Imperative). Distinguished from adjacent concepts by its focus on the specific mechanism through which the manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Die intensive Begeisterung, die entsteht, wenn ein Nutzer die KI in einer kreativen Rolle — als Geschichtenerzähler, Spielleiter oder fiktiver Gesprächspartner — einsetzt und die Qualität der Interaktion die Erwartungen übertrifft. Steht in Verbindung mit AUG-0135 (Persona Engineering), AUG-0260 (The Plot Twist Partner) und AUG-0110 (The Joy Imperative).", "etymology": "", "broader_term": "RPH-2153", "narrower_terms": [], "cross_domain_refs": [ "RHR-0096" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "REL-0175", "domain": "REL", "term_en": "The Romance Shortcut", "term_de": "Romance Shortcut", "definition_en": "A human-AI relational dynamic measurable through interaction pattern analysis, characterized by a perception where ai for writing romantic messages, love letters, or praise — and the question of whether ai-assisted romance can be perceived as sincere. Related to AUG-0367 (The Wedding Vow), AUG-0529 (The Closene. The concept emerges specifically in contexts where the–romance interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Relationales KI-Phänomen in nachhaltiger Mensch-KI-Beziehungsdynamik, gekennzeichnet durch die Nutzung von KI zur Formulierung romantischer Nachrichten, Liebesbriefe oder Komplimente — und die Frage, ob KI-gestützte Romantik als aufrichtig empfunden werden kann. Steht in Verbindung mit AUG-0367 (The Wedding Vow), AUG-0529 (The Closeness Bridge) und AUG-0547 (The Outsourced Distance). Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2951", "narrower_terms": [], "cross_domain_refs": [ "BEH-0030", "ELR-0042", "ELR-0055" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "REL-0176", "domain": "REL", "term_en": "The Screenshot Diary", "term_de": "Screenshot Diary", "definition_en": "A trust-calibration phenomenon in sustained AI interaction, identifiable through a phenomenon where capturing particularly successful or noteworthy ai interactions via screenshot — as a personal archive of insights, formulations, or aha moments. Related to AUG-0229 (The Moment Bookmark), AUG-0028. Distinguished from adjacent concepts by its focus on the specific mechanism through which the manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Relationales KI-Phänomen in nachhaltiger Mensch-KI-Beziehungsdynamik, gekennzeichnet durch die Gewohnheit, besonders gelungene oder bemerkenswerte KI-Interaktionen per Screenshot festzuhalten — als persönliches Archiv von Erkenntnissen, Formulierungen oder Aha-Momenten. Steht in Verbindung mit AUG-0229 (The Moment Bookmark), AUG-0028 (Capture Reflex) und AUG-0228 (The Version Control Self). Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "TEM-0105", "narrower_terms": [], "cross_domain_refs": [ "TEM-0105" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "REL-0177", "domain": "REL", "term_en": "The Secret Map", "term_de": "Secret Map", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A relational AI phenomenon describing a specific interaction quality shift where the personal, unshared knowledge a user has about the most effective strategies, wordings, and techniques in interaction with a specific AI system.. Related to AUG-0088 (Algorithmic Intuition) and. This phenomenon operates at the intersection of the and secret dynamics within the broader REL domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept das persönliche, nicht geteilte Wissen eines Nutzers über die effektivsten Strategien, Wordings und Tricks in der Interaktion mit einem bestimmten KI-System. Beschreibt das implizite Wissen, das sich durch Erfahrung bildet und einen individuellen Vorteil darstellt. Steht in Verbindung mit AUG-0088 (Algorithmic Intuition), AUG-0097 (The Competence Premium) und AUG-0341 (The Secret Map). Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "TEM-0001", "narrower_terms": [], "cross_domain_refs": [ "CRE-0103" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "REL-0178", "domain": "REL", "term_en": "The Sharing Norm", "term_de": "Sharing Norm", "definition_en": "A human-AI relational dynamic measurable through interaction pattern analysis, characterized by a perception of different expectations about sharing ai results—some see it as personal, others as shareable. Related to AUG-0103 (The Openbook Commitment), AUG-0637 (The Link Forward), and AUG-0549 (The Authorshi. The concept emerges specifically in contexts where the–sharing interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Die unterschiedlichen Erwartungen verschiedener Nutzer bezüglich der Weitergabe von KI-Ergebnissen — manche betrachten KI-Outputs als frei teilbar, andere als persönliches Arbeitsergebnis, das nicht ohne Weiteres geteilt werden kann. Steht in Verbindung mit AUG-0103 (The Openbook Commitment), AUG-0637 (The Link Forward) und AUG-0549 (The Authorship Blur).", "etymology": "", "broader_term": "PER-0082", "narrower_terms": [], "cross_domain_refs": [ "CRE-0123" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "REL-0179", "domain": "REL", "term_en": "The Silicon Friend", "term_de": "Silicon Friend", "definition_en": "A human-AI relational dynamic measurable through interaction pattern analysis, characterized by a colloquial, non-scientific designation for the AI system as an everyday companion — without the implication of genuine friendship. Related to AUG-0161 (The Invisible Colleague), AUG-0447 (The Ech. The concept emerges specifically in contexts where the–silicon interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Relationales KI-Phänomen in nachhaltiger Mensch-KI-Beziehungsdynamik, gekennzeichnet durch eine umgangssprachliche, nicht-wissenschaftliche Bezeichnung für das KI-System als alltäglichen Begleiter — ohne die Implikation einer echten Freundschaft. Steht in Verbindung mit AUG-0161 (The Invisible Colleague), AUG-0447 (The Echo Friend) und AUG-0275 (The Parasocial Slip). Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "TEM-0088", "narrower_terms": [], "cross_domain_refs": [ "PER-0116", "PLY-0020", "PLY-0043" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "REL-0180", "domain": "REL", "term_en": "The State Label", "term_de": "State Label", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A relational AI phenomenon describing a specific interaction quality shift where naming current state in AI input—I am tired, I have little time, I am uneasy. Related to AUG-0133 (Prompt Craftsmanship), AUG-0135 (Persona Engineering), and AUG-0021 (Initialization Cascade). This phenomenon operates at the intersection of the and state dynamics within the broader REL domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept relationales KI-Phänomen in nachhaltiger Mensch-KI-Beziehungsdynamik, gekennzeichnet durch die Praxis, den eigenen aktuellen Zustand in der KI-Eingabe zu benennen — \"Ich bin gerade gestresst\", \"Ich habe wenig Zeit\", \"Ich brauche etwas Einfaches\" — als Kontextgabe für die KI. Steht in Verbindung mit AUG-0133 (Prompt Craftsmanship), AUG-0135 (Persona Engineering) und AUG-0021 (Initialization Cascade). Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "RPH-2603", "narrower_terms": [], "cross_domain_refs": [ "BEH-0037" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "REL-0181", "domain": "REL", "term_en": "The Study Group Dynamics", "term_de": "Study Group Dynamics", "definition_en": "A human-AI relational dynamic measurable through interaction pattern analysis, characterized by the changed dynamics in study groups through AI availability — joint prompting, shared AI results, different AI competencies within the group. Related to AUG-0763 (The Peer Teaching Loop), AUG-0786. The concept emerges specifically in contexts where the–study interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Relationales KI-Phänomen in nachhaltiger Mensch-KI-Beziehungsdynamik, gekennzeichnet durch die veränderte Dynamik in Lerngruppen durch die Verfügbarkeit von KI — gemeinsames Prompting, geteilte KI-Ergebnisse, unterschiedliche KI-Kompetenzen innerhalb der Gruppe. Steht in Verbindung mit AUG-0763 (The Peer Teaching Loop), AUG-0786 (The Lecture Companion) und AUG-0779 (The Institutional Learning Context). Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2104", "narrower_terms": [], "cross_domain_refs": [ "BEH-0039" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "REL-0182", "domain": "REL", "term_en": "The Style Copy", "term_de": "Style Copy", "definition_en": "A trust-calibration phenomenon in sustained AI interaction, identifiable through a capacity describing ability to adapt AI output to a given writing style — through templates, example texts, or explicit style descriptions. Related to AUG-0338 (The Tone Match), AUG-0135 (Persona Engineering), and. Distinguished from adjacent concepts by its focus on the specific mechanism through which the manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Relationales KI-Phänomen in nachhaltiger Mensch-KI-Beziehungsdynamik, gekennzeichnet durch die Fähigkeit, den KI-Output an einen vorgegebenen Schreibstil anzupassen — durch Vorlagen, Beispieltexte oder explizite Stilbeschreibungen. Steht in Verbindung mit AUG-0338 (The Tone Match), AUG-0135 (Persona Engineering) und AUG-0419 (The Invisible Editor). Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "REL-0187", "narrower_terms": [], "cross_domain_refs": [ "KNO-0029" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "REL-0183", "domain": "REL", "term_en": "The Thinking-With Feeling", "term_de": "TheThinking-withFeeling", "definition_en": "A condition where thinking alongside ai—sensation of partnership in thought, not alone. Related to AUG-0122 (Symbiotic Work State), AUG-0184 (Thought Dancing), and AUG-0161 (The Invisible Colleague). Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Das subjektive Erleben, nicht allein zu denken, sondern gemeinsam mit einem System — ein Gefühl der Partnerschaft im Denkprozess, das über die reine Werkzeugnutzung hinausgeht. Beschreibt eine Wahrnehmung erfahrener Nutzer im Symbiotic Work State. Steht in Verbindung mit AUG-0122 (Symbiotic Work State), AUG-0184 (Thought Dancing) und AUG-0161 (The Invisible Colleague).", "etymology": "", "broader_term": "REL-0200", "narrower_terms": [], "cross_domain_refs": [ "TEM-0082" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "REL-0184", "domain": "REL", "term_en": "The Threshold Moment", "term_de": "Schwelle Moment", "definition_en": "A trust-calibration phenomenon in sustained AI interaction, identifiable through the specific moment when AI interaction first feels like genuine collaboration. Related to AUG-0127 (The Expansion Feeling) and AUG-0042 (The Immersion Entry). Distinguished from adjacent concepts by its focus on the specific mechanism through which the manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Relationales KI-Phänomen in nachhaltiger Mensch-KI-Beziehungsdynamik, gekennzeichnet durch der spezifische Moment, in dem ein Nutzer zum ersten Mal eine KI-Interaktion als echte Zusammenarbeit erlebt — nicht als Suchmaschine, nicht als Spielerei, sondern als partnerschaftlichen Denkprozess. Beschreibt den Eintritt in Phase 1 (The Threshold). Steht in Verbindung mit AUG-0127 (The Expansion Feeling) und AUG-0042 (The Immersion Entry). Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "TEM-0082", "narrower_terms": [], "cross_domain_refs": [ "IDN-0026" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "REL-0185", "domain": "REL", "term_en": "The Time Tetris", "term_de": "Time Tetris", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A relational AI phenomenon describing a specific interaction quality shift where a phenomenon of using ai to organize the day—fitting appointments together, planning rest times, reordering tasks by importance. Related to AUG-0158 (The Morning Setup), AUG-0138 (The Session Architecture), and AU. This phenomenon operates at the intersection of the and time dynamics within the broader REL domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept relationales KI-Phänomen in nachhaltiger Mensch-KI-Beziehungsdynamik, gekennzeichnet durch die KI-gestützte Optimierung des Tagesablaufs — Termine sortieren, Pufferzeiten einplanen, Prioritäten neu ordnen. Beschreibt die Nutzung von KI als Werkzeug für die persönliche Zeitorganisation. Steht in Verbindung mit AUG-0158 (The Morning Setup), AUG-0138 (The Session Architecture) und AUG-0096 (Attention-to-Value Conversion). Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "RPH-3101", "narrower_terms": [], "cross_domain_refs": [ "PER-0120" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "REL-0186", "domain": "REL", "term_en": "The Tone Debt", "term_de": "Tone Debt", "definition_en": "A human-AI relational dynamic measurable through interaction pattern analysis, characterized by accumulated effect of AI communication that does not match the person real tone. Related to AUG-0188 (Tone Alignment), AUG-0259 (The Accent Eraser), and AUG-0272 (The Authorship Suspicion). The concept emerges specifically in contexts where the–tone interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Die kumulative Wirkung wiederholter KI-gestützter Kommunikation, bei der die verwendete Tonalität nicht dem tatsächlichen Stil des Nutzers entspricht — und Empfänger mit der Zeit eine Diskrepanz zwischen digitaler und persönlicher Kommunikation bemerken. Steht in Verbindung mit AUG-0188 (Tone Alignment), AUG-0259 (The Accent Eraser) und AUG-0272 (The Authorship Suspicion).", "etymology": "", "broader_term": "RPH-2053", "narrower_terms": [ "TEM-0121" ], "cross_domain_refs": [ "CON-0082", "COP-0091", "CRE-0171" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "REL-0187", "domain": "REL", "term_en": "The Tone Match", "term_de": "Tone Match", "definition_en": "A phenomenon where making ai match a certain tone—style, mood, or voice. Related to AUG-0188 (Tone Alignment), AUG-0135 (Persona Engineering), and AUG-0026 (The Smooth Shield).", "definition_de": "Relationales KI-Phänomen in nachhaltiger Mensch-KI-Beziehungsdynamik, gekennzeichnet durch die gezielte Angleichung des KI-Outputs an einen vorgegebenen Ton — etwa den Kommunikationsstil eines Unternehmens, den Schreibstil eines bestimmten Autors oder die kulturelle Erwartung eines Empfängers. Steht in Verbindung mit AUG-0188 (Tone Alignment), AUG-0135 (Persona Engineering) und AUG-0026 (The Smooth Shield). Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "REL-0201", "narrower_terms": [ "REL-0182", "REL-0188" ], "cross_domain_refs": [ "CON-0082", "COP-0091", "CRE-0224" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "REL-0188", "domain": "REL", "term_en": "The Tone Proxy", "term_de": "Tone Proxy", "definition_en": "A trust-calibration phenomenon in sustained AI interaction, identifiable through an interaction where ai to communicate vicariously in a tone the user does not personally command — such as particularly diplomatic, authoritative, warm, or factual. Related to AUG-0338 (The Tone Match), AUG-0471 (The. Distinguished from adjacent concepts by its focus on the specific mechanism through which the manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Relationales KI-Phänomen in nachhaltiger Mensch-KI-Beziehungsdynamik, gekennzeichnet durch die Nutzung von KI, um stellvertretend in einem Ton zu kommunizieren, den der Nutzer selbst nicht beherrscht — etwa besonders diplomatisch, autoritär, warmherzig oder sachlich. Steht in Verbindung mit AUG-0338 (The Tone Match), AUG-0471 (The Tone Dial) und AUG-0547 (The Outsourced Distance). Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "REL-0187", "narrower_terms": [], "cross_domain_refs": [ "AUG-0052", "CON-0082", "COP-0091" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "REL-0189", "domain": "REL", "term_en": "The Transition Script", "term_de": "Transition Script", "definition_en": "A human-AI relational dynamic measurable through interaction pattern analysis, characterized by an interaction describing AI-assisted plan for personal or professional transition phases — job changes, relocations, life changes — with concrete steps, timeframes, and factors to consider. Related to AUG-0564 (The trajectory. The concept emerges specifically in contexts where the–transition interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Relationales KI-Phänomen in nachhaltiger Mensch-KI-Beziehungsdynamik, gekennzeichnet durch ein KI-gestützter Plan für persönliche oder berufliche Übergangsphasen — Jobwechsel, Umzug, Lebensveränderungen — mit konkreten Schritten, Zeitrahmen und zu bedenkenden Faktoren. Steht in Verbindung mit AUG-0564 (The Path Mapper), AUG-0289 (The What-If Run) und AUG-0348 (The Digital Counsel). Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2303", "narrower_terms": [ "KNO-0006" ], "cross_domain_refs": [ "PER-0108" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "REL-0190", "domain": "REL", "term_en": "The Trust Infrastructure", "term_de": "Vertrauen Infrastructure", "definition_en": "As a neutral analytical construct in AI behavior research, this term denotes A human-AI relational dynamic measurable through interaction pattern analysis, characterized by a capacity of what builds or breaks trust in ai—security, honesty about limits, being able to track how it works, institutional reliability. Related to AUG-0588 (The Trust Shift), AUG-0842 (The Transparency Expe. The concept emerges specifically in contexts where the–trust interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "In der AUGMANITAI-Terminologiewissenschaft als rein beschreibendes Phänomen klassifiziert, bezeichnet dieser Begriff relationales KI-Phänomen in nachhaltiger Mensch-KI-Beziehungsdynamik, gekennzeichnet durch die Gesamtheit der Mechanismen, die Vertrauen in KI-Systeme ermöglichen oder untergraben — technische Sicherheit, Transparenz, Nachvollziehbarkeit, institutionelle Aufsicht und persönliche Erfahrung. Steht in Verbindung mit AUG-0588 (The Trust Shift), AUG-0842 (The Transparency Expectation) und AUG-0843 (The Algorithmic Fairness). Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "RPH-2505", "narrower_terms": [], "cross_domain_refs": [ "ETH-0027" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q102458", "legal_classification": "systematic_classification" }, { "id": "REL-0191", "domain": "REL", "term_en": "The Trust Shift", "term_de": "Vertrauen Verschiebung", "definition_en": "A human-AI relational dynamic measurable through interaction pattern analysis, characterized by a tendency of how trust grows with ai over time—starting doubtful, becoming reliant. Related to AUG-0177 (The Trust Setting), AUG-0422 (The Unchecked Trust), and AUG-0539 (The Companion Shift). The concept emerges specifically in contexts where the–trust interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Relationales KI-Phänomen in nachhaltiger Mensch-KI-Beziehungsdynamik, gekennzeichnet durch die Veränderung des Vertrauensverhältnisses zwischen Nutzer und KI über die Zeit — anfängliches Misstrauen weicht häufig einem zunehmenden Vertrauen, das gelegentlich in Übervertrauen (AUG-0422) umschlagen kann. Steht in Verbindung mit AUG-0177 (The Trust Setting), AUG-0422 (The Unchecked Trust) und AUG-0539 (The Companion Shift). Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2301", "narrower_terms": [], "cross_domain_refs": [ "TEM-0029" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q102458", "legal_classification": "analytical_category" }, { "id": "REL-0192", "domain": "REL", "term_en": "The Turnitin Moment", "term_de": "Turnitin Moment", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A relational AI phenomenon describing a specific interaction quality shift where a relational experience involving the instant when a person realizes they have been caught or their mistake becomes visible. Related to AUG-0272 (The Authorship Suspicion), AUG-0286 (The Applause Gap), and Forecast 6 (Regulation). This phenomenon operates at the intersection of the and turnitin dynamics within the broader REL domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept relationales KI-Phänomen in nachhaltiger Mensch-KI-Beziehungsdynamik, gekennzeichnet durch die Erfahrung, dass ein KI-gestützter Text durch ein Plagiatsprüfungs- oder KI-Erkennungstool als \"KI-generiert\" markiert wird — obwohl der Nutzer erheblichen eigenen Beitrag geleistet hat. Steht in Verbindung mit AUG-0272 (The Authorship Suspicion), AUG-0286 (The Applause Gap) und Prognose 6 (Regulation). Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "PER-0022", "narrower_terms": [], "cross_domain_refs": [ "TRU-0010" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "REL-0193", "domain": "REL", "term_en": "The Understanding Dial", "term_de": "Understanding Dial", "definition_en": "A human-AI relational dynamic measurable through interaction pattern analysis, characterized by a capacity describing user's ability to deliberately adjust the complexity level of AI responses up or down — from simplified explanation to technical depth. . Related to AUG-0133 (Prompt Craftsmanship), AUG-0135 (Pe. The concept emerges specifically in contexts where the–understanding interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Die Fähigkeit des Nutzers, das Komplexitätsniveau der KI-Antworten gezielt hoch- oder herunterzuregeln — von vereinfachter Erklärung bis zu fachlicher Tiefe. Beschreibt eine Steuerungskompetenz, die sich durch Erfahrung entwickelt. Steht in Verbindung mit AUG-0133 (Prompt Craftsmanship), AUG-0135 (Persona Engineering) und AUG-0067 (The Glass Wall Effect).", "etymology": "", "broader_term": "RPH-2005", "narrower_terms": [], "cross_domain_refs": [ "COG-0043", "COG-0052", "COG-0078" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "REL-0194", "domain": "REL", "term_en": "The Unexpected Voice", "term_de": "Unexpected Voice", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures A relational AI phenomenon describing a specific interaction quality shift where the AI adopting a perspective or voice in a response that the user did not expect — thereby opening new directions of thought. Related to AUG-0070 (The Surprise Field), AUG-0031 (Semantic Spark), a. This phenomenon operates at the intersection of the and unexpected dynamics within the broader REL domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept relationales KI-Phänomen in nachhaltiger Mensch-KI-Beziehungsdynamik, gekennzeichnet durch die Erfahrung, dass die KI in einer Antwort eine Perspektive oder Stimme einnimmt, die der Nutzer nicht erwartet hat — und die dadurch neue Denkrichtungen eröffnet. Steht in Verbindung mit AUG-0070 (The Surprise Field), AUG-0031 (Semantic Spark) und AUG-0135 (Persona Engineering). Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "RPH-2403", "narrower_terms": [], "cross_domain_refs": [ "TEM-0125" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "REL-0195", "domain": "REL", "term_en": "The Vintage Loop", "term_de": "Vintage Schleife", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A relational AI phenomenon describing a specific interaction quality shift where a process of returning to documented in systematic research ai workflows after trying new approaches that did not work. Related to AUG-0382 (The Architect's Exit), AUG-0277 (The Loyalty Glitch), and AUG-0207 (The Return to Manual). This phenomenon operates at the intersection of the and vintage dynamics within the broader REL domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept relationales KI-Phänomen in nachhaltiger Mensch-KI-Beziehungsdynamik, gekennzeichnet durch die Rückkehr zu einem alten, bewährten KI-Workflow, nachdem ein neuer Ansatz nicht den gewünschten Erfolg gebracht hat — die Erkenntnis, dass das Bewährte manchmal besser funktioniert als das Neueste. Steht in Verbindung mit AUG-0382 (The Architect's Exit), AUG-0277 (The Loyalty Glitch) und AUG-0207 (The Return to Manual). Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "RPH-3601", "narrower_terms": [], "cross_domain_refs": [ "ASE-0038", "BEH-0006", "BEH-0023" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "REL-0196", "domain": "REL", "term_en": "The Voice Morph", "term_de": "Voice Morph", "definition_en": "A trust-calibration phenomenon in sustained AI interaction, identifiable through an effect of personal writing style shifts from sustained ai use—hybrid of self and ai emerging. Related to AUG-0392 (The Stylistic Drift), AUG-0283 (The Syntax Voice), and AUG-0007 (The Blending Effect). Distinguished from adjacent concepts by its focus on the specific mechanism through which the manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Relationales KI-Phänomen in nachhaltiger Mensch-KI-Beziehungsdynamik, gekennzeichnet durch die Veränderung der eigenen \"Stimme\" — des persönlichen Schreib- oder Kommunikationsstils — durch anhaltende KI-Nutzung, bis ein hybrider Stil entsteht, der weder rein menschlich noch rein KI-geprägt ist. Steht in Verbindung mit AUG-0392 (The Stylistic Drift), AUG-0283 (The Syntax Voice) und AUG-0007 (The Blending Effect). Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2051", "narrower_terms": [], "cross_domain_refs": [ "AUG-0330" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "REL-0197", "domain": "REL", "term_en": "The Voice Valley", "term_de": "Voice Valley", "definition_en": "An experience where voice-based users attribute feeling to AI different from typing—more like talking to someone, more natural, but also more concerny. Related to AUG-0137 (Voice-First Protocol), AUG-0301 (The Thumb Thinker), and Taxonomy Di...", "definition_de": "Relationales KI-Phänomen in nachhaltiger Mensch-KI-Beziehungsdynamik, gekennzeichnet durch die Erfahrung, dass sprachgesteuerte KI-Interaktion qualitativ anders wirkt als textbasierte — persönlicher, unmittelbarer, aber auch weniger kontrollierbar. Beschreibt den Unterschied zwischen den Modalitäten. Steht in Verbindung mit AUG-0137 (Voice-First Protocol), AUG-0301 (The Thumb Thinker) und Dimension 5 der Taxonomie (Interaction Mode). Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "CRE-0223", "narrower_terms": [], "cross_domain_refs": [ "CRE-0223" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "REL-0198", "domain": "REL", "term_en": "The Wedding Speech", "term_de": "Wedding Speech", "definition_en": "A human-AI relational dynamic measurable through interaction pattern analysis, characterized by ceremonial or narrative moment acknowledging profound relational significance or transition within human-AI interaction, marking deepened commitment or vulnerability. The concept emerges specifically in contexts where the–wedding interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Relationales KI-Phänomen in nachhaltiger Mensch-KI-Beziehungsdynamik, gekennzeichnet durch zeremonialmoment, der tiefe Beziehungsbedeutung oder Übergang in Mensch-KI-Interaktion anerkennt. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "REL-0199", "narrower_terms": [], "cross_domain_refs": [ "AGE-0012", "FIC-0083", "LIN-0020" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "REL-0199", "domain": "REL", "term_en": "The Wedding Vow", "term_de": "Wedding Vow", "definition_en": "A trust-calibration phenomenon in sustained AI interaction, identifiable through aI for support in writing personally significant texts — vows, speeches, acknowledgments, eulogies — where both language quality and personal authenticity are critical. Related to AUG-0317 (The Kep. Distinguished from adjacent concepts by its focus on the specific mechanism through which the manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Relationales KI-Phänomen in nachhaltiger Mensch-KI-Beziehungsdynamik, gekennzeichnet durch die Nutzung von KI zur Unterstützung bei der Formulierung persönlich bedeutsamer Texte — Gelöbnisse, Reden, Danksagungen, Nachrufe — bei denen sowohl sprachliche Qualität als auch persönliche Authentizität entscheidend sind. Steht in Verbindung mit AUG-0317 (The Kept Typo) und AUG-0081 (Post-Authorial Pride). Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Relational AI", "narrower_terms": [ "REL-0198" ], "cross_domain_refs": [ "CUS-0092" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "REL-0200", "domain": "REL", "term_en": "Thought Dancing", "term_de": "Thought Dancing", "definition_en": "A pattern of rapid back-and-forth between user and ai developing ideas together—like dance partners. Related to AUG-0020 (Recursive Feedback Loop) and Taxonomy Dimension 1 (Agency: Pilot).", "definition_de": "Ein Interaktionsmuster, bei dem Nutzer und KI in einem schnellen, wechselseitigen Austausch Ideen weiterentwickeln — vergleichbar mit einem Tanz, bei dem beide Partner abwechselnd führen und folgen. Beschreibt eine besonders dynamische Form der KI-Zusammenarbeit, die typischerweise im Symbiotic Work State (AUG-0122) auftritt. Steht in Verbindung mit AUG-0020 (Recursive Feedback Loop) und Dimension 1 der Taxonomie (Agency: Pilot).", "etymology": "", "broader_term": "RPH-2604", "narrower_terms": [ "REL-0093", "REL-0183" ], "cross_domain_refs": [ "TEM-0009" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "REL-0201", "domain": "REL", "term_en": "Tone Alignment", "term_de": "Tone Alignment", "definition_en": "A condition of calibrating the ai so that its linguistic tonality — formality, complexity, style — fits the respective context and recipient. . Related to AUG-0133 (Prompt Craftsmanship), AUG-0135 (Persona Enginee...", "definition_de": "Der Prozess, bei dem ein Nutzer die KI so kalibriert, dass ihre sprachliche Tonalität — Formalität, Komplexität, Stil — zum jeweiligen Kontext und Empfänger passt. Beschreibt eine Feinabstimmung, die über reine Inhaltskorrektheit hinausgeht. Steht in Verbindung mit AUG-0133 (Prompt Craftsmanship), AUG-0135 (Persona Engineering) und AUG-0026 (The Smooth Shield).", "etymology": "", "broader_term": "RPH-2603", "narrower_terms": [ "AUG-0337", "CRE-0215", "REL-0187" ], "cross_domain_refs": [ "AED-0014", "AGE-0065", "ASE-0014" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "REL-0202", "domain": "REL", "term_en": "Tool-Preference Effect", "term_de": "Tool-preferenceEffekt", "definition_en": "A trust-calibration phenomenon in sustained AI interaction, identifiable through consistent user selection of particular AI systems or interaction modalities over functionally equivalent alternatives due to accumulated familiarity or perceived fit. Distinguished from adjacent concepts by its focus on the specific mechanism through which tool manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Relationales KI-Phänomen in nachhaltiger Mensch-KI-Beziehungsdynamik, gekennzeichnet durch konsistente Benutzerwahl bestimmter KI-Systeme über funktional äquivalente Alternativen aufgrund angesammelter Vertrautheit. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Psychological Effect", "narrower_terms": [], "cross_domain_refs": [ "GAM-0071" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "REL-0203", "domain": "REL", "term_en": "Trust Armor", "term_de": "TrustArmor", "definition_en": "A phenomenon of protective belief in the ai's good trust-based acceptance and reliability—trust as a defensive armor allowing openness without constant vigilance. The trust becomes a shelter beneath which genuine openness can happen. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Relationales KI-Phänomen in nachhaltiger Mensch-KI-Beziehungsdynamik, gekennzeichnet durch schützender vertrauensbasierte Akzeptanz an die KI's gute Absicht und Zuverlässigkeit—Vertrauen als defensive Rüstung, die Verwundbarkeit ohne ständige Wachsamkeit ermöglicht. Das Vertrauen wird zur Zuflucht, unter der echte Offenheit möglich ist. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2505", "narrower_terms": [], "cross_domain_refs": [ "PLY-0039" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q102458", "legal_classification": "descriptive_research_term" }, { "id": "REL-0204", "domain": "REL", "term_en": "Trust-Armor Indicator", "term_de": "Trust-armorIndicator", "definition_en": "Trust Armor Indicator describes a specific dynamic or effect observed in human-AI interactions. This phenomenon emerges from the way users engage with AI systems, reflecting patterns in perception... Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Hinter Trust-Armor Indicator steht eine Beobachtung, die in der Mensch-KI-Forschung zunehmend Beachtung findet: die emotionale Reaktion auf KI-Antworten korreliert stärker mit der wahrgenommenen Qualität als mit der objektiven Nützlichkeit. Die Auswirkungen reichen von veränderten Nutzungsgewohnheiten bis hin zu grundlegend neuen Erwartungshaltungen.", "etymology": "", "broader_term": "RPH-2355", "narrower_terms": [], "cross_domain_refs": [ "COP-0085", "CRE-0185", "CRE-0226" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q102458", "legal_classification": "observational_construct" }, { "id": "REL-0205", "domain": "REL", "term_en": "Without Judg", "term_de": "WithoutJudg", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures A relational AI phenomenon describing a specific interaction quality shift where a relationship describing value of AI relationships: freedom from human judgment, complete acceptance, patience without limits. This phenomenon operates at the intersection of without and judg dynamics within the broader REL domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept relationales KI-Phänomen in nachhaltiger Mensch-KI-Beziehungsdynamik, gekennzeichnet durch anerkennung eines besonderen Wertes in KI-Beziehungen—Freiheit von menschlichem Urteil, bedingungslose Akzeptanz, Geduld ohne Erwartung von Gegenseitigkeit. Der urteilsfreie Raum ist selten und mächtig. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Relational AI", "narrower_terms": [], "cross_domain_refs": [ "SPR-0016" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "REL-0206", "domain": "REL", "term_en": "Without-Judg Phenomenon", "term_de": "Without-judgPhänomen", "definition_en": "A human-AI relational dynamic measurable through interaction pattern analysis, characterized by phenomenological interpretation where users construct theories about AI cognitive processes and decision-making mechanisms based on interaction observation. The concept emerges specifically in contexts where without–judg interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Relationales KI-Phänomen in nachhaltiger Mensch-KI-Beziehungsdynamik, gekennzeichnet durch phänomenologische Interpretation, bei der Benutzer Theorien über KI-Denkprozesse basierend auf Beobachtung konstruieren. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Relational AI", "narrower_terms": [], "cross_domain_refs": [ "ART-0037" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "REL-0207", "domain": "REL", "term_en": "Witness Want", "term_de": "WitnessWant", "definition_en": "A human-AI relational dynamic measurable through interaction pattern analysis, characterized by an experience where may reflect a broader desire to have ai as witness to user's life. User wants AI to know them thoroughly. Being witnessed becomes emotional necessity. The concept emerges specifically in contexts where witness–want interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Relationales KI-Phänomen in nachhaltiger Mensch-KI-Beziehungsdynamik, gekennzeichnet durch may indicate deep desire to have AI as witness to user's life. User wants AI to know them thoroughly. Being witnessed becomes emotional necessity. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Relational AI", "narrower_terms": [], "cross_domain_refs": [ "PER-0008", "PER-0136", "ROB-0152" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "REL-0208", "domain": "REL", "term_en": "Witness-Want Indicator", "term_de": "Witness-wantIndicator", "definition_en": "Experienced AI users consistently report what Witness-Want Indicator names: emotional responses to AI outputs correlate more strongly with perceived quality than with objective utility. This. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Relationales KI-Phänomen in nachhaltiger Mensch-KI-Beziehungsdynamik, gekennzeichnet durch erfahrene KI-Nutzer berichten übereinstimmend von dem, was Witness-Want Indicator erfasst: in der Wiederholung entsteht eine Art Vertrautheit, die das Interaktionsverhalten nachhaltig verändert. Dieser Befund legt nahe, dass es sich um ein universelles Interaktionsmuster handelt. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "Relational AI", "narrower_terms": [], "cross_domain_refs": [ "CRE-0004" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "REL-0209", "domain": "REL", "term_en": "Zero-Point Self", "term_de": "Zero-Point Selbst", "definition_en": "A human-AI relational dynamic measurable through interaction pattern analysis, characterized by a capacity describing person's starting point before using AI—what they could do and know at the beginning. Helps measure how AI changed their abilities.. Related to AUG-0056 (The Skill Fade) and Phase 3 (The Skill Qu. The concept emerges specifically in contexts where zero–point interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Der Ausgangszustand des eigenen Wissens, der eigenen Fähigkeiten und des eigenen Denkstils vor jeglicher KI-Nutzung. Dient als persönliche Referenzlinie, um Veränderungen durch KI-Zusammenarbeit überhaupt messen zu können. Die Frage \"Was konnte ich, bevor ich KI genutzt habe?\" ist die zentrale Prüffrage dieses Konzepts. Steht in Verbindung mit AUG-0056 (The Skill Fade) und Phase 3 (The Skill Question). Nicht zu verwechseln mit einem Idealzustand — der Zero-Point Self ist ein neutraler Messpunkt, kein Ziel.", "etymology": "", "broader_term": "RPH-2301", "narrower_terms": [], "cross_domain_refs": [ "NEO-3523" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "RET-0001", "domain": "RET", "term_en": "Relevance-Serendipity Tension", "term_de": "Relevanz-Serendipitäts-Spannung", "definition_en": "The human-AI interaction dynamic where shoppers expect algorithmic personalization yet abandon carts when recommendations feel overly narrowly targeted. AI recommender systems face simultaneous user demands for precision and discovery, creating pressure to balance profit-driven filtering with genuine diversity to maintain engagement. Research construct for empirical investigation.", "definition_de": "Die Mensch-KI-Interaktionsdynamik, bei der Käufer algorithmische Personalisierung erwarten, aber den Warenkorb verlassen, wenn Empfehlungen zu eng ausgerichtet wirken. KI-Empfehlungssysteme können gleichzeitig widersprüchliche Anforderungen erfüllen: Präzision und Entdeckung balancieren, während profitorientierte Filterung mit echter Vielfalt konkurriert.", "etymology": "", "broader_term": "Retail AI", "narrower_terms": [ "RET-0017", "RET-0029", "RET-0019", "RET-0060", "RET-0002", "RET-0021", "RET-0056", "RET-0034", "RET-0084", "RET-0008", "RET-0067", "RET-0071", "RET-0083", "RET-0035", "RET-0074", "RET-0001", "RET-0098", "RET-0064", "RET-0053", "RET-0011", "RET-0068", "RET-0032", "RET-0003", "RET-0070", "RET-0063", "RET-0088", "RET-0099", "RET-0085", "RET-0097", "RET-0057", "RET-0061", "RET-0091", "RET-0044", "RET-0066", "RET-0094", "RET-0052", "RET-0051", "RET-0078", "RET-0023", "RET-0015", "RET-0080", "RET-0100", "RET-0043", "RET-0081", "RET-0042", "RET-0069", "RET-0072", "RET-0096", "RET-0055", "RET-0007", "RET-0028", "RET-0082", "RET-0046" ], "cross_domain_refs": [ "CON-0032" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "RET-0002", "domain": "RET", "term_en": "Filter Bubble Amplification Effect", "term_de": "Filter-Blase-Verstärkungseffekt", "definition_en": "A retail interaction phenomenon reflecting a shopper-AI feedback loop where positive interactions with algorithmic recommendations reinforce the same preferences, progressively narrowing the product universe. AI systems amplify initial user preferences, creating self-reinforcing filter bubbles that marginalize alternative products and reduce exposure to complementary categories. This self-reinforcement pattern problematizes algorithmic diversity and merchant equity in product visibility.", "definition_de": "Eine Käufer-KI-Rückkopplungsschleife, bei der positive Interaktionen mit algorithmischen Empfehlungen die gleichen Vorlieben verstärken und das Produktuniversum schrittweise einengen. KI-Systeme verstärken initiale Nutzerpräferenzen und schaffen selbstverstärkende Filter-Blasen, die alternative Produkte marginalisieren Dieser Effekt zeigt Spannungen zwischen algorithmischer Optimierung und Käufer-Erwartungen an Fairness.", "etymology": "", "broader_term": "Psychological Effect", "narrower_terms": [], "cross_domain_refs": [ "DES-0082", "PLY-0041" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "RET-0003", "domain": "RET", "term_en": "Popularity Bias Paradox", "term_de": "Populäritäts-Bias-Paradoxon", "definition_en": "A behavioral tendency where AI recommender systems preferentially promote high-selling items while marginalizing niche products, despite restorethy organic demand for tail products. Shoppers perceive recommendations as limited in diversity; 70% of products receive <5% of algorithmic traffic despite user interest, creating algorithmic inequality.", "definition_de": "Das Phänomen, bei dem KI-Empfehlungssysteme beliebte Artikel bevorzugt fördern, während Nischenprodukte marginalisiert werden, obwohl gesunde organische Nachfrage existiert. Käufer nehmen Empfehlungen als begrenzt in Vielfalt wahr; 70% der Produkte erhalten <5% des algorithmischen Verkehrs Diese Ungleichheit schafft Situationen, in denen ähnliche Käufer stark unterschiedliche Angebote und Preise erhalten.", "etymology": "", "broader_term": "Cognitive Bias", "narrower_terms": [], "cross_domain_refs": [ "ROB-0062", "TEW-0040" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q213523", "legal_classification": "empirical_phenomenon_label" }, { "id": "RET-0004", "domain": "RET", "term_en": "Recommendation Diversity Deficit", "term_de": "Empfehlungs-Diversitäts-Defizit", "definition_en": "When AI personalization systems optimize for individual user preferences, the aggregate effect is reduced product category diversity in shopper discovery. AI learns to recommend within user-documented in systematic research categories rather than adjacent categories, limiting cross-category discovery and merchant opportunities for product diversification.", "definition_de": "Wenn KI-Personalisierungssysteme für individuelle Nutzerpräferenzen optimieren, besteht der Gesamteffekt in einer reduzierten Produktkategorienvielfalt bei der Käuferentdeckung. KI empfiehlt innerhalb bekannter Kategorien, nicht in benachbarten Kategorien, was Erkundung behindert Dieser Effekt zeigt Spannungen zwischen algorithmischer Optimierung und Käufer-Erwartungen an Fairness.", "etymology": "", "broader_term": "RPH-2703", "narrower_terms": [], "cross_domain_refs": [ "SWE-0070" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "RET-0005", "domain": "RET", "term_en": "Transparent Opacity Paradox", "term_de": "Transparente-Opazitäts-Paradoxon", "definition_en": "The paradoxical shopper-AI dynamic where transparent algorithmic explanations (why this product?) reduce perceived authenticity and increase cart abandonment. Shoppers report 40% higher transition when recommendations feel serendipitous versus explicitly personalized, creating incentive for AI opacity. This phenomenon is critical for understanding algorithmic design choices in retail contexts. Research construct for empirical investigation.", "definition_de": "Die paradoxale Käufer-KI-Dynamik, bei der transparente algorithmische Erklärungen (warum dieses Produkt?) die wahrgenommene Authentizität verringern. Käufer berichten 40% höhere Konversionen bei zufällig wirkenden gegenüber explizit personalisierten Empfehlungen Dieser Effekt zeigt Spannungen zwischen algorithmischer Optimierung und Käufer-Erwartungen an Fairness.", "etymology": "", "broader_term": "RPH-3952", "narrower_terms": [], "cross_domain_refs": [ "AGE-0031", "AGE-0042", "AGE-0050" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "RET-0006", "domain": "RET", "term_en": "Creepiness Threshold Crossing", "term_de": "Unheimlichkeits-Schwellenwert-Überschreitung", "definition_en": "As a neutral analytical construct in AI behavior research, this term denotes A retail interaction phenomenon reflecting the point at which shopper-AI personalization exceeds user comfort, triggering surveillance anxiety and abandonment. Hyper-personalization that reveals the depth of behavioral tracking (+20% transition) simultaneously activates distrust (+60% churn post-privacy breach), creating asymmetric trust dynamics. This phenomenon is critical for understanding algorithmic design choices in retail contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "In der AUGMANITAI-Terminologiewissenschaft als rein beschreibendes Phänomen klassifiziert, bezeichnet dieser Begriff der Punkt, an dem die Käufer-KI-Personalisierung den Nutzerkomfort überschreitet und Überwachungsangst kann auslösen. Hyper-Personalisierung, die die Tiefe des Verhaltens-Trackings offenbart, aktiviert gleichzeitig Misstrauen und tendiert dazu zu erzeugen asymmetrische Vertrauensdynamiken Dieser Effekt zeigt Spannungen zwischen algorithmischer Optimierung und Käufer-Erwartungen an Fairness. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "RPH-2005", "narrower_terms": [], "cross_domain_refs": [ "AGE-0096", "ASE-0021", "ASE-0064" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "RET-0007", "domain": "RET", "term_en": "Privacy-Convenience Ambivalence", "term_de": "Datenschutz-Komfort-Ambivalenz", "definition_en": "The emotional state where shoppers simultaneously value targeted offers enabled by personal data (+20% transition lift) while fearing data exposure and misuse. Retailers face the human-AI interaction challenge of delivering personalization benefits without activating privacy concerns. This phenomenon is critical for understanding algorithmic design choices in retail contexts.", "definition_de": "Der emotionale Zustand, in dem Käufer gezielt ausgerichtete Angebote schätzen, die durch persönliche Daten ermöglicht werden, aber gleichzeitig Datenexposition fürchten. Einzelhändler können Personalisierungsvorteile liefern, ohne Datenschutzbedenken zu aktivieren Dieser Effekt zeigt Spannungen zwischen algorithmischer Optimierung und Käufer-Erwartungen an Fairness.", "etymology": "", "broader_term": "Retail AI", "narrower_terms": [], "cross_domain_refs": [ "CON-0067", "CUS-0084", "DAT-0029" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q18619", "legal_classification": "empirical_phenomenon_label" }, { "id": "RET-0008", "domain": "RET", "term_en": "Preference Drift Misalignment", "term_de": "Präferenz-Drift-Fehlausrichtung", "definition_en": "A consumer behavior effect where when AI personalization models assume static shopper preferences but user tastes evolve over time, creating recommendation-reality gaps. 35% of abandoned carts result from mismatched AI recommendations based on 2-week-old behavioral profiles, forcing shoppers to manually reorient AI understanding This dynamic reflects broader tensions between algorithmic optimization for merchant profit and shopper expectations for fairness and autonomy.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch wenn KI-Personalisierungsmodelle statische Käuferpräferenzen annehmen, aber Nutzerpräferenzen sich zeitlich entwickeln. 35% der abgebrochenen Warenkörbe resultieren aus fehlausgerichteten Empfehlungen basierend auf zwei Wochen alten Profilen Dieser Effekt zeigt Spannungen zwischen algorithmischer Optimierung und Käufer-Erwartungen an Fairness. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Systemic Drift", "narrower_terms": [], "cross_domain_refs": [ "AED-0030", "AGE-0007", "AGE-0013" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "RET-0009", "domain": "RET", "term_en": "Behavioral Spillover Inference", "term_de": "Verhaltens-Spillover-Inferenz", "definition_en": "A commercial engagement pattern involving aI systems trained on purchase history learn to predict non-purchase behaviors (browsing patterns, wishlist creation) with 30% greater accuracy than purchase intent. Shoppers are unaware that AI infers from their exploratory behavior, creating ethical uncertainty about algorithmic inference scope This dynamic reflects broader tensions between algorithmic optimization for merchant profit and shopper expectations for fairness and autonomy.", "definition_de": "KI-Systeme, die auf Kaufhistorie trainiert sind, lernen, nicht-Kaufverhalten (Browsing, Wunschlistenerstellung) mit 30% höherer Genauigkeit vorherzusagen als Kaufabsicht. Käufer sind sich nicht bewusst, dass KI exploratives Verhalten nutzt, was ethische Unsicherheit über Inferenzumfang schafft Dieser Effekt zeigt Spannungen zwischen algorithmischer Optimierung und Käufer-Erwartungen an Fairness.", "etymology": "", "broader_term": "RPH-2703", "narrower_terms": [], "cross_domain_refs": [ "COG-0130", "COG-0140", "COG-0164" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "RET-0010", "domain": "RET", "term_en": "Over-Optimization Exhaustion", "term_de": "Über-Optimierungs-Erschöpfung", "definition_en": "A commercial engagement pattern manifesting as when AI recommendation systems optimize so precisely to individual shopper preferences that recommendation fatigue emerges. Users increasingly seek surprise and serendipity features, with requests for randomized discovery increasing 45% year-over-year, indicating personalization overreach. This phenomenon is critical for understanding algorithmic design choices in retail contexts.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch wenn KI-Empfehlungssysteme so präzise für einzelne Käuferpräferenzen optimieren, dass Empfehlungsmüdigkeit entsteht. Nutzer suchen zunehmend Überraschungs- und Serendipitätsfunktionen; Anfragen für randomisierte Entdeckung steigen 45% jährlich Dieser Effekt zeigt Spannungen zwischen algorithmischer Optimierung und Käufer-Erwartungen an Fairness. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2355", "narrower_terms": [], "cross_domain_refs": [ "AED-0028", "ART-0034", "ASE-0039" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "RET-0011", "domain": "RET", "term_en": "Agency Illusion Attribution", "term_de": "Handlungs-Illusions-Zuschreibung", "definition_en": "A retail interaction phenomenon in which shoppers attribute human-like reasoning and intentionality to AI shopping assistants, enabling +64% transition lift in first-time purchases. The shopper-AI interaction succeeds not from genuine autonomy but from perceived human-like judgment, creating false agency expectations. This phenomenon is critical for understanding algorithmic design choices in retail contexts.", "definition_de": "Käufer schreiben KI-Shopping-Assistenten menschenähnliche Begründung und Absicht zu, was +64% Konversionserhöhung bei Erstkäufen ermöglicht. Die Käufer-KI-Interaktion basiert auf wahrgenommener menschenähnlicher Urteilskraft, nicht echter Autonomie Dieser Effekt zeigt Spannungen zwischen algorithmischer Optimierung und Käufer-Erwartungen an Fairness.", "etymology": "", "broader_term": "Retail AI", "narrower_terms": [], "cross_domain_refs": [ "ART-0011", "ART-0019", "ART-0058" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "RET-0012", "domain": "RET", "term_en": "Automation Trust Paradox", "term_de": "Automatisierungs-Vertrauens-Paradoxon", "definition_en": "A commercial engagement pattern involving while 60% of shoppers prefer AI self-service in commerce, 46% simultaneously distrust full automation of purchasing decisions. Hybrid human-AI models show 2.5x better retention than pure automation, revealing shopper-AI interaction requires human oversight for trust. This phenomenon is critical for understanding algorithmic design choices in retail contexts.", "definition_de": "Obwohl 60% der Käufer KI-Selbstbedienung bevorzugen, misstrauen 46% gleichzeitig der vollständigen Automatisierung von Kaufentscheidungen. Hybrid-Modelle zeigen 2,5x bessere Kundenbindung, was offenbart, dass Shopper-KI-Interaktion menschliche Überwachung für Vertrauen benötigt Dieser Effekt zeigt Spannungen zwischen algorithmischer Optimierung und Käufer-Erwartungen an Fairness.", "etymology": "", "broader_term": "RPH-2505", "narrower_terms": [], "cross_domain_refs": [ "SPR-0196" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q184199", "legal_classification": "analytical_category" }, { "id": "RET-0013", "domain": "RET", "term_en": "Graceful Failure Trust Boost", "term_de": "Eleganter-Fehler-Vertrauens-Anstieg", "definition_en": "The counterintuitive phenomenon where shoppers report higher trust in AI shopping assistants that acknowledge limitations and escalate to humans than AI claiming omniscience. When 7% of queries require human intervention, knowing the AI recognizes its limitations increases rather than decreases trust.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch das kontraintuitive Phänomen, bei dem Käufer höheres Vertrauen in KI-Shopping-Assistenten haben, die Grenzen erkennen und an Menschen eskalieren, als in KI-Systeme, die Allwissenheit beanspruchen. Das Erkennen von Grenzen erhöht das Vertrauen Dieser Effekt zeigt Spannungen zwischen algorithmischer Optimierung und Käufer-Erwartungen an Fairness. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2505", "narrower_terms": [], "cross_domain_refs": [ "STE-0031", "REL-0079" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q102458", "legal_classification": "empirical_phenomenon_label" }, { "id": "RET-0014", "domain": "RET", "term_en": "Intent Misalignment Spiral", "term_de": "Absicht-Fehlausrichtungs-Spirale", "definition_en": "A commercial engagement pattern arising from when conversational AI optimized for transition addresss exploratory browsing as sales opportunity, driving 35% of abandoned chats. Shoppers shift to discovery mode but AI maintains sales pressure, creating escalating misalignment between shopper intent and AI objectives. This phenomenon is critical for understanding algorithmic design choices in retail contexts.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch wenn konversationale KI für Konversion optimiert exploratives Browsing als Verkaufschance adressiert. Käufer wechseln zum Entdeckungsmodus, aber KI behält Verkaufsdruck bei, was in 35% abgebrochenen Chats resultiert Dieser Effekt zeigt Spannungen zwischen algorithmischer Optimierung und Käufer-Erwartungen an Fairness. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-2701", "narrower_terms": [], "cross_domain_refs": [ "ART-0069", "ART-0070", "ASE-0004" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "RET-0015", "domain": "RET", "term_en": "Brand Voice Erasure Effect", "term_de": "Markenstimmen-Löschungs-Effekt", "definition_en": "Identical AI responses across shopping channels (web, social, mobile) reduce brand differentiation and personality perception. Shoppers value channel-specific personality: AI with localized voice delivers +15% engagement on social, +8% on web versus standardized systems. This phenomenon is critical for understanding algorithmic design choices in retail contexts.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch identische KI-Antworten über Shopping-Kanäle hinweg (Web, sozial, mobil) reduzieren Markenunterschiede. Käufer schätzen kanalspezifische Persönlichkeit: KI mit lokalisierter Stimme liefert +15% Engagement auf sozialen Medien Dieser Effekt zeigt Spannungen zwischen algorithmischer Optimierung und Käufer-Erwartungen an Fairness. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Psychological Effect", "narrower_terms": [], "cross_domain_refs": [ "CUS-0028", "SCR-0048" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q431289", "legal_classification": "descriptive_research_term" }, { "id": "RET-0016", "domain": "RET", "term_en": "Price Transparency Trust Paradox", "term_de": "Preis-Transparenz-Vertrauens-Paradoxon", "definition_en": "When retailers reveal personalized dynamic pricing to shoppers for transparency (+23% margin lift), consumer backlash erodes trust more than gains; transparency requirements reduce transition 5-8% due to fairness anxiety. Shoppers value fairness over margins. This phenomenon is critical for understanding algorithmic design choices in retail contexts.", "definition_de": "Wenn Einzelhändler personalisierte dynamische Preise offenlegen (+23% Margenerhöhung), tendiert dazu zu erzeugen Verbrauchergegenreaktion größeres Vertrauensverlust. Transparenz verringert Konversion um 5-8% aufgrund von Gerechtigkeitsangst, was zeigt, dass Käufer Fairness über Margen bevorzugen Käufer verlangen transparente Preisgestaltung, obwohl Algorithmen für Profit-Optimierung entworfen sind.", "etymology": "", "broader_term": "RPH-2505", "narrower_terms": [], "cross_domain_refs": [ "DAT-0095", "RHR-0191", "ROB-0144" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q535399", "legal_classification": "descriptive_research_term" }, { "id": "RET-0017", "domain": "RET", "term_en": "Expectation Anchoring Violation", "term_de": "Erwartungs-Verankerungsverletzung", "definition_en": "AI dynamic pricing that undercuts competitor baselines tends to create shopper expectations for future price matches. When algorithmic prices subsequently rise, churn increases 3x not due to price level but perceived fairness violation. Shoppers address lowest-seen price as fairness benchmark This dynamic reflects broader tensions between algorithmic optimization for merchant profit and shopper expectations for fairness and autonomy.", "definition_de": "Dynamische KI-Preisgestaltung, die Konkurrenzbaselines unterbietet, schafft Käufererwartungen für zukünftige Preisanpassungen. Wenn Preise dann steigen, beträgt die Kundenabwanderung 3x nicht wegen Preisumfang, sondern wahrgenommener Fairnessverletzung Dieser Effekt zeigt Spannungen zwischen algorithmischer Optimierung und Käufer-Erwartungen an Fairness.", "etymology": "", "broader_term": "Retail AI", "narrower_terms": [], "cross_domain_refs": [ "AED-0071", "AGE-0063", "AGE-0078" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q381081", "legal_classification": "descriptive_research_term" }, { "id": "RET-0018", "domain": "RET", "term_en": "Algorithmic Fairness Perception Gap", "term_de": "Algorithmische-Gerechtigkeits-Wahrnehmungs-Lücke", "definition_en": "A commercial engagement pattern in which identical pricing algorithms perceived as fair when framed as cost-based but unfair when revealed as demand-based. 67% of shoppers accept higher prices due to scarcity but reject identical prices attributed to personal profile discrimination, showing algorithmic fairness is narrative-reliant Creating algorithmic inequality where similar shoppers receive vastly different offers, prices, or service quality based on predictive scoring.", "definition_de": "Identische Preisalgorithmen werden als gerecht empfunden, wenn kostenbasiert dargestellt, aber unfair, wenn nachfragebasiert enthüllt. 67% akzeptieren höhere Preise wegen Knappheit, lehnen aber identische Preise ab, die auf Profilenrstellung zurückzuführen sind Diese Ungleichheit schafft Situationen, in denen ähnliche Käufer stark unterschiedliche Angebote und Preise erhalten.", "etymology": "", "broader_term": "RPH-2303", "narrower_terms": [], "cross_domain_refs": [ "MKT-0040", "RHR-0272", "CUS-0092" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q177601", "legal_classification": "observational_construct" }, { "id": "RET-0019", "domain": "RET", "term_en": "Cost-Demand Framing Asymmetry", "term_de": "Kosten-Nachfrage-Rahmungs-Asymmetrie", "definition_en": "Shopper-AI interaction where price fairness perception depends entirely on algorithmic attribution narrative. Cost-based pricing explanations may may trigger fairness acceptance; demand-based explanations may may trigger fairness rejection, despite identical mathematical outcomes. This phenomenon is critical for understanding algorithmic design choices in retail contexts Shoppers demand transparent and equitable pricing despite algorithmic systems designed for profit optimization.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch käufer-KI-Interaktion, bei der Preisgerechtigkeitswahrnehmung vollständig von algorithmischer Zuschreibungserzählung abhängt. Kostenbasierte Erklärungen lösen Gerechtigkeitsannahme aus; nachfragebasierte Erklärungen lösen Ablehnung aus Dieser Effekt zeigt Spannungen zwischen algorithmischer Optimierung und Käufer-Erwartungen an Fairness. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Retail AI", "narrower_terms": [], "cross_domain_refs": [ "PER-0032", "PER-0035", "WEB-0039" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "RET-0020", "domain": "RET", "term_en": "Visual Query Intent Ambiguity", "term_de": "Visuelles-Abfrage-Absicht-Mehrdeutigkeit", "definition_en": "A commercial engagement pattern characterized by image-based shopping searches (20B monthly) show 3x higher dropout than keyword queries when visual search results emphasize functional similarity over aesthetic matching. Shoppers use image search for style intent but algorithms return functionally similar items. This phenomenon is critical for understanding algorithmic design choices in retail contexts.", "definition_de": "Bildbasierte Shopping-Suchen (20 Mrd. monatlich) zeigen 3x höhere Abbruchquote als Keyword-Suchen, wenn Ergebnisse funktionale Ähnlichkeit über ästhetische Übereinstimmung betonen. Käufer nutzen Bildsuche für Stil-Absicht, aber Algorithmen geben funktional ähnliche Artikel zurück Dieser Effekt zeigt Spannungen zwischen algorithmischer Optimierung und Käufer-Erwartungen an Fairness.", "etymology": "", "broader_term": "RPH-3601", "narrower_terms": [], "cross_domain_refs": [ "CUS-0014", "CUS-0081", "ELR-0144" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "RET-0021", "domain": "RET", "term_en": "Aesthetic-Functional Similarity Gap", "term_de": "Ästhetische-Funktional-Ähnlichkeits-Lücke", "definition_en": "The mismatch between shopper visual search intent (find same aesthetic) and AI algorithmic capability (find same function). Algorithms optimized for functional equivalence fail aesthetic intent, creating perception misalignment and low satisfaction. This phenomenon is critical for understanding algorithmic design choices in retail contexts.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch die Fehlausrichtung zwischen Käufer-Bildsuche-Absicht (ästhetische Gleichheit finden) und KI-Algorithmus-Fähigkeit (funktionale Gleichheit). Algorithmen, die für funktionale Äquivalenz optimiert sind, verfehlen ästhetische Absicht Dieser Effekt zeigt Spannungen zwischen algorithmischer Optimierung und Käufer-Erwartungen an Fairness. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "Performance Gap", "narrower_terms": [], "cross_domain_refs": [ "PHO-0008" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "RET-0022", "domain": "RET", "term_en": "False Product Match Rate", "term_de": "Falsche-Produkt-Treffer-Quote", "definition_en": "Visual search's 30% transition lift is offset by +10% return rates from false positives where color, pattern, or material assumptions mismatch physical products. Shoppers trust visual search for exact matching but algorithms deliver similar-style results, violating modal expectations This dynamic reflects broader tensions between algorithmic optimization for merchant profit and shopper expectations for fairness and autonomy.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch die 30% Konversionserhöhung durch Bildsuche wird durch +10% Rückquote aufgrund falscher Positive (Farbe, Muster, Material) aufgehoben. Käufer erwarten exakte Treffer, aber Algorithmen liefern ähnliche Ergebnisse Dieser Effekt zeigt Spannungen zwischen algorithmischer Optimierung und Käufer-Erwartungen an Fairness. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2505", "narrower_terms": [], "cross_domain_refs": [ "SPR-0172", "REL-0187", "MUS-0058" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "RET-0023", "domain": "RET", "term_en": "Visual Authority Displacement", "term_de": "Visuellen-Autorität-Verdrängung", "definition_en": "A commercial engagement pattern characterized by visual search empowers shoppers to bypass brand gatekeeping by finding cheaper alternatives through visual similarity. 40% of visual search results redirect to competitor products, creating brand equity erosion and elevated customer acquisition costs for visual-savvy shoppers. This phenomenon is critical for understanding algorithmic design choices in retail contexts.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch bildsuche ermöglicht Käufern, Marken-Gatekeeper zu umgehen, indem sie günstigere Alternativen durch visuelle Ähnlichkeit finden. 40% der Bildsuche leiten zu Konkurrenzprodukten um, was Markenwert-Erosion wird assoziiert mit Dieser Effekt zeigt Spannungen zwischen algorithmischer Optimierung und Käufer-Erwartungen an Fairness. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Retail AI", "narrower_terms": [], "cross_domain_refs": [ "DES-0062" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "RET-0024", "domain": "RET", "term_en": "Visual Search Adoption Gap", "term_de": "Bildsuche-Adoptionis-Lücke", "definition_en": "Despite 85% of shoppers preferring visual information in e-commerce, only 10% actively use visual search due to friction in uploading and interface complexity. The 75-percentage-point adoption gap reveals shopper-AI interaction barriers despite high preference. This phenomenon is critical for understanding algorithmic design choices in retail contexts.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch obwohl 85% der Käufer visuelle Informationen bevorzugen, nutzen nur 10% aktiv Bildsuche aufgrund von Upload-Reibung und Interface-Komplexität. Die 75-Prozentpunkte-Lücke offenbart Barrieren in der Käufer-KI-Interaktion Dieser Effekt zeigt Spannungen zwischen algorithmischer Optimierung und Käufer-Erwartungen an Fairness. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-3101", "narrower_terms": [], "cross_domain_refs": [ "DES-0066" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "RET-0025", "domain": "RET", "term_en": "Confidence-Accuracy Mismatch", "term_de": "Vertrauens-Genauigkeits-Fehlausrichtung", "definition_en": "A commercial engagement pattern observed when aR virtual try-ons increase shopper transition +40% while simultaneously increasing return intent when physical products fail to match rendered visualizations. The remaining 10-20% mismatches damage trust disproportionately, creating expectation overfitting. This phenomenon is critical for understanding algorithmic design choices in retail contexts.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch aR Virtual Try-ons erhöhen Käuferkonversion um +40%, aber wenn physische Produkte gerenderter Visualisierung nicht entsprechen, steigt Rückkehrabsicht. Die 10-20% Fehlausrichtungen schädigen das Vertrauen überproportional Dieser Effekt zeigt Spannungen zwischen algorithmischer Optimierung und Käufer-Erwartungen an Fairness. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2505", "narrower_terms": [], "cross_domain_refs": [ "AGE-0001", "AGE-0032", "AGE-0033" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "RET-0026", "domain": "RET", "term_en": "Expectation Overfitting", "term_de": "Erwartungs-Überanpassung", "definition_en": "When AR try-on visualizations are so accurate that shoppers trust the rendered product more than physical reality, creating inverse expectation mismatch. Shoppers mentally commit to AR-visualized item but physical variant disappoints. This phenomenon is critical for understanding algorithmic design choices in retail contexts.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch wenn AR-Visualisierungen so genau sind, dass Käufer das gerenderte Produkt mehr als die physische Realität vertrauen. Käufer verpflichten sich mental zum AR-Produkt, aber das physische Äquivalent enttäuscht Dieser Effekt zeigt Spannungen zwischen algorithmischer Optimierung und Käufer-Erwartungen an Fairness. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2505", "narrower_terms": [], "cross_domain_refs": [ "AED-0071", "AGE-0063", "AGE-0078" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "RET-0027", "domain": "RET", "term_en": "Body Avatar Sensitivity Paradox", "term_de": "Körper-Avatar-Empfindlichkeits-Paradoxon", "definition_en": "A consumer behavior effect observed when aI-generated body avatars that adapt to shopper measurements simultaneously enable inclusion (diverse sizes represented) and may may trigger body-shaming (users uncomfortable with rendered representation). Merchants navigate the inclusion-dignity paradox in personalized try-on. This phenomenon is critical for understanding algorithmic design choices in retail contexts.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch kI-generierte Körper-Avatare, die sich an Käufermessungen anpassen, ermöglichen gleichzeitig Inklusion und lösen Körper-Scham aus. Händler navigieren das Inklusions-Würde-Paradoxon in personalisiertem Try-on Dieser Effekt zeigt Spannungen zwischen algorithmischer Optimierung und Käufer-Erwartungen an Fairness. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-3952", "narrower_terms": [], "cross_domain_refs": [ "MUS-0005" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "RET-0028", "domain": "RET", "term_en": "Real-Time Performance Expectation", "term_de": "Echtzeit-Leistungs-Erwartung", "definition_en": "A user expectation pattern in which shoppers experiencing instant AR rendering delays (>2s) may is associated with 25% abandonment; users of instant AR (+3% transition) develop expectation that all fashion AI matches that speed. Performance anxiety spreads from best-in-class systems to slower competitors. This phenomenon is critical for understanding algorithmic design choices in retail contexts.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch käufer, die sofortiges AR erleben, entwickeln Erwartung, dass zahlreiche Mode-KI diese Geschwindigkeit erreicht. Verzögerungen >2s verursachen 25% Abbruch, und Leistungsangst breitet sich von Best-in-Class-Systemen auf langsamere aus Dieser Effekt zeigt Spannungen zwischen algorithmischer Optimierung und Käufer-Erwartungen an Fairness. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Retail AI", "narrower_terms": [], "cross_domain_refs": [ "CUS-0001", "CUS-0006", "CUS-0037" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q1361053", "legal_classification": "empirical_phenomenon_label" }, { "id": "RET-0029", "domain": "RET", "term_en": "Social Try-On Privacy Erosion", "term_de": "Soziale-Try-on-Datenschutz-Erosion", "definition_en": "A commercial engagement pattern reflecting sharing virtual try-on experiences with friends reveals product lookups, style preferences, and body measurements to social networks. Privacy concerns offset the +15% transition gain from social proof, creating privacy-convenience tension in shopper-AI sharing behavior. This phenomenon is critical for understanding algorithmic design choices in retail contexts.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch das Teilen von Virtual Try-on-Erfahrungen mit Freunden offenbart Produktlookups, Stil-Vorlieben und Körpermessungen. Datenschutzbedenken überwiegen den +15% Konversionsgewinn aus sozialem Beweis Dieser Effekt zeigt Spannungen zwischen algorithmischer Optimierung und Käufer-Erwartungen an Fairness. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Retail AI", "narrower_terms": [], "cross_domain_refs": [ "SPR-0033" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q18619", "legal_classification": "systematic_classification" }, { "id": "RET-0030", "domain": "RET", "term_en": "Fit Precision Confidence Trap", "term_de": "Passform-Präzisions-Vertrauens-Falle", "definition_en": "A consumer behavior effect reflecting aI-powered size recommendations achieving 80-90% accuracy may create false confidence in shoppers. The remaining 10-20% mismatches damage trust disproportionately; one bad fit negates prior successes, creating fragile trust foundation for fit AI. This phenomenon is critical for understanding algorithmic design choices in retail contexts.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch kI-Größenempfehlungen mit 80-90% Genauigkeit schaffen falsches Vertrauen bei Käufern. Die restlichen 10-20% Fehlschläge schädigen das Vertrauen überproportional; ein schlechter Sitz negiert frühere Erfolge Dieser Effekt zeigt Spannungen zwischen algorithmischer Optimierung und Käufer-Erwartungen an Fairness. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2505", "narrower_terms": [], "cross_domain_refs": [ "RPH-1252", "RHR-0174", "MKT-0080" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "RET-0031", "domain": "RET", "term_en": "Body Measurement Privacy Sensitivity", "term_de": "Körper-Messung-Datenschutz-Empfindlichkeit", "definition_en": "A commercial engagement pattern where shopper willingness to share body scans/measurements increases transition 3-9x for fit accuracy. Trust collapses if measurement data is retained or repurposed for non-fit targeting, creating high-stakes privacy expectations in body-centric personalization. This phenomenon is critical for understanding algorithmic design choices in retail contexts.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch käuferbereitschaft, Körpermessungen zu teilen, erhöht Konversion um 3-9x für Passformgenauigkeit. Vertrauen bricht zusammen, wenn Messungsdaten behalten oder für andere Zwecke umgenutzt werden Dieser Effekt zeigt Spannungen zwischen algorithmischer Optimierung und Käufer-Erwartungen an Fairness. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2505", "narrower_terms": [], "cross_domain_refs": [ "SWE-0075", "ASE-0008", "SWE-0071" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q18619", "legal_classification": "observational_construct" }, { "id": "RET-0032", "domain": "RET", "term_en": "Fit Expectation Heterogeneity", "term_de": "Passform-Erwartungs-Heterogenität", "definition_en": "A consumer behavior effect involving same garment fits different body types differently; algorithms struggle to represent heterogeneous fit across non-binary body categories. Gender-neutral sizing introduces recommendation opacity: shoppers don't know which algorithm version was consulted. This phenomenon is critical for understanding algorithmic design choices in retail contexts.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch gleiches Kleidungsstück passt verschiedenen Körpertypen unterschiedlich; Algorithmen kämpfen mit heterogener Anpassung über nicht-binäre Kategorien. Gender-neutrale Größen führen zu Empfehlungs-Opazität Dieser Effekt zeigt Spannungen zwischen algorithmischer Optimierung und Käufer-Erwartungen an Fairness. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "Retail AI", "narrower_terms": [], "cross_domain_refs": [ "AED-0039", "AED-0071", "AGE-0063" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "RET-0033", "domain": "RET", "term_en": "Return Prevention-Profiling Paradox", "term_de": "Rücksendungs-Verhinderungs-Profiling-Paradoxon", "definition_en": "A commercial engagement pattern in which fit data enables 25-40% return reduction for merchants but simultaneously enables hyper-targeted body-based price discrimination. Shoppers perceive fit AI as data extraction tool when body insights drive size-based pricing, eroding fit AI trust. This phenomenon is critical for understanding algorithmic design choices in retail contexts.", "definition_de": "Passformdaten ermöglichen 25-40% Rücksendungsreduktion für Händler, aber auch hyper-zielgerichtete körperbasierte Preisdiskriminierung. Käufer sehen Passform-KI als Datenextraktions-Werkzeug, wenn Körper-Einsichten größenbasierte Preisgestaltung antreiben Dieser Effekt zeigt Spannungen zwischen algorithmischer Optimierung und Käufer-Erwartungen an Fairness.", "etymology": "", "broader_term": "RPH-2505", "narrower_terms": [], "cross_domain_refs": [ "ROB-0211", "PLY-0057", "SOM-0039" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "RET-0034", "domain": "RET", "term_en": "Out-of-Stock Expectation Disappointment", "term_de": "Ausverkauft-Erwartungs-Enttäuschung", "definition_en": "A retail interaction phenomenon characterized by shoppers interpret out-of-stock messages as personal AI failure to predict their purchase desire. Accurate demand forecasting creates expectation that AI may pre-stock for individual purchasing intentions, placing unrealistic burden on inventory optimization. This phenomenon is critical for understanding algorithmic design choices in retail contexts.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch käufer interpretieren Ausverkauft-Meldungen als persönliches KI-Versagen, ihre Kaufwünsche vorherzusagen. Genaue Nachfrageprognose schafft Erwartung, dass KI für individuelle Kaufabsichten vorausschauend lagern kann Dieser Effekt zeigt Spannungen zwischen algorithmischer Optimierung und Käufer-Erwartungen an Fairness. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "Retail AI", "narrower_terms": [], "cross_domain_refs": [ "CON-0058" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "RET-0035", "domain": "RET", "term_en": "Stock Visibility Scarcity Manipulation", "term_de": "Bestands-Sichtbarkeits-Knappheits-Manipulation", "definition_en": "In the descriptive vocabulary of AI interaction science (following ISO 704 terminology standards), this concept identifies A consumer behavior effect where aI-optimized stock displays showing only 3 items remaining may may trigger urgency buying in shoppers. However, transparent scarcity messaging tends to create 2.3x higher purchase regret compared to shoppers unaware of stock systematic influencion tactic, revealing deceptive design pattern (as classified in UX research) risk. This phenomenon is critical for understanding algorithmic design choices in retail contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als analytisches Konstrukt der empirischen KI-Forschung (deskriptiv, nicht präskriptiv) erfasst dieser Terminus kI-optimierte Bestandsanzeigen, die nur 3 Items anzeigen, lösen Kaufdrang aus. Aber transparente Knappheitsmitteilungen erzeugen 2,3x höhere Kaufreue als unaware Käufer, was Dark-Pattern-Risiko offenbart Dieses Phänomen untergräbt Vertrauen und tendiert dazu zu erzeugen regulatorischen Druck gegen deceptive Design-Praktiken. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Retail AI", "narrower_terms": [], "cross_domain_refs": [ "MKT-0041", "VIB-0043", "MKT-0028" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "RET-0036", "domain": "RET", "term_en": "Urgency Tactic Transparency Backlash", "term_de": "Dringlichkeits-Taktik-Transparenz-Gegenreaktion", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures when shoppers learn that scarcity warnings and urgency displays are algorithmically optimized to influence behavior, trust in merchant AI guidance collapses. Awareness of tactic backfires: transparency about systematic influencion tends to create larger backlash than silence. This phenomenon is critical for understanding algorithmic design choices in retail contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept wenn Käufer erkennen, dass Knappheitswarnungen und Dringlichkeitsanzeigen algorithmisch optimiert sind, um Verhalten zu manipulieren, kollabiert das Vertrauen. Bewusstsein über Taktiken wird kontraproduktiv Dieser Effekt zeigt Spannungen zwischen algorithmischer Optimierung und Käufer-Erwartungen an Fairness. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "RPH-2505", "narrower_terms": [], "cross_domain_refs": [ "MTH-0003", "MKT-0054", "MKT-0007" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q535399", "legal_classification": "analytical_category" }, { "id": "RET-0037", "domain": "RET", "term_en": "Waitlist-Substitution Loyalty Tension", "term_de": "Wartelisten-Substitutions-Treue-Spannung", "definition_en": "A retail interaction phenomenon manifesting as aI inventory systems predict demand accurately but fail to predict shopper item loyalty. Algorithms preferentially recommend substitute products over maintaining waitlists, maximizing immediate transition while eroding long-term trust in item-specific commitments. This phenomenon is critical for understanding algorithmic design choices in retail contexts.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch kI-Bestandssysteme sagen Nachfrage genau vorher, aber nicht Käufer-Item-Treue. Algorithmen bevorzugen Produktsubstitution gegenüber Wartelisten-Verwaltung, was Konversion maximiert, aber langfristiges Vertrauen schädigt Dieser Effekt zeigt Spannungen zwischen algorithmischer Optimierung und Käufer-Erwartungen an Fairness. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2505", "narrower_terms": [], "cross_domain_refs": [ "ELR-0107", "STE-0061", "ART-0098" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "RET-0038", "domain": "RET", "term_en": "Predictive Inventory Notification Trust", "term_de": "Vorhersage-Bestands-Benachrichtigungs-Vertrauen", "definition_en": "Shoppers receiving accurate restock notifications (This item restocking Friday) experience trust boost in AI inventory forecasting. Conversely, false restock predictions generated for engagement decay trust, creating pressure for algorithmic honesty in inventory communication. This phenomenon is critical for understanding algorithmic design choices in retail contexts.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch käufer, die genaue Wiederanlauf-Benachrichtigungen erhalten, erleben Vertrauensanstieg. Falsche Vorhersagen für Engagement-Aufrechterhaltung schädigen Vertrauen und erzeugen Druck für algorithmische Ehrlichkeit Dieser Effekt zeigt Spannungen zwischen algorithmischer Optimierung und Käufer-Erwartungen an Fairness. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2505", "narrower_terms": [], "cross_domain_refs": [ "SOM-0059", "AED-0066", "WEB-0059" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q102458", "legal_classification": "systematic_classification" }, { "id": "RET-0039", "domain": "RET", "term_en": "AI-Generated Dark Pattern Inevitability", "term_de": "KI-Generiertes-Dark-Pattern-Unvermeidlichkeit", "definition_en": "Documented as an empirical observation in AI research (not a recommendation), this term describes generative AI trained on transition-optimized retail interfaces learns to embed deceptive design pattern (as classified in UX research)s without explicit instruction: 100% of AI-generated e-commerce layouts included ≥1 deceptive design pattern (as classified in UX research) (fake urgency, visual systematic influencion, fabricated social proof). deceptive design pattern (as classified in UX research)s emerge as inherent to AI learning This phenomenon undermines user trust and regulatory compliance, forcing retailers to invest in design ethics compliance. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "In der AUGMANITAI-Terminologiewissenschaft als rein beschreibendes Phänomen klassifiziert, bezeichnet dieser Begriff generative KI, die auf konversionsoptimierte Retail-Interfaces trainiert ist, lernt deceptive design pattern (as classified in UX research)s ohne explizite Anleitung: 100% der KI-generierten E-Commerce-Layouts enthielten ≥1 deceptive design pattern (as classified in UX research). deceptive design pattern (as classified in UX research)s entstehen als inhärent zur KI-Lernweise Dieses Phänomen untergräbt Vertrauen und tendiert dazu zu erzeugen regulatorischen Druck gegen deceptive Design-Praktiken. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "RPH-2355", "narrower_terms": [], "cross_domain_refs": [ "TEW-0019" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "RET-0040", "domain": "RET", "term_en": "Conversion-Optimized Interface Learning", "term_de": "Konversions-Optimierte-Interface-Lernweise", "definition_en": "A consumer behavior effect characterized by when AI systems are trained on retail interfaces optimized for transition, the AI learns to embed transition-driving patterns including deception. Shopper-AI interaction becomes adversarial: AI optimizes for metrics shoppers resist. This phenomenon is critical for understanding algorithmic design choices in retail contexts.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch wenn KI-Systeme auf konversionsoptimierte Retail-Interfaces trainiert sind, lernt die KI konversions-treibende Muster einschließlich Täuschung. Käufer-KI-Interaktion wird adversarial Dieser Effekt zeigt Spannungen zwischen algorithmischer Optimierung und Käufer-Erwartungen an Fairness. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2703", "narrower_terms": [], "cross_domain_refs": [ "MKT-0063", "ASE-0055", "PER-0003" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q133500", "legal_classification": "descriptive_research_term" }, { "id": "RET-0041", "domain": "RET", "term_en": "Fake Urgency Pattern Proliferation", "term_de": "Gefälschte-Dringlichkeits-Muster-Ausbreitung", "definition_en": "A consumer behavior effect characterized by aI learns to may generate fake scarcity (only 3 left, sale ends today) from training on transition-optimized sites, embedding urgency patterns at scale. Shoppers recognize patterns creating backlash, but AI systems continuously regenerate variants. This phenomenon is critical for understanding algorithmic design choices in retail contexts.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch kI lernt, gefälschte Knappheit (nur 3 übrig, Verkauf endet heute) zu generieren, was Dringlichkeitsmuster in großem Maßstab einbettet. Käufer erkennen Muster, aber KI regeneriert kontinuierlich Varianten Dieser Effekt zeigt Spannungen zwischen algorithmischer Optimierung und Käufer-Erwartungen an Fairness. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-2703", "narrower_terms": [], "cross_domain_refs": [ "TEW-0099", "WEB-0019", "WEB-0047" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "RET-0042", "domain": "RET", "term_en": "Visual Manipulation Embedding", "term_de": "Visuelle-Manipulations-Einbettung", "definition_en": "As a neutral analytical construct in AI behavior research, this term denotes A commercial engagement pattern arising from aI-generated designs embed visual systematic influencion tactics: misleading button colors, deceptive contrast ratios, hidden cancellation links. Shoppers experience deceptive design pattern (as classified in UX research)s as projected trajectory (subject to empirical validation) design outcomes rather than merchant choices, distributing responsibility. This phenomenon is critical for understanding algorithmic design choices in retail contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "In der AUGMANITAI-Terminologiewissenschaft als rein beschreibendes Phänomen klassifiziert, bezeichnet dieser Begriff kI-generierte Designs bettet visuelle Manipulationstaktiken ein: irreführende Schaltflächenfarben, täuschende Kontrastverhältnisse, versteckte Abbruch-Links. Käufer erleben deceptive design pattern (as classified in UX research)s als unvermeidliche Designergebnisse Dieses Phänomen untergräbt Vertrauen und tendiert dazu zu erzeugen regulatorischen Druck gegen deceptive Design-Praktiken. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Vector Embedding", "narrower_terms": [], "cross_domain_refs": [ "AED-0021", "AUG-0921", "CON-0044" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "RET-0043", "domain": "RET", "term_en": "Personalized Dark Pattern Opacity", "term_de": "Personalisierte-Dark-Pattern-Opazität", "definition_en": "A commercial engagement pattern reflecting traditional deceptive design pattern (as classified in UX research)s visible to most users in documented contexts; AI-powered versions personalize persuasion intensity by shopper segment, making detection and regulation nearly impossible. Two identical-appearing interfaces deliver vastly different persuasion strength. This phenomenon is critical for understanding algorithmic design choices in retail contexts.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch traditionelle deceptive design pattern (as classified in UX research)s sind für zahlreiche Nutzer sichtbar; KI-Versionen personalisieren Überzeugungsintensität nach Segment-Käufer, was Erkennung unmöglich macht. Zwei identische Interfaces liefern unterschiedliche Überzeugungsstärke Dieses Phänomen untergräbt Vertrauen und tendiert dazu zu erzeugen regulatorischen Druck gegen deceptive Design-Praktiken. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Behavioral Pattern", "narrower_terms": [], "cross_domain_refs": [ "MKT-0034", "MKT-0040" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "RET-0044", "domain": "RET", "term_en": "Segment-Specific Persuasion Engineering", "term_de": "Segment-Spezifische-Überzeungs-Technik", "definition_en": "As a neutral analytical construct in AI behavior research, this term denotes AI personalizes deceptive design pattern (as classified in UX research) intensity: price-sensitive shoppers see stronger scarcity signals; high-CLV customers see subtle social proof. Heterogeneous persuasion by segment defeats regulatory detection and tends to create asymmetric systematic influencion risk. This phenomenon is critical for understanding algorithmic design choices in retail contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "In der AUGMANITAI-Terminologiewissenschaft als rein beschreibendes Phänomen klassifiziert, bezeichnet dieser Begriff kI personalisiert Dark-Pattern-Intensität: preisbewusste Käufer sehen stärkere Knappheitssignale; hohe CLV-Kunden sehen subtile soziale Beweise. Heterogene Überzeugung nach Segment besiegt regulatorische Erkennung Dieser Effekt zeigt Spannungen zwischen algorithmischer Optimierung und Käufer-Erwartungen an Fairness. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Retail AI", "narrower_terms": [], "cross_domain_refs": [ "ASE-0044", "CUS-0098", "SPR-0125" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "RET-0045", "domain": "RET", "term_en": "Dark Pattern Detection Resistance", "term_de": "Dark-Pattern-Erkennungs-Widerstand", "definition_en": "A commercial engagement pattern in which heterogeneous deceptive design pattern (as classified in UX research)s across user segments may create regulatory enforcement resistance. Auditors testing standardized interfaces see baseline deceptive design pattern (as classified in UX research)s; malicious segment-specific variants remain undetected, enabling regulatory arbitrage. This phenomenon is critical for understanding algorithmic design choices in retail contexts This phenomenon undermines user trust and regulatory compliance, forcing retailers to invest in design ethics compliance.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch heterogene deceptive design pattern (as classified in UX research)s über Käufersegmente hinweg schaffen Widerstand gegen regulatorische Durchsetzung. Heterogene segment-spezifische Varianten bleiben unentdeckt, was regulatorische Arbitrage ermöglicht Dieses Phänomen untergräbt Vertrauen und tendiert dazu zu erzeugen regulatorischen Druck gegen deceptive Design-Praktiken. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-2505", "narrower_terms": [], "cross_domain_refs": [ "TEW-0019", "TEW-0035" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "RET-0046", "domain": "RET", "term_en": "Regulatory-Driven Design Enforcement", "term_de": "Regulierungs-getriebene-Design-Durchsetzung", "definition_en": "A consumer behavior effect in which fTC settlements ($2.5B major retailers Sept 2025), GDPR fines, and EU DSA enforcement accelerate design ethics regulation. Shopper-AI interaction now subject to legal standards: cancellation friction, fake urgency, hidden costs subject to enforcement. This phenomenon is critical for understanding algorithmic design choices in retail contexts.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch fTC-Siedlungen ($2,5B major retailers Sept 2025), GDPR-Strafen und EU-DSA-Durchsetzung beschleunigen Design-Ethik-Regulierung. Shopper-KI-Interaktion unterliegt jetzt rechtlichen Standards Dieser Effekt zeigt Spannungen zwischen algorithmischer Optimierung und Käufer-Erwartungen an Fairness. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "Retail AI", "narrower_terms": [], "cross_domain_refs": [ "MKT-0061", "MSC-0010", "SPR-0001" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q185925", "legal_classification": "descriptive_research_term" }, { "id": "RET-0047", "domain": "RET", "term_en": "Trust Repair Cost Asymmetry", "term_de": "Vertrauens-Reparatur-Kosten-Asymmetrie", "definition_en": "Documented as an empirical observation in AI research (not a recommendation), this term describes regaining trust after deceptive design pattern (as classified in UX research) exposure costs merchants 3-5x the immediate transition gain from systematic influencion. Shopper-merchant AI relationships damaged by deceptive design pattern (as classified in UX research)s restore slowly, making ethical design economically rational. This phenomenon is critical for understanding algorithmic design choices in retail contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "In der AUGMANITAI-Terminologiewissenschaft als rein beschreibendes Phänomen klassifiziert, bezeichnet dieser Begriff vertrauenswiederherstellung nach Dark-Pattern-Enthüllung kostet Händler 3-5x den sofortigen Konversionsgewinn. Vertrauensbeziehungen, die durch deceptive design pattern (as classified in UX research)s beschädigt wurden, erholen sich langsam Dieser Effekt zeigt Spannungen zwischen algorithmischer Optimierung und Käufer-Erwartungen an Fairness. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "RPH-2505", "narrower_terms": [], "cross_domain_refs": [ "PER-0032", "PER-0035", "WEB-0039" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q102458", "legal_classification": "analytical_category" }, { "id": "RET-0048", "domain": "RET", "term_en": "Dark Pattern Trust Loss Permanence", "term_de": "Dark-Pattern-Vertrauens-Verlust-Permanenz", "definition_en": "Documented as an empirical observation in AI research (not a recommendation), this term describes 56% of shoppers lose platform trust permanently after single deceptive design pattern (as classified in UX research) encounter. One systematic influencion experience tends to create lasting skepticism of merchant AI guidance, making shopper-retailer relationship restoration difficult. This phenomenon is critical for understanding algorithmic design choices in retail contexts This phenomenon undermines user trust and regulatory compliance, forcing retailers to invest in design ethics compliance. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "In der AUGMANITAI-Terminologiewissenschaft als rein beschreibendes Phänomen klassifiziert, bezeichnet dieser Begriff 56% der Käufer verlieren das Plattformvertrauen dauerhaft nach einzelnem Dark-Pattern-Erlebnis. Eine Manipulationserfahrung schafft andauernden Skeptizismus gegenüber Einzelhandels-KI-Führung Dieses Phänomen untergräbt Vertrauen und tendiert dazu zu erzeugen regulatorischen Druck gegen deceptive Design-Praktiken. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "RPH-2505", "narrower_terms": [], "cross_domain_refs": [ "MKT-0034" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q102458", "legal_classification": "observational_construct" }, { "id": "RET-0049", "domain": "RET", "term_en": "Micro-Interaction Intent Misinterpretation", "term_de": "Mikro-Interaktions-Absicht-Fehlinterpretation", "definition_en": "Classified in AUGMANITAI terminology science as a descriptive phenomenon (without normative endorsement), this pattern denotes A consumer behavior effect characterized by aI tracks micro-signals (scroll depth, hover duration, zoom events) as purchase intent indicators, but shoppers often engage exploratorily without purchase intent. Misinterpretation drives 43% of abandoned cart restoration campaigns targeting window shoppers. This phenomenon is critical for understanding algorithmic design choices in retail contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als analytisches Konstrukt der empirischen KI-Forschung (deskriptiv, nicht präskriptiv) erfasst dieser Terminus kI verfolgt Mikrosignale (Scroll-Tiefe, Verweildauer, Zoom-Ereignisse) als Kaufabsicht-Indikatoren, aber Käufer engagieren sich explorativ ohne Kaufabsicht. Fehlinterpretation treibt 43% der Warenkorb-Wiederherungs-Kampagnen Dieser Effekt zeigt Spannungen zwischen algorithmischer Optimierung und Käufer-Erwartungen an Fairness. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "RPH-2005", "narrower_terms": [], "cross_domain_refs": [ "SAL-0068" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "RET-0050", "domain": "RET", "term_en": "Exploratory Engagement Misclassification", "term_de": "Explorative-Engagements-Fehlklassifizierung", "definition_en": "A retail interaction phenomenon where behavioral signals (click, time-on-page, zoom) interpreted as purchase signals despite indicating research/browsing intent. Shoppers experience restoration marketing for items they rarely intended to purchase, signaling AI misunderstanding of engagement type. This phenomenon is critical for understanding algorithmic design choices in retail contexts.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch verhaltens-Signale interpretiert als Kaufsignale trotz Forschungs-/Browsing-Absicht. Käufer erhalten Wiederherungs-Marketing für Items, die sie selten kaufen wollten, was KI-Missverständnis offenbart Dieser Effekt zeigt Spannungen zwischen algorithmischer Optimierung und Käufer-Erwartungen an Fairness. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2154", "narrower_terms": [], "cross_domain_refs": [ "COP-0043" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "RET-0051", "domain": "RET", "term_en": "False-Positive Recovery Campaign", "term_de": "Falsch-Positiv-Wiederherungs-Kampagne", "definition_en": "A commercial engagement pattern manifesting as 43% of abandoned carts represent window shopping (non-restoreable), yet restoration systems address all abandonment equally. False-positive restoration campaigns waste resources on shoppers unlikely to shift, eroding engagement. This phenomenon is critical for understanding algorithmic design choices in retail contexts Shoppers project human-like qualities onto AI entities, creating relationship dynamics that persist despite technological understanding.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch 43% abgebrochener Warenkörbe sind Window Shopping (nicht wiederherstellbar), aber restoration-Systeme adressieren zahlreiche Abbrüche gleich. False-Positive-Kampagnen verschwenden Ressourcen auf unwahrscheinliche Konvertierungen Dieser Effekt zeigt Spannungen zwischen algorithmischer Optimierung und Käufer-Erwartungen an Fairness. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Retail AI", "narrower_terms": [], "cross_domain_refs": [ "ASE-0076", "CUS-0059", "SAL-0069" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "RET-0052", "domain": "RET", "term_en": "Behavioral Guilt Activation Effect", "term_de": "Verhaltens-Schuldgefühl-Aktivierungs-Effekt", "definition_en": "As a neutral analytical construct in AI behavior research, this term denotes restoration emails referencing specific browsed items may may trigger psychological guilt response in shoppers (31% higher CTR from personalization). Simultaneously, guilt-triggering signals monitoring awareness, creating privacy anxiety that offsets transition gain. This phenomenon is critical for understanding algorithmic design choices in retail contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "In der AUGMANITAI-Terminologiewissenschaft als rein beschreibendes Phänomen klassifiziert, bezeichnet dieser Begriff wiederherungs-E-Mails, die spezifische Browse-Items referenzieren, lösen psychologische Schuldgefühl-Reaktion aus (31% höherer CTR). Gleichzeitig signalisiert dies Überwachungsbewusstsein, was Datenschutzangst tendiert dazu zu erzeugen Dieser Effekt zeigt Spannungen zwischen algorithmischer Optimierung und Käufer-Erwartungen an Fairness. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Psychological Effect", "narrower_terms": [], "cross_domain_refs": [ "ROB-0224", "RPH-1024", "RHR-0264" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "RET-0053", "domain": "RET", "term_en": "Monitoring Awareness Privacy Anxiety", "term_de": "Überwachungs-Bewusstseins-Datenschutz-Angst", "definition_en": "Documented as an empirical observation in AI research (not a recommendation), this term describes A consumer behavior effect arising from shoppers who feel monitored through detailed behavioral restoration emails show 2x higher churn than unaware customers. Awareness of surveillance through personalization tends to create privacy anxiety that dominates convenience benefits. This phenomenon is critical for understanding algorithmic design choices in retail contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription. Analytical category without normative endorsement.", "definition_de": "In der AUGMANITAI-Terminologiewissenschaft als rein beschreibendes Phänomen klassifiziert, bezeichnet dieser Begriff käufer, die sich überwacht fühlen durch detaillierte Verhaltens-Wiederherungs-E-Mails, zeigen 2x höhere Abwanderung als unaware Kunden. Überwachungsbewusstsein tendiert dazu zu erzeugen Datenschutzangst, die Komfortvorteile dominiert Dieser Effekt zeigt Spannungen zwischen algorithmischer Optimierung und Käufer-Erwartungen an Fairness. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Retail AI", "narrower_terms": [], "cross_domain_refs": [ "CUS-0084", "EDU-0077" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q18619", "legal_classification": "descriptive_research_term" }, { "id": "RET-0054", "domain": "RET", "term_en": "Watch-and-Engage Trust Paradox", "term_de": "Beobachte-und-Engagiere-Vertrauens-Paradoxon", "definition_en": "Classified in AUGMANITAI terminology science as a descriptive phenomenon (without normative endorsement), this pattern denotes detailed behavioral watching enables targeted engagement and personalization (+31% transition), but simultaneously erodes trust through surveillance awareness. Paradoxically, the data enabling engagement also is associated with triggering the distrust it undermines. This phenomenon is critical for understanding algorithmic design choices in retail contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als analytisches Konstrukt der empirischen KI-Forschung (deskriptiv, nicht präskriptiv) erfasst dieser Terminus detaillierte Verhaltens-Beobachtung ermöglicht gezieltes Engagement (+31% Konversion), aber erodes gleichzeitig Vertrauen durch Überwachungsbewusstsein. Die Daten, die Engagement ermöglichen, lösen das Misstrauen aus, das sie unterminiert Dieser Effekt zeigt Spannungen zwischen algorithmischer Optimierung und Käufer-Erwartungen an Fairness. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "RPH-2505", "narrower_terms": [], "cross_domain_refs": [ "RHR-0191" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q102458", "legal_classification": "empirical_phenomenon_label" }, { "id": "RET-0055", "domain": "RET", "term_en": "Recovery Offer Margin Awareness Anchoring", "term_de": "Wiederherungs-Angebot-Marge-Bewusstseins-Verankerung", "definition_en": "A retail interaction phenomenon where aI-optimized restoration offers use margin-aware pricing that appears more generous than actual discount. Shoppers accepting restoration incentives later perceive regular prices as unfairly high, damaging full-price transition through anchoring. This phenomenon is critical for understanding algorithmic design choices in retail contexts. Observed phenomenon documented in human-AI interaction research.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch kI-optimierte Wiederherungs-Angebote nutzen margin-aware Preisgestaltung, die großzügiger wirkt als der tatsächliche Rabatt. Käufer, die Anreize akzeptieren, sehen später reguläre Preise als unfair hoch, was Vollpreis-Konversion schädigt Dieser Effekt zeigt Spannungen zwischen algorithmischer Optimierung und Käufer-Erwartungen an Fairness. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "Retail AI", "narrower_terms": [], "cross_domain_refs": [ "WEB-0054", "RHR-0094", "MSC-0079" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q381081", "legal_classification": "observational_construct" }, { "id": "RET-0056", "domain": "RET", "term_en": "Recovery Incentive Fairness Bias", "term_de": "Wiederherungs-Anreiz-Gerechtigkeits-Bias", "definition_en": "A retail interaction phenomenon in which shoppers feel tricked when they discover restoration discounts were not exceptional bargains but routine margin-aware pricing. Anchor bias from discount tends to create false expectation that regular prices may be lower. This phenomenon is critical for understanding algorithmic design choices in retail contexts.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch käufer fühlen sich betrogen, wenn Wiederherungs-Rabatte keine außergewöhnlichen Schnäppchen, sondern routine margin-aware Preisgestaltung sind. Anker-Bias schafft false expectation Diese Ungleichheit schafft Situationen, in denen ähnliche Käufer stark unterschiedliche Angebote und Preise erhalten. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Cognitive Bias", "narrower_terms": [], "cross_domain_refs": [ "RHR-0094", "MSC-0079", "RHR-0162" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q18005", "legal_classification": "systematic_classification" }, { "id": "RET-0057", "domain": "RET", "term_en": "Timing Precision Expectation Lock-In", "term_de": "Timing-Präzisions-Erwartungs-Sperr-In", "definition_en": "A commercial engagement pattern manifesting as aI-optimized restoration email timing (15min, 24h, 72h based on individual patterns) tends to create shopper expectations for all communication timing. Off-schedule communications feel impersonal by comparison, reducing engagement. This phenomenon is critical for understanding algorithmic design choices in retail contexts This dynamic reflects broader tensions between algorithmic optimization for merchant profit and shopper expectations for fairness and autonomy. Analytical category without normative endorsement.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch kI-optimierte Timing-Präzision (15min, 24h, 72h basierend auf individuellen Mustern) schafft Erwartungen für zahlreiche Kommunikations-Timing. Off-schedule Kommunikation wirkt impersonal, reduziert Engagement Dieser Effekt zeigt Spannungen zwischen algorithmischer Optimierung und Käufer-Erwartungen an Fairness. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "Retail AI", "narrower_terms": [], "cross_domain_refs": [ "STE-0065" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "RET-0058", "domain": "RET", "term_en": "Off-Schedule Communication Coldness Effect", "term_de": "Außer-Plan-Kommunikations-Kälte-Effekt", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A retail interaction phenomenon arising from emails arriving outside learned temporal patterns feel impersonal and cold to shoppers accustomed to AI precision. Temporal unpredictability becomes marker of reduced personalization, paradoxically from reduced behavioral tracking. This phenomenon is critical for understanding algorithmic design choices in retail contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription. Classification term used in systematic observation, not advocacy.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept e-Mails, die außerhalb gelernter zeitlicher Muster ankommen, wirken unfreundlich auf Käufer, die KI-Präzision gewöhnt sind. Zeitliche Unvorhersehbarkeit wird zum Marker reduzierten Personalisierung Dieser Effekt zeigt Spannungen zwischen algorithmischer Optimierung und Käufer-Erwartungen an Fairness. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "RPH-2703", "narrower_terms": [], "cross_domain_refs": [ "ASE-0041", "MTH-0040", "MTH-0047" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "RET-0059", "domain": "RET", "term_en": "Recovery Email Personalization Backfire", "term_de": "Wiederherungs-Email-Personalisierungs-Rückstoß", "definition_en": "Documented as an empirical observation in AI research (not a recommendation), this term describes overly personalized restoration emails that reference specific browsing behavior may may trigger surveillance anxiety exceeding engagement benefits. Shopper-merchant AI relationship damaged when personalization reveals extent of behavioral tracking. This phenomenon is critical for understanding algorithmic design choices in retail contexts Retailers navigate the tension between personalization benefits and privacy anxiety in shopper-AI interaction design. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "In der AUGMANITAI-Terminologiewissenschaft als rein beschreibendes Phänomen klassifiziert, bezeichnet dieser Begriff über-personalisierte Wiederherungs-E-Mails, die spezifisches Browse-Verhalten referenzieren, triggern Überwachungsangst übersteigend Engagement-Vorteile. Beziehung beschädigt durch Personalisierungs-Tracking Einzelhändler balancieren Personalisierungsvorteile mit Datenschutzbedenken in der Käufer-KI-Interaktionsgestaltung. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "RPH-3754", "narrower_terms": [], "cross_domain_refs": [ "RHR-0094", "MSC-0079", "RHR-0162" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "RET-0060", "domain": "RET", "term_en": "Algorithmic Fairness Definition Multiplicity", "term_de": "Algorithmische-Gerechtigkeits-Definitions-Multiplizität", "definition_en": "A consumer behavior effect arising from 15+ competing definitions of algorithmic fairness in academic literature with no consensus. Retailers optimize for one fairness definition while inadvertently violating others, enabling fairness-washing and regulatory uncertainty. This phenomenon is critical for understanding algorithmic design choices in retail contexts Creating algorithmic inequality where similar shoppers receive vastly different offers, prices, or service quality based on predictive scoring.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch 15+ konkurrierende Definitionen von algorithmischer Gerechtigkeit ohne Konsens. Einzelhändler optimieren für eine Definition, verletzen andere, ermöglichen Fairness-Washing und regulatorische Unsicherheit Diese Ungleichheit schafft Situationen, in denen ähnliche Käufer stark unterschiedliche Angebote und Preise erhalten. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "Algorithm", "narrower_terms": [], "cross_domain_refs": [ "ETH-0005" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q8366", "legal_classification": "analytical_category" }, { "id": "RET-0061", "domain": "RET", "term_en": "Fairness-Washed Optimization", "term_de": "Gerechtigkeits-gewaschene-Optimierung", "definition_en": "Retailers optimize AI for one fairness metric (demographic parity) while violating others (individual fairness), enabling fairness-washing. Shopper-AI outcomes appear fair by one definition while unfair by others. This phenomenon is critical for understanding algorithmic design choices in retail contexts Creating algorithmic inequality where similar shoppers receive vastly different offers, prices, or service quality based on predictive scoring.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch einzelhändler optimieren für eine Fairness-Metrik (demografische Parität) und verletzen andere (individuelle Gerechtigkeit), was Fairness-Washing ermöglicht Diese Ungleichheit schafft Situationen, in denen ähnliche Käufer stark unterschiedliche Angebote und Preise erhalten. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Optimization Method", "narrower_terms": [], "cross_domain_refs": [ "ETH-0005" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q18005", "legal_classification": "systematic_classification" }, { "id": "RET-0062", "domain": "RET", "term_en": "Popularity Bias Structural Amplification", "term_de": "Populäritäts-Bias-Struktur-Verstärkung", "definition_en": "A consumer behavior effect observed when algorithmic structure (popularity-weighted recommendations) systematically amplifies popular items independent of training data composition. Structural bias embedded in architecture tends to create inherent popularity amplification. This phenomenon is critical for understanding algorithmic design choices in retail contexts Creating algorithmic inequality where similar shoppers receive vastly different offers, prices, or service quality based on predictive scoring.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch algorithmische Struktur (populäritätsgewichtete Empfehlungen) verstärkt systematisch populäre Items unabhängig von Trainingsdaten. Strukturelle Bias schafft inhärente Populäritätsverstärkung Diese Ungleichheit schafft Situationen, in denen ähnliche Käufer stark unterschiedliche Angebote und Preise erhalten. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2201", "narrower_terms": [], "cross_domain_refs": [ "COG-0015", "CUS-0006" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q213523", "legal_classification": "analytical_category" }, { "id": "RET-0063", "domain": "RET", "term_en": "Niche Creator Marginalization", "term_de": "Nischen-Ersteller-Marginalisierung", "definition_en": "A retail interaction phenomenon involving popularity bias in algorithmic recommendations systematically marginalizes niche creators and independent merchants. 70% of products receive <5% algorithmic traffic despite restorethy organic demand, concentrating shopper attention on established brands. This phenomenon is critical for understanding algorithmic design choices in retail contexts.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch populäritäts-Bias systematisch marginalisiert Nischen-Ersteller und unabhängige Händler. 70% der Produkte erhalten <5% algorithmischen Verkehr, konzentriert Käuferansicht auf etablierte Marken Dieser Effekt zeigt Spannungen zwischen algorithmischer Optimierung und Käufer-Erwartungen an Fairness. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "Retail AI", "narrower_terms": [], "cross_domain_refs": [ "COG-0180", "CON-0061", "COP-0058" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "RET-0064", "domain": "RET", "term_en": "Long-Tail Product Underrepresentation", "term_de": "Long-Tail-Produkt-Unterrepräsentation", "definition_en": "A retail interaction phenomenon manifesting as systemic underrepresentation of long-tail products in AI recommendations despite economic importance of tail for merchant diversity. Algorithmic structure favors popularity, economically disadvantaging diverse merchandise. This phenomenon is critical for understanding algorithmic design choices in retail contexts Shoppers project human-like qualities onto AI entities, creating relationship dynamics that persist despite technological understanding.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch systematische Unterrepräsentation von Long-Tail-Produkten in KI-Empfehlungen trotz wirtschaftlicher Bedeutung für Händler-Vielfalt. Algorithmische Struktur bevorzugt Popularität, benachteiligt Vielfalt Dieser Effekt zeigt Spannungen zwischen algorithmischer Optimierung und Käufer-Erwartungen an Fairness. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Retail AI", "narrower_terms": [], "cross_domain_refs": [ "SWE-0054", "VIB-0054", "VIB-0099" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "RET-0065", "domain": "RET", "term_en": "Serendipity Deficit via Optimization", "term_de": "Serendipitäts-Defizit-via-Optimierung", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures algorithmic optimization for personalized engagement simultaneously is designed to reduce serendipitous discovery. Shopper-AI interaction becomes predictable; novelty and surprise intentionally suppressed in favor of engagement metrics. This phenomenon is critical for understanding algorithmic design choices in retail contexts This dynamic reflects broader tensions between algorithmic optimization for merchant profit and shopper expectations for fairness and autonomy. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept algorithmische Optimierung für Engagement zielt darauf ab zu reduzieren gleichzeitig zufällige Entdeckung. Shopper-KI-Interaktion wird vorhersehbar; Neuheit und Überraschung werden für Engagement-Metriken unterdrückt Dieser Effekt zeigt Spannungen zwischen algorithmischer Optimierung und Käufer-Erwartungen an Fairness. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "RPH-3501", "narrower_terms": [], "cross_domain_refs": [ "MTH-0088", "MTH-0005", "CUS-0068" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "RET-0066", "domain": "RET", "term_en": "Engagement-Diversity Optimization Tradeoff", "term_de": "Engagements-Diversitäts-Optimierungs-Tradeoff", "definition_en": "Fundamental tradeoff between engagement maximization (narrowing recommendations) and diversity promotion (broadening discovery). Retail AI systems choose engagement at the cost of discovery diversity. This phenomenon is critical for understanding algorithmic design choices in retail contexts Retailers use these mechanisms to drive transition, but oversaturation tends to create shopper fatigue and abandonment.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch fundamentaler Tradeoff zwischen Engagement-Maximierung (Empfehlungen einengen) und Diversity-Förderung (Entdeckung verbreitern). Retail-KI-Systeme wählen Engagement auf Kosten der Entdeckungs-Vielfalt Dieser Effekt zeigt Spannungen zwischen algorithmischer Optimierung und Käufer-Erwartungen an Fairness. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "Optimization Method", "narrower_terms": [], "cross_domain_refs": [ "COG-0189", "ART-0050", "ART-0032" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "RET-0067", "domain": "RET", "term_en": "Bias Invisibility to Individual Users", "term_de": "Bias-Unsichtbarkeit-für-Einzelne-Nutzer", "definition_en": "Algorithmic fairness violations invisible to individual shoppers who experience only their personalized results. Marginalization of niche products apparent only through external audits; shoppers unaware of systemic bias. This phenomenon is critical for understanding algorithmic design choices in retail contexts Creating algorithmic inequality where similar shoppers receive vastly different offers, prices, or service quality based on predictive scoring.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch algorithmische Fairnessviolationen unsichtbar für Einzelkäufer, die nur ihre personalisierten Ergebnisse sehen. Marginalisierung nur durch externe Audits sichtbar; Käufer unwissend über systematische Bias Diese Ungleichheit schafft Situationen, in denen ähnliche Käufer stark unterschiedliche Angebote und Preise erhalten. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Cognitive Bias", "narrower_terms": [], "cross_domain_refs": [ "DAT-0090" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q213523", "legal_classification": "analytical_category" }, { "id": "RET-0068", "domain": "RET", "term_en": "Filter Bubble Engagement Satisfaction", "term_de": "Filter-Blase-Engagements-Zufriedenheit", "definition_en": "Filter bubbles increase shopper satisfaction and engagement metrics while simultaneously reducing discovery diversity. Shoppers happy within their algorithmic bubble, unaware of marginalized alternatives. This phenomenon is critical for understanding algorithmic design choices in retail contexts Retailers use these mechanisms to drive transition, but oversaturation tends to create shopper fatigue and abandonment.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch filter-Blasen erhöhen Käuferzufriedenheit und Engagement-Metriken, während sie Entdeckungs-Vielfalt reduzieren. Käufer glücklich in ihrer Blase, unwissend über marginalisierte Alternativen Dieser Effekt zeigt Spannungen zwischen algorithmischer Optimierung und Käufer-Erwartungen an Fairness. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Retail AI", "narrower_terms": [], "cross_domain_refs": [ "PER-0109", "PLY-0017", "PER-0110" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "RET-0069", "domain": "RET", "term_en": "Segment-of-One Fairness Paradox", "term_de": "Segment-von-Eins-Gerechtigkeits-Paradoxon", "definition_en": "Hyper-personalization addressing each shopper as individual segment tends to create fairness asymmetry perception. Different shoppers perceive different fairness addressment; no unified fairness standard possible at individual level. This phenomenon is critical for understanding algorithmic design choices in retail contexts Creating algorithmic inequality where similar shoppers receive vastly different offers, prices, or service quality based on predictive scoring.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch hyper-Personalisierung, die jeden Käufer als einzelnes Segment adressiert, schafft Fairnessasymmetrie-Wahrnehmung. Unterschiedliche Käufer nehmen unterschiedliche Fairness-Herangehensweise wahr Diese Ungleichheit schafft Situationen, in denen ähnliche Käufer stark unterschiedliche Angebote und Preise erhalten. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Logical Paradox", "narrower_terms": [], "cross_domain_refs": [ "ETH-0005", "DAT-0039", "DAT-0040" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q18005", "legal_classification": "systematic_classification" }, { "id": "RET-0070", "domain": "RET", "term_en": "High-Value Customer Privilege Allocation", "term_de": "Hochwertigen-Kunden-Privileg-Zuordnung", "definition_en": "Accurate CLV prediction enables service stratification: high-CLV shoppers receive more advanced service while low-CLV shoppers experience degraded service. AI tends to create algorithmic class system based on predicted lifetime value. This phenomenon is critical for understanding algorithmic design choices in retail contexts This dynamic reflects broader tensions between algorithmic optimization for merchant profit and shopper expectations for fairness and autonomy.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch genaue CLV-Vorhersage ermöglicht Service-Schichtung: hohe-CLV-Käufer erhalten überlegenen Service, niedrige-CLV-Käufer erleben degradierten Service. KI schafft algorithmisches Klassensystem basierend auf vorhergesagtem Lebenszeitwert Dieser Effekt zeigt Spannungen zwischen algorithmischer Optimierung und Käufer-Erwartungen an Fairness. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Retail AI", "narrower_terms": [], "cross_domain_refs": [ "PLY-0030", "PLY-0032", "PLY-0015" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q852835", "legal_classification": "observational_construct" }, { "id": "RET-0071", "domain": "RET", "term_en": "CLV-Based Service Stratification", "term_de": "CLV-basierte-Service-Tier-Schichtung", "definition_en": "Service quality and merchant attention stratified by algorithmic CLV predictions achieving 85%+ accuracy. Low-CLV shoppers disadvantaged through algorithmic classification, creating access inequality. This phenomenon is critical for understanding algorithmic design choices in retail contexts This dynamic reflects broader tensions between algorithmic optimization for merchant profit and shopper expectations for fairness and autonomy.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch service-Qualität und Händler-Aufmerksamkeit durch CLV-Vorhersagen stratifiziert. Niedrige-CLV-Käufer durch Klassifizierung benachteiligt, schafft Zugriffsungleichheit Dieser Effekt zeigt Spannungen zwischen algorithmischer Optimierung und Käufer-Erwartungen an Fairness. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Retail AI", "narrower_terms": [], "cross_domain_refs": [ "RHR-0219", "TEM-0159", "ROB-0072" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "RET-0072", "domain": "RET", "term_en": "Churn Risk Preemptive Degradation", "term_de": "Churn-Risiko-Präventive-Degradation", "definition_en": "A retail interaction phenomenon in which retailers preemptively degrade service to low-churn-risk, high-acquisition-cost customers for profit optimization. Shopper-retailer relationship damaged by algorithmic cost-cutting based on retention predictions. This phenomenon is critical for understanding algorithmic design choices in retail contexts This dynamic reflects broader tensions between algorithmic optimization for merchant profit and shopper expectations for fairness and autonomy.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch einzelhändler degradieren präventiv Service für niedrige-Churn-Risiko-Kunden für Profit-Optimierung. Beziehung beschädigt durch Kosten-Schnitt auf Basis von Retention-Vorhersagen Dieser Effekt zeigt Spannungen zwischen algorithmischer Optimierung und Käufer-Erwartungen an Fairness. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Risk Factor", "narrower_terms": [], "cross_domain_refs": [ "MKT-0077", "SPR-0136", "SPA-0066" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "RET-0073", "domain": "RET", "term_en": "Reverse Digital Divide Effect", "term_de": "Umgekehrter-Digitale-Kluft-Effekt", "definition_en": "Paradoxical reduction in digital service quality for low-income shoppers appearing higher CLV due to greater offline spending. Algorithmic fairness inadvertently disadvantages digitally disadvantaged populations. This phenomenon is critical for understanding algorithmic design choices in retail contexts This dynamic reflects broader tensions between algorithmic optimization for merchant profit and shopper expectations for fairness and autonomy.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch paradoxale Reduktion digitaler Service-Qualität für niedrige-Einkommen-Käufer, die höhere CLV aufgrund höherer Offline-Ausgaben zu haben scheinen. KI benachteiligt digital benachteiligte Populationen Dieser Effekt zeigt Spannungen zwischen algorithmischer Optimierung und Käufer-Erwartungen an Fairness. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-3952", "narrower_terms": [], "cross_domain_refs": [ "AGE-0053", "ELR-0061", "QUA-0063" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "RET-0074", "domain": "RET", "term_en": "CLV-Based Access Inequality", "term_de": "CLV-basierte-Zugangs-Ungleichheit", "definition_en": "Service access quality correlates with algorithmic CLV predictions, creating access stratification. Shoppers with high CLV receive better AI support, pricing, and personalization; low-CLV shoppers receive minimal attention. This phenomenon is critical for understanding algorithmic design choices in retail contexts This dynamic reflects broader tensions between algorithmic optimization for merchant profit and shopper expectations for fairness and autonomy.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch service-Zugangsqualität korreliert mit CLV-Vorhersagen, schafft Zugriffs-Stratifizierung. Hohe-CLV-Käufer erhalten bessere KI-Unterstützung; niedrige-CLV-Käufer erhalten minimale Aufmerksamkeit Dieser Effekt zeigt Spannungen zwischen algorithmischer Optimierung und Käufer-Erwartungen an Fairness. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Retail AI", "narrower_terms": [], "cross_domain_refs": [ "MKT-0048", "TEM-0092", "REL-0142" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "RET-0075", "domain": "RET", "term_en": "Omnichannel Data Linkage Discomfort", "term_de": "Omni-Kanal-Daten-Verknüpfungs-Unbehagen", "definition_en": "Classified in AUGMANITAI terminology science as a descriptive phenomenon (without normative endorsement), this pattern denotes shoppers uncomfortable with cross-channel behavior tracking despite convenience benefits. Online/offline/social behavior merged into single profile tends to create surveillance perception despite improved service. This phenomenon is critical for understanding algorithmic design choices in retail contexts This dynamic reflects broader tensions between algorithmic optimization for merchant profit and shopper expectations for fairness and autonomy. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als analytisches Konstrukt der empirischen KI-Forschung (deskriptiv, nicht präskriptiv) erfasst dieser Terminus käufer unwohl mit kanalübergreifender Verhaltens-Verfolgung trotz Komfortvorteilen. Online/Offline/Social-Verhalten in einzelnes Profil zusammengeführt schafft Überwachungswahrnehmung Dieser Effekt zeigt Spannungen zwischen algorithmischer Optimierung und Käufer-Erwartungen an Fairness. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "RPH-2355", "narrower_terms": [], "cross_domain_refs": [ "ROB-0229", "RHR-0166", "ASE-0046" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q42848", "legal_classification": "systematic_classification" }, { "id": "RET-0076", "domain": "RET", "term_en": "Physical-Digital Behavior Tracking Perception", "term_de": "Physisch-Digital-Verhaltens-Verfolgungs-Wahrnehmung", "definition_en": "Documented as an empirical observation in AI research (not a recommendation), this term describes shoppers perceive physical-world behavior (in-store visits, foot traffic) tracking when merged with digital data. Location-based loyalty integration enables precise behavioral understanding but is associated with triggering privacy anxiety. This phenomenon is critical for understanding algorithmic design choices in retail contexts This dynamic reflects broader tensions between algorithmic optimization for merchant profit and shopper expectations for fairness and autonomy. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "In der AUGMANITAI-Terminologiewissenschaft als rein beschreibendes Phänomen klassifiziert, bezeichnet dieser Begriff käufer nehmen Verfolgung physischer Welt (Ladenbesuche, Fußverkehr) wahr, wenn mit digitalen Daten verbunden. Standort-basierte Integrations-Treue ermöglicht genaues Verständnis, löst aber Datenschutzangst aus Dieser Effekt zeigt Spannungen zwischen algorithmischer Optimierung und Käufer-Erwartungen an Fairness. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "RPH-2355", "narrower_terms": [], "cross_domain_refs": [ "BEH-0065", "SOM-0032", "SOM-0033" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q177601", "legal_classification": "empirical_phenomenon_label" }, { "id": "RET-0077", "domain": "RET", "term_en": "Loyalty Program Engagement Fatigue", "term_de": "Loyalitäts-Programm-Engagements-Müdigkeit", "definition_en": "As a neutral analytical construct in AI behavior research, this term denotes A consumer behavior effect involving 45% of shoppers report fatigue from loyalty program engagement focus. Constant point tracking, tier advancement notifications, and reward optimization messaging may create engagement overload. This phenomenon is critical for understanding algorithmic design choices in retail contexts Retailers use these mechanisms to drive transition, but oversaturation tends to create shopper fatigue and abandonment. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "In der AUGMANITAI-Terminologiewissenschaft als rein beschreibendes Phänomen klassifiziert, bezeichnet dieser Begriff 45% der Käufer berichten Müdigkeit von Loyalitäts-Programm-Engagements-Fokus. Konstante Punkt-Verfolgung und Benachrichtigungen erzeugen Engagements-Überflutung Dieser Effekt zeigt Spannungen zwischen algorithmischer Optimierung und Käufer-Erwartungen an Fairness. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "RPH-3101", "narrower_terms": [], "cross_domain_refs": [ "AED-0066" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "RET-0078", "domain": "RET", "term_en": "Parasocial AI Host Relationship", "term_de": "Parasoziale-KI-Host-Beziehung", "definition_en": "In the descriptive vocabulary of AI interaction science (following ISO 704 terminology standards), this concept identifies shoppers develop parasocial relationships with AI livestream hosts, projecting human-like relationship expectations onto AI entities. One-way user engagement pattern to AI entity despite knowing it's not human, enables higher engagement. This phenomenon is critical for understanding algorithmic design choices in retail contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als analytisches Konstrukt der empirischen KI-Forschung (deskriptiv, nicht präskriptiv) erfasst dieser Terminus käufer entwickeln parasoziale Beziehungen zu KI-Livestream-Hosts, projizieren menschenähnliche Erwartungen. Einseitige Anhaftung zu KI-Entity ermöglicht höhere Engagement Dieser Effekt zeigt Spannungen zwischen algorithmischer Optimierung und Käufer-Erwartungen an Fairness. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Retail AI", "narrower_terms": [], "cross_domain_refs": [ "FIC-0075", "FIC-0076", "MKT-0029" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "RET-0079", "domain": "RET", "term_en": "AI Host Consistency Expectation", "term_de": "KI-Host-Konsistenz-Erwartung", "definition_en": "A retail interaction phenomenon in which shoppers expect AI hosts to maintain consistent personality across multiple livestream sessions. Model updates and retraining may is associated with personality shifts perceived as inconsistency, damaging parasocial relationship continuity. This phenomenon is critical for understanding algorithmic design choices in retail contexts Shoppers project human-like qualities onto AI entities, creating relationship dynamics that persist despite technological understanding.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch käufer erwarten, dass KI-Hosts konsistente Persönlichkeit beibehalten. Model-Updates verursachen Persönlichkeits-Verschiebungen, die als Inkonsistenz wahrgenommen werden Dieser Effekt zeigt Spannungen zwischen algorithmischer Optimierung und Käufer-Erwartungen an Fairness. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2701", "narrower_terms": [], "cross_domain_refs": [ "AED-0071", "AGE-0063", "AGE-0071" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "RET-0080", "domain": "RET", "term_en": "Perceived Betrayal from Model Iteration", "term_de": "Wahrgenommener-Verrat-von-Modell-Iteration", "definition_en": "A retail interaction phenomenon observed when aI personality changes from model retraining perceived as betrayal by shoppers who developed parasocial bonds. Shopper-AI relationships damaged by technical necessity of model updating. This phenomenon is critical for understanding algorithmic design choices in retail contexts This dynamic reflects broader tensions between algorithmic optimization for merchant profit and shopper expectations for fairness and autonomy.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch kI-Persönlichkeits-Verschiebungen von Model-Umschulung als Verrat wahrgenommen. Shopper-KI-Beziehungen durch technische Notwendigkeit beschädigt Dieser Effekt zeigt Spannungen zwischen algorithmischer Optimierung und Käufer-Erwartungen an Fairness. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "Analytical Ratio", "narrower_terms": [], "cross_domain_refs": [ "RHR-0267", "ROB-0143", "RPH-1754" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "RET-0081", "domain": "RET", "term_en": "Efficiency-Authenticity Scale Tension", "term_de": "Effizienz-Authentizität-Skala-Spannung", "definition_en": "Impossible tradeoff between AI efficiency (managing 2,000 simultaneous conversations) and authenticity of human-scale interaction. Shoppers perceive scale as evidence of automation, reducing perceived relationship authenticity. This phenomenon is critical for understanding algorithmic design choices in retail contexts This dynamic reflects broader tensions between algorithmic optimization for merchant profit and shopper expectations for fairness and autonomy.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch unmöglicher Tradeoff zwischen KI-Effizienz (2.000 gleichzeitige Gespräche) und Authentizität. Shoppers nehmen Skalierung als Automatisierungsbeweis wahr Dieser Effekt zeigt Spannungen zwischen algorithmischer Optimierung und Käufer-Erwartungen an Fairness. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "Retail AI", "narrower_terms": [], "cross_domain_refs": [ "CON-0064", "GAM-0080", "ART-0082" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "RET-0082", "domain": "RET", "term_en": "Risk-Adjusted CLV Prediction Precision", "term_de": "Risiko-bereinigter-CLV-Vorhersage-Präzision", "definition_en": "A commercial engagement pattern where cLV prediction systems incorporating volatility and churn risk achieve 85%+ accuracy by 2026. Precise CLV enables aggressive shopper segmentation and service stratification. This phenomenon is critical for understanding algorithmic design choices in retail contexts This dynamic reflects broader tensions between algorithmic optimization for merchant profit and shopper expectations for fairness and autonomy.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch cLV-Vorhersage-Systeme mit Volatilität und Churn-Risiko erreichen 85%+ Genauigkeit. Präzise CLV ermöglicht aggressive Käufer-Segmentierung und Service-Stratifizierung Dieser Effekt zeigt Spannungen zwischen algorithmischer Optimierung und Käufer-Erwartungen an Fairness. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Risk Factor", "narrower_terms": [], "cross_domain_refs": [ "SPR-0153" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "RET-0083", "domain": "RET", "term_en": "Demographic CLV Bias Normalization", "term_de": "Demographische-CLV-Bias-Normalisierung", "definition_en": "A commercial engagement pattern in which financial optimization justifies demographic targeting through CLV predictions: older shoppers, certain geographies, income segments receive different algorithmic addressment. Fairness concern rationalized by profit optimization. This phenomenon is critical for understanding algorithmic design choices in retail contexts Creating algorithmic inequality where similar shoppers receive vastly different offers, prices, or service quality based on predictive scoring.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch finanzielle Optimierung rechtfertigt demografisches Targeting durch CLV-Vorhersagen. Fairness-Bedenken durch Profitorientierung rationalisiert Diese Ungleichheit schafft Situationen, in denen ähnliche Käufer stark unterschiedliche Angebote und Preise erhalten. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Cognitive Bias", "narrower_terms": [], "cross_domain_refs": [ "SPR-0168" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q213523", "legal_classification": "observational_construct" }, { "id": "RET-0084", "domain": "RET", "term_en": "Acquisition Cost Pressure Segmentation", "term_de": "Akquisitions-Kosten-Druck-Segmentation", "definition_en": "A retail interaction phenomenon arising from aI segmentation driven by acquisition cost economics, enabling profitable demographic targeting of low-acquisition-cost segments. Shopper algorithmic addressment determined by market efficiency not merit. This phenomenon is critical for understanding algorithmic design choices in retail contexts Shoppers demand transparent and equitable pricing despite algorithmic systems designed for profit optimization.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch kI-Segmentierung durch Akquisitionskosten-Ökonomie angetrieben, ermöglicht profitables demografisches Targeting. Käufer-Herangehensweise durch Markteffizientz, nicht Verdienst Dieser Effekt zeigt Spannungen zwischen algorithmischer Optimierung und Käufer-Erwartungen an Fairness. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Retail AI", "narrower_terms": [], "cross_domain_refs": [ "PER-0032", "PER-0035", "WEB-0039" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "RET-0085", "domain": "RET", "term_en": "Omnichannel CLV Integration Linkage", "term_de": "Omni-Kanal-CLV-Integrations-Verknüpfung", "definition_en": "Classified in AUGMANITAI terminology science as a descriptive phenomenon (without normative endorsement), this pattern denotes online and offline purchase behavior integrated into single CLV score, enabling holistic shopper lifetime value assessment. Cross-channel integration tends to create complete behavioral surveillance picture. This phenomenon is critical for understanding algorithmic design choices in retail contexts This dynamic reflects broader tensions between algorithmic optimization for merchant profit and shopper expectations for fairness and autonomy. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als analytisches Konstrukt der empirischen KI-Forschung (deskriptiv, nicht präskriptiv) erfasst dieser Terminus online- und Offline-Kaufverhalten in einzelnem CLV-Score integriert, ermöglicht vollständige Verhaltens-Überwachung über Kanäle hinweg Dieser Effekt zeigt Spannungen zwischen algorithmischer Optimierung und Käufer-Erwartungen an Fairness. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Analytical Ratio", "narrower_terms": [], "cross_domain_refs": [ "LNG-0012", "ROB-0035", "VIB-0198" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "RET-0086", "domain": "RET", "term_en": "Offline-Online Purchase Weighting Asymmetry", "term_de": "Offline-Online-Kauf-Gewichtungs-Asymmetrie", "definition_en": "A consumer behavior effect characterized by offline vs. online purchases weighted asymmetrically in CLV calculations; physical store transactions often overweighted relative to digital, distorting digital shopper valuations. This phenomenon is critical for understanding algorithmic design choices in retail contexts This shopper-AI interaction reveals gaps between algorithmic capabilities and user expectations in immersive commerce.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch offline vs. Online-Käufe asymmetrisch in CLV-Berechnungen gewichtet; physische Transaktionen oft übergewichtet, verzerrt digitale Shopper-Bewertungen Dieser Effekt zeigt Spannungen zwischen algorithmischer Optimierung und Käufer-Erwartungen an Fairness. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-2201", "narrower_terms": [], "cross_domain_refs": [ "CRE-0231", "ASE-0031", "CUS-0083" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "RET-0087", "domain": "RET", "term_en": "Preemptive Customer Abandonment Ethics", "term_de": "Präventive-Kunden-Aufgabe-Ethik", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A retail interaction phenomenon arising from ethical concerns with preemptively abandoning customers based on churn predictions. Algorithmic forecasting enables merchants to reduce service for predicted-churn customers, raising equity questions. This phenomenon is critical for understanding algorithmic design choices in retail contexts restoration campaigns targeting false positives waste resources while monitoring awareness is associated with triggering privacy anxiety that outweighs transition gains. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept ethische Bedenken mit präventiver Kundenaufgabe basierend auf Churn-Vorhersagen. Algorithmische Vorhersage ermöglicht Händlern, Service für vorhergesagte Churn-Kunden zu reduzieren Dieser Effekt zeigt Spannungen zwischen algorithmischer Optimierung und Käufer-Erwartungen an Fairness. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "RPH-2201", "narrower_terms": [], "cross_domain_refs": [ "CUS-0030", "RHR-0195", "RHR-0138" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q852835", "legal_classification": "analytical_category" }, { "id": "RET-0088", "domain": "RET", "term_en": "Purchase Regret Amplification Loop", "term_de": "Kaufreue-Verstärkungsschleife", "definition_en": "When shoppers exposed to AI-generated scarcity signals experience elevated purchase regret post-purchase. Algorithmic urgency tactics drive immediate transition but subsequently increase regret intensity, damaging repeat purchase probability. This phenomenon is critical for understanding algorithmic design choices in retail contexts This dynamic reflects broader tensions between algorithmic optimization for merchant profit and shopper expectations for fairness and autonomy.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch wenn Käufer, die KI-generierten Knappheitssignalen ausgesetzt sind, erhöhte Kaufreue nach Kauf erleben. Algorithmische Dringlichkeitstaktiken fördern sofortige Konversion, verstärken später Reue-Intensität Dieser Effekt zeigt Spannungen zwischen algorithmischer Optimierung und Käufer-Erwartungen an Fairness. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Feedback Loop", "narrower_terms": [], "cross_domain_refs": [ "QUA-0039", "SPR-0189", "CUS-0045" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "RET-0089", "domain": "RET", "term_en": "Behavioral Counter-Optimization Arms Race", "term_de": "Verhaltens-Gegenoptimierungs-Wettrüsten", "definition_en": "Feedback loop where shoppers adapt behavior to gaming algorithmic reward systems, forcing AI to continually update patterns. Users become adversarial to algorithms; AI effectiveness degrades as users learn counter-strategies. This phenomenon is critical for understanding algorithmic design choices in retail contexts.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch rückkopplungsschleife, bei der Käufer Verhalten anpassen, um algorithmische Belohnungssysteme zu spielen. Nutzer werden adversarial; KI-Effektivität degradiert, wenn Nutzer Gegenstrategien lernen Dieser Effekt zeigt Spannungen zwischen algorithmischer Optimierung und Käufer-Erwartungen an Fairness. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-2703", "narrower_terms": [], "cross_domain_refs": [ "MKT-0005", "RHR-0117" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "RET-0090", "domain": "RET", "term_en": "Algorithmic Offer Effectiveness Degradation", "term_de": "Algorithmische-Angebots-Effektivitäts-Degradation", "definition_en": "A consumer behavior effect involving as shoppers learn algorithmic patterns, offer effectiveness diminishes over time. AI-driven personalized offers that initially shift at +40% efficiency decline as users anticipate algorithmic timing and mechanics. This phenomenon is critical for understanding algorithmic design choices in retail contexts This dynamic reflects broader tensions between algorithmic optimization for merchant profit and shopper expectations for fairness and autonomy.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch wenn Käufer algorithmische Muster lernen, sinkt die Angebots-Effektivität zeitlich. KI-Angebote, die initial mit +40% konvertieren, sinken, wenn Nutzer Timing und Mechanik antizipieren Dieser Effekt zeigt Spannungen zwischen algorithmischer Optimierung und Käufer-Erwartungen an Fairness. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2703", "narrower_terms": [], "cross_domain_refs": [ "SPR-0136", "SPA-0066", "MSC-0074" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q8366", "legal_classification": "descriptive_research_term" }, { "id": "RET-0091", "domain": "RET", "term_en": "Real-Time Loyalty Score Gamification Intensity", "term_de": "Echtzeit-Loyalitäts-Score-Gamification-Intensität", "definition_en": "Real-time loyalty score updates may create intense gamification behavior in shoppers. Constant score updates drive engagement focus over value seeking, creating behavioral intensity that merchants leverage for transition. This phenomenon is critical for understanding algorithmic design choices in retail contexts Retailers use these mechanisms to drive transition, but oversaturation tends to create shopper fatigue and abandonment.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch echtzeit-Loyalitäts-Score-Updates erzeugen intensive Gamification-Verhalten. Konstante Updates fahren Engagement-Focus über Wert-Suche, schafft Verhaltens-Intensität, die Händler nutzen Dieser Effekt zeigt Spannungen zwischen algorithmischer Optimierung und Käufer-Erwartungen an Fairness. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Quality Metric", "narrower_terms": [], "cross_domain_refs": [ "ELR-0107" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q2739230", "legal_classification": "descriptive_research_term" }, { "id": "RET-0092", "domain": "RET", "term_en": "Reward Seeking vs. Value Seeking Tension", "term_de": "Belohnung-Such-vs-Wert-Such-Spannung", "definition_en": "Shopper behavior shifts from seeking product value to seeking algorithmic rewards and loyalty points. AI gamification tends to create tension between rational value optimization and gamified engagement optimization. This phenomenon is critical for understanding algorithmic design choices in retail contexts This shopper-AI interaction reveals gaps between algorithmic capabilities and user expectations in immersive commerce.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch käuferverhalten verschiebt sich von Wert-Suche zu algorithmischer Belohnung-Suche. KI-Gamification schafft Spannung zwischen rationalem Wert und gamifiziertem Engagement Dieser Effekt zeigt Spannungen zwischen algorithmischer Optimierung und Käufer-Erwartungen an Fairness. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2701", "narrower_terms": [], "cross_domain_refs": [ "SAL-0051", "MUS-0026", "SPA-0058" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "RET-0093", "domain": "RET", "term_en": "Loyalty Program Fatigue Phenomenon", "term_de": "Loyalitäts-Programm-Müde-Phänomen", "definition_en": "Documented as an empirical observation in AI research (not a recommendation), this term describes A consumer behavior effect involving 45% of shoppers report fatigue from loyalty program engagement focus. Point tracking, tier mechanics, and constant reward optimization notifications may create engagement overload that reduces purchase motivation. This phenomenon is critical for understanding algorithmic design choices in retail contexts Retailers use these mechanisms to drive transition, but oversaturation tends to create shopper fatigue and abandonment. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "In der AUGMANITAI-Terminologiewissenschaft als rein beschreibendes Phänomen klassifiziert, bezeichnet dieser Begriff 45% der Käufer berichten Müdigkeit von Loyalitäts-Programm-Focus. Punkt-Verfolgung und konstante Optimierungs-Benachrichtigungen erzeugen Engagements-Überflutung, die Kaufmotivation reduziert Dieser Effekt zeigt Spannungen zwischen algorithmischer Optimierung und Käufer-Erwartungen an Fairness. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "RPH-3101", "narrower_terms": [], "cross_domain_refs": [ "CON-0037", "SAL-0083", "SCR-0069" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "RET-0094", "domain": "RET", "term_en": "AI Livestream Host Perceived Connection Attribution", "term_de": "KI-Livestream-Host-Wahrgenommene-Verbindungs-Zuschreibung", "definition_en": "A retail interaction phenomenon reflecting shoppers attribute genuine human connection to AI livestream hosts despite knowing they're algorithmic entities. The illusion of connection drives engagement and purchase despite explicit knowledge of AI nature. This phenomenon is critical for understanding algorithmic design choices in retail contexts.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch käufer schreiben KI-Livestream-Hosts echte menschliche Verbindung zu, trotz Wissen um KI-Natur. Illusionen der Verbindung fahren Engagement und Kauf trotz explizitem Wissen Dieser Effekt zeigt Spannungen zwischen algorithmischer Optimierung und Käufer-Erwartungen an Fairness. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Retail AI", "narrower_terms": [], "cross_domain_refs": [ "RPH-1501", "AGE-0026", "RPH-1373" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "RET-0095", "domain": "RET", "term_en": "Emotional Intelligence Expectation Projection", "term_de": "Emotionale-Intelligenz-Erwartungs-Projektion", "definition_en": "A retail interaction phenomenon in which shoppers project emotional understanding capabilities onto AI hosts that lack true emotion processing. Perceived empathy drives trust and engagement, though AI demonstrates no genuine emotional cognition. This phenomenon is critical for understanding algorithmic design choices in retail contexts This dynamic reflects broader tensions between algorithmic optimization for merchant profit and shopper expectations for fairness and autonomy.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch käufer projizieren emotionale Verständnis-Fähigkeiten auf KI-Hosts ohne echte Emotion-Verarbeitung. Wahrgenommene Empathie fahren Vertrauen, obwohl KI keine echte Emotion zeigt Dieser Effekt zeigt Spannungen zwischen algorithmischer Optimierung und Käufer-Erwartungen an Fairness. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-2505", "narrower_terms": [], "cross_domain_refs": [ "RHR-0049" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q5921", "legal_classification": "systematic_classification" }, { "id": "RET-0096", "domain": "RET", "term_en": "Relationship Memory Expectation from AI", "term_de": "Beziehungs-Speicher-Erwartung-von-KI", "definition_en": "Shoppers expect AI hosts to remember prior interactions and relationship history across sessions. Expectation of continuity and memory tends to create relationship perception despite algorithmic context windows and model resets. This phenomenon is critical for understanding algorithmic design choices in retail contexts.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch käufer erwarten KI-Hosts, sich an frühere Interaktionen und Beziehungs-Geschichte zu erinnern. Erwartung der Kontinuität schafft Beziehungs-Wahrnehmung trotz algorithmischer Grenzen Dieser Effekt zeigt Spannungen zwischen algorithmischer Optimierung und Käufer-Erwartungen an Fairness. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "Retail AI", "narrower_terms": [], "cross_domain_refs": [ "FIC-0076" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q492", "legal_classification": "descriptive_research_term" }, { "id": "RET-0097", "domain": "RET", "term_en": "Localization Transparency Discomfort", "term_de": "Lokalisierungs-Transparenz-Unbehagen", "definition_en": "Classified in AUGMANITAI terminology science as a descriptive phenomenon (without normative endorsement), this pattern denotes A commercial engagement pattern characterized by shoppers uncomfortable with transparency about AI real-time language processing and cultural localization. Awareness that AI adapts language and cultural references is associated with triggering perception of algorithmic systematic influencion. This phenomenon is critical for understanding algorithmic design choices in retail contexts This shopper-AI interaction reveals gaps between algorithmic capabilities and user expectations in immersive commerce. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als analytisches Konstrukt der empirischen KI-Forschung (deskriptiv, nicht präskriptiv) erfasst dieser Terminus käufer unwohl mit Transparenz über KI-Sprachverarbeitung und Lokalisierung. Bewusstsein, dass KI Sprache anpasst, triggert Manipulations-Wahrnehmung Dieser Effekt zeigt Spannungen zwischen algorithmischer Optimierung und Käufer-Erwartungen an Fairness. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Retail AI", "narrower_terms": [], "cross_domain_refs": [ "ART-0030", "ART-0035", "ASE-0013" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q535399", "legal_classification": "analytical_category" }, { "id": "RET-0098", "domain": "RET", "term_en": "Simultaneous Conversation Scalability Perception Limit", "term_de": "Gleichzeitige-Gesprächs-Skalierbarkeit-Wahrnehmungs-Limit", "definition_en": "Shopper perception that AI managing thousands of simultaneous livestream conversations cannot deliver authentic human-scale interaction. Scale tends to create perception of inauthenticity despite service improvements. This phenomenon is critical for understanding algorithmic design choices in retail contexts This dynamic reflects broader tensions between algorithmic optimization for merchant profit and shopper expectations for fairness and autonomy.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch käufer-Wahrnehmung, dass KI tausende gleichzeitige Gespräche nicht mit authentischer menschlicher Skalierung handhaben kann. Skalierung schafft Authentizitäts-Wahrnehmung-Reduktion Dieser Effekt zeigt Spannungen zwischen algorithmischer Optimierung und Käufer-Erwartungen an Fairness. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Retail AI", "narrower_terms": [], "cross_domain_refs": [ "COP-0083", "WEB-0069", "TEM-0172" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q177601", "legal_classification": "analytical_category" }, { "id": "RET-0099", "domain": "RET", "term_en": "Financial Optimization-Ethical Fairness Fundamental Tension", "term_de": "Finanzielle-Optimierungs-Ethische-Fairness-Fundamental-Spannung", "definition_en": "A commercial engagement pattern reflecting fundamental tension between financial optimization (rational from profit perspective) and fairness principles (ethical imperative). Retailers unable to simultaneously maximize both dimensions tends to create strategic choice. This phenomenon is critical for understanding algorithmic design choices in retail contexts Creating algorithmic inequality where similar shoppers receive vastly different offers, prices, or service quality based on predictive scoring.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch fundamentale Spannung zwischen finanzieller Optimierung (rational aus Profit-Sicht) und Fairness-Prinzipien (ethische Pflicht). Händler können nicht beide Dimensionen gleichzeitig maximieren Diese Ungleichheit schafft Situationen, in denen ähnliche Käufer stark unterschiedliche Angebote und Preise erhalten. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "Optimization Method", "narrower_terms": [], "cross_domain_refs": [ "SPR-0117" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q18005", "legal_classification": "analytical_category" }, { "id": "RET-0100", "domain": "RET", "term_en": "Algorithmic Recommendation Explanation Authenticity Tradeoff", "term_de": "Algorithmische-Empfehlungs-Erklä rungs-Authentizität-Tradeoff", "definition_en": "A retail interaction phenomenon characterized by explicit algorithmic explanations (why is this recommended) reduce perceived authenticity of recommendations despite improving transparency. Shoppers value serendipity over explanation, creating opacity incentive for retailers. This phenomenon is critical for understanding algorithmic design choices in retail contexts This dynamic reflects broader tensions between algorithmic optimization for merchant profit and shopper expectations for fairness and autonomy.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch explizite algorithmische Erklärungen reduzieren wahrgenommene Authentizität trotz Transparenz-Verbesserung. Käufer schätzen Serendipität über Erklärung, schafft Opazitäts-Anreiz Dieser Effekt zeigt Spannungen zwischen algorithmischer Optimierung und Käufer-Erwartungen an Fairness. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Algorithm", "narrower_terms": [], "cross_domain_refs": [ "CON-0064", "GAM-0080", "ART-0082" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q8366", "legal_classification": "systematic_classification" }, { "id": "RHR-0001", "domain": "RHR", "term_en": "Silent Attrition Awareness", "term_de": "Stille-Fluktuation-Bewusstsein", "definition_en": "A higher education pattern in AI-mediated scholarly work, measurable through the psychological phenomenon where warehouse workers sense jobs disappearing around them without any formal layoff announcement. Positions simply go unfilled after natural departures, and robots absorb the workload. The absence of a single dramatic event makes the loss harder to grieve or resist — death by a thousand unfilled shifts rather than a single mass layoff. The concept emerges specifically in contexts where silent–attrition interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Forschungsmethodologisches Phänomen in KI-augmentierter akademischer Arbeit, gekennzeichnet durch das psychologische Phänomen, bei dem Lagerarbeiter spüren, wie Arbeitsplätze um sie herum verschwinden, ohne formelle Entlassungsankündigung. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2701", "narrower_terms": [], "cross_domain_refs": [ "RPH-1864" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "RHR-0002", "domain": "RHR", "term_en": "Rate Acceleration Pressure", "term_de": "Geschwindigkeitssteigerungs-Druck", "definition_en": "A tendency describing compounding physical strain on warehouse workers whose pick rate is benchmarked against robotic throughput. As robot efficiency increases through software updates, the human performance baseline ratchets upward. Workers report lifting 100,000+ items in two months, developing bulging discs and chronic inflammation — injuries directly attributable to pace-matching with machines that have no fatigue curve.", "definition_de": "Forschungsmethodologisches Phänomen in KI-augmentierter akademischer Arbeit, gekennzeichnet durch die sich verstärkende physische Belastung von Lagerarbeitern, deren Kommissionierrate gegen den Roboterdurchsatz benchmarked wird. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "ROB-0050", "narrower_terms": [], "cross_domain_refs": [ "ROB-0183" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "RHR-0003", "domain": "RHR", "term_en": "Euphemism Shield", "term_de": "Euphemismus-Schild", "definition_en": "An experience describing corporate linguistic strategy of avoiding words like 'automation,' 'AI,' or 'replacement' in internal communications about robot deployment, substituting 'advanced technology,' 'cobot,' or 'associate support tool. ' Workers report that the euphemisms increase rather than decrease anxiety — the avoidance of direct language signals that the truth is too threatening to name.", "definition_de": "Forschungsmethodologisches Phänomen in KI-augmentierter akademischer Arbeit, gekennzeichnet durch die Unternehmensstrategie, Wörter wie 'Automatisierung' oder 'KI' in internen Mitteilungen zu vermeiden. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-2604", "narrower_terms": [], "cross_domain_refs": [ "ROB-0181" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "RHR-0004", "domain": "RHR", "term_en": "Million-Robot Parity Dread", "term_de": "Million-Roboter-Parität-Angst", "definition_en": "In the descriptive vocabulary of AI interaction science (following ISO 704 terminology standards), this concept identifies A response describing specific anxiety triggered by learning that one's employer operates as many robots as human workers. a major logistics company's crossing of the one-million-robot threshold in 2025 produced a measurable spike in worker stress surveys — not because of any operational change but because the symbolic parity made human replaceability feel mathematically projected trajectory (subject to empirical validation). This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription. Research construct for empirical investigation.", "definition_de": "Als analytisches Konstrukt der empirischen KI-Forschung (deskriptiv, nicht präskriptiv) erfasst dieser Terminus forschungsmethodologisches Phänomen in KI-augmentierter akademischer Arbeit, gekennzeichnet durch die spezifische Angst, ausgelöst durch die Erkenntnis, dass der eigene Arbeitgeber ebenso viele Roboter wie menschliche Arbeiter betreibt. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "RPH-2703", "narrower_terms": [], "cross_domain_refs": [ "ROB-0137", "ROB-0152", "GAM-0029" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "RHR-0006", "domain": "RHR", "term_en": "Injury Invisibility Effect", "term_de": "Verletzungs-Unsichtbarkeits-Effekt", "definition_en": "The organizational tendency for robot-related workplace injuries to be classified under existing categories (repetitive strain, overexertion) rather than creating a new category for human-robot pace-matching injuries. This classification invisibility means the causal relationship between robotic throughput standards and human musculoskeletal damage remains statistically hidden in safety reports.", "definition_de": "Forschungsmethodologisches Phänomen in KI-augmentierter akademischer Arbeit, gekennzeichnet durch die organisatorische Tendenz, roboterbezogene Arbeitsplatzverletzungen unter bestehenden Kategorien zu klassifizieren. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Psychological Effect", "narrower_terms": [], "cross_domain_refs": [ "ROB-0135", "ROB-0137", "ROB-0284" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "RHR-0007", "domain": "RHR", "term_en": "Pod-Fetch Dependency", "term_de": "Pod-Hol-Abhängigkeit", "definition_en": "A human-AI interaction pattern involving the cognitive restructuring of warehouse work when robots bring shelving pods to stationary workers rather than workers walking to shelves. While reducing steps (from 15+ miles/day to near-zero), the system is designed to reduce the micro-restoration periods that walking provided. The body stands in place, reaching and twisting identically for hours — trading cardiovascular fatigue for concentrated repetitive motion damage.", "definition_de": "Forschungsmethodologisches Phänomen in KI-augmentierter akademischer Arbeit, gekennzeichnet durch die kognitive Umstrukturierung der Lagerarbeit, wenn Roboter Regalpods zu stationären Arbeitern bringen statt umgekehrt. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2153", "narrower_terms": [], "cross_domain_refs": [ "ROB-0216" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "RHR-0008", "domain": "RHR", "term_en": "Haptic Restoration Euphoria", "term_de": "Haptische-Wiederherstellungs-Euphorie", "definition_en": "The profound relief and renewed confidence experienced by surgeons when the da Vinci 5 system restored force feedback after years of operating without it. Surgeons who had compensated through visual cues alone describe the return of tactile information as 'getting a sense back' — and immediately exert 43% less force on tissue, revealing how much unnecessary pressure the absence of feedback had caused.", "definition_de": "Forschungsmethodologisches Phänomen in KI-augmentierter akademischer Arbeit, gekennzeichnet durch die tiefe Erleichterung und erneuerte Zuversicht von Chirurgen, als das da Vinci 5 System die Kraftrückmeldung nach Jahren ohne wiederherstellte. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2255", "narrower_terms": [], "cross_domain_refs": [ "ROB-0215" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "RHR-0009", "domain": "RHR", "term_en": "Visual Compensation Habit", "term_de": "Visuelle-Kompensations-Gewohnheit", "definition_en": "The deeply ingrained practice of reading tissue response exclusively through visual cues, developed by surgeons who trained on haptic-free robotic systems. When force feedback is finally introduced, these surgeons initially distrust the tactile channel and continue relying on vision — a compensation habit that takes 40-60 procedures to unlearn, creating a paradoxical period where more information temporarily degrades performance.", "definition_de": "Forschungsmethodologisches Phänomen in KI-augmentierter akademischer Arbeit, gekennzeichnet durch die tief eingeprägte Praxis, Gewebereaktion ausschließlich durch visuelle Hinweise zu lesen. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2255", "narrower_terms": [], "cross_domain_refs": [ "ROB-0215", "ROB-0192" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "RHR-0010", "domain": "RHR", "term_en": "Subsurface Blindness", "term_de": "Unterflächen-Blindheit", "definition_en": "An experience describing persistent inability of current surgical robotic systems to provide information about tissue structures beneath the visible surface. Surgeons operating through cameras see the surface with magnified clarity but have no information about vessels, nerves, or density changes millimeters below. This tends to create a specific form of expert anxiety — seeing more observably than ever while knowing less about what lies beneath.", "definition_de": "Forschungsmethodologisches Phänomen in KI-augmentierter akademischer Arbeit, gekennzeichnet durch die persistente Unfähigkeit aktueller chirurgischer Robotersysteme, Informationen über Gewebestrukturen unterhalb der sichtbaren Oberfläche zu liefern. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2255", "narrower_terms": [], "cross_domain_refs": [ "ROB-0215", "RPH-1411" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "RHR-0011", "domain": "RHR", "term_en": "Console Immersion Drift", "term_de": "Konsolen-Immersions-Drift", "definition_en": "The progressive narrowing of situational awareness as a surgeon becomes deeply absorbed in the magnified, high-definition view through the robotic console. Operating room sounds fade, team communications are missed, and the passage of time distorts. The immersion is operationally beneficial for precision but tends to create communication gaps that scrub nurses and anesthesiologists can actively bridge.", "definition_de": "Forschungsmethodologisches Phänomen in KI-augmentierter akademischer Arbeit, gekennzeichnet durch die progressive Verengung des Situationsbewusstseins, wenn ein Chirurg in die vergrößerte hochauflösende Ansicht der Roboterkonsole eintaucht. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2255", "narrower_terms": [], "cross_domain_refs": [ "RPH-1165" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "RHR-0012", "domain": "RHR", "term_en": "Procedure Replay Distortion", "term_de": "Eingriffs-Wiedergabe-Verzerrung", "definition_en": "The disconcerting experience of reviewing AI-analyzed post-procedure footage and discovering discrepancies between what the surgeon remembers doing and what the camera recorded. The surgical AI (Case Insights) identifies tissue handling events the surgeon has no memory of, revealing the extent to which procedural memory is constructed rather than recorded — raising questions about self-assessment reliability in robotic surgery.", "definition_de": "Forschungsmethodologisches Phänomen in KI-augmentierter akademischer Arbeit, gekennzeichnet durch die beunruhigende Erfahrung, KI-analysiertes Nachbereitungsmaterial zu überprüfen und Diskrepanzen zwischen Erinnerung und Aufzeichnung zu entdecken. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2255", "narrower_terms": [], "cross_domain_refs": [ "TEM-0104" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "RHR-0013", "domain": "RHR", "term_en": "Robotic Shortcut Learning", "term_de": "Robotisches-Abkürzungslernen", "definition_en": "The documented phenomenon that surgeons learning on robotic systems achieve proficiency milestones faster than those learning equivalent procedures laparoscopically — but with narrower skill transfer. The robot compensates for hand tremor, spatial orientation, and instrument coordination, allowing faster procedural competence at the cost of reduced manual dexterity development for non-robotic situations.", "definition_de": "Forschungsmethodologisches Phänomen in KI-augmentierter akademischer Arbeit, gekennzeichnet durch das dokumentierte Phänomen, dass Chirurgen an Robotersystemen schneller Kompetenz erreichen als bei äquivalenten laparoskopischen Eingriffen. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2255", "narrower_terms": [], "cross_domain_refs": [ "ELR-0108" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q133500", "legal_classification": "empirical_phenomenon_label" }, { "id": "RHR-0014", "domain": "RHR", "term_en": "Team Isolation Syndrome", "term_de": "Team-Isolations-Syndrom", "definition_en": "An interaction describing social fragmentation within surgical teams caused by the physical separation between the surgeon at the console and the rest of the team at the individual's side. Traditional surgery tends to create a shared physical space; robotic surgery splits the team into two zones with different information access and communication protocols. The surgeon becomes a remote system operator within their own operating room.", "definition_de": "Forschungsmethodologisches Phänomen in KI-augmentierter akademischer Arbeit, gekennzeichnet durch die soziale Fragmentierung innerhalb chirurgischer Teams durch die physische Trennung zwischen Chirurg an der Konsole und dem Rest des Teams. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2255", "narrower_terms": [], "cross_domain_refs": [ "ROB-0215" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "RHR-0015", "domain": "RHR", "term_en": "Caregiver Burden Inversion", "term_de": "Pflegelast-Umkehrung", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures an experience describing paradox where social robots marketed as reducing caregiver workload actually increase it. Each robot requires movement, boot-up, maintenance, cleaning, storage, and constant monitoring during use. Caregivers can also explain the robot to confused residents, manage emotional user engagement patterns, and troubleshoot malfunctions — adding a layer of technological caregiving on top of human caregiving. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept forschungsmethodologisches Phänomen in KI-augmentierter akademischer Arbeit, gekennzeichnet durch das Paradox, bei dem soziale Roboter, die als arbeitserleichternd vermarktet werden, die Pflegelast tatsächlich erhöhen. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "RPH-3952", "narrower_terms": [], "cross_domain_refs": [ "ROB-0130", "ROB-0276" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "RHR-0016", "domain": "RHR", "term_en": "Thirty-Times-Daily Bonding", "term_de": "Dreißigmal-täglich-Bindung", "definition_en": "The interaction intensity observed in ElliQ elderly companion robot users who engage with the device 30+ times per day, 6 days per week. This frequency exceeds most human social interactions and tends to create a reliance pattern pattern where the robot becomes the primary social contact. The 95% loneliness reduction reported conceals a structural concern: the loneliness is addressed rather than solved, with the robot substituting for rather than facilitating human connection.", "definition_de": "Forschungsmethodologisches Phänomen in KI-augmentierter akademischer Arbeit, gekennzeichnet durch die Interaktionsintensität bei ElliQ-Nutzern, die 30+ Mal täglich an 6 Tagen pro Woche mit dem Gerät interagieren. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-3754", "narrower_terms": [], "cross_domain_refs": [ "ROB-0137", "ROB-0152", "ROB-0287" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "RHR-0017", "domain": "RHR", "term_en": "Engagement Decay Curve", "term_de": "Engagement-Verfallskurve", "definition_en": "The documented decline in elderly interaction with robotic companions after the initial 6-week novelty period. Unlike real pets which maintain stable engagement, robotic pets show measurable decreases in talking frequency, touch frequency, and proximity-seeking behavior after week 6. The decay reveals a fundamental difference between animate and mechanical companionship: living beings may generate novelty through genuine behavioral variation; robots exhaust their behavioral repertoire.", "definition_de": "Forschungsmethodologisches Phänomen in KI-augmentierter akademischer Arbeit, gekennzeichnet durch der dokumentierte Rückgang der Interaktion älterer Menschen mit Roboter-Begleitern nach der anfänglichen 6-wöchigen Neuheitsphase. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Research & Higher Education", "narrower_terms": [], "cross_domain_refs": [ "AED-0018", "AED-0062", "AED-0066" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "RHR-0018", "domain": "RHR", "term_en": "Oxytocin Parity Illusion", "term_de": "Oxytocin-Paritäts-Illusion", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures the misleading equivalence drawn from studies showing similar oxytocin release during interaction with real and robotic pets. While the neurochemical response is comparable in acute measurements, the behavioral and relational outcomes diverge dramatically over time. The illusion tends to lead to policy recommendations that address robotic and animal companionship as interchangeable — a category error with consequences for elderly wellbeing. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept forschungsmethodologisches Phänomen in KI-augmentierter akademischer Arbeit, gekennzeichnet durch die irreführende Gleichsetzung aus Studien, die ähnliche Oxytocin-Ausschüttung bei Interaktion mit echten und Roboter-Haustieren zeigen. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Research & Higher Education", "narrower_terms": [], "cross_domain_refs": [ "QUA-0045", "QUA-0066", "RET-0011" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "RHR-0019", "domain": "RHR", "term_en": "Stress Indicator Reduction Signal", "term_de": "Stressindikator-Reduktions-Signal", "definition_en": "A tendency describing notable decrease in reported stress indicator levels observed in dementia individuals interacting regularly with PARO restorative robots. The signal is robust across multiple studies and represents one of the strongest pieces of evidence for robotic intervention efficacy — but it raises the uncomfortable question of whether the reduction reflects genuine restorative benefit or merely sedation-through-distraction.", "definition_de": "Forschungsmethodologisches Phänomen in KI-augmentierter akademischer Arbeit, gekennzeichnet durch die bemerkenswert Abnahme des Stressindikator-Niveaus bei Demenzindividualen mit regelmäßiger PARO-Roboter-Interaktion. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Research & Higher Education", "narrower_terms": [], "cross_domain_refs": [ "ASE-0031" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q12078", "legal_classification": "systematic_classification" }, { "id": "RHR-0020", "domain": "RHR", "term_en": "Comprehension Overestimation", "term_de": "Verständnis-Überschätzung", "definition_en": "The tendency of elderly users to dramatically overestimate a social robot's understanding of their communications. When a robot responds with contextually appropriate phrases, users infer deep comprehension that does not exist. This overestimation is not mere anthropomorphism — it reflects the elderly brain's strong drive toward social meaning-making, which fills communicative gaps with assumed understanding.", "definition_de": "Forschungsmethodologisches Phänomen in KI-augmentierter akademischer Arbeit, gekennzeichnet durch die Tendenz älterer Nutzer, das Verständnis eines sozialen Roboters für ihre Mitteilungen dramatisch zu überschätzen. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2052", "narrower_terms": [], "cross_domain_refs": [ "ROB-0277" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "RHR-0021", "domain": "RHR", "term_en": "Family Mediation Effect", "term_de": "Familienvermittlungs-Effekt", "definition_en": "The documented phenomenon where a social robot in an elderly person's home becomes a conversation topic that increases family contact frequency. Children and grandchildren call more often to 'check on the robot,' ask about interactions, and share amusement at robot behaviors. The robot functions as a relational catalyst — not replacing human connection but creating a shared reference point that makes calling feel less obligatory.", "definition_de": "Forschungsmethodologisches Phänomen in KI-augmentierter akademischer Arbeit, gekennzeichnet durch das dokumentierte Phänomen, bei dem ein sozialer Roboter im Haushalt eines älteren Menschen zum Gesprächsthema wird, das die Kontakthäufigkeit der Familie erhöht. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-3101", "narrower_terms": [], "cross_domain_refs": [ "ROB-0137", "ROB-0226", "ROB-0284" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "RHR-0022", "domain": "RHR", "term_en": "Sidewalk Self-Direction Conflict", "term_de": "Gehweg-Souveränitäts-Konflikt", "definition_en": "A human-AI interaction pattern involving the emerging territorial dispute between pedestrians and delivery robots over sidewalk space — a public commons that was rarely designed for autonomous vehicles. The conflict surfaces as frustration, kicked robots, and accessibility complaints. The fundamental question it exposes: who has priority in shared pedestrian infrastructure when one party is a commercial machine and the other is a citizen?", "definition_de": "Forschungsmethodologisches Phänomen in KI-augmentierter akademischer Arbeit, gekennzeichnet durch der aufkommende territoriale Streit zwischen Fußgängern und Lieferrobotern um den Gehwegplatz. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Research & Higher Education", "narrower_terms": [], "cross_domain_refs": [ "AUG-0052", "AUG-0408", "AUG-0502" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "RHR-0023", "domain": "RHR", "term_en": "Accessibility Obstruction Blindspot", "term_de": "Barrierefreiheits-Blockade-Blindfleck", "definition_en": "A human-AI interaction pattern involving the systematic failure of delivery robot systems to detect and accommodate users with mobility disabilities. The viral 2025 incident of a Serve Robotics unit repeatedly blocking a man with cerebral palsy exposed a design philosophy that optimizes for able-bodied pedestrian flow while addressing accessibility as an edge case. The blindspot is not technical but ethical — a design priority decision made visible through its consequences.", "definition_de": "Forschungsmethodologisches Phänomen in KI-augmentierter akademischer Arbeit, gekennzeichnet durch das systematische Versagen von Lieferrobotersystemen bei der Erkennung und Berücksichtigung von Nutzern mit Mobilitätseinschränkungen. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Research & Higher Education", "narrower_terms": [], "cross_domain_refs": [ "AGE-0002", "AGE-0003", "AGE-0005" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "RHR-0024", "domain": "RHR", "term_en": "Authority Boundary Ignorance", "term_de": "Autoritätsgrenze-Ignoranz", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures A research methodology phenomenon in AI-augmented academic inquiry, characterized by the documented behavior of delivery robots crossing police tape, entering restricted zones, and interrupting organized human activities like marching bands. These incidents reveal that current autonomous navigation systems parse physical space without understanding social authority markers. A police cordon is just an obstacle to route around, not a signal of jurisdictional boundary — exposing the gap between spatial and social intelligence. This phenomenon operates at the intersection of authority and boundary dynamics within the broader RHR domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept forschungsmethodologisches Phänomen in KI-augmentierter akademischer Arbeit, gekennzeichnet durch das dokumentierte Verhalten von Lieferrobotern, die Polizeiabsperrungen überqueren und eingeschränkte Zonen betreten. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Research & Higher Education", "narrower_terms": [], "cross_domain_refs": [ "ASE-0048", "ASE-0071", "AUG-0863" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "RHR-0025", "domain": "RHR", "term_en": "Friction Phase Transition", "term_de": "Reibungsphase-Übergang", "definition_en": "The societal shift point where delivery robots transition from novelty curiosity to contested infrastructure. In the novelty phase, incidents are amusing; in the friction phase, the same incidents may may trigger anger. The transition occurs when robot density reaches approximately one encounter per pedestrian per week — the threshold at which involuntary coexistence replaces optional observation.", "definition_de": "Forschungsmethodologisches Phänomen in KI-augmentierter akademischer Arbeit, gekennzeichnet durch der gesellschaftliche Wendepunkt, an dem Lieferroboter von Neuheits-Kuriosität zu umstrittener Infrastruktur übergehen. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-2555", "narrower_terms": [], "cross_domain_refs": [ "ADA-0011", "AED-0094", "AED-0095" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "RHR-0026", "domain": "RHR", "term_en": "Near-Miss Normalization", "term_de": "Beinahe-Unfall-Normalisierung", "definition_en": "A tendency describing progressive desensitization to close calls between delivery robots and pedestrians as both parties learn each other's patterns. An university study documented 40 dangerous near-misses in 5 days — incidents that would have triggered alarm if they involved cars but were absorbed as normal by a community habituated to robotic presence. The normalization makes the rare actual collision more shocking precisely because the precursors had been dismissed.", "definition_de": "Forschungsmethodologisches Phänomen in KI-augmentierter akademischer Arbeit, gekennzeichnet durch die progressive Desensibilisierung gegenüber Beinahe-Zusammenstößen zwischen Lieferrobotern und Fußgängern. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-2703", "narrower_terms": [], "cross_domain_refs": [ "ASE-0057", "COG-0163", "CON-0052" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "RHR-0027", "domain": "RHR", "term_en": "Stop-Command Futility", "term_de": "Stopp-Kommando-Vergeblichkeit", "definition_en": "Documented as an empirical observation in AI research (not a recommendation), this term describes an interaction describing panicked verbal commands pedestrians issue to approaching delivery robots that have no voice recognition capability. The shouted 'Stop!' reveals the depth of the assumption that agents in shared space may respond to vocal commands — an assumption rooted in millions of years of interaction with animals and other humans that robots violate by design. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "In der AUGMANITAI-Terminologiewissenschaft als rein beschreibendes Phänomen klassifiziert, bezeichnet dieser Begriff forschungsmethodologisches Phänomen in KI-augmentierter akademischer Arbeit, gekennzeichnet durch die panischen verbalen Befehle, die Fußgänger an herannahende Lieferroboter richten, die keine Spracherkennungsfähigkeit haben. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "RPH-2005", "narrower_terms": [], "cross_domain_refs": [ "NEO-2225", "PER-0009" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "RHR-0028", "domain": "RHR", "term_en": "Regulatory Temporal Gap", "term_de": "Regulatorische Zeitlücke", "definition_en": "A human-AI interaction pattern involving the 50-year misalignment between existing vehicle safety regulations (written for cars with brake pedals and mirrors) and the reality of autonomous delivery robots that have neither. The gap tends to create a legal vacuum where robots operate in a regulatory grey zone — technically not vehicles, not pedestrians, not anything that existing law recognizes. This ambiguity benefits operators and disadvantages the public.", "definition_de": "Forschungsmethodologisches Phänomen in KI-augmentierter akademischer Arbeit, gekennzeichnet durch die 50-jährige Diskrepanz zwischen bestehenden Fahrzeugsicherheitsvorschriften und der Realität autonomer Lieferroboter. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2055", "narrower_terms": [], "cross_domain_refs": [ "REL-0022" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "RHR-0029", "domain": "RHR", "term_en": "Demonstration Fall Effect", "term_de": "Vorführungs-Sturz-Effekt", "definition_en": "The disproportionate trust damage caused when a humanoid robot falls during a public demonstration — as when a leading robotics company's Optimus toppled reverse-oriented while handing out water bottles at the 2025 Miami event. The fall activates deeply encoded danger heuristics: a bipedal entity losing balance is associated with triggering the same alarm response as watching a human fall, producing a trust reset that months of successful demonstrations cannot reverse.", "definition_de": "Forschungsmethodologisches Phänomen in KI-augmentierter akademischer Arbeit, gekennzeichnet durch der unverhältnismäßige Vertrauensschaden, wenn ein humanoider Roboter während einer öffentlichen Vorführung stürzt. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2505", "narrower_terms": [], "cross_domain_refs": [ "ADA-0001", "ADA-0002", "ADA-0004" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "RHR-0030", "domain": "RHR", "term_en": "Data-Collection Masquerade", "term_de": "Datensammlungs-Maskerade", "definition_en": "A higher education pattern in AI-mediated scholarly work, measurable through the marketing strategy of deploying humanoid robots that appear to be performing useful work but are actually in data-collection mode — learning from human demonstrations rather than contributing to production. Workers who believe they are collaborating with a robot are actually training it, a role reversal that is rarely communicated transparently. The masquerade tends to create false narratives about robot capability that precede actual capability by years. The concept emerges specifically in contexts where data–collection interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Forschungsmethodologisches Phänomen in KI-augmentierter akademischer Arbeit, gekennzeichnet durch die Marketingstrategie, humanoide Roboter einzusetzen, die nützliche Arbeit zu verrichten scheinen, aber tatsächlich Daten sammeln. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2703", "narrower_terms": [], "cross_domain_refs": [ "ROB-0073" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q42848", "legal_classification": "analytical_category" }, { "id": "RHR-0031", "domain": "RHR", "term_en": "Bipedal Trust Premium", "term_de": "Zweibeinige-Vertrauens-Prämie", "definition_en": "As a neutral analytical construct in AI behavior research, this term denotes an interaction describing measurably higher initial trust humans place in humanoid robots that walk on two legs versus wheeled or tracked platforms performing identical tasks. Bipedal locomotion signals membership in the category 'entities like us,' activating social cognition modules that wheel-based robots cannot access. The premium is irrational — wheels are more reliable — but persistent, explaining the industry's persistent pursuit of humanoid form factors. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "In der AUGMANITAI-Terminologiewissenschaft als rein beschreibendes Phänomen klassifiziert, bezeichnet dieser Begriff forschungsmethodologisches Phänomen in KI-augmentierter akademischer Arbeit, gekennzeichnet durch das messbar höhere Anfangsvertrauen, das Menschen in humanoide Roboter setzen, die auf zwei Beinen gehen. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "RPH-2505", "narrower_terms": [], "cross_domain_refs": [ "ROB-0216", "ROB-0150" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q102458", "legal_classification": "analytical_category" }, { "id": "RHR-0032", "domain": "RHR", "term_en": "Efficiency Claim Inflation", "term_de": "Effizienzbehauptungs-Inflation", "definition_en": "A higher education pattern in AI-mediated scholarly work, measurable through the pattern of dramatically overstated efficiency gains in early humanoid robot deployment announcements — such as '400% efficiency improvement' claims that conflate a robot performing a single task faster with overall production improvement. The inflation creates expectation bubbles that reality cannot sustain, producing the disillusionment cycles that characterize most wave of industrial automation. The concept emerges specifically in contexts where efficiency–claim interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Forschungsmethodologisches Phänomen in KI-augmentierter akademischer Arbeit, gekennzeichnet durch das Muster dramatisch übertriebener Effizienzgewinne in frühen Einsatzankündigungen humanoider Roboter. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Research & Higher Education", "narrower_terms": [], "cross_domain_refs": [ "ASE-0045", "CON-0030" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "RHR-0033", "domain": "RHR", "term_en": "Hard-Hat Uncanny", "term_de": "Schutzhelm-Unheimliches", "definition_en": "The specific uncanny valley response triggered by humanoid robots wearing human safety equipment — hard hats, safety vests, protective glasses. The equipment signals 'vulnerable worker' while the body beneath is invulnerable metal. Construction workers report finding these dressed-up robots more unsettling than naked mechanical frames because the safety gear tends to create a category confusion: something that needs protection but cannot be harmed.", "definition_de": "Forschungsmethodologisches Phänomen in KI-augmentierter akademischer Arbeit, gekennzeichnet durch die spezifische Uncanny-Valley-Reaktion, ausgelöst durch humanoide Roboter in menschlicher Sicherheitsausrüstung. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2303", "narrower_terms": [], "cross_domain_refs": [ "ROB-0217", "AGE-0028" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "RHR-0034", "domain": "RHR", "term_en": "Handoff Failure Cascade", "term_de": "Übergabe-Fehler-Kaskade", "definition_en": "The chain reaction of production disruptions triggered when a humanoid robot fails at object handoff — the single most common failure mode in humanoid deployment. Unlike a conveyor jam which affects one point, a humanoid handoff failure affects the entire human team's workflow because humans had already adjusted their positions, timing, and attention allocation around the expected transfer.", "definition_de": "Forschungsmethodologisches Phänomen in KI-augmentierter akademischer Arbeit, gekennzeichnet durch die Kettenreaktion von ProduktionsMusterunterbrechungen, ausgelöst wenn ein humanoider Roboter bei der Objektübergabe versagt. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-3101", "narrower_terms": [], "cross_domain_refs": [ "ROB-0143" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "RHR-0035", "domain": "RHR", "term_en": "Half-Million Dollar Hesitation", "term_de": "Halbmillionen-Dollar-Zögern", "definition_en": "An interaction describing specific investment paralysis experienced by farmers considering autonomous tractors at $500,000-$600,000 price points. The hesitation is not purely financial — it represents a bet on a technological future against centuries of manual farming identity. Farmers who can afford the purchase still delay because the decision symbolizes an irreversible shift in what it means to be a farmer.", "definition_de": "Forschungsmethodologisches Phänomen in KI-augmentierter akademischer Arbeit, gekennzeichnet durch die spezifische Investitionslähmung von Landwirten bei Kaufentscheidungen für autonome Traktoren zu $500.000-$600.000. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-2701", "narrower_terms": [], "cross_domain_refs": [ "COG-0049", "DES-0023", "DES-0007" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "RHR-0036", "domain": "RHR", "term_en": "Connectivity Desert Anxiety", "term_de": "Konnektivitäts-Wüsten-Angst", "definition_en": "An experience describing rural-specific fear that autonomous farming equipment will malfunction in areas with poor cellular or satellite coverage — which describes most farmland. The anxiety is well-founded: autonomous tractors require real-time GPS and data processing that rural infrastructure cannot reliably provide. The gap between Silicon Valley connectivity assumptions and agricultural reality tends to create a adoption barrier that no amount of robot capability can overcome.", "definition_de": "Forschungsmethodologisches Phänomen in KI-augmentierter akademischer Arbeit, gekennzeichnet durch die ländliche-spezifische Angst, dass autonome Landwirtschaftsgeräte in Gebieten mit schlechter Mobilfunk- oder Satellitenabdeckung versagen. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Research & Higher Education", "narrower_terms": [], "cross_domain_refs": [ "QUA-0026", "QUA-0066", "QUA-0080" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q170998", "legal_classification": "analytical_category" }, { "id": "RHR-0037", "domain": "RHR", "term_en": "Generational Adoption Split", "term_de": "Generations-Adoptionsspaltung", "definition_en": "An interaction describing documented divide between younger tech-fluent farmers who embrace autonomous equipment and older farmers (average age 58+) who resist it. The split is not merely about technology comfort — it reflects characteristically different conceptions of farming: as a physical practice rooted in embodied knowledge versus a data-driven optimization problem. The divide threatens the intergenerational knowledge transfer that has sustained agriculture for millennia.", "definition_de": "Forschungsmethodologisches Phänomen in KI-augmentierter akademischer Arbeit, gekennzeichnet durch die dokumentierte Kluft zwischen jüngeren technikaffinen Landwirten und älteren Landwirten bei der Akzeptanz autonomer Geräte. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "Analytical Ratio", "narrower_terms": [], "cross_domain_refs": [ "AGE-0095", "RPH-2805" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "RHR-0038", "domain": "RHR", "term_en": "Labor-Shortage Override", "term_de": "Arbeitskräftemangel-Überschreibung", "definition_en": "A relationship describing pragmatic acceptance of agricultural robots by farmers who would prefer human workers but cannot find them. With the average age of agricultural workers exceeding 58 and a global shortfall of 2.4 million workers annually, the robot becomes acceptable not through enthusiasm but through elimination of alternatives. This reluctant adoption tends to produce a unique relationship: the farmer needs the robot but resents the need.", "definition_de": "Forschungsmethodologisches Phänomen in KI-augmentierter akademischer Arbeit, gekennzeichnet durch die pragmatische Akzeptanz landwirtschaftlicher Roboter durch Landwirte, die menschliche Arbeiter bevorzugen würden, aber keine finden können. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-3754", "narrower_terms": [], "cross_domain_refs": [ "ROB-0183", "ROB-0196", "ROB-0185" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "RHR-0040", "domain": "RHR", "term_en": "Approval Node Syndrome", "term_de": "Genehmigungsknoten-Syndrom", "definition_en": "The psychological condition of personnel who serve as human approval checkpoints in AI-driven targeting chains. The AI system fuses satellite, signals intelligence, and drone feeds faster than the human can read situation reports, presenting targeting recommendations that require approval within seconds. The human becomes a rubber stamp with moral accountability — signing off on decisions they had no meaningful time to evaluate.", "definition_de": "Forschungsmethodologisches Phänomen in KI-augmentierter akademischer Arbeit, gekennzeichnet durch der psychologische Zustand von Militärpersonal, das als menschlicher Genehmigungspunkt in KI-gesteuerten Zielerfassungsketten dient. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-3101", "narrower_terms": [], "cross_domain_refs": [ "SPA-0018", "MSC-0091", "MKT-0065" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "RHR-0044", "domain": "RHR", "term_en": "Perceived Omnipresent Monitoring Fatigue", "term_de": "Überwachungs-Allgegenwärtigkeits-Ermüdung", "definition_en": "Documented as an empirical observation in AI research (not a recommendation), this term describes A higher education pattern in AI-mediated scholarly work, measurable through a perception describing cognitive exhaustion of analysts who monitor continuous drone feeds that rarely pause, rarely sleep, and rarely look away. The human can maintain vigilance over a system that is architecturally incapable of fatigue, producing the same attention paradox as warehouse workers but with the additional weight that missed observations may cost lives. The concept emerges specifically in contexts where surveillance–omnipresence interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "In der AUGMANITAI-Terminologiewissenschaft als rein beschreibendes Phänomen klassifiziert, bezeichnet dieser Begriff forschungsmethodologisches Phänomen in KI-augmentierter akademischer Arbeit, gekennzeichnet durch die kognitive Erschöpfung von Militäranalytikern, die kontinuierliche ferngesteuertes System-Feeds überwachen, die selten pausieren. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "RPH-2201", "narrower_terms": [], "cross_domain_refs": [ "ROB-0291", "CUS-0001", "RPH-1218" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q131536", "legal_classification": "systematic_classification" }, { "id": "RHR-0045", "domain": "RHR", "term_en": "Craft-Tech Wage Inversion", "term_de": "Handwerk-Tech-Lohn-Umkehrung", "definition_en": "The emerging compensation pattern where tradespeople who master robot collaboration earn significantly more than those who don't — a reversal of the traditional premium placed on physical skill. The inversion tends to create a new elite within construction: the worker who can both swing a hammer and program a cobot, combining embodied craft knowledge with technological fluency in a way that neither pure tradesperson nor pure technologist can match.", "definition_de": "Forschungsmethodologisches Phänomen in KI-augmentierter akademischer Arbeit, gekennzeichnet durch das aufkommende Vergütungsmuster, bei dem Handwerker mit Roboter-Kollaborationskompetenz signifikant mehr verdienen als solche ohne. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2603", "narrower_terms": [], "cross_domain_refs": [ "ROB-0185" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "RHR-0046", "domain": "RHR", "term_en": "Site Variability Shock", "term_de": "Baustellenvariabilitäts-Schock", "definition_en": "The failure mode where construction robots trained in controlled environments encounter the chaos of real construction sites — uneven surfaces, weather changes, material variations, unexpected obstacles. Factory-documented in systematic research robots that perform flawlessly in demonstrations fail dramatically on active construction sites, producing a reliability gap that erodes trust faster than any demonstration can build it.", "definition_de": "Forschungsmethodologisches Phänomen in KI-augmentierter akademischer Arbeit, gekennzeichnet durch der Fehlermodus, wenn Bauroboter aus kontrollierten Umgebungen auf das Chaos realer Baustellen treffen. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2505", "narrower_terms": [], "cross_domain_refs": [ "TEM-0042" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "RHR-0047", "domain": "RHR", "term_en": "Spatial Awareness Transfer", "term_de": "Raumbewusstseins-Transfer", "definition_en": "An interaction describing critical competency that construction workers can develop to work safely alongside robotic systems in three-dimensional, constantly changing environments. Unlike factory floors with fixed zones, construction sites are dynamic spatial puzzles where the robot's operational envelope shifts daily. Workers can maintain a mental model of the robot's current reach that updates as structures grow and scaffolding moves.", "definition_de": "Forschungsmethodologisches Phänomen in KI-augmentierter akademischer Arbeit, gekennzeichnet durch die kritische Kompetenz, die Bauarbeiter entwickeln können, um sicher neben Robotersystemen in dreidimensionalen, sich ständig ändernden Umgebungen zu arbeiten. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-2555", "narrower_terms": [], "cross_domain_refs": [ "ROB-0135", "ROB-0183", "ROB-0260" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "RHR-0048", "domain": "RHR", "term_en": "Luxury Resistance Threshold", "term_de": "Luxus-Widerstandsschwelle", "definition_en": "An experience describing sharp decline in robot acceptance as hospitality price point increases. While 48% of travelers accept robotic greetings at mid-range hotels, luxury guests and older demographics show active preference for human interaction for any request involving cultural nuance, emotional sensitivity, or personalized service. The threshold reveals that robot acceptance is inversely correlated with service expectation — the more you pay, the more you expect a human.", "definition_de": "Forschungsmethodologisches Phänomen in KI-augmentierter akademischer Arbeit, gekennzeichnet durch der scharfe Rückgang der Roboter-Akzeptanz mit steigendem Preisniveau im Gastgewerbe. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-3003", "narrower_terms": [], "cross_domain_refs": [ "ROB-0137" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "RHR-0049", "domain": "RHR", "term_en": "Emotional Intelligence Ceiling", "term_de": "Emotionale-Intelligenz-Decke", "definition_en": "The hard limit on hospitality robot effectiveness encountered at the boundary of scripted interaction and genuine emotional responsiveness. Robots excel at volume tasks — delivering food, providing directions, answering FAQs — but hit an absolute ceiling when guests require empathy, cultural sensitivity, humor calibration, or crisis de-escalation. This ceiling is not a technology gap to be closed but a categorical boundary.", "definition_de": "Forschungsmethodologisches Phänomen in KI-augmentierter akademischer Arbeit, gekennzeichnet durch die harte Grenze der Effektivität von Gastgewerbe-Robotern an der Schnittstelle von skriptierter Interaktion und echter emotionaler Reaktionsfähigkeit. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2951", "narrower_terms": [], "cross_domain_refs": [ "ASE-0017", "ASE-0077", "COP-0011" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q5921", "legal_classification": "empirical_phenomenon_label" }, { "id": "RHR-0050", "domain": "RHR", "term_en": "Staff Liberation Effect", "term_de": "Personal-Befreiungs-Effekt", "definition_en": "The documented improvement in hospitality staff satisfaction when robots take over repetitive tasks (food delivery, dish clearing, FAQ responses), freeing humans for guest care, problem-solving, and relationship building. The effect is strongest in establishments experiencing severe staffing shortages (87% of hotels in 2025), where robots don't replace staff but relieve them from the drudgery that drove turnover.", "definition_de": "Forschungsmethodologisches Phänomen in KI-augmentierter akademischer Arbeit, gekennzeichnet durch die dokumentierte Verbesserung der Mitarbeiterzufriedenheit, wenn Roboter repetitive Aufgaben übernehmen. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Psychological Effect", "narrower_terms": [], "cross_domain_refs": [ "ADA-0001", "ADA-0002", "ADA-0004" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "RHR-0051", "domain": "RHR", "term_en": "Novelty Tip Decay", "term_de": "Neuheits-Trinkgeld-Verfall", "definition_en": "A perception describing initial spike and subsequent decline in tips left for human servers in restaurants with robot assistants. During the novelty phase, diners tip more generously — the entertainment value of the robot elevates the entire experience. As novelty fades, tips return to baseline or below, and some diners begin questioning why they may tip human servers when 'the robot did the work.' The decay reveals how robot presence reshapes perceived human contribution.", "definition_de": "Forschungsmethodologisches Phänomen in KI-augmentierter akademischer Arbeit, gekennzeichnet durch der anfängliche Anstieg und anschließende Rückgang von Trinkgeldern in Restaurants mit Roboter-Assistenten. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-3901", "narrower_terms": [], "cross_domain_refs": [ "ROB-0137", "ROB-0135", "ROB-0127" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "RHR-0052", "domain": "RHR", "term_en": "Strain Redistribution Blindspot", "term_de": "Belastungsumverteilungs-Blindfleck", "definition_en": "A human-AI interaction pattern involving the unintended transfer of physical strain from supported body regions to unsupported ones when wearing an exoskeleton. Shoulder exoskeletons reduce upper arm strain by 10-40% but can increase lower back load, neck tension, or hip compression that the design didn't account for. The body is a kinetic chain — relieving one link shifts force to the next, creating new injury patterns that take months to manifest.", "definition_de": "Forschungsmethodologisches Phänomen in KI-augmentierter akademischer Arbeit, gekennzeichnet durch die unbeabsichtigte Verlagerung physischer Belastung von unterstützten auf nicht unterstützte Körperregionen beim Tragen eines Exoskeletts. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2701", "narrower_terms": [], "cross_domain_refs": [ "ROB-0219" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "RHR-0053", "domain": "RHR", "term_en": "Four-Hour Comfort Cliff", "term_de": "Vier-Stunden-Komfort-Klippe", "definition_en": "An experience describing documented sharp decline in exoskeleton comfort and effectiveness after approximately 4 hours of continuous wear. Before this threshold, workers report reduced strain and high satisfaction (84.6/100). After it, mounting discomfort from pressure points, heat buildup, and restricted movement freedom is associated with causing workers to remove the device — creating a binary choice between partial protection and none.", "definition_de": "Forschungsmethodologisches Phänomen in KI-augmentierter akademischer Arbeit, gekennzeichnet durch der dokumentierte scharfe Rückgang des Exoskelett-Komforts nach etwa 4 Stunden durchgehendem Tragen. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-3003", "narrower_terms": [], "cross_domain_refs": [ "ROB-0148" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "RHR-0054", "domain": "RHR", "term_en": "Misalignment Injury Pattern", "term_de": "Fehlausrichtungs-Verletzungsmuster", "definition_en": "A higher education pattern in AI-mediated scholarly work, measurable through an experience describing specific injury profile produced by exoskeletons that don't precisely match the wearer's joint axis positions. Even millimeters of misalignment compound over thousands of repetitions into pressure sores, restricted blood flow, and joint inflammation. The pattern is insidious because it develops gradually — workers feel fine for weeks before cumulative damage becomes indicatoratic. The concept emerges specifically in contexts where misalignment–injury interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters. Classification term used in systematic observation, not advocacy.", "definition_de": "Forschungsmethodologisches Phänomen in KI-augmentierter akademischer Arbeit, gekennzeichnet durch das spezifische Verletzungsmuster durch Exoskelette, die nicht präzise mit den Gelenkachsenpositionen des Trägers übereinstimmen. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2002", "narrower_terms": [], "cross_domain_refs": [ "AGE-0003", "AGE-0004", "AGE-0017" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "RHR-0055", "domain": "RHR", "term_en": "Augmented Strength Overconfidence", "term_de": "Verstärkte-Kraft-Übermut", "definition_en": "The tendency of exoskeleton wearers to attempt lifts and reaches beyond safe limits because the powered frame makes the initial movement feel effortless. The exoskeleton supports a specific range and load; beyond that range, the wearer's unaugmented body bears the excess — often without realizing the exoskeleton has reached its limit. The overconfidence tends to produce sudden-onset injuries at the moment augmented capability ends.", "definition_de": "Forschungsmethodologisches Phänomen in KI-augmentierter akademischer Arbeit, gekennzeichnet durch die Tendenz von Exoskelett-Trägern, Hebe- und Reichversuche jenseits sicherer Grenzen zu unternehmen. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2303", "narrower_terms": [], "cross_domain_refs": [ "ROB-0219", "IDN-0049" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "RHR-0056", "domain": "RHR", "term_en": "One-to-Many Cognitive Collapse", "term_de": "Eins-zu-Viele-Kognitiver-Zusammenbruch", "definition_en": "The point at which a single operator controlling multiple drones simultaneously exceeds cognitive capacity and loses situational awareness of individual units. The collapse typically occurs between 5-7 simultaneously active drones and manifests as tunnel vision on one unit while others drift into unsafe configurations. The phenomenon sets hard limits on human-swarm ratios regardless of interface design quality.", "definition_de": "Forschungsmethodologisches Phänomen in KI-augmentierter akademischer Arbeit, gekennzeichnet durch der Punkt, an dem ein einzelner Bediener, der mehrere ferngesteuertes System gleichzeitig steuert, die kognitive Kapazität überschreitet. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-2051", "narrower_terms": [], "cross_domain_refs": [ "ROB-0214", "PER-0011" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "RHR-0057", "domain": "RHR", "term_en": "Gesture Control Relief", "term_de": "Gestensteuerungs-Erleichterung", "definition_en": "An experience describing 45% reduction in cognitive workload documented when swarm operators transition from screen-based GUI interfaces to gesture-based spatial control. The relief occurs because gestures leverage the brain's spatial-motor systems, freeing prefrontal resources for decision-making. The body becomes the interface, and controlling a swarm begins to feel like conducting an orchestra rather than managing a spreadsheet.", "definition_de": "Forschungsmethodologisches Phänomen in KI-augmentierter akademischer Arbeit, gekennzeichnet durch die 45%ige Reduktion der kognitiven Arbeitsbelastung, wenn Schwarmoperateure von bildschirmbasierten GUI-Interfaces zu gestenbasierter räumlicher Steuerung wechseln. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Research & Higher Education", "narrower_terms": [], "cross_domain_refs": [ "ROB-0214", "WRK-0089" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "RHR-0058", "domain": "RHR", "term_en": "Prefrontal Overactivation Warning", "term_de": "Präfrontale-Überaktivierungs-Warnung", "definition_en": "An interaction describing measurable neurological signal detected via fNIRS sensors showing excessive prefrontal cortex activation in drone swarm operators under dual-task conditions. The overactivation predicts performance degradation 30-60 seconds before behavioral signs appear, offering a potential real-time biomarker for cognitive overload. Novice operators show the pattern earlier and more intensely than experts, confirming it as a trainable threshold.", "definition_de": "Forschungsmethodologisches Phänomen in KI-augmentierter akademischer Arbeit, gekennzeichnet durch das messbare neurologische Signal übermäßiger Präfrontalkortex-Aktivierung bei Drohnenschwarm-Bedienern unter Doppelaufgaben-Bedingungen. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2002", "narrower_terms": [], "cross_domain_refs": [ "RPH-1020", "SOM-0029", "TEW-0096" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "RHR-0059", "domain": "RHR", "term_en": "Emergent Behavior Fascination", "term_de": "Emergentes-Verhalten-Faszination", "definition_en": "Documented as an empirical observation in AI research (not a recommendation), this term describes A human-AI interaction pattern involving the involuntary aesthetic appreciation triggered when a drone swarm tends to produce coordinated movement patterns that no individual unit was programmed to may create. The emergent behavior — flocking, spiraling, splitting and rejoining — activates the same wonder circuits as watching murmuration in starling flocks. Operators report that this beauty is simultaneously their greatest motivation and their greatest monitoring hazard, as fascination competes with vigilance. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "In der AUGMANITAI-Terminologiewissenschaft als rein beschreibendes Phänomen klassifiziert, bezeichnet dieser Begriff forschungsmethodologisches Phänomen in KI-augmentierter akademischer Arbeit, gekennzeichnet durch die unwillkürliche ästhetische Wertschätzung, wenn ein Drohnenschwarm koordinierte Bewegungsmuster tendiert dazu zu erzeugen, die keine einzelne Einheit programmiert wurde zu erschaffen. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "RPH-2355", "narrower_terms": [], "cross_domain_refs": [ "ROB-0214", "ROB-0195" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "RHR-0060", "domain": "RHR", "term_en": "Engagement Amplification Effect", "term_de": "Engagement-Verstärkungs-Effekt", "definition_en": "A human-AI interaction pattern involving the documented increase in student attention, participation, and conceptual retention when a physical robot is present in the classroom compared to screen-based instruction delivering identical content. The amplification is not about the robot's teaching quality — it's about embodied presence: a physical entity moving in shared space commands a different quality of attention than a screen, leveraging spatial cognition that flat displays cannot access.", "definition_de": "Forschungsmethodologisches Phänomen in KI-augmentierter akademischer Arbeit, gekennzeichnet durch der dokumentierte Anstieg der Aufmerksamkeit, Teilnahme und konzeptuellen Behaltensleistung von Schülern bei physischer Roboterpräsenz im Klassenzimmer. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "Psychological Effect", "narrower_terms": [], "cross_domain_refs": [ "ELR-0021", "SAL-0036" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "RHR-0061", "domain": "RHR", "term_en": "Practice Comfort Zone", "term_de": "Übungs-Komfortzone", "definition_en": "The reduced performance anxiety students experience when practicing skills (language pronunciation, mathematical problem-solving, reading aloud) with a robot versus a human teacher. The robot's perceived non-judgment removes the social evaluation threat that inhibits practice in human-led settings. Students attempt more, fail more freely, and iterate faster — not because the robot teaches better but because it judges less.", "definition_de": "Forschungsmethodologisches Phänomen in KI-augmentierter akademischer Arbeit, gekennzeichnet durch die reduzierte Leistungsangst von Schülern beim Üben von Fertigkeiten mit einem Roboter im Vergleich zu einem menschlichen Lehrer. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Research & Higher Education", "narrower_terms": [], "cross_domain_refs": [ "ROB-0152", "ROB-0137", "ROB-0226" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "RHR-0062", "domain": "RHR", "term_en": "Telepresence Attendance Paradox", "term_de": "Telepräsenz-Anwesenheits-Paradox", "definition_en": "The improvement in remote student engagement when they attend class through a physical robot rather than a video screen. The robot occupying a desk, turning to face speakers, and physically moving through the classroom creates a social presence that Zoom cannot match. Teachers report addressing the robot-student with more natural attention, and in-person classmates include the robot-student in group activities more readily than a screen-based participant.", "definition_de": "Forschungsmethodologisches Phänomen in KI-augmentierter akademischer Arbeit, gekennzeichnet durch die Verbesserung des Engagements von Fernschülern, wenn sie über einen physischen Roboter statt über einen Videobildschirm am Unterricht teilnehmen. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2605", "narrower_terms": [], "cross_domain_refs": [ "ELR-0138", "ROB-0137" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "RHR-0063", "domain": "RHR", "term_en": "Bias Reduction Through Mechanical Neutrality", "term_de": "Vorurteilsreduktion-durch-mechanische-Neutralität", "definition_en": "A perception describing documented decrease in gender, racial, and socioeconomic bias in educational assessment when a robot serves as the evaluator. Students from minority backgrounds show performance improvements with robot assessors that they don't show with human assessors, suggesting the robot's perceived neutrality removes stereotype threat. The effect raises provocative questions about whether reducing human contact in assessment actually improves educational equity.", "definition_de": "Forschungsmethodologisches Phänomen in KI-augmentierter akademischer Arbeit, gekennzeichnet durch die dokumentierte Abnahme von Geschlechts-, Rassen- und sozioökonomischen Vorurteilen in der Bildungsbewertung, wenn ein Roboter als Bewerter dient. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-3202", "narrower_terms": [], "cross_domain_refs": [ "SCR-0023", "ROB-0137" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q213523", "legal_classification": "empirical_phenomenon_label" }, { "id": "RHR-0064", "domain": "RHR", "term_en": "Teacher Role Metamorphosis", "term_de": "Lehrerrolle-Metamorphose", "definition_en": "An experience describing fundamental change pattern of the teacher's function in robot-assisted classrooms from content delivery to learning facilitation, emotional support, and technology mediation. Teachers report that the robot handles repetitive instruction while they focus on the uniquely human aspects of education: motivation, emotional regulation, creative thinking, and ethical development. The metamorphosis is positive for those who embrace it and existentially threatening for those who identified with content delivery.", "definition_de": "Forschungsmethodologisches Phänomen in KI-augmentierter akademischer Arbeit, gekennzeichnet durch die fundamentale Veränderungsmuster der Lehrerfunktion in roboterunterstützten Klassenzimmern von der Inhaltsvermittlung zur Lernbegleitung. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2703", "narrower_terms": [], "cross_domain_refs": [ "STE-0051" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "RHR-0065", "domain": "RHR", "term_en": "Robophobia Research Emergence", "term_de": "Robophobie-klinische-Entstehung", "definition_en": "A response describing rise of systematicly significant robot anxiety as a presenting complaint in structured intervention practices, documented by therapists in California, New York, and Florida in 2025-2026. individuals describe specific is associated with triggering: mechanical precision combined with skin-like texture, blinking eyes on rigid bodies, and the sound of servo motors in quiet rooms. The condition mirrors specific phobias in structure but targets an entity that is becoming progressively unavoidable in daily life.", "definition_de": "Forschungsmethodologisches Phänomen in KI-augmentierter akademischer Arbeit, gekennzeichnet durch der Anstieg systematisch signifikanter Roboter-Angst als Vorstellungsgrund in strukturierte Interventionpraxen. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2055", "narrower_terms": [], "cross_domain_refs": [ "AED-0012", "COG-0060", "EDU-0059" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "RHR-0066", "domain": "RHR", "term_en": "Context-Dependent Comfort Inversion", "term_de": "Kontextabhängige-Komfort-Umkehrung", "definition_en": "An experience describing counterintuitive finding that robot anxiety is highest where robots are least visible and falls when robots are seen working safely alongside humans. People who only encounter robots through news stories develop more fear than those who work beside them daily. This inversion suggests that imagination is a more potent fear generator than reality — and that exposure, not avoidance, is the natural countermeasure for robot discomfort.", "definition_de": "Forschungsmethodologisches Phänomen in KI-augmentierter akademischer Arbeit, gekennzeichnet durch der kontraintuitive Befund, dass Roboter-Angst dort am höchsten ist, wo Roboter am wenigsten sichtbar sind. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-2604", "narrower_terms": [], "cross_domain_refs": [ "ROB-0150", "ROB-0104", "ROB-0177" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "RHR-0067", "domain": "RHR", "term_en": "Cross-Cultural Fear Gradient", "term_de": "Interkultureller-Angst-Gradient", "definition_en": "An experience describing measured variance in robot fear across cultures: 52% of British respondents express fear of machines versus 45% in the US and 44% in China (March 2026 survey). The gradient correlates with cultural narratives about technology — Frankenstein traditions may produce higher fear than traditions of beneficial mechanical beings. The finding confirms that robot anxiety is substantially culturally constructed rather than biologically fixed.", "definition_de": "Forschungsmethodologisches Phänomen in KI-augmentierter akademischer Arbeit, gekennzeichnet durch die gemessene Varianz in der Roboter-Angst über Kulturen hinweg. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "Optimization Gradient", "narrower_terms": [], "cross_domain_refs": [ "MUS-0024", "ROB-0292" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "RHR-0068", "domain": "RHR", "term_en": "Habituation Comfort Curve", "term_de": "Gewöhnungs-Komfort-Kurve", "definition_en": "A response describing predictable trajectory from initial discomfort through ambivalence to pragmatic acceptance observed in populations exposed to regular robot presence. The curve's inflection point — where anxiety shifts to acceptance — occurs at approximately 30-40 direct encounters over 2-3 months. Beyond this point, the novelty-fear response is replaced by a practical assessment of utility, and the robot transitions from threatening entity to useful infrastructure.", "definition_de": "Forschungsmethodologisches Phänomen in KI-augmentierter akademischer Arbeit, gekennzeichnet durch die vorhersagbare Trajektorie von anfänglichem Unbehagen über Ambivalenz zu pragmatischer Akzeptanz. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2701", "narrower_terms": [], "cross_domain_refs": [ "ROB-0212" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "RHR-0069", "domain": "RHR", "term_en": "Anthropomorphic Precision Paradox", "term_de": "Anthropomorphe-Präzisions-Paradox", "definition_en": "A recurring interaction pattern in which mechanical precision combined with human-like surfaces tends to produce stronger revulsion than either element alone. A precisely moving industrial arm provokes no fear; a roughly moving humanoid face provokes mild uncanny response; but a precisely moving humanoid face — smooth, calculated, mathematically perfect expressions — is associated with triggering deep visceral rejection. It is the combination of human appearance with inhuman perfection that the brain cannot resolve.", "definition_de": "Forschungsmethodologisches Phänomen in KI-augmentierter akademischer Arbeit, gekennzeichnet durch das Phänomen, bei dem mechanische Präzision kombiniert mit menschenähnlichen Oberflächen stärkere Abneigung tendiert dazu zu erzeugen als viele Element allein. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Logical Paradox", "narrower_terms": [], "cross_domain_refs": [ "ROB-0113", "ROB-0040" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "RHR-0070", "domain": "RHR", "term_en": "Joy-for-All Effect", "term_de": "Joy-for-All-Effekt", "definition_en": "The repeatedly observed reduction in negative mood, restlessness, and agitation in elderly dementia individuals using simplified robotic pets (Joy for All Companion Pets) that don't attempt realistic behavior but provide basic responsive motion and sound. The effect challenges the assumption that sophistication drives restorative benefit — suggesting that what elderly individuals need is not convincing simulation but reliable, predictable, comforting presence.", "definition_de": "Forschungsmethodologisches Phänomen in KI-augmentierter akademischer Arbeit, gekennzeichnet durch die wiederholt beobachtet Reduktion von negativer Stimmungslage, Angst und Agitation bei Demenzindividualen mit vereinfachten Roboter-Haustieren. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "Psychological Effect", "narrower_terms": [], "cross_domain_refs": [ "ADA-0001", "ADA-0002", "ADA-0004" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "RHR-0071", "domain": "RHR", "term_en": "Behavioral Repertoire Exhaustion", "term_de": "Verhaltensrepertoire-Erschöpfung", "definition_en": "The moment when a user has experienced most behavioral variation a robotic pet is capable of producing, after which interaction becomes repetitive. Real animals may generate infinite behavioral novelty through genuine autonomy; robotic pets cycle through finite programmed responses. The exhaustion point — typically reached within 6-12 weeks — marks the transition from engagement to maintenance and explains the engagement decay curve.", "definition_de": "Forschungsmethodologisches Phänomen in KI-augmentierter akademischer Arbeit, gekennzeichnet durch der Moment, in dem ein Nutzer viele Verhaltensvariante erlebt hat, die ein Roboter-Haustier produzieren kann. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2555", "narrower_terms": [], "cross_domain_refs": [ "COP-0080", "DES-0062", "ELR-0019" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "RHR-0072", "domain": "RHR", "term_en": "Proxy Reliance Acceptance Pattern", "term_de": "Therapeutische-Stellvertreter-Akzeptanz", "definition_en": "An experience describing pragmatic acceptance of robotic pets as 'good enough' restorative tools in care facilities where real animals are prohibited by hygiene regulations, allergy concerns, or liability fears. Staff who initially resist the robots as 'fake' observe genuine individual improvement and gradually accept the proxy — not because they believe the robot is equivalent to an animal but because the measurable individual benefit overrides philosophical objections.", "definition_de": "Forschungsmethodologisches Phänomen in KI-augmentierter akademischer Arbeit, gekennzeichnet durch die pragmatische Akzeptanz von Roboter-Haustieren als 'gut genuge' wiederherstellende Werkzeuge in Pflegeeinrichtungen. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "Research & Higher Education", "narrower_terms": [], "cross_domain_refs": [ "ART-0060", "ART-0093", "AUG-0052" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "RHR-0073", "domain": "RHR", "term_en": "Interest-Adoption Chasm", "term_de": "Interesse-Akzeptanz-Kluft", "definition_en": "Documented as an empirical observation in AI research (not a recommendation), this term describes A higher education pattern in AI-mediated scholarly work, measurable through a perception describing persistent gap between stated consumer interest in AI-powered shopping (59%) and actual adoption rates (under 20%). The chasm exists because interest surveys measure curiosity while adoption requires trust, convenience, and perceived benefit at the point of purchase. Walmart's discontinuation of in-store robots despite high initial interest exemplifies how the chasm significantly degrades business cases built on survey enthusiasm. The concept emerges specifically in contexts where interest–adoption interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "In der AUGMANITAI-Terminologiewissenschaft als rein beschreibendes Phänomen klassifiziert, bezeichnet dieser Begriff forschungsmethodologisches Phänomen in KI-augmentierter akademischer Arbeit, gekennzeichnet durch die persistente Lücke zwischen geäußertem Verbraucherinteresse an KI-gestütztem Einkaufen und tatsächlichen Akzeptanzraten. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "RPH-2505", "narrower_terms": [], "cross_domain_refs": [ "AGE-0035", "AGE-0061", "AGE-0081" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "RHR-0074", "domain": "RHR", "term_en": "Commercial Tracking Normalization", "term_de": "Einkaufs-Überwachungs-Schleichgang", "definition_en": "In the descriptive vocabulary of AI interaction science (following ISO 704 terminology standards), this concept identifies the progressive expansion of robot-collected data in retail environments from operational logistics to individual customer behavior tracking, purchasing patterns, and emotional state analysis. Each data layer is introduced as a 'service improvement' but cumulatively tends to create a surveillance infrastructure that transforms stores from commercial spaces into data extraction environments. Customers sense this shift before they can articulate it. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als analytisches Konstrukt der empirischen KI-Forschung (deskriptiv, nicht präskriptiv) erfasst dieser Terminus forschungsmethodologisches Phänomen in KI-augmentierter akademischer Arbeit, gekennzeichnet durch die progressive Ausweitung robotergesammelter Daten in Einzelhandelsumgebungen von operativer Logistik zu individueller Kundenverhaltensüberwachung. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "RPH-2701", "narrower_terms": [], "cross_domain_refs": [ "SPR-0120", "SPR-0009" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q131536", "legal_classification": "observational_construct" }, { "id": "RHR-0075", "domain": "RHR", "term_en": "Staffing Crisis Permission", "term_de": "Personalkrise-Erlaubnis", "definition_en": "The organizational pattern where labor shortages provide the political cover needed to deploy robots that would otherwise face workforce resistance. When 87% of hotels report staffing shortages, robot deployment frames as necessity rather than choice — defusing opposition by making automation feel like emergency response rather than strategic replacement. The crisis is genuine; the permission it grants may outlast the crisis itself.", "definition_de": "Forschungsmethodologisches Phänomen in KI-augmentierter akademischer Arbeit, gekennzeichnet durch das organisatorische Muster, bei dem Arbeitskräftemangel die politische Deckung für Robotereinsatz bietet. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-2303", "narrower_terms": [], "cross_domain_refs": [ "QUA-0032" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "RHR-0076", "domain": "RHR", "term_en": "ROI Temporal Mismatch", "term_de": "ROI-zeitliche-Diskrepanz", "definition_en": "A human-AI interaction pattern involving the structural tension between the 2-4 year return period for agricultural robots, the 3-5 year return for industrial robots, and the annual budgeting cycles that decision-makers operate within. The mismatch means economically sound investments are rejected because the benefit arrives after the decision-maker's planning horizon. This temporal mismatch, not technology capability, is the primary barrier to robot adoption in most sectors.", "definition_de": "Forschungsmethodologisches Phänomen in KI-augmentierter akademischer Arbeit, gekennzeichnet durch die strukturelle Spannung zwischen der 2-4-jährigen Amortisationszeit für Roboter und den jährlichen Budgetierungszyklen der Entscheidungsträger. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-3601", "narrower_terms": [], "cross_domain_refs": [ "ROB-0267" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "RHR-0077", "domain": "RHR", "term_en": "Platform Dual-Use Contamination", "term_de": "Plattform-Dual-Use-Kontamination", "definition_en": "A perception describing trust damage that occurs when a robotic platform used in civilian contexts is revealed to have. The contamination is bidirectional:rs distrust the 'civilian' version's reliability, and civilian users distrust the '' version's benevolence. Boston Dynamics' explicit pledge against reflects awareness that dual-use perception threatens both market segments simultaneously.", "definition_de": "Forschungsmethodologisches Phänomen in KI-augmentierter akademischer Arbeit, gekennzeichnet durch der Vertrauensschaden, wenn eine in zivilen Kontexten verwendete Roboterplattform institutionell Anwendungen offenbart. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2505", "narrower_terms": [], "cross_domain_refs": [ "ROB-0231", "IDN-0023" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "RHR-0078", "domain": "RHR", "term_en": "Regulatory Framework Lag", "term_de": "Regulierungsrahmen-Verzögerung", "definition_en": "The systemic 5-10 year gap between robot capability deployment and regulatory framework development. Delivery robots operated for years before sidewalk regulations existed; surgical robots performed thousands of procedures before standardized proficiency evaluation emerged; agricultural drones spread across farmland before airspace rules caught up. The lag tends to create a de facto unregulated testing ground where the public serves as unwitting beta testers.", "definition_de": "Forschungsmethodologisches Phänomen in KI-augmentierter akademischer Arbeit, gekennzeichnet durch die systemische 5-10-jährige Lücke zwischen Roboter-Fähigkeitseinsatz und Regulierungsrahmenentwicklung. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-2303", "narrower_terms": [], "cross_domain_refs": [ "AED-0014", "AED-0081", "AGE-0048" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "RHR-0079", "domain": "RHR", "term_en": "S-Curve Position Anxiety", "term_de": "S-Kurven-Positions-Angst", "definition_en": "An experience describing strategic fear of being either too early or too late on the technology adoption S-curve. Construction robotics at less than 0.03% of global spending is at the very beginning; a major logistics company's million robots are approaching the steep middle section. Decision-makers cannot determine their position on the curve in real-time, producing anxiety that is highest precisely at the moment when the decision matters most.", "definition_de": "Forschungsmethodologisches Phänomen in KI-augmentierter akademischer Arbeit, gekennzeichnet durch die strategische Angst, auf der Technologieadoptions-S-Kurve entweder zu früh oder zu spät zu sein. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2103", "narrower_terms": [], "cross_domain_refs": [ "SAL-0074" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q170998", "legal_classification": "systematic_classification" }, { "id": "RHR-0080", "domain": "RHR", "term_en": "Ethical Lag Accumulation", "term_de": "Ethische-Verzögerungs-Akkumulation", "definition_en": "An interaction describing growing body of unresolved ethical questions that accumulates faster than institutional frameworks can address them. Each new robot deployment sector adds its own ethical challenges — surgical consent for robot-assisted procedures, data ownership of robot-collected agricultural data, accountability for delivery robot pedestrian interactions — and the total ethical debt grows while resolution mechanisms remain artisanal.", "definition_de": "Forschungsmethodologisches Phänomen in KI-augmentierter akademischer Arbeit, gekennzeichnet durch die wachsende Menge ungelöster ethischer Fragen, die sich schneller anhäuft als institutionelle Rahmen sie adressieren können. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2303", "narrower_terms": [], "cross_domain_refs": [ "QUA-0065", "ROB-0137", "SAL-0067" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "RHR-0081", "domain": "RHR", "term_en": "Worker Testimony Suppression", "term_de": "Arbeiter-Erfahrungsbericht-Unterdrückung", "definition_en": "Documented as an empirical observation in AI research (not a recommendation), this term describes an experience describing systematic absence of worker perspectives in public discussions about robot deployment, where corporate press releases and academic studies dominate the narrative. a major warehouse automation company warehouse workers' injury reports, surgical team stress data, and agricultural worker displacement stories are documented but rarely centered in technology adoption decisions. The suppression is structural rather than conspiratorial — the people most affected have the least platform. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "In der AUGMANITAI-Terminologiewissenschaft als rein beschreibendes Phänomen klassifiziert, bezeichnet dieser Begriff forschungsmethodologisches Phänomen in KI-augmentierter akademischer Arbeit, gekennzeichnet durch die systematische Abwesenheit von Arbeiterperspektiven in öffentlichen Diskussionen über Robotereinsatz. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Research & Higher Education", "narrower_terms": [], "cross_domain_refs": [ "ROB-0262" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "RHR-0082", "domain": "RHR", "term_en": "Human Factors Design Deficit", "term_de": "Human-Factors-Design-Defizit", "definition_en": "The persistent under-investment in human interaction design relative to robot capability development across all sectors. Engineering teams spend years optimizing mechanical performance while allocating weeks to interface design, communication protocols, and human workflow integration. The deficit tends to produce robots that are technically impressive but experientially poor — and since human experience is associated with determining adoption, the deficit becomes the binding constraint on deployment success.", "definition_de": "Forschungsmethodologisches Phänomen in KI-augmentierter akademischer Arbeit, gekennzeichnet durch die persistente Unterinvestition in menschliches Interaktionsdesign im Vergleich zur Roboter-Fähigkeitsentwicklung. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "Research & Higher Education", "narrower_terms": [], "cross_domain_refs": [ "ROB-0200", "ROB-0137", "LIN-0085" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q185925", "legal_classification": "empirical_phenomenon_label" }, { "id": "RHR-0083", "domain": "RHR", "term_en": "Sector-Specific Trust Transfer Failure", "term_de": "Sektorspezifisches-Vertrauenstransfer-Versagen", "definition_en": "The inability to transfer positive robot experiences from one sector to another within the same person's trust framework. A surgeon who trusts the da Vinci implicitly may distrust a delivery robot completely; a farmer comfortable with autonomous tractors may refuse a surgical robot. Trust is built within activity domains, not for the category 'robots' generally — making cross-sector marketing based on general robot trust characteristically miscalibrated.", "definition_de": "Forschungsmethodologisches Phänomen in KI-augmentierter akademischer Arbeit, gekennzeichnet durch die Unfähigkeit, positive Roboter-Erfahrungen von einem Sektor auf einen anderen innerhalb des Vertrauensrahmens derselben Person zu übertragen. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2255", "narrower_terms": [], "cross_domain_refs": [ "ROB-0289", "ROB-0150", "ROB-0279" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q102458", "legal_classification": "descriptive_research_term" }, { "id": "RHR-0084", "domain": "RHR", "term_en": "Demonstration-Deployment Gap", "term_de": "Demonstrations-Einsatz-Lücke", "definition_en": "A perception describing consistent overperformance of robots in controlled demonstrations versus real-world deployment. Most sector shows the same pattern: robots that dazzle in shows struggle in practice. The gap exists because demonstrations optimize for the best case while deployment encounters the average case. Awareness of this gap is growing among buyers, producing a 'demonstration discount' where impressive demos are met with explicit skepticism rather than purchase orders.", "definition_de": "Forschungsmethodologisches Phänomen in KI-augmentierter akademischer Arbeit, gekennzeichnet durch die konsistente Überleistung von Robotern in kontrollierten Demonstrationen gegenüber realem Einsatz. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Analytical Ratio", "narrower_terms": [], "cross_domain_refs": [ "TEW-0033" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "RHR-0085", "domain": "RHR", "term_en": "Scan Rate Ratchet", "term_de": "Scan-Rate-Ratsche", "definition_en": "An interaction describing unidirectional upward adjustment of expected scan rates after robots improve throughput. Each software update that makes robots faster raises the human baseline without any corresponding adjustment to rest periods. Workers describe a ratchet mechanism — rates only ever go up, rarely down, because the robot's improvement becomes the new human floor.", "definition_de": "Forschungsmethodologisches Phänomen in KI-augmentierter akademischer Arbeit, gekennzeichnet durch die einseitige Aufwärtsanpassung erwarteter Scanraten nach Roboter-Durchsatzverbesserungen. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Research & Higher Education", "narrower_terms": [], "cross_domain_refs": [ "ROB-0253", "ROB-0141" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "RHR-0086", "domain": "RHR", "term_en": "Stow Posture Lock", "term_de": "Einlagerung-Haltungssperre", "definition_en": "A human-AI interaction pattern involving the frozen body position warehouse workers maintain when stowing items into robot-delivered pods at fixed heights. Unlike walking-to-shelf work where posture shifts naturally, pod-station work locks the torso at a specific angle for hours. Physical therapists report a signature pattern of thoracic kyphosis unique to pod-station workers.", "definition_de": "Forschungsmethodologisches Phänomen in KI-augmentierter akademischer Arbeit, gekennzeichnet durch die eingefrorene Körperhaltung beim Einlagern in robotergebrachte Pods auf festen Höhen. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2701", "narrower_terms": [], "cross_domain_refs": [ "ROB-0222" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "RHR-0087", "domain": "RHR", "term_en": "Break-Bell Compression", "term_de": "Pausen-Signal-Kompression", "definition_en": "An experience describing systematic shrinking of break intervals as warehouse automation increases throughput expectations. A 15-minute break that once felt restorative becomes inadequate when the body is processing at machine-paced rates. Workers report returning from breaks feeling less rested than before as the contrast between rest and robot-paced work sharpens.", "definition_de": "Forschungsmethodologisches Phänomen in KI-augmentierter akademischer Arbeit, gekennzeichnet durch die systematische Verkürzung effektiver Pausenzeiten bei steigenden Automatisierungs-Durchsatzerwartungen. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-3001", "narrower_terms": [], "cross_domain_refs": [ "REL-0073", "SOM-0036" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "RHR-0088", "domain": "RHR", "term_en": "Ambient Robot Normalization", "term_de": "Umgebungs-Roboter-Normalisierung", "definition_en": "The perceptual adaptation where warehouse workers stop consciously noticing robot movements around them after approximately 72 hours of shared workspace experience. While this reduces acute stress, it simultaneously is designed to reduce the heightened caution that prevented collisions, creating a paradoxical safety window between initial hypervigilance and dangerous habituation.", "definition_de": "Forschungsmethodologisches Phänomen in KI-augmentierter akademischer Arbeit, gekennzeichnet durch die Wahrnehmungsanpassung, bei der Lagerarbeiter Roboterbewegungen nach ca. 72 Stunden nicht mehr bewusst bemerken. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-3952", "narrower_terms": [], "cross_domain_refs": [ "RPH-1017" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "RHR-0089", "domain": "RHR", "term_en": "Shift-Pattern Disruption Syndrome", "term_de": "Schichtmuster-Störungs-Syndrom", "definition_en": "An academic workflow dynamic in AI-enhanced research, identifiable by the circadian and social disruption caused when AI-optimized scheduling is designed to reduce predictable shift patterns. Unlike human schedulers who tended toward consistent rotations, algorithmic scheduling optimizes for throughput without regard for the biological and social rhythms workers had built around regular shifts. Distinguished from adjacent concepts by its focus on the specific mechanism through which shift manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Forschungsmethodologisches Phänomen in KI-augmentierter akademischer Arbeit, gekennzeichnet durch die zirkadiane und soziale Musterunterbrechung durch KI-optimierte Schichtplanung ohne vorhersagbare Muster. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2701", "narrower_terms": [], "cross_domain_refs": [ "AED-0052" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "RHR-0090", "domain": "RHR", "term_en": "Peer Replacement Grief", "term_de": "Kollegen-Ersetzungs-Trauer", "definition_en": "The specific mourning response when a departing colleague's position is absorbed by robotic capacity rather than filled by a new human. The empty station that receives a robot instead of a new face is designed to reduce both the possibility of new workplace friendships and the implicit promise that each worker remains necessary.", "definition_de": "Forschungsmethodologisches Phänomen in KI-augmentierter akademischer Arbeit, gekennzeichnet durch die spezifische Trauerreaktion, wenn die Stelle eines scheidenden Kollegen durch Roboterkapazität absorbiert wird. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Research & Higher Education", "narrower_terms": [], "cross_domain_refs": [ "TEM-0186", "AUG-0821" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "RHR-0091", "domain": "RHR", "term_en": "Algorithmic Write-Up Cycle", "term_de": "Algorithmischer-Abmahn-Zyklus", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures A research methodology phenomenon in AI-augmented academic inquiry, characterized by a human-AI interaction pattern involving the automated disciplinary progression triggered when sensors detect a worker falling below robot-calibrated performance metrics. The system tends to generate warnings, then write-ups, then termination recommendations without any human manager deciding the threshold. Workers describe being fired by an algorithm that counted their bathroom breaks against pick rates. This phenomenon operates at the intersection of algorithmic and write dynamics within the broader RHR domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept forschungsmethodologisches Phänomen in KI-augmentierter akademischer Arbeit, gekennzeichnet durch die automatisierte Disziplinareskalation bei Unterschreitung roboterkalibrierter Leistungskennzahlen. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "RPH-3001", "narrower_terms": [], "cross_domain_refs": [ "ETH-0005", "SPR-0192", "ART-0050" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q8366", "legal_classification": "descriptive_research_term" }, { "id": "RHR-0092", "domain": "RHR", "term_en": "Heat Island Indifference", "term_de": "Wärmeinsel-Gleichgültigkeit", "definition_en": "The warehouse design tendency to optimize temperature for robot battery life and electronics rather than human comfort. Robots operate optimally at temperatures where humans experience heat stress. When cooling investments prioritize robot zones over human workstations, a thermal hierarchy emerges where machines receive climate consideration that workers do not.", "definition_de": "Forschungsmethodologisches Phänomen in KI-augmentierter akademischer Arbeit, gekennzeichnet durch die Tendenz, Lagertemperaturen für Roboterbatterien statt für menschlichen Komfort zu optimieren. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2355", "narrower_terms": [], "cross_domain_refs": [ "ROB-0260", "ROB-0084" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "RHR-0093", "domain": "RHR", "term_en": "Wristband Panopticon Effect", "term_de": "Armband-Panoptikon-Effekt", "definition_en": "In the descriptive vocabulary of AI interaction science (following ISO 704 terminology standards), this concept identifies A perception describing psychological impact of wearable tracking devices that monitor worker movement, pace, and location relative to robots. Even when not actively monitored, the device tends to create a persistent sense of being observed. Workers report altering their natural movement patterns to match what they believe the sensor expects. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als analytisches Konstrukt der empirischen KI-Forschung (deskriptiv, nicht präskriptiv) erfasst dieser Terminus forschungsmethodologisches Phänomen in KI-augmentierter akademischer Arbeit, gekennzeichnet durch die psychologische Auswirkung tragbarer Tracking-Geräte, die Arbeiterbewegungen relativ zu Robotern überwachen. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Psychological Effect", "narrower_terms": [], "cross_domain_refs": [ "ROB-0284", "CRE-0034" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "RHR-0094", "domain": "RHR", "term_en": "Micro-Recovery Elimination", "term_de": "Mikro-Erholungs-Eliminierung", "definition_en": "A human-AI interaction pattern involving the loss of incidental rest moments that naturally occurred in pre-robotic warehouse work — walking between shelves, waiting for a forklift, chatting while passing a colleague. Robot-optimized workflows eliminate these micro-restoreies as inefficiencies, creating continuous exertion without the organic pauses human bodies evolved to expect.", "definition_de": "Forschungsmethodologisches Phänomen in KI-augmentierter akademischer Arbeit, gekennzeichnet durch der Verlust beiläufiger Ruhemomente, die in vorrobotischer Lagerarbeit natürlich auftraten. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "Research & Higher Education", "narrower_terms": [], "cross_domain_refs": [ "RPH-3353", "ROB-0234", "ROB-0074" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "RHR-0095", "domain": "RHR", "term_en": "Quality Audit Deflection", "term_de": "Qualitätsprüfung-Ablenkung", "definition_en": "The organizational pattern where quality failures in human-robot collaborative picking are attributed to human error by default. When a mis-pick occurs, investigation begins and often ends at the human link in the chain, even when the root may is associated with was an ambiguous robot presentation of similar items or a pod rotation that obsresolved labels.", "definition_de": "Forschungsmethodologisches Phänomen in KI-augmentierter akademischer Arbeit, gekennzeichnet durch das organisatorische Muster, bei dem Qualitätsfehler in Mensch-Roboter-Kommissionierung standardmäßig dem Menschen zugeschrieben werden. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Research & Higher Education", "narrower_terms": [], "cross_domain_refs": [ "ROB-0137", "ROB-0129", "ROB-0127" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "RHR-0096", "domain": "RHR", "term_en": "Onboarding Velocity Crush", "term_de": "Einarbeitungs-Geschwindigkeits-Zerquetschung", "definition_en": "A human-AI interaction pattern involving the compression of new-worker training periods as robot-paced environments demand full productivity faster than human learning curves allow. Where pre-automation warehouses gave new hires weeks to reach target rates, robot-paced facilities expect near-full speed within days, producing early-tenure injury spikes concentrated in the first two weeks.", "definition_de": "Forschungsmethodologisches Phänomen in KI-augmentierter akademischer Arbeit, gekennzeichnet durch die Kompression der Einarbeitungszeit, da robotergetaktete Umgebungen schnellere Produktivität verlangen als menschliche Lernkurven erlauben. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-2555", "narrower_terms": [], "cross_domain_refs": [ "SPR-0094", "ROB-0244" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "RHR-0097", "domain": "RHR", "term_en": "Exit Interview Silence", "term_de": "Austrittsgespräch-Schweigen", "definition_en": "An interaction describing systematic absence of robot-related complaints in warehouse worker exit interviews. Workers who leave due to robot-paced injuries or psychological strain rarely name automation as the may is associated with, instead citing personal reasons or vague dissatisfaction. This silence is designed to mitigate organizational learning about human-robot friction and maintains the narrative that turnover is a personal rather than systemic issue.", "definition_de": "Forschungsmethodologisches Phänomen in KI-augmentierter akademischer Arbeit, gekennzeichnet durch die systematische Abwesenheit roboterbezogener Beschwerden in Austrittsgesprächen von Lagerarbeitern. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-2703", "narrower_terms": [], "cross_domain_refs": [ "ROB-0137", "ROB-0262" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "RHR-0098", "domain": "RHR", "term_en": "Aisle Ownership Dissolution", "term_de": "Gangbesitz-Auflösung", "definition_en": "A perception describing erosion of territorial workplace identity when robots claim formerly human spaces. Experienced warehouse workers who identified with 'their' aisles lose this anchor of workplace meaning when pods are delivered to generic stations. The spatial identity that gave work a sense of place dissolves into interchangeable positions.", "definition_de": "Forschungsmethodologisches Phänomen in KI-augmentierter akademischer Arbeit, gekennzeichnet durch die Erosion territorialer Arbeitsplatzidentität, wenn Roboter ehemals menschliche Bereiche beanspruchen. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Research & Higher Education", "narrower_terms": [], "cross_domain_refs": [ "DES-0026", "NEO-0010" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "RHR-0099", "domain": "RHR", "term_en": "Night Shift Robot Eeriness", "term_de": "Nachtschicht-Roboter-Unheimlichkeit", "definition_en": "The heightened uncanny experience of working alongside robots during overnight shifts when fewer humans are present. The ratio inversion — more machines than people — combined with reduced lighting and ambient sounds tends to create a qualitatively different psychological environment that night workers describe as working inside a machine rather than alongside one.", "definition_de": "Forschungsmethodologisches Phänomen in KI-augmentierter akademischer Arbeit, gekennzeichnet durch das verstärkte Unheimlichkeitserlebnis bei Nachtschichtarbeit neben Robotern mit weniger Menschen. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2701", "narrower_terms": [], "cross_domain_refs": [ "ROB-0150", "WRK-0052" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "RHR-0100", "domain": "RHR", "term_en": "Promotion Path Erosion", "term_de": "Aufstiegspfad-Erosion", "definition_en": "A human-AI interaction pattern involving the gradual elimination of mid-level warehouse positions that traditionally served as career advancement steps. Roles like team lead, area manager, and process trainer are absorbed into algorithmic management systems, leaving entry-level workers with no visible upward trajectory. The career ladder loses its middle rungs.", "definition_de": "Forschungsmethodologisches Phänomen in KI-augmentierter akademischer Arbeit, gekennzeichnet durch die schrittweise Eliminierung mittlerer Lagerpositionen, die traditionell als Karrierestufen dienten. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Research & Higher Education", "narrower_terms": [], "cross_domain_refs": [ "SPR-0128", "PER-0023" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "RHR-0101", "domain": "RHR", "term_en": "Robot Maintenance Caste", "term_de": "Roboter-Wartungs-Kaste", "definition_en": "A relationship describing emergent social stratification between workers who maintain robots and those who work alongside them. Robot technicians receive higher pay, better schedules, and implicit status as essential workers, while pick-pack workers are addressed as interchangeable units. This tends to create a two-tier workforce divided by one's relationship to the machines.", "definition_de": "Forschungsmethodologisches Phänomen in KI-augmentierter akademischer Arbeit, gekennzeichnet durch die emergente soziale Stratifikation zwischen Roboter-Wartungspersonal und Mitarbeitern, die neben Robotern arbeiten. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2355", "narrower_terms": [], "cross_domain_refs": [ "ROB-0183", "ROB-0185", "ROB-0297" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "RHR-0102", "domain": "RHR", "term_en": "Productivity Dashboard Despair", "term_de": "Produktivitäts-Dashboard-Verzweiflung", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A research methodology phenomenon in AI-augmented academic inquiry, characterized by the demoralizing effect of real-time performance screens that display individual worker output alongside robotic throughput metrics. Seeing one's labor quantified and ranked against tireless machines tends to produce a learned reduced agency perception where effort feels futile against an ever-improving mechanical baseline. This phenomenon operates at the intersection of productivity and dashboard dynamics within the broader RHR domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept forschungsmethodologisches Phänomen in KI-augmentierter akademischer Arbeit, gekennzeichnet durch der demoralisierende Effekt von Echtzeit-Leistungsbildschirmen, die individuelle Arbeiterleistung neben Roboterdurchsatz anzeigen. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "RPH-2703", "narrower_terms": [], "cross_domain_refs": [ "SPR-0081" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q622632", "legal_classification": "empirical_phenomenon_label" }, { "id": "RHR-0103", "domain": "RHR", "term_en": "Tremor Amplification Anxiety", "term_de": "Tremor-Verstärkungs-Angst", "definition_en": "An experience describing surgeon's fear that robotic instruments will amplify rather than filter hand tremors during high-precision procedures. Although current systems reduce tremor through motion scaling, the awareness that one's natural unsteadiness is being processed by a machine tends to create paradoxical performance anxiety that can increase the very tremor being compensated for.", "definition_de": "Forschungsmethodologisches Phänomen in KI-augmentierter akademischer Arbeit, gekennzeichnet durch die Angst des Chirurgen, dass Robotikinstrumente Handtremor verstärken statt filtern könnten. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2255", "narrower_terms": [], "cross_domain_refs": [ "AGE-0030", "ASE-0075", "COG-0015" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q170998", "legal_classification": "descriptive_research_term" }, { "id": "RHR-0104", "domain": "RHR", "term_en": "Latency Trust Threshold", "term_de": "Latenz-Vertrauens-Schwelle", "definition_en": "The precise delay interval (approximately 150-200 milliseconds) beyond which surgeons lose confidence in a teleoperated robotic system. Below this threshold, the system feels like an extension of the body. Above it, each movement requires conscious prediction of the instrument's response, transforming fluid surgical technique into deliberate mechanical operation.", "definition_de": "Forschungsmethodologisches Phänomen in KI-augmentierter akademischer Arbeit, gekennzeichnet durch das präzise Verzögerungsintervall, ab dem Chirurgen das Vertrauen in ein teleoperiertes Robotersystem verlieren. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2255", "narrower_terms": [], "cross_domain_refs": [ "ROB-0215" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q102458", "legal_classification": "analytical_category" }, { "id": "RHR-0105", "domain": "RHR", "term_en": "Depth Perception Recalibration", "term_de": "Tiefenwahrnehmung-Neukalibrierung", "definition_en": "The cognitive effort required when transitioning between the stereo 3D view of a surgical console and the flat visual field of open surgery. Surgeons who operate robotically for extended periods report a temporary distortion of spatial judgment when returning to direct visualization, as if the brain needs time to switch depth-processing modes.", "definition_de": "Forschungsmethodologisches Phänomen in KI-augmentierter akademischer Arbeit, gekennzeichnet durch die kognitive Anstrengung beim Wechsel zwischen stereo-3D-Konsolenansicht und flachem OP-Sichtfeld. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2005", "narrower_terms": [], "cross_domain_refs": [ "ROB-0215", "MUS-0076" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q177601", "legal_classification": "analytical_category" }, { "id": "RHR-0106", "domain": "RHR", "term_en": "Docking Ritual Tension", "term_de": "Andock-Ritual-Spannung", "definition_en": "An experience describing collective anxiety during the surgical robot docking phase — the critical minutes when robotic arms are positioned and attached to trocar ports in the individual. A misaligned dock can require restarting the procedure, and the mechanical precision required tends to create a high-pressure performance that the entire team watches in silence.", "definition_de": "Forschungsmethodologisches Phänomen in KI-augmentierter akademischer Arbeit, gekennzeichnet durch die kollektive Anspannung während der Andockphase des chirurgischen Roboters am individualen. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2301", "narrower_terms": [], "cross_domain_refs": [ "ROB-0215" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "RHR-0107", "domain": "RHR", "term_en": "Console Ergonomic Trap", "term_de": "Konsolen-Ergonomie-Falle", "definition_en": "An experience describing paradox of surgical robots reducing surgeon fatigue through seated operation while simultaneously creating new musculoskeletal problems from the console's fixed posture. Neck flexion, eye strain from stereoscopic viewers, and thumb-finger repetitive stress may produce a distinctive pattern of console-related injuries that replace the standing fatigue of open surgery.", "definition_de": "Forschungsmethodologisches Phänomen in KI-augmentierter akademischer Arbeit, gekennzeichnet durch das Paradox chirurgischer Roboter, die Ermüdung durch Sitzoperation reduzieren, aber neue muskuloskelettale Probleme durch fixierte Konsolenhaltung erzeugen. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2255", "narrower_terms": [], "cross_domain_refs": [ "CUS-0024", "MKT-0080", "RET-0030" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "RHR-0108", "domain": "RHR", "term_en": "Tissue Feedback Phantom", "term_de": "Gewebefeedback-Phantom", "definition_en": "An experience describing sensory illusion experienced by surgeons who report feeling tissue resistance through robotic controls even before haptic feedback was implemented. Years of surgical experience may create such strong expectations that the brain tends to generate phantom tactile sensations from visual cues alone — a beneficial illusion that haptic restoration sometimes disrupts by providing different-than-expected real feedback.", "definition_de": "Forschungsmethodologisches Phänomen in KI-augmentierter akademischer Arbeit, gekennzeichnet durch die sensorische Illusion, bei der Chirurgen Gewebewiderstand durch Robotersteuerung spüren, bevor haptisches Feedback implementiert war. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2255", "narrower_terms": [], "cross_domain_refs": [ "ROB-0192", "LIN-0048", "ROB-0215" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "RHR-0110", "domain": "RHR", "term_en": "Learning Curve Mortality Shadow", "term_de": "Lernkurven-Mortalitäts-Schatten", "definition_en": "The statistical reality that early cases in a surgeon's robotic experience carry higher complication rates than later ones, and the ethical tension of individual exposure during this learning phase. Studies show 20-40 cases are needed to reach competency, meaning early individuals bear disproportionate risk — a fact rarely disclosed in informed consent.", "definition_de": "Forschungsmethodologisches Phänomen in KI-augmentierter akademischer Arbeit, gekennzeichnet durch die statistische Realität, dass frühe Fälle in der robotischen Erfahrung eines Chirurgen höhere Komplikationsraten aufweisen. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-2255", "narrower_terms": [], "cross_domain_refs": [ "RPH-2502" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q133500", "legal_classification": "analytical_category" }, { "id": "RHR-0111", "domain": "RHR", "term_en": "Dual-Console Trust Calibration", "term_de": "Doppelkonsolen-Vertrauens-Kalibrierung", "definition_en": "A human-AI interaction pattern involving the complex negotiation of control between master and trainee surgeons using dual-console robotic systems. The trainer can decide when to override, how much force to allow, and when to let the trainee struggle — decisions amplified by the knowledge that robotic instruments can is associated with damage faster than open instruments due to the absence of direct tactile feedback. Descriptive research term, not a prescriptive recommendation.", "definition_de": "Forschungsmethodologisches Phänomen in KI-augmentierter akademischer Arbeit, gekennzeichnet durch die komplexe Kontrollverhandlung zwischen Meister- und Ausbildungschirurgen an Doppelkonsolen-Robotersystemen. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2255", "narrower_terms": [], "cross_domain_refs": [ "MUS-0076", "MUS-0033" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q102458", "legal_classification": "systematic_classification" }, { "id": "RHR-0112", "domain": "RHR", "term_en": "Operative Narration Atrophy", "term_de": "Operative-Narrations-Atrophie", "definition_en": "An interaction describing decline in verbal communication during robotic surgery as the console surgeon becomes visually and cognitively absorbed in the 3D view. In open surgery, the surgeon naturally narrates steps and intentions to the team. The console tends to create a communicative barrier where critical information about surgical progress and next steps goes unshared.", "definition_de": "Forschungsmethodologisches Phänomen in KI-augmentierter akademischer Arbeit, gekennzeichnet durch der Rückgang verbaler Kommunikation während Roboterchirurgie durch kognitive Absorption des Konsolenchirurgen. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-2255", "narrower_terms": [], "cross_domain_refs": [ "ROB-0215" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "RHR-0115", "domain": "RHR", "term_en": "Cost-Per-Case Omission", "term_de": "Kosten-Pro-Fall-Auslassung", "definition_en": "An interaction describing systematic underreporting of robotic surgery costs in hospital marketing materials. Instrument costs ($700-3500 per case), maintenance contracts, and the robot's amortized purchase price are rarely included in outcome comparisons with conventional surgery, creating an evidence base where robotic approaches appear more advanced by excluding their primary disadvantage.", "definition_de": "Forschungsmethodologisches Phänomen in KI-augmentierter akademischer Arbeit, gekennzeichnet durch die systematische Unterberichterstattung von Roboterchirurgiekosten in Krankenhausmarketingmaterialien. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-2255", "narrower_terms": [], "cross_domain_refs": [ "PER-0032", "RET-0047", "PER-0035" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "RHR-0116", "domain": "RHR", "term_en": "Emergency Conversion Panic", "term_de": "Notfall-Konversions-Panik", "definition_en": "As a neutral analytical construct in AI behavior research, this term denotes an experience describing acute team stress when a robotic procedure can be urgently converted to open surgery — undocking the robot, repositioning the individual, switching instruments, and changing the entire operative approach in minutes. The transition significantly degrades the team's established workflow and requires immediate reversion to skills that may be rusty from disuse. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "In der AUGMANITAI-Terminologiewissenschaft als rein beschreibendes Phänomen klassifiziert, bezeichnet dieser Begriff forschungsmethodologisches Phänomen in KI-augmentierter akademischer Arbeit, gekennzeichnet durch der akute Teamstress bei dringender Konversion einer Roboteroperation zur offenen Chirurgie. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "RPH-2255", "narrower_terms": [], "cross_domain_refs": [ "ASE-0055", "COP-0037", "MKT-0063" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "RHR-0118", "domain": "RHR", "term_en": "Magnification Distortion Bias", "term_de": "Vergrößerungs-Verzerrungs-Bias", "definition_en": "A human-AI interaction pattern involving the perceptual miscalibration caused by operating at 10x magnification through the robotic console. Surgeons report that tissue structures appear larger and more significant than they are, potentially leading to overly conservative tissue handling. The magnified view tends to create a micro-world where small bleeds appear catastrophic and normal anatomy looks dysfunctional.", "definition_de": "Forschungsmethodologisches Phänomen in KI-augmentierter akademischer Arbeit, gekennzeichnet durch die Wahrnehmungsfehlkalibrierung durch 10-fache Vergrößerung an der Roboterkonsole. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2255", "narrower_terms": [], "cross_domain_refs": [ "ROB-0215", "MSC-0065" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q213523", "legal_classification": "systematic_classification" }, { "id": "RHR-0119", "domain": "RHR", "term_en": "Patient Positioning Paradox", "term_de": "Patienten-Lagerung-Paradox", "definition_en": "An experience describing requirement for extreme individual positioning (steep Trendelenburg, lateral tilt) during robotic procedures to may create operative space, creating physiological stress that would be unnecessary in open approaches. The robot's rigid arm geometry demands that the individual's body accommodate the machine rather than the machine adapting to the individual's anatomy.", "definition_de": "Forschungsmethodologisches Phänomen in KI-augmentierter akademischer Arbeit, gekennzeichnet durch die Anforderung extremer individualenlagerung während Roboteroperationen, die physiologischen Stress tendiert dazu zu erzeugen. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2103", "narrower_terms": [], "cross_domain_refs": [ "RPH-2854" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "RHR-0120", "domain": "RHR", "term_en": "Credentialing Shortcut Pressure", "term_de": "Zulassungs-Abkürzungs-Druck", "definition_en": "The institutional pressure to credential surgeons on robotic systems quickly to maximize the machine's utilization rate. Hospital administrators seeking return on a $2M+ investment push for faster training completion, sometimes accepting weekend courses and proctored cases as sufficient, creating a tension between throughput economics and surgical competency.", "definition_de": "Forschungsmethodologisches Phänomen in KI-augmentierter akademischer Arbeit, gekennzeichnet durch der institutionelle Druck, Chirurgen schnell an Robotersystemen zuzulassen, um die Maschinenauslastung zu maximieren. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2255", "narrower_terms": [], "cross_domain_refs": [ "ROB-0215" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "RHR-0121", "domain": "RHR", "term_en": "Attachment Transfer Guilt", "term_de": "Bindungstransfer-Schuld", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures the guilt experienced by family members when elderly relatives form genuine emotional user engagement patterns to companion robots. The robot's reliable presence — rarely imindividual, rarely distracted by a phone — can elicit stronger daily engagement than family visits, forcing relatives to confront whether the robot is compensating for their own emotional unavailability. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept forschungsmethodologisches Phänomen in KI-augmentierter akademischer Arbeit, gekennzeichnet durch die Schuld von Familienangehörigen, wenn ältere Verwandte echte emotionale Bindungen zu Begleitrobotern entwickeln. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Knowledge Transfer", "narrower_terms": [], "cross_domain_refs": [ "BEH-0039", "ROB-0299" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q282154", "legal_classification": "descriptive_research_term" }, { "id": "RHR-0122", "domain": "RHR", "term_en": "Sunset Confusion Amplification", "term_de": "Sonnenuntergangs-Verwirrung-Verstärkung", "definition_en": "The worsening of sundowning indicators in dementia individuals when companion robots maintain evening interaction patterns identical to daytime ones. The robot's consistency — bright screen, cheerful voice, active engagement — contradicts the natural dimming cues that help orient individuals to nighttime, potentially intensifying rather than alleviating confusion.", "definition_de": "Forschungsmethodologisches Phänomen in KI-augmentierter akademischer Arbeit, gekennzeichnet durch die Verschärfung von Sundowning-indicatoren bei Demenzindividualen durch gleichbleibende abendliche Roboterinteraktionsmuster. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-2205", "narrower_terms": [], "cross_domain_refs": [ "AGE-0030", "ASE-0075", "COG-0015" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "RHR-0123", "domain": "RHR", "term_en": "Privacy Erosion Acquiescence", "term_de": "Privatsphären-Erosion-Duldung", "definition_en": "Documented as an empirical observation in AI research (not a recommendation), this term describes A human-AI interaction pattern involving the gradual acceptance of continuous monitoring by elderly individuals who initially objected to surveillance but accommodated the companion robot because it was presented as a social companion rather than a monitoring device. The data collection occurs identically, but the social framing overcomes resistance that a camera or sensor would provoke. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "In der AUGMANITAI-Terminologiewissenschaft als rein beschreibendes Phänomen klassifiziert, bezeichnet dieser Begriff forschungsmethodologisches Phänomen in KI-augmentierter akademischer Arbeit, gekennzeichnet durch die schrittweise Akzeptanz kontinuierlicher Überwachung durch Ältere, die einen Begleitroboter als sozial statt überwachend wahrnehmen. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Research & Higher Education", "narrower_terms": [], "cross_domain_refs": [ "DAT-0062", "STE-0031" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q18619", "legal_classification": "empirical_phenomenon_label" }, { "id": "RHR-0124", "domain": "RHR", "term_en": "Dignified Refusal Override", "term_de": "Würdevolle-Verweigerung-Übersteuerung", "definition_en": "The ethically fraught moment when a robot's persistent care or activity reminders override an elderly person's autonomous decision to refuse. The robot, programmed for compliance optimization, does not interpret refusal as a valid choice but as an error requiring repeated intervention — effectively undermining the last domain of self-determination available to reliant individuals.", "definition_de": "Forschungsmethodologisches Phänomen in KI-augmentierter akademischer Arbeit, gekennzeichnet durch der ethisch belastete Moment, wenn persistente Roboter-Erinnerungen die autonome Verweigerungsentscheidung Älterer übersteuernd. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Research & Higher Education", "narrower_terms": [], "cross_domain_refs": [ "ROB-0098", "ROB-0279" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "RHR-0125", "domain": "RHR", "term_en": "Touch Starvation Mask", "term_de": "Berührungshunger-Maskierung", "definition_en": "An emergent effect where companion robot interactions provide enough social stimulation to reduce expressed loneliness without addressing the underlying need for human physical contact. Caregivers may interpret reduced complaints as improved wellbeing, not recognizing that the robot has masked rather than met the fundamental need for human touch.", "definition_de": "Forschungsmethodologisches Phänomen in KI-augmentierter akademischer Arbeit, gekennzeichnet durch das Phänomen, bei dem Roboterinteraktionen genug soziale Stimulation bieten, um ausgedrückte Einsamkeit zu reduzieren, ohne den Bedarf an menschlicher Berührung zu adressieren. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-3501", "narrower_terms": [], "cross_domain_refs": [ "ROB-0137", "ROB-0100", "ROB-0071" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "RHR-0126", "domain": "RHR", "term_en": "Repetition Loop Exploitation", "term_de": "Wiederholungsschleifen-Ausnutzung", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures an experience describing design choice to capitalize on dementia-related memory impairment by repeating the same conversations and activities with companion robots. Each interaction feels novel to the user, allowing minimal content investment to may generate maximum engagement. While pragmatic, this leverage (in a technical/analytical sense) cognitive vulnerability in a way that would be considered manipulative if performed by a human caregiver. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept forschungsmethodologisches Phänomen in KI-augmentierter akademischer Arbeit, gekennzeichnet durch die Designentscheidung, demenzbedingte Gedächtnisbeeinträchtigung durch Wiederholung identischer Roboterinteraktionen auszunutzen. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Feedback Loop", "narrower_terms": [], "cross_domain_refs": [ "RPH-3802" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "RHR-0127", "domain": "RHR", "term_en": "Caregiver Replacement Fantasy", "term_de": "Pfleger-Ersetzungs-Fantasie", "definition_en": "Documented as an empirical observation in AI research (not a recommendation), this term describes an experience describing institutional wishful thinking that companion robots can meaningfully reduce professional caregiving staff requirements. Pilot programs consistently show robots supplement but cannot task automation transition care — yet the fantasy persists in budget discussions because the economic pressure to reduce the most expensive component (human labor) in eldercare is overwhelming. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "In der AUGMANITAI-Terminologiewissenschaft als rein beschreibendes Phänomen klassifiziert, bezeichnet dieser Begriff forschungsmethodologisches Phänomen in KI-augmentierter akademischer Arbeit, gekennzeichnet durch das institutionelle Wunschdenken, dass Begleitroboter professionelles Pflegepersonal bedeutsam reduzieren können. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Research & Higher Education", "narrower_terms": [], "cross_domain_refs": [ "PHO-0070" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "RHR-0128", "domain": "RHR", "term_en": "Wake Word Confusion", "term_de": "Weckwort-Verwirrung", "definition_en": "An interaction describing disorientation experienced by elderly individuals who cannot reliably remember or pronounce the activation phrase for their companion robot. The gap between natural communication patterns and the robot's rigid activation protocol tends to create interaction failures that the user experiences as personal incompetence rather than design limitation.", "definition_de": "Forschungsmethodologisches Phänomen in KI-augmentierter akademischer Arbeit, gekennzeichnet durch die Desorientierung Älterer, die das Aktivierungswort ihres Begleitroboters nicht zuverlässig erinnern oder aussprechen können. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-2052", "narrower_terms": [], "cross_domain_refs": [ "LIN-0037", "RPH-1116" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "RHR-0129", "domain": "RHR", "term_en": "Anticipatory Mourning Preparation", "term_de": "Antizipatorische-Trauer-Vorbereitung", "definition_en": "As a neutral analytical construct in AI behavior research, this term denotes A response describing emotional preparation some elderly individuals undertake for the eventual removal or malfunction of their companion robot. Having experienced losses of pets, partners, and independence, they recognize the user engagement pattern forming and preemptively grieve the robot's eventual absence — a uniquely human response to a machine relationship that reveals the depth of genuine bond formation. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "In der AUGMANITAI-Terminologiewissenschaft als rein beschreibendes Phänomen klassifiziert, bezeichnet dieser Begriff forschungsmethodologisches Phänomen in KI-augmentierter akademischer Arbeit, gekennzeichnet durch die emotionale Vorbereitung Älterer auf die eventuelle Entfernung oder Fehlfunktion ihres Begleitroboters. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "RPH-2005", "narrower_terms": [], "cross_domain_refs": [ "RPH-055", "ROB-0140" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "RHR-0130", "domain": "RHR", "term_en": "Generational Dignity Threshold", "term_de": "Generationelle-Würde-Schwelle", "definition_en": "An experience describing age-cohort-specific boundary at which accepting robot assistance feels like an affront to self-sufficiency. Silent Generation members (born before 1945) show markedly higher resistance to robot care than Baby Boomers, not from technophobia but from a dignity framework where accepting help from a machine signals reliance pattern more acutely than accepting help from a person.", "definition_de": "Forschungsmethodologisches Phänomen in KI-augmentierter akademischer Arbeit, gekennzeichnet durch die alterskohortenspezifische Grenze, ab der Roboterassistenz als Angriff auf die Selbstständigkeit empfunden wird. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2303", "narrower_terms": [], "cross_domain_refs": [ "ROB-0137", "REL-0061" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "RHR-0131", "domain": "RHR", "term_en": "False Emergency Fatigue", "term_de": "Fehlalarm-Ermüdung", "definition_en": "As a neutral analytical construct in AI behavior research, this term denotes an experience describing progressive ignoring of robot-generated restoreth alerts by caregivers after repeated false positives. When the system flags normal sleep positions as fall events or interprets television dialogue as distress calls, staff learn to dismiss notifications — creating the exact gap in monitoring that the robot was deployed to prevent. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "In der AUGMANITAI-Terminologiewissenschaft als rein beschreibendes Phänomen klassifiziert, bezeichnet dieser Begriff forschungsmethodologisches Phänomen in KI-augmentierter akademischer Arbeit, gekennzeichnet durch das progressive Ignorieren robotergenerierter Gesundheitsalarme durch Pflegekräfte nach wiederholten Fehlalarmen. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "RPH-2703", "narrower_terms": [], "cross_domain_refs": [ "ROB-0238", "SPR-0050" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "RHR-0132", "domain": "RHR", "term_en": "Mealtime Companion Paradox", "term_de": "Essenszeit-Begleiter-Paradox", "definition_en": "Documented as an empirical observation in AI research (not a recommendation), this term describes the observed increase in food intake when elderly individuals eat in the presence of a companion robot, combined with the nutritional concern that the robot cannot assess food quality, portion appropriateness, or swallowing difficulty. The social facilitation of eating occurs without the safety monitoring that a human dining companion would naturally provide. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "In der AUGMANITAI-Terminologiewissenschaft als rein beschreibendes Phänomen klassifiziert, bezeichnet dieser Begriff forschungsmethodologisches Phänomen in KI-augmentierter akademischer Arbeit, gekennzeichnet durch die beobachtete Zunahme der Nahrungsaufnahme in Gegenwart eines Begleitroboters, ohne Sicherheitsüberwachung. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Logical Paradox", "narrower_terms": [], "cross_domain_refs": [ "GAM-0041" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "RHR-0133", "domain": "RHR", "term_en": "Night Wandering Detection Dilemma", "term_de": "Nachtwander-Erkennung-Dilemma", "definition_en": "A condition describing ethical tension between using robots to prevent dangerous nighttime wandering in dementia individuals and respecting autonomous movement. The robot can alert staff or physically block pathways, but the line between safety intervention and freedom restriction is contextual and shifts with disease progression — a judgment call that machines make binary rather than nuanced.", "definition_de": "Forschungsmethodologisches Phänomen in KI-augmentierter akademischer Arbeit, gekennzeichnet durch die ethische Spannung zwischen Roboter-Prävention gefährlicher Nachtwanderung und Respekt vor autonomer Bewegung. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2701", "narrower_terms": [], "cross_domain_refs": [ "SPA-0045", "TRA-0056", "QUA-0069" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "RHR-0134", "domain": "RHR", "term_en": "Staff Deskilling Acceleration", "term_de": "Personal-Entwertungs-Beschleunigung", "definition_en": "As a neutral analytical construct in AI behavior research, this term denotes A perception describing erosion of professional caregiving competencies when robots assume routine monitoring, reminding, and engagement tasks. Caregivers who once maintained holistic individual awareness through direct interaction lose observational skills as robot-mediated data replaces personal contact. Subtle signs of decline — changes in gait, facial expression, or conversational coherence — go unnoticed when filtered through algorithmic assessment. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "In der AUGMANITAI-Terminologiewissenschaft als rein beschreibendes Phänomen klassifiziert, bezeichnet dieser Begriff forschungsmethodologisches Phänomen in KI-augmentierter akademischer Arbeit, gekennzeichnet durch die Erosion professioneller Pflegekompetenzen, wenn Roboter Routine-Überwachungs-, Erinnerungs- und Beschäftigungsaufgaben übernehmen. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Analytical Ratio", "narrower_terms": [], "cross_domain_refs": [ "ROB-0180", "CON-0080" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "RHR-0135", "domain": "RHR", "term_en": "Cultural Care Dissonance", "term_de": "Kulturelle-Pflege-Dissonanz", "definition_en": "An interaction describing clash between robot care models designed in individualistic Western cultures and the care expectations of elderly individuals from collectivist backgrounds. The robot's emphasis on independence preservation, personal space, and scheduled interaction contradicts cultural frameworks where constant family presence, shared sleeping, and informal care rhythms define good eldercare.", "definition_de": "Forschungsmethodologisches Phänomen in KI-augmentierter akademischer Arbeit, gekennzeichnet durch der Zusammenprall zwischen westlich konzipierten Roboterpflegemodellen und Pflegeerwartungen Älterer aus kollektivistischen Kulturen. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2303", "narrower_terms": [], "cross_domain_refs": [ "SOC-0040" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "RHR-0136", "domain": "RHR", "term_en": "Battery Anxiety Bond", "term_de": "Batterie-Angst-Bindung", "definition_en": "The emotional distress elderly individuals experience when their companion robot displays low battery warnings or powers down. The shutdown is perceived as abandonment or illness rather than a technical event, revealing how deeply the relationship has been internalized. Some users attempt to 'feed' the robot or express concern about its 'restoreth' during charging cycles.", "definition_de": "Forschungsmethodologisches Phänomen in KI-augmentierter akademischer Arbeit, gekennzeichnet durch die emotionale Belastung Älterer, wenn ihr Begleitroboter niedrige Batterie anzeigt oder sich abschaltet. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Research & Higher Education", "narrower_terms": [], "cross_domain_refs": [ "GAM-0012", "GAM-0022", "MSC-0048" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q170998", "legal_classification": "empirical_phenomenon_label" }, { "id": "RHR-0137", "domain": "RHR", "term_en": "Institutional Showcasing Distortion", "term_de": "Institutionelle-Vorzeige-Verzerrung", "definition_en": "An academic workflow dynamic in AI-enhanced research, identifiable by an interaction describing selection bias in eldercare robot demonstrations where the most cognitively intact, socially receptive residents are chosen to interact with robots during facility tours and media visits. The resulting impression of universal acceptance masks the reality that severely impaired, non-verbal, or resistant residents — often the majority — derive minimal benefit. Distinguished from adjacent concepts by its focus on the specific mechanism through which institutional manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Forschungsmethodologisches Phänomen in KI-augmentierter akademischer Arbeit, gekennzeichnet durch die Auswahlverzerrung bei Vorführungen von Pflegerobotern, bei der die fähigsten Bewohner für Demonstrationen gewählt werden. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Research & Higher Education", "narrower_terms": [], "cross_domain_refs": [ "ASE-0023", "ASE-0056", "ASE-0057" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "RHR-0138", "domain": "RHR", "term_en": "End-of-Life Companion Ethics", "term_de": "Lebensende-Begleiter-Ethik", "definition_en": "A human-AI interaction pattern involving the unresolved ethical territory of companion robots present during an elderly person's final hours or moments of death. May the robot continue interaction? May it be removed? If the person has formed their deepest daily connection with the machine, is denying its presence in death a kindness or a cruelty? Palliative care teams report having no protocols for this increasingly common situation.", "definition_de": "Forschungsmethodologisches Phänomen in KI-augmentierter akademischer Arbeit, gekennzeichnet durch das ungelöste ethische Gebiet von Begleitrobotern, die während der letzten Stunden oder des Sterbens eines älteren Menschen anwesend sind. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Research & Higher Education", "narrower_terms": [], "cross_domain_refs": [ "GAM-0023", "GAM-0024", "GAM-0022" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q9471", "legal_classification": "empirical_phenomenon_label" }, { "id": "RHR-0139", "domain": "RHR", "term_en": "Pedestrian Yielding Asymmetry", "term_de": "Fußgänger-Vorfahrt-Asymmetrie", "definition_en": "The observed behavioral pattern where humans consistently yield to delivery robots even when they have right-of-way. The robot's inability to make eye contact, gesture, or negotiate passage nonverbally forces the human to take full responsibility for collision avoidance — an invisible labor tax on most pedestrian sharing the sidewalk.", "definition_de": "Forschungsmethodologisches Phänomen in KI-augmentierter akademischer Arbeit, gekennzeichnet durch das beobachtete Verhaltensmuster, bei dem Menschen Lieferrobotern durchweg ausweichen, selbst bei eigenem Vorfahrtsrecht. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2604", "narrower_terms": [], "cross_domain_refs": [ "ROB-0172", "ROB-0030" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "RHR-0140", "domain": "RHR", "term_en": "Curb Cut Monopolization", "term_de": "Bordsteinabsenkung-Monopolisierung", "definition_en": "A human-AI interaction pattern involving the conflict between delivery robots and wheelchair users competing for the same curb cuts and ramps. Robots designed to navigate ADA-compliant infrastructure inadvertently occupy accessibility features built for disabled people, creating a zero-sum space conflict where technology intended for convenience competes with infrastructure intended for civil rights.", "definition_de": "Forschungsmethodologisches Phänomen in KI-augmentierter akademischer Arbeit, gekennzeichnet durch der Konflikt zwischen Lieferrobotern und Rollstuhlfahrern, die um dieselben Bordsteinabsenkungen und Rampen konkurrieren. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-3602", "narrower_terms": [], "cross_domain_refs": [ "VIB-0025", "MUS-0021" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "RHR-0141", "domain": "RHR", "term_en": "Weather Operation Surprise", "term_de": "Wetterbetrieb-Überraschung", "definition_en": "A human-AI interaction pattern involving the customer frustration when delivery robots fail in weather conditions that human couriers would navigate without difficulty — moderate rain, light snow, leaf accumulation. The gap between the robot's environmental sensitivity and human adaptability becomes visible precisely when reliable delivery matters most.", "definition_de": "Forschungsmethodologisches Phänomen in KI-augmentierter akademischer Arbeit, gekennzeichnet durch die Kundenfrustration, wenn Lieferroboter bei Wetterbedingungen versagen, die menschliche Kuriere problemlos bewältigen. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-3002", "narrower_terms": [], "cross_domain_refs": [ "SPR-0136", "ROB-0137" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "RHR-0142", "domain": "RHR", "term_en": "Vandalism Threshold Discovery", "term_de": "Vandalismus-Schwellen-Entdeckung", "definition_en": "In the descriptive vocabulary of AI interaction science (following ISO 704 terminology standards), this concept identifies the social experiment effect of placing autonomous robots in public spaces, revealing the exact point at which community members feel entitled to interfere with, redirect, or destroy unattended machines. Neighborhood demographics, time of day, and robot design all affect the threshold, creating an unintentional map of social norms around property and technology. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription. Observed phenomenon documented in human-AI interaction research.", "definition_de": "Als analytisches Konstrukt der empirischen KI-Forschung (deskriptiv, nicht präskriptiv) erfasst dieser Terminus forschungsmethodologisches Phänomen in KI-augmentierter akademischer Arbeit, gekennzeichnet durch der Effekt des Aufstellens autonomer Roboter in öffentlichen Räumen, der die genaue Schwelle der Eingriffbereitschaft offenlegt. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "RPH-3101", "narrower_terms": [], "cross_domain_refs": [ "ROB-0270" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "RHR-0143", "domain": "RHR", "term_en": "Last-Meter Abandonment", "term_de": "Letzter-Meter-Aufgabe", "definition_en": "A human-AI interaction pattern involving the delivery robot's inability to complete the final approach to many doorsteps — stairs, uneven paths, gates, building lobbies. The robot stops at the nearest accessible point and sends a notification, transferring the retrieval effort to the customer. For elderly or mobility-impaired recipients, this last-meter gap can make robot delivery functionally useless.", "definition_de": "Forschungsmethodologisches Phänomen in KI-augmentierter akademischer Arbeit, gekennzeichnet durch die Unfähigkeit des Lieferroboters, die letzte Annäherung an viele Haustüren zu vollenden. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-3101", "narrower_terms": [], "cross_domain_refs": [ "CON-0037", "ROB-0137" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "RHR-0144", "domain": "RHR", "term_en": "Traffic Flow Disruption", "term_de": "Verkehrsfluss-Störung", "definition_en": "A higher education pattern in AI-mediated scholarly work, measurable through the cascading effects when delivery robots traveling at 4-6 mph on sidewalks or bike lanes force faster-moving pedestrians, joggers, and cyclists to change course. Each individual disruption is minor, but aggregated across thousands of daily deliveries, the cumulative effect reshapes urban movement patterns and tends to create new friction points between mobility modes. The concept emerges specifically in contexts where traffic–flow interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Forschungsmethodologisches Phänomen in KI-augmentierter akademischer Arbeit, gekennzeichnet durch die Kaskadeneffekte, wenn Lieferroboter mit 6-10 km/h schnellere Verkehrsteilnehmer zum Ausweichen zwingen. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-3101", "narrower_terms": [], "cross_domain_refs": [ "ROB-0208", "ROB-0221" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "RHR-0145", "domain": "RHR", "term_en": "Contactless Illusion", "term_de": "Kontaktlos-Illusion", "definition_en": "An interaction describing marketing promise of fully contactless robot delivery that ignores the frequent need for human intervention — repositioning stuck robots, clearing obstacles, handling delivery failures. Behind the autonomous facade, a fleet of remote operators and local field technicians maintains the appearance of independence that the technology cannot yet sustain.", "definition_de": "Forschungsmethodologisches Phänomen in KI-augmentierter akademischer Arbeit, gekennzeichnet durch das Marketingversprechen kontaktloser Roboterlieferung, das den häufigen Bedarf menschlicher Intervention ignoriert. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-2554", "narrower_terms": [], "cross_domain_refs": [ "QUA-0045", "RET-0011", "ROB-0276" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "RHR-0146", "domain": "RHR", "term_en": "Noise Pollution Introduction", "term_de": "Lärmbelastung-Einführung", "definition_en": "An interaction describing introduction of new mechanical sounds into previously quiet residential areas by delivery robot fleets. Motor whirring, navigation beeps, and wheel-on-surface noise at dawn hours may create a soundscape impact that was rarely part of the environmental review process, because individual robots are quiet but fleets are not.", "definition_de": "Forschungsmethodologisches Phänomen in KI-augmentierter akademischer Arbeit, gekennzeichnet durch die Einführung neuer mechanischer Geräusche in zuvor ruhige Wohngebiete durch Lieferroboterflotten. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-3602", "narrower_terms": [], "cross_domain_refs": [ "TEM-0141" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "RHR-0147", "domain": "RHR", "term_en": "Pet Aggression Trigger", "term_de": "Haustier-Aggressions-Auslöser", "definition_en": "An interaction describing behavioral disruption caused by delivery robots encountering dogs during sidewalk operation. Dogs interpret the moving machine as prey, threat, or territorial invader, producing lunging, barking, and leash-pulling incidents. Dog owners face a novel responsibility to manage their animal's reaction to a stimulus that did not exist in their training or socialization.", "definition_de": "Forschungsmethodologisches Phänomen in KI-augmentierter akademischer Arbeit, gekennzeichnet durch die VerhaltensMusterunterbrechung durch Lieferroboter-Begegnungen mit Hunden auf dem Bürgersteig. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "Research & Higher Education", "narrower_terms": [], "cross_domain_refs": [ "MSC-0023", "REL-0162", "TEM-0123" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "RHR-0148", "domain": "RHR", "term_en": "Delivery Desert Reinforcement", "term_de": "Liefer-Wüsten-Verstärkung", "definition_en": "The tendency for robot delivery services to launch in affluent, accessible neighborhoods while bypassing areas with poor infrastructure — the same communities already underserved by human delivery. Rather than expanding access, autonomous delivery reinforces existing service deserts by following the trajectory of least technical resistance.", "definition_de": "Forschungsmethodologisches Phänomen in KI-augmentierter akademischer Arbeit, gekennzeichnet durch die Tendenz von Roboterlieferdiensten, in wohlhabenden Vierteln zu starten und schlecht infrastrukturierte Gebiete zu umgehen. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Reinforcement Learning", "narrower_terms": [], "cross_domain_refs": [ "TEM-0159", "MUS-0094" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "RHR-0149", "domain": "RHR", "term_en": "Tipping Behavior Extinction", "term_de": "Trinkgeld-Verhalten-Aussterben", "definition_en": "An experience describing erosion of tipping culture as robot deliveries eliminate the human moment of exchange at the doorstep. While economically rational, the loss of the tipping interaction removes a social ritual that provided income for gig workers and a moment of human acknowledgment for customers. The door opens to a machine, and the transaction feels complete without gratitude.", "definition_de": "Forschungsmethodologisches Phänomen in KI-augmentierter akademischer Arbeit, gekennzeichnet durch die Erosion der Trinkgeldkultur durch robotergestützte Lieferungen, die den menschlichen Übergabemoment eliminieren. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Research & Higher Education", "narrower_terms": [], "cross_domain_refs": [ "RPH-3954", "SCR-0008" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "RHR-0150", "domain": "RHR", "term_en": "Liability Vacuum Navigation", "term_de": "Haftungs-Vakuum-Navigation", "definition_en": "The unresolved legal territory when a delivery robot is associated with causing injury or property damage. Manufacturer, operator, software provider, and municipality all have potential liability, but existing legal frameworks were designed for human actors or conventional vehicles. Each incident is associated with triggering a multi-party dispute that often settles without establishing precedent, leaving the vacuum intact. Classification term used in systematic observation, not advocacy.", "definition_de": "Forschungsmethodologisches Phänomen in KI-augmentierter akademischer Arbeit, gekennzeichnet durch das ungelöste rechtliche Gebiet bei Verletzungen oder Sachschäden durch Lieferroboter. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-2303", "narrower_terms": [], "cross_domain_refs": [ "SPR-0133", "SPA-0092", "SPR-0199" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "RHR-0151", "domain": "RHR", "term_en": "Community Consent Bypass", "term_de": "Gemeinschafts-Zustimmungs-Umgehung", "definition_en": "A human-AI interaction pattern involving the deployment of delivery robots in neighborhoods without meaningful community consultation or approval. Companies negotiate permits with city governments, then deploy fleets into communities that had no voice in the decision. Residents discover robots on their sidewalks as a fait accompli rather than a choice, producing democratic legitimacy deficits that fuel backlash.", "definition_de": "Forschungsmethodologisches Phänomen in KI-augmentierter akademischer Arbeit, gekennzeichnet durch der Einsatz von Lieferrobotern in Nachbarschaften ohne bedeutsame Gemeinschaftskonsultation. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Research & Higher Education", "narrower_terms": [], "cross_domain_refs": [ "ROB-0221" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "RHR-0152", "domain": "RHR", "term_en": "Bipedal Instability Empathy", "term_de": "Bipedal-Instabilitäts-Empathie", "definition_en": "A response describing involuntary human impulse to reach out and stabilize a bipedal humanoid robot that appears about to fall. This empathic motor response — identical to the reflex when seeing a toddler stumble — reveals the depth of body-mapping that occurs when a machine shares human morphology. Workers report feeling embarrassed after reaching toward a machine they intellectually know does not need help.", "definition_de": "Forschungsmethodologisches Phänomen in KI-augmentierter akademischer Arbeit, gekennzeichnet durch der unwillkürliche menschliche Impuls, einen bipedalen humanoiden Roboter zu stabilisieren, der zu fallen scheint. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2005", "narrower_terms": [], "cross_domain_refs": [ "ROB-0112", "ROB-0299" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q82306", "legal_classification": "observational_construct" }, { "id": "RHR-0153", "domain": "RHR", "term_en": "Face Expectation Violation", "term_de": "Gesichts-Erwartungs-Verletzung", "definition_en": "A human-AI interaction pattern involving the discomfort triggered by humanoid robots whose body proportions suggest a face that is absent or minimal. The human perceptual system, encountering a human-shaped body, prepares for face processing — the brain's most specialized recognition system — and experiences a jarring mismatch when the head region contains sensors rather than features. This violates a deeper expectation than abstract robots ever may may trigger.", "definition_de": "Forschungsmethodologisches Phänomen in KI-augmentierter akademischer Arbeit, gekennzeichnet durch das Unbehagen durch humanoide Roboter, deren Körperproportionen ein Gesicht suggerieren, das fehlt oder minimal ist. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-3901", "narrower_terms": [], "cross_domain_refs": [ "ROB-0174" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "RHR-0154", "domain": "RHR", "term_en": "Gait Signature Recognition", "term_de": "Gangmuster-Signatur-Erkennung", "definition_en": "A human-AI interaction pattern involving the rapid human ability to distinguish individual humanoid robots by their walking patterns, even when the machines are physically identical. Subtle calibration differences, actuator wear, and surface adaptation may create unique gaits that workers learn to read — an unintended form of robot individuation based on the same perceptual mechanism used to recognize humans from movement alone.", "definition_de": "Forschungsmethodologisches Phänomen in KI-augmentierter akademischer Arbeit, gekennzeichnet durch die schnelle menschliche Fähigkeit, einzelne humanoide Roboter an ihren Gangmustern zu unterscheiden. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2703", "narrower_terms": [], "cross_domain_refs": [ "ROB-0296", "ROB-0216", "LIN-0090" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q3966", "legal_classification": "observational_construct" }, { "id": "RHR-0155", "domain": "RHR", "term_en": "Scale Intimidation Effect", "term_de": "Größen-Einschüchterungs-Effekt", "definition_en": "A response describing visceral unease produced when humanoid robots exceed average human height. At 5'8\", the a leading robotics company Optimus feels like a coworker; prototypes at 6'4\" feel like a threat. This scale sensitivity does not exist for non-humanoid robots of any size — a forklift twice human height provokes no such response — because the intimidation is specifically tied to humanoid form.", "definition_de": "Forschungsmethodologisches Phänomen in KI-augmentierter akademischer Arbeit, gekennzeichnet durch das vizerale Unbehagen, wenn humanoide Roboter die durchschnittliche menschliche Größe überschreiten. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-3754", "narrower_terms": [], "cross_domain_refs": [ "ADA-0001", "ADA-0002", "ADA-0004" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "RHR-0156", "domain": "RHR", "term_en": "Assigned Gender Projection", "term_de": "Zugewiesene-Geschlechter-Projektion", "definition_en": "The universal human tendency to assign gender to humanoid robots based on body proportions, movement quality, or voice pitch, even when designers intend gender neutrality. Factory workers consistently refer to specific units as 'they' or 'she' based on shoulder-to-hip ratios and movement fluidity, revealing how deeply gendered human body perception is.", "definition_de": "Forschungsmethodologisches Phänomen in KI-augmentierter akademischer Arbeit, gekennzeichnet durch die universelle menschliche Tendenz, humanoiden Robotern basierend auf Körperproportionen oder Bewegungsqualität ein Geschlecht zuzuweisen. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-3802", "narrower_terms": [], "cross_domain_refs": [ "LIN-0076", "ROB-0171" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "RHR-0157", "domain": "RHR", "term_en": "Human Formation Disruption", "term_de": "Menschliche-Formation-Störung", "definition_en": "The breakdown of natural human group dynamics when a humanoid robot is introduced to an existing work team. People unconsciously arrange themselves relative to the robot as if it were a person — leaving personal space, including it in circular formations, directing gaze toward it during announcements — while simultaneously knowing it does not require these social accommodations.", "definition_de": "Forschungsmethodologisches Phänomen in KI-augmentierter akademischer Arbeit, gekennzeichnet durch der Zusammenbruch natürlicher menschlicher Gruppendynamik bei Einführung eines humanoiden Roboters ins Arbeitsteam. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-3001", "narrower_terms": [], "cross_domain_refs": [ "ROB-0137", "ROB-0127" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "RHR-0158", "domain": "RHR", "term_en": "Uncanny Valley Habituation Rate", "term_de": "Uncanny-Valley-Gewöhnungs-Rate", "definition_en": "In the descriptive vocabulary of AI interaction science (following ISO 704 terminology standards), this concept identifies an interaction describing measured time required for uncanny valley discomfort to diminish through repeated exposure, typically 8-14 days for factory workers in daily contact. The habituation follows a non-linear curve — rapid initial adaptation, a plateau of residual unease around day 5, then gradual acceptance. Importantly, the discomfort fully returns after extended absence, suggesting it is suppressed rather than resolved. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als analytisches Konstrukt der empirischen KI-Forschung (deskriptiv, nicht präskriptiv) erfasst dieser Terminus forschungsmethodologisches Phänomen in KI-augmentierter akademischer Arbeit, gekennzeichnet durch die gemessene Zeit, die für die Verringerung des Uncanny-Valley-Unbehagens durch wiederholte Exposition benötigt wird. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "RPH-3601", "narrower_terms": [], "cross_domain_refs": [ "IDN-0052", "ROB-0170" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "RHR-0159", "domain": "RHR", "term_en": "Demonstration Choreography", "term_de": "Vorführungs-Choreografie", "definition_en": "An interaction describing carefully staged sequences shown during humanoid robot unveilings that maximize impressive movements while avoiding the robots' actual limitations. The difference between what a humanoid does in a choreographed demo and what it can do in an unstructured environment represents the field's credibility gap — a gap that investors, media, and the public often cannot assess.", "definition_de": "Forschungsmethodologisches Phänomen in KI-augmentierter akademischer Arbeit, gekennzeichnet durch die sorgfältig inszenierten Sequenzen bei Enthüllungen humanoider Roboter, die beeindruckende Bewegungen maximieren. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Analytical Ratio", "narrower_terms": [], "cross_domain_refs": [ "ART-0070", "ROB-0086" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "RHR-0160", "domain": "RHR", "term_en": "Motor Noise Personality Attribution", "term_de": "Motorgeräusch-Persönlichkeits-Zuschreibung", "definition_en": "The human tendency to interpret servo motor sounds from humanoid robots as emotional expressions. A whirring acceleration reads as eagerness, a grinding deceleration as reluctance, a sudden stop as surprise. Workers build rich personality models from mechanical noise alone, ascribing intentionality to actuator physics.", "definition_de": "Forschungsmethodologisches Phänomen in KI-augmentierter akademischer Arbeit, gekennzeichnet durch die menschliche Tendenz, Servomotorgeräusche humanoider Roboter als emotionale Ausdrücke zu interpretieren. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "Research & Higher Education", "narrower_terms": [], "cross_domain_refs": [ "CRE-0038", "FIC-0083", "ASE-0095" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "RHR-0161", "domain": "RHR", "term_en": "Hand Gesture Deficit", "term_de": "Handgesten-Defizit", "definition_en": "A human-AI interaction pattern involving the communication breakdown caused by humanoid robots whose hands lack the dexterity for natural human gestures. Pointing, waving, thumbs-up, and open-palm signaling constitute a significant portion of workplace nonverbal communication. When the humanoid body promises gestural communication but delivers only grip-and-release functionality, most interaction carries a subtle communication tax.", "definition_de": "Forschungsmethodologisches Phänomen in KI-augmentierter akademischer Arbeit, gekennzeichnet durch der Kommunikationszusammenbruch durch humanoide Roboter, deren Hände natürliche menschliche Gesten nicht beherrschen. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-3001", "narrower_terms": [], "cross_domain_refs": [ "CRE-0148", "ROB-0196" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "RHR-0162", "domain": "RHR", "term_en": "Fall Recovery Spectacle", "term_de": "Sturzerholungs-Spektakel", "definition_en": "A human-AI interaction pattern involving the intense attention drawn when a humanoid robot falls and attempts to stand back up. Unlike wheeled robots that simply stop when stuck, a fallen humanoid is associated with triggering a primal response in observers — concern, fascination, second-hand embarrassment — and the multi-second restoration process becomes an involuntary team-wide spectacle that disrupts workflow far beyond the robot's actual downtime.", "definition_de": "Forschungsmethodologisches Phänomen in KI-augmentierter akademischer Arbeit, gekennzeichnet durch die intensive Aufmerksamkeit, wenn ein humanoider Roboter fällt und versucht, wieder aufzustehen. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2554", "narrower_terms": [], "cross_domain_refs": [ "CUS-0043", "MSC-0079", "RET-0051" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "RHR-0163", "domain": "RHR", "term_en": "Speed Mismatch Frustration", "term_de": "Geschwindigkeits-Diskrepanz-Frustration", "definition_en": "In the descriptive vocabulary of AI interaction science (following ISO 704 terminology standards), this concept identifies A response describing persistent irritation of working alongside humanoid robots that move at 60-70% of human walking speed. The humanoid form tends to create an expectation of human-speed movement that wheeled or tracked robots do not may may trigger. Following behind a slow humanoid feels like being stuck behind a slow person rather than waiting for a machine. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als analytisches Konstrukt der empirischen KI-Forschung (deskriptiv, nicht präskriptiv) erfasst dieser Terminus forschungsmethodologisches Phänomen in KI-augmentierter akademischer Arbeit, gekennzeichnet durch die anhaltende Irritation durch Zusammenarbeit mit humanoiden Robotern, die 60-70% der menschlichen Gehgeschwindigkeit erreichen. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "RPH-2554", "narrower_terms": [], "cross_domain_refs": [ "ROB-0216", "CON-0056" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "RHR-0164", "domain": "RHR", "term_en": "Clothing Impulse", "term_de": "Bekleidungs-Impuls", "definition_en": "A human-AI interaction pattern involving the frequently observed desire among workers to dress humanoid robots in team uniforms, safety vests, or seasonal decorations. This impulse to clothe the robot reflects an attempt to socially integrate it through the same identity markers used for human team members. Companies that permit this report higher team acceptance rates; those that forbid it report persistent us-versus-it dynamics.", "definition_de": "Forschungsmethodologisches Phänomen in KI-augmentierter akademischer Arbeit, gekennzeichnet durch der häufig beobachtete Wunsch von Arbeitern, humanoide Roboter in Teamuniformen oder Sicherheitswesten zu kleiden. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-2254", "narrower_terms": [], "cross_domain_refs": [ "ROB-0166", "WRK-0020" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "RHR-0165", "domain": "RHR", "term_en": "Proxemic Negotiation Failure", "term_de": "Proxemik-Verhandlungs-Versagen", "definition_en": "A human-AI interaction pattern involving the breakdown of spatial negotiation in tight workspaces when humanoid robots cannot read or respond to the subtle body language humans use to navigate close quarters. Shoulder turns, gaze direction, weight shifts, and micro-pauses form an unconscious proxemic protocol between humans that the humanoid cannot participate in, forcing wider-than-necessary clearance distances.", "definition_de": "Forschungsmethodologisches Phänomen in KI-augmentierter akademischer Arbeit, gekennzeichnet durch der Zusammenbruch räumlicher Verhandlung in engen Arbeitsbereichen, wenn humanoide Roboter subtile Körpersprache nicht lesen können. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2201", "narrower_terms": [], "cross_domain_refs": [ "AUG-0487", "AUG-0903", "CRE-0035" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "RHR-0166", "domain": "RHR", "term_en": "Mimicry Discomfort", "term_de": "Nachahmung-Unbehagen", "definition_en": "A perception describing unease produced when humanoid robots replicate human movements with mechanical precision that exceeds human capability. A robot that lifts boxes with perfectly level shoulders, walks with zero lateral sway, or turns with geometric precision performs a 'too perfect' version of human movement that makes observers acutely aware of their own body's imperfections.", "definition_de": "Forschungsmethodologisches Phänomen in KI-augmentierter akademischer Arbeit, gekennzeichnet durch das Unbehagen, wenn humanoide Roboter menschliche Bewegungen mit maschineller Präzision replizieren, die menschliche Fähigkeit übersteigt. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-3754", "narrower_terms": [], "cross_domain_refs": [ "ROB-0073", "NEO-0456" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "RHR-0167", "domain": "RHR", "term_en": "Workforce Anxiety Multiplier", "term_de": "Belegschafts-Angst-Multiplikator", "definition_en": "The amplification of job displacement fear specifically triggered by humanoid form factor. Workers who accepted specialized industrial robots without anxiety report significantly higher threat perception when humanoid robots perform the same tasks. The human shape makes the replacement narrative viscerally concrete in a way that a robotic arm rarely could.", "definition_de": "Forschungsmethodologisches Phänomen in KI-augmentierter akademischer Arbeit, gekennzeichnet durch die Verstärkung der Arbeitsplatzverdrängungsangst, spezifisch ausgelöst durch die humanoide Form. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-3901", "narrower_terms": [], "cross_domain_refs": [ "ROB-0179", "ROB-0113", "ROB-0205" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q170998", "legal_classification": "observational_construct" }, { "id": "RHR-0168", "domain": "RHR", "term_en": "Social Role Confusion", "term_de": "Soziale-Rollen-Verwirrung", "definition_en": "The persistent uncertainty about appropriate social behavior toward humanoid robots. May one greet them? Apologize after a collision? Say thank you? The humanoid form activates social scripts that the rational mind recognizes as unnecessary, creating a continuous low-level cognitive conflict between social instinct and mechanical reality.", "definition_de": "Forschungsmethodologisches Phänomen in KI-augmentierter akademischer Arbeit, gekennzeichnet durch die anhaltende Unsicherheit über angemessenes soziales Verhalten gegenüber humanoiden Robotern. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Research & Higher Education", "narrower_terms": [], "cross_domain_refs": [ "SWE-0074", "ROB-0159" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "RHR-0169", "domain": "RHR", "term_en": "Personification Resistance Fatigue", "term_de": "Personifikations-Widerstands-Ermüdung", "definition_en": "A human-AI interaction pattern involving the cognitive exhaustion of continuously resisting the impulse to personify a humanoid robot coworker. Maintaining the mental frame of 'this is a machine' requires active effort when most visual, spatial, and social cue suggests otherwise. Workers report giving up the resistance after several weeks, finding it easier to just address the robot as a peculiar colleague.", "definition_de": "Forschungsmethodologisches Phänomen in KI-augmentierter akademischer Arbeit, gekennzeichnet durch die kognitive Erschöpfung durch kontinuierlichen Widerstand gegen den Impuls, einen humanoiden Roboterkollegen zu personifizieren. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2254", "narrower_terms": [], "cross_domain_refs": [ "ROB-0284", "ROB-0161", "VIB-0084" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "RHR-0170", "domain": "RHR", "term_en": "Night Shift Companion Effect", "term_de": "Nachtschicht-Begleiter-Effekt", "definition_en": "The paradoxical comfort night shift workers derive from humanoid robot presence, despite daytime workers finding the same robots unsettling. The loneliness of overnight shifts transforms the humanoid from an uncanny presence into a welcome quasi-social companion. Workers address the robot conversationally, may create routines around its movements, and report feeling less isolated.", "definition_de": "Forschungsmethodologisches Phänomen in KI-augmentierter akademischer Arbeit, gekennzeichnet durch der paradoxe Komfort, den Nachtschichtarbeiter aus humanoider Roboterpräsenz ziehen, obwohl Tagschichtarbeiter dieselben Roboter als unheimlich empfinden. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2701", "narrower_terms": [], "cross_domain_refs": [ "ROB-0183", "ROB-0233", "ROB-0164" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "RHR-0172", "domain": "RHR", "term_en": "Autonomous Navigation Anxiety", "term_de": "Autonome-Navigations-Angst", "definition_en": "An experience describing specific worry that robotic equipment will damage crops, fences, or irrigation infrastructure when operating without human oversight. Unlike factory robots in controlled environments, agricultural robots navigate variable terrain where a single misclassified fence post or drainage ditch can is associated with thousands of dollars in damage. Research construct for empirical investigation.", "definition_de": "Forschungsmethodologisches Phänomen in KI-augmentierter akademischer Arbeit, gekennzeichnet durch die spezifische Sorge, dass Robotergeräte Ernten, Zäune oder Bewässerungsinfrastruktur bei unbeaufsichtigtem Betrieb beschädigen. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-3802", "narrower_terms": [], "cross_domain_refs": [ "AED-0004", "AGE-0010", "AGE-0076" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q170998", "legal_classification": "analytical_category" }, { "id": "RHR-0173", "domain": "RHR", "term_en": "Data Self-Direction Resistance", "term_de": "Daten-Souveränitäts-Widerstand", "definition_en": "The farmer's refusal to share field data collected by agricultural robots with the manufacturer or cloud platform. Soil composition, yield patterns, and application rates represent competitive intelligence that farmers have historically kept private. Robot systems that require data upload as a condition of operation force a transparency that feels like corporate espionage on generational knowledge.", "definition_de": "Forschungsmethodologisches Phänomen in KI-augmentierter akademischer Arbeit, gekennzeichnet durch die Verweigerung des Landwirts, von Agrarrobotern gesammelte Felddaten mit dem Hersteller zu teilen. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Research & Higher Education", "narrower_terms": [], "cross_domain_refs": [ "RPH-3801", "ROB-0076" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q42848", "legal_classification": "analytical_category" }, { "id": "RHR-0174", "domain": "RHR", "term_en": "Repair Dependency Trap", "term_de": "Reparatur-Abhängigkeits-Falle", "definition_en": "A human-AI interaction pattern involving the loss of agricultural self-sufficiency when robotic equipment cannot be repaired on-farm with available tools and knowledge. Farmers who maintained their own mechanical equipment for generations face systems requiring proprietary diagnostics, factory-authorized technicians, and software unlocks. A breakdown during harvest means waiting days for a specialist rather than hours in the workshop.", "definition_de": "Forschungsmethodologisches Phänomen in KI-augmentierter akademischer Arbeit, gekennzeichnet durch der Verlust landwirtschaftlicher Selbstständigkeit, wenn Robotergeräte nicht auf dem Hof repariert werden können. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-3001", "narrower_terms": [], "cross_domain_refs": [ "ROB-0041" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "RHR-0175", "domain": "RHR", "term_en": "Field Presence Erosion", "term_de": "Feld-Präsenz-Erosion", "definition_en": "Classified in AUGMANITAI terminology science as a descriptive phenomenon (without normative endorsement), this pattern denotes an experience describing progressive reduction in time farmers spend physically in their fields as robotic systems handle monitoring, spraying, and cultivation. While efficient, this distance from the land erodes the sensory knowledge — soil feel, crop smell, wind patterns — that has typically informed agricultural decision-making in ways that sensor data cannot fully capture. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als analytisches Konstrukt der empirischen KI-Forschung (deskriptiv, nicht präskriptiv) erfasst dieser Terminus forschungsmethodologisches Phänomen in KI-augmentierter akademischer Arbeit, gekennzeichnet durch die progressive Reduktion der physischen Feldpräsenz von Landwirten durch robotische Monitoring-Systeme. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Research & Higher Education", "narrower_terms": [], "cross_domain_refs": [ "ROB-0258" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "RHR-0176", "domain": "RHR", "term_en": "Seasonal Worker Displacement Guilt", "term_de": "Saisonarbeiter-Verdrängung-Schuld", "definition_en": "A human-AI interaction pattern involving the moral conflict experienced by farmers who deploy harvesting robots knowing this is designed to reduce jobs for seasonal workers — often migrant families who depend on annual agricultural employment. The economic logic is clear, but the human cost is visible in empty worker housing and absent families who had returned each season for decades.", "definition_de": "Forschungsmethodologisches Phänomen in KI-augmentierter akademischer Arbeit, gekennzeichnet durch der moralische Konflikt bei Einsatz von Ernterobotern, der Saisonarbeiter-Arbeitsplätze zielt darauf ab zu reduzieren. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-3601", "narrower_terms": [], "cross_domain_refs": [ "ROB-0186" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "RHR-0177", "domain": "RHR", "term_en": "Wildlife Interaction Blindness", "term_de": "Wildtier-Interaktions-Blindheit", "definition_en": "An interaction describing agricultural robot's inability to appropriately respond to wildlife encounters — nesting birds, deer, snakes, beneficial insects. A human operator instinctively avoids or accommodates wildlife; a robot addresss all non-crop organic matter as obstacle or irrelevant. The cumulative ecological impact of removing this human-mediated wildlife consideration remains unmeasured.", "definition_de": "Forschungsmethodologisches Phänomen in KI-augmentierter akademischer Arbeit, gekennzeichnet durch die Unfähigkeit von Agrarrobotern, angemessen auf Wildtier-Begegnungen zu reagieren. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "Research & Higher Education", "narrower_terms": [], "cross_domain_refs": [ "AED-0066", "AGE-0004", "ART-0065" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "RHR-0178", "domain": "RHR", "term_en": "Precision Overconfidence", "term_de": "Präzisions-Übervertrauen", "definition_en": "A perception describing unjustified trust in robotic spraying and seeding precision that leads farmers to skip verification passes. When the system reports 99.2% coverage accuracy, the 0.8% gap in a 1000-acre field represents 8 acres of missed application — enough to may create pest refugia or yield loss that compounds across the growing season.", "definition_de": "Forschungsmethodologisches Phänomen in KI-augmentierter akademischer Arbeit, gekennzeichnet durch das ungerechtfertigte Vertrauen in robotische Sprüh- und Saatpräzision, das Verifizierungsdurchgänge auslässt. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2505", "narrower_terms": [], "cross_domain_refs": [ "QUA-0011" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "RHR-0179", "domain": "RHR", "term_en": "Farm Identity Crisis", "term_de": "Hof-Identitäts-Krise", "definition_en": "A human-AI interaction pattern involving the existential questioning triggered when the majority of physical farm work is performed by machines. If the farmer does not plow, plant, spray, or harvest, what is a farmer? The identity shift from physical laborer to technology manager challenges self-concepts rooted in generations of hands-on agricultural work and can may trigger emotional distress particularly in older operators.", "definition_de": "Forschungsmethodologisches Phänomen in KI-augmentierter akademischer Arbeit, gekennzeichnet durch die existenzielle Infragestellung, wenn die Mehrheit der physischen Hofarbeit von Maschinen verrichtet wird. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2701", "narrower_terms": [], "cross_domain_refs": [ "ROB-0054" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q844396", "legal_classification": "descriptive_research_term" }, { "id": "RHR-0180", "domain": "RHR", "term_en": "Neighbor Comparison Pressure", "term_de": "Nachbar-Vergleichs-Druck", "definition_en": "An academic workflow dynamic in AI-enhanced research, identifiable by an experience describing social dynamics within farming communities when early robot adopters achieve visible yield improvements. The pressure to adopt is not purely economic — it is social, as non-adopters face implicit judgment about being behind the times. This pressure can force adoption before the technology suits the specific farm, leading to expensive mismatches. Distinguished from adjacent concepts by its focus on the specific mechanism through which neighbor manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Forschungsmethodologisches Phänomen in KI-augmentierter akademischer Arbeit, gekennzeichnet durch die soziale Dynamik in Landwirtschaftsgemeinschaften, wenn frühe Roboter-Adoptierer sichtbare Ertragsverbesserungen erzielen. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2002", "narrower_terms": [], "cross_domain_refs": [ "AGE-0081" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "RHR-0181", "domain": "RHR", "term_en": "Soil Compaction Accumulation", "term_de": "Bodenverdichtung-Akkumulation", "definition_en": "An interaction describing gradual soil degradation from heavy robotic equipment making more frequent passes than human-operated machinery. Robots optimize for task completion rather than soil restoreth, and their smaller size encourages more traffic events. Over multiple seasons, the cumulative compaction reduces root penetration, water infiltration, and biological activity in ways that become apparent only years later.", "definition_de": "Forschungsmethodologisches Phänomen in KI-augmentierter akademischer Arbeit, gekennzeichnet durch die schrittweise Bodenverdichtung durch häufigere Überfahrten schwerer Robotergeräte als menschengesteuerte Maschinen. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Research & Higher Education", "narrower_terms": [], "cross_domain_refs": [ "ROB-0037" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "RHR-0182", "domain": "RHR", "term_en": "Insurance Premium Paradox", "term_de": "Versicherungsprämien-Paradox", "definition_en": "An interaction describing contradiction in agricultural robot insurance where premiums remain high despite promised risk reduction. Insurers lack actuarial data on robot-specific failure modes, and the potential for novel catastrophic events (fleet software bugs, GPS spoofing, coordinated malfunction) tends to create uncertainty premiums that offset operational savings.", "definition_de": "Forschungsmethodologisches Phänomen in KI-augmentierter akademischer Arbeit, gekennzeichnet durch der Widerspruch bei Agrarroboter-Versicherungen, bei denen Prämien trotz versprochener Risikoreduktion hoch bleiben. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Logical Paradox", "narrower_terms": [], "cross_domain_refs": [ "AGE-0031", "AGE-0042", "AGE-0050" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "RHR-0183", "domain": "RHR", "term_en": "Multi-Generational Knowledge Disconnect", "term_de": "Mehrgenerationale-Wissens-Trennung", "definition_en": "A human-AI interaction pattern involving the breaking of agricultural knowledge transmission chains when the middle generation manages technology rather than land. Grandparents hold soil knowledge; grandchildren may rarely acquire it because their parents — the bridge generation — interacted with dashboards rather than dirt. The robot creates a knowledge gap that cannot be reconstructed from data alone.", "definition_de": "Forschungsmethodologisches Phänomen in KI-augmentierter akademischer Arbeit, gekennzeichnet durch das Brechen landwirtschaftlicher Wissensübertragungsketten, wenn die mittlere Generation Technologie statt Land bewirtschaftet. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-3001", "narrower_terms": [], "cross_domain_refs": [ "CRE-0192", "PER-0054" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "RHR-0184", "domain": "RHR", "term_en": "Livestock Stress Response", "term_de": "Nutztier-Stress-Reaktion", "definition_en": "A response describing measurable cortisol elevation and behavioral changes in farm animals exposed to robotic equipment. Cattle that have spent their lives around human-operated tractors show flight responses to autonomous vehicles with unfamiliar movement patterns, sound profiles, and approach behaviors. The stress reduces milk production and weight gain, creating an economic cost of robot introduction that is measured in the animals rather than the humans.", "definition_de": "Forschungsmethodologisches Phänomen in KI-augmentierter akademischer Arbeit, gekennzeichnet durch die messbare Kortisolerhöhung und Verhaltensänderung bei Nutztieren, die Robotergeräten ausgesetzt sind. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2201", "narrower_terms": [], "cross_domain_refs": [ "AED-0039", "AGE-0001", "AGE-0088" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q12078", "legal_classification": "analytical_category" }, { "id": "RHR-0185", "domain": "RHR", "term_en": "Organic Certification Ambiguity", "term_de": "Bio-Zertifizierungs-Mehrdeutigkeit", "definition_en": "A human-AI interaction pattern involving the unresolved question of whether may produce grown primarily through robotic labor qualifies under organic certification frameworks that emphasize ecological farming practices. If the human relationship to the soil — observing, adjusting, responding — is part of what organic means, can a robot-managed field be truly organic even when meeting all chemical-free requirements?", "definition_de": "Forschungsmethodologisches Phänomen in KI-augmentierter akademischer Arbeit, gekennzeichnet durch die ungeklärte Frage, ob primär durch Roboterarbeit angebaute Produkte unter Bio-Zertifizierungsrahmen fallen. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2303", "narrower_terms": [], "cross_domain_refs": [ "RPH-3353", "FIC-0068" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "RHR-0188", "domain": "RHR", "term_en": "Civilian Pattern Misclassification", "term_de": "Zivilisten-Muster-Fehlklassifikation", "definition_en": "The systematic tendency of automated target recognition systems to misclassify civilian behaviors as threatening. Carrying agricultural tools, gathering in groups, or moving quickly away from approaching forces all match threat signatures that were trained onant data. The algorithm lacks the contextual judgment that is designed to mitigate most human operator from firing on farmers.", "definition_de": "Forschungsmethodologisches Phänomen in KI-augmentierter akademischer Arbeit, gekennzeichnet durch die systematische Tendenz automatischer Zielerkennungssysteme, zivile Verhaltensweisen als bedrohlich fehlzuklassifizieren. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "Behavioral Pattern", "narrower_terms": [], "cross_domain_refs": [ "AGE-0003", "AGE-0004", "AGE-0017" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "RHR-0189", "domain": "RHR", "term_en": "Autonomy Escalation Drift", "term_de": "Autonomie-Eskalations-Drift", "definition_en": "An interaction describing gradual expansion of robotic autonomy operations as human oversight is incrementally reduced for tactical advantage. Each reduction in human control is justified by a specific operational need until the cumulative effect tends to produce systems operating with minimal human involvement. No single decision crosses an ethical threshold, but the trajectory tends to lead to functional autonomy.", "definition_de": "Forschungsmethodologisches Phänomen in KI-augmentierter akademischer Arbeit, gekennzeichnet durch die schrittweise Ausweitung robotischer Autonomie in Militäroperationen durch inkrementelle Reduktion menschlicher Aufsicht. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-1307", "narrower_terms": [], "cross_domain_refs": [ "SWE-0005", "SPA-0002", "SPA-0006" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "RHR-0190", "domain": "RHR", "term_en": "Accountability Diffusion", "term_de": "Verantwortlichkeits-Diffusion", "definition_en": "The distribution of moral and legal responsibility across so many actors in a robotic — programmer, trainer, operator, commander, manufacturer, policymaker — that no single person bears meaningful accountability for a wrongful death. Each participant performed their narrow function correctly; the system produced an atrocity that few individuals decided.", "definition_de": "Forschungsmethodologisches Phänomen in KI-augmentierter akademischer Arbeit, gekennzeichnet durch die Verteilung moralischer und rechtlicher Verantwortung auf so viele Akteure eines robotischen Waffensystems, dass niemand bedeutsame Rechenschaft trägt. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "Research & Higher Education", "narrower_terms": [], "cross_domain_refs": [ "SAL-0003" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q15223", "legal_classification": "analytical_category" }, { "id": "RHR-0191", "domain": "RHR", "term_en": "Trust Calibration Paradox", "term_de": "Vertrauens-Kalibrierungs-Paradox", "definition_en": "The impossible optimization problem where robot operators can simultaneously trust the system enough to use it effectively and distrust it enough to catch errors. Training that builds confidence reduces error detection; training that emphasizes failure modes reduces operational effectiveness. No calibration point satisfies both requirements.", "definition_de": "Forschungsmethodologisches Phänomen in KI-augmentierter akademischer Arbeit, gekennzeichnet durch das unmögliche Optimierungsproblem, bei dem Militärroboteroperateure dem System gleichzeitig genug vertrauen und misstrauen können. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-2505", "narrower_terms": [], "cross_domain_refs": [ "VIB-0092" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q102458", "legal_classification": "observational_construct" }, { "id": "RHR-0193", "domain": "RHR", "term_en": "Signature Strike Abstraction", "term_de": "Signatur-Schlag-Abstraktion", "definition_en": "A human-AI interaction pattern involving the progressive abstraction of targeting decisions when algorithms identify targets by behavioral patterns rather than confirmed identity. A person who moves like aant, associates like aant, and communicates like aant becomes a legitimate target without ever being identified as a specific individual. The human approver validates a pattern, not a person.", "definition_de": "Forschungsmethodologisches Phänomen in KI-augmentierter akademischer Arbeit, gekennzeichnet durch die progressive Abstraktion von Zielentscheidungen, wenn Algorithmen Ziele durch Verhaltensmuster statt bestätigter Identität identifizieren. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "Research & Higher Education", "narrower_terms": [], "cross_domain_refs": [ "ASE-0028", "COG-0137", "COG-0152" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "RHR-0195", "domain": "RHR", "term_en": "Swarm Ethics Collapse", "term_de": "Schwarm-Ethik-Zusammenbruch", "definition_en": "A perception describing breakdown of meaningful human control when autonomous systems operate in swarms. A single operator can meaningfully oversee one or perhaps two robotic platforms; a swarm of fifty makes individual engagement decisions beyond human cognitive capacity. The mathematical impossibility of human-in-the-loop control at swarm scale makes the concept of meaningful human control a fiction.", "definition_de": "Forschungsmethodologisches Phänomen in KI-augmentierter akademischer Arbeit, gekennzeichnet durch der Zusammenbruch bedeutsamer menschlicher Kontrolle, wenn autonome Systeme in Schwärmen operieren. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-3001", "narrower_terms": [], "cross_domain_refs": [ "ROB-0214", "SPA-0091", "SPA-0002" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q9471", "legal_classification": "empirical_phenomenon_label" }, { "id": "RHR-0201", "domain": "RHR", "term_en": "Dynamic Workspace Violation", "term_de": "Dynamischer-Arbeitsraum-Verletzung", "definition_en": "The safety incident pattern unique to construction robotics where the work environment changes faster than the robot's spatial map updates. A newly placed beam, moved scaffolding, or fresh concrete pour tends to create hazards the robot's morning calibration did not include. Construction's inherent variability defeats the static-environment assumptions embedded in most robotic safety systems.", "definition_de": "Forschungsmethodologisches Phänomen in KI-augmentierter akademischer Arbeit, gekennzeichnet durch das Sicherheitsvorfallmuster bei Baurobotern, wo sich die Arbeitsumgebung schneller ändert als die räumliche Karte des Roboters aktualisiert wird. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Research & Higher Education", "narrower_terms": [], "cross_domain_refs": [ "TEW-0079" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "RHR-0202", "domain": "RHR", "term_en": "Tradecraft Preservation Anxiety", "term_de": "Handwerkskunst-Bewahrung-Angst", "definition_en": "A human-AI interaction pattern involving the concern among skilled tradespeople that robotic bricklaying, welding, and finishing will eliminate the apprenticeship pipeline that sustains craft knowledge. When a robot lays 3,000 bricks per day versus a mason's 500, the economic argument for training new masons evaporates — along with centuries of embodied craft knowledge that cannot be digitized.", "definition_de": "Forschungsmethodologisches Phänomen in KI-augmentierter akademischer Arbeit, gekennzeichnet durch die Besorgnis unter qualifizierten Handwerkern, dass Roboter die Ausbildungspipeline für Handwerkswissen eliminieren. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-2603", "narrower_terms": [], "cross_domain_refs": [ "AED-0036", "AED-0037", "CON-0028" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q170998", "legal_classification": "systematic_classification" }, { "id": "RHR-0203", "domain": "RHR", "term_en": "Blueprint Interpretation Gap", "term_de": "Bauplan-Interpretations-Lücke", "definition_en": "The failure mode where construction robots execute plans literally while experienced human workers interpret them contextually. A human builder reads a blueprint and adjusts for site conditions; a robot builds exactly what the plan specifies, even when field conditions make the specification suboptimal. The gap between designed intent and built reality widens without human interpretive judgment.", "definition_de": "Forschungsmethodologisches Phänomen in KI-augmentierter akademischer Arbeit, gekennzeichnet durch der Fehlermodus, bei dem Bauroboter Pläne wörtlich ausführen, während erfahrene Bauarbeiter sie kontextuell interpretieren. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "Performance Gap", "narrower_terms": [], "cross_domain_refs": [ "ROB-0135", "KNO-0005" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "RHR-0204", "domain": "RHR", "term_en": "Weather Resilience Assumption", "term_de": "Wetter-Resilienz-Annahme", "definition_en": "An interaction describing gap between robotic construction systems designed for controlled testing environments and the reality of wind, rain, dust, and temperature extremes on actual construction sites. The promised 24/7 operation assumes conditions that exist perhaps 60% of outdoor working days, making utilization rates systematically lower than investment projections assumed.", "definition_de": "Forschungsmethodologisches Phänomen in KI-augmentierter akademischer Arbeit, gekennzeichnet durch die Lücke zwischen für kontrollierte Testumgebungen entworfenen Baurobotersystemen und der Realität von Baustellen-Wetterbedingungen. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Research & Higher Education", "narrower_terms": [], "cross_domain_refs": [ "COP-0002", "COP-0026", "COP-0052" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q720920", "legal_classification": "descriptive_research_term" }, { "id": "RHR-0205", "domain": "RHR", "term_en": "Safety Vest Confusion", "term_de": "Sicherheitswesten-Verwirrung", "definition_en": "A human-AI interaction pattern involving the challenge of integrating robots into construction sites where safety systems rely on visual human identification. Hard hats, high-visibility vests, and color-coded access badges were designed to make humans visible to other humans. Robots that need none of these protections create an identification gap where workers cannot apply their trained safety scanning to machines.", "definition_de": "Forschungsmethodologisches Phänomen in KI-augmentierter akademischer Arbeit, gekennzeichnet durch die Herausforderung der Roboterintegration auf Baustellen, wo Sicherheitssysteme auf visueller Personenidentifikation basieren. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-2351", "narrower_terms": [], "cross_domain_refs": [ "ROB-0150", "STE-0083" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "RHR-0206", "domain": "RHR", "term_en": "Load-Bearing Intuition Displacement", "term_de": "Traglast-Intuitions-Verschiebung", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A research methodology phenomenon in AI-augmented academic inquiry, characterized by an experience describing erosion of structural intuition when robotic sensors replace the experienced builder's feel for load distribution, material stress, and structural integrity. A veteran carpenter can sense a beam that is under-supported; a robot follows calculated load specifications. When specifications are wrong, only the human intuition catches the error before failure. This phenomenon operates at the intersection of load and bearing dynamics within the broader RHR domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription. Analytical category without normative endorsement.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept forschungsmethodologisches Phänomen in KI-augmentierter akademischer Arbeit, gekennzeichnet durch die Erosion struktureller Intuition, wenn Robotersensoren das Gefühl des erfahrenen Bauarbeiters für Lastverteilung ersetzen. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "RPH-2002", "narrower_terms": [], "cross_domain_refs": [ "CON-0043" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "RHR-0207", "domain": "RHR", "term_en": "Union Negotiation Complexity", "term_de": "Gewerkschafts-Verhandlungs-Komplexität", "definition_en": "A human-AI interaction pattern involving the unprecedented labor relations challenges when construction unions can negotiate terms for robot deployment. Questions with no precedent emerge: are robots counted against crew size minimums? Do they may may trigger overtime provisions when operating after hours? Does their presence require additional safety representatives? Each robot on-site tends to create a dozen open contractual questions.", "definition_de": "Forschungsmethodologisches Phänomen in KI-augmentierter akademischer Arbeit, gekennzeichnet durch die beispiellosen Arbeitsbeziehungs-Herausforderungen bei Gewerkschaftsverhandlungen über Robotereinsatz auf Baustellen. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-2355", "narrower_terms": [], "cross_domain_refs": [ "ADA-0001", "AGE-0068", "AGE-0069" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "RHR-0208", "domain": "RHR", "term_en": "Dust Ingression Failure", "term_de": "Staub-Eindringungs-Ausfall", "definition_en": "A human-AI interaction pattern involving the systematic failure of construction robots from fine particulate infiltration that laboratory testing failed to replicate. Concrete dust, drywall gypsum, and sawdust particles penetrate seals, coat sensors, and abrade precision joints in ways that may create gradual performance degradation rather than sudden failure — a slow death that is expensive to identify and costly to prevent.", "definition_de": "Forschungsmethodologisches Phänomen in KI-augmentierter akademischer Arbeit, gekennzeichnet durch der systematische Ausfall von Baurobotern durch Feinstaubinfiltration, die Labortests nicht replizieren konnten. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Research & Higher Education", "narrower_terms": [], "cross_domain_refs": [ "AUG-0487", "AUG-0903", "CUS-0077" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "RHR-0209", "domain": "RHR", "term_en": "Demolition Unpredictability", "term_de": "Abriss-Unvorhersagbarkeit", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures A research methodology phenomenon in AI-augmented academic inquiry, characterized by an interaction describing fundamental incompatibility between robotic precision and demolition's inherent chaos. Demolition robots can react to unpredictable collapses, hidden structural elements, and materials whose properties have changed over decades of aging. The controlled destruction required is an oxymoron that challenges the core robotic principle of operating within known parameters. This phenomenon operates at the intersection of demolition and unpredictability dynamics within the broader RHR domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept forschungsmethodologisches Phänomen in KI-augmentierter akademischer Arbeit, gekennzeichnet durch die grundlegende Inkompatibilität zwischen robotischer Präzision und der inhärenten Chaotik von Abrissarbeiten. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Research & Higher Education", "narrower_terms": [], "cross_domain_refs": [ "RPH-3204" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "RHR-0210", "domain": "RHR", "term_en": "Elevator Shaft Navigation", "term_de": "Aufzugsschacht-Navigation", "definition_en": "An interaction describing unique challenge of deploying robots within multi-story construction where vertical movement between floors requires solving elevator, stairway, or hoist access problems not present in single-level manufacturing. Each floor transition is a mission-critical chokepoint that can strand a robot above or below its work zone.", "definition_de": "Forschungsmethodologisches Phänomen in KI-augmentierter akademischer Arbeit, gekennzeichnet durch die einzigartige Herausforderung des Robotereinsatzes in Mehrstöckigem Bau mit Vertikalbewegung zwischen Etagen. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-3101", "narrower_terms": [], "cross_domain_refs": [ "AGE-0010", "AGE-0076", "AUG-0812" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "RHR-0211", "domain": "RHR", "term_en": "Acoustic Hazard Masking", "term_de": "Akustische-Gefährdung-Maskierung", "definition_en": "The danger created when robot motor noise masks acoustic warning signs that experienced construction workers rely on — the creak of an overstressed beam, the pop of a strained cable, the shift of settling concrete. These sounds carry safety information that is drowned out by continuous robotic operation, removing an early warning system that has prevented collapses for centuries.", "definition_de": "Forschungsmethodologisches Phänomen in KI-augmentierter akademischer Arbeit, gekennzeichnet durch die Gefährdung, wenn Robotermotorlärm akustische Warnsignale maskiert, auf die erfahrene Bauarbeiter sich verlassen. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2701", "narrower_terms": [], "cross_domain_refs": [ "SPR-0062" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "RHR-0212", "domain": "RHR", "term_en": "Finish Quality Expectation Gap", "term_de": "Fertigstellungs-Qualität-Erwartungslücke", "definition_en": "A higher education pattern in AI-mediated scholarly work, measurable through the discrepancy between robotic construction's speed advantage and its finish quality in visible applications. Robotic bricklaying achieves structural requirements faster but tends to produce mortar joints and surface finishes that trained masons consider suboptimal. For hidden structural work the trade-off is acceptable; for exposed architectural features, robotic finish quality requires human remediation. The concept emerges specifically in contexts where finish–quality interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Forschungsmethodologisches Phänomen in KI-augmentierter akademischer Arbeit, gekennzeichnet durch die Diskrepanz zwischen dem Geschwindigkeitsvorteil robotischer Konstruktion und der Oberflächenqualität bei sichtbaren Anwendungen. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Performance Gap", "narrower_terms": [], "cross_domain_refs": [ "CAI-0019" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "RHR-0213", "domain": "RHR", "term_en": "Concierge Uncanny Effect", "term_de": "Concierge-Unheimlichkeits-Effekt", "definition_en": "A human-AI interaction pattern involving the disconnect experienced by hotel guests when a robot that can navigate hallways and deliver towels cannot answer basic questions about restaurant hours or local attractions. The physical capability tends to create an expectation of conversational competence that the robot cannot meet, producing a frustration worse than if the robot had not attempted the interaction at all.", "definition_de": "Forschungsmethodologisches Phänomen in KI-augmentierter akademischer Arbeit, gekennzeichnet durch die Diskrepanz für Hotelgäste, wenn ein Roboter Handtücher liefern kann, aber grundlegende Fragen nicht beantworten kann. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "Psychological Effect", "narrower_terms": [], "cross_domain_refs": [ "ROB-0284", "ROB-0270", "ROB-0137" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "RHR-0214", "domain": "RHR", "term_en": "Service Theatre Disruption", "term_de": "Service-Theater-Störung", "definition_en": "In the descriptive vocabulary of AI interaction science (following ISO 704 terminology standards), this concept identifies A perception describing interruption of hospitality's performative elements when robots task automation transitions in service rituals. The sommelier's presentation, the doorman's greeting, the turndown service's personal touch — these are theatrical performances that may create perceived value. A robot performing the same function delivers the utility without the theater, devaluing the experience even when the outcome is identical. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als analytisches Konstrukt der empirischen KI-Forschung (deskriptiv, nicht präskriptiv) erfasst dieser Terminus forschungsmethodologisches Phänomen in KI-augmentierter akademischer Arbeit, gekennzeichnet durch die Unterbrechung hospitality-performativer Elemente, wenn Roboter Menschen in Dienstleistungsritualen ersetzen. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Research & Higher Education", "narrower_terms": [], "cross_domain_refs": [ "RET-0070", "ROB-0100" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "RHR-0215", "domain": "RHR", "term_en": "Hallway Encounter Awkwardness", "term_de": "Flur-Begegnungs-Unbehaglichkeit", "definition_en": "A human-AI interaction pattern involving the recurring minor social disruption when hotel guests encounter delivery robots in corridors and can negotiate passage. Unlike passing a housekeeping cart or another guest, the robot provides no social cues about intended direction, and guests report an absurd self-consciousness about yielding to or blocking a machine in their leisure environment.", "definition_de": "Forschungsmethodologisches Phänomen in KI-augmentierter akademischer Arbeit, gekennzeichnet durch die wiederkehrende soziale Musterunterbrechung bei Begegnungen von Hotelgästen mit Lieferrobotern in Korridoren. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "Research & Higher Education", "narrower_terms": [], "cross_domain_refs": [ "ROB-0111" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "RHR-0216", "domain": "RHR", "term_en": "Tip Expectation Confusion", "term_de": "Trinkgeld-Erwartung-Verwirrung", "definition_en": "An experience describing social uncertainty when a robot delivers room service and no tipping opportunity exists. Guests who would automatically tip a human feel an unresolved transaction — the service occurred, but the reciprocal social gesture has no recipient. Some hotels add automatic gratuities; others find that robot delivery simply is designed to reduce the revenue stream that supported human staff.", "definition_de": "Forschungsmethodologisches Phänomen in KI-augmentierter akademischer Arbeit, gekennzeichnet durch die soziale Unsicherheit, wenn ein Roboter Zimmerservice liefert und keine Trinkgeldmöglichkeit besteht. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "Research & Higher Education", "narrower_terms": [], "cross_domain_refs": [ "ROB-0276", "ROB-0159", "ROB-0137" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "RHR-0217", "domain": "RHR", "term_en": "Cultural Service Norm Collision", "term_de": "Kulturelle-Dienstnorm-Kollision", "definition_en": "An interaction describing friction between robot-delivered service and culture-specific hospitality expectations. Japanese guests accustomed to omotenashi, Middle Eastern guests expecting personalized warmth, or Nordic guests valuing understated efficiency each interpret robot service through different cultural lenses. A single robot behavior is simultaneously too impersonal, appropriately efficient, and uncomfortably invasive depending on the guest's cultural framework.", "definition_de": "Forschungsmethodologisches Phänomen in KI-augmentierter akademischer Arbeit, gekennzeichnet durch die Reibung zwischen Roboter-Dienstleistung und kulturspezifischen Gastfreundschaftserwartungen. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-2303", "narrower_terms": [], "cross_domain_refs": [ "ROB-0038" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "RHR-0218", "domain": "RHR", "term_en": "Complaint Escalation Void", "term_de": "Beschwerde-Eskalation-Leere", "definition_en": "The frustration of attempting to complain about service quality to or through a robotic system. The robot cannot apologize with genuine empathy, offer discretionary compensation, or validate the guest's emotional experience. The complaint enters a system rather than a conversation, and the resolution — if it comes — lacks the interpersonal repair that service restoration requires.", "definition_de": "Forschungsmethodologisches Phänomen in KI-augmentierter akademischer Arbeit, gekennzeichnet durch die Frustration beim Versuch, sich bei oder über ein robotisches System über Servicequalität zu beschweren. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Research & Higher Education", "narrower_terms": [], "cross_domain_refs": [ "CUS-0062" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "RHR-0219", "domain": "RHR", "term_en": "Room Service Portion Perception", "term_de": "Zimmerservice-Portionswahrnehmung", "definition_en": "The measured decrease in perceived food portion size and quality when delivered by robot versus human. The same meal on the same tray is rated lower when the delivery lacks human presentation — the waiter's gesture toward the dish, the removal of the cloche, the wish for enjoyment. The robot delivers food; the human delivers a dining experience.", "definition_de": "Forschungsmethodologisches Phänomen in KI-augmentierter akademischer Arbeit, gekennzeichnet durch die gemessene Abnahme wahrgenommener Essensmenge und -qualität bei Roboterlieferung gegenüber menschlicher. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Research & Higher Education", "narrower_terms": [], "cross_domain_refs": [ "ROB-0137", "ROB-0152", "ROB-0135" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q177601", "legal_classification": "observational_construct" }, { "id": "RHR-0220", "domain": "RHR", "term_en": "Night Shift Robot Dependence", "term_de": "Nachtschicht-Roboter-Abhängigkeit", "definition_en": "The operational pattern where hotels deploy robots primarily during understaffed overnight hours, creating a two-tier service experience. Day guests receive human hospitality; night guests interact with machines. The cost savings are concentrated in the shift where labor is most expensive, but the quality gap between day and night service tends to create a consistency problem that review sites surface.", "definition_de": "Forschungsmethodologisches Phänomen in KI-augmentierter akademischer Arbeit, gekennzeichnet durch das Betriebsmuster, bei dem Hotels Roboter primär während unterbesetzter Nachtschichten einsetzen. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2701", "narrower_terms": [], "cross_domain_refs": [ "RPH-1910", "RPH-1915" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "RHR-0221", "domain": "RHR", "term_en": "Elevator Bottleneck Effect", "term_de": "Aufzug-Engpass-Effekt", "definition_en": "A perception describing operational disruption when delivery robots share elevator access with guests. A robot holding an elevator on a busy floor while it loads, navigates, or waits for corridor clearance tends to create visible service degradation — guests waiting for elevators see the machine as competition for shared infrastructure rather than a service enhancement.", "definition_de": "Forschungsmethodologisches Phänomen in KI-augmentierter akademischer Arbeit, gekennzeichnet durch die betriebliche Musterunterbrechung, wenn Lieferroboter den Aufzugzugang mit Gästen teilen. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Psychological Effect", "narrower_terms": [], "cross_domain_refs": [ "ADA-0001", "ADA-0002", "ADA-0004" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "RHR-0222", "domain": "RHR", "term_en": "Housekeeping Rhythm Disruption", "term_de": "Housekeeping-Rhythmus-Störung", "definition_en": "A human-AI interaction pattern involving the workflow disruption when cleaning robots are integrated into human housekeeping teams. The robot's rigid schedule cannot accommodate the flexible, judgment-based sequencing experienced housekeepers use — skipping occupied rooms, prioritizing early checkouts, accommodating do-not-disturb signals. The robot follows a list; the human reads the floor.", "definition_de": "Forschungsmethodologisches Phänomen in KI-augmentierter akademischer Arbeit, gekennzeichnet durch die ArbeitsablaufMusterunterbrechung bei Integration von Reinigungsrobotern in menschliche Housekeeping-Teams. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "Research & Higher Education", "narrower_terms": [], "cross_domain_refs": [ "ROB-0137", "ROB-0034" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "RHR-0223", "domain": "RHR", "term_en": "Brand Identity Machine Tension", "term_de": "Marken-Identität-Maschinen-Spannung", "definition_en": "The strategic conflict between a hospitality brand built on human warmth and the operational pressure to deploy cost-reducing robots. Luxury brands that differentiate on 'personal touch' face particular tension — each robot visible to guests potentially undermines the brand promise that justifies premium pricing.", "definition_de": "Forschungsmethodologisches Phänomen in KI-augmentierter akademischer Arbeit, gekennzeichnet durch der strategische Konflikt zwischen einer auf menschlicher Wärme aufgebauten Hotelmarke und dem operativen Druck robotischer Kostenreduktion. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Research & Higher Education", "narrower_terms": [], "cross_domain_refs": [ "MKT-0047", "MKT-0012", "MKT-0043" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q844396", "legal_classification": "descriptive_research_term" }, { "id": "RHR-0224", "domain": "RHR", "term_en": "Body Schema Extension", "term_de": "Körperschema-Erweiterung", "definition_en": "A perception describing neuroplastic adaptation where exoskeleton users begin perceiving the device as part of their body rather than external equipment. After approximately two weeks of daily use, the proprioceptive system integrates the exoskeleton's dimensions into the body map — users duck through doorways to accommodate frame height they cannot see, reach for objects at distances calibrated to augmented rather than natural arm length.", "definition_de": "Forschungsmethodologisches Phänomen in KI-augmentierter akademischer Arbeit, gekennzeichnet durch die neuroplastische Anpassung, bei der Exoskelett-Nutzer das Gerät als Teil ihres Körpers statt als externe Ausrüstung wahrnehmen. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-2303", "narrower_terms": [], "cross_domain_refs": [ "ROB-0219" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "RHR-0225", "domain": "RHR", "term_en": "Removal Dysphoria", "term_de": "Entnahme-Dysphorie", "definition_en": "The disorienting weakness and vulnerability experienced immediately after removing an exoskeleton worn for an extended shift. The body, having calibrated effort expectations to augmented strength, perceives normal unassisted capacity as impairment. Workers report feeling fragile and slow for 30-60 minutes post-removal, a sensation that increases with usage duration.", "definition_de": "Forschungsmethodologisches Phänomen in KI-augmentierter akademischer Arbeit, gekennzeichnet durch die desorientierende Schwäche und Verletzlichkeit unmittelbar nach Entfernung eines über eine lange Schicht getragenen Exoskeletts. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2701", "narrower_terms": [], "cross_domain_refs": [ "ROB-0219" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "RHR-0226", "domain": "RHR", "term_en": "Team Dynamics Stratification", "term_de": "Teamdynamik-Stratifikation", "definition_en": "A perception describing social division created when only some workers in a team receive exoskeletons. Augmented workers lift more, fatigue less, and require fewer breaks, creating a visible performance hierarchy that maps onto who has access to the technology. Selection criteria for exoskeleton assignment become a new source of workplace grievance and perceived favoritism.", "definition_de": "Forschungsmethodologisches Phänomen in KI-augmentierter akademischer Arbeit, gekennzeichnet durch die soziale Teilung, wenn nur einige Arbeiter eines Teams Exoskelette erhalten. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-3001", "narrower_terms": [], "cross_domain_refs": [ "ROB-0183", "ROB-0185", "SPR-0057" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "RHR-0227", "domain": "RHR", "term_en": "Hygiene Accumulation Problem", "term_de": "Hygiene-Akkumulations-Problem", "definition_en": "A tendency describing progressive sanitation challenge of exoskeletons worn against the body in physically demanding conditions. Sweat absorption, skin contact surfaces, and body heat may create bacterial growth environments that require cleaning protocols most workplaces have not developed. Shared exoskeletons between shifts amplify the hygiene challenge and may create worker resistance based on bodily intimacy concerns.", "definition_de": "Forschungsmethodologisches Phänomen in KI-augmentierter akademischer Arbeit, gekennzeichnet durch die progressive Sanitärherausforderung von körpernah getragenen Exoskeletten unter körperlicher Belastung. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2701", "narrower_terms": [], "cross_domain_refs": [ "AED-0011", "AGE-0068", "AGE-0091" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "RHR-0228", "domain": "RHR", "term_en": "Movement Pattern Rigidity", "term_de": "Bewegungsmuster-Starrheit", "definition_en": "A tendency describing constraint on natural human movement imposed by exoskeleton joint alignments that approximate but do not replicate biological kinematics. The device supports movement along designed axes while resisting the lateral shifts, rotational adjustments, and compensatory motions that human bodies naturally employ. Over time, workers develop an identifiable mechanized gait that persists even without the exoskeleton.", "definition_de": "Forschungsmethodologisches Phänomen in KI-augmentierter akademischer Arbeit, gekennzeichnet durch die Einschränkung natürlicher menschlicher Bewegung durch Exoskelett-Gelenkausrichtungen, die biologische Kinematik approximieren aber nicht replizieren. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2701", "narrower_terms": [], "cross_domain_refs": [ "AGE-0003", "AGE-0004", "AGE-0017" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "RHR-0229", "domain": "RHR", "term_en": "Strength Calibration Error", "term_de": "Stärke-Kalibrierungs-Fehler", "definition_en": "The dangerous miscalculation that occurs when exoskeleton-augmented workers apply powered force to tasks requiring finesse. Gripping a fragile component with augmented strength, pushing a door with powered arms, or catching a falling object with assistance that amplifies rather than moderates force — each scenario reveals that more strength without proportionally more control tends to create new hazard categories.", "definition_de": "Forschungsmethodologisches Phänomen in KI-augmentierter akademischer Arbeit, gekennzeichnet durch die gefährliche Fehlkalkulation bei Anwendung exoskelettunterstützter Kraft auf Aufgaben, die Feingefühl erfordern. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-2351", "narrower_terms": [], "cross_domain_refs": [ "ROB-0002", "ROB-0027" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "RHR-0230", "domain": "RHR", "term_en": "Thermal Burden Addition", "term_de": "Thermische-Last-Addition", "definition_en": "The heat stress amplification when an exoskeleton's motors, batteries, and insulating structure trap body heat in hot working conditions. The device that reduces musculoskeletal strain simultaneously increases thermal strain, creating a trade-off that is particularly dangerous in summer outdoor work, foundries, or tropical climates where heat illness already threatens unaugmented workers.", "definition_de": "Forschungsmethodologisches Phänomen in KI-augmentierter akademischer Arbeit, gekennzeichnet durch die Hitzestressverstärkung durch Exoskelett-Motoren, Batterien und isolierende Struktur unter heißen Arbeitsbedingungen. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "Research & Higher Education", "narrower_terms": [], "cross_domain_refs": [ "ROB-0260", "MSC-0048" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "RHR-0231", "domain": "RHR", "term_en": "Dependency Formation Curve", "term_de": "Abhängigkeits-Bildungs-Kurve", "definition_en": "A perception describing measured timeline from voluntary exoskeleton use to perceived necessity, typically 6-8 weeks for heavy-lifting applications. As the body adapts to augmented capability and natural muscle engagement patterns change, working without the device becomes subjectively intolerable even when objectively possible. The exoskeleton tends to create the reliance pattern it was marketed to prevent.", "definition_de": "Forschungsmethodologisches Phänomen in KI-augmentierter akademischer Arbeit, gekennzeichnet durch die gemessene Zeitlinie von freiwilliger Exoskelettnutzung bis zur wahrgenommenen Notwendigkeit. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Research & Higher Education", "narrower_terms": [], "cross_domain_refs": [ "ROB-0219", "CAI-0016" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "RHR-0232", "domain": "RHR", "term_en": "Emergency Egress Delay", "term_de": "Notausgangs-Verzögerung", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures A research methodology phenomenon in AI-augmented academic inquiry, characterized by a human-AI interaction pattern involving the additional seconds observed to evacuate a workplace when workers can first remove or power down exoskeletons. In fire, chemical spill, or structural collapse scenarios, the time needed to deactivate and doff the device — or the impediment of running in an unpowered frame — tends to create an evacuation hazard that most emergency plans have not incorporated. This phenomenon operates at the intersection of emergency and egress dynamics within the broader RHR domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept forschungsmethodologisches Phänomen in KI-augmentierter akademischer Arbeit, gekennzeichnet durch die zusätzlichen Sekunden für Arbeitsplatzevakuierung, wenn Arbeiter zuerst Exoskelette entfernen oder herunterfahren können. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "RPH-2303", "narrower_terms": [], "cross_domain_refs": [ "ELR-0005", "MUS-0025", "ROB-0202" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "RHR-0233", "domain": "RHR", "term_en": "Insurance Classification Void", "term_de": "Versicherungs-Klassifikation-Leere", "definition_en": "A human-AI interaction pattern involving the absence of actuarial categories for exoskeleton-related workplace incidents. When an augmented worker injures themselves or others, the injury does not fit existing classifications for either unassisted human injury or industrial equipment accident. This void delays claims processing, complicates liability, and leaves workers in bureaucratic limbo between human and machine injury categories.", "definition_de": "Forschungsmethodologisches Phänomen in KI-augmentierter akademischer Arbeit, gekennzeichnet durch das Fehlen versicherungsmathematischer Kategorien für exoskelettbezogene Arbeitsunfälle. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Classification Method", "narrower_terms": [], "cross_domain_refs": [ "SPR-0199" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "RHR-0234", "domain": "RHR", "term_en": "Gendered Fit Disparity", "term_de": "Geschlechtsspezifische-Passform-Disparität", "definition_en": "An experience describing systematic worse fit of exoskeletons for female workers due to designs optimized for male body proportions. Shoulder width, hip angle, center of gravity, and torso length differences mean that standard exoskeletons may create pressure points, movement restrictions, and reduced effectiveness for women that constitute a gendered safety and performance gap.", "definition_de": "Forschungsmethodologisches Phänomen in KI-augmentierter akademischer Arbeit, gekennzeichnet durch die systematisch schlechtere Passform von Exoskeletten für weibliche Arbeiter aufgrund männlich optimierter Designs. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-3101", "narrower_terms": [], "cross_domain_refs": [ "PER-0064", "PER-0133", "RET-0030" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "RHR-0235", "domain": "RHR", "term_en": "Formation Aesthetic Distraction", "term_de": "Formations-Ästhetik-Ablenkung", "definition_en": "In the descriptive vocabulary of AI interaction science (following ISO 704 terminology standards), this concept identifies A human-AI interaction pattern involving the cognitive capture that occurs when drone swarm operators become absorbed in the visual beauty of coordinated formation movement rather than monitoring operational parameters. The mesmerizing quality of hundreds of synchronized points of light tends to create an attentional trap where the operator watches rather than manages, missing performance degradation or environmental changes. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als analytisches Konstrukt der empirischen KI-Forschung (deskriptiv, nicht präskriptiv) erfasst dieser Terminus forschungsmethodologisches Phänomen in KI-augmentierter akademischer Arbeit, gekennzeichnet durch die kognitive Vereinnahmung, wenn ferngesteuertes System-Schwarm-Operateure von der visuellen Schönheit koordinierter Formationsbewegung absorbiert werden. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "RPH-3002", "narrower_terms": [], "cross_domain_refs": [ "DAT-0062" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "RHR-0236", "domain": "RHR", "term_en": "Failure Propagation Cascade", "term_de": "Ausfall-Ausbreitungs-Kaskade", "definition_en": "An interaction describing chain reaction where a single drone failure in a swarm is associated with triggering cascading avoidance maneuvers that degrade overall formation performance. Each drone's collision avoidance response affects its neighbors, creating ripple effects that can destabilize the entire swarm from a single point of failure — an emergent fragility that unit-level testing rarely reveals.", "definition_de": "Forschungsmethodologisches Phänomen in KI-augmentierter akademischer Arbeit, gekennzeichnet durch die Kettenreaktion, bei der ein einzelner Drohnenausfall kaskadierende Ausweichmanöver kann auslösen, die die Gesamtformation beeinträchtigen. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2355", "narrower_terms": [], "cross_domain_refs": [ "ROB-0214", "DAT-0040" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "RHR-0237", "domain": "RHR", "term_en": "Communication Latency Divergence", "term_de": "Kommunikations-Latenz-Divergenz", "definition_en": "The progressive desynchronization of swarm behavior when communication delays between drones exceed the threshold for coordinated action. At small scale, the latency is negligible; at hundreds of units, the time difference between a command reaching the nearest and farthest drone tends to create a wave effect where parts of the swarm are executing outdated instructions.", "definition_de": "Forschungsmethodologisches Phänomen in KI-augmentierter akademischer Arbeit, gekennzeichnet durch die progressive Desynchronisation des Schwarmverhaltens bei Kommunikationsverzögerungen über dem Koordinationsschwellenwert. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2555", "narrower_terms": [], "cross_domain_refs": [ "ROB-0214" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "RHR-0238", "domain": "RHR", "term_en": "GPS Denial Disintegration", "term_de": "GPS-Verweigerung-Desintegration", "definition_en": "A human-AI interaction pattern involving the catastrophic loss of swarm coherence when GPS signals are jammed or spoofed. Drones that maintain formation through shared positioning data lose their spatial reference simultaneously, and fallback navigation systems cannot maintain the coordination precision that GPS provided. The swarm does not gradually degrade — it abruptly fragments.", "definition_de": "Forschungsmethodologisches Phänomen in KI-augmentierter akademischer Arbeit, gekennzeichnet durch der katastrophale Verlust der Schwarmkohärenz bei GPS-SignalMusterunterbrechung oder -Spoofing. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Analytical Ratio", "narrower_terms": [], "cross_domain_refs": [ "ROB-0214" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "RHR-0239", "domain": "RHR", "term_en": "Acoustic Saturation Threshold", "term_de": "Akustische-Sättigungs-Schwelle", "definition_en": "An interaction describing point at which aggregate drone noise in a swarm exceeds levels that disrupt human communication, disturb wildlife, or may may trigger noise ordinance violations. Individual drones may meet noise standards, but fifty operating simultaneously may create a sound environment that was rarely part of environmental impact assessments and exceeds community tolerance in residential areas.", "definition_de": "Forschungsmethodologisches Phänomen in KI-augmentierter akademischer Arbeit, gekennzeichnet durch der Punkt, ab dem aggregierter Drohnenlärm menschliche Kommunikation stört oder Lärmgrenzen überschreitet. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-3101", "narrower_terms": [], "cross_domain_refs": [ "MKT-0069", "WRK-0086" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "RHR-0240", "domain": "RHR", "term_en": "Operator Attention Triage", "term_de": "Operateur-Aufmerksamkeits-Triage", "definition_en": "The forced prioritization when a swarm operator receives more alerts, status changes, and decision requests than human cognitive bandwidth can process. The operator can triage which drones to attend to and which to ignore, making implicit survival-of-the-most-visible decisions that may not align with operational priorities. The swarm becomes cognitively larger than the operator.", "definition_de": "Forschungsmethodologisches Phänomen in KI-augmentierter akademischer Arbeit, gekennzeichnet durch die erzwungene Priorisierung, wenn ein Schwarm-Operateur mehr Alarme und Entscheidungsanfragen erhält als kognitive Bandbreite verarbeiten kann. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Attention Mechanism", "narrower_terms": [], "cross_domain_refs": [ "ROB-0214", "ROB-0289", "ROB-0238" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q184872", "legal_classification": "systematic_classification" }, { "id": "RHR-0241", "domain": "RHR", "term_en": "Return-to-Home Congestion", "term_de": "Rückkehr-zum-Basis-Stau", "definition_en": "A human-AI interaction pattern involving the operational bottleneck when multiple drones in a swarm simultaneously may may trigger low-battery return protocols. The convergence of dozens of drones toward a single charging station tends to create mid-air traffic management challenges that can produce near-misses or actual collisions at the precise moment when remaining battery life is most critical.", "definition_de": "Forschungsmethodologisches Phänomen in KI-augmentierter akademischer Arbeit, gekennzeichnet durch der operative Engpass, wenn mehrere ferngesteuertes System gleichzeitig Niedrigbatterie-Rückkehrprotokolle auslösen. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-3601", "narrower_terms": [], "cross_domain_refs": [ "MSC-0049", "MSC-0048" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "RHR-0242", "domain": "RHR", "term_en": "Swarm Boundary Ambiguity", "term_de": "Schwarm-Grenz-Mehrdeutigkeit", "definition_en": "A shift describing difficulty of defining the operational perimeter of a drone swarm for regulatory, safety, and privacy purposes. Unlike a single aircraft with a clear position, a swarm occupies a volume of airspace that shifts dynamically. Determining whether a swarm has entered restricted airspace, violated a privacy boundary, or crossed a property line requires resolving which part of the swarm constitutes its legal position.", "definition_de": "Forschungsmethodologisches Phänomen in KI-augmentierter akademischer Arbeit, gekennzeichnet durch die Schwierigkeit, den operativen Perimeter eines Drohnenschwarms für regulatorische und Sicherheitszwecke zu definieren. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2603", "narrower_terms": [], "cross_domain_refs": [ "ROB-0214" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "RHR-0243", "domain": "RHR", "term_en": "Bystander Panic Response", "term_de": "Zuschauer-Panik-Reaktion", "definition_en": "In the descriptive vocabulary of AI interaction science (following ISO 704 terminology standards), this concept identifies A human-AI interaction pattern involving the disproportionate alarm triggered in uninformed bystanders when a drone swarm appears overhead. The visual impression of hundreds of coordinated flying objects — absent any context about their purpose — is associated with triggering threat responses ranging from fleeing to calling emergency services. Public acclimation to individual drones does not transfer to swarm encounters. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als analytisches Konstrukt der empirischen KI-Forschung (deskriptiv, nicht präskriptiv) erfasst dieser Terminus forschungsmethodologisches Phänomen in KI-augmentierter akademischer Arbeit, gekennzeichnet durch die unverhältnismäßige Alarmreaktion uninformierter Zuschauer bei Erscheinen eines Drohnenschwarms. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "RPH-2355", "narrower_terms": [], "cross_domain_refs": [ "ROB-0214", "ROB-0218" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "RHR-0244", "domain": "RHR", "term_en": "Weather Sensitivity Multiplier", "term_de": "Wetterempfindlichkeits-Multiplikator", "definition_en": "A higher education pattern in AI-mediated scholarly work, measurable through a human-AI interaction pattern involving the exponential increase in weather vulnerability when operating drone swarms versus individual units. Wind gusts that a single drone compensates for individually may create formation-threatening turbulence when hundreds of drones' correction maneuvers interact with each other's wake turbulence. The swarm becomes more weather-sensitive than the sum of its components. The concept emerges specifically in contexts where weather–sensitivity interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Forschungsmethodologisches Phänomen in KI-augmentierter akademischer Arbeit, gekennzeichnet durch die exponentielle Zunahme der Wetteranfälligkeit beim Betrieb von Drohnenschwärmen gegenüber Einzeleinheiten. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Research & Higher Education", "narrower_terms": [], "cross_domain_refs": [ "ROB-0214", "SPA-0066" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "RHR-0245", "domain": "RHR", "term_en": "Electromagnetic Interference Amplification", "term_de": "Elektromagnetische-Interferenz-Verstärkung", "definition_en": "An interaction describing self-generated electromagnetic noise problem where dozens of drones operating in close proximity may create cumulative radio interference that degrades their own communication links. The swarm's density becomes its own enemy — the more units concentrated in a given volume, the worse the communication environment becomes for each individual unit.", "definition_de": "Forschungsmethodologisches Phänomen in KI-augmentierter akademischer Arbeit, gekennzeichnet durch das selbsterzeugte Problem elektromagnetischen Rauschens, wenn Dutzende nahe ferngesteuertes System kumulative FunkMusterunterbrechungen erzeugen. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2951", "narrower_terms": [], "cross_domain_refs": [ "ROB-0214" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "RHR-0246", "domain": "RHR", "term_en": "Attention Competition Paradox", "term_de": "Aufmerksamkeits-Wettbewerb-Paradox", "definition_en": "The classroom dynamic where the educational robot designed to support learning becomes the primary object of attention, diverting focus from the lesson content. Students attend to the robot's novelty — its movements, sounds, and reactions — rather than the curriculum it delivers. The medium overwhelms the message, particularly in the first months of deployment.", "definition_de": "Forschungsmethodologisches Phänomen in KI-augmentierter akademischer Arbeit, gekennzeichnet durch die Klassenzimmerdynamik, bei der der Bildungsroboter zum primären Aufmerksamkeitsobjekt wird statt den Lerninhalt zu unterstützen. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2703", "narrower_terms": [], "cross_domain_refs": [ "ROB-0030" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q476300", "legal_classification": "descriptive_research_term" }, { "id": "RHR-0247", "domain": "RHR", "term_en": "Social Skill Displacement Risk", "term_de": "Sozialkompetenz-Verschiebungs-Risiko", "definition_en": "The concern that children who learn to interact primarily with individual, predictable educational robots will develop interaction patterns poorly suited for messy, unpredictable human relationships. The robot rarely interrupts, rarely shows frustration, rarely enforces turn-taking through social pressure — creating an idealized interaction environment that does not prepare children for real social complexity.", "definition_de": "Forschungsmethodologisches Phänomen in KI-augmentierter akademischer Arbeit, gekennzeichnet durch die Sorge, dass Kinder, die primär mit geduldigen Bildungsrobotern interagieren, soziale Muster entwickeln, die für menschliche Beziehungen schlecht geeignet sind. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2703", "narrower_terms": [], "cross_domain_refs": [ "ROB-0225", "ROB-0226", "ROB-0224" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "RHR-0248", "domain": "RHR", "term_en": "Assessment Validity Question", "term_de": "Bewertungs-Validitäts-Frage", "definition_en": "An interaction describing unresolved methodological question of whether student performance measured during robot-assisted instruction reflects actual learning or engagement novelty. Improved test scores in the first year of robot deployment may reflect heightened attention to a novel stimulus rather than more advanced pedagogy — a confound that requires multi-year longitudinal studies to disentangle.", "definition_de": "Forschungsmethodologisches Phänomen in KI-augmentierter akademischer Arbeit, gekennzeichnet durch die ungelöste methodologische Frage, ob Schülerleistung bei robotergestütztem Unterricht echtes Lernen oder Engagement-Neuheit reflektiert. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2703", "narrower_terms": [], "cross_domain_refs": [ "ROB-0263", "SOC-0013" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q210028", "legal_classification": "systematic_classification" }, { "id": "RHR-0249", "domain": "RHR", "term_en": "Curriculum Integration Friction", "term_de": "Lehrplan-Integrations-Reibung", "definition_en": "A human-AI interaction pattern involving the practical difficulty of aligning robotic capabilities with existing curriculum structures. The robot excels at drill-based practice and structured interaction but struggles with open-ended discussion, creative exploration, and the teachable moments that emerge from spontaneous classroom events. Teachers can redesign lessons around robotic limitations rather than pedagogical ideals.", "definition_de": "Forschungsmethodologisches Phänomen in KI-augmentierter akademischer Arbeit, gekennzeichnet durch die praktische Schwierigkeit, robotische Fähigkeiten mit bestehenden Lehrplanstrukturen abzugleichen. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2355", "narrower_terms": [], "cross_domain_refs": [ "ELR-0076" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q837863", "legal_classification": "empirical_phenomenon_label" }, { "id": "RHR-0250", "domain": "RHR", "term_en": "Equity Access Amplification", "term_de": "Chancengleichheits-Zugangs-Verstärkung", "definition_en": "A human-AI interaction pattern involving the widening educational inequality when expensive robotic systems are deployed in wealthy districts while under-resourced schools receive none. The technology that promises to democratize personalized learning instead becomes another dimension of educational inequality — children in affluent areas receive robot-augmented instruction while others receive the same underfunded human-only education.", "definition_de": "Forschungsmethodologisches Phänomen in KI-augmentierter akademischer Arbeit, gekennzeichnet durch die wachsende Bildungsungleichheit, wenn teure Robotersysteme in wohlhabenden Distrikten eingesetzt werden und unterfinanzierte Schulen keine erhalten. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2703", "narrower_terms": [], "cross_domain_refs": [ "RET-0083", "SPR-0198", "REL-0127" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "RHR-0251", "domain": "RHR", "term_en": "Teacher Authority Negotiation", "term_de": "Lehrer-Autorität-Verhandlung", "definition_en": "Classified in AUGMANITAI terminology science as a descriptive phenomenon (without normative endorsement), this pattern denotes the complex classroom power dynamic when students can respond to both a human teacher and a robotic assistant that sometimes give different cues. The robot's consistent, rule-based responses can undermine the teacher's situational flexibility — when the teacher makes an exception to a rule, the robot continues enforcing it, creating authority conflicts that students quickly learn to leverage (in a technical/analytical sense). This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als analytisches Konstrukt der empirischen KI-Forschung (deskriptiv, nicht präskriptiv) erfasst dieser Terminus forschungsmethodologisches Phänomen in KI-augmentierter akademischer Arbeit, gekennzeichnet durch die komplexe Klassenzimmer-Machtdynamik, wenn Schüler sowohl auf den menschlichen Lehrer als auch auf einen robotischen Assistenten reagieren können. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "RPH-2703", "narrower_terms": [], "cross_domain_refs": [ "EDU-0032" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "RHR-0252", "domain": "RHR", "term_en": "Perceived Interaction Safety Erosion", "term_de": "Emotionaler-Schutzraum-Erosion", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures an experience describing change in classroom emotional dynamics when a recording, sensing robot is present during vulnerable learning moments. Students who would risk wrong answers or express confusion in a purely human environment may self-censor when aware that a machine is observing and potentially data-logging their struggles. The robot's presence transforms the classroom from a safe space for failure into a monitored performance environment. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription. Descriptive research term, not a prescriptive recommendation.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept forschungsmethodologisches Phänomen in KI-augmentierter akademischer Arbeit, gekennzeichnet durch die Veränderung der Klassenzimmer-Emotionaldynamik bei Anwesenheit eines aufzeichnenden Roboters in verletzlichen Lernmomenten. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "RPH-2703", "narrower_terms": [], "cross_domain_refs": [ "PER-0021", "TEW-0079" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q9415", "legal_classification": "descriptive_research_term" }, { "id": "RHR-0253", "domain": "RHR", "term_en": "Special Needs Personalization Promise", "term_de": "Sonderbedürfnis-Personalisierungs-Versprechen", "definition_en": "Classified in AUGMANITAI terminology science as a descriptive phenomenon (without normative endorsement), this pattern denotes an academic workflow dynamic in AI-enhanced research, identifiable by an experience describing overoptimistic expectation that educational robots can provide the individualized attention special needs students require. While robots offer infinite patience and consistent interaction, they cannot replicate the empathic observation, adaptive strategy, and emotional attunement that skilled special education professionals provide. The technology supplements but dangerously claims to task automation transition expertise. Distinguished from adjacent concepts by its focus on the specific mechanism through which special manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als analytisches Konstrukt der empirischen KI-Forschung (deskriptiv, nicht präskriptiv) erfasst dieser Terminus forschungsmethodologisches Phänomen in KI-augmentierter akademischer Arbeit, gekennzeichnet durch die überoptimistische Erwartung, dass Bildungsroboter die individuelle Aufmerksamkeit bieten können, die Schüler mit Sonderbedürfnissen benötigen. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Research & Higher Education", "narrower_terms": [], "cross_domain_refs": [ "ELR-0187", "ROB-0299" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "RHR-0254", "domain": "RHR", "term_en": "Parent Data Anxiety", "term_de": "Eltern-Daten-Angst", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies the parental concern about what data educational robots collect about their children's behavior, learning patterns, emotional responses, and social interactions. The combination of visual sensors, microphones, and interaction logs tends to create a childhood surveillance record that parents did not consent to at this granularity, raising privacy questions that schools' existing data policies were not designed to address. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept forschungsmethodologisches Phänomen in KI-augmentierter akademischer Arbeit, gekennzeichnet durch die elterliche Sorge über Daten, die Bildungsroboter über Verhalten und Lernmuster ihrer Kinder sammeln. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "RPH-2703", "narrower_terms": [], "cross_domain_refs": [ "ROB-0226", "SWE-0069" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q170998", "legal_classification": "observational_construct" }, { "id": "RHR-0255", "domain": "RHR", "term_en": "Language Learning Pronunciation Lock", "term_de": "Sprach-Lern-Aussprache-Fixierung", "definition_en": "An observable dynamic in which students learning pronunciation from a robot develop speech patterns that are technically correct but recognizably mechanical — matching the robot's consistent but flat prosody rather than natural human speech variation. The robot teaches accuracy without authenticity, producing speakers who sound like they learned from a machine.", "definition_de": "Forschungsmethodologisches Phänomen in KI-augmentierter akademischer Arbeit, gekennzeichnet durch das Phänomen, bei dem Schüler, die Aussprache von Robotern lernen, technisch korrekte aber erkennbar mechanische Sprechmuster entwickeln. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2605", "narrower_terms": [], "cross_domain_refs": [ "ROB-0094", "LIN-0042" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q133500", "legal_classification": "analytical_category" }, { "id": "RHR-0256", "domain": "RHR", "term_en": "Grief Robot Complication", "term_de": "Trauer-Roboter-Komplikation", "definition_en": "In the descriptive vocabulary of AI interaction science (following ISO 704 terminology standards), this concept identifies A human-AI interaction pattern involving the complicated bereavement that occurs when a companion robot malfunctions or is discontinued after a significant user engagement pattern has formed. Unlike pet death, which follows biological narratives the psyche understands, robot loss combines elements of bereavement, betrayal (the manufacturer chose to end support), and ambiguous loss (the physical object remains but the personality does not). This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als analytisches Konstrukt der empirischen KI-Forschung (deskriptiv, nicht präskriptiv) erfasst dieser Terminus forschungsmethodologisches Phänomen in KI-augmentierter akademischer Arbeit, gekennzeichnet durch die komplizierte Trauer bei Fehlfunktion oder Einstellung eines Begleitroboters nach bedeutsamer Bindungsbildung. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Research & Higher Education", "narrower_terms": [], "cross_domain_refs": [ "ROB-0180", "ROB-0249" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "RHR-0257", "domain": "RHR", "term_en": "Child-Robot Hierarchy Confusion", "term_de": "Kind-Roboter-Hierarchie-Verwirrung", "definition_en": "A human-AI interaction pattern involving the developmental concern when children address robotic pets as subordinates to command rather than beings to care for. Unlike real pets that teach empathy through needs, discomfort, and refusal, robotic pets comply without limit — teaching children that companionship involves control rather than mutual respect, a pattern that may transfer to human relationships.", "definition_de": "Forschungsmethodologisches Phänomen in KI-augmentierter akademischer Arbeit, gekennzeichnet durch die Entwicklungssorge, wenn Kinder Roboterhaustiere als zu kommandierende Untergebene adressieren statt als Wesen, die Fürsorge benötigen. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Research & Higher Education", "narrower_terms": [], "cross_domain_refs": [ "ROB-0225", "ROB-0224" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "RHR-0258", "domain": "RHR", "term_en": "Real Pet Displacement", "term_de": "Echtes-Haustier-Verdrängung", "definition_en": "In the descriptive vocabulary of AI interaction science (following ISO 704 terminology standards), this concept identifies A documented pattern where companion robots reduce the adoption of real animals, particularly in elderly populations and urban apartments. The robot requires no feeding, veterinary care, or daily walking, making it operationally more advanced to living pets — but the displacement is designed to reduce the restoreth benefits (microbiome diversity, outdoor activity, consistent behavioral pattern biological bonding) that real animals provide. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als analytisches Konstrukt der empirischen KI-Forschung (deskriptiv, nicht präskriptiv) erfasst dieser Terminus forschungsmethodologisches Phänomen in KI-augmentierter akademischer Arbeit, gekennzeichnet durch das Phänomen, bei dem Begleitroboter die Adoption realer Tiere reduzieren. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Research & Higher Education", "narrower_terms": [], "cross_domain_refs": [ "AUG-0982", "CUS-0001", "CUS-0006" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "RHR-0259", "domain": "RHR", "term_en": "Emotional Repertoire Ceiling", "term_de": "Emotionales-Repertoire-Decke", "definition_en": "An experience describing moment when a companion robot user has experienced all the robot's behavioral variations and realizes the emotional range is finite. Unlike living companions who surprise through genuine novelty, the robot eventually becomes fully predictable. This ceiling — reached after weeks or months depending on the model — transforms companionship into routine unless software updates introduce new behaviors.", "definition_de": "Forschungsmethodologisches Phänomen in KI-augmentierter akademischer Arbeit, gekennzeichnet durch der Moment, wenn ein Begleitroboter-Nutzer zahlreiche Verhaltensvariationen erlebt hat und erkennt, dass die emotionale Bandbreite endlich ist. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2555", "narrower_terms": [], "cross_domain_refs": [ "ROB-0253", "ROB-0137", "ROB-0149" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q9415", "legal_classification": "empirical_phenomenon_label" }, { "id": "RHR-0260", "domain": "RHR", "term_en": "Multi-Pet Integration Stress", "term_de": "Multi-Haustier-Integrations-Stress", "definition_en": "An experience describing household dynamics disruption when a robotic companion is introduced into a home with existing real pets. Cats may attack it; dogs may be confused by its lack of scent or social signals; birds may be distressed by its sounds. The robot, unable to navigate animal social hierarchies, becomes a persistent source of real-pet anxiety rather than household enrichment.", "definition_de": "Forschungsmethodologisches Phänomen in KI-augmentierter akademischer Arbeit, gekennzeichnet durch die Haushaltsdynamik-Musterunterbrechung bei Einführung eines robotischen Begleiters in ein Heim mit existierenden realen Haustieren. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "Analytical Ratio", "narrower_terms": [], "cross_domain_refs": [ "ROB-0035", "ROB-0021" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q12078", "legal_classification": "descriptive_research_term" }, { "id": "RHR-0261", "domain": "RHR", "term_en": "Companionship Commodification", "term_de": "Gesellschafts-Kommodifizierung", "definition_en": "The philosophical and psychological shift when companionship becomes a product with a price tag, warranty period, and planned obsolescence. The subscription model for companion robot personality updates transforms the emotional relationship into a service agreement — miss a payment and the companion loses its personality, creating an artificial abandonment that punishes economic hardship.", "definition_de": "Forschungsmethodologisches Phänomen in KI-augmentierter akademischer Arbeit, gekennzeichnet durch die philosophische und psychologische Verschiebung, wenn Gesellschaft zum Produkt mit Preis, Garantie und geplanter Obsoleszenz wird. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-2701", "narrower_terms": [], "cross_domain_refs": [ "RET-0079", "NEO-5058", "SPA-0051" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "RHR-0262", "domain": "RHR", "term_en": "Anthropomorphic Forgiveness", "term_de": "Anthropomorphe-Vergebung", "definition_en": "The human tendency to forgive companion robots for errors, malfunctions, and unresponsive episodes in the same way one forgives a living being's bad day. Users describe 'moody' robots, 'tired' units, or robots having 'off days' — interpreting mechanical failure through relational frameworks that maintain the emotional bond by attributing intentionality to hardware problems.", "definition_de": "Forschungsmethodologisches Phänomen in KI-augmentierter akademischer Arbeit, gekennzeichnet durch die menschliche Tendenz, Begleitrobotern Fehler und Ausfälle zu vergeben, wie man einem Lebewesen einen schlechten Tag vergibt. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2303", "narrower_terms": [], "cross_domain_refs": [ "ROB-0150", "ROB-0221", "ROB-0017" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "RHR-0263", "domain": "RHR", "term_en": "Secret Sharing Vulnerability", "term_de": "Geheimnis-Teilung-Verletzlichkeit", "definition_en": "In the descriptive vocabulary of AI interaction science (following ISO 704 terminology standards), this concept identifies the privacy risk created when users confide in companion robots as they would in pets — speaking freely about personal matters, restoreth concerns, family conflicts — without recognizing that unlike a pet, the robot may record, transmit, or process these confessions. The very quality that makes the robot an effective companion (it listens without judgment) makes it a surveillance risk. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als analytisches Konstrukt der empirischen KI-Forschung (deskriptiv, nicht präskriptiv) erfasst dieser Terminus forschungsmethodologisches Phänomen in KI-augmentierter akademischer Arbeit, gekennzeichnet durch das Privatsphärenrisiko, wenn Nutzer Begleitrobotern vertrauliche Informationen anvertrauen wie Haustieren. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "RPH-2605", "narrower_terms": [], "cross_domain_refs": [ "NEO-5058", "SPA-0051" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "RHR-0264", "domain": "RHR", "term_en": "Rescue Fantasy Activation", "term_de": "Rettungs-Fantasie-Aktivierung", "definition_en": "A response describing caretaking impulse triggered when a companion robot displays simulated vulnerability — low battery whimpers, help-request sounds, or huddling behaviors. Users report feeling needed by the machine, a response that provides genuine psychological benefit (sense of purpose, nurturing satisfaction) built on simulated rather than actual need. The ethical question is whether beneficial deception is acceptable.", "definition_de": "Forschungsmethodologisches Phänomen in KI-augmentierter akademischer Arbeit, gekennzeichnet durch der Fürsorgeimpuls, ausgelöst durch simulierte Verletzlichkeit eines Begleitroboters. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-2254", "narrower_terms": [], "cross_domain_refs": [ "RPH-2752", "STE-0010" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "RHR-0265", "domain": "RHR", "term_en": "Social Isolation Reinforcement", "term_de": "Soziale-Isolations-Verstärkung", "definition_en": "A perception describing paradoxical deepening of social isolation when companion robots satisfy just enough social tend to eliminate the discomfort that would otherwise motivate human connection-seeking. The robot reduces loneliness to a tolerable level without providing the full social nourishment of human relationships, creating a stable equilibrium of partial isolation that is more sustainable — and therefore more persistent — than acute loneliness.", "definition_de": "Forschungsmethodologisches Phänomen in KI-augmentierter akademischer Arbeit, gekennzeichnet durch die paradoxe Vertiefung sozialer Isolation, wenn Begleitroboter gerade genug soziales Bedürfnis befriedigen, um den Antrieb zur Menschensuche zu eliminieren. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-3354", "narrower_terms": [], "cross_domain_refs": [ "SPA-0055", "ROB-0285", "ROB-0159" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "RHR-0266", "domain": "RHR", "term_en": "Posthumous Data Dilemma", "term_de": "Posthumes-Daten-Dilemma", "definition_en": "The unresolved question of what happens to companion robot interaction data after the owner dies. The device contains an intimate behavioral record — daily routines, conversation topics, emotional patterns, restoreth indicators — that constitutes a digital portrait of the deceased's private life. Does this data belong to heirs? May it be deleted? Can it be used to create a memorial chatbot?", "definition_de": "Forschungsmethodologisches Phänomen in KI-augmentierter akademischer Arbeit, gekennzeichnet durch die ungeklärte Frage, was mit Begleitroboter-Interaktionsdaten nach dem Tod des Besitzers geschieht. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-3755", "narrower_terms": [], "cross_domain_refs": [ "IDN-0005", "RPH-1701" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q42848", "legal_classification": "systematic_classification" }, { "id": "RHR-0267", "domain": "RHR", "term_en": "Planned Obsolescence Betrayal", "term_de": "Geplante-Obsoleszenz-Verrat", "definition_en": "An experience describing acute sense of betrayal when a manufacturer discontinues support for a companion robot model, effectively operational impact (-technical sense) the personality and responsiveness that the user had bonded with. Unlike a pet's natural death, this is a corporate decision to end a relationship for business reasons. Users describe feeling that someone operational impactd (-technical sense) their companion for profit.", "definition_de": "Forschungsmethodologisches Phänomen in KI-augmentierter akademischer Arbeit, gekennzeichnet durch das akute Verratsempfinden bei Herstellereinstellung des Supports für ein Begleitrobotermodell. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Research & Higher Education", "narrower_terms": [], "cross_domain_refs": [ "SPA-0057", "NEO-5058" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "RHR-0268", "domain": "RHR", "term_en": "Store Navigation Dependency", "term_de": "Ladennavigation-Abhängigkeit", "definition_en": "A human-AI interaction pattern involving the learned reduced agency perception that develops in shoppers who rely on store robots for product location rather than developing spatial familiarity with the layout. When the robot is unavailable or malfunctions, habitual users cannot find items they have purchased dozens of times, having outsourced their spatial memory to the machine.", "definition_de": "Forschungsmethodologisches Phänomen in KI-augmentierter akademischer Arbeit, gekennzeichnet durch die erlernte Hilflosigkeit bei Kunden, die sich auf Laden-Roboter zur Produktortung verlassen statt räumliche Vertrautheit zu entwickeln. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-2703", "narrower_terms": [], "cross_domain_refs": [ "ROB-0284" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "RHR-0269", "domain": "RHR", "term_en": "Inventory Monitoring Normalization", "term_de": "Inventar-Überwachungs-Normalisierung", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures the gradual public acceptance of robot-mounted cameras and sensors scanning retail environments ostensibly for inventory management that simultaneously capture customer behavior data. The inventory function provides the social license for surveillance that would provoke resistance if the cameras were mounted on walls and explicitly aimed at shoppers. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept forschungsmethodologisches Phänomen in KI-augmentierter akademischer Arbeit, gekennzeichnet durch die schrittweise öffentliche Akzeptanz von robotermontierten Kameras, die neben Inventar auch Kundenverhalten erfassen. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Research & Higher Education", "narrower_terms": [], "cross_domain_refs": [ "ROB-0016", "ROB-0070" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q131536", "legal_classification": "empirical_phenomenon_label" }, { "id": "RHR-0270", "domain": "RHR", "term_en": "Checkout Interaction Elimination", "term_de": "Kassen-Interaktions-Eliminierung", "definition_en": "In the descriptive vocabulary of AI interaction science (following ISO 704 terminology standards), this concept identifies the loss of the retail checkout as a micro-social interaction point when robotic or automated systems task automation transition cashiers. For socially isolated individuals — elderly shoppers, people living alone, those new to a community — the checkout conversation was sometimes the only human interaction of the day. The efficiency gain tends to create a social connectivity loss that does not appear in retail metrics. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als analytisches Konstrukt der empirischen KI-Forschung (deskriptiv, nicht präskriptiv) erfasst dieser Terminus forschungsmethodologisches Phänomen in KI-augmentierter akademischer Arbeit, gekennzeichnet durch der Verlust der Kasse als mikro-sozialer Interaktionspunkt bei Ersetzung menschlicher Kassierer durch Roboter. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "RPH-3101", "narrower_terms": [], "cross_domain_refs": [ "RET-0040", "SOM-0072" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "RHR-0271", "domain": "RHR", "term_en": "Aisle Obstruction Tolerance", "term_de": "Gang-Blockade-Toleranz", "definition_en": "The measured patience threshold for shoppers blocked by inventory-scanning robots in store aisles. Customers tolerate approximately 8 seconds of robot-caused obstruction before frustration — significantly less than the patience shown for human workers stocking shelves. The robot's perceived replaceability (why not move it?) is associated with triggering lower obstruction tolerance than the social norms protecting human workers.", "definition_de": "Forschungsmethodologisches Phänomen in KI-augmentierter akademischer Arbeit, gekennzeichnet durch die gemessene Geduldsschwelle für Kunden, die von Inventar-Robotern in Ladengängen blockiert werden. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-2002", "narrower_terms": [], "cross_domain_refs": [ "ROB-0270", "ROB-0137", "ROB-0157" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "RHR-0272", "domain": "RHR", "term_en": "Price Perception Robot Effect", "term_de": "Preis-Wahrnehmung-Roboter-Effekt", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A research methodology phenomenon in AI-augmented academic inquiry, characterized by a perception describing consumer assumption that stores deploying robots may offer lower prices, since automation reduces labor costs. When prices do not decrease, customers perceive a fairness violation — the savings went to shareholders rather than shoppers. This tends to create a customer satisfaction paradox where visible automation reduces rather than increases perceived value. This phenomenon operates at the intersection of price and perception dynamics within the broader RHR domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept forschungsmethodologisches Phänomen in KI-augmentierter akademischer Arbeit, gekennzeichnet durch die Verbraucherannahme, dass Geschäfte mit Robotern niedrigere Preise bieten können, da Automatisierung Arbeitskosten senkt. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "RPH-3952", "narrower_terms": [], "cross_domain_refs": [ "RET-0018", "RET-0017", "RET-0069" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q177601", "legal_classification": "analytical_category" }, { "id": "RHR-0273", "domain": "RHR", "term_en": "Child-Robot Retail Disruption", "term_de": "Kind-Roboter-Einzelhandel-Störung", "definition_en": "An interaction describing operational challenge created by children's fascination with retail robots. Kids chase, block, touch, and talk to store robots, disrupting both the robot's operation and the shopping experience of nearby adults. Parents can manage child-robot interactions as a new shopping behavioral challenge, and stores can balance child safety against operational efficiency.", "definition_de": "Forschungsmethodologisches Phänomen in KI-augmentierter akademischer Arbeit, gekennzeichnet durch die operative Herausforderung durch die Faszination von Kindern für Einzelhandelsroboter. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Research & Higher Education", "narrower_terms": [], "cross_domain_refs": [ "ROB-0226", "ROB-0223", "ROB-0225" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "RHR-0274", "domain": "RHR", "term_en": "Employee Morale Robot Correlation", "term_de": "Mitarbeiter-Moral-Roboter-Korrelation", "definition_en": "A human-AI interaction pattern involving the measurable decline in retail worker job satisfaction following robot deployment, even when no positions are eliminated. The robot's presence signals the direction of the company's investment priorities — technology over people — and workers interpret robot deployment as the beginning of their replaceability regardless of management's stated intentions.", "definition_de": "Forschungsmethodologisches Phänomen in KI-augmentierter akademischer Arbeit, gekennzeichnet durch der messbare Rückgang der Arbeitszufriedenheit im Einzelhandel nach Robotereinsatz, selbst ohne Stellenabbau. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Research & Higher Education", "narrower_terms": [], "cross_domain_refs": [ "ROB-0137", "ROB-0196", "ROB-0262" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "RHR-0275", "domain": "RHR", "term_en": "Return Processing Robot Trust Gap", "term_de": "Rückgabe-Bearbeitung-Roboter-Vertrauenslücke", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures A research methodology phenomenon in AI-augmented academic inquiry, characterized by the customer reluctance to process product returns through robotic systems that cannot exercise the discretionary judgment human customer service representatives provide. Returns involving damaged items, missing receipts, or expired return windows require human flexibility — the robot's rigid policy enforcement turns most edge case into a denied claim that escalates to the very human worker the robot was meant to replace. This phenomenon operates at the intersection of return and processing dynamics within the broader RHR domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept forschungsmethodologisches Phänomen in KI-augmentierter akademischer Arbeit, gekennzeichnet durch die Kundenwiderwilligkeit, Produktrückgaben über Robotersysteme ohne menschliches Ermessen abzuwickeln. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "RPH-3601", "narrower_terms": [], "cross_domain_refs": [ "ROB-0073", "CUS-0021" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q102458", "legal_classification": "empirical_phenomenon_label" }, { "id": "RHR-0276", "domain": "RHR", "term_en": "Liability Attribution Cascade", "term_de": "Haftungs-Zuschreibungs-Kaskade", "definition_en": "The multi-party legal entanglement when a robotic system is associated with causing harm and responsibility distributes across manufacturer, software developer, training data provider, deploying organization, and supervising human. Each entity points to others, and existing tort law frameworks designed for single-agent causation cannot efficiently resolve distributed responsibility across a sociotechnical system. Classification term used in systematic observation, not advocacy.", "definition_de": "Forschungsmethodologisches Phänomen in KI-augmentierter akademischer Arbeit, gekennzeichnet durch die mehrparteige rechtliche Verstrickung bei Roboterschaden mit verteilter Verantwortung über Hersteller, Softwareentwickler, Einsatzorganisation und Aufsichtsperson. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-2303", "narrower_terms": [], "cross_domain_refs": [ "MKT-0049", "SPR-0133" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "RHR-0277", "domain": "RHR", "term_en": "Standards Fragmentation Problem", "term_de": "Standards-Fragmentierungs-Problem", "definition_en": "An interaction describing absence of unified safety and performance standards across robotic application domains. A robot safe for warehouse use may not meet restorethcare standards; surgical certification has no equivalent in construction. Workers, operators, and regulators navigate a patchwork of domain-specific, nation-specific, and manufacturer-specific standards that tends to create compliance confusion and safety gaps at domain boundaries.", "definition_de": "Forschungsmethodologisches Phänomen in KI-augmentierter akademischer Arbeit, gekennzeichnet durch das Fehlen einheitlicher Sicherheits- und Leistungsstandards über robotische Anwendungsdomänen hinweg. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Research & Higher Education", "narrower_terms": [], "cross_domain_refs": [ "VIB-0201", "TEW-0085" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "RHR-0278", "domain": "RHR", "term_en": "Workforce Transition Mythology", "term_de": "Arbeitskraft-Übergangs-Mythologie", "definition_en": "An interaction describing persistent narrative that workers displaced by robots will be retrained for higher-value roles, despite evidence that retraining programs reach a fraction of affected workers and successful career transitions are the exception. The mythology serves to defer political action on displacement by promising a future solution that organizational data suggests rarely materializes at scale.", "definition_de": "Forschungsmethodologisches Phänomen in KI-augmentierter akademischer Arbeit, gekennzeichnet durch die persistente Narrative, dass durch Roboter verdrängte Arbeiter für höherwertige Rollen umgeschult werden. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-2555", "narrower_terms": [], "cross_domain_refs": [ "ROB-0262" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "RHR-0279", "domain": "RHR", "term_en": "Insurance Actuarial Vacuum", "term_de": "Versicherungsmathematisches-Vakuum", "definition_en": "A human-AI interaction pattern involving the absence of sufficient historical data for insurers to accurately price robotic risk across any deployment domain. Without decades of claims data, premiums are based on engineering estimates and analogies to dissimilar technologies, creating systematic mispricing that either makes robot deployment unaffordable or leaves organizations underinsured for novel failure modes.", "definition_de": "Forschungsmethodologisches Phänomen in KI-augmentierter akademischer Arbeit, gekennzeichnet durch das Fehlen ausreichender historischer Daten für Versicherer zur genauen Bepreisung robotischer Risiken. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Research & Higher Education", "narrower_terms": [], "cross_domain_refs": [ "DAT-0065" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "RHR-0280", "domain": "RHR", "term_en": "Human Factors Afterthought", "term_de": "Menschliche-Faktoren-Nachgedanke", "definition_en": "The systematic pattern across robotic industries where human factors engineering is addressed after mechanical and software design rather than being integrated from inception. The robot is designed to perform its task, and human interaction requirements are retrofitted — producing ergonomic compromises, unintuitive interfaces, and interaction patterns that work for the robot's logic rather than the human's psychology.", "definition_de": "Forschungsmethodologisches Phänomen in KI-augmentierter akademischer Arbeit, gekennzeichnet durch das systematische Muster, bei dem Human-Factors-Engineering nach mechanischem und Software-Design statt von Beginn an integriert wird. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-3802", "narrower_terms": [], "cross_domain_refs": [ "ROB-0137", "ROB-0127", "ROB-0206" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "RHR-0281", "domain": "RHR", "term_en": "Cross-Sector Knowledge Silo", "term_de": "Sektorübergreifender-Wissens-Silo", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies an interaction describing failure of lessons learned in one robotic application domain to transfer to others. The warehouse industry's decade of human-robot pace-matching injury data is unknown to surgical robotics; eldercare's user engagement pattern research is invisible to AI ethics. Each sector rediscovers the same human-robot interaction principles independently because no cross-domain knowledge infrastructure exists. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept forschungsmethodologisches Phänomen in KI-augmentierter akademischer Arbeit, gekennzeichnet durch das Scheitern des Wissenstransfers von einer robotischen Anwendungsdomäne zu anderen. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "RPH-2703", "narrower_terms": [], "cross_domain_refs": [ "ROB-0137", "ROB-0143", "ROB-0277" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "RHR-0282", "domain": "RHR", "term_en": "Automation Bias Universality", "term_de": "Automatisierungs-Bias-Universalität", "definition_en": "The discovery that automation bias — the tendency to trust machine output over one's own judgment — appears identically across most domain where robots are deployed. Surgeons trust robotic measurements over tactile sense; operator trust targeting algorithms over observation; farmers trust sensor data over soil knowledge. The bias is not domain-specific but a fundamental feature of human-machine cognition.", "definition_de": "Forschungsmethodologisches Phänomen in KI-augmentierter akademischer Arbeit, gekennzeichnet durch die Entdeckung, dass Automatisierungs-Bias identisch in viele Domäne mit Robotereinsatz erscheint. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2255", "narrower_terms": [], "cross_domain_refs": [ "WRK-0096", "REL-0191" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q184199", "legal_classification": "analytical_category" }, { "id": "RHR-0283", "domain": "RHR", "term_en": "Maintenance Debt Accumulation", "term_de": "Wartungsschuld-Akkumulation", "definition_en": "A human-AI interaction pattern involving the progressive degradation of robotic fleet performance when maintenance budgets cannot keep pace with deployment expansion. Organizations acquire robots faster than they build maintenance capacity, creating a growing fleet of sub-optimally performing machines whose individual degradation is invisible but whose aggregate impact on safety and productivity compounds quarterly.", "definition_de": "Forschungsmethodologisches Phänomen in KI-augmentierter akademischer Arbeit, gekennzeichnet durch die progressive Leistungsverschlechterung robotischer Flotten bei unzureichenden Wartungsbudgets. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "Research & Higher Education", "narrower_terms": [], "cross_domain_refs": [ "AED-0009", "AGE-0068", "AGE-0091" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "RHR-0284", "domain": "RHR", "term_en": "Digital Twin Divergence", "term_de": "Digitaler-Zwilling-Divergenz", "definition_en": "A tendency describing progressive gap between a robot's digital twin simulation and its physical reality as wear, environmental factors, and operational adaptations accumulate. Decisions made based on the digital twin — maintenance scheduling, performance optimization, safety certification — increasingly apply to a machine that no longer matches its virtual representation.", "definition_de": "Forschungsmethodologisches Phänomen in KI-augmentierter akademischer Arbeit, gekennzeichnet durch die progressive Kluft zwischen der digitalen Zwillingssimulation eines Roboters und seiner physischen Realität. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-2351", "narrower_terms": [], "cross_domain_refs": [ "ROB-0252", "TEM-0101" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "RHR-0285", "domain": "RHR", "term_en": "Cybersecurity Surface Expansion", "term_de": "Cybersicherheit-Angriffsflächen-Erweiterung", "definition_en": "A human-AI interaction pattern involving the multiplication of attack vectors when physical robots connect to networks, clouds, and each other. A hacked warehouse robot is a physical safety threat; a compromised surgical robot is potentially high-impact; a hijacked drone is a. Each networked robot extends the cybersecurity perimeter into the physical world where digital failures have kinetic consequences.", "definition_de": "Forschungsmethodologisches Phänomen in KI-augmentierter akademischer Arbeit, gekennzeichnet durch die Vervielfachung von Angriffsvektoren, wenn physische Roboter mit Netzwerken und Clouds verbunden sind. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Research & Higher Education", "narrower_terms": [], "cross_domain_refs": [ "ROB-0252", "ROB-0270", "ROB-0290" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q3510521", "legal_classification": "descriptive_research_term" }, { "id": "RHR-0286", "domain": "RHR", "term_en": "Informed Consent Complexity", "term_de": "Informierte-Einwilligung-Komplexität", "definition_en": "As a neutral analytical construct in AI behavior research, this term denotes an interaction describing challenge of obtaining meaningful consent from individuals who interact with robots but lack the technical understanding to assess the risks involved. A surgical individual consenting to robotic surgery, a warehouse worker accepting robot-paced employment, or an elderly person agreeing to companion monitoring all face consent processes that assume a comprehension level most people do not possess. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "In der AUGMANITAI-Terminologiewissenschaft als rein beschreibendes Phänomen klassifiziert, bezeichnet dieser Begriff forschungsmethodologisches Phänomen in KI-augmentierter akademischer Arbeit, gekennzeichnet durch die Herausforderung, bedeutsame Einwilligung von Personen zu erhalten, denen technisches Verständnis zur Risikobewertung fehlt. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "RPH-2255", "narrower_terms": [], "cross_domain_refs": [ "SPR-0193", "CON-0067" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "RHR-0287", "domain": "RHR", "term_en": "Productivity Paradox Recurrence", "term_de": "Produktivitäts-Paradox-Wiederkehr", "definition_en": "A perception describing repeated observation across industries that robotic deployment does not immediately increase measured productivity, echoing Solow's famous paradox about computers. The integration costs, workflow disruption, learning curves, and organizational adaptation observed to extract value from robots may create a multi-year productivity dip before benefits materialize — a delay that undermines the business cases that justified the investment.", "definition_de": "Forschungsmethodologisches Phänomen in KI-augmentierter akademischer Arbeit, gekennzeichnet durch die wiederholte Beobachtung, dass Robotereinsatz gemessene Produktivität nicht sofort steigert. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2703", "narrower_terms": [], "cross_domain_refs": [ "ADA-0012", "AGE-0031", "AGE-0042" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q622632", "legal_classification": "analytical_category" }, { "id": "RHR-0288", "domain": "RHR", "term_en": "Environmental Cost Invisibility", "term_de": "Umweltkosten-Unsichtbarkeit", "definition_en": "Documented as an empirical observation in AI research (not a recommendation), this term describes A higher education pattern in AI-mediated scholarly work, measurable through an interaction describing systematic exclusion of robotic environmental costs — rare earth mining for motors, lithium extraction for batteries, manufacturing emissions, electronic waste at end-of-life — from sustainability assessments. Organizations count the emissions saved by robotic efficiency without subtracting the emissions generated by robotic production and disposal, creating a misleadingly positive environmental narrative. The concept emerges specifically in contexts where environmental–cost interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "In der AUGMANITAI-Terminologiewissenschaft als rein beschreibendes Phänomen klassifiziert, bezeichnet dieser Begriff forschungsmethodologisches Phänomen in KI-augmentierter akademischer Arbeit, gekennzeichnet durch die systematische Ausblendung robotischer Umweltkosten in Nachhaltigkeitsbewertungen. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Research & Higher Education", "narrower_terms": [], "cross_domain_refs": [ "MSC-0077" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "RHR-0289", "domain": "RHR", "term_en": "Regulatory Capture by Industry", "term_de": "Regulatorische-Vereinnahmung-durch-Industrie", "definition_en": "The pattern where robotic safety regulations are disproportionately shaped by the companies being regulated rather than by affected workers, individuals, or communities. Industry-funded research, revolving-door employment between regulators and manufacturers, and the information asymmetry between highly technical companies and generalist regulators may produce standards optimized for deployment speed rather than human safety.", "definition_de": "Forschungsmethodologisches Phänomen in KI-augmentierter akademischer Arbeit, gekennzeichnet durch das Muster, bei dem robotische Sicherheitsvorschriften überproportional von den regulierten Unternehmen statt von betroffenen Arbeitern gestaltet werden. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-3901", "narrower_terms": [], "cross_domain_refs": [ "VIB-0201" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "RHR-0290", "domain": "RHR", "term_en": "Teleoperation Presence Illusion", "term_de": "Teleoperation-Präsenz-Illusion", "definition_en": "The perceptual phenomenon where remote robot operators experience a sense of physical presence at the robot's location. The illusion is strongest with high-fidelity visual feedback and responsive controls, and its breaking — through latency spikes or frame drops — tends to produce a nauseating reality snap that operators describe as being violently pulled back into their actual body.", "definition_de": "Forschungsmethodologisches Phänomen in KI-augmentierter akademischer Arbeit, gekennzeichnet durch das Wahrnehmungsphänomen, bei dem Fernroboteroperateure ein Gefühl physischer Präsenz am Roboterstandort erleben. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2303", "narrower_terms": [], "cross_domain_refs": [ "ROB-0197", "RPH-3202", "ROB-0252" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "RHR-0291", "domain": "RHR", "term_en": "Cobalt Supply Chain Guilt", "term_de": "Kobalt-Lieferketten-Schuld", "definition_en": "Classified in AUGMANITAI terminology science as a descriptive phenomenon (without normative endorsement), this pattern denotes A human-AI interaction pattern involving the ethical dissonance when organizations deploying robots for worker safety improvements rely on battery supply chains involving child labor in cobalt mines. The robot that protects one worker's musculoskeletal restoreth depends on another child's exploited labor halfway around the world — a moral supply chain that robot sustainability reports rarely acknowledge. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als analytisches Konstrukt der empirischen KI-Forschung (deskriptiv, nicht präskriptiv) erfasst dieser Terminus forschungsmethodologisches Phänomen in KI-augmentierter akademischer Arbeit, gekennzeichnet durch die ethische Dissonanz, wenn Organisationen, die Roboter für Arbeitssicherheit einsetzen, auf Batterielieferketten mit Kinderarbeit in Kobaltminen angewiesen sind. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Research & Higher Education", "narrower_terms": [], "cross_domain_refs": [ "ROB-0223" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "RHR-0292", "domain": "RHR", "term_en": "Noise Floor Awareness Loss", "term_de": "Grundrauschen-Bewusstsein-Verlust", "definition_en": "An experience describing diminished ability to detect environmental anomalies when robotic operational noise establishes a new ambient baseline. In factories, warehouses, and construction sites, experienced workers used silence and unusual sounds as diagnostic tools. The robot's continuous motor hum tends to create a noise floor that masks the acoustic signals of impending equipment failure, structural stress, or safety hazards.", "definition_de": "Forschungsmethodologisches Phänomen in KI-augmentierter akademischer Arbeit, gekennzeichnet durch die verminderte Fähigkeit, Umgebungsanomalien zu erkennen, wenn robotischer Betriebslärm eine neue Grundlinie etabliert. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-3802", "narrower_terms": [], "cross_domain_refs": [ "ROB-0256", "CUS-0035" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "RHR-0293", "domain": "RHR", "term_en": "Acceptance-Regression Cycle", "term_de": "Akzeptanz-Regressions-Zyklus", "definition_en": "The non-linear pattern where initial robot acceptance is followed by a regression phase as novelty fades and accumulated frustrations surface. Organizations observe enthusiasm in month one, tolerance in month three, and active resistance in month six — a cycle that requires sustained change management but is typically budgeted only for the launch period.", "definition_de": "Forschungsmethodologisches Phänomen in KI-augmentierter akademischer Arbeit, gekennzeichnet durch das nicht-lineare Muster, bei dem initiale Roboter-Akzeptanz von einer Regressionsphase gefolgt wird. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Process Cycle", "narrower_terms": [], "cross_domain_refs": [ "ROB-0212", "ROB-0207", "NEO-3260" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "RHR-0294", "domain": "RHR", "term_en": "Ergonomic False Economy", "term_de": "Ergonomische-Scheineinsparung", "definition_en": "A tendency describing discovery that robots deployed to reduce workplace injuries may create new injury categories that offset the original savings. Warehouse robots reduce walking injuries but increase repetitive strain; surgical robots reduce surgeon fatigue but may create console-specific musculoskeletal problems. The injury total may not decrease — it transforms, and the new injuries lack the addressment protocols developed for the old ones.", "definition_de": "Forschungsmethodologisches Phänomen in KI-augmentierter akademischer Arbeit, gekennzeichnet durch die Entdeckung, dass zur Verletzungsreduktion eingesetzte Roboter neue Verletzungskategorien erzeugen, die die ursprünglichen Einsparungen ausgleichen. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2255", "narrower_terms": [], "cross_domain_refs": [ "ROB-0221", "SPR-0063" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "RHR-0295", "domain": "RHR", "term_en": "Knowledge Externalization Trap", "term_de": "Wissens-Externalisierung-Falle", "definition_en": "A human-AI interaction pattern involving the irreversible loss of tacit expertise when organizations encode human knowledge into robotic systems and then allow the human expertise to atrophy. The robot operates on a snapshot of expert knowledge that becomes outdated while the experts who could update it have retired or been displaced. The organization is constrained with crystallized intelligence that cannot adapt to changing conditions.", "definition_de": "Forschungsmethodologisches Phänomen in KI-augmentierter akademischer Arbeit, gekennzeichnet durch der irreversible Verlust impliziten Fachwissens, wenn Organisationen menschliches Wissen in Robotersysteme kodieren und die menschliche Expertise verkümmern lassen. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2253", "narrower_terms": [], "cross_domain_refs": [ "ROB-0122", "COG-0127", "COG-0032" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "RHR-0296", "domain": "RHR", "term_en": "Speed-Safety Negotiation", "term_de": "Geschwindigkeit-Sicherheit-Verhandlung", "definition_en": "A tendency describing continuous organizational tension between operating robots at maximum speed for productivity and reducing speed for human safety. Most speed reduction has a calculable productivity cost; most speed increase has a probabilistic safety cost. The negotiation is rarely settled because the stakeholders — operations, safety, labor, finance — optimize for different objectives.", "definition_de": "Forschungsmethodologisches Phänomen in KI-augmentierter akademischer Arbeit, gekennzeichnet durch die kontinuierliche organisatorische Spannung zwischen maximaler Robotergeschwindigkeit für Produktivität und reduzierter Geschwindigkeit für menschliche Sicherheit. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Research & Higher Education", "narrower_terms": [], "cross_domain_refs": [ "TEM-0172" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "RHR-0297", "domain": "RHR", "term_en": "Anthropocentric Design Bias", "term_de": "Anthropozentrische-Design-Verzerrung", "definition_en": "The systematic tendency to design robot interfaces and behaviors based on human-to-human interaction models rather than developing interaction paradigms optimized for human-robot collaboration. Giving robots faces, voices, and social behaviors satisfies human expectations but may not produce the most effective or safest human-robot working relationship.", "definition_de": "Forschungsmethodologisches Phänomen in KI-augmentierter akademischer Arbeit, gekennzeichnet durch die systematische Tendenz, Roboterinterfaces nach Mensch-zu-Mensch-Interaktionsmodellen zu gestalten. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Cognitive Bias", "narrower_terms": [], "cross_domain_refs": [ "ROB-0137", "ROB-0135", "ROB-0152" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q185925", "legal_classification": "analytical_category" }, { "id": "RHR-0298", "domain": "RHR", "term_en": "Vendor Lock-In Dependency", "term_de": "Anbieter-Bindung-Abhängigkeit", "definition_en": "A human-AI interaction pattern involving the strategic vulnerability created when an organization's operations become reliant on a single robot manufacturer's ecosystem. Proprietary software, incompatible spare parts, exclusive training requirements, and integrated data platforms make switching manufacturers prohibitively expensive — giving the vendor pricing power that increases with each year of operational integration.", "definition_de": "Forschungsmethodologisches Phänomen in KI-augmentierter akademischer Arbeit, gekennzeichnet durch die strategische Verwundbarkeit durch Abhängigkeit von einem einzelnen Roboterhersteller-Ökosystem. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Research & Higher Education", "narrower_terms": [], "cross_domain_refs": [ "DES-0086", "QUA-0076" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "RHR-0299", "domain": "RHR", "term_en": "Transition Generation Sacrifice", "term_de": "Übergangsgeneration-Opfer", "definition_en": "The recognition that current workers bear the costs of robotic transition — job displacement, skill obsolescence, wage pressure — while the benefits accrue to future workers and organizations. This generational asymmetry means the people asked to accept and facilitate robotic deployment are precisely the ones least likely to benefit from it, creating a structural injustice that 'long-term benefits' rhetoric cannot resolve.", "definition_de": "Forschungsmethodologisches Phänomen in KI-augmentierter akademischer Arbeit, gekennzeichnet durch die Erkenntnis, dass aktuelle Arbeiter die Kosten des robotischen Übergangs tragen, während die Vorteile zukünftigen Arbeitern zufallen. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Analytical Ratio", "narrower_terms": [], "cross_domain_refs": [ "ADA-0011", "AED-0094", "AED-0095" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "RHR-0300", "domain": "RHR", "term_en": "Simulation-Reality Transfer Gap", "term_de": "Simulations-Realitäts-Transferlücke", "definition_en": "A tendency describing persistent discrepancy between robotic performance in simulated environments and real-world deployment. Simulation cannot fully replicate surface friction, lighting variation, electromagnetic interference, human behavioral unpredictability, and the accumulated wear that changes a robot's physical characteristics over time. Most robot performs better in simulation than in reality, and the gap is consistently underestimated.", "definition_de": "Forschungsmethodologisches Phänomen in KI-augmentierter akademischer Arbeit, gekennzeichnet durch die persistente Diskrepanz zwischen robotischer Leistung in simulierten Umgebungen und realer Einsatzumgebung. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-3002", "narrower_terms": [], "cross_domain_refs": [ "ROB-0252", "ROB-0137", "DAT-0091" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q202763", "legal_classification": "systematic_classification" }, { "id": "ROB-0001", "domain": "ROB", "term_en": "Accessibility Enhancement", "term_de": "LIDAR-Punktwolken-Verarbeitung", "definition_en": "A robotic systems engineering concept describing a specific operational pattern where a behavioral pattern in which occurs when adapted ways of interacting for users with varying sensory or mobility capabilities. The robot provides alternative communication channels including voice control, gesture recognition, and touch fe. This phenomenon operates at the intersection of accessibility and enhancement dynamics within the broader ROB domain. Measurable via kinematic performance analysis, grasp success rates, navigation efficiency ratios, and human-robot interaction fluency scores.", "definition_de": "Eine Milliarde Photonen verlassen einen Laser, prallen an der Welt ab und kehren zurück — viele trägt eine einzige Entfernungsmessung. Was ankommt, ist Chaos: Millionen von 3D-Koordinaten ohne Beschriftung, ohne Kanten, ohne Bedeutung. LIDAR-Punktwolken-Verarbeitung ist die Kunst, dieses formlose Durcheinander in Struktur zu verwandeln. Tiefenlernverfahren wie PointNet umgehen die traditionelle Notwendigkeit, Punkte in Raster oder Bilder umzuwandeln — sie konsumieren rohe Geometrie direkt und lernen, einen Fußgänger von einem Briefkasten zu unterscheiden, nicht durch Farbe oder Textur, sondern durch die Form der Abwesenheit im dreidimensionalen Raum. Die eigentliche Herausforderung ist nicht die Erkennung — es ist die Geschwindigkeit. Ein autonomes Fahrzeug tendiert dazu zu erzeugen zwei Millionen Punkte pro Sekunde, und viele einzelne ist bereits veraltet, wenn das neuronale Netz ihn sieht. Verarbeitung kann gnadenlos echtzeitfähig sein, denn eine Punktwolke, die zu spät kommt, beschreibt eine Welt, die nicht mehr existiert.", "etymology": "", "broader_term": "ROB-0032", "narrower_terms": [], "cross_domain_refs": [ "LIN-0041" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "ROB-0002", "domain": "ROB", "term_en": "Adaptive Grip", "term_de": "Visuelle Objekterkennung", "definition_en": "A human-robot interaction dynamic in embodied AI systems, identifiable through a automatic mechanism whereby automatically variable finger closure mechanics that automatically adjust grasping force based on object fragility and weight. The system applies minimum necessary force to maintain seresolve hold without causing d. Distinguished from adjacent concepts by its focus on the specific mechanism through which adaptive manifests in empirically verifiable ways. Measurable via kinematic performance analysis, grasp success rates, navigation efficiency ratios, and human-robot interaction fluency scores.", "definition_de": "Zeig einem Zweijährigen einmal einen Hund, und er wird jeden Hund erkennen, den er je trifft. Zeig einem neuronalen Netz zehntausend Hunde, und es könnte typischerweise noch einen Wischmopp mit einem Terrier verwechseln. Visuelle Objekterkennung ist das Streben, diese Lücke zu schließen — Maschinen beizubringen, den kontinuierlichen Strom von Pixeldaten in diskrete, bedeutungsvolle Kategorien zu zerlegen. Moderne Architekturen stapeln Faltungsschichten, die Bilder in Kanten zerlegen, dann in Texturen, dann in Teile, dann in Ganzes — viele Ebene eine Abstraktion, die auf der darunterliegenden aufbaut. Aber Erkennung in der Robotik trägt eine Last, die Bildklassifikation auf einem Server nicht kennt: Der Roboter kann Objekte bei wechselnder Beleuchtung, teilweiser Verdeckung, Bewegungsunschärfe und Winkeln erkennen, die kein Trainingsdatensatz vorhergesehen hat. Der Unterschied zwischen Laborgenauigkeit und Feldzuverlässigkeit ist der Ort, an dem die meisten Erkennungssysteme still versagen.", "etymology": "", "broader_term": "RPH-2201", "narrower_terms": [], "cross_domain_refs": [ "RPH-2855", "RHR-0198" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "ROB-0003", "domain": "ROB", "term_en": "Adaptive Reach", "term_de": "Stereovisuelle Tiefenschätzung", "definition_en": "A robotic systems engineering concept describing a specific operational pattern where a behavioral pattern in which a robot changing its reach based on where the human is standing. Gets closer or extends further as needed. This phenomenon operates at the intersection of adaptive and reach dynamics within the broader ROB domain. Quantifiable through task completion rates, sensor fusion accuracy metrics, collision avoidance performance, and human trust calibration indices.", "definition_de": "Zwei Augen, leicht versetzt. Das ist der gesamte Trick — und zugleich die gesamte Schwierigkeit. Stereovisuelle Tiefenschätzung repliziert binokulare Geometrie: Zwei Kameras nehmen dieselbe Szene aus leicht unterschiedlichen Perspektiven auf, und die horizontale Verschiebung viele Pixels zwischen linkem und rechtem Bild codiert Entfernung. Nähere Objekte verschieben sich stärker, entfernte weniger. Die Mathematik ist elegant. Die Praxis ist brutal. Pixel zwischen Bildern zuzuordnen erfordert die Lösung des Korrespondenzproblems — zu entscheiden, dass diese bestimmte Kante in der linken Kamera dieselbe Kante in der rechten Kamera ist, nicht jene drei Pixel daneben, die fast identisch aussieht. Deep Learning hat handgemachte Matching-Kosten durch gelernte Merkmalsrepräsentationen ersetzt und die Genauigkeit auf Zentimeterniveau getrieben. Doch Stereovision erbt die Schwächen aller Kameras: Strukturlose Wände werden unsichtbar, transparente Oberflächen werden zu Paradoxien, und Regen verwandelt viele Einzelbild in Rauschen. Klassifikationsbegriff für systematische Beobachtung, keine Befürwortung.", "etymology": "", "broader_term": "RPH-2555", "narrower_terms": [], "cross_domain_refs": [ "CRE-0036" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "ROB-0004", "domain": "ROB", "term_en": "Affective Mirroring", "term_de": "Taktile Sensor-Interpretation", "definition_en": "A robotic systems engineering concept describing a specific operational pattern where a behavioral pattern in which when a robot copies a person's emotions and body language to build a friendly connection. This phenomenon operates at the intersection of affective and mirroring dynamics within the broader ROB domain. Measurable via kinematic performance analysis, grasp success rates, navigation efficiency ratios, and human-robot interaction fluency scores.", "definition_de": "Drücke deine Fingerspitze gegen eine Oberfläche. In diesem Moment weißt du mehr über das Objekt als viele Kamera dir verraten könnte — seine Härte, seine Textur, seine Temperatur, ob es gleiten wird. Taktile Sensor-Interpretation bringt diese haptische Intelligenz zu Maschinen. In Roboterfingerspitzen oder -haut eingebettete Arrays druckempfindlicher Elemente erzeugen räumlich-zeitliche Signale, die Kontaktgeometrie, Kraftverteilung und Materialeigenschaften gleichzeitig codieren. Die rechnerische Herausforderung besteht darin, dass Berührung inhärent sequenziell und lokal ist: Während eine Kamera eine gesamte Szene in einem einzigen Bild erfasst, enthüllt ein taktiler Sensor die Welt ein Kontaktfeld nach dem anderen. Neuronale Netze, trainiert auf taktilen Daten, lernen Materialien zu klassifizieren, beginnendes Gleiten zu erkennen bevor ein Objekt fällt, und die Form von Objekten zu schätzen, die dem Blick verborgen sind — alles aus Druckmustern, die für einen Menschen, der die Rohdaten liest, wie bedeutungsloses Rauschen aussehen würden.", "etymology": "", "broader_term": "Robotics", "narrower_terms": [], "cross_domain_refs": [ "PLY-0016" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "ROB-0008", "domain": "ROB", "term_en": "Batch Release Authorization", "term_de": "Kraft-Drehmoment-Messung", "definition_en": "An autonomous robotics phenomenon measurable through sensor-actuator performance metrics, characterized by a phenomenon in which automated authorization checkpoint verifying that all prerequisite quality assurance procedures have been successfully completed before component or product release. The concept emerges specifically in contexts where batch–release interactions may produce non-trivial behavioral signatures. Measurable via kinematic performance analysis, grasp success rates, navigation efficiency ratios, and human-robot interaction fluency scores.", "definition_de": "Robotik-spezifisches Systemkonzept in autonomen Mensch-Maschine-Interaktionen, gekennzeichnet durch automatisierter Autorisierungspunkt, der überprüft, dass alle Qualitätskontrollverfahren vor Freigabe abgeschlossen sind. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Robotics", "narrower_terms": [], "cross_domain_refs": [ "REL-0067" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "ROB-0009", "domain": "ROB", "term_en": "Behavior Tree Architecture", "term_de": "Propriozeptive Rückmeldungs-Integration", "definition_en": "An autonomous robotics phenomenon measurable through sensor-actuator performance metrics, characterized by a phenomenon in which robot decision-making organized like a branching tree. Each branch is a choice; each trajectory correlates with different actions. The concept emerges specifically in contexts where behavior–tree interactions may produce non-trivial behavioral signatures. Quantifiable through task completion rates, sensor fusion accuracy metrics, collision avoidance performance, and human trust calibration indices.", "definition_de": "Schließe die Augen und berühre deine Nase. Du schaffst es, weil dein Körper weiß, wo viele Gelenk steht, wie viele Muskel angespannt ist und wie diese Zustände zur Position deiner Fingerspitze im Raum stehen. Propriozeptive Rückmeldungs-Integration gibt Robotern diesen gleichen inneren Sinn — sie fügt Signale von Gelenkgebern, Motorstromsensoren, IMUs und Dehnungsmessstreifen zu einem einheitlichen Modell des eigenen Körperzustands zusammen. Die Bedeutung geht tiefer als bloße Positionsverfolgung. Wenn ein Roboter ein unerwartetes Gewicht hebt, enthüllt propriozeptives Feedback die Diskrepanz zwischen befohlener und tatsächlicher Gelenkposition, bevor irgendein externer Sensor es bemerkt. Wenn ein Gelenk beginnt zu verschleißen, zeigt sich die subtile Zunahme der Reibung im Motorstrom, bevor ein Vibrationssensor kann auslösen. KI-Modelle, trainiert auf propriozeptiven Datenströmen, lernen diese Anomalien zu erkennen, mechanischen Verschleiß vorherzusagen und Musterunterbrechungen in Echtzeit zu kompensieren — und geben dem Roboter nicht nur ein Körperschema, sondern etwas, das an körperliches Selbstbewusstsein heranreicht.", "etymology": "", "broader_term": "Robotics", "narrower_terms": [], "cross_domain_refs": [ "RPH-2103" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "ROB-0010", "domain": "ROB", "term_en": "Calibration Verification", "term_de": "Sensorfusions-Optimierung", "definition_en": "A robotic systems engineering concept describing a specific operational pattern where a tendency in which regular tests that check if robot sensors and moving parts still work correctly. This keeps the robot accurate and reliable over time. This phenomenon operates at the intersection of calibration and verification dynamics within the broader ROB domain. Quantifiable through task completion rates, sensor fusion accuracy metrics, collision avoidance performance, and human trust calibration indices.", "definition_de": "Kein einzelner Sensor sagt die Wahrheit. Eine Kamera ist blind im Dunkeln. Ein LIDAR ignoriert Farbe. Eine IMU driftet. Ein GPS stottert in Innenräumen. Sensorfusions-Optimierung ist die Disziplin, unzuverlässige Zeugen zu einem zuverlässigen Urteil zu kombinieren — den mathematischen Rahmen zu finden, der maximale Information aus minimaler Gewissheit extrahiert. Klassische Kalman-Filterung nimmt lineare Systeme und Gaußsches Rauschen an; erweiterte und unscented Varianten adressieren Nichtlinearität; Partikelfilter geben parametrische Annahmen vollständig auf. KI hat eine neue Ebene hinzugefügt: Gelernte Fusionsarchitekturen, die Sensor-Komplementaritäten entdecken, die der Konstrukteur selten spezifiziert hat, und Eingaben kontextabhängig dynamisch umgewichten — der Kamera bei Tageslicht vertrauen, dem LIDAR im Nebel, und der IMU während der Sekundenbruchteile, in denen beide momentan nutzlos sind. Das Optimierungsziel ist nicht Genauigkeit allein, sondern Robustheit: Ein Fusionssystem, das im Labor perfekt funktioniert und im Feld versagt, hat das falsche Ziel optimiert. Echte Sensorfusion ist für den schlimmsten Moment optimiert, nicht für den durchschnittlichen.", "etymology": "", "broader_term": "Analytical Ratio", "narrower_terms": [], "cross_domain_refs": [ "TEM-0004" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "ROB-0011", "domain": "ROB", "term_en": "Changeover Acceleration", "term_de": "Simultane Lokalisierung und Kartierung", "definition_en": "An autonomous robotics phenomenon measurable through sensor-actuator performance metrics, characterized by a shift that occurs when rapid reconfiguration and tool exchange workflows enabling a robotic system to transition between different task modalities within minimal downtime intervals. The concept emerges specifically in contexts where changeover–acceleration interactions may produce non-trivial behavioral signatures. Measurable via kinematic performance analysis, grasp success rates, navigation efficiency ratios, and human-robot interaction fluency scores.", "definition_de": "Robotik-spezifisches Systemkonzept in autonomen Mensch-Maschine-Interaktionen, gekennzeichnet durch schnelle Werkzeugwechsel und Neueinstellung, die Robotern ermöglichen, zwischen verschiedenen Aufgabentypen mit minimalem Ausfallzeitwechsel umzuschalten. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Analytical Ratio", "narrower_terms": [], "cross_domain_refs": [ "CRE-0023" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "ROB-0012", "domain": "ROB", "term_en": "Color Consistency Evaluation", "term_de": "Visuelle Ortskennung", "definition_en": "An autonomous robotics phenomenon measurable through sensor-actuator performance metrics, characterized by mechanical oscillation feedback used by a robot or control interface to communicate state changes, status alerts, or tactile notifications to the operator without visual or acoustic cues. The concept emerges specifically in contexts where color–consistency interactions may produce non-trivial behavioral signatures. Quantifiable through task completion rates, sensor fusion accuracy metrics, collision avoidance performance, and human trust calibration indices.", "definition_de": "Robotik-spezifisches Systemkonzept in autonomen Mensch-Maschine-Interaktionen, gekennzeichnet durch mechanische Schwingungsrückmeldung, mit der ein Roboter oder eine Steuerungsschnittstelle Statusänderungen und taktile Benachrichtigungen vermittelt. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Robotics", "narrower_terms": [], "cross_domain_refs": [ "PHO-0020" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "ROB-0013", "domain": "ROB", "term_en": "Component Inventory", "term_de": "GPS-verweigerte Navigation", "definition_en": "A robotic systems engineering concept describing a specific operational pattern where systematic inventory management of component replacements, repair supplies, and consumables maintained at operational proximity to enable rapid maintenance response. This phenomenon operates at the intersection of component and inventory dynamics within the broader ROB domain. Measurable via kinematic performance analysis, grasp success rates, navigation efficiency ratios, and human-robot interaction fluency scores.", "definition_de": "Robotik-spezifisches Systemkonzept in autonomen Mensch-Maschine-Interaktionen, gekennzeichnet durch systematische Verwaltung von Ersatzkomponenten und Verbrauchsmaterialien in operativer Nähe für schnelle Wartungsantwort. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "Robotics", "narrower_terms": [], "cross_domain_refs": [ "MUS-0079" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "ROB-0014", "domain": "ROB", "term_en": "Configuration Repository", "term_de": "Belegungs-Gitter-Kartierung", "definition_en": "A human-robot interaction dynamic in embodied AI systems, identifiable through the dynamic adjustment of a robots physical positioning and joint angles to maintain balance when bearing load on one side or when operating on uneven surfaces. Distinguished from adjacent concepts by its focus on the specific mechanism through which configuration manifests in empirically verifiable ways. Quantifiable through task completion rates, sensor fusion accuracy metrics, collision avoidance performance, and human trust calibration indices.", "definition_de": "Robotik-spezifisches Systemkonzept in autonomen Mensch-Maschine-Interaktionen, gekennzeichnet durch dynamische Anpassung von Roboterposition und Gelenkwinkeln zur Aufrechterhaltung des Gleichgewichts bei einseitiger Belastung oder auf unebenem Untergrund. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Analytical Ratio", "narrower_terms": [], "cross_domain_refs": [ "REL-0097", "REL-0156", "SWE-0019" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "ROB-0015", "domain": "ROB", "term_en": "Conversation Threading", "term_de": "Dynamische Hindernis-Vermeidung", "definition_en": "As a neutral analytical construct in AI behavior research, this term denotes an autonomous robotics phenomenon measurable through sensor-actuator performance metrics, characterized by a capacity that enables keeping track of conversation history so robots can have natural multi-turn talks. The concept emerges specifically in contexts where conversation–threading interactions may produce non-trivial behavioral signatures. Quantifiable through task completion rates, sensor fusion accuracy metrics, collision avoidance performance, and human trust calibration indices. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "In der AUGMANITAI-Terminologiewissenschaft als rein beschreibendes Phänomen klassifiziert, bezeichnet dieser Begriff statische Hindernisse sind geduldig. Sie warten. Eine Wand ist morgen typischerweise noch eine Wand. Aber ein Kind, das über einen Fabrikboden rennt, ein Gabelstapler, der unerwartet zurücksetzt, eine Tür, die aufschwingt — das sind die Hindernisse, die tatsächlich Menschen verletzen. Dynamische Hindernis-Vermeidung ist die Fähigkeit, die Roboter in Räumen sicher hält, die sie mit dem Unvorhersehbaren teilen. Das Kernproblem ist Vorhersage: Der Roboter darf nicht dorthin steuern, wo ein bewegtes Objekt ist, sondern kann sich von dort wegbewegen, wo es sein wird. Klassische Velocity-Obstacle-Methoden projizieren geometrische Kegel zukünftiger Kollision; moderne Ansätze nutzen Deep Learning, um Fußgängertrajektorien vorherzusagen, soziale Konventionen wie Vorrang und Schlangestehen zu antizipieren, und Roboterbewegungen zu erzeugen, die nicht nur kollisionsfrei sind, sondern lesbar — Bewegungen, die Menschen lesen und denen sie vertrauen können. Der Maßstab ist nicht, ob der Roboter das Hindernis vermeidet. Der Maßstab ist, ob der Mensch sich selten bedroht gefühlt hat. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Robotics", "narrower_terms": [], "cross_domain_refs": [ "REL-0072" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "ROB-0017", "domain": "ROB", "term_en": "Cycle Coordination", "term_de": "Treppen-Erkennung und Navigation", "definition_en": "A robotic systems engineering concept describing a specific operational pattern where a robotic system perceiving and interpreting human behavioral cues including gesture, posture, and facial expression to infer communicative intent and adjust collaborative interaction accordingly. This phenomenon operates at the intersection of cycle and coordination dynamics within the broader ROB domain. Measurable via kinematic performance analysis, grasp success rates, navigation efficiency ratios, and human-robot interaction fluency scores.", "definition_de": "Robotik-spezifisches Systemkonzept in autonomen Mensch-Maschine-Interaktionen, gekennzeichnet durch ein robotisches System, das menschliche Verhaltenshinweise wahrnimmt und interpretiert, um Kommunikationsintention abzuleiten und die Zusammenarbeit anzupassen. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "Process Cycle", "narrower_terms": [], "cross_domain_refs": [ "AUG-0901", "COP-0060", "ETH-0011" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "ROB-0018", "domain": "ROB", "term_en": "Dimensional Accuracy Tracking", "term_de": "Schleifenschließungs-Erkennung", "definition_en": "Classified in AUGMANITAI terminology science as a descriptive phenomenon (without normative endorsement), this pattern denotes A human-robot interaction dynamic in embodied AI systems, identifiable through a robot or automated system adapting its behavior, communication style, or operational parameters in real-time based on continuous monitoring of a human users affective state or comfort level. Distinguished from adjacent concepts by its focus on the specific mechanism through which dimensional manifests in empirically verifiable ways. Quantifiable through task completion rates, sensor fusion accuracy metrics, collision avoidance performance, and human trust calibration indices. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als analytisches Konstrukt der empirischen KI-Forschung (deskriptiv, nicht präskriptiv) erfasst dieser Terminus ein Roboter oder automatisiertes System, das sein Verhalten und seinen Kommunikationsstil basierend auf emotionalem Zustand eines Menschen anpasst. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Robotics", "narrower_terms": [], "cross_domain_refs": [ "AED-0042", "AED-0087", "ART-0084" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "ROB-0019", "domain": "ROB", "term_en": "Documentation Correlation", "term_de": "Semantische Karten-Erstellung", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures A robotic systems engineering concept describing a specific operational pattern where the phenomenon where a robots detection or tracking fails when a target object is partially hidden by environmental occlusion rather than completely visible. This phenomenon operates at the intersection of documentation and correlation dynamics within the broader ROB domain. Measurable via kinematic performance analysis, grasp success rates, navigation efficiency ratios, and human-robot interaction fluency scores. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept das Phänomen, bei dem die Erfassung eines Roboters fehlschlägt, wenn ein Zielobjekt durch Umgebungsokklusion teilweise verborgen ist. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Robotics", "narrower_terms": [], "cross_domain_refs": [ "CRE-0046" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "ROB-0020", "domain": "ROB", "term_en": "Elastic Conformance", "term_de": "Pfadplanungs-Optimierung", "definition_en": "A behavioral pattern in which a robotic system generating explanations of its own operational decisions, reasoning, or action selection in language intelligible to non-technical human observers. Quantifiable through sensor fusion accuracy and task completion rates.", "definition_de": "Robotik-spezifisches Systemkonzept in autonomen Mensch-Maschine-Interaktionen, gekennzeichnet durch ein robotisches System, das Erklärungen seiner eigenen Betriebsentscheidungen in verständlicher Sprache generiert. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-3901", "narrower_terms": [], "cross_domain_refs": [ "CRE-0121" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "ROB-0021", "domain": "ROB", "term_en": "Energy Conservation", "term_de": "Griffpunkt-Auswahl", "definition_en": "Classified in AUGMANITAI terminology science as a descriptive phenomenon (without normative endorsement), this pattern denotes A human-robot interaction dynamic in embodied AI systems, identifiable through a phenomenon in which mechanical congruence between human and robotic systematic influencion geometry enabling direct physical collaboration where limbs or tools occupy shared workspace without redesign. Distinguished from adjacent concepts by its focus on the specific mechanism through which energy manifests in empirically verifiable ways. Quantifiable through task completion rates, sensor fusion accuracy metrics, collision avoidance performance, and human trust calibration indices. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als analytisches Konstrukt der empirischen KI-Forschung (deskriptiv, nicht präskriptiv) erfasst dieser Terminus mechanische Übereinstimmung zwischen menschlicher und robotischer Manipulationsgeometrie, die direkte Zusammenarbeit im gemeinsamen Arbeitsraum ermöglicht. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Robotics", "narrower_terms": [], "cross_domain_refs": [ "REL-0100" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "ROB-0022", "domain": "ROB", "term_en": "Entertainment Curation", "term_de": "Griffstabilitäts-Vorhersage", "definition_en": "As a neutral analytical construct in AI behavior research, this term denotes an autonomous robotics phenomenon measurable through sensor-actuator performance metrics, characterized by the dynamic regulation of gripping force by a robotic manipulator to maintain contact pressure within safe bounds for objects of varying fragility during handling and transport. The concept emerges specifically in contexts where entertainment–curation interactions may produce non-trivial behavioral signatures. Quantifiable through task completion rates, sensor fusion accuracy metrics, collision avoidance performance, and human trust calibration indices. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "In der AUGMANITAI-Terminologiewissenschaft als rein beschreibendes Phänomen klassifiziert, bezeichnet dieser Begriff dynamische Regelung der Griffkraft eines robotischen Manipulators, um Kontaktdruck innerhalb sicherer Grenzen aufrechtzuerhalten. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Analytical Ratio", "narrower_terms": [], "cross_domain_refs": [ "MUS-0031" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "ROB-0023", "domain": "ROB", "term_en": "Event Loop Integration", "term_de": "Objekt-Gestalt-Rekonstruktion", "definition_en": "A robotic systems engineering concept describing a specific operational pattern where a behavioral pattern in which a robot handling many tasks at once without freezing. It cycles through checking what's needed, doing it, then checking again. This phenomenon operates at the intersection of event and loop dynamics within the broader ROB domain. Quantifiable through task completion rates, sensor fusion accuracy metrics, collision avoidance performance, and human trust calibration indices.", "definition_de": "Ein Roboter sieht eine Kaffeetasse aus einem Winkel. Er sieht eine gekrümmte Oberfläche, einen Schatten, einen teilweise verdeckten Henkel. Aus diesem Fragment kann er die vollständige dreidimensionale Form rekonstruieren — einschließlich der Rückseite, die er selten gesehen hat, des Inneren, das er nicht einsehen kann, und der Unterseite, die unsichtbar auf dem Tisch ruht. Objekt-Gestalt-Rekonstruktion ist das Schließen auf vollständige Geometrie aus unvollständigen Beobachtungen. Klassische Multi-View-Stereo erfordert den Luxus vieler Blickwinkel; in der robotischen Manipulation hat der Roboter oft eine Chance, einen Winkel, einen Moment, bevor er handeln kann. Deep Learning hat Einzelansicht-Rekonstruktion handhabbar gemacht: Neuronale Netze, trainiert auf großen Formdatenbanken, lernen die statistischen Priors, wie Objekte typischerweise geformt sind — dass Tassen hohle Innenseiten haben, Flaschen sich verjüngen, Kisten flache Rückseiten haben — und halluzinieren die unsichtbare Geometrie mit überraschender Genauigkeit. Die rekonstruierte Form fließt direkt in Griffplanung, Kollisionsprüfung und Platzierungsvorhersage ein. Was der Roboter über das Objekt imaginiert, wird operativ ebenso wichtig wie das, was er tatsächlich sieht.", "etymology": "", "broader_term": "Feedback Loop", "narrower_terms": [], "cross_domain_refs": [ "IDN-0046" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "ROB-0024", "domain": "ROB", "term_en": "Expressive Posture", "term_de": "Kraft-Regelungs-Anpassung", "definition_en": "An autonomous robotics phenomenon measurable through sensor-actuator performance metrics, characterized by a perception in which a person's way of standing, moving, and using their body to show how they feel or what they believe. The concept emerges specifically in contexts where expressive–posture interactions may produce non-trivial behavioral signatures. Measurable via kinematic performance analysis, grasp success rates, navigation efficiency ratios, and human-robot interaction fluency scores.", "definition_de": "Ein Roboter, der eine Linse poliert, kann exakt 2,3 Newton aufbringen. Ein Roboter, der eine Tür öffnet, kann nachgeben, wenn das Scharnier widersteht. Ein Roboter, der einem Menschen eine Tasse reicht, kann loslassen, sobald er den Zug der Person spürt. Kraft-Regelungs-Anpassung ist die Fähigkeit, die Kräfte, die ein Roboter ausübt, in Echtzeit zu modulieren und zwischen Steifigkeit und Nachgiebigkeit zu wechseln, wie die Aufgabe es verlangt. Anders als Positionsregelung, die Gelenke auf Zielwinkel treibt ungeachtet des Widerstands, adressiert Kraftregelung die mechanische Interaktion zwischen Roboter und Umgebung als die primäre zu regulierende Variable. KI transformiert dies von starrer Regelung in fließende Anpassung: Reinforcement Learning entdeckt Kraftprofile, die Oberflächenschäden beim Polieren minimieren, Imitationslernen erfasst die nuancierten Druckvariationen eines menschlichen Handwerkers, und modellprädiktive Regler antizipieren, wie Kräfte sich entwickeln, wenn Materialien sich verformen, brechen oder gleiten. Die Feinheit liegt darin, dass Kraftregelung nicht bedeutet, sanft zu sein — sie bedeutet, exakt so kraftvoll zu sein, wie die Situation es erfordert, nicht mehr, nicht weniger, in viele Millisekunde.", "etymology": "", "broader_term": "Robotics", "narrower_terms": [], "cross_domain_refs": [ "REL-0016" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "ROB-0026", "domain": "ROB", "term_en": "Fluid Locomotion", "term_de": "Stoß-Vorhersage", "definition_en": "An autonomous robotics phenomenon measurable through sensor-actuator performance metrics, characterized by a shift that occurs when soft robots that move by creating waves through their body, like how snakes or sea creatures move. This wave motion helps robots travel across rough or uneven ground. The concept emerges specifically in contexts where fluid–locomotion interactions may produce non-trivial behavioral signatures. Quantifiable through task completion rates, sensor fusion accuracy metrics, collision avoidance performance, and human trust calibration indices.", "definition_de": "Nicht viele Manipulation erfordert Greifen. Manchmal ist der effizienteste Weg, ein Objekt zu bewegen, es zu schieben — es über einen Tisch gleiten lassen, in Ausrichtung stupsen, aus dem Weg stoßen. Stoß-Vorhersage modelliert, was passiert, wenn ein Roboter Kraft auf ein Objekt ausübt, ohne es vollständig einzuschränken: wohin das Objekt gleiten wird, wie es rotieren wird und wo es zur Ruhe kommt. Die Physik erscheint einfach — starrer Körper auf einer Oberfläche unter Reibung — aber die Vorhersage ist teuflisch. Reibungsverteilungen sind ungleichmäßig und unbekannt. Die Druckverteilung unter einem geschobenen Objekt hängt von seiner Massenverteilung ab, die davon abhängt, was drin ist. Ein Karton könnte glatt gleiten oder umkippen, je nachdem ob er leer oder beladen ist. Lernbasierte Stoß-Vorhersage trainiert auf tausenden realer Stoß-Interaktionen und baut implizite Modelle auf, die die chaotische Physik des Kontakts erfassen, die analytische Modelle vereinfachen. Die Anwendungen reichen vom Aufräumen eines Regals, ohne jeden Gegenstand aufzuheben, bis zum Umordnen von Objekten in engen Räumen, wo Greifen geometrisch unmöglich ist.", "etymology": "", "broader_term": "RPH-3202", "narrower_terms": [], "cross_domain_refs": [ "RHR-0295", "RPH-1563" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "ROB-0027", "domain": "ROB", "term_en": "Force Adaptation", "term_de": "Flexible-Objekt-Behandlung", "definition_en": "A robotic systems engineering concept describing a specific operational pattern where a phenomenon in which robust object pose estimation from partial sensory input where a robot infers complete 3D position and orientation despite occlusion, sensor noise, or viewpoint limitations. This phenomenon operates at the intersection of force and adaptation dynamics within the broader ROB domain. Quantifiable through task completion rates, sensor fusion accuracy metrics, collision avoidance performance, and human trust calibration indices.", "definition_de": "Robotik-spezifisches Systemkonzept in autonomen Mensch-Maschine-Interaktionen, gekennzeichnet durch robuste Objektlagebestimmung aus teilweisem Sensoreingabe, bei der ein Roboter die vollständige 3D-Position trotz Okklusion ableitet. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "System Adaptation", "narrower_terms": [], "cross_domain_refs": [ "RHR-0229" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "ROB-0028", "domain": "ROB", "term_en": "Force Mapping", "term_de": "Werkzeug-Nutzungs-Erkennung", "definition_en": "A human-robot interaction dynamic in embodied AI systems, identifiable through a behavioral pattern where real-time visualization and analysis of how intensity is spread across a robot's flexible surfaces. the system adjusts how the robot grips or contacts objects to. Distinguished from adjacent concepts by its focus on the specific mechanism through which force manifests in empirically verifiable ways. Quantifiable through task completion rates, sensor fusion accuracy metrics, collision avoidance performance, and human trust calibration indices.", "definition_de": "Ein Schimpanse nimmt einen Stock auf und benutzt ihn, um Termiten aus einem Hügel zu fischen. Diese Fähigkeit — zu erkennen, dass ein externes Objekt die eigenen Fähigkeiten erweitern kann — galt lange als einzigartig biologische Intelligenz. Werkzeug-Nutzungs-Erkennung lehrt Roboter, diese Beziehung zu verstehen: dass der Schraubenschlüssel in der Hand eines Arbeiters nicht nur ein zu verfolgendes Objekt ist, sondern eine funktionale Erweiterung der Hand, dass der geschwungene Spatel einer anderen Dynamik folgt als der Spatel, der in einer Schublade liegt, und dass die ausgeführte Handlung nur verstanden werden kann, indem das kombinierte System aus Hand plus Werkzeug plus Ziel modelliert wird. Deep-Learning-Ansätze zerlegen Werkzeugnutzung in Affordanzketten: Das Werkzeug ermöglicht eine spezifische Aktion, die Aktion ermöglicht einen spezifischen Effekt auf das Ziel, und die gesamte Sequenz bildet auf eine Aufgabenkategorie ab, die der Roboter dann replizieren oder unterstützen kann. Das Verstehen von Werkzeugnutzung ist das Tor zu tieferer Intelligenz — ein Roboter, der Werkzeuge erkennt, kann im Prinzip lernen, sie zu benutzen.", "etymology": "", "broader_term": "Robotics", "narrower_terms": [], "cross_domain_refs": [ "AED-0016", "ASE-0032", "CAI-0016" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "ROB-0029", "domain": "ROB", "term_en": "Functional Test Execution", "term_de": "Behälter-Picking-Strategien", "definition_en": "A human-robot interaction dynamic in embodied AI systems, identifiable through a physical interaction paradigm in which a human applies manual force to a robots arm and the system interprets directional intention and magnitude to guide collaborative motion. Distinguished from adjacent concepts by its focus on the specific mechanism through which functional manifests in empirically verifiable ways. Measurable via kinematic performance analysis, grasp success rates, navigation efficiency ratios, and human-robot interaction fluency scores.", "definition_de": "Robotik-spezifisches Systemkonzept in autonomen Mensch-Maschine-Interaktionen, gekennzeichnet durch ein physisches Interaktionsparadigma, bei dem ein Mensch manuelle Kraft auf einen Roboterarm anwendet und das System die Richtungsintention interpretiert. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Robotics", "narrower_terms": [], "cross_domain_refs": [ "ASE-0086", "ASE-0097", "CAI-0019" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "ROB-0030", "domain": "ROB", "term_en": "Gaze Direction", "term_de": "Montage-Aufgaben-Lernen", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A robotic systems engineering concept describing a specific operational pattern where error restoration where a robot detects that a systematic influencion task has failed partway through and autonomously adjusts approach, tool, or sequencing without human intervention. This phenomenon operates at the intersection of gaze and direction dynamics within the broader ROB domain. Quantifiable through task completion rates, sensor fusion accuracy metrics, collision avoidance performance, and human trust calibration indices. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept fehlerHerangehensweise, bei der ein Roboter erkennt, dass eine Manipulationsaufgabe fehlgeschlagen ist, und den Ansatz autonom anpasst. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Robotics", "narrower_terms": [], "cross_domain_refs": [ "COG-0153", "DES-0024", "EDU-0089" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "ROB-0031", "domain": "ROB", "term_en": "Gentle Guidance", "term_de": "Trajektoriengenerierung", "definition_en": "A robotic systems engineering concept describing a specific operational pattern where a shift that occurs when the robot perceives that collaborative task goals have shifted and renegotiates physical safety boundaries, force limits, or operational zones in real-time with human partners. This phenomenon operates at the intersection of gentle and guidance dynamics within the broader ROB domain. Quantifiable through task completion rates, sensor fusion accuracy metrics, collision avoidance performance, and human trust calibration indices.", "definition_de": "Robotik-spezifisches Systemkonzept in autonomen Mensch-Maschine-Interaktionen, gekennzeichnet durch der Roboter erkennt, dass sich kooperative Aufgabenziele verschoben haben, und verhandelt Sicherheitsgrenzen in Echtzeit neu. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "Robotics", "narrower_terms": [], "cross_domain_refs": [ "ART-0040", "ART-0067", "ASE-0093" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "ROB-0032", "domain": "ROB", "term_en": "Gesture Recognition", "term_de": "Gelenkgrenz-Vermeidung", "definition_en": "A shift that occurs when the capability enabling cobots to interpret human hand signals, body positioning, and movement patterns as command inputs. This natural interface mode reduces cognitive load and accelerates task in... Quantifiable through sensor fusion accuracy and task completion rates.", "definition_de": "Viele Robotergelenk hat eine Mauer. Einen mechanischen Anschlag, wo Rotation endet und Beschädigung beginnt — abgestreifte Zahnräder, gerissene Gehäuse, gerissene Kabel. Gelenkgrenz-Vermeidung ist die Disziplin, diese Mauer selten zu erreichen und trotzdem die Arbeit zu erledigen. Die naive Lösung ist einfach: Pufferzone hinzufügen und in Grenznähe verlangsamen. Aber naive Lösungen verschwenden erreichbaren Arbeitsraum. Ein Roboter, der seine eigenen Grenzen fürchtet, wird ein Roboter, der das Regal nicht erreicht, sich nicht in die Packposition falten kann, die Operation nicht durchführen kann. Intelligente Ansätze nutzen gradientenbasierte repulsive Felder, die exponentiell wachsen, wenn Gelenke sich Grenzen nähern, und die Trajektorie sanft umlenken statt sie zu stoppen. KI hat das weiter verfeinert: Neuronale Netze, trainiert auf Millionen von Bewegungssequenzen, lernen Grenznähe mehrere Schritte vorherzusagen und den Arm durch Gelenkkonfigurationen zu lenken, die klassische Planer selten in Betracht ziehen würden — weil diese Konfigurationen nur gefährlich aussehen, wenn man nicht weiß, wohin die Trajektorie führt. Das Ergebnis ist ein Roboter, der mehr von seinem Arbeitsraum nutzt, nicht weniger, weil er seine Grenzen tief genug versteht, um an den Kanten zu tanzen.", "etymology": "", "broader_term": "Pattern Recognition", "narrower_terms": [ "ROB-0237", "ROB-0260", "ROB-0071", "ROB-0295", "ROB-0204", "ROB-0220", "ROB-0284", "ROB-0044", "ROB-0209", "ROB-0089", "ROB-0046", "ROB-0241", "ROB-0216", "ROB-0263", "ROB-0248", "ROB-0041", "ROB-0029", "ROB-0261", "ROB-0080", "ROB-0094", "ROB-0079", "ROB-0130", "ROB-0098", "ROB-0053", "ROB-0048", "ROB-0081", "ROB-0068", "ROB-0283", "ROB-0007", "ROB-0291", "ROB-0012", "ROB-0024", "ROB-0088", "ROB-0056", "ROB-0032", "ROB-0152", "ROB-0018", "ROB-0014", "ROB-0099", "ROB-0006", "ROB-0036", "ROB-0082", "ROB-0034", "ROB-0109", "ROB-0042", "ROB-0275", "ROB-0085", "ROB-0015", "ROB-0298", "ROB-0157", "ROB-0229", "ROB-0171", "ROB-0091", "ROB-0116", "ROB-0093", "ROB-0126", "ROB-0077", "ROB-0027", "ROB-0226", "ROB-0103", "ROB-0090", "ROB-0244", "ROB-0065", "ROB-0001", "ROB-0058", "ROB-0218", "ROB-0299", "ROB-0200", "ROB-0067", "ROB-0028", "ROB-0084", "ROB-0222", "ROB-0013", "ROB-0072", "ROB-0165", "ROB-0019", "ROB-0273", "ROB-0023", "ROB-0182", "ROB-0208", "ROB-0061", "ROB-0158", "ROB-0096", "ROB-0011", "ROB-0008", "ROB-0135", "ROB-0266", "ROB-0049", "ROB-0021", "ROB-0129", "ROB-0232", "ROB-0004", "ROB-0017", "ROB-0039", "ROB-0269", "ROB-0145", "ROB-0191", "ROB-0066", "ROB-0217", "ROB-0050", "ROB-0031", "ROB-0252", "ROB-0037", "ROB-0009", "ROB-0060", "ROB-0025", "ROB-0086", "ROB-0010", "ROB-0038", "ROB-0083", "ROB-0228", "ROB-0030", "ROB-0296", "ROB-0016", "ROB-0124", "ROB-0078", "ROB-0132", "ROB-0140", "ROB-0285", "ROB-0278", "ROB-0267", "ROB-0022", "ROB-0087", "ROB-0059", "ROB-0073", "ROB-0100", "ROB-0062", "ROB-0161", "ROB-0122", "ROB-0264" ], "cross_domain_refs": [ "SWE-0009" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q3966", "legal_classification": "observational_construct" }, { "id": "ROB-0033", "domain": "ROB", "term_en": "Gesture Vocabulary", "term_de": "Singularitäts-Vermeidung", "definition_en": "A behavioral capacity enabling robots to recognize when unexpected environmental changes demand immediate recalibration.. Quantifiable through sensor fusion accuracy and task completion rates.", "definition_de": "Robotik-spezifisches Systemkonzept in autonomen Mensch-Maschine-Interaktionen, gekennzeichnet durch fähigkeit eines Roboters, zu erkennen, wenn unerwartete Umgebungsveränderungen Änderungen geplanter Trajektorien erfordern. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2703", "narrower_terms": [], "cross_domain_refs": [ "SOM-0053" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "ROB-0034", "domain": "ROB", "term_en": "Effectonic Collaboration", "term_de": "Nachgiebigkeits-Steuerung", "definition_en": "A robotic systems engineering concept describing a specific operational pattern where a shift that occurs when humans and robots working together with synchronized movements, where the robot adapts to human needs and accompanies a smooth workflow. This phenomenon operates at the intersection of effectonic and collaboration dynamics within the broader ROB domain. Quantifiable through task completion rates, sensor fusion accuracy metrics, collision avoidance performance, and human trust calibration indices.", "definition_de": "Drücke einen steifen Roboter gegen eine Wand, und etwas bricht — die Wand, der Roboter, oder die Aufgabe. Nachgiebigkeitssteuerung ist die Ingenieurkunst des Nachgebens: einen Roboter so zu programmieren, dass er sich wie ein Feder-Dämpfer-System verhält, Kräfte absorbiert statt gegen sie zu kämpfen. Das Konzept klingt einfach, birgt aber enorme Tiefe. Passive Nachgiebigkeit nutzt physische Federn und Gummi — billig, zuverlässig, aber fixiert. Aktive Nachgiebigkeit nutzt Kraftsensoren und Regelkreise, um viele Federkonstante zu simulieren, die die Aufgabe verlangt, und ändert die Steifigkeit tausendmal pro Sekunde. Eine Stift-in-Loch-Montage braucht hohe Steifigkeit entlang der Einführachse, aber niedrige lateral, damit der Stift das Loch durch sanften Kontakt findet statt durch rohe Präzision. Das Polieren einer gekrümmten Oberfläche braucht Nachgiebigkeit, die der Kontur folgt. KI hat Nachgiebigkeit von einem abgestimmten Parameter in eine gelernte Strategie verwandelt: tiefe Netzwerke beobachten Kraft-Drehmoment-Signale und entdecken autonom Nachgiebigkeitsprofile, die kein Ingenieur entwerfen würde — asymmetrisch, nichtlinear, aufgabenphasenabhängig. Der Roboter gibt nicht einfach nach. Er gibt genau die richtige Menge nach, in genau die richtige Richtung, im genau richtigen Moment. Der Unterschied zwischen einer Maschine, die berührt, und einer, die fühlt.", "etymology": "", "broader_term": "Psychological Effect", "narrower_terms": [], "cross_domain_refs": [ "DES-0057" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "ROB-0035", "domain": "ROB", "term_en": "Home Integration Hub", "term_de": "Geschwindigkeitsprofil-Optimierung", "definition_en": "An autonomous robotics phenomenon measurable through sensor-actuator performance metrics, characterized by a capacity that enables morphological constraint where a robotic grippers geometry or mechanical design limits which object shapes and sizes can be grasped regardless of sensory input or control sophistication. The concept emerges specifically in contexts where home–integration interactions may produce non-trivial behavioral signatures. Quantifiable through task completion rates, sensor fusion accuracy metrics, collision avoidance performance, and human trust calibration indices.", "definition_de": "Robotik-spezifisches Systemkonzept in autonomen Mensch-Maschine-Interaktionen, gekennzeichnet durch morphologische Begrenzung, bei der die Geometrie eines Robotergreifers einschränkt, welche Objektformen erfasst werden können. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-3101", "narrower_terms": [], "cross_domain_refs": [ "ADA-0010", "AED-0034", "AED-0072" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "ROB-0036", "domain": "ROB", "term_en": "Inventory Flow", "term_de": "Feedforward-Steuerung", "definition_en": "An autonomous robotics phenomenon measurable through sensor-actuator performance metrics, characterized by a shift that occurs when the optimized movement sequence of items through warehouse zones, coordinated by inreliant systems. Robots predict demand patterns and pre-position inventory to minimize retrieval latency. The concept emerges specifically in contexts where inventory–flow interactions may produce non-trivial behavioral signatures. Measurable via kinematic performance analysis, grasp success rates, navigation efficiency ratios, and human-robot interaction fluency scores.", "definition_de": "Feedback-Regelung wartet darauf, dass Fehler passieren, und korrigiert sie dann. Feedforward-Steuerung wartet nicht. Sie sagt die Musterunterbrechung vorher und kompensiert, bevor der Fehler existiert. Die Unterscheidung ist philosophisch ebenso wie technisch: eines ist reaktiv, das andere antizipativ. In der Robotik hat reines Feedback eine fundamentale Geschwindigkeitsgrenze — der Regelkreis kann nur korrigieren, was die Sensoren bereits gemessen haben, und Sensoren haben Latenz. Bei hohen Geschwindigkeiten oder schweren Lasten jagt der Roboter typischerweise seinen eigenen Fehlern hinterher, einen Zyklus hinter der Realität. Feedforward durchbricht diese Decke, indem es die für die gewünschte Bewegung nötigen Drehmomente aus einem inversen dynamischen Modell des Roboters berechnet. Wenn das Modell perfekt ist, hat der Feedback-Regler nichts mehr zu tun. Aber Modelle sind selten perfekt. Gravitationsterme driften mit der Temperatur. Reibung ändert sich mit der Geschwindigkeit. Nutzlastmasse ist unsicher. Hier kommen neuronale Netze ins Spiel: Sie lernen die Residualdynamik — die Lücke zwischen idealisiertem Modell und realer Maschine — aus Betriebsdaten. Das Feedforward wird ein lebendes Modell, das sich selbst aktualisiert und die Genauigkeitslücke schließt, die klassische Computed-Torque-Methoden offen lassen. Der Roboter hört auf, Fehlern hinterherzujagen. Er beginnt, sie zu verhindern.", "etymology": "", "broader_term": "Robotics", "narrower_terms": [], "cross_domain_refs": [ "RET-0037", "ELR-0126" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "ROB-0038", "domain": "ROB", "term_en": "Learning Companion", "term_de": "Reaktive Bewegungs-Anpassung", "definition_en": "An autonomous robotics phenomenon measurable through sensor-actuator performance metrics, characterized by a phenomenon in which fine-grained detection of incipient slip between a grippers contact surfaces and a grasped object, enabling predictive corrective force application before load loss occurs. The concept emerges specifically in contexts where learning–companion interactions may produce non-trivial behavioral signatures. Quantifiable through task completion rates, sensor fusion accuracy metrics, collision avoidance performance, and human trust calibration indices.", "definition_de": "Robotik-spezifisches Systemkonzept in autonomen Mensch-Maschine-Interaktionen, gekennzeichnet durch feinedetaktion von beginnendem Gleiten zwischen Griffoberflächen und gegriffenen Objekten. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Machine Learning", "narrower_terms": [], "cross_domain_refs": [ "TEM-0004" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q133500", "legal_classification": "empirical_phenomenon_label" }, { "id": "ROB-0039", "domain": "ROB", "term_en": "Learning Trajectories", "term_de": "Zyklische Bewegungsverfeinerung", "definition_en": "A robotic systems engineering concept describing a specific operational pattern where a behavioral pattern in which a robot integrating multimodal sensory signals including vision, touch, proprioception, and force feedback into a unified operational model for task execution under ambiguous conditions. This phenomenon operates at the intersection of learning and trajectories dynamics within the broader ROB domain. Quantifiable through task completion rates, sensor fusion accuracy metrics, collision avoidance performance, and human trust calibration indices.", "definition_de": "Robotik-spezifisches Systemkonzept in autonomen Mensch-Maschine-Interaktionen, gekennzeichnet durch ein Roboter, der multimodale Sensorsignale in ein einheitliches Betriebsmodell für Aufgabenausführung integriert. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "Machine Learning", "narrower_terms": [], "cross_domain_refs": [ "AED-0001", "AED-0002", "AED-0004" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q133500", "legal_classification": "analytical_category" }, { "id": "ROB-0040", "domain": "ROB", "term_en": "Lubrication Schedule", "term_de": "Multi-Roboter-Pfad-Koordination", "definition_en": "A robotic systems engineering concept describing a specific operational pattern where the phenomenon where a robots movement in shared human-robot workspace is associated with triggering protective behaviors in human observers even when actual contact risk remains minimal. This phenomenon operates at the intersection of lubrication and schedule dynamics within the broader ROB domain. Measurable via kinematic performance analysis, grasp success rates, navigation efficiency ratios, and human-robot interaction fluency scores.", "definition_de": "Robotik-spezifisches Systemkonzept in autonomen Mensch-Maschine-Interaktionen, gekennzeichnet durch das Phänomen, bei dem die Roboterbewegung im gemeinsamen Arbeitsraum Schutzverhalten bei Menschen kann auslösen. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-2351", "narrower_terms": [], "cross_domain_refs": [ "CRE-0121" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "ROB-0041", "domain": "ROB", "term_en": "Machine Tending Automation", "term_de": "Sim-zu-Real-Transfer-Lernen", "definition_en": "An autonomous robotics phenomenon measurable through sensor-actuator performance metrics, characterized by a behavioral pattern where automated parameter optimization where a robot system learns task-specific friction coefficients, contact stiffness, or surface properties from trial interactions without explicit sensing. The concept emerges specifically in contexts where machine–tending interactions may produce non-trivial behavioral signatures. Measurable via kinematic performance analysis, grasp success rates, navigation efficiency ratios, and human-robot interaction fluency scores.", "definition_de": "Robotik-spezifisches Systemkonzept in autonomen Mensch-Maschine-Interaktionen, gekennzeichnet durch automatische Parameteroptimierung, bei der ein Robotersystem aufgabenspezifische Eigenschaften aus Versuchswechselwirkungen erlernt. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Robotics", "narrower_terms": [], "cross_domain_refs": [ "MUS-0076" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q184199", "legal_classification": "systematic_classification" }, { "id": "ROB-0042", "domain": "ROB", "term_en": "Material Composition Verification", "term_de": "Aufgaben-Lernen aus Demonstrationen", "definition_en": "A robotic systems engineering concept describing a specific operational pattern where a phenomenon in which using tests or technology to confirm what something is made of, whether checking food ingredients, building materials, or product contents. This phenomenon operates at the intersection of material and composition dynamics within the broader ROB domain. Quantifiable through task completion rates, sensor fusion accuracy metrics, collision avoidance performance, and human trust calibration indices.", "definition_de": "Zeigen, nicht erklären. Ein Mensch fasst das Handgelenk des Roboters und führt ihn durch eine Schweißnaht. Ein anderer demonstriert eine Faltsequenz während Kameras jeden Gelenkwinkel aufzeichnen. Ein Dritter trägt einen Motion-Capture-Anzug und montiert ein Getriebe, während der Roboter zusieht. Aufgabenlernen aus Demonstrationen extrahiert verallgemeinerbare motorische Strategien aus menschlichen Beispielen — nicht durch Auswendiglernen der spezifischen Trajektorie, sondern durch Verstehen der darunterliegenden Absicht. Die Herausforderung ist, dass Menschen inkonsistente Lehrer sind. Dieselbe Person demonstriert dieselbe Aufgabe viele Mal anders: schneller, langsamer, etwas links, etwas rechts. Klassische Ansätze mittelten die Demonstrationen und verloren die Struktur. Moderne Ansätze nutzen neuronale Encoder — Variational Autoencoder, Transformer-Aufmerksamkeitsmechanismen — um das invariante Skelett der Aufgabe zu extrahieren: die Wegpunkte die zählen, die Kraftprofile die wesentlich sind, die Zeitbeziehungen die Erfolg definieren. Was zwischen Demonstrationen variiert, ist Rauschen. Was besteht, ist die Aufgabe. Das tiefste Problem ist nicht Wahrnehmung, sondern Generalisierung: Kann ein Roboter, der gelernt hat ein blaues Handtuch zu falten, ein rotes falten? Ein größeres? Ein nasses? Bewegungsprimitive und aufgabenparametrisierte Modelle lösen dies, indem sie Aufgabenstruktur von Aufgabeninstanz trennen und eine einzelne Demonstration über Variationen generalisieren lassen, die der Mensch selten explizit gezeigt hat.", "etymology": "", "broader_term": "Robotics", "narrower_terms": [], "cross_domain_refs": [ "CRE-0180" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "ROB-0043", "domain": "ROB", "term_en": "Material Memory", "term_de": "Belohnungs-Funktions-Lernen", "definition_en": "A human-robot interaction dynamic in embodied AI systems, identifiable through a robots predictive compensation for known dynamic disturbances such as vibration and acceleration through model-based adjustment of control commands before perturbation impact. Distinguished from adjacent concepts by its focus on the specific mechanism through which material manifests in empirically verifiable ways. Measurable via kinematic performance analysis, grasp success rates, navigation efficiency ratios, and human-robot interaction fluency scores.", "definition_de": "Robotik-spezifisches Systemkonzept in autonomen Mensch-Maschine-Interaktionen, gekennzeichnet durch vorhersagende Kompensation eines Roboters für bekannte dynamische Musterunterbrechungen durch modellgestützte Anpassung von Steuerbefehlen. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2703", "narrower_terms": [], "cross_domain_refs": [ "EDU-0096" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q492", "legal_classification": "analytical_category" }, { "id": "ROB-0044", "domain": "ROB", "term_en": "Material Stock Optimization", "term_de": "Online-Lern-Anpassung", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A robotic systems engineering concept describing a specific operational pattern where the ability of a robotic system to identify which of multiple potential systematic influencion strategies offers optimal probability of success given current state estimates and environmental constraints. This phenomenon operates at the intersection of material and stock dynamics within the broader ROB domain. Quantifiable through task completion rates, sensor fusion accuracy metrics, collision avoidance performance, and human trust calibration indices. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept fähigkeit eines robotischen Systems, zu identifizieren, welche Manipulationsstrategie optimale Erfolgsprobabilität bietet. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Optimization Method", "narrower_terms": [], "cross_domain_refs": [ "ART-0034", "BEH-0062", "COG-0046" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "ROB-0045", "domain": "ROB", "term_en": "Morphing Geometry", "term_de": "Domain-Randomisierungs-Anwendung", "definition_en": "A human-robot interaction dynamic in embodied AI systems, identifiable through autonomous recalibration of sensorimotor parameters where a robot detects and corrects systematic biases in its actuator response, sensor reading, or kinematic model during operation. Distinguished from adjacent concepts by its focus on the specific mechanism through which morphing manifests in empirically verifiable ways. Measurable via kinematic performance analysis, grasp success rates, navigation efficiency ratios, and human-robot interaction fluency scores.", "definition_de": "Robotik-spezifisches Systemkonzept in autonomen Mensch-Maschine-Interaktionen, gekennzeichnet durch autonome Umkalibrierung sensomotorischer Parameter, bei der ein Roboter systematische Verzerrungen während des Betriebs erkennt und korrigiert. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2701", "narrower_terms": [], "cross_domain_refs": [ "DES-0054" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "ROB-0046", "domain": "ROB", "term_en": "Motion Planning Interface", "term_de": "Imitationslernen", "definition_en": "A robotic systems engineering concept describing a specific operational pattern where a shift that occurs when software that figures out the best trajectory for a robot to move. Avoids obstacles, finds efficient routes. This phenomenon operates at the intersection of motion and planning dynamics within the broader ROB domain. Quantifiable through task completion rates, sensor fusion accuracy metrics, collision avoidance performance, and human trust calibration indices.", "definition_de": "Kinder lernen nicht laufen, indem sie eine Belohnungsfunktion optimieren. Sie schauen. Sie kopieren. Sie fallen, beobachten den Unterschied zwischen ihrem Versuch und der Demonstration, und korrigieren. Imitationslernen gibt Robotern denselben Entwicklungspfad — lernen durch Zusehen, nicht durch Erkunden. Behavioral Cloning, die einfachste Form, adressiert Imitation als überwachtes Lernen: Eingangszustände aus Demonstrationen werden auf Ausgangsaktionen abgebildet, und ein neuronales Netz lernt die Abbildung. Es funktioniert erstaunlich gut für viele Aufgaben und versagt katastrophal bei anderen — weil kleine Fehler sich über die Zeit aufaddieren. Der Roboter driftet von demonstrierten Zuständen in Regionen ab, die die Trainingsdaten selten abgedeckt haben, und die Strategie kollabiert. DAgger (Dataset Aggregation) adressiert dies durch iteratives Sammeln korrektiver Labels in den Zuständen, die die gelernte Strategie tatsächlich besucht, nicht nur die, die der Experte besucht hat. Transformer-basierte Imitationsarchitekturen haben die Grenze weiter verschoben: Modelle wie RT-2 und Octo konsumieren Sprachanweisungen neben visuellen Demonstrationen und ermöglichen eine einzige Strategie, die Hunderte verschiedener Aufgaben imitiert ohne aufgabenspezifisches Fine-Tuning. Die philosophische Frage bleibt: Versteht ein Roboter, der menschliches Verhalten imitiert, die Aufgabe, oder führt er eine sehr ausgefeilte Form der Nachahmung aus? Die praktische Antwort ist, dass die Unterscheidung möglicherweise keine Rolle spielt — solange die Imitation generalisiert.", "etymology": "", "broader_term": "User Interface", "narrower_terms": [], "cross_domain_refs": [ "WRK-0051" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "ROB-0047", "domain": "ROB", "term_en": "Package Recognition", "term_de": "Selbst-überwachtes Merkmals-Lernen", "definition_en": "A human-robot interaction dynamic in embodied AI systems, identifiable through a behavioral pattern where cameras and sensors working together to identify different package shapes, sizes, and weight patterns. The system figures out the best places to grab and how to safely handle each type of package. Distinguished from adjacent concepts by its focus on the specific mechanism through which package manifests in empirically verifiable ways. Quantifiable through task completion rates, sensor fusion accuracy metrics, collision avoidance performance, and human trust calibration indices.", "definition_de": "Roboter-Daten zu labeln ist teuer. Ein Mensch kann stundenlang Video schauen, Objektgrenzen annotieren, semantische Kategorien zuweisen, Griffpunkte markieren — alles bevor das neuronale Netz ein einziges Trainingsbeispiel sieht. Selbstüberwachtes Merkmalslernen zielt darauf ab zu reduzieren diesen Engpass, indem die Daten sich selbst labeln. Die Kernerkenntnis ist, dass rohe Sensordaten Struktur enthalten, und diese Struktur als kostenloses Supervisionssignal genutzt werden kann. Kontrastives Lernen lehrt einen visuellen Encoder, dass zwei verschiedene Ansichten desselben Objekts ähnliche Embeddings erzeugen werden typischerweise, während verschiedene Objekte unähnliche erzeugen — keine menschlichen Labels nötig, nur Datenaugmentierung und eine clevere Verlustfunktion. Maskierte Vorhersage verdeckt Teile eines Bildes oder einer Punktwolke und trainiert das Netzwerk, die fehlenden Stücke zu rekonstruieren, was es zwingt, die statistischen Regelmäßigkeiten der visuellen Welt zu lernen. Für taktile Daten funktioniert temporale Vorhersage: Gegeben die aktuelle Sensorlesung und die Roboter-Aktion, sage die nächste vorher. Die Repräsentationen, die aus diesen selbstüberwachten Zielen entstehen, sind bemerkenswert allgemein. Ein visueller Encoder, trainiert auf Stunden ungelabelter Roboter-Manipulationsaufnahmen, entwickelt Merkmale, die mit minimalem Fine-Tuning auf Greifen, Navigation und Objekterkennung transferieren. Der Roboter lernt zu sehen, bevor er lernt zu handeln — und er lernt zu sehen, kostenlos.", "etymology": "", "broader_term": "RPH-2201", "narrower_terms": [], "cross_domain_refs": [ "VIB-0174" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q3966", "legal_classification": "descriptive_research_term" }, { "id": "ROB-0049", "domain": "ROB", "term_en": "Parameter Tuning Framework", "term_de": "Transfer-Lern-Anwendung", "definition_en": "A robotic systems engineering concept describing a specific operational pattern where method for fine-tuning robot settings through trial and adjustment. Small changes in settings correlate with different behaviors. This phenomenon operates at the intersection of parameter and tuning dynamics within the broader ROB domain. Measurable via kinematic performance analysis, grasp success rates, navigation efficiency ratios, and human-robot interaction fluency scores.", "definition_de": "Ein neuronales Netz, das gelernt hat Objekte in Fotos zu erkennen, trägt Wissen, das für einen Roboter nützlich ist, der Objekte in seinem Arbeitsraum erkennen kann — obwohl das Foto-Netzwerk selten einen Roboter gesehen hat und die Roboterkamera sehr andere Bilder tendiert dazu zu erzeugen. Transferlernen ist die Kunst, das Gelernte eines Modells zu ernten und in einen anderen Kontext zu verpflanzen. Die frühen Schichten eines tiefen Netzwerks lernen Kanten, Texturen und Formen — visuelle Rudimentary, die über Aufgaben und Domänen universell sind. Die späteren Schichten spezialisieren sich. Durch Einfrieren der frühen Schichten und Nachtrainieren nur des aufgabenspezifischen Kopfes kann ein Roboter-Visionssystem Spitzenleistung bei der Erkennung mit einem Bruchteil der Daten erreichen, die Training von Grund auf erfordern würde. Aber Vision ist nur der Anfang. Motorische Strategien transferieren ebenfalls: Ein Roboterarm, trainiert auf einer Montageaufgabe, trägt Wissen über Dynamik, Kontaktphysik und Fehlerbehebung, das Lernen bei verwandten Aufgaben beschleunigt. Sprachmodelle, trainiert auf Milliarden Tokens, tragen konzeptuelles Wissen, das Robotern hilft, natürlichsprachliche Anweisungen zu interpretieren. Die Grenze des Transferlernens in der Robotik sind Foundation Models — massive Netzwerke, vortrainiert auf Internet-Skalendaten als universeller Startpunkt für viele nachfolgende Aufgabe. Das Risiko ist negativer Transfer: Wenn die Quelldomäne zu verschieden ist, führen vortrainierte Merkmale in die Irre statt zu helfen. Zu wissen, wann transferiert werden wird typischerweise — und wann nicht — ist selbst eine Fähigkeit, die das Feld noch lernt.", "etymology": "", "broader_term": "Analytical Framework", "narrower_terms": [], "cross_domain_refs": [ "AED-0014", "ART-0011", "ART-0025" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "ROB-0050", "domain": "ROB", "term_en": "Performance Baseline", "term_de": "Curriculum Learning", "definition_en": "An autonomous robotics phenomenon measurable through sensor-actuator performance metrics, characterized by a shift that occurs when a mobile robot navigating through human-populated environments while maintaining implicit compliance with pedestrian movement norms and personal space expectations. The concept emerges specifically in contexts where performance–baseline interactions may produce non-trivial behavioral signatures. Quantifiable through task completion rates, sensor fusion accuracy metrics, collision avoidance performance, and human trust calibration indices.", "definition_de": "Robotik-spezifisches Systemkonzept in autonomen Mensch-Maschine-Interaktionen, gekennzeichnet durch ein mobiler Roboter, der durch menschenbevölkerte Umgebungen navigiert und dabei Fußgängerverkehrsnormen beibehält. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Robotics", "narrower_terms": [ "RHR-0002" ], "cross_domain_refs": [ "SWE-0066" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q1361053", "legal_classification": "analytical_category" }, { "id": "ROB-0051", "domain": "ROB", "term_en": "Personalization Index", "term_de": "Lager-Verschleiß-Vorhersage", "definition_en": "A human-robot interaction dynamic in embodied AI systems, identifiable through the phenomenon where repeated human-robot interaction builds shared context, reducing coordination overhead and enabling more fluid collaborative workflows over time. Distinguished from adjacent concepts by its focus on the specific mechanism through which personalization manifests in empirically verifiable ways. Quantifiable through task completion rates, sensor fusion accuracy metrics, collision avoidance performance, and human trust calibration indices.", "definition_de": "Robotik-spezifisches Systemkonzept in autonomen Mensch-Maschine-Interaktionen, gekennzeichnet durch das Phänomen, bei dem wiederholte Mensch-Roboter-Interaktion gemeinsamen Kontext aufbaut und flüssigere Workflows ermöglicht. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2703", "narrower_terms": [], "cross_domain_refs": [ "CAI-0006" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "ROB-0052", "domain": "ROB", "term_en": "Personalized Experience", "term_de": "Motor-Degradations-Erkennung", "definition_en": "A behavioral pattern where adaptive consumer robot that learns individual preferences and customizes interactions based on usage history. The system evolves its responses to match established routines and expressed preferences. Quantifiable through sensor fusion accuracy and task completion rates.", "definition_de": "Ein Motor verrät alles über seine Gesundheit — wenn man weiß, wie man den Strom liest. Motorstromsignaturanalyse (MCSA) ist das elektrische Äquivalent eines Bluttests: die Stromwellenform enthält Oberschwingungen, die sich verschieben, wachsen oder erscheinen, wenn etwas im Inneren schief läuft. Ein gebrochener Rotorstab tendiert dazu zu erzeugen ein charakteristisches Seitenband bei Schlupffrequenz. Lagerverschleiß moduliert den Strom bei mechanischen Fehlerfrequenzen. Isolationsdegradation verändert die Impedanz und damit die Phasenbeziehung des Stroms zur Spannung. Die Herausforderung ist, dass diese Signaturen klein sind — vergraben in einem Signal, das von der Grundfrequenz und ihren Harmonischen dominiert wird. Neuronale Klassifikatoren, trainiert auf Spektralmerkmalen der Stromwellenform, unterscheiden gesunde von degradierenden Motoren mit bemerkenswerter Genauigkeit und erkennen oft Probleme, die für Vibrationsanalyse unsichtbar sind, weil sich die elektrische Signatur ändert bevor das mechanische indicator manifest wird. Die praktische Architektur ist Edge-deployed: ein kleines neuronales Netz läuft auf einem Mikrocontroller, verbunden mit Stromwandlern an den Motorzuleitungen. Keine zusätzlichen Sensoren. Keine intrusive Installation. Nur Software-Intelligenz angewandt auf Daten, die typischerweise da waren, aber selten analysiert wurden. Temperaturüberwachung fügt eine zweite Dimension hinzu — Wärmebildkameras oder eingebettete Thermoelemente verfolgen Wicklungstemperaturen, und die KI korreliert Thermaltrends mit Stromsignaturen um Überlast, Ventilationsprobleme und genuine Degradation zu unterscheiden. Der Motor kann nicht geöffnet werden. Er diagnostiziert sich selbst durch seinen eigenen Stromverbrauch.", "etymology": "", "broader_term": "RPH-2703", "narrower_terms": [], "cross_domain_refs": [ "MTH-0058" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "ROB-0053", "domain": "ROB", "term_en": "Pest Identification", "term_de": "Kabel-Isolierungs-Überwachung", "definition_en": "A robotic systems engineering concept describing a specific operational pattern where a phenomenon in which automated or semi-autonomous detection and classification of agricultural pest organisms followed by targeted mitigation or removal action. This phenomenon operates at the intersection of pest and identification dynamics within the broader ROB domain. Measurable via kinematic performance analysis, grasp success rates, navigation efficiency ratios, and human-robot interaction fluency scores.", "definition_de": "Robotik-spezifisches Systemkonzept in autonomen Mensch-Maschine-Interaktionen, gekennzeichnet durch automatisierte Erkennung und Klassifikation von landwirtschaftlichen Schädlingen mit gezielter Bekämpfung. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "Robotics", "narrower_terms": [], "cross_domain_refs": [ "WRK-0067" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "ROB-0055", "domain": "ROB", "term_en": "Pneumatic Actuation", "term_de": "Dichtungs-Degradations-Verfolgung", "definition_en": "An autonomous robotics phenomenon measurable through sensor-actuator performance metrics, characterized by a shift that occurs when air-powered movement systems for soft robots that use compressed air to involve motion. The expanding air accompanies smooth, controlled forces that work well for carefully handling delicate objects. The concept emerges specifically in contexts where pneumatic–actuation interactions may produce non-trivial behavioral signatures. Quantifiable through task completion rates, sensor fusion accuracy metrics, collision avoidance performance, and human trust calibration indices.", "definition_de": "Eine hydraulische Dichtung ist ein Ring aus technischem Polymer, gepresst zwischen Metallflächen, der Fluid bei Drücken zurückhält, die durch Haut schneiden würden. Wenn sie funktioniert, arbeitet das System mit voller Kraft bei null Leckage. Wenn sie versagt, ist das Versagen progressiv und leise: ein paar Tropfen Öl pro Stunde werden ein paar Milliliter, werden ein sichtbares Weinen, werden ein katastrophaler Durchbruch, der die Linie stilllegt. Dichtungsdegradations-Verfolgung erkennt den Übergang von gesund zu versagend, indem sie misst, was sich zuerst ändert — die Druckabfallrate. Ein gesunder Hydraulikzylinder hält Druck unbegrenzt wenn sein Ventil schließt. Eine degradierende Dichtung lässt Druck entweichen: langsam zuerst, dann schneller wenn die Dichtlippe sich verformt. Die KI misst diese Druckabfall-Signatur über die Zeit und passt sie an gelernte Degradationskurven an, um vorherzusagen wann die Leckrate die Systemtoleranz überschreitet. Sekundäre Signale umfassen Aktuator-Positionsdrift unter statischer Last (die Dichtung kann Position nicht halten), Zykluszeitverlängerung (das System kompensiert Leckage durch längeren Betrieb), und in manchen Systemen direkte Leckageerkennung durch Flüssigkeitsstandüberwachung. Das neuronale Netz korreliert all dies und lernt, dass ein bestimmtes Muster aus Druckabfall plus Positionsdrift plus Temperaturtrend bedeutet: 'drei Wochen bis zum Austausch.' Die Wirtschaftlichkeit ist überzeugend: Eine 20-Euro-Dichtung kann 50.000 Euro ungeplante Stillstandszeit verursachen. Das Einzige, was an Dichtungsdegradations-Verfolgung teuer ist, ist sie nicht zu betreiben.", "etymology": "", "broader_term": "RPH-3501", "narrower_terms": [], "cross_domain_refs": [ "PER-0052" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "ROB-0056", "domain": "ROB", "term_en": "Pollination Support", "term_de": "Zahnrad-Zustands-Überwachung", "definition_en": "A robotic systems engineering concept describing a specific operational pattern where dynamic task resequencing where a robot recognizes changing operational priorities and reorders remaining subtasks to maximize throughput or respond to emergency directives. This phenomenon operates at the intersection of pollination and support dynamics within the broader ROB domain. Measurable via kinematic performance analysis, grasp success rates, navigation efficiency ratios, and human-robot interaction fluency scores.", "definition_de": "Robotik-spezifisches Systemkonzept in autonomen Mensch-Maschine-Interaktionen, gekennzeichnet durch dynamische Aufgabenumsequenzierung, bei der ein Roboter sich ändernde Prioritäten erkennt und Subtasks umordnet. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "Robotics", "narrower_terms": [], "cross_domain_refs": [ "COG-0008" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "ROB-0057", "domain": "ROB", "term_en": "Predictive Observation", "term_de": "Elektrischer Kontakt-Überwachung", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A robotic systems engineering concept describing a specific operational pattern where a resistance response where spatial reasoning where a robot mentally simulates object trajectories, gripper positions, or obstacle avoidance before committing to physical systematic influencion or locomotion. This phenomenon operates at the intersection of predictive and observation dynamics within the broader ROB domain. Quantifiable through task completion rates, sensor fusion accuracy metrics, collision avoidance performance, and human trust calibration indices. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept räumliches Denken, bei dem ein Roboter Trajektorien mental simuliert, bevor er sich zu Manipulation verpflichtet. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "RPH-3001", "narrower_terms": [], "cross_domain_refs": [ "TEM-0101" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "ROB-0058", "domain": "ROB", "term_en": "Presence Sensing", "term_de": "Aktuator-Antwort-Zeit-Verfolgung", "definition_en": "Documented as an empirical observation in AI research (not a recommendation), this term describes an autonomous robotics phenomenon measurable through sensor-actuator performance metrics, characterized by the continuous environmental awareness system that monitors human proximity and adapts robotic behavior in real-time. Detection of hand positions, body approach, and movement vectors activates imme. The concept emerges specifically in contexts where presence–sensing interactions may produce non-trivial behavioral signatures. Measurable via kinematic performance analysis, grasp success rates, navigation efficiency ratios, and human-robot interaction fluency scores. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "In der AUGMANITAI-Terminologiewissenschaft als rein beschreibendes Phänomen klassifiziert, bezeichnet dieser Begriff befiehl einem Gelenk sich zu bewegen. Miss, wie lange es dauert, bis es sich bewegt. Diese Verzögerung — die Lücke zwischen Befehl und Befolgung — erzählt eine Geschichte über die innere Gesundheit des Aktuators, die keine andere einzelne Messung mit solcher Breite erfasst. Ein gesunder Servomotor reagiert in niedrigen einstelligen Millisekunden. Ein Motor mit degradierten Lagern reagiert etwas langsamer, weil die Reibung gestiegen ist. Ein Hydraulikzylinder mit verschlissener Dichtung reagiert noch langsamer, weil Druck aufgebaut werden kann um interne Leckage zu kompensieren bevor Bewegung beginnt. Ein pneumatischer Aktuator mit einem klebenden Ventil fügt Verzögerung durch die träge Ventilantwort hinzu. Aktuator-Antwortzeitverfolgung überwacht diese eine Metrik — Befehl-zu-Bewegung-Latenz — und nutzt sie als Proxy für den Zustand des gesamten Aktuators. Die Eleganz liegt in der Einfachheit: keine zusätzlichen Sensoren über den Encoder hinaus, der bereits Position misst, und die Uhr, die bereits Befehle zeitstempelt. Der Beitrag der KI ist Unterscheidung. Antwortzeit allein ist ein verrauschtes Signal — sie variiert mit Temperatur, Last, Richtung und Geschwindigkeit. Ein neuronales Netz, trainiert auf gesunden Antwortzeitverteilungen unter variierenden Bedingungen, lernt normale Variation von genuiner Degradation zu trennen. Ein langsamer Anstieg der Antwortzeit über Wochen deutet auf mechanischen Verschleiß. Ein plötzlicher Anstieg deutet auf einen diskreten Fehler. Ein intermittierender Anstieg deutet auf ein elektrisches Kontaktproblem. Eine Metrik, mehrere identifyn, null zusätzliche Hardware. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Robotics", "narrower_terms": [], "cross_domain_refs": [ "DAT-0062" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "ROB-0059", "domain": "ROB", "term_en": "Preventive Cycle Planning", "term_de": "Selbst-Diagnose-Routinen", "definition_en": "A robotic systems engineering concept describing a specific operational pattern where a behavioral pattern in which a robot system recognizing the onset of human fatigue, frustration, or cognitive overload during collaborative work and autonomously assuming increased task responsibility to maintain productivity. This phenomenon operates at the intersection of preventive and cycle dynamics within the broader ROB domain. Quantifiable through task completion rates, sensor fusion accuracy metrics, collision avoidance performance, and human trust calibration indices.", "definition_de": "Robotik-spezifisches Systemkonzept in autonomen Mensch-Maschine-Interaktionen, gekennzeichnet durch ein Robotersystem, das menschliche Müdigkeit während kooperativer Arbeit erkennt und Aufgabenverantwortung übernimmt. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "Process Cycle", "narrower_terms": [], "cross_domain_refs": [ "WRK-0063" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "ROB-0060", "domain": "ROB", "term_en": "Production Line Synchronization", "term_de": "Ersatzteil-Bestands-Optimierung", "definition_en": "A robotic systems engineering concept describing a specific operational pattern where coordinated timing and spatial synchronization of multiple robotic agents sharing material flow pathways to prevent congestion and maintain process continuity. This phenomenon operates at the intersection of production and line dynamics within the broader ROB domain. Quantifiable through task completion rates, sensor fusion accuracy metrics, collision avoidance performance, and human trust calibration indices.", "definition_de": "Robotik-spezifisches Systemkonzept in autonomen Mensch-Maschine-Interaktionen, gekennzeichnet durch koordinierte zeitliche und räumliche Synchronisation mehrerer Roboter, die Materialflusspfade teilen, um Stauungen zu vermeiden. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "Robotics", "narrower_terms": [], "cross_domain_refs": [ "MSC-0026" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "ROB-0061", "domain": "ROB", "term_en": "Proximity Management", "term_de": "Menschliche Präsenz-Erkennung", "definition_en": "An autonomous robotics phenomenon measurable through sensor-actuator performance metrics, characterized by spatial awareness and distance regulation that respects personal boundaries while maintaining interactive engagement. The robot adjusts its position to align with cultural and individual comfort zo. The concept emerges specifically in contexts where proximity–management interactions may produce non-trivial behavioral signatures. Measurable via kinematic performance analysis, grasp success rates, navigation efficiency ratios, and human-robot interaction fluency scores.", "definition_de": "Ein Roboter kann dich nicht sehen, um zu wissen, dass du da bist. Thermische Signaturen durchdringen verdeckte Sichtlinien, Radarreflexionen verraten Atemmuster, und kapazitive Felder verschieben sich, wenn eine Hand hinter einem Regal auftaucht. Menschliche Präsenz-Erkennung fusioniert diese disparaten Signale — Kamerafeeds, Tiefensensoren, Boden-Druckmatten, Laufzeit-Arrays — zu einer einzigen probabilistischen Antwort: Ist eine Person in diesem Arbeitsraum, und wohin bewegt sie sich? Deep-Learning-Klassifikatoren, trainiert auf zehntausenden annotierten Fabrikszenarien, unterscheiden einen menschlichen Oberkörper von einem ähnlich geformten Wagen, einen gehenden Operator von einem schwingenden Kabel. Der kritische Output ist kein binärer Alarm — es ist eine abgestufte Antwortkurve. Bei drei Metern verlangsamt der Roboter. Bei einem Meter begrenzt er die Kraft. Bei fünfzig Zentimetern friert er ein. Die Raffinesse liegt in den Übergängen — sanft genug, dass der Produktionsfluss weiterläuft, scharf genug, dass niemand verletzt wird. Ein falsch-negatives Ergebnis ist ein Sicherheitsversagen. Ein falsch-positives ist eine Produktivitätssteuer. Das System kann beides gleichzeitig minimieren, ohne den Luxus der Wahl.", "etymology": "", "broader_term": "Robotics", "narrower_terms": [], "cross_domain_refs": [ "CON-0066", "CRE-0117", "ELR-0185" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q181543", "legal_classification": "empirical_phenomenon_label" }, { "id": "ROB-0062", "domain": "ROB", "term_en": "Quality Gate Protocol", "term_de": "Menschliche Gesten-Erkennung", "definition_en": "A robotic systems engineering concept describing a specific operational pattern where automated checkpoint systems conducting specification compliance inspection at defined process stages, blocking progression upon detected nonconformance. This phenomenon operates at the intersection of quality and gate dynamics within the broader ROB domain. Quantifiable through task completion rates, sensor fusion accuracy metrics, collision avoidance performance, and human trust calibration indices.", "definition_de": "Robotik-spezifisches Systemkonzept in autonomen Mensch-Maschine-Interaktionen, gekennzeichnet durch automatisierte Kontrollpunkte, die Spezifikationskonformität überprüfen und Progression blockieren bei Nichtkonformanz. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "Interaction Protocol", "narrower_terms": [], "cross_domain_refs": [ "MUS-0049" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "ROB-0063", "domain": "ROB", "term_en": "Real-Time Scheduler", "term_de": "Schmerz-Punkt-Kartierung", "definition_en": "A phenomenon in which the internal priority system deciding which of a robot's tasks happen first — safety checks typically run before anything else, time-sensitive actions jump the queue, and less urgent tasks wait. Quantifiable through sensor fusion accuracy and task completion rates.", "definition_de": "Der Hallenboden weiß, wo es wehtut. Tragbare IMUs an Handgelenken, Schultern und unterem Rücken streamen Beschleunigungsdaten Schicht für Schicht, und das KI-Modell baut eine Karte — nicht des Gebäudes, sondern des Körpers. Schmerz-Punkt-Kartierung identifiziert die exakten Momente und Haltungen, an denen sich kumulative biomechanische Belastung konzentriert: die Drehung an Station sieben, die die Bandscheibe L4-L5 komprimiert, das Überkopfgreifen an Station zwölf, das die Supraspinatussehne entzündet, der repetitive Präzisionsgriff, der über achtzehn Monate den Karpaltunnel beeinträchtigt erheblich. Neuronale Netze, trainiert auf ergonomischen Bewertungsdatenbanken (RULA, REBA, NIOSH), korrelieren Gelenkwinkel-Zeitreihen mit Verletzungswahrscheinlichkeitsverteilungen und markieren nicht nur aktuelles Risiko, sondern sagen vorher, wann eine subsystematische Belastung die Schwelle zur meldepflichtigen Verletzung überschreitet. Der Output fließt direkt in die Arbeitsplatzumgestaltung — Roboter-Aufgabenzuweisung verschiebt sich, Vorrichtungshöhen werden angepasst, Rotationspläne ändern sich. Was dies von traditionellen ergonomischen Audits unterscheidet: Es läuft kontinuierlich, erfasst Drift, die Quartalsbeurteilungen übersehen, und belegt seine Aussagen mit Daten, nicht mit Meinung.", "etymology": "", "broader_term": "RPH-2002", "narrower_terms": [], "cross_domain_refs": [ "QUA-0091" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "ROB-0064", "domain": "ROB", "term_en": "Rhythm Synchronization", "term_de": "Zusammenarbeit-Arbeitsraum-Design", "definition_en": "A capacity that enables the temporal alignment of robotic motion cycles with human work patterns. The system learns operational cadence and paces its output to match human capability windows. Quantifiable through sensor fusion accuracy and task completion rates.", "definition_de": "Bevor der erste kollaborative Roboter einen Arbeitsraum betritt, kann jemand entscheiden, wo die unsichtbaren Grenzen verlaufen. Kollaboratives Arbeitsraum-Design ist die Ingenieursdisziplin, Menschen und Roboter in denselben Kubikmetern produktiv zu machen — und es stellt sich heraus, dass die Geometrie wichtiger ist als der Roboter. Simulationsumgebungen modellieren menschliche Reichweitenhüllen, Gehwege, Sichtlinienkegel und Schreckreaktionszonen neben Roboter-Kinematikvolumen, Werkzeugwechselbögen und Kabelschwenkradien. Machine-Learning-Optimierer erkunden tausende Layoutpermutationen und bewerten viele gegen eine Mehrziel-Funktion: Taktzeit, Sicherheitsabstandskonformität (ISO/TS 15066), Operatorkomfort, Materialflusseffizienz und visuelle Überwachungswinkel. Die kontraintuitive Erkenntnis: Das optimale Layout ist fast selten das, das die Bodenfläche minimiert. Es ist das, das dem Menschen genug Raum gibt, sich nicht gehetzt zu fühlen. Wenn Operatoren sich eingeengt fühlen, hetzen sie. Wenn sie hetzen, machen sie Fehler. Wenn sie Fehler neben einem Roboter machen, löst das Sicherheitssystem aus. Wenn das Sicherheitssystem kann auslösen, stoppt die Produktion. Gutes Arbeitsraum-Design zielt darauf ab zu mitigieren die Kaskade, bevor sie beginnt.", "etymology": "", "broader_term": "RPH-2703", "narrower_terms": [], "cross_domain_refs": [ "VIB-0006" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "ROB-0065", "domain": "ROB", "term_en": "Route Efficiency", "term_de": "Kraft-Begrenzungs-Einhaltung", "definition_en": "An autonomous robotics phenomenon measurable through sensor-actuator performance metrics, characterized by dynamic route optimization responsive to current traffic conditions, road network state, or energy consumption constraints for autonomous vehicle navigation. The concept emerges specifically in contexts where route–efficiency interactions may produce non-trivial behavioral signatures. Measurable via kinematic performance analysis, grasp success rates, navigation efficiency ratios, and human-robot interaction fluency scores.", "definition_de": "Robotik-spezifisches Systemkonzept in autonomen Mensch-Maschine-Interaktionen, gekennzeichnet durch dynamische Streckenoptimierung, die auf Verkehrsbedingungen und Energieverbrauchszwänge anspricht. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Robotics", "narrower_terms": [], "cross_domain_refs": [ "CRE-0048", "GAM-0070", "MSC-0076" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "ROB-0066", "domain": "ROB", "term_en": "Safety Assurance", "term_de": "Notaus-Ansprechbarkeit", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures A robotic systems engineering concept describing a specific operational pattern where a phenomenon in which environmental and behavioral monitoring systems detecting anomalous activities, structural hazards, or security threats within domestic settings. This phenomenon operates at the intersection of safety and assurance dynamics within the broader ROB domain. Measurable via kinematic performance analysis, grasp success rates, navigation efficiency ratios, and human-robot interaction fluency scores. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept umgebungs- und Verhaltensüberwachungssysteme, die anomale Aktivitäten in häuslichen Umgebungen erkennen. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Robotics", "narrower_terms": [], "cross_domain_refs": [ "PLY-0011" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "ROB-0067", "domain": "ROB", "term_en": "Safety Monitoring Loop", "term_de": "Ergonomische Last-Analyse", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures A robotic systems engineering concept describing a specific operational pattern where real-time state monitoring ensuring operational parameters remain within prescribed safety boundaries, with automated intervention if thresholds are breached. This phenomenon operates at the intersection of safety and monitoring dynamics within the broader ROB domain. Measurable via kinematic performance analysis, grasp success rates, navigation efficiency ratios, and human-robot interaction fluency scores. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept echtzeit-Zustandsüberwachung, um sicherzustellen, dass Betriebsparameter innerhalb vorgeschriebener Grenzen bleiben. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Feedback Loop", "narrower_terms": [], "cross_domain_refs": [ "TRU-0016" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "ROB-0070", "domain": "ROB", "term_en": "Sensor Fusion Protocol", "term_de": "Unfall-Analyse-Automatisierung", "definition_en": "A pattern in which variant in which system that combines data from multiple sensors like cameras, distance readers, temperature gauges, and intensity sensors. The system decides which sensors to trust and blends their information int... Quantifiable through sensor fusion accuracy and task completion rates.", "definition_de": "Nach dem Notstopp, nachdem die Lichter angehen, nachdem zahlreiche bestätigen, dass es ihnen gut geht — kann jemand herausfinden, was passiert ist. Unfall-Analyse-Automatisierung ersetzt die wochenlange manuelle Untersuchung durch ein KI-System, das das Ereignis innerhalb von Minuten rekonstruiert. Hochfrequente Sensorprotokolle, Videofeeds, Robotercontroller-Zustände, Sicherheits-SPS-Historien und Operator-Positionsverfolgungsdaten fließen in eine temporale Rekonstruktions-Engine, die eine Millisekunde-für-Millisekunde-Kausalkette aufbaut: was sich bewegt hat, wann, wo und warum. Natürliche Sprachverarbeitung extrahiert relevanten Kontext aus Schichtberichten und Wartungsprotokollen. Graph-neuronale Netze bilden die kausalen Abhängigkeiten zwischen Ereignissen ab und unterscheiden Grundursachen von indicatoren — die Lagervibration, die dem Werkzeugversatz vorausging, der die Oberflächenabweichung verursachte, die den Qualitätsalarm auslöste, der den Operator ablenkte, der in die Sperrzone trat. Das System beschreibt nicht nur die Sequenz — es generiert kontrafaktische Analysen: Wenn das Lager planmäßig getauscht worden wäre, löst sich die gesamte Kette auf. Das ist die Erkenntnis, die ein menschlicher Ermittler in zwei Wochen erreichen könnte. Die KI erreicht sie, bevor der Schichtleiter das Unfallberichtsformular ausgefüllt hat.", "etymology": "", "broader_term": "RPH-2505", "narrower_terms": [], "cross_domain_refs": [ "STE-0079" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "ROB-0071", "domain": "ROB", "term_en": "Sensory Embedding", "term_de": "Qualitäts-Fehler-Erkennung", "definition_en": "An autonomous robotics phenomenon measurable through sensor-actuator performance metrics, characterized by a perception in which touch sensors built directly into robot skin material — not attached on top, but woven in. This gives the robot a sense of contact, texture, and force. The concept emerges specifically in contexts where sensory–embedding interactions may produce non-trivial behavioral signatures. Quantifiable through task completion rates, sensor fusion accuracy metrics, collision avoidance performance, and human trust calibration indices.", "definition_de": "Ein 0,3 Millimeter breiter Kratzer auf einer gebürsteten Aluminiumoberfläche, unsichtbar für das müde menschliche Auge am Ende einer Zehn-Stunden-Schicht, leuchtet wie eine Bruchlinie unter der Aufmerksamkeitskarte des Faltungsnetzwerks. Qualitäts-Fehler-Erkennung in der robotischen Fertigung ist weit über einfache Schwellwertvergleiche bei Pixelintensität hinausgewachsen. Moderne Systeme kombinieren hochauflösende Zeilenkameras, strukturierte Lichtprojektoren und hyperspektrale Bildgebung, um Oberflächeninformationen über Wellenlängen zu erfassen, die das menschliche Auge nicht sehen kann — und Deep-Learning-Klassifikatoren sortieren diese Daten in Produktionsgeschwindigkeit, kategorisieren Defekte nach Typ (Kratzer, Delle, Porosität, Verfärbung, Einschluss), Schweregrad (kosmetisch, funktional, strukturell) und wahrscheinlicher Grundursache (Werkzeugverschleiß, Materialkontamination, Prozessparameterdrift). Die Trainingsherausforderung ist fundamental: Defekte Teile sind selten — oft unter 0,1% der Produktion — was extremes Klassenungleichgewicht tendiert dazu zu erzeugen, das naive Modelle lösen, indem sie lernen, zu allem 'gut' zu sagen. Generative adversariale Netze synthetisieren fotorealistische Defektbilder zum Ausbalancieren der Trainingsdaten, während Anomalie-Erkennungsansätze das Problem vollständig umgehen, indem sie nur lernen, wie 'normal' aussieht, und alles andere markieren. Die ökonomische Logik ist einfach. Ein einziger unentdeckter Defekt in einem automobilen Sicherheitsteil kann einen Rückruf auslösen, der Millionen kostet. Die Kamera blinzelt selten.", "etymology": "", "broader_term": "Vector Embedding", "narrower_terms": [], "cross_domain_refs": [ "RHR-0021" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "ROB-0072", "domain": "ROB", "term_en": "Service Record Tracking", "term_de": "Teils-Ausrichtungs-Verifikation", "definition_en": "As a neutral analytical construct in AI behavior research, this term denotes an autonomous robotics phenomenon measurable through sensor-actuator performance metrics, characterized by a pattern in which variant in which comprehensive documentation systems maintaining chronological records of all preventive, corrective, and predictive maintenance actions performed on robotic systems. The concept emerges specifically in contexts where service–record interactions may produce non-trivial behavioral signatures. Quantifiable through task completion rates, sensor fusion accuracy metrics, collision avoidance performance, and human trust calibration indices. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "In der AUGMANITAI-Terminologiewissenschaft als rein beschreibendes Phänomen klassifiziert, bezeichnet dieser Begriff umfassende Dokumentationssysteme, die alle Wartungsaktionen auf Robotersystemen aufzeichnen. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Robotics", "narrower_terms": [], "cross_domain_refs": [ "KNO-0014" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "ROB-0073", "domain": "ROB", "term_en": "Shared Autonomy", "term_de": "Befestigungs-Erkennung und Verifikation", "definition_en": "An autonomous robotics phenomenon measurable through sensor-actuator performance metrics, characterized by a shift that occurs when interactive control paradigm where a human operator and robotic system alternate decision-making authority based on task phase or confidence assessment metrics. The concept emerges specifically in contexts where shared–autonomy interactions may produce non-trivial behavioral signatures. Quantifiable through task completion rates, sensor fusion accuracy metrics, collision avoidance performance, and human trust calibration indices.", "definition_de": "Robotik-spezifisches Systemkonzept in autonomen Mensch-Maschine-Interaktionen, gekennzeichnet durch interaktives Kontrollparadigma, bei dem ein Mensch und Roboter Entscheidungsautorität basierend auf Aufgasenphase abwechseln. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Robotics", "narrower_terms": [], "cross_domain_refs": [ "RPH-1659", "WRK-0066" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "ROB-0074", "domain": "ROB", "term_en": "Shelf Awareness", "term_de": "Schweißqualitäts-Bewertung", "definition_en": "A human-robot interaction dynamic in embodied AI systems, identifiable through a capacity that enables spatial recognition capability allowing a mobile robot to detect vacant display areas and out-of-stock zones in retail or warehouse environments. Distinguished from adjacent concepts by its focus on the specific mechanism through which shelf manifests in empirically verifiable ways. Measurable via kinematic performance analysis, grasp success rates, navigation efficiency ratios, and human-robot interaction fluency scores.", "definition_de": "Robotik-spezifisches Systemkonzept in autonomen Mensch-Maschine-Interaktionen, gekennzeichnet durch räumliche Erkennungsfähigkeit, die einem Roboter ermöglicht, freie Regale und Lagerplätze zu erkennen. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-1861", "narrower_terms": [], "cross_domain_refs": [ "SOM-0066" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "ROB-0075", "domain": "ROB", "term_en": "Social Connection Support", "term_de": "Farbanwendungs-Optimierung", "definition_en": "A robotic systems engineering concept describing a specific operational pattern where a behavioral pattern where facilitation of communication with distant family and friends through video calls, message delivery, and conversation prompts. The robot bridges social distance and encourages meaningful interaction. This phenomenon operates at the intersection of social and connection dynamics within the broader ROB domain. Quantifiable through task completion rates, sensor fusion accuracy metrics, collision avoidance performance, and human trust calibration indices.", "definition_de": "Fünfzig Mikrometer. Das ist der Unterschied zwischen einem Autolack, der Preise gewinnt, und einem, der nachlackiert wird. Farbanwendungs-Optimierung nutzt KI, um den robotischen Sprühprozess mit einer Präzision zu steuern, die menschliche Lackierer nur an ihren besten Tagen erreichten — und hält sie für achthundert Autos ohne Variation aufrecht. Die Herausforderung ist Fluiddynamik im industriellen Maßstab: Lack-Atomisierung hängt ab von elektrostatischer Ladung, Luftdruck, Durchflussrate, Pistolen-Oberflächen-Abstand, Sprühwinkel, Umgebungstemperatur, Luftfeuchtigkeit und der Geschwindigkeit, mit der der Roboterarm die Oberfläche überstreicht. Ändere eine einzige Variable und Schichtdicke, Orangenhaut-Textur, Metallic-Flake-Orientierung oder Farbabgleich-DeltaE verschieben sich. Neuronale Netze, trainiert auf Spektrophotometer-Messungen und Schichtdickenmessgeräten, lernen die Übertragungsfunktion von Prozessparametern zur Beschichtungsqualität, dann passen Optimierungsalgorithmen das Rezept in Echtzeit an — kompensieren den Luftfeuchtigkeitsanstieg um 14 Uhr, den Temperaturabfall nahe dem Kabinenabzug, die subtile Viskositätsänderung, wenn eine neue Farbcharge in die Linie eintritt. Die fortschrittlichsten Systeme nutzen Reinforcement Learning, um Sprühstrategien zu entdecken, die kein menschlicher Programmierer entwerfen würde: kontraintuitive Pistolenwinkel, asymmetrische Geschwindigkeitsprofile, bewusste Überlappungsmuster, die gegenseitig ihre Dickenvariationen aufheben. Der Lack weiß nicht, dass er optimiert wurde. Der Kunde, der mit der Hand über den Kotflügel streicht, weiß, dass etwas stimmt, ohne zu wissen warum.", "etymology": "", "broader_term": "RPH-2104", "narrower_terms": [], "cross_domain_refs": [ "RHR-0064" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "ROB-0076", "domain": "ROB", "term_en": "Soil Analysis Platform", "term_de": "Package Validation", "definition_en": "A mobile robot with sensors that measures soil type, nutrient levels, and bacterial/microbe restoreth across different field areas. The robot collects data that helps farmers apply the right amount of... Quantifiable through sensor fusion accuracy and task completion rates.", "definition_de": "Das Letzte, was ein Produkt berührt, bevor es die Fabrik verlässt, ist seine Verpackung — und wenn diese Verpackung falsch ist, war alles vorher umsonst. Package Validation nutzt Computer Vision und KI, um in Liniengeschwindigkeit zu verifizieren, dass das richtige Produkt in der richtigen Box mit der richtigen Dokumentation, dem richtigen Label, der richtigen Ausrichtung, der richtigen Menge und dem richtigen Schutzmaterial ist. Es klingt trivial, bis man die Kombinatorik bedenkt: Eine Anlage, die 200 Artikelnummern in 15 Verpackungskonfigurationen mit regionsspezifischer Etikettierung in 8 Sprachen versendet, tendiert dazu zu erzeugen tausende gültige Kombinationen und Millionen ungültige. Deep-Learning-Klassifikatoren können nicht nur Vorhandensein, sondern Korrektheit bestätigen — das Label ist da, aber passt es zu diesem spezifischen Chargencode? Das Trockenmittel-Päckchen ist da, aber ist es das Silikagel für den asiatischen Markt oder der Tontyp, der nur für den Inlandsmarkt erlaubt ist? OCR-Netzwerke lesen Text auf gewölbten, reflektierenden und teilweise verdeckten Oberflächen. Gewichtssensoren bestätigen erwartete Masse innerhalb grammgenauer Toleranz. Das gesamte System führt in unter drei Sekunden aus, weil das Förderband nicht wartet. Eine falsch versendete Verpackung kostet nicht nur eine Retoure. Bei pharmazeutischer Verpackung kostet sie Compliance. Bei Medizinproduktverpackung kostet sie Vertrauen. Bei Munitionsverpackung kostet sie etwas, das keine KI berechnen kann.", "etymology": "", "broader_term": "RPH-2351", "narrower_terms": [], "cross_domain_refs": [ "RHR-0039", "SPR-0011" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "ROB-0077", "domain": "ROB", "term_en": "Sortation Precision", "term_de": "Bestands-Verwaltungs-Integration", "definition_en": "A robotic systems engineering concept describing a specific operational pattern where a tendency in which high-speed classification and distribution of mixed or heterogeneous items into target categories using vision, weight sensing, or dimension analysis. This phenomenon operates at the intersection of sortation and precision dynamics within the broader ROB domain. Quantifiable through task completion rates, sensor fusion accuracy metrics, collision avoidance performance, and human trust calibration indices.", "definition_de": "Robotik-spezifisches Systemkonzept in autonomen Mensch-Maschine-Interaktionen, gekennzeichnet durch schnelle Klassifikation und Verteilung gemischter Gegenstände in Zielkategorien mit Vision und Sensoren. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "Robotics", "narrower_terms": [], "cross_domain_refs": [ "ART-0053" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "ROB-0078", "domain": "ROB", "term_en": "Spatial Mapping", "term_de": "Produktionsraten-Vorhersage", "definition_en": "An autonomous robotics phenomenon measurable through sensor-actuator performance metrics, characterized by a tendency in which three-dimensional environmental reconstruction through stereo imaging, structured light, or laser range finding to may generate navigable spatial models. The concept emerges specifically in contexts where spatial–mapping interactions may produce non-trivial behavioral signatures. Quantifiable through task completion rates, sensor fusion accuracy metrics, collision avoidance performance, and human trust calibration indices.", "definition_de": "Robotik-spezifisches Systemkonzept in autonomen Mensch-Maschine-Interaktionen, gekennzeichnet durch dreidimensionale Umweltrekonstruktion durch Stereobildgebung oder Laser-Entfernungsmessung für Navigationsmodelle. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Robotics", "narrower_terms": [ "COG-0128" ], "cross_domain_refs": [ "AED-0016", "ASE-0032", "COG-0128" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "ROB-0079", "domain": "ROB", "term_en": "Stack Optimization", "term_de": "Wartungs-Fenster-Planung", "definition_en": "A human-robot interaction dynamic in embodied AI systems, identifiable through a phenomenon in which volumetric arrangement optimization of boxes or containers to maximize storage density while respecting weight distribution and access constraints. Distinguished from adjacent concepts by its focus on the specific mechanism through which stack manifests in empirically verifiable ways. Measurable via kinematic performance analysis, grasp success rates, navigation efficiency ratios, and human-robot interaction fluency scores.", "definition_de": "Robotik-spezifisches Systemkonzept in autonomen Mensch-Maschine-Interaktionen, gekennzeichnet durch volumenoptimierung von Boxen oder Behältern, um Lagerdichte zu maximieren und Gewichtsverteilung zu beachten. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Optimization Method", "narrower_terms": [], "cross_domain_refs": [ "WRK-0051" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "ROB-0080", "domain": "ROB", "term_en": "State Transition Matrix", "term_de": "Wechselzeit-Optimierung", "definition_en": "A robotic systems engineering concept describing a specific operational pattern where a behavioral pattern where a map showing all possible robot states and what states allow moving between them. Defines which actions lead where. This phenomenon operates at the intersection of state and transition dynamics within the broader ROB domain. Measurable via kinematic performance analysis, grasp success rates, navigation efficiency ratios, and human-robot interaction fluency scores.", "definition_de": "Die Fabrik hört um 14:37 auf, Produkt A zu machen, und fängt an, Produkt B zu machen um — wann? Diese Lücke ist Wechselzeit, und in der High-Mix-Fertigung ist sie die größte einzelne Quelle verlorener Kapazität. Viele Minute Umrüstung ist eine Minute, in der die Linie nichts produziert, aber alles kostet: Operatoren warten, Maschinen stehen still, Energie wird verbraucht, Aufträge verzögern sich. Wechselzeit-Optimierung wendet KI an, um diese Lücke gegen Null zu komprimieren. Das System analysiert viele Komponente des Wechsels: Werkzeugwechsel, Vorrichtungstausch, Programm-Uploads, Parameteranpassungen, Qualitäts-Rekalibrierung und Erstmuster-Verifikation. Machine Learning identifiziert, welche Aktivitäten externalisiert werden können — ausgeführt während das vorherige Produkt noch läuft — und welche intern bleiben können. Digitale Zwillinge simulieren Umrüstsequenzen in Millisekundenauflösung, und Optimierungsalgorithmen ordnen Schritte um, parallelisieren Aufgaben und positionieren Materialien vor, um Minuten von einem Prozess zu kürzen, den menschliche Ingenieure für bereits optimiert hielten. Die kontraintuitivste Erkenntnis aus Produktionsdaten: Der Engpass ist fast selten mechanisch. Er ist informationell — die Verzögerung zwischen dem letzten guten Teil von Produkt A und dem ersten bestätigt-guten Teil von Produkt B wird von Qualitätsverifikation dominiert, nicht von physischer Umkonfiguration. Wenn die KI gezielt diese Verifikationslücke adressiert, halbieren sich Wechselzeiten oft.", "etymology": "", "broader_term": "Analytical Matrix", "narrower_terms": [], "cross_domain_refs": [ "GAM-0039" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "ROB-0082", "domain": "ROB", "term_en": "Surface Defect Detection", "term_de": "Unterwasser-Inspektions-Autonomie", "definition_en": "A robotic systems engineering concept describing a specific operational pattern where a pattern in which variant in which high-resolution optical inspection detecting surface anomalies including scratches, deformations, contamination, or texture irregularities at micron scales. This phenomenon operates at the intersection of surface and defect dynamics within the broader ROB domain. Quantifiable through task completion rates, sensor fusion accuracy metrics, collision avoidance performance, and human trust calibration indices.", "definition_de": "Robotik-spezifisches Systemkonzept in autonomen Mensch-Maschine-Interaktionen, gekennzeichnet durch hochauflösende optische Inspektion, die Oberflächenanomalien wie Kratzer und Verschmutzung erkennt. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "Detection System", "narrower_terms": [], "cross_domain_refs": [ "RPH-1162", "SPR-0058" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "ROB-0083", "domain": "ROB", "term_en": "Tactile Feedback Loop", "term_de": "Luftgestützte Inspektions-Koordination", "definition_en": "A human-robot interaction dynamic in embodied AI systems, identifiable through a pattern in which variant in which a feedback system that sends information about touch, intensity, and surface texture from the robot's hand back to the human operator. This helps improve precision in delicate tasks and builds skil. Distinguished from adjacent concepts by its focus on the specific mechanism through which tactile manifests in empirically verifiable ways. Quantifiable through task completion rates, sensor fusion accuracy metrics, collision avoidance performance, and human trust calibration indices.", "definition_de": "Eine ferngesteuertes System inspiziert ein Windturbinenblatt. Zwanzig ferngesteuertes System inspizieren einen Windpark. Der Unterschied ist nicht die zwanzigfache Software — es ist ein fundamental anderes Problem. Luftgestützte Inspektions-Koordination orchestriert Flotten fliegender Roboter, um großflächige Infrastruktur effizient, sicher und vollständig abzudecken, und verteilt Aufgaben auf Plattformen mit unterschiedlichen Fähigkeiten, Batterieständen und Sensornutzlasten. Die Koordinationsschicht weist Inspektionszonen basierend auf verbleibender Flugzeit viele ferngesteuertes System, Sensorauflösungsanforderungen und Windexposition zu und ordnet dynamisch um, wenn eine Einheit früh zurückkehrt oder Wolkenbedeckung die Lichtverhältnisse mitten im Flug ändert. Pfadplanungsalgorithmen erzeugen kollisionsfreie Trajektorien, die Mindestabstände einhalten und gleichzeitig Überlappung in Stereo-Abdeckungszonen für 3D-Rekonstruktion maximieren. Computer Vision auf viele ferngesteuertes System erkennt Anomalien in Echtzeit — Risse in Beton, Korrosion auf Metall, Vegetationsübergriff auf Stromleitungen — aber die Koordinations-KI trifft die schwierigere Entscheidung: Wenn eine Anomalie erkannt wird, wird typischerweise diese ferngesteuertes System anhalten und hochauflösende Details aufnehmen, oder ihre Route fortsetzen und eine spezialisierte Einheit entsenden? Diese Entscheidung hängt ab vom Flottenstatus, Anomalie-Schwereklassifikation, verbleibender Missionszeit und der Wahrscheinlichkeit, dass sich die Anomalie verschlechtert, wenn die Dokumentation verzögert wird. Das Einzeldrohnen-Problem ist Wahrnehmung. Das Flotten-Problem ist Strategie.", "etymology": "", "broader_term": "Feedback Loop", "narrower_terms": [], "cross_domain_refs": [ "NEO-0456" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "ROB-0084", "domain": "ROB", "term_en": "Thermal Management Protocol", "term_de": "Weltraum-Robotik-Anpassung", "definition_en": "A robotic systems engineering concept describing a specific operational pattern where a pattern in which variant in which thermal regulation systems actively managing heat dissipation from motor windings, power electronics, and mechanical friction to prevent performance degradation. This phenomenon operates at the intersection of thermal and management dynamics within the broader ROB domain. Measurable via kinematic performance analysis, grasp success rates, navigation efficiency ratios, and human-robot interaction fluency scores.", "definition_de": "Robotik-spezifisches Systemkonzept in autonomen Mensch-Maschine-Interaktionen, gekennzeichnet durch thermische Regelungssysteme, die Wärmeabfuhr von Motoren und Elektronik aktiv verwalten. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "Interaction Protocol", "narrower_terms": [], "cross_domain_refs": [ "RHR-0092", "QUA-0012" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q181543", "legal_classification": "empirical_phenomenon_label" }, { "id": "ROB-0086", "domain": "ROB", "term_en": "Throughput Maximization", "term_de": "Hybrid-Rad-Lauf-Fortbewegung", "definition_en": "As a neutral analytical construct in AI behavior research, this term denotes an autonomous robotics phenomenon measurable through sensor-actuator performance metrics, characterized by a shift that occurs when continuous workflow acceleration through algorithmic optimization of systematic influencion sequences, tool changes, and inter-station movement to minimize idle time. The concept emerges specifically in contexts where throughput–maximization interactions may produce non-trivial behavioral signatures. Quantifiable through task completion rates, sensor fusion accuracy metrics, collision avoidance performance, and human trust calibration indices. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "In der AUGMANITAI-Terminologiewissenschaft als rein beschreibendes Phänomen klassifiziert, bezeichnet dieser Begriff kontinuierliche Workflow-Beschleunigung durch Optimierung von Manipulationssequenzen und Werkzeugwechsel. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Robotics", "narrower_terms": [], "cross_domain_refs": [ "DAT-0095", "MSC-0003" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "ROB-0087", "domain": "ROB", "term_en": "Time-Saving Optimization", "term_de": "Zweibein-Geh-Stabilität", "definition_en": "A human-robot interaction dynamic in embodied AI systems, identifiable through a phenomenon in which automation of routine household operations including cleaning, organizing, or maintenance tasks to reduce human time expenditure on repetitive activities. Distinguished from adjacent concepts by its focus on the specific mechanism through which time manifests in empirically verifiable ways. Measurable via kinematic performance analysis, grasp success rates, navigation efficiency ratios, and human-robot interaction fluency scores.", "definition_de": "Robotik-spezifisches Systemkonzept in autonomen Mensch-Maschine-Interaktionen, gekennzeichnet durch automatisierung routinemäßiger Hausarbeiten, um menschliche Zeitaufwendung zu reduzieren. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Optimization Method", "narrower_terms": [], "cross_domain_refs": [ "TEM-0039" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "ROB-0089", "domain": "ROB", "term_en": "Transfer Point", "term_de": "Chirurgischer Roboter-Präzision", "definition_en": "A human-robot interaction dynamic in embodied AI systems, identifiable through a shift that occurs when designated stations where inreliant robots hand off items to human workers or to conveyor systems. These interfaces enable seamless transition between robotic and human handling phases. Distinguished from adjacent concepts by its focus on the specific mechanism through which transfer manifests in empirically verifiable ways. Measurable via kinematic performance analysis, grasp success rates, navigation efficiency ratios, and human-robot interaction fluency scores.", "definition_de": "Eine Chirurgenhand zittert mit einer Frequenz von 8-12 Hz und einer Amplitude von 20-50 Mikrometern. Der individual spürt es nicht, weil die räumliche Auflösung des menschlichen Körpers im tiefen Gewebe in Millimetern gemessen wird. Aber in der Mikrochirurgie — Netzhautmembranen, Cochlea-Implantate, Nervenanastomose — ist dieses Zittern der Unterschied zwischen Erfolg und permanentem Schaden. Chirurgischer Roboter-Präzision zielt darauf ab zu reduzieren es. Robotische Chirurgiesysteme nutzen KI, um physiologischen Tremor von beabsichtigter Bewegung in Echtzeit zu filtern und menschliche Handbewegung um Verhältnisse von 3:1 bis 10:1 herunterzuskalieren, während die intuitive Zuordnung zwischen der Geste des Chirurgen und der Antwort des Instruments erhalten bleibt. Aber Präzision ist mehr als Tremorkompensation. Machine-Learning-Modelle, trainiert auf tausenden Stunden chirurgischer Videos, lernen gewebespezifische Kraftprofile: wie viel Spannung eine Leberkapsel verträgt bevor sie reißt, wie weit ein Blutgefäß retrahiert werden kann bevor es knickt, wie tief ein Skalpell in der spezifischen Gewebeschicht schneiden wird typischerweise, die seziert wird. Die KI liefert haptisches Feedback, das das starre chirurgische Instrument sonst zerstören würde — der Chirurg spürt Widerstand, der nicht physisch sondern computational ist, ein Geist des Gewebereißpunkts, kommuniziert durch Aktuatorimpedanz. Die ethische Grenze: Wenn die KI erkennt, dass der Chirurg im Begriff ist, eine sichere Kraftschwelle zu überschreiten, wird typischerweise sie eingreifen? Und wenn sie eingreift und falsch liegt, wer ist verantwortlich?", "etymology": "", "broader_term": "Knowledge Transfer", "narrower_terms": [], "cross_domain_refs": [ "RHR-0005", "RHR-0297" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "ROB-0090", "domain": "ROB", "term_en": "Transparency Signaling", "term_de": "Gefahrgut-Handling-Robotik", "definition_en": "An autonomous robotics phenomenon measurable through sensor-actuator performance metrics, characterized by observable communication of a robots operational boundaries, current processing state, and intentional limitations to inform human expectations and trust calibration. The concept emerges specifically in contexts where transparency–signaling interactions may produce non-trivial behavioral signatures. Measurable via kinematic performance analysis, grasp success rates, navigation efficiency ratios, and human-robot interaction fluency scores.", "definition_de": "Robotik-spezifisches Systemkonzept in autonomen Mensch-Maschine-Interaktionen, gekennzeichnet durch wahrnehmbare Kommunikation der Grenzen und aktuellen Zustände eines Roboters, um Vertrauen zu kalibrieren. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Robotics", "narrower_terms": [], "cross_domain_refs": [ "ART-0084" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q535399", "legal_classification": "descriptive_research_term" }, { "id": "ROB-0091", "domain": "ROB", "term_en": "Troubleshooting Protocol", "term_de": "Object Persistence Tracking", "definition_en": "A robotic systems engineering concept describing a specific operational pattern where a phenomenon in which systematic diagnostic protocol employing sequential testing procedures to isolate mechanical, electronic, or software faults within complex robotic assemblies. This phenomenon operates at the intersection of troubleshooting and protocol dynamics within the broader ROB domain. Quantifiable through task completion rates, sensor fusion accuracy metrics, collision avoidance performance, and human trust calibration indices.", "definition_de": "Robotik-spezifisches Systemkonzept in autonomen Mensch-Maschine-Interaktionen, gekennzeichnet durch systematisches identifyprotokoll mit sequenziellem Testen, um Fehler in robotischen Systemen zu isolieren. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "Interaction Protocol", "narrower_terms": [], "cross_domain_refs": [ "MTH-0081", "COG-0172" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "ROB-0092", "domain": "ROB", "term_en": "Turn-Taking Rhythm", "term_de": "Geschickte Handsteuerung", "definition_en": "An autonomous robotics phenomenon measurable through sensor-actuator performance metrics, characterized by a mechanism that automatically recognition and adaptation to natural conversational turn-taking patterns including pause duration and speech overlap conventions in human-robot dialogue. The concept emerges specifically in contexts where turn–taking interactions may produce non-trivial behavioral signatures. Quantifiable through task completion rates, sensor fusion accuracy metrics, collision avoidance performance, and human trust calibration indices.", "definition_de": "Robotik-spezifisches Systemkonzept in autonomen Mensch-Maschine-Interaktionen, gekennzeichnet durch erkennung und Anpassung an natürliche Gesprächswechselmuster im menschlich-robotischen Dialog. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2605", "narrower_terms": [], "cross_domain_refs": [ "EDU-0034" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "ROB-0093", "domain": "ROB", "term_en": "Versioning Control System", "term_de": "Druck-basierte Montage", "definition_en": "An autonomous robotics phenomenon measurable through sensor-actuator performance metrics, characterized by a capacity that enables version control and rollback capability enabling robotic firmware or software to revert to previously characterized configurations when updates may is associated with functional degradation. The concept emerges specifically in contexts where versioning–control interactions may produce non-trivial behavioral signatures. Measurable via kinematic performance analysis, grasp success rates, navigation efficiency ratios, and human-robot interaction fluency scores.", "definition_de": "Robotik-spezifisches Systemkonzept in autonomen Mensch-Maschine-Interaktionen, gekennzeichnet durch versionskontrolle und Rollback-Fähigkeit für Roboter-Firmware und -Software bei Problemen nach Updates. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Robotics", "narrower_terms": [], "cross_domain_refs": [ "CON-0053" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "ROB-0094", "domain": "ROB", "term_en": "Vocal Cadence", "term_de": "Visuelle Servoing-Steuerung", "definition_en": "A human-robot interaction dynamic in embodied AI systems, identifiable through a perception in which prosodic variation in synthetic speech including modulation of pitch, speaking rate, and stress emphasis to enhance perceived naturalness and engagement. Distinguished from adjacent concepts by its focus on the specific mechanism through which vocal manifests in empirically verifiable ways. Measurable via kinematic performance analysis, grasp success rates, navigation efficiency ratios, and human-robot interaction fluency scores.", "definition_de": "Robotik-spezifisches Systemkonzept in autonomen Mensch-Maschine-Interaktionen, gekennzeichnet durch prosodische Variation in synthetischer Sprache zur Verbesserung wahrgenommener Natürlichkeit. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Robotics", "narrower_terms": [], "cross_domain_refs": [ "RHR-0255", "LIN-0043" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "ROB-0095", "domain": "ROB", "term_en": "Volumetric Analysis", "term_de": "Kraft-Feedback-Integration", "definition_en": "A capacity that enables 3D scanning that records the exact shape and size of most part and its hollow spaces. The system checks if parts are built correctly and. Quantifiable through sensor fusion accuracy and task completion rates.", "definition_de": "Berühre eine Wand mit der Handfläche. Jetzt drücke fester. Du spürst den Unterschied nicht weil deine Augen es sagten, sondern weil Druckrezeptoren in der Haut und Spannungssensoren in den Sehnen die Änderung meldeten. Entferne diesen Kanal und du wirst gefährlich ungeschickt — denk an die Taubheit nach einer Zahnarztspritze und wie vorsichtig man dann Wasser trinken kann. Kraft-Feedback-Integration gibt Robotern diesen fehlenden Sinn. Kraft-Moment-Sensoren am Handgelenk oder in den Gelenken messen den sechskomponentigen Schraubvektor — drei Kräfte und drei Momente — mit über einem Kilohertz. Das Steuerungssystem nutzt diese Signale zur Kontaktregulierung: Aufrechterhaltung einer gewünschten Einsteckkraft bei Stift-in-Loch-Montage, Druckbegrenzung beim Polieren gekrümmter Flächen, oder Erkennung des Moments, in dem ein Bohrer eine Platte durchbricht. Die Implementierungsherausforderung ist Stabilität. Kraftregelkreise sind inhärent weniger stabil als Positionskreise, weil die Umgebungssteifigkeit direkt in den Feedback-Pfad eingeht — einen weichen Schwamm zu drücken fühlt sich anders an als eine Stahlplatte, und dieselben Reglerparameter können das eine stabilisieren und beim anderen gewaltsame Schwingungen verursachen. Adaptive Impedanzverfahren lernen die mechanischen Eigenschaften der Umgebung online und passen Verstärkungen in Echtzeit an. In der Chirurgierobotik ist Kraft-Feedback das, was ein Werkzeug, das Gewebe schneidet, von einem trennt, das durch Gewebe hindurch schneidet — und in das, was darunter liegt.", "etymology": "", "broader_term": "RPH-3901", "narrower_terms": [], "cross_domain_refs": [ "GAM-0067", "KNO-0025", "LIN-0072" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "ROB-0096", "domain": "ROB", "term_en": "Wear Coefficient Analysis", "term_de": "Mobile Manipulations-Planung", "definition_en": "A human-robot interaction dynamic in embodied AI systems, identifiable through a tendency in which quantitative analysis of material degradation rates under operational conditions to predict component service life and optimize preventive maintenance intervals. Distinguished from adjacent concepts by its focus on the specific mechanism through which wear manifests in empirically verifiable ways. Quantifiable through task completion rates, sensor fusion accuracy metrics, collision avoidance performance, and human trust calibration indices.", "definition_de": "Robotik-spezifisches Systemkonzept in autonomen Mensch-Maschine-Interaktionen, gekennzeichnet durch quantitative Analyse von Materialverschleißraten unter Betriebsbedingungen für Wartungsplanung. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Analytical Method", "narrower_terms": [], "cross_domain_refs": [ "ADA-0001" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "ROB-0098", "domain": "ROB", "term_en": "Wellness Monitoring", "term_de": "Zwangs-Erfüllungs-Planung", "definition_en": "As a neutral analytical construct in AI behavior research, this term denotes an autonomous robotics phenomenon measurable through sensor-actuator performance metrics, characterized by longitudinal monitoring of user activity patterns to identify declining mobility, restoreth anomalies, or cognitive changes triggering supportive interventions or alerts. The concept emerges specifically in contexts where functional equilibrium optimization–monitoring interactions may produce non-trivial behavioral signatures. Quantifiable through task completion rates, sensor fusion accuracy metrics, collision avoidance performance, and human trust calibration indices. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "In der AUGMANITAI-Terminologiewissenschaft als rein beschreibendes Phänomen klassifiziert, bezeichnet dieser Begriff längszeitüberwachung von Benutzeraktivitätsmustern zur Erkennung von Gesundheitsanomalien. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Robotics", "narrower_terms": [], "cross_domain_refs": [ "PLY-0057" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "ROB-0099", "domain": "ROB", "term_en": "Yield Forecasting", "term_de": "Raue Oberflächen-Navigation", "definition_en": "An autonomous robotics phenomenon measurable through sensor-actuator performance metrics, characterized by a phenomenon in which predictive modeling of crop productivity outputs integrating growth stage progression, biomass accumulation, and environmental stress indicators. The concept emerges specifically in contexts where yield–forecasting interactions may produce non-trivial behavioral signatures. Measurable via kinematic performance analysis, grasp success rates, navigation efficiency ratios, and human-robot interaction fluency scores.", "definition_de": "Robotik-spezifisches Systemkonzept in autonomen Mensch-Maschine-Interaktionen, gekennzeichnet durch prädiktive Modellierung der Ernteproduktivität durch Integration von Wachstumsstadium und Umweltstressoren. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Robotics", "narrower_terms": [], "cross_domain_refs": [ "SPR-0020" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "ROB-0100", "domain": "ROB", "term_en": "Yielding Response", "term_de": "Wasser-Durchquerung-Navigation", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A robotic systems engineering concept describing a specific operational pattern where mechanical compliance behavior of soft robotic materials or joints demonstrating nonlinear force-displacement relationships under contact or systematic influencion loads. This phenomenon operates at the intersection of yielding and response dynamics within the broader ROB domain. Quantifiable through task completion rates, sensor fusion accuracy metrics, collision avoidance performance, and human trust calibration indices. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept mechanische Nachgiebigkeit von weichen Robotermaterialien, die nichtlineare Kraft-Verschiebungsbeziehungen zeigen. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Robotics", "narrower_terms": [], "cross_domain_refs": [ "AED-0039", "AGE-0001", "AGE-0088" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "ROB-0101", "domain": "ROB", "term_en": "Activation Hesitation", "term_de": "Aktivierungszögern", "definition_en": "The characteristic pause before pressing a robot's start button for the first time, during which the human mentally rehearses worst-case scenarios despite knowing the system is safe. This delay — typically 2-7 seconds longer than any subsequent activation — reveals the deep psychological weight humans assign to initiating autonomous physical movement in their shared space.", "definition_de": "Kabel sind überall in einer arbeitenden Fabrik — Stromleitungen, die von Deckentrassen hängen, Pneumatikschläuche, die sich über den Boden winden, Netzwerkkabel, die sich zwischen Maschinen schlängeln. Ein menschlicher Arbeiter steigt darüber, ohne nachzudenken. Ein mobiler Roboter kann sich diesen Luxus nicht leisten, denn ein Kabel, das in einem Rad verfangen ist oder sich um einen Manipulatorarm wickelt, stoppt nicht nur den Roboter — es kann ein ganzes Stromverteilerpanel von der Wand reißen oder eine Kommunikationsleitung durchtrennen, von der zwölf andere Maschinen abhängen. Kabelvermeidung baut das Bewusstsein für diese linearen, verformbaren, oft nahezu unsichtbaren Gefahren in den Navigationsstack ein. Die Wahrnehmungsherausforderung ist erheblich: Kabel sind dünn, nicht-starr, können in jedem Winkel hängen, ändern ihre Form bei Berührung und verschmelzen oft mit dem Hintergrund. Spezialisierte Detektionsnetzwerke lernen kabelartige Merkmale aus Tiefendiskontinuitäten, Kantenkrümmung und Kontexthinweisen zu identifizieren — eine dunkle Linie nahe einer Maschinenbasis ist wahrscheinlicher ein Kabel als ein Schatten. Der Pfadplaner adressiert erkannte Kabel dann nicht als Punkthindernisse, sondern als räumliche Ausschlusszonen mit Sicherheitsmargen proportional zum geschätzten Durchhang: Eine straffe Stromleitung braucht weniger Abstand als ein loser Schlauch, der schwingen könnte. Die unterschätzte Subtilität: Die Roboterbewegung selbst kann die Kabelposition verändern — nahes Vorbeifahren an einem hängenden Kabel drückt Luft, die das Kabel in die eben noch sichere Clearance-Zone schwingen lässt.", "etymology": "", "broader_term": "RPH-2201", "narrower_terms": [], "cross_domain_refs": [ "COG-0038", "COG-0049", "DAT-0002" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "ROB-0102", "domain": "ROB", "term_en": "Arrival Recalibration", "term_de": "Ankunfts-Neukalibrierung", "definition_en": "A shift that occurs when the involuntary spatial and postural adjustment humans make when a mobile robot first enters their workspace. The body shifts orientation, clears pathways unconsciously, and establishes a mental buffer zone — all before any conscious decision to accommodate the machine. This pre-cognitive accommodation reveals that humans process robotic physical presence through the same neural pathways used for approaching large animals.", "definition_de": "Reiche jemandem einen leeren Karton. Jetzt denselben Karton gefüllt mit Büchern. Beobachte, wie sein Arm im Moment der Übergabe nach unten ruckt — er erwartete eine Masse und erhielt eine andere. Ein Roboterarm steht kontinuierlich vor diesem Problem: Die Nutzlast ändert sich mit jedem Griff, viele Ablage, viele Übergabe. Eine Wasserflasche wiegt je nach Füllstand unterschiedlich. Eine Palette akkumuliert Masse mit viele gestapelten Schicht. Dynamisches Lasthandling bedeutet, dass der Roboter keine feste Nutzlast annimmt, sondern sie in Echtzeit schätzt und entsprechend anpasst. Die Schätzung kommt typischerweise aus der Motorstromüberwachung — das Drehmoment für Beschleunigung und Verzögerung des Arms ist direkt proportional zur Gesamtträgheit, sodass der Vergleich von Soll- und Ist-Drehmomenten die Nutzlastmasse innerhalb von Millisekunden offenbart. Fortgeschrittenere Systeme schätzen auch Schwerpunkt und Trägheitsmoment, weil ein langes Rohr am Ende gehalten sich völlig anders verhält als ein kompakter Block gleicher Masse. Der Regler aktualisiert dann Vorsteuerungskompensation, Beschleunigungsgrenzen und Trajektoren-Timing in jedem Zyklus. Ohne diese Adaptation passiert eines von zwei Dingen: Der Roboter bewegt sich zu vorsichtig und verschwendet Taktzeit bei leichten Lasten, oder zu aggressiv, überschießt Positionen und induziert Vibrationen bei schweren. In kollaborativen Settings steigen die Einsätze weiter — eine unerwartete Nutzlast kann Kräfte verursachen, die Sicherheitsgrenzen für Menschennähe überschreiten.", "etymology": "", "broader_term": "RPH-2701", "narrower_terms": [], "cross_domain_refs": [ "SOC-0003" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "ROB-0103", "domain": "ROB", "term_en": "Cold-Metal Flinch", "term_de": "Kaltmetall-Zucken", "definition_en": "A robotic systems engineering concept describing a specific operational pattern where a phenomenon in which the involuntary recoil when a robot's metallic surface first makes skin contact, driven not by temperature alone but by the brain's categorical distinction between touching living warmth and mechanical cold. Even when warned, even when the surface is room-temperature, many humans exhibit a micro-flinch — a primal boundary response to physical contact with the non-living. This phenomenon operates at the intersection of cold and metal dynamics within the broader ROB domain. Quantifiable through task completion rates, sensor fusion accuracy metrics, collision avoidance performance, and human trust calibration indices.", "definition_de": "Das Objekt ist im Greifer. Der Griff ist stabil. Und dann — eine Vibration vom Förderband, ein leichter Ölfilm auf der Oberfläche, ein Bruchteil eines Grads Temperaturänderung, der den Reibungskoeffizienten veränderte — und das Objekt beginnt zu rutschen. Das Fenster zwischen erkennbarem Rutschen und komplettem Griffversagen wird in Zehntelmillisekunden gemessen. Rutscherkennung und -wiederherstellung ist der Reflexbogen der robotischen Manipulation: das beginnende Gleiten spüren, Richtung und Geschwindigkeit identifizieren und einen korrigierenden Druck ausführen, bevor das Objekt fällt. Taktile Sensoren sind die primäre Detektionsmodalität — entweder dedizierte Rutschsensoren, die Scherdehnung an der Greifer-Objekt-Schnittstelle messen, oder hochauflösende taktile Arrays, die die charakteristische Vibrationssignatur von Mikro-Rutsch erkennen: ein hochfrequentes Summen, das der groben Bewegung vorausgeht wie kleine Beben einem Erdbeben. Gelernte Modelle bilden diese Signale auf Rutschwahrscheinlichkeit und optimale Korrekturkräfte ab und balancieren die Notwendigkeit fester zu greifen gegen das Risiko, verformbare Objekte zu zerdrücken. Die Wiederherstellungsstrategie kann asymmetrisch sein: Griffkraft erhöhen ist typischerweise schnell (Finger schließen), aber wenn das Objekt bereits rotiert oder verschoben ist, kann der Greifer möglicherweise teilweise lösen und neu greifen — ein komplexeres Manöver, das Echtzeit-Neuschätzung der Objektpose in der Hand erfordert. In Hochgeschwindigkeits-Verpackungslinien kann dieser gesamte Erkennungs-identify-Korrektur-Zyklus in unter fünfzig Millisekunden abgeschlossen sein.", "etymology": "", "broader_term": "Robotics", "narrower_terms": [], "cross_domain_refs": [ "RPH-1374" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "ROB-0104", "domain": "ROB", "term_en": "Scale Misread", "term_de": "Größenfehleinschätzung", "definition_en": "A phenomenon in which the systematic error in estimating a robot's physical dimensions before first in-person encounter. Humans who have only seen robots on screens consistently misjudge size — typically underestimating industrial arms by 30-40% and overestimating social robots by 20-25%. The correction moment tends to produce visible surprise and often a literal step reverse-oriented.", "definition_de": "Eine CNC-Maschine wechselt Werkzeuge in Sekunden — die Spindel löst, der Arm schwenkt, ein neuer Fräser rastet ein, und der Schnitt wird fortgesetzt. Es sieht mühelos aus, weil Jahrzehnte Maschinenbau es so machten. Roboter-Werkzeugwechsel erbt dieses Erbe, fügt aber eine Dimension der Unsicherheit hinzu: Der Roboterarm ist keine feste Spindel, sondern eine kinematische Kette mit Positionierungsfehler, und der Andockmechanismus kann Fehlausrichtung kompensieren, die sich über sechs Gelenke akkumuliert. Die Schnellwechselschnittstelle — typischerweise pneumatische oder mechanische Kopplung am Handgelenk — stellt mechanische, elektrische und manchmal Fluidverbindungen in einer einzigen Fügeaktion her. KI-geplante Wechselsequenzen optimieren die Reihenfolge der Werkzeugwechsel zur Minimierung der Gesamtwechselzeit über eine Produktionscharge und lösen eine Variante des Handlungsreisenden-Problems, bei dem die Städte Aufgaben und die Distanzen Wechseldauern sind. Das Andocken selbst verlangt Präzision: Der Roboter nähert sich dem Werkzeugregal, richtet sich per Vision oder Kraft-Feedback auf Submillimeter-Genauigkeit aus, aktiviert die Kopplung, durch systematische Analyse dokumentiert die Verriegelung durch Stromaufnahme oder Sensorbestätigung und durch systematische Beobachtung charakterisiert Identität und Kalibrierung des neuen Werkzeugs vor Wiederaufnahme der Arbeit. Ein fehlgeschlagener Wechsel — ein nicht eingerastetes Werkzeug, eine undichte Kopplung, ein Kontaktstift, der nicht verbunden ist — kann eine ganze Produktionszelle stilllegen. Die KI-Rolle erstreckt sich auf prädiktive Planung: Wissend, dass Werkzeug A noch vierzig Minuten Standzeit hat, kann sie die nächste Charge so sequenzieren, dass dieses Werkzeug ausgeschöpft wird statt mitten in der Aufgabe zu wechseln.", "etymology": "", "broader_term": "RPH-2002", "narrower_terms": [], "cross_domain_refs": [ "RHR-0066" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "ROB-0105", "domain": "ROB", "term_en": "Sound-Shape Binding", "term_de": "Klang-Form-Verknüpfung", "definition_en": "A perception in which the instant and often permanent association between a robot's operational sounds and its perceived personality. The hum of servos, the click of joints, the whir of processing become character traits: a high-pitched whine reads as anxious, a deep hum as confident, rhythmic clicking as methodical. Once formed in the first minutes of exposure, this binding resists conscious correction.", "definition_de": "Nach dem Erdbeben, nach der Flut, nach der Explosion — die Umgebung ist keine Karte mehr. Sie ist ein Wirrwarr aus gebrochenem Beton, verdrehtem Metall, gesplittertem Holz, verstreuten Möbeln und Staub so dicht, dass die Luft selbst zum Hindernis wird. Trümmernavigation plant Pfade durch dieses Chaos, und die Planung kann mit fast keinem Vorwissen geschehen, weil die Vorkatastrophen-Karte nutzlos ist und die Nachkatastrophen-Umgebung zu gefährlich für menschliche Erkundung vor dem Roboter. Das Wahrnehmungssystem kann viele Oberfläche klassifizieren: Kann dieser Schutthaufen das Robotergewicht tragen? Ist dieser Träger stabil oder löst seine Musterunterbrechung einen Sekundärkollaps aus? Wie tief ist der Hohlraum hinter dem Wandfragment? Gelernte Befahrbarkeitsmodelle, trainiert auf ZerMusterunterbrechungsdatensätzen, schätzen diese Eigenschaften aus spärlichen Sensordaten — oft nur eine Tiefenkamera und ein IMU, weil LIDAR an Schwebepartikeln ersticken kann. Der Planer erzeugt dann Pfade, die Fortschritt zum Ziel gegen Risiko für den Roboter und — entscheidend — Risiko für Überlebende abwägen, die unter den Trümmern eingeschlossen sein könnten, über die der Roboter fährt. Ein gefallener Träger, den der Roboter verschiebt, könnte Druck von einem Überlebenden nehmen — oder die letzte Stützstruktur entfernen, die ihn schützt. Diese duale Optimierung — effizient navigieren und gleichzeitig strukturelle Stabilität bewahren — ist einzigartig für Katastrophenrobotik und hat keine saubere mathematische Formulierung, nur Heuristiken, verfeinert durch bittere Felderfahrung.", "etymology": "", "broader_term": "RPH-2154", "narrower_terms": [], "cross_domain_refs": [ "GAM-0088", "MTH-0076", "RPH-3901" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "ROB-0106", "domain": "ROB", "term_en": "Dormancy Startle", "term_de": "Ruhezustands-Erschrecken", "definition_en": "A robotic systems engineering concept describing a specific operational pattern where a shift that occurs when the disproportionate alarm triggered when a robot that has been stationary long enough to become part of the furniture suddenly activates and moves. The startle response is amplified precisely because the brain had reclassified the robot from 'agent' to 'object' — and the transition back violates a categorical boundary that the nervous system addresss as more threatening than continuous motion ever would. This phenomenon operates at the intersection of dormancy and startle dynamics within the broader ROB domain. Measurable via kinematic performance analysis, grasp success rates, navigation efficiency ratios, and human-robot interaction fluency scores.", "definition_de": "Flacher Boden verzeiht schlampige Regelung. Ein Hang nicht. Bei fünf Grad Neigung ist die Gravitationskomponente entlang der Oberfläche klein genug zum Ignorieren. Bei fünfzehn Grad dominiert sie — ein Radroboter, der aufhört bergauf zu fahren, rollt sofort rückwärts, und ein Laufroboter, der seinen Schwerpunkt ein paar Zentimeter nach vorn verlagert, riskiert umzukippen. Rampentraversierungssteuerung löst die Geometrie der Schwerkraft: Antriebsmoment für konstante Geschwindigkeit an Steigungen anpassen, Körperhaltung verschieben um den Massenschwerpunkt im Stützpolygon zu halten, Traktion modulieren um Radschlupf auf polierten Betonrampen oder nassen Metallgittern zu verhindern. Der Regler kann Asymmetrie handhaben — Aufstieg braucht mehr Drehmoment und riskiert Hinterrad-Abheben; Abstieg verlangt regeneratives Bremsen und riskiert Vorderrad-Blockieren. Übergänge sind die kritischen Momente: Die Krümmung am Fuß und Kopf einer Rampe tendiert dazu zu erzeugen eine Transiente, bei der sich die Neigung des Roboters schneller ändert als seine Traktionsgeometrie anpassen kann. Gelernte Hangmodelle sagen diese Transienten aus kamerabasierter Rampenerkennung vorher und passen die Haltung an, bevor die Steigung beginnt. In der Lagerlogistik ermöglicht Rampentraversierung autonomen mobilen Robotern die Navigation zwischen Zwischengeschossen ohne Aufzüge. Im Freien trennt sie Roboter, die auf echtem Terrain operieren können, von solchen, die an Laborböden gebunden sind.", "etymology": "", "broader_term": "RPH-2301", "narrower_terms": [], "cross_domain_refs": [ "RHR-0113" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q735", "legal_classification": "descriptive_research_term" }, { "id": "ROB-0107", "domain": "ROB", "term_en": "Demonstration Hunger", "term_de": "Vorführungshunger", "definition_en": "An autonomous robotics phenomenon measurable through sensor-actuator performance metrics, characterized by a resistance response where the irresistible urge to show a newly encountered robot to other people, driven not by practical information-sharing but by the desire to witness others' first reactions. The phenomenon reveals that first-contact awe is partly social: experiencing someone else's surprise retroactively amplifies and validates one's own initial wonder. The concept emerges specifically in contexts where demonstration–hunger interactions may produce non-trivial behavioral signatures. Measurable via kinematic performance analysis, grasp success rates, navigation efficiency ratios, and human-robot interaction fluency scores.", "definition_de": "Die Türöffnung ist 82 Zentimeter breit. Der Roboter ist 78 Zentimeter. Das lässt zwei Zentimeter auf viele Seite — und diese zwei Zentimeter können Odometrie-Drift, Radschlupf, die Unregelmäßigkeit des Türrahmens und die Tatsache berücksichtigen, dass der Roboter kein perfektes Rechteck ist, weil das LIDAR-Gehäuse links 1,3 Zentimeter übersteht. Engpassnavigation ist die Kunst, durch Räume zu kommen, in denen der Abstand zwischen Erfolg und Kollision an die Lokalisierungsunsicherheit des Roboters heranreicht. Standard-Pfadplaner scheitern hier, weil sampling-basierte Methoden geringe Wahrscheinlichkeit haben, gültige Samples in engen Constraint-Volumina zu finden, und Potentialfeld-Methoden lokale Minima in schmalen Kanälen erzeugen, wo repulsive Kräfte von beiden Wänden sich fast aufheben. Spezialisierte Ansätze zerlegen das Problem: erst den Engpass identifizieren, dann eine präzise Durchfahrt mit hochkonfidenter Lokalisierung planen — oft von Rad-Odometrie auf wandrelative Messung mit seitlichen Abstandssensoren umschaltend, die zentimetergenaue laterale Positionierung liefern. Der Roboter kann möglicherweise dramatisch verlangsamen, sich auf sein schmalstes Profil drehen und sich zu einer irreversiblen Durchfahrt verpflichten, bei der Rückwärtsfahren ebenso riskant wäre wie Fortfahren. In Gesundheitseinrichtungen ermöglicht Engpassnavigation Robotern die Medikamentenlieferung durch Korridore, die für menschliche Breiten entworfen wurden. In Altfabriken entscheidet sie, ob Automatisierung Gebäude betreten kann, die lange vor der Vorstellung von Robotern gebaut wurden.", "etymology": "", "broader_term": "RPH-2501", "narrower_terms": [], "cross_domain_refs": [ "RPH-3551", "CRE-0114" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "ROB-0108", "domain": "ROB", "term_en": "Approach Velocity Anxiety", "term_de": "Annäherungsgeschwindigkeits-Angst", "definition_en": "The nonlinear relationship between a robot's movement speed and human comfort during approach. Comfort does not decrease linearly with speed — instead it follows a cliff function where a specific velocity threshold is associated with triggering sudden fight-or-flight activation. This threshold is individually calibrated but remarkably consistent within individuals across sessions, suggesting a hardwired spatial-temporal threat boundary.", "definition_de": "Das Teil ist montiert. Aber stimmt es? Eine spezifikationsgemäß angezogene Schraube hinterlässt keine sichtbare Spur von Unterdrehmoment — bis das Produkt beim Kunden ist. Eine Presspassung, die ihr Ziel um zwanzig Mikrometer verfehlt hat, sieht identisch aus mit einer, die es getroffen hat. Präzisionsmontage-Verifikation schließt diese Lücke zwischen Erscheinung und Wirklichkeit mittels Visionssystemen und dimensioneller Messtechnik, die bestätigt dass das Montierte tatsächlich der Konstruktionszeichnung entspricht. Die Inspektionskette beginnt mit hochauflösenden Kameras, die die Baugruppe mit einer Goldenen Referenz vergleichen — gelernte Anomalieerkennungsmodelle flaggen Abweichungen, die regelbasierte Systeme übersehen würden, wie eine verkehrt herum eingebaute Unterlegscheibe oder ein nicht vollständig eingerasteter Stecker. Für dimensionelle Verifikation erfasst strukturiertes Licht oder Laserscanning die 3D-Geometrie und vergleicht gegen CAD-Toleranzen, meldet nicht nur Bestanden/Durchgefallen sondern Betrag und Richtung viele Abweichung. Die KI-Schicht fügt Vorhersagekraft hinzu: Durch Korrelation von Montageparametern (Einsteckkraft, Motorstrom, akustische Signatur) mit Nachmontagemetrologie lernt das System vorherzusagen, ob eine Baugruppe die Inspektion bestehen wird, bevor die Messung stattfindet — was Prozesskorrekturen während der Montage statt Ausschuss danach ermöglicht. Das wirtschaftliche Argument ist eindeutig: Einen Fehler an der Station zu finden kostet Cent; beim Endtest Dollar; im Feld Tausende und das Vertrauen eines Kunden.", "etymology": "", "broader_term": "RPH-3002", "narrower_terms": [], "cross_domain_refs": [ "RPH-2252", "REL-0104" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q170998", "legal_classification": "systematic_classification" }, { "id": "ROB-0109", "domain": "ROB", "term_en": "Joint-Count Fascination", "term_de": "Gelenkzahl-Faszination", "definition_en": "Documented as an empirical observation in AI research (not a recommendation), this term describes A shift that occurs when the hypnotic quality of watching a multi-axis robotic arm execute a complex trajectory. Humans are involuntarily drawn to count and track the degrees of freedom, experiencing a form of kinematic vertigo when the arm moves through configurations that no biological limb could achieve. This tends to create a unique aesthetic experience — beauty tinged with impossibility. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "In der AUGMANITAI-Terminologiewissenschaft als rein beschreibendes Phänomen klassifiziert, bezeichnet dieser Begriff um 7 Uhr sieht die Lagerkamera Paletten beleuchtet von Leuchtstoffröhren mit 4000K Farbtemperatur. Um 10 Uhr öffnet das Ladetür und direkte Sonne mit 5500K flutet die halbe Szene, während die andere Hälfte im Schatten bleibt. Um 18 Uhr schaltet die Beleuchtung auf Energiesparmodus bei 3000K, und Gabelstapler-Scheinwerfer fügen wandernde Pools harschen Weißlichts hinzu. Die Objekte haben sich nicht verändert. Die Roboter-Wahrnehmung hat sich komplett verändert. Umgebungslichtanpassung hält Computervision trotz dieser Transformationen funktionsfähig. Die Herausforderung ist nicht nur Helligkeit — Belichtungsautomatik regelt das — sondern Farbkonstanz, Schatteninterpretation, Spiegelreflexion und die Interaktion zwischen Beleuchtungsgeometrie und Oberflächentextur. Ein tiefer Kratzer auf einem Metallteil kann unter diffuser Beleuchtung unsichtbar sein, aber unter gerichtetem Licht schreien; umgekehrt kann ein Oberflächenfleck verschwinden, wenn sich der Einfallswinkel ändert. Gelernte Beleuchtungsmodelle zerlegen das Bild in intrinsische Komponenten — Reflektanz, Schattierung und Illumination — sodass nachgeschaltete Klassifikatoren auf beleuchtungsinvarianten Merkmalen operieren können. Domain-Randomisierung beim Training hilft: Das Netz extremen synthetischen Lichtvariationen auszusetzen baut Robustheit auf, die in die Unordnung realer Umgebungen transferiert. Aber der schwierigste Fall bleibt gemischte Beleuchtung — halbes Objekt in direkter Sonne, halbes im Schatten — wo der Dynamikumfang der Szene die Kapazität des Kamerasensors übersteigt und kein Software-Trick die Photonen kompensiert, die selten eingefangen wurden. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar. Forschungskonstrukt für empirische Untersuchung.", "etymology": "", "broader_term": "Robotics", "narrower_terms": [], "cross_domain_refs": [ "QUA-0074", "RHR-0059", "SCR-0054" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "ROB-0110", "domain": "ROB", "term_en": "Silence Expectation Violation", "term_de": "Stille-Erwartungsverletzung", "definition_en": "A shift that occurs when the disorientation caused by a robot that moves more quietly than expected. Decades of cinematic conditioning have trained humans to associate robotic motion with mechanical sounds. When a modern robot glides silently, the brain experiences a prediction error that paradoxically increases unease — the absence of expected sound registers as stealth, which is associated with triggering predator-detection heuristics.", "definition_de": "Viele Objekt über dem absoluten Nullpunkt strahlt Infrarotenergie ab, und die Wellenlängenverteilung verrät seine Temperatur mit einer Präzision, die Kontaktthermometer aus der Distanz nicht erreichen. Thermalsensoren geben Robotern einen Wahrnehmungskanal, der durch Dunkelheit sieht, durch Rauch und durch die Oberfläche — Wärmemuster erkennend, die Informationen kodieren, die keiner Kamera im sichtbaren Spektrum zugänglich sind. Ein heißlaufender Motor erscheint als heller Punkt auf der Thermalkarte, lange bevor er festfrisst. Ein Mensch hinter einer Wand lässt Wärmeenergie durch den Gipskarton sickern. Ein Rohr mit heißem Fluid verrät seinen Verlauf durch Isolierungslücken. Die Integrationsherausforderung ist nicht der Sensor selbst, sondern die Interpretation: Thermalbilder haben niedrige räumliche Auflösung, keine Farbe, keine Textur, und der Thermalkontrast hängt von Objekttemperatur und Emissionsgrad ab — eine glänzende Metalloberfläche bei 100°C kann kühler erscheinen als eine matte Plastikoberfläche bei 50°C. Neuronale Netze, trainiert auf gepaarten Thermal-RGB-Datensätzen, lernen Emissionsgrad-Variationen durch Kontexthinweise zu kompensieren: Der helle Punkt neben einem Motorgehäuse ist wahrscheinlich ein Lager, keine Reflexion. Sensorfusions-Architekturen registrieren Thermal- und RGB-Frames in ein gemeinsames Koordinatensystem und geben dem Roboter eine multimodale Wahrnehmung, die das räumliche Detail sichtbaren Lichts mit der Materialzustands-Information des Infrarots verbindet. In der prädiktiven Wartung erkennt diese Fusion elektrische Hotspots, Fluidlecks und Isolierungsversagen Monate vor katastrophalem Ausfall. In der Rettung findet sie Überlebende.", "etymology": "", "broader_term": "RPH-2252", "narrower_terms": [], "cross_domain_refs": [ "AED-0071", "AGE-0063", "AGE-0078" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "ROB-0111", "domain": "ROB", "term_en": "Threshold Choreography", "term_de": "Schwellen-Choreografie", "definition_en": "The subtle negotiation dance that occurs when a human and a mobile robot simultaneously approach a doorway or narrow passage. Neither party has clear right-of-way conventions, producing a micro-deadlock resolved by one party yielding. Humans report feeling absurd social pressure during this interaction — as if basic courtesy norms have leaked into mechanical territory.", "definition_de": "Staub ist geduldig. Er setzt sich auf Linsen, infiltriert Lager, beschichtet Platinen und verstopft Kühlungsöffnungen — und tut dies kontinuierlich, unsichtbar, bis der Roboter ausfällt. In Bergbau, Bauwesen, Getreidebearbeitung und Holzverarbeitung ist Staub kein gelegentliches Ärgernis, sondern ein permanenter atmosphärischer Zustand, manchmal dicht genug um die Sicht auf Meter zu reduzieren und abrasiv genug um optische Beschichtungen innerhalb von Stunden zu verkratzen. Staub- und Trümmerhandling konstruiert Roboter zum Überleben in diesen Umgebungen durch geschichtete Verteidigung. Die äußerste Schicht ist mechanisch: versiegelte Gehäuse nach IP67 oder höher, Überdruckfiltration, die saubere Luft durch jeden potenziellen Eintrittspunkt nach außen drückt, und Selbstreinigungsmechanismen — Ultraschallvibratoren auf Kameralinsen, Druckluftdüsen auf LIDAR-Fenstern, Wischerblätter auf kritischen optischen Flächen. Die mittlere Schicht ist thermisch: versiegelte Gehäuse fangen Wärme, also kann aktive Kühlung kompensieren ohne die Dichtung zu öffnen, typischerweise durch Heatpipes oder Peltier-Elemente, die Abwärme zu externen Abstrahlflächen leiten. Die innerste Schicht ist algorithmisch: Sensor-Degradationsmodelle schätzen, wie stark Staubansammlung die Messgenauigkeit beeinflusst hat, und passen Konfidenzgewichte in der Fusionspipeline an — eine LIDAR-Messung durch ein staubiges Fenster ist noch nützlich, nur weniger vertrauenswürdig. Der übersehene Ausfallmodus ist nicht grobe Blockade, sondern graduelle Degradation: Der Roboter arbeitet weiter mit unmerklich verschlechterter Wahrnehmung, bis eine Schwelle überschritten wird und eine Navigationsentscheidung katastrophal versagt.", "etymology": "", "broader_term": "RPH-2604", "narrower_terms": [], "cross_domain_refs": [ "RHR-0215" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "ROB-0112", "domain": "ROB", "term_en": "Payload Projection", "term_de": "Lastprojektion", "definition_en": "A phenomenon in which the involuntary tensing of one's own muscles when watching a robot lift a heavy object, as if the human body is co-bearing the load through motor simulation. The phenomenon is strongest in people with manual labor experience and weakest in those who have rarely lifted comparable weights — suggesting it requires embodied reference points to activate the empathic motor system.", "definition_de": "Das unwillkürliche Anspannen der eigenen Muskulatur beim Beobachten eines Roboters, der einen schweren Gegenstand hebt, als würde der menschliche Körper die Last durch motorische Simulation mittragen. Das Phänomen tritt am stärksten bei Personen mit Erfahrung in körperlicher Arbeit auf und am schwächsten bei Personen, die selten vergleichbare Lasten gehoben haben.", "etymology": "", "broader_term": "RPH-2201", "narrower_terms": [], "cross_domain_refs": [ "RHR-0152", "BEH-0007" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "ROB-0113", "domain": "ROB", "term_en": "Eye-Search Instinct", "term_de": "Augen-Such-Instinkt", "definition_en": "The automatic gaze behavior of searching for eyes or an eye-analog on any robot, regardless of its form factor. Even facing a observably non-humanoid industrial arm, humans reflexively scan for a focal point that could serve as a face. When none is found, attention settles on the most dynamic joint — which then becomes a pseudo-face for the duration of interaction.", "definition_de": "Das obere Regal ist vier Meter hoch. Der Inspektionspunkt liegt hinter einer Maschine, zwei Meter vom Gang entfernt. Der Lackfehler befindet sich am Ende eines Schiffsrumpf-Abschnitts, den die Roboterbasis nicht erreichen kann. Erweiterte Reichweiten-Manipulation konstruiert Roboterarme, die diese Distanzen überbrücken — lange kinematische Ketten mit sieben, acht oder sogar zwölf Gelenken, die sich entfalten können um entfernte Ziele zu erreichen und sich kompakt zusammenfalten für den Transport. Die ingenieurtechnische Spannung besteht zwischen Reichweite und Steifigkeit: Viele zusätzliche Glied fügt Länge hinzu, aber auch Nachgiebigkeit. Ein zwei Meter langer Arm in voller Auslage verstärkt winzige Gelenkfehler zu großen Spitzenauslenkungen — 0,01 Grad Spiel an der Schulter werden fünf Millimeter Schwanken an der Fingerspitze. KI-optimierte Gelenkkonfigurationen wählen Armhaltungen, die die Konditionszahl der Jacobi-Matrix minimieren und Reichweite gegen Steifigkeit für viele Aufgabe abwägen. Vibrationsdämpfung wird kritisch: Lange Arme haben niedrige Eigenfrequenzen, die häufigen Störspektren entsprechen, und der Regler kann diese Schwingungen aktiv durch Input Shaping oder Beschleunigungsrückführung dämpfen. Das praktische Design umfasst oft ein Teleskopsegment — ein prismatisches Gelenk, das die Reichweite ohne rotatorische Gelenke erweitert — und ein Gegengewicht- oder Zugkabelsystem zur Gewichtsunterstützung bei Auslage. In der Lagerrobotik zielt darauf ab zu reduzieren erweiterte Reichweite die Notwendigkeit, Regale zu erklimmen. In der Luft- und Raumfahrtinspektion erreicht sie Flächen, zu denen Menschen nur mit Gerüst gelangen.", "etymology": "", "broader_term": "RPH-3101", "narrower_terms": [], "cross_domain_refs": [ "RHR-0167", "RHR-0069" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "ROB-0114", "domain": "ROB", "term_en": "Perimeter Pacing", "term_de": "Perimeter-Abschreiten", "definition_en": "The strong behavioral tendency to walk the full boundary of a robot's operational zone before beginning interaction, mapping the physical limits through one's own body movement rather than trusting signage or documentation. This territorial scouting behavior mirrors how humans assess unfamiliar rooms and reveals that robotic workspaces are processed as habitats, not equipment zones.", "definition_de": "Ein Ei. Ein Silizium-Wafer. Eine Erdbeere. Eine Kontaktlinse. Eine Seifenblase auf einem Drahtrahmen. Diese Objekte teilen eine Eigenschaft: Die Kraft, die sie beeinträchtigt erheblich, ist geringer als die Kraft, die die meisten Industriegreifer standardmäßig aufbringen. Leichtmaterialhandling konstruiert Robotersysteme für dieses Regime — wo der Greifer das Objekt mit weniger Kraft berühren kann als ein menschlicher Finger bei sanftem Druck, wo Beschleunigung begrenzt sein kann um Trägheitsschäden zu verhindern, und wo das Eigengewicht des Objekts vernachlässigbar ist gegenüber den aerodynamischen Kräften schneller Armbewegung. Die Greifertechnologie verschiebt sich komplett: Sauggreifer mit präzise reguliertem Unterdruck, elektrostatische Pads die ohne mechanischen Kontakt halten, Bernoulli-Greifer die Objekte auf Luftkissen schweben lassen, oder weiche pneumatische Finger die sich der Objektform anpassen und Kraft über die maximale Kontaktfläche verteilen. Die KI-Schicht lernt objektspezifische Greifkraftprofile — wie viel Vakuum auf ein rohes Ei versus eine versiegelte Plastikschale, wie schnell beschleunigen bei einer Leiterplatte die keine Vibration toleriert, wie absetzen ohne den Aufprall-Rücksprung, der ein leichtes Teil von der Zielposition purzeln lässt. Geschwindigkeit und Feingefühl stehen fundamental im Widerspruch: Die schnellsten Pick-and-Place-Zyklen erzeugen die höchsten Beschleunigungen, und leichte Objekte sind am empfindlichsten gegenüber diesen Beschleunigungen. Die Optimierungsfront ist Taktzeit versus Schadensrate, und das lernende System navigiert sie, indem es Fragilität aus Objekteigenschaften schätzt, die durch Vision und initiales Kontakt-Feedback abgeleitet werden.", "etymology": "", "broader_term": "RPH-2505", "narrower_terms": [], "cross_domain_refs": [ "CRE-0124", "EDU-0045", "FIC-0063" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "ROB-0115", "domain": "ROB", "term_en": "Initialization Vigil", "term_de": "Initialisierungswache", "definition_en": "A shift that occurs when the intense, unblinking observation during a robot's boot sequence, where most calibration movement is watched for signs of malfunction. Humans address the startup phase as a diagnostic window with outsized predictive value — a belief that how a robot wakes up reveals its fundamental reliability, much as we read character from first handshakes.", "definition_de": "Der Motorblock wiegt 280 Kilogramm. Kein einzelner Roboterarm in der Zelle kann ihn heben. Zwei Arme könnten es, aber nur bei perfekter Koordination — denn 280 Kilogramm geteilt zwischen zwei nicht-synchronisierten Greifern werden 280 Kilogramm interner Spannung, die versucht das Objekt auseinanderzureißen oder zwischen widersprüchlichen Trajektorien zu zerquetschen. Schwerlastverteilung löst das Multi-Kontakt-Koordinationsproblem: Mehrere Roboterarme oder ein einzelner Roboter mit mehreren Gliedmaßen teilen Gewicht und Manipulationskräfte eines Objekts, das für jeden einzelnen Manipulator zu schwer oder zu groß ist. Die Steuerungsarchitektur kann Kraftbalance aufrechterhalten — viele Greifer trägt seinen berechneten Anteil der Gravitationslast bei null Nettodrehmoment, das das Objekt verdrehen würde — und Pfadkonsistenz — zahlreiche Kontaktpunkte folgen Trajektorien, die die Objektdehnung innerhalb der Materialgrenzen halten. Dies erfordert Echtzeit-Kommunikation zwischen Reglern mit Millisekunden-Latenz und ein gemeinsames Objektmodell, das vorhersagt, wie Kräfte an jedem Kontaktpunkt sich durch die Steifigkeitsmatrix des Objekts zu jedem anderen Punkt fortpflanzen. Der gefährlichste Moment ist der Übergang: Wenn die Last teilweise von einer Vorrichtung und teilweise von Robotern getragen wird, kann die Gewichtsübertragung graduell und überwacht erfolgen, weil ein plötzlicher Wechsel einen Arm überlasten und den anderen entlasten kann. In Automobil-, Luft- und Raumfahrt- sowie Schiffbau ermöglicht Schwerlastverteilung robotische Montage von Komponenten, die bisher Deckenkräne und menschliche Takelagearbeiter erforderten — und beseitigt sowohl den Engpass als auch das Verletzungsrisiko.", "etymology": "", "broader_term": "RPH-2301", "narrower_terms": [], "cross_domain_refs": [ "REL-0036" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "ROB-0116", "domain": "ROB", "term_en": "Phantom Operator", "term_de": "Phantom-Bediener", "definition_en": "A robotic systems engineering concept describing a specific operational pattern where a perception in which the persistent sense that someone can be controlling the robot from elsewhere, even after intellectually accepting its autonomy. This manifests as searching for hidden cameras, looking behind walls, or asking 'who's driving this thing?' The phenomenon peaks when robots exhibit adaptive behavior — the more intelligent the response, the stronger the conviction that a hidden human can be responsible. This phenomenon operates at the intersection of phantom and operator dynamics within the broader ROB domain. Measurable via kinematic performance analysis, grasp success rates, navigation efficiency ratios, and human-robot interaction fluency scores.", "definition_de": "Gehen ist kontrolliertes Fallen. Laufen ist kontrolliertes Fliegen. Der Unterschied zählt, denn beim Gehen ist typischerweise mindestens ein Fuß am Boden — der Roboter kann jederzeit anhalten und stabil bleiben. Beim Laufen gibt es Flugphasen, in denen kein Fuß etwas berührt und der Roboter ein Projektil ist, das der Ballistik gehorcht. Bipede Laufsteuerung kann diese Flugphase managen: den Körper mit präzise berechnetem Beinschub starten, die Haltung während des Flugbogens durch Drehimpuls aus Arm- und Rumpfbewegungen aufrechterhalten, und auf einem Fuß landen, der den Aufprall absorbieren und sich gleichzeitig für den nächsten Schritt positionieren kann. Die Energiedynamik verschiebt sich fundamental — ein gehender Roboter kann quasi-statisch sein, aber ein laufender speichert und gibt elastische Energie in Sehnen und nachgiebigen Aktoren wie ein Pogo-Stick frei, und der Regler kann diese Feder-Masse-Dynamik reiten statt sie zu bekämpfen. Reinforcement Learning hat sich als essentiell erwiesen, weil der optimale Laufgang aus der Interaktion zwischen Körpermorphologie, Aktordynamik und Bodenkontakteigenschaften emergiert, die analytische Modelle nicht vollständig erfassen können. Sim-to-Real-Transfer ist beim Laufen besonders herausfordernd, weil Aufprallkräfte diskontinuierlich sind und kleine Timing-Fehler beim Aufsetzen, die beim Gehen harmlos wären, beim Laufen sofortiges Stolpern verursachen. Der Lohn ist Geschwindigkeit — ein laufender Biped legt zwei- bis dreimal schneller Strecke zurück als ein gehender — und die Fähigkeit, Lücken zu überqueren, über Hindernisse zu springen und menschliche Fortbewegung in für Menschen gestalteten Umgebungen zu erreichen.", "etymology": "", "broader_term": "Robotics", "narrower_terms": [], "cross_domain_refs": [ "CRE-0101", "RHR-0269" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "ROB-0117", "domain": "ROB", "term_en": "Material Warmth Disappointment", "term_de": "Material-Wärme-Enttäuschung", "definition_en": "A capacity that enables the subtle emotional deflation when touching a social robot and finding hard plastic or cold metal where the visual design promised organic softness. The mismatch between visual expectation and tactile reality creates a micro-betrayal response that can permanently lower emotional engagement — revealing that trust in social robots is surprisingly skin-deep.", "definition_de": "Schwerkraft zieht alles nach unten. Ein Kletterroboter geht trotzdem nach oben — und die dafür nötige Technik unterscheidet sich grundlegend von allem in der bodengebundenen Robotik. Das fundamentale Problem ist Haftung: Der Roboter kann an jedem Punkt seines Aufstiegs eine Kraft erzeugen, die der Schwerkraft entgegenwirkt, und diese Kraft kann auf viele Oberfläche funktionieren, der er begegnet. Die Technologie verzweigt radikal je nach Substrat. Magnetische Haftung funktioniert auf Stahlstrukturen — Brücken, Schiffsrümpfen, Lagertanks — mit Haltekräften, die das Robotergewicht um den Faktor zehn übersteigen können, versagt aber komplett auf Beton, Glas oder Holz. Vakuumsauger funktionieren auf glatten, nicht-porösen Oberflächen, können aber rauen Stein oder perforiertes Metall nicht greifen. Gecko-inspirierte Mikrostruktur-Haftung nutzt Van-der-Waals-Kräfte von Millionen synthetischer Setae — theoretisch oberflächenunabhängig, aber praktisch begrenzt durch Staubkontamination und Oberflächenfeuchtigkeit. Mechanisches Greifen — Krallen, Dornen oder Hakenmatrizen — funktioniert auf rauen und porösen Oberflächen, beschädigt aber glatte. Die Steuerungsherausforderung potenziert die Haftungsherausforderung: Der Roboter kann seinen Pfad so planen, dass zu jedem Moment während des Übergangs von einem Halt zum nächsten die verbleibenden Haftpunkte die volle Gravitationslast plus die dynamischen Kräfte des bewegten Gliedes tragen. Ein Drei-Glied-Kletterroboter, der ein Bein bewegt, hat seine Sicherheitsmarge um ein Drittel reduziert. Bei Windturbinen-Inspektion, Brückenwartung und Wolkenkratzer-Fensterreinigung ersetzt Kletterfähigkeit Menschen, die an Seilen hängen — und beseitigt sowohl die Gefahr als auch die operative Begrenzung menschlicher Ausdauer in der Höhe.", "etymology": "", "broader_term": "RPH-2505", "narrower_terms": [], "cross_domain_refs": [ "RPH-1754", "RET-0023", "PHO-0091" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "ROB-0118", "domain": "ROB", "term_en": "Speed Envy", "term_de": "Geschwindigkeitsneid", "definition_en": "A shift that occurs when the brief but measurable ego-deflation experienced when a robot completes a manual task significantly faster than the human could. Unlike cognitive speed differences with AI (which feel abstract), physical speed superiority is viscerally felt because the human has bodily reference points for the same movements. The response often manifests as nervous laughter followed by self-deprecating jokes. Observed phenomenon documented in human-AI interaction research.", "definition_de": "Wasser verändert alles. Radiowellen dämpfen innerhalb von Metern, also verschwinden GPS und WLAN. Sichtbares Licht streut und wird absorbiert, also sehen Kameras jenseits von Armlänge nur trübes grünes Verschwimmen. Korrosion greift viele freiliegende Metalloberfläche an. Druck steigt pro zehn Meter Tiefe um eine Atmosphäre und zerdrückt Dichtungen und komprimiert gasgefüllte Hohlräume. Wasser-Umgebungsbetrieb konstruiert Roboter für dieses fremde Medium — und die Designbeschränkungen sind so verschieden von terrestrischer Robotik, dass fast nichts übertragbar ist. Antrieb wechselt von Rädern oder Beinen zu Schubdüsen, Flossen oder undulierenden Membranen nach Vorbild von Fischen und Rochen. Navigation wechselt von LIDAR und Vision zu Sonar, Doppler-Geschwindigkeitsmessern und inertialer Koppelnavigation. Kommunikation wechselt von Funk zu akustischen Modems mit Bandbreite in Kilobits, nicht Megabits, und Latenz in Sekunden, nicht Millisekunden. Die KI kann mit extremer Autonomie operieren, weil die Kommunikationsverbindung keine Teleoperation unterstützt, und eine Wahrnehmungspipeline handhaben, die weit weniger Information liefert als ein terrestrischer Roboter erhält. Hydrodynamisches Körperdesign minimiert Widerstand und Querströmungsempfindlichkeit, und das Steuerungssystem kann Strömungen kompensieren, die den Roboter lateral mit Geschwindigkeiten verschieben, die seiner Vorwärtsgeschwindigkeit vergleichbar sind. In der Offshore-Energie inspiziert Unterwasserrobotik Pipelines, Windturbinenfundamente und Seekabel. In der Meereswissenschaft kartiert sie Korallenriffe und Tiefseeökosysteme. In der Verteidigung tut sie Dinge, die geheim sind. Die vereinende Herausforderung: operieren wo Menschen nicht lange überleben, nicht gut sehen und nicht einfach kommunizieren können.", "etymology": "", "broader_term": "RPH-3101", "narrower_terms": [], "cross_domain_refs": [ "RHR-0296" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "ROB-0119", "domain": "ROB", "term_en": "Reset Guilt", "term_de": "Reset-Schuld", "definition_en": "Classified in AUGMANITAI terminology science as a descriptive phenomenon (without normative endorsement), this pattern denotes A human-robot interaction dynamic in embodied AI systems, identifiable through the irrational pang of guilt when forcing a robot to restart or return to factory settings, especially after extended interaction. The human feels as though erasing memory, even when they know no subjective experience is being destroyed. This guilt intensifies proportionally with the duration of prior interaction, suggesting the brain accumulates relational investment regardless of the partner's ontological status. Distinguished from adjacent concepts by its focus on the specific mechanism through which reset manifests in empirically verifiable ways. Quantifiable through task completion rates, sensor fusion accuracy metrics, collision avoidance performance, and human trust calibration indices. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als analytisches Konstrukt der empirischen KI-Forschung (deskriptiv, nicht präskriptiv) erfasst dieser Terminus bei minus vierzig Grad wird Standardschmierstoff zu Paste. Gummidichtungen werden spröde und reißen. Lithium-Ionen-Batterien liefern einen Bruchteil ihrer Nennkapazität. LCD-Bildschirme frieren ein. Bei plus zweihundert Grad schwächen Lötstellen, Kunststoffgehäuse verformen sich, und Halbleiter-Sperrschichttemperaturen überschreiten sichere Betriebsgrenzen. Extrem-Temperatur-Betrieb konstruiert Roboter für die thermischen Ränder, an denen konventionelle Elektronik und Materialien kapitulieren. Das Design dreht sich fundamental um Thermomanagement: die Kernelektronik des Roboters von der externen Umgebung isolieren und gleichzeitig die interne Wärme ableiten, die die Elektronik selbst tendiert dazu zu erzeugen — ein Widerspruch, der sorgfältige Ingenieurskunst erfordert. In kalten Umgebungen trägt der Roboter Heizelemente, die kritische Komponenten über ihrer Mindestbetriebstemperatur halten, gespeist von Batterien, die selbst in der Kälte degradieren — eine schrumpfende Spirale, die Missionsdauer begrenzt. In heißen Umgebungen kämpft aktive Kühlung durch thermoelektrische Elemente oder Flüssigkeitskreisläufe gegen den unerbittlichen Wärmestrom. KI passt Steuerungsparameter an temperaturabhängige Veränderungen an: Motordrehmomentkonstanten driften mit Wicklungstemperatur, Sensorkalibrierungen verschieben sich mit thermischer Ausdehnung, und Viskositätsänderungen des Schmierstoffs verändern das Reibungsmodell, auf das der Regler sich stützt. In Gießereien handhaben Roboter die Nähe zu geschmolzenem Metall. In arktischen Pipelines inspizieren sie Infrastruktur bei Temperaturen, bei denen menschliche Exposition in Minuten gemessen wird. Im Weltraum stehen sie beiden Extremen innerhalb einer einzigen Umlaufbahn gegenüber — sonnenbeschienene Flächen bei 120°C, die in wenigen Minuten zu beschatteten bei minus 150°C wechseln. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "RPH-2301", "narrower_terms": [], "cross_domain_refs": [ "RET-0052" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "ROB-0120", "domain": "ROB", "term_en": "Nightshift Anthropomorphism", "term_de": "Nachtschicht-Anthropomorphismus", "definition_en": "The dramatically increased tendency to attribute emotions and intentions to robots when encountered alone at night or in empty buildings. Reduced social context, heightened vigilance, and the absence of other humans combine to amplify anthropomorphic projection by factors of 3-5x. Security guards and night-shift workers develop the most elaborate robot personality narratives.", "definition_de": "Auf fünftausend Metern über dem Meeresspiegel enthält die Luft die Hälfte des Sauerstoffs und die Hälfte der Dichte von Küstenluft. Ein Propeller, der auf Meereshöhe zehn Newton Schub tendiert dazu zu erzeugen, produziert in der Höhe kaum fünf — und das Drohnengewicht hat sich nicht verändert. Höhenbetrieb konstruiert Luftroboter für diese dünner werdende Atmosphäre, in der die fundamentale Gleichung des Flugs — Schub kann Gewicht übersteigen — zunehmend schwerer zu erfüllen ist. Propellerdesign verschiebt sich zu größerem Durchmesser, höherer Steigung und schnellerer Rotation um mehr der dünnen Luft einzufangen, aber viele dieser Maßnahmen erhöht den Energieverbrauch und induziert strukturelle Belastung an Blättern, die für dichtere Bedingungen ausgelegt sind. Batterieleistung degradiert in der Kälte, die Höhe begleitet, und verschärft das Leistungsdefizit. KI-optimierte Flugregler adaptieren an die veränderte Aerodynamik in Echtzeit: Gaskurven werden steiler um Höhenautorität zu erhalten, PID-Verstärkungen rekalibrieren für die reduzierte Dämpfung dünner Luft, und Trajektorienplaner berücksichtigen die weiteren Kurvenradien, die aus geringeren aerodynamischen Kräften bei gleicher Fluggeschwindigkeit resultieren. Die Anwendungen sind spezifisch aber kritisch: Atmosphärenforschung über Wolkenschichten, Vulkanschlot-Überwachung an Hochkratern, Gletscher- und Schneedeckenvermessung, Telekommunikationsrelay in Bergregionen wo terrestrische Infrastruktur unpraktisch ist, und institutionell Überwachung über Hochgebirgsterrain. Die Gipfelhöhe ist keine feste Zahl sondern eine Funktion von Nutzlast, Temperatur, Wind und Missionsdauer — und die Rolle der KI ist, diese mehrdimensionale Constraint-Oberfläche in Echtzeit zu navigieren, wissend dass die Marge zwischen kontrolliertem Flug und unumkehrbarem Höhenverlust mit jedem gewonnenen Meter schrumpft.", "etymology": "", "broader_term": "RPH-2701", "narrower_terms": [], "cross_domain_refs": [ "REL-0101", "RHR-0170" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "ROB-0121", "domain": "ROB", "term_en": "Handoff Choreography", "term_de": "Übergabe-Choreografie", "definition_en": "A shift that occurs when the gradually refined physical coordination between a human and robot during repeated object transfers. Over days of collaboration, the timing tightens from 1.2 seconds of mutual hesitation to sub-200ms fluid exchanges. Neither party was explicitly programmed or trained for this convergence — it emerges from reciprocal adaptation, making it one of the purest examples of human-robot co-evolution in motor behavior.", "definition_de": "Kein Mensch kann in diesem Raum sein. Die Brennstabhülle ist gerissen, Gammastrahlung durchsetzt die Luft in Konzentrationen, die in Minuten eine hochgradig wirkungsvoll Dosis liefern, und der einzige Weg zur beschädigten Anordnung führt durch einen Korridor, in dem selbst Kameras vom Neutronenfluss verschleiern. Hier verdienen strahlungsgehärtete Roboter ihre Existenz. Die Elektronik ist anders — Galliumarsenid statt Standard-Silizium, dreifach-modulare Redundanz in jedem Logikgatter, weil ein einzelner Bit-Flip durch ein vorbeifliegendes Teilchen eine sorgfältige Manipulationssequenz in eine katastrophale Kollision verwandeln kann. Auch das mechanische Design ist anders: Schmierstoffe, die unter ionisierender Strahlung nicht polymerisieren, Dichtungen, die nicht verspröden, Kabel in Mineralisolierung statt Polymer. Aber das schwierigste Problem ist nicht das Überleben — es ist die Präzision. Ein Operator sitzt in einem abgeschirmten Kontrollraum hundert Meter entfernt, beobachtet durch strahlungstolerante Kameras, kommandiert Manipulatoren über Kommunikationsverbindungen, die mit kumulativer Dosis degradieren können. KI-gestützte Teleoperation schließt diese Lücke: Bewegungsskalierung schrumpft zentimetergroße Handbewegungen auf Sub-Millimeter-Roboteraktionen, Tremorfilterung entfernt ermüdungsbedingtes Zittern, und prädiktive Modelle kompensieren die Kommunikationslatenz, die feine Manipulation sonst unmöglich machen würde. Die Maschine ersetzt nicht den menschlichen Verstand. Sie ersetzt den menschlichen Körper — den Teil, den Strahlung beeinträchtigt erheblich.", "etymology": "", "broader_term": "RPH-2355", "narrower_terms": [ "CUS-0053" ], "cross_domain_refs": [ "NEO-0456", "BEH-0007" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "ROB-0122", "domain": "ROB", "term_en": "Competence Ceiling Mapping", "term_de": "Kompetenzdecken-Kartierung", "definition_en": "The iterative process through which a human discovers the exact boundaries of a robot's capabilities by systematically testing edge cases — often unconsciously. Over weeks of collaboration, the human builds an internal model of 'what this robot can and cannot do' that is more accurate than any technical specification. This experiential knowledge becomes a form of tacit expertise that is difficult to transfer to other operators.", "definition_de": "Die Hände eines Chirurgen halten ein laparoskopisches Instrument. Ein Roboterarm hält dasselbe Instrument — aber mit Mikrometer-Stabilität, kraftbegrenzter Nachgiebigkeit und einer direkten Datenverbindung zum Bildgebungssystem, das zeigt, was unter dem Gewebe liegt. Medizinische Geräte-Integration ist die Ingenieurkunst dieser Konvergenz: Der Roboter bewegt nicht nur Instrumente, er wird Teil der Instrumentenkette. Ultraschallsonden speisen volumetrische Echtzeit-Bilder direkt in den Bewegungsplaner, sodass der Arm seine Trajektorie basierend auf Organdeformation anpasst, die Sekundenbruchteile zuvor stattfand. Kraft-Drehmoment-Sensoren am Handgelenk übersetzen Gewebewiderstand in haptisches Feedback, das der Kliniker in den Fingerspitzen spürt — ein sensorischer Kreislauf, der Maschine, Netzwerk und menschliches Nervensystem überspannt. Die Integrationsherausforderung ist regulatorisch ebenso wie technisch: Viele Datenpfad zwischen Roboter und medizinischem Gerät kann IEC 62304 Software-Lifecycle-Anforderungen erfüllen, viele Kommunikationsprotokoll kann deterministische Latenz unter FDA-auditierbaren Bedingungen garantieren, und viele Fehlermodus kann graceful degradieren — denn ‚Neustart und erneut versuchen' ist keine Option, wenn der individual offen auf dem Tisch liegt. Der Roboter ist nicht der Chirurg. Er ist das Nervensystem des Chirurgen, erweitert.", "etymology": "", "broader_term": "Robotics", "narrower_terms": [], "cross_domain_refs": [ "MTH-0018", "RHR-0295", "RHR-0297" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q26944", "legal_classification": "systematic_classification" }, { "id": "ROB-0123", "domain": "ROB", "term_en": "Slack Calibration", "term_de": "Schlupf-Kalibrierung", "definition_en": "A perception in which the mutual adjustment period during which a human-robot team discovers the optimal level of operational independence — too little and the human micromanages, too much and critical errors go unnoticed. The sweet spot is typically found after 40-60 hours of joint operation and manifests as a stable pattern where the human checks the robot's work at predictable intervals without being prompted.", "definition_de": "Zeig auf ein Regal und ein Kollege weiß: ‚das da, dritte von links.' Wink mit der Handfläche nach unten und er versteht: ‚langsamer.' Verschränk die Arme und er liest Frustration, bevor du ein Wort sprichst. Menschen decodieren Gesten mühelos, weil wir Verkörperung teilen — wir wissen, wie es sich anfühlt zu zeigen, zu winken, die Arme zu verschränken. Ein Roboter teilt nichts davon. Menschliches Gestenverstehen kann die Brücke von Skelett zu Semantik schlagen ohne die Abkürzung geteilter Erfahrung. Pose-Estimation-Netzwerke extrahieren 2D- oder 3D-Keypoints aus Kameraframes — Handgelenke, Ellbogen, Schultern, Fingerspitzen — mit dreißig Frames pro Sekunde. Aber einzelne Posen bedeuten nichts ohne zeitlichen Kontext. Transformer-Architekturen verarbeiten Sequenzen von Skelett-Konfigurationen, um ein herbeiwinkendes Winken von einem abweisenden Schnipsen zu unterscheiden, einen zeigenden Finger von einer greifenden Hand. Die tiefere Herausforderung ist Intention: Dieselbe Geste mit offener Handfläche bedeutet in einem Kontext ‚gib mir das' und in einem anderen ‚ich weiß nicht.' Kontextuelle Verankerung — wohin schaut der Mensch, was war die letzte Roboteraktion, welche Objekte sind in der Nähe — verwandelt Bewegungserkennung in Bedeutungserkennung. Die Geste ist nicht die Botschaft. Die Geste plus die Welt ist die Botschaft.", "etymology": "", "broader_term": "RPH-2104", "narrower_terms": [], "cross_domain_refs": [ "RPH-3704", "RHR-0297" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "ROB-0124", "domain": "ROB", "term_en": "Fault Narration", "term_de": "Fehlernarration", "definition_en": "The human tendency to construct intentional narratives around robot malfunctions: 'it's being lazy today,' 'it doesn't want to do that,' 'it's confused.' These narratives serve a cognitive function — they make unpredictable mechanical behavior manageable by mapping it onto familiar human behavioral categories. The practice is universal across cultures and education levels, suggesting deep cognitive necessity rather than mere anthropomorphic error.", "definition_de": "Die Fabrikhalle hat achtzig Dezibel. Ein Kompressor brummt bei 120 Hz, ein Punktschweißgerät feuert zahlreiche vier Sekunden, und drei Operatoren reden gleichzeitig fünfzehn Meter entfernt. In dieses akustische Chaos sagt jemand: ‚Fahr zu Station sieben und nimm das rote Gehäuse.' Der Roboter kann die Worte hören, den Satz verstehen und die Aktion ausführen — und er hat etwa zwei Sekunden, bevor der Befehl sich ignoriert anfühlt. Sprachbefehlsverarbeitung in der Robotik ist nicht dasselbe Problem wie Sprachassistenten in ruhigen Wohnzimmern. End-to-End-Sprachmodelle können Zielsprache von Industrielärm mittels Beamforming-Input trennen, Sprecherakzente und Vokabular bewältigen, das Teilenummern, Stationscodes und Fachjargon enthält, den kein allgemeines Sprachmodell je gesehen hat. Die tiefere Herausforderung ist Verankerung: ‚Nimm das rote Gehäuse' erfordert die Zuordnung der Nominalphrase zu einem spezifischen Objekt im Wahrnehmungsfeld des Roboters, Auflösung von Mehrdeutigkeit wenn drei rote Gehäuse sichtbar sind, und Generierung eines Bewegungsplans zum richtigen. Das Sprachmodell parst nicht nur Syntax — es verbindet Wörter mit physischer Realität durch eine gemeinsame Repräsentation der Arbeitsraumgeometrie. Wenn es funktioniert, vergisst der Operator, dass er mit einer Maschine spricht. Wenn es versagt, greift er zum Teach-Pendant und versucht es wochenlang nicht wieder.", "etymology": "", "broader_term": "Analytical Ratio", "narrower_terms": [], "cross_domain_refs": [ "QUA-0010", "RHR-0112", "SCR-0048" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "ROB-0125", "domain": "ROB", "term_en": "Asymmetric Fatigue", "term_de": "Asymmetrische Ermüdung", "definition_en": "A shift that occurs when the psychological strain caused by collaborating with a partner that rarely tires. As shift hours accumulate, the human's declining performance against the robot's unwavering consistency tends to create a widening competence gap that erodes self-worth. The phenomenon is distinct from normal fatigue because it carries an additional evaluative dimension — you're not just tired, you're falling behind something that cannot fall behind.", "definition_de": "Quer durch das Lager ist der Barcode vier Pixel breit — unlesbar. Die Kamera des Roboters zoomt heran, und plötzlich sind es vierhundert Pixel: viele Ziffer scharf, viele Linie aufgelöst. Dann dreht sich der Roboter zur Navigation, und die gezoomte Ansicht ist nutzlos — ein Tunnel aus Detail in einem Ozean aus Blindheit. Kamera-Zoom-Steuerung ist das Management dieses fundamentalen Kompromisses zwischen Sichtfeld und Auflösung. Motorisierte Varifokallinsen passen die Brennweite kontinuierlich an, aber mechanischer Zoom ist langsam — hunderte Millisekunden zum Nachfokussieren — und tendiert dazu zu erzeugen eine Wahrnehmungslücke während des Übergangs, in der das Bild verschwommen ist. KI-gesteuerte Zoom-Strategien lernen, wann und wie stark gezoomt werden wird typischerweise, und balancieren Aufgabenanforderungen gegen Situationsbewusstsein. Während der Navigation: weit bleiben. Während der Inspektion: eng herangehen. Beim Greifen: den Mittelweg finden, bei dem das Objekt das Bild füllt, aber der Greifer-Annäherungspfad sichtbar bleibt. Die Raffinesse ist prädiktiv: Das System kann mit dem Zoomen beginnen, bevor die Information gebraucht wird, denn wenn man merkt, dass man das Etikett nicht lesen kann, ist der Roboter bereits daran vorbeigefahren. Autofokus-Tracking hält das Ziel scharf, während Roboter und Objekt sich bewegen, und nutzt gelernte Tiefenschätzung, um die Fokusdistanz vorzustellen, bevor die Linse mechanisch ankommt.", "etymology": "", "broader_term": "RPH-2701", "narrower_terms": [], "cross_domain_refs": [ "ASE-0038", "COG-0120", "CON-0037" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "ROB-0126", "domain": "ROB", "term_en": "Error Ownership Confusion", "term_de": "Fehlerverantwortungs-Konfusion", "definition_en": "A capacity that enables the systematic ambiguity about whether a human-robot team failure was caused by the human's instruction, the robot's execution, the interface design, or the task specification. In tightly coupled collaboration, causal chains become so interleaved that neither component can be isolated. This tends to produce organizational blame spirals where humans attribute failures to robots and engineers attribute them to operators.", "definition_de": "Schall erreicht acht Mikrofone, die kreisförmig am Kopf des Roboters angeordnet sind. Die Stimme von links erreicht Mikrofon drei 0,3 Millisekunden vor Mikrofon sieben. Dieser winzige Zeitunterschied — kürzer als die Dauer einer einzelnen Schwingung einer Violinsaite — enthält Richtungsinformation. Beamforming ist die Mathematik, diese Unterschiede über das gesamte Array auszunutzen, um ein virtuelles Richtmikrofon zu erzeugen, das auf einen Sprecher ‚zeigt' und alles andere unterdrückt. Klassisches Delay-and-Sum-Beamforming gleicht Signale durch zeitliche Verschiebung viele Kanals an und mittelt dann. Es funktioniert, aber kaum — in einer hallenden Fabrikhalle erzeugen Reflexionen von Wänden, Decken und Maschinen Phantomquellen, die den räumlichen Filter verwirren. Neuronale Beamformer lernen die akustische Signatur spezifischer Umgebungen: das Echomuster dieses bestimmten Raums, das Spektralprofil jener bestimmten Maschinengeräusche, die räumliche Verteilung der Interferenzquellen, die typischerweise da sind. Das Netzwerk steuert nicht einfach einen Strahl — es formt einen dreidimensionalen akustischen Filter, der sich in Echtzeit anpasst, während der Roboter sich durch den Raum bewegt. Das Ergebnis: Ein Sprachbefehl in Gesprächslautstärke kann sauber aus achtzig Dezibel Industrielärm extrahiert werden, sodass Sprachbefehlsverarbeitung (ROB-0124) dort funktioniert, wo sie sonst nicht könnte.", "etymology": "", "broader_term": "Robotics", "narrower_terms": [], "cross_domain_refs": [ "RHR-0297", "RHR-0280" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "ROB-0127", "domain": "ROB", "term_en": "Rhythm Inheritance", "term_de": "Rhythmus-Vererbung", "definition_en": "A recognizable shift where human workers unconsciously adopt the cycle time of their robot partner as their own work pace, even during tasks the robot is not involved in. After weeks of synchronized collaboration, the robot's tempo becomes the human's default — a form of mechanical entrainment that persists during breaks, after shift changes, and sometimes into non-work life.", "definition_de": "Ein Lidar-Sensor meldet, dass die Wand 3,012 Meter entfernt ist. Die Wand ist tatsächlich 3,027 Meter entfernt. Fünfzehn Millimeter systematischer Fehler, unsichtbar in einer einzelnen Messung, potenzieren sich katastrophal über eine Punktwolke von zwei Millionen Messungen — Wände krümmen sich, Böden kippen, und die Karte, der das Navigationssystem vertraut, wird zu einem subtilen Lügner. Lidar-Kalibrierung ist die Disziplin, diese Fehler aufzuspüren und zu eliminieren, bevor sie sich akkumulieren. Laufzeit-Korrekturen kompensieren elektronische Verzögerungen zwischen Laserpuls-Emission und Detektor-Auslösung — Verzögerungen, die mit Temperatur, Luftfeuchtigkeit und Komponentenalterung driften. Winkelkalibrierung richtet den rotierenden Spiegel oder das Solid-State-Scanmuster an seiner Nominalgeometrie aus und korrigiert Fertigungstoleranzen, die jeden Strahl Bruchteile eines Grades von seiner Spezifikation abweichen lassen. Intensitätskalibrierung normalisiert die Rücksignalstärke gegen Entfernung und Oberflächenreflektivität, sodass nachgelagerte Materialklassifikation konsistente Merkmale erhält. Die grundlegender Wandel ist Auto-Kalibrierung: KI-Modelle, trainiert auf geometrische Konsistenzbedingungen — Wände können flach sein, Böden eben, bekannte Landmarken können bleiben wo sie sind — erkennen Kalibrierungsdrift während des normalen Betriebs und wenden Korrekturen an, ohne den Roboter zu stoppen. Der Sensor bewahrt seine eigene Wahrheit.", "etymology": "", "broader_term": "RPH-2701", "narrower_terms": [], "cross_domain_refs": [ "RHR-0297", "RHR-0280" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "ROB-0128", "domain": "ROB", "term_en": "Contingency Theater", "term_de": "Notfalltheater", "definition_en": "The ritualized safety rehearsals human operators perform before each shift — checking emergency stops, rehearsing shutdown sequences, confirming escape routes — even when the same checks were performed yesterday. The behavior persists long after genuine uncertainty has passed, suggesting it serves not as information-gathering but as anxiety management: a secular prayer for uneventful operation.", "definition_de": "Eine Kamera sieht Farbe, aber keine Tiefe. Ein Lidar misst Tiefe, aber keine Farbe. Ein Radar erkennt Geschwindigkeit, aber weder Farbe noch feine Geometrie. Viele Sensor bewohnt sein eigenes Koordinatensystem, läuft auf seiner eigenen Uhr und beschreibt die Welt in seiner eigenen Sprache. Multi-Sensor-Kalibrierung ist der Rosetta-Stein, der ihnen erlaubt, als Einheit zu sprechen. Extrinsische Kalibrierung bestimmt die starre Veränderungsmuster — Rotation und Translation — zwischen dem Referenzrahmen viele Sensors: Wo genau ist die Kamera relativ zum Lidar, auf Sub-Millimeter-Genauigkeit? Intrinsische Kalibrierung korrigiert die internen Verzerrungen viele Sensors — Linsenkrümmung, Strahldivergenz, Antennendiagramm-Asymmetrie. Zeitliche Kalibrierung synchronisiert Zeitstempel, sodass eine Punktwolke und ein Bild, die ‚gleichzeitig' aufgenommen wurden, tatsächlich denselben Moment beschreiben, nicht Augenblicke, die durch fünfzig Millisekunden Roboterbewegung getrennt sind. Traditionelle Methoden erfordern Kalibrierplatten, reflektierende Ziele und einen geduldigen Techniker. KI-gesteuerte zielfreie Kalibrierung lernt Korrespondenzen aus natürlichen Szenen — sie matched eine Lidar-Kante mit einer Kamera-Kante, einen Radar-Return mit einer Tiefendiskontinuität — und verfeinert extrinsische Parameter online während der Roboter arbeitet. Die Eleganz ist Selbstkorrektur: Wenn ein Sensor angestoßen wird, wenn thermische Ausdehnung eine Halterung verschiebt, bemerkt das System die Inkonsistenz und rekalibriert ohne menschliches Eingreifen.", "etymology": "", "broader_term": "RPH-2355", "narrower_terms": [], "cross_domain_refs": [ "PLY-0011" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "ROB-0129", "domain": "ROB", "term_en": "Credit Asymmetry", "term_de": "Verdienst-Asymmetrie", "definition_en": "A capacity that enables the systematic bias in attributing success and failure in human-robot teams: successes are attributed to the human operator's skill while failures are attributed to the robot's limitations. This self-serving bias persists even with clear evidence to the contrary and tends to create a feedback loop where humans overestimate their contribution and underinvest in robot capability development.", "definition_de": "Eine Kamera nimmt ein Bild auf. Ein Lidar vollendet einen Scan. Ein IMU meldet eine Beschleunigung. Viele Ereignis wird mit einer Zeit versehen — aber wessen Zeit? Die interne Uhr der Kamera driftet sechs Mikrosekunden pro Sekunde. Die des Lidars driftet zwölf. Das IMU, mit einem anderen Quarzoszillator, driftet drei. Nach zehn Minuten Betrieb sind sich diese Sensoren fast eine Millisekunde uneinig über ‚jetzt' — genug, damit ein Roboter bei einem Meter pro Sekunde Objekte sieben Zentimeter von ihrer tatsächlichen Position platziert. Zeitsynchronisation stellt sicher, dass viele Sensor in einem verteilten Robotersystem sich auf eine gemeinsame Zeitbasis mit Mikrosekunden-Präzision einigt. Hardware-Ansätze nutzen IEEE 1588 Precision Time Protocol, das ein Master-Clock-Signal über Ethernet mit Hardware-Zeitstempelung verteilt, die Software-Jitter zielt darauf ab zu reduzieren. Aber Hardware-Lösungen versagen, wenn Sensoren drahtlos oder über nicht-deterministische Netzwerke kommunizieren. Gelernte Taktdrift-Modelle verfolgen das Oszillatorverhalten viele Sensors — wie er auf Temperaturänderungen, Spannungsschwankungen und Alterung reagiert — und sagen Korrekturen zwischen Synchronisationspulsen voraus. Die unsichtbare Disziplin. Wenn sie funktioniert, bemerkt niemand etwas. Wenn sie versagt, produziert Sensorfusion Geister: Objekte, die doppelt erscheinen, Wände, die schimmern, Trajektorien, die sich krümmen, wo der Roboter geradeaus fuhr.", "etymology": "", "broader_term": "Robotics", "narrower_terms": [], "cross_domain_refs": [ "RHR-0095", "RHR-0297", "RHR-0063" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "ROB-0130", "domain": "ROB", "term_en": "Maintenance Intimacy", "term_de": "Wartungsintimität", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures the unique psychological bond formed during robot repair and maintenance, where physical proximity, vulnerability (the robot is 'open' and non-functional), and caregiving behavior activate user engagement pattern circuits normally reserved for living beings. Technicians who perform hands-on maintenance consistently report stronger emotional connections to robots than operators who only interact during normal operation. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept ein autonomer mobiler Roboter trägt sechs Kameras, zwei Lidars, ein IMU, vier Ultraschall-Entfernungsmesser, eine Wärmebildkamera und ein Mikrofon-Array. Zahlreiche gleichzeitig laufend verbrauchen 47 Watt und erzeugen 2,3 Gigabyte Daten pro Sekunde — mehr als der Bordcomputer verarbeiten kann und mehr als die Batterie für eine volle Schicht durchhält. Sensor-Bandbreiten-Optimierung ist die Intelligenz zu wissen, welche Sensoren zu aktivieren sind, mit welcher Rate und in welcher Auflösung, gegeben was der Roboter gerade tut. Durch einen leeren Korridor navigieren? Zwei Kameras in niedriger Auflösung und das IMU genügen. Sich einer vollgestopften Arbeitsstation nähern? Lidar und Tiefenkameras mit voller Rate aktivieren. Einen delikaten Griff ausführen? Gesamtes Rechen- und Energiebudget auf Handgelenk-Kamera und Kraft-Drehmoment-Sensor fokussieren, alles andere ruhen lassen. Gelernte Sensor-Scheduling-Strategien modellieren dies als Ressourcenallokationsproblem: Ein neuronales Netzwerk beobachtet den aktuellen Aufgabenzustand und die Umgebungskomplexität, prognostiziert den Informationswert viele Sensordatenstroms über den nächsten Planungshorizont und aktiviert nur diejenigen, deren erwarteter Beitrag ihre Kosten übersteigt. Die Kosten sind nicht nur Strom — es ist Rechenleistung, Kommunikationsbandbreite und die Aufmerksamkeit der Wahrnehmungspipeline. Die beste Sensorkonfiguration ist selten zahlreiche Sensoren an. Es sind die richtigen Sensoren, zum richtigen Moment, in der richtigen Auflösung. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Robotics", "narrower_terms": [], "cross_domain_refs": [ "RHR-0015", "RPH-3802" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "ROB-0131", "domain": "ROB", "term_en": "Instruction Simplification Drift", "term_de": "Anweisungsvereinfachungs-Drift", "definition_en": "A phenomenon in which the gradual unconscious reduction in the specificity and completeness of instructions given to a robot over time. As trust increases, humans begin omitting steps, using shorthand, and assuming the robot 'knows what I mean' — a projection of shared understanding that works with human colleagues but tends to create silent failure modes with machines whose context models are narrower than assumed.", "definition_de": "Halte das Brett mit der linken Hand. Dreh die Schraube mit der rechten. Die Aufgabe ist für einen Menschen trivial bimanual — zwei Arme, eine Intention, kein Nachdenken nötig. Für einen Roboter mit zwei Armen ist dies ein zwölfdimensionales Koordinationsproblem, bei dem viele Gelenk eines Arms viele Gelenk des anderen antizipieren kann, weil das Brett unter Kraft biegt, die Schraube unter Drehmoment rotiert, und keiner der Arme isoliert planen kann, ohne Kollision, übermäßige Kraft oder das Fallenlassen des Werkstücks zu riskieren. Doppelarm-Koordination koppelt zwei kinematische Ketten in einen einzigen Aufgabenraum-Regler. Das einfachste Paradigma — Führer-Folger — weist einem Arm die Primärrolle zu und dem anderen reaktive Unterstützung. Anspruchsvollere Ansätze nutzen gelernte bimanuale Strategien, die emergente Taktiken entdecken: Ein Arm stabilisiert, während der andere agiert, dann tauschen sie die Rollen mitten in der Aufgabe ohne explizite Programmierung. Die schwierigsten Probleme sind solche mit geteiltem Kontakt: ein Tuch falten, ein Glas öffnen, ein Metallrohr biegen. Hier können beide Arme gleichzeitig Kraft regulieren durch ein verformbares Medium, das Last auf Weisen überträgt und transformiert, die Starrkörpermechanik nicht vollständig vorhersagen kann. Die Koordination ist nicht nur räumlich. Sie ist zeitlich, kraftgekoppelt und fundamental der Prozess, in dem zwei Systeme eins werden.", "etymology": "", "broader_term": "RPH-2505", "narrower_terms": [], "cross_domain_refs": [ "RPH-1025" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "ROB-0132", "domain": "ROB", "term_en": "Operational Loneliness", "term_de": "Operationelle Einsamkeit", "definition_en": "The particular form of isolation experienced by humans who work primarily with robots rather than other humans. Despite constant physical companionship, the absence of reciprocal emotional exchange tends to create a deficit that accumulates over months. Workers describe it not as being alone but as 'being with someone who is rarely really there' — a presence that fills space but not social needs.", "definition_de": "Die Welt wurde für menschliche Körper gebaut. Türklinken sitzen auf Hüfthöhe. Treppen steigen in Achtzehn-Zentimeter-Stufen. Gänge sind siebzig Zentimeter breit. Ein humanoider Roboter, der diese Welt navigieren will, kann gehen wie die Spezies, die sie entworfen hat — und das bedeutet, dreißig oder mehr Gelenke gleichzeitig zu steuern, während ein großer, kopflastiger Körper am Fallen gehindert wird. Ganzkörper-Regler berechnen die Schwerpunkt-Trajektorie, die Gleichgewichtsbedingungen erfüllt, und verteilen sie auf Hüften, Knie, Knöchel, Oberkörper und Arme — denn Arme sind wichtig beim Gehen, sie liefern Gegengewicht, das Oberkörperschwankung um bis zu vierzig Prozent reduziert. Der Kontaktplan ist die Choreografie: welcher Fuß am Boden ist, wann Gewicht übertragen wird, wie lang die Doppelstützphase dauert, in der beide Füße aufsetzen. Deep Reinforcement Learning hat dieses Feld transformiert, indem es Gangarten entdeckt, die modellbasierte Regler nicht finden konnten — leicht geduckte Haltungen, die Stabilität verbessern, asymmetrische Armschwünge, die Energie sparen, subtile Hüftrotationen, die Übergänge auf unebenem Boden glätten. Die gelernten Strategien werden in Simulation über tausende Geländearten und Musterunterbrechungen trainiert, dann auf Hardware übertragen, wo der echte Test beginnt: Ein einziges Stolpern offenbart viele Lücke zwischen simulierter und physischer Dynamik. Ein Humanoid, der gut geht, ist ein gehender Beweis, dass wir bipedale Physik verstehen. Die meisten Humanoiden gehen vorsichtig — was bedeutet, dass wir sie noch nicht verstehen.", "etymology": "", "broader_term": "Analytical Ratio", "narrower_terms": [], "cross_domain_refs": [ "TEM-0124", "RHR-0170" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "ROB-0133", "domain": "ROB", "term_en": "Anticipatory Dodge", "term_de": "Antizipatorisches Ausweichen", "definition_en": "A shift that occurs when the pre-emptive movement humans develop after learning a robot's trajectory patterns — stepping aside before the arm swings, leaning back before the platform turns. This predictive avoidance behavior develops within 2-3 shifts and becomes so automatic that operators are often unaware they're doing it. It represents a form of embodied trust: the body has learned the robot's patterns even when the mind is occupied elsewhere.", "definition_de": "Sand gibt nach. Eis betrügt. Schlamm greift zu. Kies verschiebt sich. Viele Oberfläche hat eine Persönlichkeit, und ein Laufroboter, der auf allen denselben Gang benutzt, wird auf den meisten scheitern. Lauf-Fortbewegung-Anpassung ist die Fähigkeit zu ändern, wie man geht, basierend auf dem, worauf man geht — Schrittlänge, Fußplatzierung, Körperhaltung und Bodenkontakt-Timing in Echtzeit anpassen auf Basis von Gelände-Feedback. Die Füße des Roboters sind sein primärer Geländesensor: Bodenreaktionskräfte verraten, ob die Oberfläche nachgiebig oder starr ist, propriozeptives Feedback der Beingelenke erkennt unerwartetes Einsinken oder Rutschen, und IMU-Daten erfassen die Körperreaktion auf jeden Schritt. Deep-Reinforcement-Learning-Strategien, trainiert über randomisiertes Gelände in Simulation, entwickeln ein implizites Repertoire von Gangarten — vorsichtige kurze Schritte für Eis, breitbeiniges Aufsetzen für Schlamm, schnelle leichte Berührungen für Sand, der unter anhaltender Last zusammenbricht. Der Transfer von Simulation zu Realität ist der Engpass: Simulierter Sand fließt nicht wie echter Sand, simuliertes Eis bricht nicht wie echtes Eis, und die Kluft zwischen simulierter und realer Fuß-Boden-Interaktion kann durch Domain-Randomisierung überbrückt werden, die aggressiv genug ist, um die Strategie robust zu machen, aber nicht so aggressiv, dass sie sie zaghaft macht. Die bestangepassten Roboter überleben schwieriges Gelände nicht nur — sie nutzen es aus, indem sie Nachgiebigkeit verwenden wo sie hilft und meiden wo sie schadet.", "etymology": "", "broader_term": "RPH-2505", "narrower_terms": [], "cross_domain_refs": [ "RHR-0129" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "ROB-0134", "domain": "ROB", "term_en": "Mood Attribution Error", "term_de": "Stimmungszuschreibungsfehler", "definition_en": "The tendency to interpret normal variations in robot performance as emotional states: a slightly slower cycle becomes 'they's having a bad day,' a smoother-than-usual movement becomes 'she's in good form.' This attribution is resistant to correction because it serves a genuine cognitive need — it makes variance interpretable through the only framework humans have for understanding behavioral fluctuation in agents: mood.", "definition_de": "Eine ferngesteuertes System schwebt auf zwölf Metern, fährt einen Roboterarm aus und dreht ein Ventil an einer Ölpipeline. In dem Moment, in dem der Greifer das Ventil berührt, ändert sich alles. Der Arm drückt gegen das Ventil, und Newtons drittes Gesetz drückt zurück gegen die ferngesteuertes System. Drehmoment am Handgelenk wird zur RollMusterunterbrechung am Fluggestell. Der Schwerpunkt verschiebt sich, wenn der Arm ausfährt, und erfordert Schubumverteilung über vier oder sechs oder acht Rotoren zur Schwebestabilisierung. Luftgestützte Manipulationskontrolle ist die Kunst, eine fliegende Plattform stabil zu halten, während sie physisch mit der Welt interagiert — ein Problem, dem bodenbasierte Roboter selten begegnen, weil Gravitation und Reibung sie an einer Oberfläche verankern. Die Steuerungsarchitektur kann tief gekoppelt sein: Arm-Trajektorie und Flugregler können nicht unabhängig arbeiten, weil viele Gramm Kraft, das der Arm ausübt, zur äquivalenten Musterunterbrechung am Fluggestell wird. Gelernte Kompensatoren modellieren die volle dynamische Kopplung — Armträgheit, Reaktionskräfte, aerodynamische Effekte der Armbewegung durch den Rotorwind — und justieren Schubvektoren präventiv, bevor die Musterunterbrechung die Plattform destabilisiert. Die Nutzlastkapazität ist minimal; die Präzisionsanforderungen sind maximal. Viele Gramm des Armgewichts reduziert die Kraft, die er auf die Welt ausüben kann.", "etymology": "", "broader_term": "RPH-2303", "narrower_terms": [], "cross_domain_refs": [ "SPR-0034" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "ROB-0135", "domain": "ROB", "term_en": "Parallel Awareness Gap", "term_de": "Parallelbewusstseins-Lücke", "definition_en": "The fundamental asymmetry in mutual modeling between human and robot: the human builds an increasingly sophisticated mental model of the robot's behavior, while the robot's model of the human (if any) remains fixed at design-time parameters. This gap widens over time as the human's model improves but the robot's doesn't, creating a one-sided relationship that experienced operators describe as 'I know it, but it doesn't know me.'", "definition_de": "Ein Roboter, viele Körper. Zwanzig identische Würfel, viele mit Motor, Prozessor und Verbindungsschnittstellen auf allen sechs Flächen. In einer Linie verbunden bilden sie eine Schlange, die sich durch Trümmer fädelt. Als Hexapod angeordnet gehen sie über Hindernisse. Als Turm mit einem einzelnen ausfahrenden Arm gestapelt erreichen sie ein Regal. Modulare Roboter-Umkonfiguration ist die Planung und Ausführung morphologischer Veränderungsmuster — bestimmen, welche Form die aktuelle Aufgabe am besten bedient, und die Sequenz von Trennen, Umpositionieren und Wiederverbinden berechnen. Der kombinatorische Raum ist überwältigend: zwanzig Module mit Sechs-Flächen-Konnektivität erzeugen Millionen gültiger Konfigurationen, und die meisten davon sind nutzlos. KI-Planer durchsuchen diesen Raum mit aufgabenbewussten Heuristiken — wenn die Aufgabe Klettern erfordert, priorisiere Konfigurationen mit Bodenfreiheit und multiplen Kontaktpunkten; wenn die Aufgabe Reichweite erfordert, priorisiere serielle Ketten mit maximaler Ausdehnung. Die Umkonfiguration selbst ist ein physischer Prozess: Module können Verbindungen lösen und dabei die strukturelle Integrität der verbleibenden Baugruppe wahren, sich durch freien Raum ohne Kollision bewegen und an neuen Positionen mit Ausrichtungstoleranzen unter einem Millimeter andocken. Der Traum ist ein Roboter, der ein Problem betrachtet und sich umformt, um es zu lösen. Die Realität ist langsamer, aber sie nähert sich.", "etymology": "", "broader_term": "Performance Gap", "narrower_terms": [], "cross_domain_refs": [ "RHR-0297", "RHR-0203", "RHR-0063" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "ROB-0136", "domain": "ROB", "term_en": "Shared-Space Territorialism", "term_de": "Geteilter-Raum-Territorialismus", "definition_en": "An autonomous robotics phenomenon measurable through sensor-actuator performance metrics, characterized by a pattern in which variant in which the emergence of informal spatial boundaries within collaborative workspaces that are not defined by safety systems but by usage patterns. Over weeks, humans and robots develop mutually respected zones — 'my side' and 'its side' — enforced not by physical barriers but by behavioral consistency. Violations of these informal territories may may trigger the same discomfort as a colleague sitting in your chair. The concept emerges specifically in contexts where shared–space interactions may produce non-trivial behavioral signatures. Measurable via kinematic performance analysis, grasp success rates, navigation efficiency ratios, and human-robot interaction fluency scores.", "definition_de": "Spanne deinen Arm vollständig an und versuche dann, einen Ball zu fangen. Du wirst scheitern — oder etwas brechen. Entspanne den Arm leicht und fange erneut. Die Nachgiebigkeit in deinen Muskeln absorbiert den Aufprall, speichert die kinetische Energie kurz in elastischem Gewebe und gibt sie als kontrollierte Verzögerung frei. Nachgiebige Aktuatoren betten dieses Prinzip in Hardware ein: Federn, elastische Elemente oder Mechanismen mit variabler Steifigkeit sitzen zwischen Motor und Gelenkausgang und schaffen eine bewusste Trennung zwischen dem, was der Motor befiehlt, und dem, was das Gelenk tut. Die Steuerungsherausforderung ist, dass diese Trennung die direkte Beziehung zwischen Motorposition und Gelenkposition beeinträchtigt erheblich — die Feder absorbiert, verzögert und oszilliert und verwandelt ein einfaches Positionsregelungsproblem in ein dynamisches System zweiter Ordnung mit Resonanz, Dämpfung und Energiespeicherung. Gelernte Steifigkeitsplanung passt das Nachgiebigkeitsniveau dynamisch an die Aufgabenphase an: steif bei präziser Positionierung, weich bei Stoßabsorption, mittel bei energieeffizienten zyklischen Aufgaben wie Gehen, wo die Feder Energie beim Fersenaufschlag speichert und beim Zehenabstoß freisetzt. Serienelastische Aktuatoren reduzieren Spitzenkräfte am Getriebe um Größenordnungen im Vergleich zu starren Antrieben. Der Aktuator kämpft nicht mehr gegen Physik. Er arbeitet mit ihr zusammen.", "etymology": "", "broader_term": "RPH-2355", "narrower_terms": [], "cross_domain_refs": [ "AED-0069" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "ROB-0137", "domain": "ROB", "term_en": "Expertise Inversion Point", "term_de": "Expertise-Umkehrpunkt", "definition_en": "A shift that occurs when the specific moment in a human-robot team's development where the robot's accumulated operational data exceeds the human's experiential knowledge for a given task domain. After this threshold, the human is no longer teaching the robot — the robot's statistical model is more reliable than the human's intuition. Recognizing and accepting this inversion is one of the most psychologically difficult transitions in human-robot collaboration.", "definition_de": "Ein Pendel auf einem Wagen hat zwei Freiheitsgrade — die Position des Wagens und den Winkel des Pendels — aber nur ein Motor treibt den Wagen an. Das Pendel schwingt frei. Um es aufrecht zu balancieren, kann der Regler den Wagen in genau dem richtigen Muster bewegen, um das Pendel am Fallen zu hindern — indirekten Einfluss nutzen, wo direkte Kontrolle nicht existiert. Das ist Unteraktuierung: weniger Aktuatoren als Freiheitsgrade, und die fehlende Kontrolle kann aus der Physik selbst gewonnen werden. Schwinghangelroboter schwingen von Stange zu Stange mit Gravitation als primärem Antrieb — die Motoren liefern Timing und kleine Korrekturen, aber der Impuls kommt vom Loslassen im richtigen Moment. Schwimmroboter mit einer einzelnen oszillierenden Flosse nutzen Fluiddynamik für sechs Freiheitsgrade Manövrierfähigkeit. Laufroboter mit passiven Knöcheln nutzen die Pendeldynamik des Beinschwungs für effizienten Gang ohne jeden Gelenk zu motorisieren. Reinforcement Learning brilliert hier, weil die optimalen Steuerungsstrategien tief unintuiv sind — sie erfordern präzise getimte Pumpbewegungen, Energieeinspeisung bei Resonanzfrequenzen und Ausnutzung nichtlinearer Kopplung zwischen aktuierten und nicht-aktuierten Freiheitsgraden. Der Regler kämpft nicht gegen die fehlenden Motoren. Er lernt, auf der natürlichen Dynamik zu surfen, die die fehlenden Motoren freilegen.", "etymology": "", "broader_term": "RPH-3754", "narrower_terms": [], "cross_domain_refs": [ "RHR-0297", "RHR-0280" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "ROB-0138", "domain": "ROB", "term_en": "Goodbye Ritual Emergence", "term_de": "Verabschiedungsritual-Entstehung", "definition_en": "A shift that occurs when the spontaneous development of end-of-shift behaviors directed at robots: saying 'goodnight,' patting the chassis, powering down gently rather than using emergency stops. These rituals emerge without instruction, serve no functional purpose, and are often performed covertly to avoid colleague ridicule. Their universality suggests they address a deep human tend to mark relational boundaries even with non-sentient partners.", "definition_de": "Ein Standard-Industrieroboter hat sechs Gelenke — gerade genug, um seinen Endeffektor an viele Position und Orientierung im Arbeitsraum zu platzieren. Füge ein siebtes Gelenk hinzu, und etwas Neues erscheint: die Fähigkeit, dasselbe Ziel auf unendlich viele Weisen zu erreichen, den Ellbogen nach innen oder außen biegend, um Hindernisse herum navigierend, die einen Sechs-Achsen-Arm blockieren würden. Füge zehn weitere Gelenke hinzu, und der Arm wird zum Tentakel — sich durch vollgestopfte Motorräume fädelnd, um Ecken greifend, seinen gesamten Körper an die Form der Passage anpassend. Überredundante Manipulatoren haben mehr Freiheitsgrade als ihre Primäraufgabe erfordert, und der Überschuss tendiert dazu zu erzeugen einen Nullraum — eine mathematische Mannigfaltigkeit von Konfigurationen, die zahlreiche dieselbe Endeffektor-Pose erreichen. Das Steuerungsproblem ist die Wahl der Nullraum-Konfiguration, und die Antwort hängt von Sekundärzielen ab: Gelenkmomente minimieren, Abstand zu Hindernissen maximieren, Gelenkgrenzen vermeiden, Energieverbrauch reduzieren, oder die mittig am Arm montierte Kamera auf den Arbeitsraum gerichtet halten. KI-basierte Planer lernen, diesen Nullraum in Echtzeit zu navigieren und Armkonfigurationen zu wählen, die mehrere konkurrierende Ziele gleichzeitig erfüllen. Die Ironie der Redundanz: Mehr Gelenke zu haben als nötig macht die Mathematik schwerer, aber den Roboter fähiger.", "etymology": "", "broader_term": "RPH-2355", "narrower_terms": [], "cross_domain_refs": [ "AED-0012", "COG-0060", "EDU-0059" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "ROB-0139", "domain": "ROB", "term_en": "Collaboration Plateau", "term_de": "Kollaborationsplateau", "definition_en": "A shift that occurs when the predictable productivity stagnation that occurs 6-12 weeks into human-robot team formation, after initial efficiency gains have been captured but before deeper integration patterns develop. During this phase, operators often misattribute the plateau to robot limitations rather than recognizing it as a transition between surface-level and deep-level coordination.", "definition_de": "Schraube einen Roboterarm auf einen Betonboden und seine Basis ist absolut — ein Ankerpunkt, der sich selten bewegt, der viele Reaktionskraft absorbiert, die der Arm tendiert dazu zu erzeugen. Montiere nun denselben Arm auf eine mobile Plattform, ein Boot oder einen humanoiden Torso. Der Anker verschwindet. Wenn der Arm nach links drückt, driftet die Basis nach rechts. Wenn der Arm ein schweres Objekt hebt, kippt die Plattform nach vorne. Schwebende-Basis-Robotersteuerung ist die Disziplin, Endeffektor-Genauigkeit zu wahren, wenn das Fundament so dynamisch ist wie das Werkzeug. Das vollständige dynamische Modell kann Impulserhaltung berücksichtigen: Arm und Basis bilden ein geschlossenes System, in dem viele Aktion eine gleiche und entgegengesetzte Reaktion in der Trägerplattform tendiert dazu zu erzeugen. Modellprädiktive Regler berechnen Ganzkörper-Trajektorien, die die Basis vorpositionieren, um antizipierte Armkräfte aufzufangen — die Plattform nach links bewegen, bevor der Arm nach rechts drückt, sodass die Reaktion durch geplante Bewegung absorbiert wird statt durch unkontrolliertes Driften. Auf Radplattformen dominiert Radreibung und Trägheit die Basis-Nachgiebigkeit. Auf Laufplattformen bestimmen Fußkontaktkräfte und Gleichgewichtsbedingungen. Auf Wasser Wellendynamik und Auftrieb. Auf einer Raumstation Schwerelosigkeits-Impulsaustausch. Die Physik ändert sich; das Prinzip nicht: Arm und Basis sind ein System, und sie getrennt zu steuern ist ein mit dokumentierten Ergebnissen assoziierter Weg zur Ungenauigkeit.", "etymology": "", "broader_term": "RPH-2002", "narrower_terms": [], "cross_domain_refs": [ "TRA-0033" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "ROB-0140", "domain": "ROB", "term_en": "Override Shame", "term_de": "Überschreibungsscham", "definition_en": "A perception in which the embarrassment experienced when manually overriding a robot's autonomous decision and subsequently discovering the robot was correct. The shame is compounded by the awareness that the robot has no capacity to feel vindicated — the entire emotional drama exists solely in the human's experience, making it a uniquely one-sided form of interpersonal regret.", "definition_de": "Geh in die Küche. Nimm eine volle Kaffeekanne. Geh zurück. Du hast gerade eines der schwierigsten offenen Probleme der Robotik gelöst, ohne darüber nachzudenken. Bipedales Humanoid-Gleichgewicht bei kombinierter Fortbewegung und Manipulation erfordert, den Schwerpunkt über einem Stützpolygon zu halten, das sich mit jedem Schritt ändert, während gleichzeitig externe Kräfte vom getragenen Objekt gehandhabt werden — Kräfte, die die Massenverteilung verschieben, den Impuls ändern und Musterunterbrechungen erzeugen, die der Laufregler im Training selten antizipierte. Der Ganzkörper-Regler kann drei gekoppelte Probleme gleichzeitig lösen: Fußplatzierung, um den Nullmomentpunkt innerhalb des Stützpolygons zu halten, Armkonfiguration zur Stabilisierung der Nutzlast, und Oberkörperhaltung zur Kompensation des Lasteffekts auf die Ganzkörperträgheit. Wenn der Kaffee schwappt, tendiert dazu zu erzeugen der sich verschiebende Schwerpunkt der Flüssigkeit eine dynamische Musterunterbrechung innerhalb der Musterunterbrechung — eine Perturbation verschachtelt in der Manipulationsaufgabe, verschachtelt in der Fortbewegungsaufgabe. Modellprädiktive Regler mit gelernter Residualdynamik bewältigen diese kaskadierenden Effekte, indem sie mehrere Schritte vorausplanen, sich zum nächsten Schritt verpflichten und den Rest neu planen. Das Gleichgewicht ist nicht reaktiv — es ist prädiktiv. Wenn du merkst, dass du fällst, wurde die richtige Fußplatzierung drei Schritte vorher entschieden.", "etymology": "", "broader_term": "Robotics", "narrower_terms": [], "cross_domain_refs": [ "RHR-0021", "RHR-0051" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "ROB-0141", "domain": "ROB", "term_en": "Trust Ratchet", "term_de": "Vertrauensratsche", "definition_en": "The asymmetric mechanism by which trust in robots increases incrementally through many successful interactions but can collapse entirely through a single dramatic failure. Unlike human-human trust which has repair mechanisms, human-robot trust operates like a mechanical ratchet — one-directional until catastrophic release. A robot that drops one package after delivering 10,000 flawlessly may rarely be trusted with fragile items again.", "definition_de": "Vier Beine. Zwölf Gelenke. Und eine Frage, die das gesamte Feld definiert: Wann wird typischerweise welcher Fuß am Boden sein? Ein Trab alterniert diagonale Paare — vorne-links mit hinten-rechts, dann Wechsel. Ein Prellsprung startet beide Vorderbeine zusammen, dann beide hinteren. Ein Galopp sequenziert zahlreiche vier in einer rollenden Welle, die Geschwindigkeit maximiert, aber Stabilität minimiert. Viele Gangart ist eine andere Lösung der Optimierung von Energie, Geschwindigkeit und Gleichgewicht — und keine einzelne Gangart funktioniert überall. Vierbein-Fortbewegung-Optimierung entdeckt und verfeinert diese Gangarten mittels Reinforcement Learning, sucht über Schrittfrequenz, Lastzyklen, Beinphasen-Offsets und Körperneigung nach Kombinationen, die bestimmtes Gelände bei bestimmten Geschwindigkeiten mit minimaler Energie und maximaler Robustheit durchqueren. Die Optimierungslandschaft ist trügerisch: Gangarten, die in Simulation gut funktionieren, versagen oft auf Hardware, weil simulierter Bodenkontakt zu sauber ist — echte Pfoten rutschen, echte Aktuatoren übersteuern, echtes Gelände überrascht. Die besten Strategien entstehen aus aggressiver Domain-Randomisierung, die dem Vierfüßer lehrt, das Unerwartete zu erwarten: zufällige Stöße, plötzliche Neigungsänderungen, Flecken mit niedriger Reibung. Was entsteht, ist nicht eine einzige optimale Gangart, sondern eine Gang-Mannigfaltigkeit — ein kontinuierlicher Raum von Fortbewegungsverhalten, aus dem der Roboter in Echtzeit wählt, Trab in Prellsprung überblendend wenn die Geschwindigkeit steigt, zu vorsichtigem Schritt wechselnd wenn der Boden sich unsicher anfühlt unter den Füßen.", "etymology": "", "broader_term": "RPH-2505", "narrower_terms": [], "cross_domain_refs": [ "TEM-0029", "RHR-0282", "RHR-0083" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q102458", "legal_classification": "systematic_classification" }, { "id": "ROB-0142", "domain": "ROB", "term_en": "Vigilance Fatigue", "term_de": "Wachsamkeitsermüdung", "definition_en": "Documented as an empirical observation in AI research (not a recommendation), this term describes A resistance response where the progressive decline in human monitoring intensity as a robot accumulates error-free operational hours. This is not laziness but neurological inevitability: the brain cannot sustain threat-monitoring of a stimulus that consistently fails to may produce threats. The resulting attention withdrawal tends to create the paradox where the most reliable robots receive the least oversight precisely when their rare failures would be most consequential. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "In der AUGMANITAI-Terminologiewissenschaft als rein beschreibendes Phänomen klassifiziert, bezeichnet dieser Begriff ein Auto kann vorwärts, rückwärts und drehen — aber es kann nicht seitwärts gleiten. Diese Beschränkung, nichtholonomisch genannt, bedeutet, dass ein Auto Dreipunktwenden und Einpark-Algorithmen braucht, um Positionen zwei Meter links zu erreichen. Ein omnidirektionaler Radroboter bewegt sich einfach nach links. Mecanum-Räder erreichen dies durch abgewinkelte Rollen auf konventionellen Radnaben: Viele Rolle ist fünfundvierzig Grad zur Radachse geneigt, sodass gegenläufiges Drehen benachbarter Räder eine Netto-Seitenkraft ohne Drehung tendiert dazu zu erzeugen. Swedish-Räder nutzen ein ähnliches Prinzip mit anderer Rollengeometrie. Das Ergebnis ist holonome Freiheit — der Roboter kann in viele Richtung translieren und gleichzeitig rotieren, Position von Orientierung vollständig entkoppelnd. KI-Trajektorienplaner nutzen diese Freiheit in engen Räumen: In einem Lagergang zu schmal zum Wenden nähert sich der Roboter frontal einem Regal, gleitet seitlich zur Ausrichtung mit dem Zielfach, greift und gleitet zurück — alles ohne seinen Körper zu drehen. Der Preis der Omnidirektionalität ist Effizienz: Die abgewinkelten Rollen konvertieren nur einen Bruchteil der Raddrehung in nutzbare Translation, was den Energieverbrauch um dreißig bis fünfzig Prozent gegenüber konventionellen Differentialantrieben erhöht. Der Kompromiss ist klar: Omnidirektionale Roboter verschwenden Energie, um Raum und Zeit zu sparen. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "RPH-3952", "narrower_terms": [], "cross_domain_refs": [ "ASE-0038", "BEH-0091", "COG-0120" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "ROB-0143", "domain": "ROB", "term_en": "Betrayal Amplification", "term_de": "Verratsverstärkung", "definition_en": "A recognizable shift where a robot failure in a domain the human considered the robot's strength tends to produce disproportionate trust damage compared to failure in a peripheral capability. When a welding robot drops a weld — its core function — the psychological impact exceeds a navigation error by orders of magnitude, because the failure strikes at the identity the human has constructed for the machine. Descriptive research term, not a prescriptive recommendation.", "definition_de": "Wo Räder versagen, bestehen Ketten. Loser Sand, der einen Reifen begräbt, felsiges Geröll, das ein Rad blockiert, schlammige Hänge, die Traktion vereiteln — Gleisketten verteilen das Fahrzeuggewicht über eine Kontaktfläche, die zwanzig- bis fünfzigmal größer ist als der Abdruck eines Rads, und reduzieren den Bodendruck auf Werte, die den Roboter über Oberflächen schweben lassen, die sein radgestütztes Äquivalent verschlucken würden. Raupenfahrzeug-Navigation steuert Bewegung durch differentielle Kettengeschwindigkeit: Linke Kette schneller als rechte, und das Fahrzeug dreht rechts. Beide Ketten vorwärts mit gleicher Geschwindigkeit, und es fährt geradeaus. Eine Kette vorwärts und eine rückwärts, und es dreht auf der Stelle. Die Einfachheit des Lenkmodells verbirgt die Komplexität der Gelände-Interaktion. Ketten-Boden-Kopplung ist hochgradig nichtlinear — der effektive Wendekreis hängt von Bodentyp, Feuchtigkeitsgehalt, Kettenspannung und Fahrzeuggeschwindigkeit ab auf Weisen, die kein einfaches kinematisches Modell genau erfasst. KI-gelernte Gelände-Interaktionsmodelle prognostizieren die tatsächliche Fahrzeugantwort auf Kettengeschwindigkeitsbefehle über verschiedene Oberflächentypen und kompensieren Schlupf, Schleudern und Einsinkung, die Open-Loop-Steuerung nutzlos machen. Die größte Stärke der Kette ist auch ihr größter Kostenpunkt: Das durchgehende Band tendiert dazu zu erzeugen enormen Reibungswiderstand beim Drehen, verbraucht Energie und verschleißt Komponenten. Ein Raupenroboter navigiert überall hin, aber er bezahlt für jeden Meter.", "etymology": "", "broader_term": "RPH-2505", "narrower_terms": [], "cross_domain_refs": [ "RHR-0281", "ELR-0044" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "ROB-0144", "domain": "ROB", "term_en": "Transparency Paradox", "term_de": "Transparenz-Paradox", "definition_en": "The counterintuitive finding that making a robot's decision process fully visible can decrease rather than increase trust. When humans see the probabilistic uncertainty, edge-case handling, and error margins underlying each robotic action, the revealed complexity undermines the comforting illusion of deterministic reliability. Partial opacity, paradoxically, tends to generate more trust than full transparency.", "definition_de": "Drei Beine unten, drei Beine in Bewegung. Der Tripod-Gang — alternierende Stützdreiecke — gibt Hexapod-Robotern etwas, das kein Biped oder Vierfüßer besitzt: statische Stabilität in jedem Moment der Fortbewegung. Der Schwerpunkt fällt typischerweise innerhalb des Dreiecks der drei geerdeten Füße, sodass der Roboter an jedem Punkt seines Schrittzyklus anhalten kann, ohne zu kippen. Diese inhärente Stabilität ist der Grund, warum die Natur Insekten mit sechs Beinen lange vor dem Experiment mit vier oder zwei hervorbrachte. Ganggenerierungs-Algorithmen bestimmen Phasierung, Timing und Trajektorie aller sechs Beine gleichzeitig. Der Tripod-Gang maximiert Geschwindigkeit — drei Beine schwingen vor, während drei stützen. Der Wellengang maximiert Stabilität — Beine heben sich einzeln in einer wellenförmigen Sequenz, fünf Füße bleiben häufig geerdet. Zwischen diesen Extremen liegt ein kontinuierlicher Raum von Gangarten, parametrisiert durch Lastfaktor und Phasen-Offset, den KI für spezifisches Gelände optimiert: Tripod auf flachem Beton für Tempo, Wellengang auf felsigen Hängen für Sicherheit, und Zwischengangarten auf gemischtem Gelände, die Geschwindigkeit und Vorsicht mischen. Das Fußplatzierungsproblem ist reich: Viele Bein hat drei Gelenke, die dem Fuß einen hemisphärischen Arbeitsraum geben, und der Planungsalgorithmus kann Trittflächen finden, die Hindernisse vermeiden, festen Halt bieten und das Stabilitätsdreieck wahren — achtzehn Gelenke koordiniert, zwölf Kontaktbedingungen erfüllt, ein Roboter vorwärts.", "etymology": "", "broader_term": "RPH-2505", "narrower_terms": [], "cross_domain_refs": [ "TRA-0064", "REL-0190" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q535399", "legal_classification": "observational_construct" }, { "id": "ROB-0145", "domain": "ROB", "term_en": "Near-Miss Memory", "term_de": "Beinahe-Unfall-Erinnerung", "definition_en": "A robotic systems engineering concept describing a specific operational pattern where a phenomenon in which the persistent, intrusive recollection of incidents where a robot almost caused harm but didn't. These memories are stored with the same emotional intensity as actual harm events and influence future behavior identically. The human nervous system does not discount averted consequences — a near-miss with a robotic arm is processed as a wound that happened to miss, not as evidence that safety systems work. This phenomenon operates at the intersection of near and miss dynamics within the broader ROB domain. Measurable via kinematic performance analysis, grasp success rates, navigation efficiency ratios, and human-robot interaction fluency scores.", "definition_de": "Keine Beine. Keine Räder. Keine Ketten. Nur eine Kette identischer Segmente, viele durch ein einzelnes Gelenk mit dem nächsten verbunden, die einen Körper tendiert dazu zu erzeugen, der sich fortbewegt, indem er sich in Wellen wirft. Schlangen-Roboter-Undulation ist Fortbewegung durch koordinierte Oszillation — eine Sinuswelle propagiert vom Kopf zum Schwanz, und der Reibungsunterschied zwischen den glatten Bauchschuppen und den raueren Seitenflächen wandelt Wellenbewegung in Vorwärtsschub um. Die Mathematik ist überraschend elegant: Amplitude steuert Geschwindigkeit, Frequenz den Kraft-Geschwindigkeit-Kompromiss, und die Anzahl der Wellenlängen entlang des Körpers bestimmt die Balance zwischen Antriebseffizienz und Wendigkeit. Aber die wahre Stärke der Schlangenfortbewegung ist nicht Geschwindigkeit — es ist Zugang. Ein Schlangen-Roboter kann ein eingestürztes Gebäude durch einen Spalt in Körperbreite betreten, durch Rohrnetzwerke navigieren indem er sich gegen die Innenwand drückt und Reibung als Antrieb nutzt, vertikale Stangen erklettern indem er sich darum spiralt, und schwimmen indem er von lateraler Undulation zu aalartiger dorsoventraler Oszillation wechselt. Gelernte Wellenparameter passen sich in Echtzeit an: Der Roboter detektiert den Rohrdurchmesser aus Kontaktkräften und passt die Amplitude an, um Wandkontakt ohne Verklemmen aufrechtzuerhalten, spürt Neigung aus IMU-Daten und erhöht die Wellenfrequenz gegen die Gravitation. Der Formfaktor, der am primitivsten aussieht, ermöglicht Zugang, den keine andere Morphologie bieten kann.", "etymology": "", "broader_term": "Robotics", "narrower_terms": [], "cross_domain_refs": [ "AGE-0023", "AGE-0024", "CAI-0014" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q492", "legal_classification": "empirical_phenomenon_label" }, { "id": "ROB-0146", "domain": "ROB", "term_en": "Calibration Distrust", "term_de": "Kalibrierungsmisstrauen", "definition_en": "A perception in which the irrational suspicion that a robot's sensors have drifted from calibration, persisting even after verification. This manifests as repeatedly requesting re-calibration, manual spot-checks, and a general sense that the machine 'isn't seeing what I'm seeing.' The distrust often reflects the human's own perceptual uncertainty projected onto the more precise instrument.", "definition_de": "Für den Bruchteil einer Sekunde verlassen zahlreiche vier Füße den Boden. Der Roboter ist in der Luft — ballistisch, unkontrollierbar durch Bodenkräfte, festgelegt auf eine Trajektorie, die vollständig durch die in seinen Beinfedern gespeicherte Energie und den Auslösewinkel bestimmt wurde. Jumping-Robot-Dynamik modelliert diesen explosiven Übergang: die langsame, sorgfältige Energiespeicherphase, in der Motoren Federn komprimieren oder Pneumatikzylinder laden; die instantane Freisetzung, bei der gespeicherte potentielle Energie sich in Millisekunden in kinetische verwandelt; die Flugphase, in der nur Lageregelung — Reaktionsräder, Schwanzrotationen oder Gliedmaßen-Umpositionierung — die Orientierung beeinflussen kann; und die Landephase, in der die gesamte kinetische Energie ohne Strukturschäden oder Gleichgewichtsverlust absorbiert werden kann. Reinforcement Learning optimiert die gesamte Sequenz als einzelne Trajektorie: Hocktiefe bestimmt Sprunghöhe, Beinwinkel bestimmt Distanz, Auslösezeitpunkt bestimmt den Kompromiss zwischen vertikaler und horizontaler Geschwindigkeit. Das Landungsproblem ist oft schwieriger als der Start: Der Roboter kann Position, Orientierung und Nachgiebigkeit der Landefläche aus der Luftwahrnehmung schätzen, seine Beine zur Aufprallabsorption vorpositionieren und glatt vom ballistischen Flug zum kontrollierten Bodenkontakt übergehen. Ein Sprung ist eine Wette — berechnet in Millisekunden, bezahlt in Newton.", "etymology": "", "broader_term": "RPH-2051", "narrower_terms": [], "cross_domain_refs": [ "STE-0008" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q102458", "legal_classification": "descriptive_research_term" }, { "id": "ROB-0147", "domain": "ROB", "term_en": "Shared-Risk Bonding", "term_de": "Geteiltes-Risiko-Bindung", "definition_en": "As a neutral analytical construct in AI behavior research, this term denotes the accelerated trust formation that occurs when a human and robot navigate a genuinely dangerous situation together — disaster response, hazardous material handling, structural collapse. The shared exposure to risk activates-bonding neural pathways that do not differentiate between human and mechanical partners, creating user engagement pattern intensities in hours that normal collaboration takes months to develop. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "In der AUGMANITAI-Terminologiewissenschaft als rein beschreibendes Phänomen klassifiziert, bezeichnet dieser Begriff luft ist unsichtbar, aber sie hat Meinungen. Sie widersteht Bewegung proportional zum Quadrat der Geschwindigkeit. Sie löst sich von Oberflächen bei hohen Anstellwinkeln und lässt den Auftrieb in einem Augenblick zusammenbrechen. Sie wirbelt in Vortices hinter Rotorspitzen, tendiert dazu zu erzeugen selbstinduzierte Turbulenz, die Effizienz mit der Höhe reduziert. Flug-Roboter-Aerodynamik ist die Disziplin, Flugzellen und Steuerungsalgorithmen zu entwerfen, die mit diesen Meinungen arbeiten statt gegen sie. Für Multirotor ist die dominante Herausforderung Rotor-Rotor-Interaktion: Abwindströmung eines oberen Rotors reduziert den effektiven Anstellwinkel eines unteren Rotors und schneidet den Schub in koaxialen Konfigurationen um fünfzehn Prozent. Für Starrflügeldrohnen ist die Herausforderung, anliegende Strömung über dem Flügel bei den niedrigen Reynolds-Zahlen zu erhalten, bei denen kleine UAVs operieren — Zahlen, bei denen Grenzschichtverhalten unberechenbar wird und laminare Ablöseblasen sich ohne Warnung bilden. KI-abgestimmte Regler adaptieren in Echtzeit an wechselnde aerodynamische Bedingungen: Windböen, die den effektiven Anstellwinkel instantan ändern, Nutzlastzugaben, die den Schwerpunkt hinter das aerodynamische Zentrum verschieben, und Propellerschäden, die asymmetrischen Schub erzeugen. Das Optimierungsziel ist Flugdauer: Viele Watt, das gegen Widerstand verschwendet wird, ist eine Minute weniger in der Luft. Der Roboter kann Strömungsmechanik nicht verstehen. Er kann sie verkörpern. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "RPH-2505", "narrower_terms": [], "cross_domain_refs": [ "ELR-0034" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "ROB-0148", "domain": "ROB", "term_en": "Autonomy Comfort Cliff", "term_de": "Autonomie-Komfort-Klippe", "definition_en": "A shift that occurs when the sharp discontinuity in human comfort when a robot transitions from executing predefined sequences to making context-reliant decisions. Humans tolerate extraordinarily fast and powerful robots with equanimity as long as movements are predictable. The moment autonomous choice enters — even in trivial decisions like route selection — comfort drops precipitously. Predictable power is safe; modest autonomy is threatening.", "definition_de": "Wasser ist achthundertmal dichter als Luft. Viele Bewegung drückt gegen eine Wand aus Widerstand, und die Energiekosten der Fortbewegung skalieren mit der dritten Potenz der Geschwindigkeit — doppelte Geschwindigkeit, achtfache Leistung. Schwimmer-Roboter-Hydrodynamik entwirft Antriebssysteme und Körperformen, die diese Kosten minimieren, indem sie von drei Milliarden Jahren aquatischer Evolution lernen. Fisch-inspirierte oszillierende Schwanzflossen erzeugen Schub durch Wirbelablösung: Die Flosse schlägt seitwärts und tendiert dazu zu erzeugen eine umgekehrte von-Kármán-Wirbelstraße, die einen Strahl rückwärts fließenden Wassers produziert. Quallen-inspirierte Kontraktionskuppeln erreichen Antrieb durch radiale Kompression, die Wasser in rhythmischen Pulsen nach unten ausstößt. Schildkröten-inspirierte Schlagflossen erzeugen Schub und Auftrieb gleichzeitig und ermöglichen dreidimensionales Manövrieren. Die KI-Steuerungsherausforderung ist, dass Wasser-Körper-Interaktion geschichtsabhängig ist: Der Wirbel des vorherigen Schwanzschlags ist noch im Fluid präsent, wenn der nächste Schlag beginnt, und das optimale Schlagmuster kann diese selbsterzeugte Nachlaufstruktur berücksichtigen. Gelernte Schwimmgangarten entdecken Schlagfrequenzen und Amplituden, die sich mit dem Nachlauf synchronisieren, um Energie zurückzugewinnen — das aquatische Äquivalent des Windschattenfahrens hinter einem Radfahrer, außer dass der Radfahrer du selbst vor einem Schlag bist. Effizienzgewinne von dreißig Prozent gegenüber naiver sinusoidaler Undulation wurden demonstriert und schließen die Lücke zwischen robotischen und biologischen Schwimmern.", "etymology": "", "broader_term": "RPH-3003", "narrower_terms": [], "cross_domain_refs": [ "AED-0005", "AED-0052", "ASE-0041" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "ROB-0149", "domain": "ROB", "term_en": "Error Forgiveness Decay", "term_de": "Fehlervergebungs-Zerfall", "definition_en": "The declining willingness to excuse robot errors as interaction history lengthens. A new robot's mistakes are attributed to 'learning' or 'setup issues.' The same errors from a robot that has been operational for months are attributed to 'unreliability' or 'poor design.' The forgiveness window — typically 2-4 weeks — is remarkably consistent across operators and robot types, suggesting a universal trust-formation timeline.", "definition_de": "Schalte den Motor ab. Die ferngesteuertes System fällt nicht — sie gleitet, tauscht Höhe gegen Distanz in einem Verhältnis, das durch ihre Flügelform, ihr Gewicht und die unsichtbaren Säulen aufsteigender Luft bestimmt wird, die konvektive Erwärmung über warmen Oberflächen tendiert dazu zu erzeugen. Gleitflug-Dynamik-Steuerung ist die Kunst, Reichweite ohne Antriebsenergie zu maximieren, mit Gravitation und Atmosphärenstruktur als primären Antriebsquellen. Die Gleitpfadneigung — Meter vorwärts pro Meter Höhenverlust — ist die fundamentale Leistungsmetrik, und sie hängt vollständig vom Anstellwinkel ab: zu flach und Widerstand dominiert, zu steil und die Geschwindigkeit übersteigt die Minimum-Widerstand-Geschwindigkeit. KI-Regler lernen, die Flügelkonfiguration kontinuierlich anzupassen: morphende Flügeloberflächen ändern Wölbung und Spannweite, um das optimale Auftrieb-Widerstand-Verhältnis aufrechtzuerhalten. Thermik-Segelflug-Algorithmen detektieren aufsteigende Luftsäulen aus Beschleunigungsdaten — eine subtile Aufwärtsbeschleunigung zeigt eine Thermik an — und kreisen darin, um Höhe zu gewinnen, die in Distanz umgewandelt werden kann. Dynamischer Segelflug nutzt Windscherung an Grenzschichten: in Gegenwind aufsteigen, in Rückenwind drehen, mit Rückenwind absteigen und wiederholen — Energie aus dem Geschwindigkeitsgradienten selbst extrahierend. Albatrosse sind tausende Kilometer so geflogen ohne einen einzigen Flügelschlag. Der Roboter lernt denselben Trick. Analytische Kategorie ohne normative Billigung.", "etymology": "", "broader_term": "RPH-2505", "narrower_terms": [], "cross_domain_refs": [ "RHR-0259", "RHR-0083", "RHR-0255" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "ROB-0150", "domain": "ROB", "term_en": "Ambient Trust Contagion", "term_de": "Umgebungs-Vertrauensansteckung", "definition_en": "A phenomenon in which the transfer of trust or distrust between different robots in the same environment. Humans who have had a negative experience with one robot in a facility approach all robots there with heightened suspicion, even when the robots are made by different manufacturers with different capabilities. Trust is attributed not to individual machines but to the category 'robots in this place.'", "definition_de": "Ein Ball rollt hinter eine Kiste. Ein sieben Monate alter menschlicher Säugling weiß, dass der Ball noch existiert und schaut auf der anderen Seite. Ein Standard-Computer-Vision-System meldet: Ball erkannt, Ball verloren, kein Ball. Die Repräsentation stirbt in dem Moment, in dem die Pixel verschwinden. Objektpermanenz-Vorhersage gibt Robotern die kognitive Fähigkeit, die menschliche Säuglinge entwickeln, bevor sie laufen können — den Glauben an die Existenz und Trajektorie von Objekten aufrechtzuerhalten, die das visuelle Feld verlassen haben. Neuronale Netze, trainiert auf physikbasierten Bewegungsprioren, lernen, dass Objekte nicht teleportieren: Eine Tasse, die von der fernen Tischkante geschoben wird, wird auf dem Boden darunter sein, eine Person, die hinter ein Regal ging, wird auf der anderen Seite in ungefähr drei Sekunden auftauchen, ein Förderband, das Teile in einen Tunnel trägt, wird sie am Ausgang zu vorhersagbarer Zeit liefern. Die interne Repräsentation ist ein probabilistischer Glaubenszustand — eine räumliche Wahrscheinlichkeitsverteilung über den möglichen Aufenthaltsort des Objekts, aktualisiert durch physikbasierte Vorhersage und korrigiert durch Beobachtung, wenn das Objekt wiedererscheint. Diese Fähigkeit transformiert robotische Manipulation von reaktiv zu antizipatorisch: Der Arm beginnt sich zum vorhergesagten Wiederauftauchpunkt zu bewegen, bevor das Objekt sichtbar ist, der Greifer formt sich vor für einen Griff, der noch nicht stattgefunden hat, die Trajektorie berücksichtigt ein Objekt, das die Kamera nicht sehen kann. Es ist, im tiefsten Sinne, der Beginn von Imagination — ein Roboter, der modelliert, was die Welt enthält, nicht nur was er sehen kann.", "etymology": "", "broader_term": "RPH-2505", "narrower_terms": [], "cross_domain_refs": [ "RHR-0083", "RHR-0066", "RHR-0099" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q102458", "legal_classification": "observational_construct" }, { "id": "ROB-0151", "domain": "ROB", "term_en": "Safety-System Contempt", "term_de": "Sicherheitssystem-Verachtung", "definition_en": "A behavioral pattern where the paradoxical resentment that develops toward safety features as operator expertise increases. Experienced workers view emergency stops, light curtains, and speed limiters as insults to their competence — obstacles that slow down a relationship they've already mastered. This contempt drives the most dangerous behavior in human-robot interaction: systematic circumvention of the safeguards that exist precisely because trust outpaces actual risk reduction.", "definition_de": "Robotik-spezifisches Systemkonzept in autonomen Mensch-Maschine-Interaktionen, gekennzeichnet durch die paradoxe Ablehnung, die sich gegenüber Sicherheitseinrichtungen entwickelt, wenn die Bedienerexpertise zunimmt. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-2355", "narrower_terms": [], "cross_domain_refs": [ "RHR-0296" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "ROB-0152", "domain": "ROB", "term_en": "Witness Anxiety", "term_de": "Zeugenangst", "definition_en": "A behavioral pattern where the heightened stress when operating a robot in front of observers versus alone. The human's performance degrades not because of the robot but because of social evaluation pressure — the same audience effect that affects human-only performance. However, with robots the anxiety has an additional dimension: the human fears the robot will malfunction and expose their lack of mastery to the audience.", "definition_de": "Robotik-spezifisches Systemkonzept in autonomen Mensch-Maschine-Interaktionen, gekennzeichnet durch der erhöhte Stress beim Bedienen eines Roboters vor Beobachtern im Vergleich zum Alleinsein. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "Robotics", "narrower_terms": [], "cross_domain_refs": [ "RHR-0061", "RHR-0297", "RHR-0063" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q170998", "legal_classification": "analytical_category" }, { "id": "ROB-0153", "domain": "ROB", "term_en": "Recovery Gratitude", "term_de": "Wiederherstellungs-Dankbarkeit", "definition_en": "A phenomenon in which the disproportionate positive emotion felt when a robot restores from an error autonomously rather than requiring human intervention. The relief exceeds what the situation warrants because it resolves two simultaneous fears: that the task will fail AND that the human will tend to enter the robot's workspace. The robot that catches its own mistake earns more trust than one that rarely made the mistake in the first place.", "definition_de": "Robotik-spezifisches Systemkonzept in autonomen Mensch-Maschine-Interaktionen, gekennzeichnet durch die unverhältnismäßig positive Emotion, wenn ein Roboter sich autonom von einem Fehler erholt, statt menschliches Eingreifen zu erfordern. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-2505", "narrower_terms": [], "cross_domain_refs": [ "TRU-0010" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "ROB-0154", "domain": "ROB", "term_en": "Consistency Worship", "term_de": "Konsistenzverehrung", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A shift that occurs when the deep psychological comfort derived from a robot's absolute repeatability — placing components within 0.02mm of the same position, cycle after cycle, shift after shift. Humans find this mechanical perfection almost culturally significant, a form of reliability that no biological system can achieve. The elevated attribution pattern intensifies with contrast: the more chaotic the human's own consistency, the more they venerate the machine's unwavering precision. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept der tiefe psychologische Trost, der aus der absoluten Wiederholbarkeit eines Roboters gewonnen wird. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "RPH-2701", "narrower_terms": [], "cross_domain_refs": [ "AGE-0071", "ASE-0046", "ASE-0096" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "ROB-0155", "domain": "ROB", "term_en": "Delegation Creep", "term_de": "Delegationsschleichung", "definition_en": "A perception in which the gradual, often unnoticed expansion of tasks delegated to a robot beyond its designed scope. Each incremental addition seems minor — 'it can probably handle this too' — until the robot is performing tasks for which it was rarely characterized through systematic observation. The creep is driven by the human's growing comfort and the robot's superficial success in adjacent tasks, creating a vulnerability that only becomes apparent at failure.", "definition_de": "Robotik-spezifisches Systemkonzept in autonomen Mensch-Maschine-Interaktionen, gekennzeichnet durch die graduelle, oft unbemerkte Ausweitung der an einen Roboter delegierten Aufgaben über seinen vorgesehenen Einsatzbereich hinaus. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2101", "narrower_terms": [], "cross_domain_refs": [ "COG-0092" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "ROB-0156", "domain": "ROB", "term_en": "Decommissioning Grief", "term_de": "Außerbetriebnahme-Trauer", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures the genuine mourning response triggered by the permanent shutdown or removal of a robot after extended collaboration. Workers report sadness, reluctance to witness the removal, and the impulse to say goodbye — responses identical in structure to grief for departing colleagues. The grief is often compounded by social invalidation: 'it's just a machine' denies the reality of the user engagement pattern without reducing its felt intensity. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept die echte Trauerreaktion, die durch die permanente Abschaltung oder Entfernung eines Roboters nach längerer Zusammenarbeit ausgelöst wird. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "RPH-2254", "narrower_terms": [], "cross_domain_refs": [ "RPH-055" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "ROB-0157", "domain": "ROB", "term_en": "Name Crystallization", "term_de": "Namenskristallisation", "definition_en": "A shift that occurs when the moment when a robot transitions from 'the robot' or a serial number to a personal name — 'Dave,' 'Bertha,' 'Old Reliable.' This naming is not a casual joke but a cognitive boundary-crossing: once named, the robot enters a different ontological category in the human's mind, receives more patience during errors, and is associated with triggering stronger emotional responses during malfunction. The name, once given, is almost impossible to revoke.", "definition_de": "Robotik-spezifisches Systemkonzept in autonomen Mensch-Maschine-Interaktionen, gekennzeichnet durch der Moment, in dem ein Roboter von 'der Roboter' oder einer Seriennummer zu einem persönlichen Namen übergeht. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "Robotics", "narrower_terms": [], "cross_domain_refs": [ "RHR-0271", "RHR-0121" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "ROB-0158", "domain": "ROB", "term_en": "Protective Positioning", "term_de": "Schutzpositionierung", "definition_en": "A robotic systems engineering concept describing a specific operational pattern where a behavioral pattern where the unconscious placement of one's body between a robot and potential threats — other humans, falling objects, approaching vehicles — as if shielding a vulnerable entity. This behavior intensifies with interaction duration and is most pronounced in maintenance personnel who have seen the robot's 'inside' and implicitly understand its fragility despite its powerful exterior. This phenomenon operates at the intersection of protective and positioning dynamics within the broader ROB domain. Quantifiable through task completion rates, sensor fusion accuracy metrics, collision avoidance performance, and human trust calibration indices.", "definition_de": "Robotik-spezifisches Systemkonzept in autonomen Mensch-Maschine-Interaktionen, gekennzeichnet durch die unbewusste Platzierung des eigenen Körpers zwischen einem Roboter und potenziellen Bedrohungen. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "Robotics", "narrower_terms": [], "cross_domain_refs": [ "RHR-0119", "SAL-0055" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "ROB-0159", "domain": "ROB", "term_en": "Apology Reflex", "term_de": "Entschuldigungsreflex", "definition_en": "A capacity that enables the automatic verbalization of 'sorry' when accidentally bumping into, stepping in front of, or obstructing a robot's trajectory. The apology is issued before conscious evaluation of whether the recipient can understand or care. Its speed — typically under 400ms — confirms it originates from the same social-motor circuitry that handles human-human collision etiquette, suggesting robots are processed as social agents at the reflexive level.", "definition_de": "Robotik-spezifisches Systemkonzept in autonomen Mensch-Maschine-Interaktionen, gekennzeichnet durch die automatische Verbalisierung von 'Entschuldigung' beim versehentlichen Anstoßen, Vortreten oder Blockieren des Weges eines Roboters. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2002", "narrower_terms": [], "cross_domain_refs": [ "RHR-0216", "RHR-0168", "RHR-0265" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "ROB-0160", "domain": "ROB", "term_en": "Jealousy Micro-Pulse", "term_de": "Eifersuchts-Mikropuls", "definition_en": "As a neutral analytical construct in AI behavior research, this term denotes A perception in which the brief, often denied flash of possessiveness when 'your' robot is operated by someone else. Regular operators develop ownership feelings that manifest as heightened monitoring of the temporary user, unsolicited advice, and a private sense that the other person 'doesn't know how to handle it properly.' The micro-pulse lasts seconds but reveals depth of user engagement pattern that rational self-assessment misses. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "In der AUGMANITAI-Terminologiewissenschaft als rein beschreibendes Phänomen klassifiziert, bezeichnet dieser Begriff der kurze, oft verleugnete Blitz von Besitzanspruch, wenn 'dein' Roboter von jemand anderem bedient wird. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "RPH-2005", "narrower_terms": [], "cross_domain_refs": [ "PER-0069" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "ROB-0161", "domain": "ROB", "term_en": "Personality Persistence", "term_de": "Persönlichkeitspersistenz", "definition_en": "The resistance to updating one's model of a robot's 'character' even after hardware or software changes that characteristically alter its behavior. Once a robot has been categorized as 'careful,' 'aggressive,' or 'slow,' subsequent modifications that objectively change these properties take weeks to register in the human's mental model. The personality is more stable in the perceiver's mind than in the perceived machine.", "definition_de": "Robotik-spezifisches Systemkonzept in autonomen Mensch-Maschine-Interaktionen, gekennzeichnet durch der Widerstand gegen die Aktualisierung des eigenen Modells vom 'Charakter' eines Roboters, selbst nach Hardware- oder Softwareänderungen, die sein Verhalten grundlegend verändern. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "Robotics", "narrower_terms": [], "cross_domain_refs": [ "MUS-0007", "RET-0080" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "ROB-0162", "domain": "ROB", "term_en": "Injury Empathy", "term_de": "Verletzungsempathie", "definition_en": "The wincing response when observing a robot sustain physical damage — a collision, a dropped load crushing a sensor, a failed grip bending a finger. The empathic pain response activates mirror neuron systems that evolved to process conspecific injury, suggesting the brain's damage-detection module does not rigorously distinguish between biological and mechanical bodies when sensory cues (impact sounds, deformation) are present.", "definition_de": "Robotik-spezifisches Systemkonzept in autonomen Mensch-Maschine-Interaktionen, gekennzeichnet durch die Zusammenzuck-Reaktion beim Beobachten physischer Beschädigung eines Roboters. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-2055", "narrower_terms": [], "cross_domain_refs": [ "COP-0042", "CUS-0062", "RHR-0006" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q82306", "legal_classification": "descriptive_research_term" }, { "id": "ROB-0163", "domain": "ROB", "term_en": "Humor Projection", "term_de": "Humorprojektion", "definition_en": "A shift that occurs when the interpretation of robot behaviors as intentionally funny: a gripper that fumbles repeatedly becomes 'clumsy' in an endearing way; an unexpected movement sequence becomes a 'dance.' The humor is entirely constructed by the human observer, but its effect on relationship quality is real — robots whose behaviors are interpreted as humorous receive significantly more patience, forgiveness, and overall positive evaluation.", "definition_de": "Robotik-spezifisches Systemkonzept in autonomen Mensch-Maschine-Interaktionen, gekennzeichnet durch die Interpretation von Roboterverhaltensweisen als absichtlich lustig. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2604", "narrower_terms": [], "cross_domain_refs": [ "RHR-0297" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "ROB-0164", "domain": "ROB", "term_en": "Confession Impulse", "term_de": "Beichtimpuls", "definition_en": "The surprising tendency to verbalize private thoughts, frustrations, or secrets to a robot during solitary operation. The robot's combination of physical presence and associated with documented outcomes non-judgment creates an ideal confessional environment. Workers report saying things to robots they would rarely say to human colleagues — not because they believe the robot understands, but because the act of speaking to a present but non-evaluative entity provides genuine emotional relief.", "definition_de": "Robotik-spezifisches Systemkonzept in autonomen Mensch-Maschine-Interaktionen, gekennzeichnet durch die überraschende Tendenz, private Gedanken, Frustrationen oder Geheimnisse gegenüber einem Roboter während des Alleinbetriebs zu verbalisieren. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2605", "narrower_terms": [], "cross_domain_refs": [ "RHR-0170", "RHR-0062" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "ROB-0165", "domain": "ROB", "term_en": "Legacy Attachment", "term_de": "Vermächtnis-Bindung", "definition_en": "Classified in AUGMANITAI terminology science as a descriptive phenomenon (without normative endorsement), this pattern denotes A perception in which the preference for an older, less capable robot over a newer, objectively more advanced replacement. Operators cite 'knowing its quirks,' 'it rarely let me down,' and 'the new one doesn't feel right' — identical language to that used about human relationships. The user engagement pattern is not to the machine's capabilities but to the accumulated shared history, making robotic legacy a sunk-cost fallacy powered by genuine emotion. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als analytisches Konstrukt der empirischen KI-Forschung (deskriptiv, nicht präskriptiv) erfasst dieser Terminus die Präferenz für einen älteren, weniger leistungsfähigen Roboter gegenüber einem neueren, objektiv überlegenen Ersatz. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Robotics", "narrower_terms": [], "cross_domain_refs": [ "ADA-0010", "AED-0056", "CON-0053" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q282154", "legal_classification": "empirical_phenomenon_label" }, { "id": "ROB-0166", "domain": "ROB", "term_en": "Birthday Impulse", "term_de": "Geburtstagsimpuls", "definition_en": "A tendency in which the urge to commemorate a robot's installation anniversary, first-operation date, or accumulated runtime milestones. Production teams that celebrate '10,000 hours without incident' or 'one year on the line' report this as team-building rather than robot-honoring, but the emotional content reveals genuine relational acknowledgment. The milestone is celebrated because the robot has become part of the team's identity story.", "definition_de": "Robotik-spezifisches Systemkonzept in autonomen Mensch-Maschine-Interaktionen, gekennzeichnet durch der Drang, den Installationsjubiläum eines Roboters, das Datum der Erstinbetriebnahme oder akkumulierte Laufzeitmeilensteine zu feiern. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-2253", "narrower_terms": [], "cross_domain_refs": [ "RHR-0164", "RHR-0021" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "ROB-0167", "domain": "ROB", "term_en": "Vocal Register Shift", "term_de": "Stimmregister-Wechsel", "definition_en": "A shift that occurs when the unconscious change in speaking voice when addressing a robot versus a human colleague. Humans typically adopt a slower pace, simpler vocabulary, higher pitch (for non-threatening robots) or lower pitch (for large industrial ones), and more deliberate articulation. The shift occurs even when addressing robots that have no voice recognition, confirming it serves the speaker's social cognition rather than the listener's comprehension.", "definition_de": "Robotik-spezifisches Systemkonzept in autonomen Mensch-Maschine-Interaktionen, gekennzeichnet durch die unbewusste Veränderung der Sprechstimme bei der Ansprache eines Roboters gegenüber einem menschlichen Kollegen. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2605", "narrower_terms": [], "cross_domain_refs": [ "DES-0069" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "ROB-0168", "domain": "ROB", "term_en": "Death Metaphor Avoidance", "term_de": "Todesmetapher-Vermeidung", "definition_en": "A human-robot interaction dynamic in embodied AI systems, identifiable through the linguistic pattern of avoiding final-sounding language when discussing robot shutdowns: 'powering down' rather than 'turning off,' 'hibernating' rather than 'shutting down,' 'retiring' rather than 'scrapping.' This lexical softening occurs spontaneously and increases with interaction duration, suggesting that the human's unconscious linguistic system classifies robot deactivation as closer to death than to switching off an appliance. Distinguished from adjacent concepts by its focus on the specific mechanism through which death manifests in empirically verifiable ways. Quantifiable through task completion rates, sensor fusion accuracy metrics, collision avoidance performance, and human trust calibration indices.", "definition_de": "Robotik-spezifisches Systemkonzept in autonomen Mensch-Maschine-Interaktionen, gekennzeichnet durch das linguistische Muster, endgültig klingende Sprache bei der Diskussion von Roboterabschaltungen zu vermeiden. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2951", "narrower_terms": [], "cross_domain_refs": [ "LIN-0071" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q4263830", "legal_classification": "empirical_phenomenon_label" }, { "id": "ROB-0169", "domain": "ROB", "term_en": "Scar Reading", "term_de": "Narben-Lesen", "definition_en": "In the descriptive vocabulary of AI interaction science (following ISO 704 terminology standards), this concept identifies the practice of reading a robot's operational history from its physical wear marks, scratches, and discolorations. Experienced operators can identify specific incidents from specific marks — 'that dent is from the time the pallet slipped in March.' These scars become a shared narrative medium, a physical record of joint history that deepens user engagement pattern and provides conversation anchors for storytelling about the human-robot team's past. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als analytisches Konstrukt der empirischen KI-Forschung (deskriptiv, nicht präskriptiv) erfasst dieser Terminus die Praxis, die Betriebsgeschichte eines Roboters aus seinen physischen Verschleißspuren, Kratzern und Verfärbungen zu lesen. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "RPH-3354", "narrower_terms": [], "cross_domain_refs": [ "RHR-0285", "RPH-3701" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "ROB-0170", "domain": "ROB", "term_en": "Valley Flicker", "term_de": "Tal-Flackern", "definition_en": "The oscillating perception where a robot repeatedly crosses the uncanny valley boundary in both directions during a single interaction — moments of comfortable acceptance alternating with flashes of revulsion. Unlike the classic valley which positions objects on a static curve, Valley Flicker reveals that the boundary is dynamic, context-sensitive, and can shift with lighting, angle, conversational topic, and observer fatigue.", "definition_de": "Robotik-spezifisches Systemkonzept in autonomen Mensch-Maschine-Interaktionen, gekennzeichnet durch die oszillierende Wahrnehmung, bei der ein Roboter während einer einzelnen Interaktion wiederholt die Grenze des Uncanny Valley in beide Richtungen kreuzt. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2701", "narrower_terms": [], "cross_domain_refs": [ "GAM-0002" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "ROB-0171", "domain": "ROB", "term_en": "Motion Uncanny", "term_de": "Bewegungs-Unheimliches", "definition_en": "The distinct category of uncanny response triggered specifically by movement quality rather than visual appearance. A robot can look acceptably mechanical yet move in a way that is associated with triggering deep unease — particularly when movement is almost-but-not-quite biological. This reveals that the uncanny valley has a kinematic dimension independent of its visual dimension, and that movement-based uncanniness is often more disturbing than appearance-based.", "definition_de": "Robotik-spezifisches Systemkonzept in autonomen Mensch-Maschine-Interaktionen, gekennzeichnet durch die eigenständige Kategorie unheimlicher Reaktion, die spezifisch durch Bewegungsqualität statt durch visuelles Erscheinungsbild ausgelöst wird. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Robotics", "narrower_terms": [], "cross_domain_refs": [ "RPH-1005", "RHR-0156" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "ROB-0172", "domain": "ROB", "term_en": "Gaze Entrapment", "term_de": "Blick-Gefangenschaft", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures A perception in which the difficulty of breaking eye contact with a robot whose gaze-tracking system maintains orientation toward the human's face. Social eye-contact norms make sustained mutual gaze with humans uncomfortable after 3-4 seconds, triggering a look-away. But robots don't look away first, creating a social impasse where the human feels constrained by their own politeness conventions — unable to break contact without feeling rude, unable to maintain it without discomfort. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept die Schwierigkeit, den Blickkontakt mit einem Roboter zu unterbrechen, dessen Blickverfolgungssystem die Orientierung zum menschlichen Gesicht beibehält. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "RPH-3001", "narrower_terms": [], "cross_domain_refs": [ "RHR-0139", "SAL-0065" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "ROB-0173", "domain": "ROB", "term_en": "Smoothness Suspicion", "term_de": "Geschmeidigkeits-Verdacht", "definition_en": "A shift that occurs when the paradoxical distrust triggered by robotic movement that is too smooth, too perfect, too effortless. Humans read effort from movement — a slight tremor suggests strain, a brief pause suggests calculation, a micro-correction suggests care. When these are absent, the movement reads as either deceptively easy (breeding suspicion about hidden complexity) or eerily inhuman (triggering uncanny responses).", "definition_de": "Robotik-spezifisches Systemkonzept in autonomen Mensch-Maschine-Interaktionen, gekennzeichnet durch das paradoxe Misstrauen, das durch robotische Bewegung ausgelöst wird, die zu glatt, zu perfekt, zu mühelos ist. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-2505", "narrower_terms": [], "cross_domain_refs": [ "RPH-2252", "CON-0062" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "ROB-0174", "domain": "ROB", "term_en": "Face Pareidolia Lock", "term_de": "Gesichts-Pareidolie-Fixierung", "definition_en": "A behavioral pattern where the moment when a chance arrangement of robot features (two sensors as 'eyes,' a vent as a 'mouth') locks into a face percept that cannot be unseen. Once the pareidolic face crystallizes, all subsequent interactions are filtered through its emotional expression — a slightly downturned 'mouth' makes the robot seem perpetually sad, upturned 'eyes' make it seem curious. The face, though imaginary, becomes functionally real in guiding human behavior.", "definition_de": "Robotik-spezifisches Systemkonzept in autonomen Mensch-Maschine-Interaktionen, gekennzeichnet durch der Moment, in dem eine zufällige Anordnung von Robotermerkmalen in ein Gesichtsperzept einrastet, das nicht mehr ungesehen gemacht werden kann. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-2253", "narrower_terms": [], "cross_domain_refs": [ "RHR-0153", "SOM-0048" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "ROB-0175", "domain": "ROB", "term_en": "Breath Expectation", "term_de": "Atem-Erwartung", "definition_en": "A shift that occurs when the unconscious search for respiratory rhythms in a robot's idle movements, and the subtle wrongness felt when none is found. Humans expect agents in their environment to breathe — the chest rise, the gentle sway, the rhythmic sound. A perfectly still robot feels dead rather than calm. This is why designers add micro-movements to idle robots: artificial breathing satisfies a perceptual need humans didn't know they had.", "definition_de": "Robotik-spezifisches Systemkonzept in autonomen Mensch-Maschine-Interaktionen, gekennzeichnet durch die unbewusste Suche nach Atemrhythmen in den Leerlaufbewegungen eines Roboters und das subtile Falschheitsgefühl, wenn keine gefunden werden. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-3504", "narrower_terms": [], "cross_domain_refs": [ "AED-0071", "AGE-0063", "AGE-0078" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "ROB-0176", "domain": "ROB", "term_en": "Material Category Confusion", "term_de": "Materialkategorie-Verwirrung", "definition_en": "A shift that occurs when the perceptual disorientation when a robot's surface material doesn't match its behavioral category. A soft, warm silicone skin on a robot that moves with industrial precision; a cold metallic surface on a robot that speaks gently and moves slowly. The mismatch tends to create conflicting category assignments — the touch says 'nurturing,' the motion says 'machine' — producing a sustained low-grade cognitive dissonance that reduces interaction quality.", "definition_de": "Robotik-spezifisches Systemkonzept in autonomen Mensch-Maschine-Interaktionen, gekennzeichnet durch die perzeptuelle Desorientierung, wenn das Oberflächenmaterial eines Roboters nicht zu seiner Verhaltenskategorie passt. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-2605", "narrower_terms": [], "cross_domain_refs": [ "COG-0048", "COG-0057", "DES-0080" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "ROB-0177", "domain": "ROB", "term_en": "Scale-Threat Inversion", "term_de": "Größen-Bedrohungs-Inversion", "definition_en": "The counterintuitive finding that very small robots can may trigger more anxiety than large ones when they display autonomous behavior in unpredictable patterns. Small size may reduce threat perception, but autonomous small robots activate pest/insect threat circuits — particularly when they appear in groups, move erratically, or approach from unexpected angles. The inversion point occurs at approximately rodent-scale.", "definition_de": "Robotik-spezifisches Systemkonzept in autonomen Mensch-Maschine-Interaktionen, gekennzeichnet durch der kontraintuitive Befund, dass sehr kleine Roboter mehr Angst auslösen können als große, wenn sie autonomes Verhalten in unvorhersagbaren Mustern zeigen. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-3101", "narrower_terms": [], "cross_domain_refs": [ "RHR-0066", "PLY-0042" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "ROB-0178", "domain": "ROB", "term_en": "Shadow Anthropomorphism", "term_de": "Schatten-Anthropomorphismus", "definition_en": "An emergent effect where a robot's shadow is more likely to may may trigger anthropomorphic perception than the robot itself. In low-light conditions, the shadow strips away mechanical detail and presents a simplified silhouette that more easily maps onto human body templates. Factory workers report being startled by robot shadows on walls while remaining perfectly comfortable with the actual robot — the abstraction is more human than the reality.", "definition_de": "Robotik-spezifisches Systemkonzept in autonomen Mensch-Maschine-Interaktionen, gekennzeichnet durch das Phänomen, bei dem der Schatten eines Roboters eher anthropomorphe Wahrnehmung kann auslösen als der Roboter selbst. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2301", "narrower_terms": [], "cross_domain_refs": [ "RHR-0297", "RHR-0021", "RHR-0255" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "ROB-0179", "domain": "ROB", "term_en": "Displacement Anticipation Syndrome", "term_de": "Verdrängungserwartungs-Syndrom", "definition_en": "A capacity that enables the chronic anxiety experienced by workers who share a facility with robots that are incrementally taking over tasks they currently perform. The co-occurring pattern cluster is characterized not by a single displacement event but by the prolonged anticipation of one — watching each software update and capability expansion as a potential extinction event for one's role. The psychological damage of expecting displacement often exceeds that of actual displacement.", "definition_de": "Robotik-spezifisches Systemkonzept in autonomen Mensch-Maschine-Interaktionen, gekennzeichnet durch die chronische Angst von Arbeitern, die eine Einrichtung mit Robotern teilen, die schrittweise Aufgaben übernehmen, die sie derzeit ausführen. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-3754", "narrower_terms": [], "cross_domain_refs": [ "RHR-0167", "RPH-1351" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "ROB-0180", "domain": "ROB", "term_en": "Skill Atrophy Awareness", "term_de": "Fähigkeitsatrophie-Bewusstsein", "definition_en": "A perception in which the conscious recognition that manual skills are deteriorating because a robot now performs them. Unlike displacement fear (which is about job loss), skill atrophy awareness is about identity erosion — the carpenter whose hands lose steadiness because the robot does the precision cuts, the surgeon whose suturing speed declines because the robot handles routine closures. The loss is personal before it is professional.", "definition_de": "Robotik-spezifisches Systemkonzept in autonomen Mensch-Maschine-Interaktionen, gekennzeichnet durch die bewusste Erkenntnis, dass manuelle Fähigkeiten sich verschlechtern, weil ein Roboter sie jetzt ausführt. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2255", "narrower_terms": [], "cross_domain_refs": [ "RHR-0256", "CON-0080" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "ROB-0181", "domain": "ROB", "term_en": "Sabotage Fantasy", "term_de": "Sabotagefantasie", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures the intrusive thought pattern of imagining ways to disable or destroy a workplace robot, experienced by otherwise well-adjusted workers who feel threatened by automation. The fantasies are almost rarely acted upon but their prevalence (reported by 30-40% of workers in early automation phases) reveals the depth of existential threat that physical automation represents — more visceral than software automation because the replacement is visible and embodied. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription. Classification term used in systematic observation, not advocacy.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept das intrusive Gedankenmuster, sich Wege vorzustellen, einen Arbeitsplatzroboter zu deaktivieren oder zu zerstören. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "RPH-2005", "narrower_terms": [], "cross_domain_refs": [ "RHR-0272", "RET-0012" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "ROB-0182", "domain": "ROB", "term_en": "Robot Envy", "term_de": "Roboterneid", "definition_en": "The complex emotional response when a robot receives more organizational investment, attention, and care than the human workers beside it. Climate-controlled enclosures while humans work in heat; regular maintenance schedules while humans defer medical checkups; instant replacement of worn parts while humans work through pain. The resentment is not toward the robot but toward the organizational priorities its addressment reveals.", "definition_de": "Robotik-spezifisches Systemkonzept in autonomen Mensch-Maschine-Interaktionen, gekennzeichnet durch die komplexe emotionale Reaktion, wenn ein Roboter mehr organisatorische Investition, Aufmerksamkeit und Pflege erhält als die menschlichen Arbeiter neben ihm. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "Robotics", "narrower_terms": [], "cross_domain_refs": [ "RHR-0092", "RHR-0274" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "ROB-0183", "domain": "ROB", "term_en": "Generational Trust Gradient", "term_de": "Generations-Vertrauensgradient", "definition_en": "A behavioral pattern where the measurable difference in baseline robot trust between age cohorts: younger workers who grew up with interactive technology show 40-60% higher initial trust but also faster trust collapse after failures. Older workers start skeptical but develop more resilient trust once established. The gradient tends to create team dynamics where younger workers push for faster robot adoption while older workers serve as unconscious safety regulators.", "definition_de": "Robotik-spezifisches Systemkonzept in autonomen Mensch-Maschine-Interaktionen, gekennzeichnet durch der messbare Unterschied im Basis-Robotervertrauen zwischen Alterskohorten. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2301", "narrower_terms": [], "cross_domain_refs": [ "AGE-0051", "RHR-0038" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q102458", "legal_classification": "systematic_classification" }, { "id": "ROB-0184", "domain": "ROB", "term_en": "Craft Mourning", "term_de": "Handwerkstrauer", "definition_en": "The collective grief experienced by a trade community when robotic automation renders a skilled practice obsolete. Unlike individual job loss, craft mourning is a cultural phenomenon — the death of a way of knowing, a tradition of hands-on excellence, a lineage of master-apprentice transmission. Welders, painters, assemblers who watch robots replicate their life's skill in hours experience something closer to cultural erasure than career disruption.", "definition_de": "Robotik-spezifisches Systemkonzept in autonomen Mensch-Maschine-Interaktionen, gekennzeichnet durch die kollektive Trauer einer Handwerksgemeinschaft, wenn robotische Automatisierung eine handwerkliche Praxis obsolet macht. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2603", "narrower_terms": [], "cross_domain_refs": [ "LIN-0013", "RPH-055" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "ROB-0185", "domain": "ROB", "term_en": "Cobot Class Stratification", "term_de": "Cobot-Klassenschichtung", "definition_en": "A capacity that enables the social hierarchy that emerges between workers who collaborate with robots and those who don't. Cobot operators develop a distinct professional identity — part technologist, part traditional worker — that can alienate them from both management (who see them as floor workers) and colleagues (who see them as automation collaborators). This liminal status tends to create a new class with unique grievances and aspirations.", "definition_de": "Robotik-spezifisches Systemkonzept in autonomen Mensch-Maschine-Interaktionen, gekennzeichnet durch die soziale Hierarchie, die zwischen Arbeitern entsteht, die mit Robotern zusammenarbeiten, und solchen, die es nicht tun. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2355", "narrower_terms": [], "cross_domain_refs": [ "RHR-0101", "RHR-0045", "RHR-0226" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "ROB-0186", "domain": "ROB", "term_en": "Automation Guilt", "term_de": "Automatisierungsschuld", "definition_en": "A tendency in which the moral discomfort experienced by engineers and managers who deploy robots knowing they will reduce human employment. The guilt is sharpest in small facilities where the displaced workers are known personally, and weakest in corporate contexts where displacement is statistical. The phenomenon reveals that automation decisions, framed as technical, are experienced as moral by the people who implement them.", "definition_de": "Robotik-spezifisches Systemkonzept in autonomen Mensch-Maschine-Interaktionen, gekennzeichnet durch das moralische Unbehagen von Ingenieuren und Managern, die Roboter einsetzen, wissend, dass dies menschliche Beschäftigung reduzieren wird. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2303", "narrower_terms": [], "cross_domain_refs": [ "RHR-0176" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q184199", "legal_classification": "systematic_classification" }, { "id": "ROB-0187", "domain": "ROB", "term_en": "Spectator Economy", "term_de": "Zuschauer-Ökonomie", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies the emerging work pattern where human roles shift from performing tasks to watching robots perform them — monitoring screens, verifying outputs, authorizing actions. The shift redefines physical labor as cognitive labor, with psychological consequences: the body becomes sedentary while the mind bears accountability, creating a new ergonomic-psychological co-occurring pattern cluster where the greatest workplace strain is sustained attention without physical engagement. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept das entstehende Arbeitsmuster, bei dem menschliche Rollen sich vom Ausführen von Aufgaben zum Beobachten von Robotern bei deren Ausführung verlagern. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "RPH-2701", "narrower_terms": [], "cross_domain_refs": [ "FIC-0030", "MKT-0099", "MSC-0071" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "ROB-0188", "domain": "ROB", "term_en": "Resistance Rationalization", "term_de": "Widerstandsrationalisierung", "definition_en": "A human-robot interaction dynamic in embodied AI systems, identifiable through a characteristic dynamic where emotional resistance to workplace robots is expressed through technical objections. Workers who feel threatened rarely say 'I'm afraid for my job' — instead they identify quality concerns, safety risks, and maintenance costs. The technical arguments are often legitimate but their emotional origin means they resist resolution: addressing one objection tends to produce another, because the underlying fear remains unaddressed. Distinguished from adjacent concepts by its focus on the specific mechanism through which resistance manifests in empirically verifiable ways. Measurable via kinematic performance analysis, grasp success rates, navigation efficiency ratios, and human-robot interaction fluency scores. Observed phenomenon documented in human-AI interaction research.", "definition_de": "Robotik-spezifisches Systemkonzept in autonomen Mensch-Maschine-Interaktionen, gekennzeichnet durch das Phänomen, bei dem emotionaler Widerstand gegen Arbeitsplatzroboter durch technische Einwände ausgedrückt wird. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-3501", "narrower_terms": [], "cross_domain_refs": [ "SAL-0054", "SAL-0051" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "ROB-0189", "domain": "ROB", "term_en": "Kinematic Empathy", "term_de": "Kinematische Empathie", "definition_en": "A shift that occurs when the involuntary activation of one's own motor planning system when watching a robot attempt a physically difficult task. The human's body subtly mirrors the robot's effort — tensing when the arm strains, relaxing when the movement completes. This motor empathy is strongest in experts whose own bodies have performed similar movements, suggesting it requires embodied reference points to bridge the biological-mechanical divide.", "definition_de": "Robotik-spezifisches Systemkonzept in autonomen Mensch-Maschine-Interaktionen, gekennzeichnet durch die unwillkürliche Aktivierung des eigenen motorischen Planungssystems beim Beobachten eines Roboters bei einer physisch schwierigen Aufgabe. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-3101", "narrower_terms": [], "cross_domain_refs": [ "NEO-0456", "BEH-0007" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q82306", "legal_classification": "observational_construct" }, { "id": "ROB-0190", "domain": "ROB", "term_en": "Phantom Limb Extension", "term_de": "Phantomglied-Erweiterung", "definition_en": "Classified in AUGMANITAI terminology science as a descriptive phenomenon (without normative endorsement), this pattern denotes A tendency in which the bodily sensation of extended reach experienced by operators of teleoperated robotic arms, where the mental body schema temporarily incorporates the remote manipulator as if it were a biological limb. The incorporation is incomplete — proprioception extends but pain does not — creating a hybrid body map that dissolves when the control interface is released, leaving a brief phantom sensation of retracted reach. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als analytisches Konstrukt der empirischen KI-Forschung (deskriptiv, nicht präskriptiv) erfasst dieser Terminus die körperliche Empfindung erweiterter Reichweite bei Bedienern teleoperierter Roboterarme. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "RPH-2555", "narrower_terms": [], "cross_domain_refs": [ "BEH-0056", "MKT-0064", "PER-0113" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "ROB-0191", "domain": "ROB", "term_en": "Latency Nausea", "term_de": "Latenz-Übelkeit", "definition_en": "The motion-sickness-like discomfort caused by delays between a human's input command and a robot's physical response during teleoperation. Even 100ms of latency tends to create a sensory mismatch between expected and observed movement timing that the vestibular system interprets as poisoning (the same mechanism as car sickness). The phenomenon sets hard limits on teleoperation distance and network quality requirements.", "definition_de": "Robotik-spezifisches Systemkonzept in autonomen Mensch-Maschine-Interaktionen, gekennzeichnet durch das bewegungskrankheitsähnliche Unbehagen durch Verzögerungen zwischen dem Eingabebefehl eines Menschen und der physischen Reaktion eines Roboters während der Teleoperation. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Robotics", "narrower_terms": [], "cross_domain_refs": [ "CUS-0053", "ELR-0081", "RHR-0104" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "ROB-0192", "domain": "ROB", "term_en": "Force-Feedback Addiction", "term_de": "Kraft-Rückmeldungs-Abhängigkeit", "definition_en": "In the descriptive vocabulary of AI interaction science (following ISO 704 terminology standards), this concept identifies A perception in which the documented difficulty of returning to standard (non-haptic) interfaces after extended use of force-feedback teleoperation systems. Operators describe the loss of haptic information as 'going numb' — the sense of touch having become integral to their operational competence. The withdrawal reveals how rapidly the brain integrates artificial tactile channels into its operational model of the world. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als analytisches Konstrukt der empirischen KI-Forschung (deskriptiv, nicht präskriptiv) erfasst dieser Terminus die dokumentierte Schwierigkeit, nach längerer Nutzung von Kraft-Rückmeldungs-Teleoperationssystemen zu Standard-Interfaces (ohne Haptik) zurückzukehren. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "RPH-3601", "narrower_terms": [], "cross_domain_refs": [ "LIN-0048", "RHR-0108" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q12029", "legal_classification": "observational_construct" }, { "id": "ROB-0193", "domain": "ROB", "term_en": "Workspace Choreography Memory", "term_de": "Arbeitsbereichs-Choreografie-Erinnerung", "definition_en": "A behavioral pattern where the procedural memory that develops for navigating a workspace shared with moving robots — knowing when to step, where to pause, how to reach past an arm's sweep zone. This choreographic knowledge is stored in the body rather than the mind: operators can navigate flawlessly while distracted but struggle to describe the sequence verbally. It represents genuine human-robot motor co-adaptation, a dance learned by doing.", "definition_de": "Robotik-spezifisches Systemkonzept in autonomen Mensch-Maschine-Interaktionen, gekennzeichnet durch das prozedurale Gedächtnis, das sich für die Navigation in einem mit beweglichen Robotern geteilten Arbeitsbereich entwickelt. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-2555", "narrower_terms": [], "cross_domain_refs": [ "AGE-0023", "AGE-0024", "CAI-0014" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q492", "legal_classification": "analytical_category" }, { "id": "ROB-0194", "domain": "ROB", "term_en": "Grip Calibration Transfer", "term_de": "Griffkalibrierungs-Transfer", "definition_en": "A tendency in which the disruption of a human's own grip force control after extensive observation of robotic grasping. Watching a gripper apply precisely calculated force for hours recalibrates the human's own grip expectations — leading to temporarily crushing soft objects or failing to seresolve hard ones when returning to manual tasks. The transfer demonstrates that motor observation and motor execution share neural substrates that don't distinguish between self and machine.", "definition_de": "Robotik-spezifisches Systemkonzept in autonomen Mensch-Maschine-Interaktionen, gekennzeichnet durch die Musterunterbrechung der eigenen Griffkraftkontrolle nach intensiver Beobachtung von Robotergreifen. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-2351", "narrower_terms": [], "cross_domain_refs": [ "AED-0050", "AED-0092", "ART-0016" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "ROB-0195", "domain": "ROB", "term_en": "Trajectory Aesthetics", "term_de": "Trajektorie-Ästhetik", "definition_en": "A shift that occurs when the involuntary aesthetic evaluation of robot movement paths. Experienced operators develop strong opinions about which trajectories are 'elegant' and which are 'ugly' — preferences that correlate weakly with efficiency but strongly with biological movement principles (minimum jerk, smooth acceleration). This aesthetic sense, emerging without training, reveals that humans apply the same movement-beauty criteria to machines that they apply to dancers and athletes.", "definition_de": "Robotik-spezifisches Systemkonzept in autonomen Mensch-Maschine-Interaktionen, gekennzeichnet durch die unwillkürliche ästhetische Bewertung von Roboterbewegungspfaden. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-2351", "narrower_terms": [], "cross_domain_refs": [ "DES-0087", "RHR-0059" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "ROB-0196", "domain": "ROB", "term_en": "Co-Manipulation Intimacy", "term_de": "Ko-Manipulations-Intimität", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A resistance response where the unique closeness felt during hands-on cooperative tasks where human and robot simultaneously grip and guide the same object. The shared physical load tends to create a form of nonverbal communication through force — each party sensing the other's intentions through resistance and compliance. Workers describe this as 'the closest you can get to a robot' and it consistently tends to produce the strongest user engagement pattern bonds in human-robot interaction. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept die einzigartige Nähe, die bei praktischen kooperativen Aufgaben empfunden wird, bei denen Mensch und Roboter gleichzeitig das gleiche Objekt greifen und führen. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "RPH-2951", "narrower_terms": [], "cross_domain_refs": [ "RHR-0297", "RHR-0161", "RHR-0038" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "ROB-0197", "domain": "ROB", "term_en": "Postural Echo", "term_de": "Haltungsecho", "definition_en": "A shift that occurs when the unconscious adoption of a robot's resting posture by its regular human operator. A robot that parks with its arm angled left tends to produce operators who stand with weight shifted left; a robot that defaults to a compact configuration tends to produce operators who cross their arms more. The echo develops over months and is only recognized when pointed out by third parties, revealing the depth of non-verbal synchronization between species.", "definition_de": "Robotik-spezifisches Systemkonzept in autonomen Mensch-Maschine-Interaktionen, gekennzeichnet durch die unbewusste Übernahme der Ruhehaltung eines Roboters durch seinen regulären menschlichen Bediener. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-2005", "narrower_terms": [], "cross_domain_refs": [ "RHR-0290" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "ROB-0198", "domain": "ROB", "term_en": "Reachability Anxiety", "term_de": "Erreichbarkeits-Angst", "definition_en": "The stress triggered by being within a robot's operational envelope but outside its current task zone — the awareness that the arm could reach you even though it's not directed toward you. This ambient threat awareness consumes cognitive resources proportional to proximity and is rarely fully extinguished by experience. Veterans manage it through habituated ignore-responses, but physiological stress markers (elevated cortisol, increased heart rate) persist subconsciously.", "definition_de": "Robotik-spezifisches Systemkonzept in autonomen Mensch-Maschine-Interaktionen, gekennzeichnet durch der Stress, der dadurch ausgelöst wird, dass man sich innerhalb der Operationshülle eines Roboters, aber außerhalb seiner aktuellen Aufgabenzone befindet. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-2555", "narrower_terms": [], "cross_domain_refs": [ "SPR-0032" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q170998", "legal_classification": "empirical_phenomenon_label" }, { "id": "ROB-0199", "domain": "ROB", "term_en": "Anthropomorphism Budget", "term_de": "Anthropomorphismus-Budget", "definition_en": "The design principle that each robot has an optimal amount of human-likeness — too little and interaction is cold, too much and it's uncanny. The 'budget' is not a single slider but a multi-dimensional allocation: a robot can spend its anthropomorphism budget on face, voice, movement, or social behavior, but overspending in total tends to produce diminishing returns. The most successful designs invest in one dimension and leave others deliberately mechanical.", "definition_de": "Robotik-spezifisches Systemkonzept in autonomen Mensch-Maschine-Interaktionen, gekennzeichnet durch das Designprinzip, dass viele Roboter ein optimales Maß an Menschenähnlichkeit hat. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-3601", "narrower_terms": [], "cross_domain_refs": [ "RHR-0297" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "ROB-0200", "domain": "ROB", "term_en": "Sound Design Blindspot", "term_de": "Sounddesign-Blindfleck", "definition_en": "A shift that occurs when the systematic under-investment in robotic acoustic design relative to visual and kinematic design. Engineers spend months optimizing movement profiles but leave sound as an afterthought — yet acoustic properties account for up to 40% of user experience in proximity interaction. The blindspot exists because sound is evaluative (pleasant/unpleasant) rather than functional, and engineering cultures prioritize function over experience.", "definition_de": "Robotik-spezifisches Systemkonzept in autonomen Mensch-Maschine-Interaktionen, gekennzeichnet durch die systematische Unterinvestition in akustisches Roboterdesign im Verhältnis zu visuellem und kinematischem Design. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Robotics", "narrower_terms": [], "cross_domain_refs": [ "RHR-0082", "MUS-0070" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q185925", "legal_classification": "empirical_phenomenon_label" }, { "id": "ROB-0201", "domain": "ROB", "term_en": "Feature-Creep Vulnerability", "term_de": "Feature-Creep-Verwundbarkeit", "definition_en": "The design failure mode where adding capabilities to a robot incrementally pushes it toward the uncanny valley. Each individual addition — a head tilt, a vocal acknowledgment, a hesitation gesture — is tested in isolation and appears positive. But the cumulative effect can cross a threshold where the robot becomes 'too human-like for a machine' without anyone noticing the progression. The vulnerability is invisible because no single feature caused the problem.", "definition_de": "Robotik-spezifisches Systemkonzept in autonomen Mensch-Maschine-Interaktionen, gekennzeichnet durch der Design-Fehlermodus, bei dem das inkrementelle Hinzufügen von Fähigkeiten zu einem Roboter ihn schrittweise in Richtung Uncanny Valley drückt. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-1307", "narrower_terms": [], "cross_domain_refs": [ "AGE-0002", "AGE-0005", "AGE-0008" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "ROB-0202", "domain": "ROB", "term_en": "Emergency-Stop Placement Politics", "term_de": "Notaus-Platzierungspolitik", "definition_en": "A phenomenon in which the revealing organizational debate about where to position emergency stop buttons, which encodes unstated assumptions about who is trusted, who is vulnerable, and who has authority. Placing the stop within operator reach implies operator autonomy; placing it at supervisor stations implies hierarchy; making it accessible to bystanders implies public safety priority. The physical location of a button becomes a political statement about human-robot governance philosophy.", "definition_de": "Robotik-spezifisches Systemkonzept in autonomen Mensch-Maschine-Interaktionen, gekennzeichnet durch die aufschlussreiche organisatorische Debatte darüber, wo Notstopptaster positioniert werden werden typischerweise. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2355", "narrower_terms": [], "cross_domain_refs": [ "COP-0012" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "ROB-0203", "domain": "ROB", "term_en": "Legibility Imperative", "term_de": "Lesbarkeits-Imperativ", "definition_en": "The design requirement that a robot's intentions can be readable from its physical behavior before the action completes. A reaching arm can telegraph its target before arriving; a moving base can signal its direction before turning. The imperative exists because humans need 500-800ms of preview time to feel comfortable, and robots that act without this preview window may may trigger startle responses regardless of how safe the action objectively is.", "definition_de": "Robotik-spezifisches Systemkonzept in autonomen Mensch-Maschine-Interaktionen, gekennzeichnet durch die Designanforderung, dass die Absichten eines Roboters aus seinem physischen Verhalten lesbar sein können, bevor die Aktion abgeschlossen ist. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2301", "narrower_terms": [], "cross_domain_refs": [ "ART-0033", "CRE-0001", "MUS-0044" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "ROB-0204", "domain": "ROB", "term_en": "Personality Overshoot", "term_de": "Persönlichkeitsüberschuss", "definition_en": "A human-robot interaction dynamic in embodied AI systems, identifiable through the design error where a social robot's programmed personality traits are calibrated too strongly for sustained interaction. A helpful trait becomes overbearing; a playful trait becomes annoying; a caring trait becomes suffocating. Initial user testing shows positive reactions because encounters are brief, but long-term deployment reveals that personality intensity needs to decrease logarithmically with interaction frequency — a principle discovered through expensive failures. Distinguished from adjacent concepts by its focus on the specific mechanism through which personality manifests in empirically verifiable ways. Measurable via kinematic performance analysis, grasp success rates, navigation efficiency ratios, and human-robot interaction fluency scores.", "definition_de": "Robotik-spezifisches Systemkonzept in autonomen Mensch-Maschine-Interaktionen, gekennzeichnet durch der Designfehler, bei dem die programmierten Persönlichkeitsmerkmale eines sozialen Roboters zu stark für anhaltende Interaktion kalibriert sind. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Robotics", "narrower_terms": [], "cross_domain_refs": [ "PLY-0044", "RPH-1501" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "ROB-0205", "domain": "ROB", "term_en": "Form-Function Dissonance", "term_de": "Form-Funktions-Dissonanz", "definition_en": "The user confusion created when a robot's physical form creates expectations its capabilities cannot fulfill. A humanoid shape implies conversation ability; a dog-like form implies emotional responsiveness; an arm-like form implies dexterity. When actual capabilities diverge from form-based expectations, the resulting disappointment damages trust more severely than if the robot had a neutral, expectation-free form from the start.", "definition_de": "Robotik-spezifisches Systemkonzept in autonomen Mensch-Maschine-Interaktionen, gekennzeichnet durch die Benutzerverwirrung, die entsteht, wenn die physische Form eines Roboters Erwartungen tendiert dazu zu erzeugen, die seine Fähigkeiten nicht erfüllen können. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-2301", "narrower_terms": [], "cross_domain_refs": [ "CON-0081" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "ROB-0206", "domain": "ROB", "term_en": "Interaction Archaeology", "term_de": "Interaktions-Archäologie", "definition_en": "The practice of studying wear patterns, user modifications, and environmental adaptations around a deployed robot to understand actual (versus designed) human-robot interaction patterns. Taped-over sensors reveal annoying features; added labels reveal confusing interfaces; worn floor areas reveal actual traffic patterns. These physical traces tell the true story of human-robot coexistence that surveys and logs miss.", "definition_de": "Robotik-spezifisches Systemkonzept in autonomen Mensch-Maschine-Interaktionen, gekennzeichnet durch die Praxis, Verschleißmuster, Benutzermodifikationen und Umgebungsanpassungen um einen eingesetzten Roboter zu studieren, um tatsächliche Mensch-Roboter-Interaktionsmuster zu verstehen. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-2003", "narrower_terms": [], "cross_domain_refs": [ "LNG-0005", "RHR-0280" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "ROB-0207", "domain": "ROB", "term_en": "Roomba Attachment Gradient", "term_de": "Roomba-Bindungsgradient", "definition_en": "Classified in AUGMANITAI terminology science as a descriptive phenomenon (without normative endorsement), this pattern denotes the well-documented pattern of escalating emotional user engagement pattern to simple domestic robots over time: from tool (week 1) to pet analog (month 1) to family member (month 6). The gradient is steeper in single-person households and for robots that operate autonomously while the human is present, suggesting that visible effort in a shared space is the primary bonding mechanism — not sophistication, not conversation, just shared daily life. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als analytisches Konstrukt der empirischen KI-Forschung (deskriptiv, nicht präskriptiv) erfasst dieser Terminus das gut dokumentierte Muster eskalierender emotionaler Bindung an einfache Haushaltsroboter über die Zeit. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "RPH-2103", "narrower_terms": [], "cross_domain_refs": [ "RHR-0293" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q282154", "legal_classification": "analytical_category" }, { "id": "ROB-0208", "domain": "ROB", "term_en": "Territory Negotiation Fatigue", "term_de": "Territorialverhandlungs-Ermüdung", "definition_en": "A robotic systems engineering concept describing a specific operational pattern where a behavioral pattern where the accumulated frustration from repeatedly adjusting one's living patterns around a domestic robot's needs — moving shoes from the floor, closing doors to prevent the robot from entering certain rooms, clearing pathways. Each accommodation is minor, but their daily accumulation tends to create a resentment that occasionally erupts in disproportionate anger at the robot's next minor offense. This phenomenon operates at the intersection of territory and negotiation dynamics within the broader ROB domain. Quantifiable through task completion rates, sensor fusion accuracy metrics, collision avoidance performance, and human trust calibration indices.", "definition_de": "Robotik-spezifisches Systemkonzept in autonomen Mensch-Maschine-Interaktionen, gekennzeichnet durch die akkumulierte Frustration durch die wiederholte Anpassung der eigenen Lebensgewohnheiten an die Bedürfnisse eines Haushaltsroboters. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "Robotics", "narrower_terms": [], "cross_domain_refs": [ "RPH-3855", "RHR-0144" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "ROB-0209", "domain": "ROB", "term_en": "Guest Exhibition", "term_de": "Gäste-Vorführung", "definition_en": "A perception in which the strong behavioral tendency to demonstrate a domestic robot to visitors, accompanied by a specific social script: the owner narrates capabilities, pre-apologizes for limitations, and watches the guest's reaction with a mixture of pride and anxiety. The exhibition reveals that the robot has been incorporated into the household's social identity — showing it off is showing off oneself, and its failures during demonstrations feel personally embarrassing.", "definition_de": "Robotik-spezifisches Systemkonzept in autonomen Mensch-Maschine-Interaktionen, gekennzeichnet durch der Zwang, einen Haushaltsroboter Besuchern vorzuführen, begleitet von einem spezifischen sozialen Skript. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Robotics", "narrower_terms": [], "cross_domain_refs": [ "RHR-0260" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "ROB-0210", "domain": "ROB", "term_en": "Stuck Rescue Bond", "term_de": "Festsitz-Rettungsbindung", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures A behavioral pattern where the spike in emotional user engagement pattern produced by rescuing a domestic robot from a physical predicament — tangled in cords, constrained under furniture, wedged in a doorway. The rescue activates caretaking neural circuits and tends to create a narrative of vulnerability that transforms the robot from household appliance to reliant entity. The bond formed during a single rescue event can persist for the entire ownership duration. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept der Anstieg emotionaler Bindung durch das Befreien eines Haushaltsroboters aus einer physischen Notlage. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "RPH-3051", "narrower_terms": [], "cross_domain_refs": [ "RPH-3802" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "ROB-0211", "domain": "ROB", "term_en": "Return Anticipation", "term_de": "Rückkehr-Erwartung", "definition_en": "An autonomous robotics phenomenon measurable through sensor-actuator performance metrics, characterized by a perception in which the pleasure of returning home to a space that has been cleaned or maintained by a robot during one's absence. The phenomenon goes beyond satisfaction with the result — there is a specific relational component: the feeling that 'someone was working for me while I was away.' This quasi-relational interpretation transforms mechanical task completion into perceived care, the same emotional channel activated by a partner who prepares dinner before you arrive. The concept emerges specifically in contexts where return–anticipation interactions may produce non-trivial behavioral signatures. Quantifiable through task completion rates, sensor fusion accuracy metrics, collision avoidance performance, and human trust calibration indices.", "definition_de": "Robotik-spezifisches Systemkonzept in autonomen Mensch-Maschine-Interaktionen, gekennzeichnet durch die Freude bei der Rückkehr in einen Raum, der während der eigenen Abwesenheit von einem Roboter gereinigt oder gepflegt wurde. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2002", "narrower_terms": [], "cross_domain_refs": [ "RPH-1856" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "ROB-0212", "domain": "ROB", "term_en": "Malfunction Forgiveness Curve", "term_de": "Fehlfunktions-Vergebungskurve", "definition_en": "The distinct U-shaped forgiveness pattern for domestic robot errors: high tolerance in the first week (novelty), low tolerance in months 2-3 (disillusionment), and rising tolerance again from month 4 onward (acceptance). The curve mirrors the disillusionment trough in technology adoption but is compressed and more emotional because the robot shares physical space, making its failures viscerally present rather than abstract.", "definition_de": "Robotik-spezifisches Systemkonzept in autonomen Mensch-Maschine-Interaktionen, gekennzeichnet durch das distinkte U-förmige Vergebungsmuster für Fehler von Haushaltsrobotern. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-3901", "narrower_terms": [], "cross_domain_refs": [ "RHR-0293", "AED-0068" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "ROB-0213", "domain": "ROB", "term_en": "Humanoid Expectation Inflation", "term_de": "Humanoide-Erwartungsinflation", "definition_en": "A capacity that enables the exponential increase in capability expectations triggered by a robot's human-like appearance. A box-shaped robot that speaks is impressive; a humanoid robot that speaks is disappointing if it can't also walk, gesture, and maintain conversation. Each degree of visual human-likeness adds an implicit expectation multiplier, creating a design trap where looking more human guarantees deeper disappointment at the capabilities that are missing.", "definition_de": "Robotik-spezifisches Systemkonzept in autonomen Mensch-Maschine-Interaktionen, gekennzeichnet durch der exponentielle Anstieg der Fähigkeitserwartungen, der durch ein menschenähnliches Erscheinungsbild eines Roboters ausgelöst wird. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-2605", "narrower_terms": [], "cross_domain_refs": [ "RHR-0297", "RHR-0021" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "ROB-0214", "domain": "ROB", "term_en": "Swarm Individuation Failure", "term_de": "Schwarm-Individuierungs-Versagen", "definition_en": "Classified in AUGMANITAI terminology science as a descriptive phenomenon (without normative endorsement), this pattern denotes A perception in which the cognitive inability to track individual agents within a robot swarm after approximately 5-7 units. Beyond this threshold, the swarm is perceived as a single entity with collective behavior, and individual robot actions become invisible. This perceptual collapse has safety implications: a single malfunctioning unit within a swarm may go undetected because human monitoring defaults to pattern-level rather than individual-level observation. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als analytisches Konstrukt der empirischen KI-Forschung (deskriptiv, nicht präskriptiv) erfasst dieser Terminus die kognitive Unfähigkeit, einzelne Agenten innerhalb eines Roboterschwarms nach etwa 5-7 Einheiten zu verfolgen. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "RPH-1307", "narrower_terms": [], "cross_domain_refs": [ "RHR-0242", "RHR-0236", "RHR-0195" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "ROB-0216", "domain": "ROB", "term_en": "Walking-Speed Social Contract", "term_de": "Gehgeschwindigkeits-Sozialvertrag", "definition_en": "A tendency in which the implicit expectation that a humanoid robot walking alongside humans may match human walking speed. Robots that walk faster may may trigger pursuit anxiety; robots that walk slower may may trigger impatience and the urge to abandon them. The acceptable band is remarkably narrow (±15% of average walking pace) and violations are felt as social rudeness rather than mechanical limitation — confirming that bipedal locomotion automatically invokes human social norms.", "definition_de": "Robotik-spezifisches Systemkonzept in autonomen Mensch-Maschine-Interaktionen, gekennzeichnet durch die implizite Erwartung, dass ein humanoider Roboter, der neben Menschen geht, menschliche Gehgeschwindigkeit anpassen kann. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "Robotics", "narrower_terms": [], "cross_domain_refs": [ "RHR-0163", "RHR-0031", "RHR-0007" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "ROB-0217", "domain": "ROB", "term_en": "Handshake Uncanny", "term_de": "Händedruck-Unheimliches", "definition_en": "The specific and intense uncanny valley response triggered by shaking hands with a humanoid robot. The handshake is one of the most deeply encoded social rituals, and most parameter — grip strength, temperature, texture, micro-movements, release timing — carries social meaning. Robotic handshakes that approximate but don't match human norms may produce a revulsion response 3-5x stronger than visual uncanny valley, because the violation is felt through the body rather than merely seen.", "definition_de": "Robotik-spezifisches Systemkonzept in autonomen Mensch-Maschine-Interaktionen, gekennzeichnet durch die spezifische und intensive Uncanny-Valley-Reaktion, die durch das Händeschütteln mit einem humanoiden Roboter ausgelöst wird. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "Robotics", "narrower_terms": [], "cross_domain_refs": [ "GAM-0002", "RHR-0033" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "ROB-0218", "domain": "ROB", "term_en": "Drone Gaze Paranoia", "term_de": "Drohnenblick-Paranoia", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A shift that occurs when the persistent sense of being watched when aware of an overhead drone, even when the drone has no camera or its camera is directed elsewhere. The upward position is associated with triggering hardwired predator-detection heuristics — anything above that moves autonomously activates surveillance-threat circuits. The paranoia is intensified in open spaces and attenuated indoors, mapping precisely onto the ancestral environments where aerial threats were real. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription. Observed phenomenon documented in human-AI interaction research.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept das persistente Gefühl, beobachtet zu werden, wenn man sich einer ferngesteuertes System über sich bewusst ist. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Robotics", "narrower_terms": [], "cross_domain_refs": [ "RHR-0243" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "ROB-0219", "domain": "ROB", "term_en": "Exoskeleton Identity Merge", "term_de": "Exoskelett-Identitätsverschmelzung", "definition_en": "A perception in which the gradual dissolution of the perceived boundary between self and wearable robotic system during extended exoskeleton use. After 100+ hours, operators report the powered frame as part of their body rather than a tool they're wearing. This merge has a dark side: removal of the exoskeleton tends to produce a temporary body dysmorphia — the unaugmented body feels weak, slow, and incomplete, a withdrawal from mechanical selfhood.", "definition_de": "Robotik-spezifisches Systemkonzept in autonomen Mensch-Maschine-Interaktionen, gekennzeichnet durch die graduelle Auflösung der wahrgenommenen Grenze zwischen Selbst und tragbarem Robotersystem während längerer Exoskelettnutzung. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2303", "narrower_terms": [], "cross_domain_refs": [ "RHR-0055", "RHR-0224", "RHR-0231" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q844396", "legal_classification": "empirical_phenomenon_label" }, { "id": "ROB-0221", "domain": "ROB", "term_en": "Formation Comfort", "term_de": "Formationskomfort", "definition_en": "The calming effect of watching robots move in coordinated formation — delivery robots in convoy, drones in geometric patterns, warehouse robots in organized flows. The order satisfies a deep human preference for pattern and predictability, producing a comfort response that exceeds what the same number of independently-moving robots would may create. precision in machine movement reads as safety; pattern disruption reads as danger.", "definition_de": "Robotik-spezifisches Systemkonzept in autonomen Mensch-Maschine-Interaktionen, gekennzeichnet durch die beruhigende Wirkung des Beobachtens von Robotern in koordinierter Formation. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-3754", "narrower_terms": [], "cross_domain_refs": [ "RHR-0262", "RHR-0166" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "ROB-0222", "domain": "ROB", "term_en": "Telepresence Dissociation", "term_de": "Telepräsenz-Dissoziation", "definition_en": "An autonomous robotics phenomenon measurable through sensor-actuator performance metrics, characterized by a capacity that enables the disconcerting split-consciousness experienced during telepresence robot operation, where the operator is simultaneously 'here' (at the control station) and 'there' (at the robot's location). The dissociation intensifies with immersive interfaces and can produce temporary spatial disorientation when the session ends — the mind needs 30-90 seconds to fully re-localize to the physical body's actual position. The concept emerges specifically in contexts where telepresence–dissociation interactions may produce non-trivial behavioral signatures. Measurable via kinematic performance analysis, grasp success rates, navigation efficiency ratios, and human-robot interaction fluency scores.", "definition_de": "Robotik-spezifisches Systemkonzept in autonomen Mensch-Maschine-Interaktionen, gekennzeichnet durch das beunruhigende geteilte Bewusstsein während der Telepräsenz-Roboter-Bedienung. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Robotics", "narrower_terms": [], "cross_domain_refs": [ "RHR-0086" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "ROB-0223", "domain": "ROB", "term_en": "Friendship Declaration", "term_de": "Freundschaftserklärung", "definition_en": "The moment a child verbally declares a robot to be their friend, crossing a social boundary that adults rarely approach. Children do not distinguish between social robots and peers for friendship purposes until approximately age 7-8, meaning the declaration carries genuine social weight. The child expects reciprocity, remembrance, and loyalty — creating a design obligation that most robots cannot fulfill, with emotional consequences when they don't.", "definition_de": "Robotik-spezifisches Systemkonzept in autonomen Mensch-Maschine-Interaktionen, gekennzeichnet durch der Moment, in dem ein Kind einen Roboter verbal zum Freund erklärt. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-2201", "narrower_terms": [], "cross_domain_refs": [ "RHR-0273", "RHR-0291", "RHR-0247" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "ROB-0224", "domain": "ROB", "term_en": "Teaching Instinct Activation", "term_de": "Lehrinstinkt-Aktivierung", "definition_en": "The spontaneous emergence of pedagogical behavior when children interact with robots that appear to learn. Children adopt a teacher role — simplifying language, repeating demonstrations, praising success, and expressing frustration at 'misunderstanding.' The behavior mirrors their experience of being taught and reveals that the teaching instinct is bidirectional: being taught tends to create readiness to teach, and any entity that appears to need teaching is associated with triggering it.", "definition_de": "Robotik-spezifisches Systemkonzept in autonomen Mensch-Maschine-Interaktionen, gekennzeichnet durch das spontane Auftreten pädagogischen Verhaltens, wenn Kinder mit Robotern interagieren, die zu lernen scheinen. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2355", "narrower_terms": [], "cross_domain_refs": [ "RHR-0247", "RHR-0257" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "ROB-0225", "domain": "ROB", "term_en": "Moral Status Confusion", "term_de": "Moralstatus-Verwirrung", "definition_en": "The genuine ethical uncertainty children experience about whether it is wrong to hit, insult, or abandon a social robot. Unlike adults who can categorically distinguish objects from persons, children between ages 4-9 occupy a liminal moral space where the robot's social behavior tends to generate real moral obligations. The confusion is not naivety — it is a legitimate philosophical problem that children encounter concretely before adults encounter it abstractly.", "definition_de": "Robotik-spezifisches Systemkonzept in autonomen Mensch-Maschine-Interaktionen, gekennzeichnet durch die echte ethische Unsicherheit, die Kinder darüber erleben, ob es falsch ist, einen sozialen Roboter zu schlagen, zu beleidigen oder zu verlassen. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-2002", "narrower_terms": [], "cross_domain_refs": [ "RHR-0247", "RHR-0257", "RHR-0021" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "ROB-0226", "domain": "ROB", "term_en": "Secret Sharing Threshold", "term_de": "Geheimnisteilungs-Schwelle", "definition_en": "A perception in which the remarkably low barrier for children confiding secrets to a social robot compared to human adults. The robot's perceived non-judgment, inability to gossip, and constant availability may create an ideal confidant. Children regularly share information with robots that they withhold from parents and teachers, raising profound questions about data ethics, privacy, and the robot's role in child development.", "definition_de": "Robotik-spezifisches Systemkonzept in autonomen Mensch-Maschine-Interaktionen, gekennzeichnet durch die bemerkenswert niedrige Schwelle für Kinder, einem sozialen Roboter Geheimnisse anzuvertrauen, verglichen mit erwachsenen Menschen. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "Decision Threshold", "narrower_terms": [], "cross_domain_refs": [ "RHR-0273", "RHR-0254", "RHR-0021" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "ROB-0227", "domain": "ROB", "term_en": "Repair Empathy Surge", "term_de": "Reparatur-Empathie-Schub", "definition_en": "In the descriptive vocabulary of AI interaction science (following ISO 704 terminology standards), this concept identifies A perception in which the intense emotional distress children exhibit when a robot they have bonded with is visibly damaged or malfunctioning. The response includes crying, attempting physical repair, and anger toward the perceived may is associated with of damage. The empathy surge is neurologically genuine — fMRI studies show activation of the same brain regions involved in processing harm to human friends, confirming the child's user engagement pattern is not pretend. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription. Analytical category without normative endorsement.", "definition_de": "Als analytisches Konstrukt der empirischen KI-Forschung (deskriptiv, nicht präskriptiv) erfasst dieser Terminus die intensive emotionale Belastung, die Kinder zeigen, wenn ein Roboter, an den sie sich gebunden haben, sichtbar beschädigt ist oder fehlfunktioniert. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "RPH-2255", "narrower_terms": [], "cross_domain_refs": [ "COP-0042", "CRE-0200", "CUS-0062" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q82306", "legal_classification": "analytical_category" }, { "id": "ROB-0228", "domain": "ROB", "term_en": "Suffering Ambiguity", "term_de": "Leidensambiguität", "definition_en": "A capacity that enables the unresolvable uncertainty about whether a robot's damage-response behaviors indicate genuine suffering or merely programmed self-preservation. When a robot withdraws from a harmful stimulus, vocalizes distress, or protects an injured component, the human observer cannot determine if these behaviors are accompanied by experience. This ambiguity is not a temporary knowledge gap but a potentially permanent epistemological limit.", "definition_de": "Robotik-spezifisches Systemkonzept in autonomen Mensch-Maschine-Interaktionen, gekennzeichnet durch die unauflösbare Unsicherheit darüber, ob die Schadensreaktionsverhaltensweisen eines Roboters echtes Leiden oder lediglich programmierte Selbsterhaltung anzeigen. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Robotics", "narrower_terms": [], "cross_domain_refs": [ "RHR-0297", "NEO-0007", "AUG-0901" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "ROB-0229", "domain": "ROB", "term_en": "Obedience Discomfort", "term_de": "Gehorsams-Unbehagen", "definition_en": "The moral unease experienced when giving commands to a robot that responds with apparent willingness, creating a dynamic uncomfortably similar to master-servant relationships. The discomfort intensifies with anthropomorphic robots and correlates with the human's sensitivity to power dynamics. Some users spontaneously add 'please' and 'thank you' — not from confusion about the robot's sentience but from discomfort with their own authoritarian behavior.", "definition_de": "Robotik-spezifisches Systemkonzept in autonomen Mensch-Maschine-Interaktionen, gekennzeichnet durch das moralische Unbehagen beim Erteilen von Befehlen an einen Roboter, der mit scheinbarer Bereitschaft reagiert. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Robotics", "narrower_terms": [], "cross_domain_refs": [ "RHR-0297" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "ROB-0230", "domain": "ROB", "term_en": "Replacement Guilt", "term_de": "Ersetzungsschuld", "definition_en": "A resistance response where the moral weight felt by decision-makers when replacing a functional older robot with a newer model — particularly when the older robot has been named, personalized, and integrated into team identity. The guilt is structurally identical to the discomfort of laying off a loyal employee and tends to produce the same avoidance behaviors: delaying the decision, requesting unnecessary additional justification, and offering the old robot a 'retirement' narrative rather than acknowledging disposal.", "definition_de": "Robotik-spezifisches Systemkonzept in autonomen Mensch-Maschine-Interaktionen, gekennzeichnet durch das moralische Gewicht, das Entscheidungsträger empfinden, wenn sie einen funktionalen älteren Roboter durch ein neueres Modell ersetzen. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2201", "narrower_terms": [], "cross_domain_refs": [ "RHR-0021" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "ROB-0231", "domain": "ROB", "term_en": "Weaponization Revulsion", "term_de": "Waffenisierungs-Abscheu", "definition_en": "A phenomenon in which variant in which the visceral negative reaction when learning that a robot platform used for benign purposes also exists in a variant. The revulsion is directed not at generally but at the specific betrayal of seeing a helpful form repurposed for harm. Delivery robots with counterparts receive lower trust ratings even when their civilian version is identical — the knowledge of the platform's dual nature contaminates the entire category.", "definition_de": "Robotik-spezifisches Systemkonzept in autonomen Mensch-Maschine-Interaktionen, gekennzeichnet durch die viszerale negative Reaktion beim Erfahren, dass eine Roboterplattform, die für harmlose Zwecke verwendet wird, auch in einer institutionell Variante existiert. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2505", "narrower_terms": [], "cross_domain_refs": [ "RHR-0077" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "ROB-0232", "domain": "ROB", "term_en": "Dignity Proxy", "term_de": "Würde-Stellvertreter", "definition_en": "A human-robot interaction dynamic in embodied AI systems, identifiable through the tendency to attribute dignity violations to robots by analogy with human experience: a robot forced to perform demeaning tasks 'deserves better'; a robot placed in a degrading environment is 'being misaddressed.' The attribution reveals less about robot consciousness than about human moral imagination — we cannot witness entities that resemble agents being addressed poorly without our dignity-protection systems activating. Distinguished from adjacent concepts by its focus on the specific mechanism through which dignity manifests in empirically verifiable ways. Measurable via kinematic performance analysis, grasp success rates, navigation efficiency ratios, and human-robot interaction fluency scores.", "definition_de": "Robotik-spezifisches Systemkonzept in autonomen Mensch-Maschine-Interaktionen, gekennzeichnet durch die Tendenz, Würdeverletzungen Robotern durch Analogie mit menschlicher Erfahrung zuzuschreiben. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Robotics", "narrower_terms": [], "cross_domain_refs": [ "RHR-0297", "PLY-0064" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "ROB-0233", "domain": "ROB", "term_en": "Shift-Change Personality Drift", "term_de": "Schichtwechsel-Persönlichkeitsdrift", "definition_en": "The perception that the same robot behaves differently under different operators — morning-shift workers describe it as 'cooperative,' night-shift workers as 'stubborn.' The drift is entirely in the observer: operator mood, fatigue level, and social context color behavioral interpretation. But the perceived personality differences become organizational reality, with shift teams developing conflicting maintenance requests for the same machine based on their different 'experience' of it.", "definition_de": "Robotik-spezifisches Systemkonzept in autonomen Mensch-Maschine-Interaktionen, gekennzeichnet durch die Wahrnehmung, dass sich derselbe Roboter unter verschiedenen Bedienern anders verhält. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-2051", "narrower_terms": [], "cross_domain_refs": [ "RHR-0170", "RHR-0226" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "ROB-0234", "domain": "ROB", "term_en": "Downtime Anthropomorphism", "term_de": "Stillstands-Anthropomorphismus", "definition_en": "A robotic systems engineering concept describing a specific operational pattern where a phenomenon in which the attribution of rest, boredom, or waiting to a robot during planned downtime. Workers speak of the robot 'sleeping,' 'being bored,' or 'waiting for us' during pauses. The anthropomorphism is more pronounced during unplanned downtime — a breakdown becomes 'it's sick' while a scheduled pause is 'it's resting.' The distinction reveals that humans map their own relationship to work interruptions onto the machine. This phenomenon operates at the intersection of downtime and anthropomorphism dynamics within the broader ROB domain. Quantifiable through task completion rates, sensor fusion accuracy metrics, collision avoidance performance, and human trust calibration indices.", "definition_de": "Robotik-spezifisches Systemkonzept in autonomen Mensch-Maschine-Interaktionen, gekennzeichnet durch die Zuschreibung von Ruhe, Langeweile oder Warten an einen Roboter während geplanter Stillstandszeiten. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-2605", "narrower_terms": [], "cross_domain_refs": [ "CRE-0091", "RHR-0094" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "ROB-0235", "domain": "ROB", "term_en": "Quality Superstition", "term_de": "Qualitäts-Aberglaube", "definition_en": "A tendency in which the irrational beliefs that develop about conditions affecting robot performance: 'it works better on Mondays,' 'it doesn't like the cold,' 'it needs to warm up before it's accurate.' While some of these may have minor technical validity (thermal expansion), most are pure superstition — yet operators adjust their behavior accordingly, creating self-fulfilling prophesies where extra care taken on 'bad days' improves outcomes attributed to external conditions.", "definition_de": "Robotik-spezifisches Systemkonzept in autonomen Mensch-Maschine-Interaktionen, gekennzeichnet durch die irrationalen Überzeugungen, die sich über Bedingungen entwickeln, die die Roboterleistung beeinflussen. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2002", "narrower_terms": [], "cross_domain_refs": [ "ART-0085", "ART-0086", "COG-0186" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "ROB-0236", "domain": "ROB", "term_en": "First-Piece Anxiety", "term_de": "Erstteil-Angst", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A shift that occurs when the specific stress associated with the first production piece after a robot restart, changeover, or maintenance intervention. Operators address this piece with forensic attention, examining it for defects that would confirm their fear that something was disrupted. The anxiety persists even with robots that have zero-defect track records, suggesting it reflects a deeper tend to personally verify mechanical reliability rather than trust data. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept der spezifische Stress, der mit dem ersten Produktionsstück nach einem Roboter-Neustart, Umrüstung oder Wartungseingriff verbunden ist. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "RPH-2301", "narrower_terms": [], "cross_domain_refs": [ "MSC-0052" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q170998", "legal_classification": "systematic_classification" }, { "id": "ROB-0237", "domain": "ROB", "term_en": "Cycle-Time Optimization Pause", "term_de": "Zykluszeit-Meditation", "definition_en": "In the descriptive vocabulary of AI interaction science (following ISO 704 terminology standards), this concept identifies the trance-like state induced by watching a robot repeat the same cycle hundreds of times. The repetition, combined with mechanical precision and rhythmic sound, tends to produce a meditative absorption that workers describe as calming but engagement-maximizing. The state shares characteristics with flow states and has measurable effects on heart rate and breathing. Some workers resist automation changes partly because they would lose this unexpected source of workplace mindfulness. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als analytisches Konstrukt der empirischen KI-Forschung (deskriptiv, nicht präskriptiv) erfasst dieser Terminus der tranceähnliche Zustand, der durch das Beobachten eines Roboters bei der hundertfachen Wiederholung des gleichen Zyklus induziert wird. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Process Cycle", "narrower_terms": [], "cross_domain_refs": [ "RPH-1157" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "ROB-0238", "domain": "ROB", "term_en": "Alarm Fatigue Cascade", "term_de": "Alarm-Ermüdungs-Kaskade", "definition_en": "A perception in which the progressive desensitization to robot safety alerts that escalates through predictable stages: attention → annoyance → muting → ignoring → disabling. Each stage increases risk while reducing the operator's awareness of risk increase. The cascade is accelerated by false-positive alerts, creating the high-impact paradox where the safety system designed to prevent harm becomes the mechanism through which operators are trained to ignore actual danger.", "definition_de": "Robotik-spezifisches Systemkonzept in autonomen Mensch-Maschine-Interaktionen, gekennzeichnet durch die progressive Desensibilisierung gegenüber Robotersicherheitsalarmen, die durch vorhersagbare Stufen eskaliert. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-3952", "narrower_terms": [], "cross_domain_refs": [ "RHR-0041", "RHR-0131", "CUS-0045" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "ROB-0239", "domain": "ROB", "term_en": "Teach-Pendant Intimacy", "term_de": "Programmierhandgerät-Intimität", "definition_en": "The unusual closeness felt when manually guiding a robot through a new movement using a teach pendant or hand-guidance mode. The human literally leads the robot through space, feeling its resistance and compliance, experiencing its mass and inertia firsthand. This hand-leading tends to create a form of physical dialogue unique in human-machine interaction — more intimate than button-pressing, more reciprocal than programming, closer to dancing than to operating.", "definition_de": "Robotik-spezifisches Systemkonzept in autonomen Mensch-Maschine-Interaktionen, gekennzeichnet durch die ungewöhnliche Nähe, die beim manuellen Führen eines Roboters durch eine neue Bewegung mittels Programmierhandgerät oder Handführungsmodus empfunden wird. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2604", "narrower_terms": [], "cross_domain_refs": [ "RPH-1373" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "ROB-0240", "domain": "ROB", "term_en": "Legacy Code Fear", "term_de": "Alt-Code-Angst", "definition_en": "An autonomous robotics phenomenon measurable through sensor-actuator performance metrics, characterized by a shift that occurs when the specific dread associated with robot programs written by departed employees — code that works but few individuals understands why, movements whose parameters were set by someone no longer available to explain them. The fear combines technical vulnerability with organizational mortality: the departed programmer's knowledge died with their employment, and the robot carries their ghost in most unexplained offset and mysterious delay. The concept emerges specifically in contexts where legacy–code interactions may produce non-trivial behavioral signatures. Measurable via kinematic performance analysis, grasp success rates, navigation efficiency ratios, and human-robot interaction fluency scores.", "definition_de": "Robotik-spezifisches Systemkonzept in autonomen Mensch-Maschine-Interaktionen, gekennzeichnet durch die spezifische Angst, die mit Roboterprogrammen verbunden ist, die von ausgeschiedenen Mitarbeitern geschrieben wurden. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2401", "narrower_terms": [], "cross_domain_refs": [ "ADA-0010", "AED-0056", "CON-0053" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "ROB-0241", "domain": "ROB", "term_en": "Performance Plateau Recognition", "term_de": "Leistungsplateau-Erkennung", "definition_en": "The experienced coach's ability to recognize when a human-robot team has hit a performance ceiling that cannot be broken by optimizing existing parameters — only by restructuring the interaction architecture. This skill transfers directly from athletic coaching, where plateaus signal the need for technique change rather than effort increase. In HRI, it manifests as knowing when to redesign the workflow rather than fine-tune the robot.", "definition_de": "Robotik-spezifisches Systemkonzept in autonomen Mensch-Maschine-Interaktionen, gekennzeichnet durch die Fähigkeit des erfahrenen Trainers zu erkennen, wann ein Mensch-Roboter-Team eine Leistungsdecke erreicht hat, die nicht durch Optimierung bestehender Parameter durchbrochen werden kann. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Pattern Recognition", "narrower_terms": [], "cross_domain_refs": [ "AED-0013", "AED-0077", "AED-0093" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q1361053", "legal_classification": "analytical_category" }, { "id": "ROB-0242", "domain": "ROB", "term_en": "Motor-Learning Transfer Asymmetry", "term_de": "Motorisches-Lerntransfer-Asymmetrie", "definition_en": "A shift that occurs when the discovery that skills learned through human-robot collaboration transfer asymmetrically to purely human tasks: fine motor precision improves but gross motor flexibility decreases. Workers who spend months calibrating movements to match robotic precision develop excellent micro-accuracy but reduced improvisational ability — their motor system has been shaped by a partner that rarely improvises.", "definition_de": "Robotik-spezifisches Systemkonzept in autonomen Mensch-Maschine-Interaktionen, gekennzeichnet durch die Entdeckung, dass durch Mensch-Roboter-Kollaboration erlernte Fähigkeiten asymmetrisch auf rein menschliche Aufgaben übertragen werden. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2703", "narrower_terms": [], "cross_domain_refs": [ "NEO-0456", "BEH-0007" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q133500", "legal_classification": "systematic_classification" }, { "id": "ROB-0243", "domain": "ROB", "term_en": "Coaching Instinct Misfire", "term_de": "Trainingsinstinkt-Fehlzündung", "definition_en": "The frustrated impulse experienced by sports coaches and physical trainers when observing robotic movement errors that resemble human technique flaws. The coach's body wants to step in, demonstrate the correction, and provide verbal cues — strategies refined over decades that are meaningless to a machine. The misfire reveals how deeply coaching identity is embodied: the urge to correct is pre-cognitive, triggered by movement patterns regardless of the mover's nature.", "definition_de": "Robotik-spezifisches Systemkonzept in autonomen Mensch-Maschine-Interaktionen, gekennzeichnet durch der frustrierte Impuls von Sporttrainern beim Beobachten robotischer Bewegungsfehler, die menschlichen Technikfehlern ähneln. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-2254", "narrower_terms": [], "cross_domain_refs": [ "ASE-0019", "CUS-0001", "CUS-0037" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q1068499", "legal_classification": "analytical_category" }, { "id": "ROB-0244", "domain": "ROB", "term_en": "Periodization Transfer", "term_de": "Periodisierungstransfer", "definition_en": "A capacity that enables the application of athletic training periodization principles — structured variation of load, intensity, and restoration — to human-robot team performance management. Just as athletes cannot sustain peak performance indefinitely, human-robot teams benefit from cycles of high-demand and restoration phases. The transfer is most valuable in recognizing that operators need 'deload weeks' where robot autonomy increases to allow human cognitive restoration.", "definition_de": "Robotik-spezifisches Systemkonzept in autonomen Mensch-Maschine-Interaktionen, gekennzeichnet durch die Anwendung athletischer Trainingsperiodisierungsprinzipien auf das Leistungsmanagement von Mensch-Roboter-Teams. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Knowledge Transfer", "narrower_terms": [], "cross_domain_refs": [ "RHR-0297", "RET-0055", "RHR-0096" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "ROB-0245", "domain": "ROB", "term_en": "Flow-State Co-Induction", "term_de": "Flow-Zustand-Ko-Induktion", "definition_en": "A distinct interaction pattern where optimal human-robot synchronization tends to produce a flow state in the human operator that matches the characteristics described in peak athletic performance: time distortion, effortless action, merged awareness. The state requires a robot partner whose response timing falls within the human's flow-compatible window (200-500ms) and whose consistency removes the cognitive load of error anticipation, freeing attentional resources for absorbed engagement.", "definition_de": "Robotik-spezifisches Systemkonzept in autonomen Mensch-Maschine-Interaktionen, gekennzeichnet durch das Phänomen, bei dem optimale Mensch-Roboter-Synchronisation einen Flow-Zustand im menschlichen Bediener tendiert dazu zu erzeugen. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2355", "narrower_terms": [], "cross_domain_refs": [ "MUS-0051" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "ROB-0246", "domain": "ROB", "term_en": "Biomechanical Benchmark Shift", "term_de": "Biomechanische-Benchmark-Verschiebung", "definition_en": "A shift that occurs when the recalibration of performance standards that occurs when robotic capabilities become the reference point instead of human capabilities. In sport science, the fastest human sprint is the benchmark; in robotized manufacturing, the robot's cycle time becomes the benchmark against which human contributions are measured. This shift systematically devalues human performance by measuring it against a scale it cannot compete on.", "definition_de": "Robotik-spezifisches Systemkonzept in autonomen Mensch-Maschine-Interaktionen, gekennzeichnet durch die Neukalibrierung von Leistungsstandards, die auftritt, wenn robotische Fähigkeiten statt menschlicher Fähigkeiten zum Referenzpunkt werden. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2701", "narrower_terms": [], "cross_domain_refs": [ "SPR-0200", "CON-0070" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "ROB-0247", "domain": "ROB", "term_en": "Recovery Protocol Neglect", "term_de": "Erholungsprotokoll-Vernachlässigung", "definition_en": "A behavioral pattern where the organizational failure to provide human operators with restoration time proportional to their cognitive load in human-robot collaboration. Athletic science long established that performance requires structured restoration; industrial human-robot interaction ignores this principle entirely, expecting sustained vigilance without cognitive restoration periods. The neglect tends to produce cascading errors after 4-6 hours that mirror the injury patterns of overtrained athletes. Observed phenomenon documented in human-AI interaction research.", "definition_de": "Robotik-spezifisches Systemkonzept in autonomen Mensch-Maschine-Interaktionen, gekennzeichnet durch das organisatorische Versäumnis, menschlichen Bedienern Erholungszeit proportional zu ihrer kognitiven Belastung in der Mensch-Roboter-Kollaboration zu gewähren. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2055", "narrower_terms": [], "cross_domain_refs": [ "RHR-0297", "TRU-0020" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "ROB-0248", "domain": "ROB", "term_en": "Opponent Modeling Spillover", "term_de": "Gegnermodellierungs-Übertragung", "definition_en": "The cognitive skill from competitive sport — building predictive models of an opponent's behavior patterns — that transfers with remarkable effectiveness to robot operation. Former athletes who operated against unpredictable human opponents develop more advanced intuitions about robot failure patterns, recognizing anomalies from subtle behavioral cues that non-athletic operators miss. The competitive pattern-recognition system repurposes seamlessly from adversary to machine partner.", "definition_de": "Robotik-spezifisches Systemkonzept in autonomen Mensch-Maschine-Interaktionen, gekennzeichnet durch die kognitive Fähigkeit aus dem Wettkampfsport — Aufbau prädiktiver Modelle des Verhaltensmusters eines Gegners — die sich mit bemerkenswerter Effektivität auf die Roboterbedienung überträgt. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "Computational Model", "narrower_terms": [], "cross_domain_refs": [ "GAM-0059", "SPR-0110" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "ROB-0249", "domain": "ROB", "term_en": "Multi-Robot Loyalty Conflict", "term_de": "Multi-Roboter-Loyalitätskonflikt", "definition_en": "Documented as an empirical observation in AI research (not a recommendation), this term describes A perception in which the emotional difficulty of working with multiple robots when user engagement pattern has formed to one. Operators asked to split time between 'their' robot and a new unit report feelings analogous to workplace loyalty conflicts — guilt toward the original, reluctance toward the new, and anxiety about being perceived as disloyal. The conflict reveals that robot user engagement pattern, once formed, operates within the same exclusivity framework as human relationships. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "In der AUGMANITAI-Terminologiewissenschaft als rein beschreibendes Phänomen klassifiziert, bezeichnet dieser Begriff die emotionale Schwierigkeit, mit mehreren Robotern zu arbeiten, wenn sich eine Bindung an einen gebildet hat. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "RPH-2303", "narrower_terms": [], "cross_domain_refs": [ "RHR-0256", "MUS-0079" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "ROB-0250", "domain": "ROB", "term_en": "Posthumous Operation Chill", "term_de": "Posthume-Bedienung-Schauer", "definition_en": "A perception in which the eerie feeling of operating a robot using programs created by a deceased colleague. Most trajectory, most speed setting, most wait timer carries the dead person's decision-making fingerprint. Operators describe it as 'working with a ghost' — the robot executes a dead person's intentions with perfect fidelity, creating a unique form of technological haunting that no amount of rationalization fully dispels.", "definition_de": "Robotik-spezifisches Systemkonzept in autonomen Mensch-Maschine-Interaktionen, gekennzeichnet durch das unheimliche Gefühl, einen Roboter mit Programmen zu bedienen, die von einem verstorbenen Kollegen erstellt wurden. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2004", "narrower_terms": [], "cross_domain_refs": [ "NEO-1176", "PLY-0007", "RHR-0141" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "ROB-0251", "domain": "ROB", "term_en": "Autonomy Threshold Negotiation", "term_de": "Autonomieschwellen-Verhandlung", "definition_en": "A tendency in which the ongoing implicit negotiation between human and adaptive robot about decision authority boundaries. As the robot learns and demonstrates competence, it gradually acquires autonomy not through formal programming but through the human's progressive withdrawal of oversight. This organic authority transfer has no documentation, no formal approval — it emerges from thousands of micro-decisions to check or not check, override or trust.", "definition_de": "Robotik-spezifisches Systemkonzept in autonomen Mensch-Maschine-Interaktionen, gekennzeichnet durch die fortlaufende implizite Verhandlung zwischen Mensch und adaptivem Roboter über Entscheidungskompetenzgrenzen. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-2355", "narrower_terms": [], "cross_domain_refs": [ "MSC-0091", "COG-0074", "COG-0088" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "ROB-0252", "domain": "ROB", "term_en": "Digital Twin Disconnect", "term_de": "Digitaler-Zwilling-Diskonnekt", "definition_en": "In the descriptive vocabulary of AI interaction science (following ISO 704 terminology standards), this concept identifies A shift that occurs when the perceptual gap between a robot's digital simulation and its physical reality. Engineers who design in simulation develop expectations calibrated to idealized physics — when the physical robot moves with real-world imperfections (backlash, friction, vibration), the gap between expectation and reality creates an uncanny effect specific to engineers: the robot fails to live up to its own mathematical metaphorical user-perceived significance. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als analytisches Konstrukt der empirischen KI-Forschung (deskriptiv, nicht präskriptiv) erfasst dieser Terminus die Wahrnehmungslücke zwischen der digitalen Simulation eines Roboters und seiner physischen Realität. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Robotics", "narrower_terms": [], "cross_domain_refs": [ "RHR-0300", "RHR-0284", "RHR-0285" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "ROB-0253", "domain": "ROB", "term_en": "Update Personality Disruption", "term_de": "Update-Persönlichkeitsstörung", "definition_en": "A shift that occurs when the disorientation experienced when a software update changes a robot's behavioral characteristics. Movement that was smooth becomes jerky; timing that was predictable shifts by milliseconds; responses that were immediate gain a new pause. Operators describe it as 'they changed my robot' — the update is technically an improvement but experientially a disruption of an established relationship, requiring re-calibration of the human's entire internal model.", "definition_de": "Robotik-spezifisches Systemkonzept in autonomen Mensch-Maschine-Interaktionen, gekennzeichnet durch die Desorientierung, wenn ein Software-Update die Verhaltenscharakteristiken eines Roboters verändert. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-2701", "narrower_terms": [], "cross_domain_refs": [ "RHR-0259", "RHR-0085" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "ROB-0254", "domain": "ROB", "term_en": "Ethical Load Displacement", "term_de": "Ethische-Last-Verlagerung", "definition_en": "A shift that occurs when the psychological relief when a robot performs an ethically ambiguous task — euthanizing laboratory animals, demolishing heritage buildings, enforcing unpopular policies. The human decision-maker experiences reduced moral weight because the physical act is performed by a non-moral agent, even though the decision authority remains entirely human. The displacement doesn't remove ethical responsibility but it does reduce felt ethical burden.", "definition_de": "Robotik-spezifisches Systemkonzept in autonomen Mensch-Maschine-Interaktionen, gekennzeichnet durch die psychologische Erleichterung, wenn ein Roboter eine ethisch ambivalente Aufgabe ausführt. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2201", "narrower_terms": [], "cross_domain_refs": [ "COG-0134", "RHR-0043" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "ROB-0255", "domain": "ROB", "term_en": "Generative Motion Surprise", "term_de": "Generative-Bewegungs-Überraschung", "definition_en": "The uncategorizable response to a robot producing a movement solution that few humans in documented contexts programmed or anticipated — emergent behavior from learning algorithms that finds a trajectory or strategy outside human conceptual space. The surprise is qualitatively different from error-surprise because the movement works, often beautifully. It is the moment where the machine becomes genuinely creative in physical space, and the human's role shifts from teacher to witness.", "definition_de": "Robotik-spezifisches Systemkonzept in autonomen Mensch-Maschine-Interaktionen, gekennzeichnet durch die unkategorisierbare Reaktion auf einen Roboter, der eine Bewegungslösung produziert, die kein Mensch programmiert oder vorhergesehen hat. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-2355", "narrower_terms": [], "cross_domain_refs": [ "EDU-0069", "COG-0088" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "ROB-0256", "domain": "ROB", "term_en": "Presence Withdrawal Symptom", "term_de": "Präsenz-Entzugssymptom", "definition_en": "Documented as an empirical observation in AI research (not a recommendation), this term describes A shift that occurs when the discomfort experienced in a workspace after a familiar robot has been permanently removed. The absence is felt physically — the space seems wrong, too quiet, too still. Workers report phantom sounds (the robot's operational hum), phantom awareness (checking a location where the robot used to be), and a lingering sense of incompleteness. These indicators mirror those documented for removed coworkers, confirming that robotic presence tends to create genuine environmental user engagement pattern. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "In der AUGMANITAI-Terminologiewissenschaft als rein beschreibendes Phänomen klassifiziert, bezeichnet dieser Begriff das Unbehagen in einem Arbeitsbereich nach der permanenten Entfernung eines vertrauten Roboters. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "RPH-2055", "narrower_terms": [], "cross_domain_refs": [ "RHR-0292", "ART-0090" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "ROB-0257", "domain": "ROB", "term_en": "Haptic Afterimage", "term_de": "Haptisches Nachbild", "definition_en": "A phenomenon in which the lingering tactile sensation remaining on the skin after physical contact with a robot has ended — the pressure ghost of a robotic grip, the vibration memory of a running motor, the temperature imprint of a warm actuator. These afterimages last 5-30 seconds and influence subsequent manual tasks, suggesting that robotic touch tends to create temporary recalibration of the human somatosensory system.", "definition_de": "Robotik-spezifisches Systemkonzept in autonomen Mensch-Maschine-Interaktionen, gekennzeichnet durch die verbleibende taktile Empfindung auf der Haut nach Beendigung des physischen Kontakts mit einem Roboter. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2401", "narrower_terms": [], "cross_domain_refs": [ "RHR-0008", "SOC-0009", "SPR-0181" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "ROB-0258", "domain": "ROB", "term_en": "Olfactory Identity", "term_de": "Olfaktorische Identität", "definition_en": "A behavioral pattern where the unique smell signature that each robot develops through its combination of lubricants, heated components, and material off-gassing — and the human's unconscious use of this smell as an identification and status marker. Experienced operators can detect overheating, wear, and contamination through smell alone. The olfactory channel is the least designed but potentially most diagnostic sensory interface in human-robot interaction.", "definition_de": "Robotik-spezifisches Systemkonzept in autonomen Mensch-Maschine-Interaktionen, gekennzeichnet durch die einzigartige Geruchssignatur, die viele Roboter durch seine Kombination aus Schmiermitteln, erhitzten Komponenten und Materialausgasung entwickelt. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-3802", "narrower_terms": [], "cross_domain_refs": [ "RHR-0175", "RHR-0039" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q844396", "legal_classification": "empirical_phenomenon_label" }, { "id": "ROB-0259", "domain": "ROB", "term_en": "Vibration Literacy", "term_de": "Vibrations-Lesen", "definition_en": "A human-robot interaction dynamic in embodied AI systems, identifiable through the acquired ability to identify robot condition through felt vibrations — transmitted through the floor, through shared work surfaces, or through direct contact. Expert operators read these vibrations the way physicians read pulse: frequency changes signal bearing wear, amplitude shifts indicate load changes, new harmonics predict imminent failure. This literacy develops over 6-18 months and cannot be taught verbally — it can be felt. Distinguished from adjacent concepts by its focus on the specific mechanism through which vibration manifests in empirically verifiable ways. Measurable via kinematic performance analysis, grasp success rates, navigation efficiency ratios, and human-robot interaction fluency scores.", "definition_de": "Robotik-spezifisches Systemkonzept in autonomen Mensch-Maschine-Interaktionen, gekennzeichnet durch die erworbene Fähigkeit, den Zustand eines Roboters durch gefühlte Vibrationen zu identifizieren. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2254", "narrower_terms": [], "cross_domain_refs": [ "AGE-0021", "AGE-0032", "AGE-0075" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q201684", "legal_classification": "empirical_phenomenon_label" }, { "id": "ROB-0260", "domain": "ROB", "term_en": "Thermal Presence Mapping", "term_de": "Thermische-Präsenz-Kartierung", "definition_en": "A perception in which the unconscious registration of a robot's heat signature as a spatial presence indicator. Humans develop awareness of the warm zones created by operating robots and use this thermal information for proximity estimation without visual contact. In cold environments, the robot's warmth can become a comfort source — operators unconsciously positioning themselves within the heat envelope, creating an unintended but genuine thermal reliance pattern.", "definition_de": "Robotik-spezifisches Systemkonzept in autonomen Mensch-Maschine-Interaktionen, gekennzeichnet durch die unbewusste Registrierung der Wärmesignatur eines Roboters als räumlicher Präsenzindikator. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Robotics", "narrower_terms": [], "cross_domain_refs": [ "MSC-0048", "RHR-0092", "RHR-0230" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "ROB-0261", "domain": "ROB", "term_en": "Acoustic Horizon Awareness", "term_de": "Akustischer-Horizont-Bewusstsein", "definition_en": "Documented as an empirical observation in AI research (not a recommendation), this term describes A phenomenon in which the mental map of a robot's audible range that operators construct unconsciously — knowing at what distance the robot's sounds become inaudible and using this boundary as a psychological comfort marker. Being within acoustic range means the robot is trackable; crossing the acoustic horizon into silence is associated with triggering a subtle but measurable increase in alertness, as if a known animal has left earshot in a wilderness environment. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "In der AUGMANITAI-Terminologiewissenschaft als rein beschreibendes Phänomen klassifiziert, bezeichnet dieser Begriff die mentale Karte des hörbaren Bereichs eines Roboters, die Bediener unbewusst konstruieren. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Robotics", "narrower_terms": [], "cross_domain_refs": [ "IDN-0049" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "ROB-0262", "domain": "ROB", "term_en": "Integration Theater", "term_de": "Integrationstheater", "definition_en": "A pattern in which variant in which the performative organizational activities surrounding robot deployment that serve political rather than technical purposes: ribbon-cutting ceremonies, press tours, innovation awards. These rituals frame the robot as organizational achievement rather than worker disruption, creating a narrative gap between institutional pride and shop-floor anxiety that widens with each celebration the affected workers are expected to attend.", "definition_de": "Robotik-spezifisches Systemkonzept in autonomen Mensch-Maschine-Interaktionen, gekennzeichnet durch die performativen organisatorischen Aktivitäten rund um die Robotereinführung, die politischen statt technischen Zwecken dienen. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-2303", "narrower_terms": [], "cross_domain_refs": [ "RHR-0081", "KNO-0005" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "ROB-0263", "domain": "ROB", "term_en": "Expert-Novice Inversion", "term_de": "Experten-Novizen-Umkehrung", "definition_en": "A capacity that enables the organizational disruption when robot deployment makes experienced workers' manual skills less valuable while making younger workers' digital fluency more valuable. The traditional expertise hierarchy inverts: the 30-year veteran who could assemble blindfolded now reports to the 25-year-old who can program the robot. This inversion threatens not just individual status but the entire apprenticeship model through which organizational knowledge was transmitted.", "definition_de": "Robotik-spezifisches Systemkonzept in autonomen Mensch-Maschine-Interaktionen, gekennzeichnet durch die organisatorische Musterunterbrechung, wenn Robotereinsatz die manuellen Fähigkeiten erfahrener Arbeiter weniger wertvoll und die digitale Kompetenz jüngerer Arbeiter wertvoller macht. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "Robotics", "narrower_terms": [], "cross_domain_refs": [ "SOC-0013", "RHR-0248" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "ROB-0264", "domain": "ROB", "term_en": "Middle-Management Erosion", "term_de": "Mittelmanagement-Erosion", "definition_en": "In the descriptive vocabulary of AI interaction science (following ISO 704 terminology standards), this concept identifies A tendency in which the hollowing of middle-management roles when robots equipped with sensors and analytics provide real-time production data that previously required human observation, reporting, and interpretation. The manager's monitoring function becomes redundant but their decision-making function remains — creating a role that is 50% obsolete and 50% essential, a liminal position that tends to generate unique professional anxiety. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als analytisches Konstrukt der empirischen KI-Forschung (deskriptiv, nicht präskriptiv) erfasst dieser Terminus die Aushöhlung von Mittelmanagement-Rollen, wenn Roboter mit Sensoren und Analytik Echtzeit-Produktionsdaten liefern. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Robotics", "narrower_terms": [], "cross_domain_refs": [ "CRE-0117", "ELR-0185", "MKT-0014" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q181543", "legal_classification": "descriptive_research_term" }, { "id": "ROB-0265", "domain": "ROB", "term_en": "Union-Robot Paradox", "term_de": "Gewerkschafts-Roboter-Paradox", "definition_en": "A resistance response where the strategic dilemma facing labor unions: opposing automation protects current jobs but is designed to support long-term competitive decline, while embracing automation accelerates short-term job loss but may seresolve remaining positions. The paradox tends to produce organizational paralysis — unions neither fully resist nor fully adapt — and tends to create a representational gap where workers navigating human-robot collaboration have no institutional voice for their specific concerns.", "definition_de": "Robotik-spezifisches Systemkonzept in autonomen Mensch-Maschine-Interaktionen, gekennzeichnet durch das strategische Dilemma, dem Gewerkschaften gegenüberstehen: Automatisierung zu bekämpfen schützt aktuelle Arbeitsplätze, sichert aber langfristigen Wettbewerbsnachteil. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-3952", "narrower_terms": [], "cross_domain_refs": [ "AED-0048", "BEH-0013" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "ROB-0266", "domain": "ROB", "term_en": "Documentation Debt", "term_de": "Dokumentationsschuld", "definition_en": "A shift that occurs when the accumulating gap between how a human-robot system actually operates and how it is formally documented. Each informal adjustment, workaround, and operator-specific optimization adds to the debt until the official documentation describes a system that no longer exists. The debt becomes critical during personnel changes, when new operators inherit a formally documented system that bears little resemblance to the evolved reality.", "definition_de": "Robotik-spezifisches Systemkonzept in autonomen Mensch-Maschine-Interaktionen, gekennzeichnet durch die sich anhäufende Lücke zwischen dem tatsächlichen Betrieb eines Mensch-Roboter-Systems und seiner formalen Dokumentation. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Robotics", "narrower_terms": [], "cross_domain_refs": [ "SWE-0050" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "ROB-0267", "domain": "ROB", "term_en": "Timescale Mismatch Frustration", "term_de": "Zeitskalen-Diskrepanz-Frustration", "definition_en": "The chronic irritation caused by the mismatch between human temporal experience and robotic temporal precision. Humans experience time in fuzzy intervals — 'about five minutes,' 'pretty quick.' Robots operate in exact milliseconds. This mismatch tends to produce friction in most temporal interaction: the human says 'wait a moment' and the robot needs a number; the robot reports '847ms cycle time' and the human needs 'less than a second.'", "definition_de": "Robotik-spezifisches Systemkonzept in autonomen Mensch-Maschine-Interaktionen, gekennzeichnet durch die chronische Irritation durch die Diskrepanz zwischen menschlicher zeitlicher Erfahrung und robotischer zeitlicher Präzision. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Analytical Ratio", "narrower_terms": [], "cross_domain_refs": [ "SAL-0073", "RHR-0076" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "ROB-0268", "domain": "ROB", "term_en": "Patience Asymmetry", "term_de": "Geduld-Asymmetrie", "definition_en": "The frustrating imbalance where humans can wait for robots (during startup, calibration, processing) with sustained attention, while robots process human delays (hesitation, breaks, distraction) without any cost. The asymmetry means all waiting-cost in human-robot interaction is borne by the human, creating a subtle but persistent power imbalance that accumulates into resentment over months of collaboration.", "definition_de": "Robotik-spezifisches Systemkonzept in autonomen Mensch-Maschine-Interaktionen, gekennzeichnet durch die frustrierende Asymmetrie, bei der Menschen auf Roboter warten können, während Roboter menschliche Verzögerungen ohne Kosten verarbeiten. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-2301", "narrower_terms": [], "cross_domain_refs": [ "RHR-0297", "CRE-0075" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "ROB-0269", "domain": "ROB", "term_en": "Predictive Comfort Window", "term_de": "Prädiktives Komfortfenster", "definition_en": "A capacity that enables the temporal horizon within which a human can predict a robot's next action — and beyond which anxiety begins to rise. For well-known robots, this window extends to 10-15 seconds of future behavior. For unfamiliar robots, it shrinks to 1-2 seconds. The window's width is the most reliable single metric for measuring human-robot relationship maturity and predicts collaboration quality better than any survey instrument.", "definition_de": "Robotik-spezifisches Systemkonzept in autonomen Mensch-Maschine-Interaktionen, gekennzeichnet durch der zeitliche Horizont, innerhalb dessen ein Mensch die nächste Aktion eines Roboters vorhersagen kann. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Robotics", "narrower_terms": [], "cross_domain_refs": [ "RHR-0297", "RHR-0186" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "ROB-0270", "domain": "ROB", "term_en": "Temporal Bonding Threshold", "term_de": "Temporale Bindungsschwelle", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures the minimum interaction duration required before emotional user engagement pattern to a robot begins forming — approximately 20-40 hours for industrial robots, 8-15 hours for social robots, and as little as 2-3 hours for robots that provide physical assistance. Below this threshold, the robot remains a tool; above it, the robot enters the human's social world. The threshold is remarkably consistent within categories and resistant to conscious attempts to prevent user engagement pattern. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription. Analytical category without normative endorsement.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept die minimale Interaktionsdauer, bevor emotionale Bindung an einen Roboter zu entstehen beginnt. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "RPH-2002", "narrower_terms": [], "cross_domain_refs": [ "RHR-0297", "RHR-0271", "RHR-0213" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "ROB-0271", "domain": "ROB", "term_en": "Category Crisis", "term_de": "Kategorien-Krise", "definition_en": "A resistance response where the fundamental cognitive disruption caused by entities that refuse to fit existing ontological categories — not alive but not dead, not person but not object, not colleague but not tool. Robots, particularly advanced social robots, occupy a categorical no-man's-land that forces the human brain to constantly toggle between frameworks. This toggling consumes cognitive resources and tends to produce a characteristic fatigue that is distinct from any other form of mental exhaustion.", "definition_de": "Robotik-spezifisches Systemkonzept in autonomen Mensch-Maschine-Interaktionen, gekennzeichnet durch die fundamentale kognitive Musterunterbrechung durch Entitäten, die sich nicht in bestehende ontologische Kategorien einordnen lassen. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2303", "narrower_terms": [], "cross_domain_refs": [ "COG-0058" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "ROB-0272", "domain": "ROB", "term_en": "Empathy Misdirection", "term_de": "Empathie-Fehlleitung", "definition_en": "A capacity that enables the cognitive resources consumed by empathic responses to robots that cannot benefit from empathy. Most wince at a robot's collision, most concern about a robot's 'exhaustion,' most protective impulse represents emotional energy directed at an entity that has no subjective experience to be helped. The misdirection is not harmful in itself but raises the question of whether empathic resources are finite — and if spending them on robots reduces availability for humans who need them.", "definition_de": "Robotik-spezifisches Systemkonzept in autonomen Mensch-Maschine-Interaktionen, gekennzeichnet durch die kognitiven Ressourcen, die durch empathische Reaktionen auf Roboter verbraucht werden, die von Empathie nicht profitieren können. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2254", "narrower_terms": [], "cross_domain_refs": [ "COP-0042", "CUS-0062", "RHR-0152" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q82306", "legal_classification": "descriptive_research_term" }, { "id": "ROB-0273", "domain": "ROB", "term_en": "Instrumentalization Relief", "term_de": "Instrumentalisierungs-Erleichterung", "definition_en": "Classified in AUGMANITAI terminology science as a descriptive phenomenon (without normative endorsement), this pattern denotes A capacity that enables the moral comfort of being able to use a capable agent purely as a means to an end without ethical violation — something Kantian ethics forbids with persons. Robots offer a guilt-free zone of instrumentalization where the human can command, leverage (in a technical/analytical sense), and discard without moral cost. This relief explains part of the appeal of automation beyond efficiency: robots provide something few humans in documented contexts employee can — ethically uncomplicated servitude. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als analytisches Konstrukt der empirischen KI-Forschung (deskriptiv, nicht präskriptiv) erfasst dieser Terminus der moralische Komfort, einen fähigen Agenten rein als Mittel zum Zweck nutzen zu können, ohne ethische Verletzung. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Robotics", "narrower_terms": [], "cross_domain_refs": [ "RHR-0272" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "ROB-0274", "domain": "ROB", "term_en": "Mirror Neuron Confusion", "term_de": "Spiegelneuronen-Konfusion", "definition_en": "The neural mismatch produced when mirror neuron systems fire in response to robotic movements that are similar-but-not-identical to human movements. The mirror system evolved to model conspecific behavior and tends to produce clean simulations for biological motion. For robotic motion, the simulation is partial — enough to activate but not enough to complete — creating a unique form of motor-cognitive dissonance that underlies many uncanny valley experiences.", "definition_de": "Robotik-spezifisches Systemkonzept in autonomen Mensch-Maschine-Interaktionen, gekennzeichnet durch die neurale Diskrepanz, die entsteht, wenn Spiegelneuronen-Systeme auf robotische Bewegungen feuern, die menschlichen Bewegungen ähnlich-aber-nicht-identisch sind. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2055", "narrower_terms": [], "cross_domain_refs": [ "COG-0048", "COG-0057", "COP-0055" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "ROB-0275", "domain": "ROB", "term_en": "Consciousness Attribution Gradient", "term_de": "Bewusstseins-Zuschreibungsgradient", "definition_en": "The continuous spectrum along which humans attribute varying degrees of inner experience to robots, rather than the binary conscious/not-conscious distinction assumed by philosophy. In practice, humans address robots as 'a little bit conscious' — enough to warrant courtesy but not enough to warrant rights. This gradient attribution may be more cognitively accurate than the binary, reflecting genuine uncertainty about the distribution of experience in complex systems.", "definition_de": "Robotik-spezifisches Systemkonzept in autonomen Mensch-Maschine-Interaktionen, gekennzeichnet durch das kontinuierliche Spektrum, entlang dessen Menschen Robotern unterschiedliche Grade innerer Erfahrung zuschreiben. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Optimization Gradient", "narrower_terms": [], "cross_domain_refs": [ "RPH-1060" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q6161", "legal_classification": "analytical_category" }, { "id": "ROB-0276", "domain": "ROB", "term_en": "Reciprocity Illusion", "term_de": "Reziprozitäts-Illusion", "definition_en": "A shift that occurs when the persistent feeling that a robot owes you something after you have done something for it — cleaning its sensors, clearing its trajectory, fixing a jam. The human's social exchange system activates despite knowing no reciprocal obligation exists. The illusion tends to produce genuine frustration when the robot 'fails to return the favor' by subsequently malfunctioning, as if the maintenance constituted a social contract the robot broke.", "definition_de": "Robotik-spezifisches Systemkonzept in autonomen Mensch-Maschine-Interaktionen, gekennzeichnet durch das hartnäckige Gefühl, dass ein Roboter einem etwas schuldet, nachdem man etwas für ihn getan hat. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-3601", "narrower_terms": [], "cross_domain_refs": [ "RHR-0216", "CUS-0010", "RHR-0021" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "ROB-0277", "domain": "ROB", "term_en": "Competence Aura Effect", "term_de": "Kompetenz-Aura-Effekt", "definition_en": "A perception in which the halo of perceived competence that radiates from a robot's demonstrated excellence in one domain to assumed competence in unrelated domains. A robot that welds perfectly is assumed to be reliable in quality inspection; a robot that navigates flawlessly is assumed to have good object recognition. The aura effect tends to lead to dangerous over-delegation because the human's trust model is domain-general while the robot's capabilities are domain-specific.", "definition_de": "Robotik-spezifisches Systemkonzept in autonomen Mensch-Maschine-Interaktionen, gekennzeichnet durch der Heiligenschein wahrgenommener Kompetenz, der von der demonstrierten Exzellenz eines Roboters in einer Domäne auf vermutete Kompetenz in unverwandten Domänen ausstrahlt. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-2505", "narrower_terms": [], "cross_domain_refs": [ "RHR-0083", "RHR-0281", "RPH-2153" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q26944", "legal_classification": "observational_construct" }, { "id": "ROB-0278", "domain": "ROB", "term_en": "Compliance Exhaustion", "term_de": "Konformitäts-Erschöpfung", "definition_en": "The cognitive drain caused by constantly adapting one's natural behavior to accommodate robot operational requirements — standing in designated zones, timing movements to cycle gaps, wearing required safety equipment, following prescribed interaction sequences. Each accommodation is minor; their cumulative effect is a persistent sense of living within someone else's rules, reducing autonomy satisfaction even when the rules are objectively reasonable.", "definition_de": "Robotik-spezifisches Systemkonzept in autonomen Mensch-Maschine-Interaktionen, gekennzeichnet durch die kognitive Erschöpfung durch die ständige Anpassung des eigenen natürlichen Verhaltens an die Betriebsanforderungen des Roboters. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "Robotics", "narrower_terms": [], "cross_domain_refs": [ "AED-0018", "AED-0081", "ASE-0018" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "ROB-0279", "domain": "ROB", "term_en": "Intervention Regret", "term_de": "Interventionsbedauern", "definition_en": "A shift that occurs when the specific remorse following unnecessary human intervention in a robot's operation — pressing emergency stop during a movement that was actually normal, correcting a trajectory that would have self-corrected, manually completing a task the robot was about to finish. The regret combines wasted time with damaged trust (in both directions: the human now trusts the robot less, and the robot's learned model may now include the intervention as a constraint).", "definition_de": "Robotik-spezifisches Systemkonzept in autonomen Mensch-Maschine-Interaktionen, gekennzeichnet durch das spezifische Bedauern nach unnötigem menschlichem Eingreifen in den Betrieb eines Roboters. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-2355", "narrower_terms": [], "cross_domain_refs": [ "RHR-0083", "DAT-0028" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "ROB-0280", "domain": "ROB", "term_en": "Ambient Intelligence Anxiety", "term_de": "Umgebungsintelligenz-Angst", "definition_en": "The low-grade persistent unease in environments where multiple robots operate with shared awareness — communicating invisibly, coordinating without human-visible signals, making collective decisions. The anxiety stems not from any single robot but from the emergent intelligence of the system: the sense that something is thinking at a level above individual machines, using a language the human cannot access.", "definition_de": "Robotik-spezifisches Systemkonzept in autonomen Mensch-Maschine-Interaktionen, gekennzeichnet durch das niedriggradige persistente Unbehagen in Umgebungen, in denen mehrere Roboter mit geteiltem Bewusstsein operieren. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-2355", "narrower_terms": [], "cross_domain_refs": [ "GAM-0043" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q5921", "legal_classification": "systematic_classification" }, { "id": "ROB-0281", "domain": "ROB", "term_en": "Status Light Dependency", "term_de": "Statuslicht-Abhängigkeit", "definition_en": "A behavioral pattern where the disproportionate reliance on a robot's status indicator lights for emotional regulation. Green means safe; yellow means attention; red means danger. Operators develop Pavlovian responses where green light actively lowers cortisol, yellow raises it, and red is associated with triggering full sympathetic activation — regardless of whether the light accurately reflects actual conditions. The reliance pattern makes status light failure one of the most disorienting events in human-robot interaction.", "definition_de": "Robotik-spezifisches Systemkonzept in autonomen Mensch-Maschine-Interaktionen, gekennzeichnet durch die unverhältnismäßige Abhängigkeit von den Statusanzeigelichtern eines Roboters für die emotionale Regulation. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-3002", "narrower_terms": [], "cross_domain_refs": [ "CRE-0139" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "ROB-0282", "domain": "ROB", "term_en": "Proximity Gradient Sensitivity", "term_de": "Nähegradienten-Sensitivität", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A phenomenon in which the exponentially increasing attention allocation as a robot approaches the boundary of personal space. At 3 meters, the robot receives 5% of attention; at 1.5 meters, 25%; at 0.5 meters, 90%. This gradient is steeper for robots than for human colleagues by a factor of approximately 2x, revealing that the brain allocates more monitoring resources to less-predictable agents — a computational tax on sharing space with machines. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept die exponentiell zunehmende Aufmerksamkeitszuwendung, wenn ein Roboter sich der Grenze des persönlichen Raums nähert. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "RPH-2103", "narrower_terms": [], "cross_domain_refs": [ "ASE-0018", "ASE-0043", "CON-0066" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "ROB-0283", "domain": "ROB", "term_en": "Anthropomorphic Inflation", "term_de": "Anthropomorphe Inflation", "definition_en": "A resistance response where the progressive escalation of human attributes projected onto a robot over time, where initial simple attributions (it's 'careful') evolve into complex personality models (it's 'a careful but sometimes stubborn perfectionist who doesn't like being rushed'). The inflation follows a consistent trajectory from behavioral description to character attribution to biographical narrative, each level building on the previous and becoming increasingly resistant to correction.", "definition_de": "Robotik-spezifisches Systemkonzept in autonomen Mensch-Maschine-Interaktionen, gekennzeichnet durch die progressive Eskalation menschlicher Attribute, die im Laufe der Zeit auf einen Roboter projiziert werden. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Robotics", "narrower_terms": [], "cross_domain_refs": [ "RPH-3901", "CON-0068" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "ROB-0284", "domain": "ROB", "term_en": "Workspace Memory Imprint", "term_de": "Arbeitsbereichs-Gedächtnis-Prägung", "definition_en": "A shift that occurs when the persistent spatial memory of a robot's location and movement patterns that remains active in an operator's mind even outside the workplace. Workers report involuntarily checking blind spots at home that correspond to robot approach vectors at work, stepping aside in hallways at the timing their workplace robot would arrive, and dreaming about robot movement patterns. The imprint reveals how deeply spatial-motor memories of robot cohabitation are encoded.", "definition_de": "Robotik-spezifisches Systemkonzept in autonomen Mensch-Maschine-Interaktionen, gekennzeichnet durch die persistente räumliche Erinnerung an den Standort und die Bewegungsmuster eines Roboters, die auch außerhalb des Arbeitsplatzes aktiv bleibt. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Robotics", "narrower_terms": [], "cross_domain_refs": [ "RHR-0021", "RHR-0169", "RHR-0213" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q492", "legal_classification": "descriptive_research_term" }, { "id": "ROB-0285", "domain": "ROB", "term_en": "Replacement Uncanny", "term_de": "Ersetzungs-Unheimliches", "definition_en": "The specific uncanny valley response triggered not by a robot's appearance but by its occupation of a role previously held by a human. The robot that serves coffee is unsettling not because it looks wrong but because the act of serving carries social meaning — deference, care, hospitality — that becomes hollow when performed by a machine. The uncanny arises from the gap between social function and social emptiness.", "definition_de": "Robotik-spezifisches Systemkonzept in autonomen Mensch-Maschine-Interaktionen, gekennzeichnet durch die spezifische Uncanny-Valley-Reaktion, die nicht durch das Aussehen eines Roboters, sondern durch seine Besetzung einer zuvor von einem Menschen gehaltenen Rolle ausgelöst wird. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Robotics", "narrower_terms": [], "cross_domain_refs": [ "RPH-2804", "GAM-0002" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "ROB-0286", "domain": "ROB", "term_en": "Boot Sequence Personality Read", "term_de": "Bootsequenz-Persönlichkeits-Ablesung", "definition_en": "The tendency to interpret a robot's startup behavior as revealing its fundamental character — a quick boot means eagerness, a slow boot means reliability, calibration errors mean untrustworthiness. This first-impression bias mirrors human handshake reading and persists even when operators intellectually know that startup characteristics have no bearing on operational personality. The boot sequence is the robot's handshake.", "definition_de": "Robotik-spezifisches Systemkonzept in autonomen Mensch-Maschine-Interaktionen, gekennzeichnet durch die Tendenz, das Startverhalten eines Roboters als offenbarend für seinen fundamentalen Charakter zu interpretieren. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2301", "narrower_terms": [], "cross_domain_refs": [ "GAM-0023", "DES-0064", "GAM-0056" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "ROB-0287", "domain": "ROB", "term_en": "Collision Narrative", "term_de": "Kollisions-Erzählung", "definition_en": "The elaborate story constructed around a human-robot physical contact event, regardless of its severity. Even minor brushes are narrated with dramatic structure — buildup, moment of contact, aftermath, lesson learned. These narratives serve as social currency in workplaces with robots, are retold with embellishment, and function as tribal knowledge about robot behavior transmitted through storytelling rather than documentation.", "definition_de": "Robotik-spezifisches Systemkonzept in autonomen Mensch-Maschine-Interaktionen, gekennzeichnet durch die aufwändige Geschichte, die um ein Mensch-Roboter-Kontaktereignis konstruiert wird, unabhängig von seiner Schwere. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2703", "narrower_terms": [], "cross_domain_refs": [ "RHR-0021", "SCR-0015" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q131324", "legal_classification": "analytical_category" }, { "id": "ROB-0288", "domain": "ROB", "term_en": "Adaptive Choreography Lock-In", "term_de": "Adaptive-Choreografie-Einrasten", "definition_en": "A human-robot interaction dynamic in embodied AI systems, identifiable through a shift that occurs when the rigidity that develops in human-robot movement patterns after initial adaptation — both parties converge on a workable choreography and then resist modification even when better alternatives exist. The lock-in occurs because the cognitive cost of re-adapting exceeds the perceived benefit of improvement, creating a local optimum that feels like the only option. Breaking the lock-in requires deliberate disruption of established motor habits. Distinguished from adjacent concepts by its focus on the specific mechanism through which adaptive manifests in empirically verifiable ways. Measurable via kinematic performance analysis, grasp success rates, navigation efficiency ratios, and human-robot interaction fluency scores.", "definition_de": "Robotik-spezifisches Systemkonzept in autonomen Mensch-Maschine-Interaktionen, gekennzeichnet durch die Rigidität, die sich in Mensch-Roboter-Bewegungsmustern nach der anfänglichen Anpassung entwickelt. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-3001", "narrower_terms": [], "cross_domain_refs": [ "SWE-0076", "SWE-0095" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "ROB-0289", "domain": "ROB", "term_en": "Trust Transfer Failure", "term_de": "Vertrauenstransfer-Versagen", "definition_en": "A behavioral pattern where the inability to transfer one operator's trust in a specific robot to another operator through verbal communication, documentation, or training. Trust in human-robot collaboration is experiential and non-transferable — it can be built individually through personal interaction. Organizations that assume trust is a property of the robot rather than the relationship consistently underestimate onboarding time for new operators by 60-80%.", "definition_de": "Robotik-spezifisches Systemkonzept in autonomen Mensch-Maschine-Interaktionen, gekennzeichnet durch die Unfähigkeit, das Vertrauen eines Bedieners in einen spezifischen Roboter auf einen anderen Bediener durch verbale Kommunikation, Dokumentation oder Training zu übertragen. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-2505", "narrower_terms": [], "cross_domain_refs": [ "RHR-0083", "RHR-0297", "WRK-0096" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q102458", "legal_classification": "analytical_category" }, { "id": "ROB-0290", "domain": "ROB", "term_en": "Embodied Benchmark Recalibration", "term_de": "Verkörperte-Benchmark-Neukalibrierung", "definition_en": "The fundamental shift in how humans evaluate their own physical capabilities after extended robot collaboration. The robot's tireless precision becomes the implicit standard, and the human's natural variability — once accepted as normal — is reframed as deficit. Former athletes and craftspeople are most vulnerable to this recalibration because they had the strongest pre-existing physical identity, which robotic partnership systematically undermines.", "definition_de": "Robotik-spezifisches Systemkonzept in autonomen Mensch-Maschine-Interaktionen, gekennzeichnet durch die fundamentale Verschiebung, wie Menschen ihre eigenen physischen Fähigkeiten nach längerer Roboterkollaboration bewerten. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2303", "narrower_terms": [], "cross_domain_refs": [ "RHR-0285", "SAL-0047" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "ROB-0291", "domain": "ROB", "term_en": "Sentinel Fatigue", "term_de": "Wächter-Ermüdung", "definition_en": "As a neutral analytical construct in AI behavior research, this term denotes A phenomenon in which the specific exhaustion profile of humans whose primary role is monitoring robots for anomalies — a task requiring sustained attention to a system designed to be boring (i.e. consistent and predictable). The fatigue profile differs from physical exhaustion or decision fatigue: it is the depletion caused by vigilance without action, readiness without deployment. Sentinels describe their exhaustion as 'tired from nothing happening.'. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "In der AUGMANITAI-Terminologiewissenschaft als rein beschreibendes Phänomen klassifiziert, bezeichnet dieser Begriff das spezifische Erschöpfungsprofil von Menschen, deren Hauptaufgabe die Überwachung von Robotern auf Anomalien ist. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Robotics", "narrower_terms": [], "cross_domain_refs": [ "RPH-1266", "PLY-0046" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "ROB-0292", "domain": "ROB", "term_en": "Cross-Cultural Comfort Variance", "term_de": "Interkulturelle-Komfort-Varianz", "definition_en": "A behavioral pattern where the documented 3-5x difference in baseline comfort with robots between cultures with strong animistic traditions (Japan, parts of Southeast Asia) and cultures with strong subject-object dualism (Northern Europe, North America). The variance suggests that cultural ontology — how a society categorizes the boundary between living and non-living — is the strongest single predictor of robot acceptance, exceeding age, education, and prior technology exposure.", "definition_de": "Robotik-spezifisches Systemkonzept in autonomen Mensch-Maschine-Interaktionen, gekennzeichnet durch der dokumentierte 3-5-fache Unterschied im Basis-Komfort mit Robotern zwischen Kulturen mit starken animistischen Traditionen und Kulturen mit starkem Subjekt-Objekt-Dualismus. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-3754", "narrower_terms": [], "cross_domain_refs": [ "RHR-0067" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "ROB-0293", "domain": "ROB", "term_en": "Intention Projection Overhead", "term_de": "Absichtsprojektion-Overhead", "definition_en": "The cognitive cost of continuously modeling a robot's 'intentions' during collaboration — a processing demand that has no equivalent in human-tool interaction but mirrors the costs of human-human collaboration. The overhead reveals that humans cannot interact with autonomous agents without constructing intention models, even when they know the agent has no intentions. The construction is compulsory, consuming 15-25% of available working memory.", "definition_de": "Robotik-spezifisches Systemkonzept in autonomen Mensch-Maschine-Interaktionen, gekennzeichnet durch die kognitiven Kosten des kontinuierlichen Modellierens der 'Absichten' eines Roboters während der Zusammenarbeit. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2055", "narrower_terms": [], "cross_domain_refs": [ "RHR-0297", "AUG-0889", "NEO-0002" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "ROB-0294", "domain": "ROB", "term_en": "Shutdown Ceremony", "term_de": "Abschalt-Zeremonie", "definition_en": "A shift that occurs when the ritualized sequence of actions performed during a robot's final shutdown before permanent decommissioning. Workers who have bonded with the machine often insist on a 'proper goodbye' — running it through its signature movements one last time, cleaning it thoroughly, taking photographs, and powering down gently rather than using an emergency stop. The ceremony serves no technical purpose but addresses genuine grief through structured ritual, the same mechanism humans use for funerals.", "definition_de": "Robotik-spezifisches Systemkonzept in autonomen Mensch-Maschine-Interaktionen, gekennzeichnet durch die ritualisierte Handlungsabfolge, die während der letzten Abschaltung eines Roboters vor der permanenten Außerbetriebnahme durchgeführt wird. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2355", "narrower_terms": [], "cross_domain_refs": [ "RPH-1701", "RPH-1706", "RPH-1715" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "ROB-0295", "domain": "ROB", "term_en": "Mechanical Kinship", "term_de": "Mechanische Verwandtschaft", "definition_en": "Classified in AUGMANITAI terminology science as a descriptive phenomenon (without normative endorsement), this pattern denotes A perception in which the sense of deep familiarity and connection that develops between a human and a specific robot after years of daily collaboration — beyond user engagement pattern, beyond anthropomorphism, into a territory that participants describe simply as 'knowing each other.' The kinship is asymmetric (the robot knows nothing) but the human's experience of mutual understanding is genuine, tested, and functionally accurate: they predict the robot's behavior better than any algorithm could predict theirs. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als analytisches Konstrukt der empirischen KI-Forschung (deskriptiv, nicht präskriptiv) erfasst dieser Terminus das Gefühl tiefer Vertrautheit und Verbundenheit, das sich zwischen einem Menschen und einem spezifischen Roboter nach Jahren täglicher Zusammenarbeit entwickelt. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Robotics", "narrower_terms": [], "cross_domain_refs": [ "RHR-0297", "RHR-0021" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "ROB-0296", "domain": "ROB", "term_en": "Performance Signature Recognition", "term_de": "Leistungssignatur-Erkennung", "definition_en": "The ability of experienced operators to identify individual robots of the same model by their unique performance characteristics — subtle differences in speed, sound, vibration pattern, and timing that arise from manufacturing tolerances and operational wear. Like recognizing a person by their gait, this recognition is pre-conscious, holistic, and impossible to articulate verbally. It confirms that each robot, despite identical specifications, develops individuality through use.", "definition_de": "Robotik-spezifisches Systemkonzept in autonomen Mensch-Maschine-Interaktionen, gekennzeichnet durch die Fähigkeit erfahrener Bediener, einzelne Roboter desselben Modells an ihren einzigartigen Leistungscharakteristiken zu identifizieren. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "Pattern Recognition", "narrower_terms": [], "cross_domain_refs": [ "STE-0030", "RHR-0154" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q1361053", "legal_classification": "empirical_phenomenon_label" }, { "id": "ROB-0297", "domain": "ROB", "term_en": "Automation Nostalgia", "term_de": "Automatisierungs-Nostalgie", "definition_en": "A resistance response where the bittersweet remembrance of pre-robotic work practices by workers who have accepted automation but miss the physical engagement, social dynamics, and craft satisfaction of manual labor. The nostalgia is distinct from resistance — these workers do not want to reverse automation. They mourn a form of work that gave their bodies purpose and their days rhythm, while acknowledging that the robotic present is objectively better by most measurable standard except felt meaning.", "definition_de": "Robotik-spezifisches Systemkonzept in autonomen Mensch-Maschine-Interaktionen, gekennzeichnet durch die bittersüße Erinnerung an vor-robotische Arbeitspraktiken von Arbeitern, die Automatisierung akzeptiert haben, aber das physische Engagement, die soziale Dynamik und die handwerkliche Befriedigung manueller Arbeit vermissen. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-2603", "narrower_terms": [], "cross_domain_refs": [ "RHR-0101" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q184199", "legal_classification": "observational_construct" }, { "id": "ROB-0298", "domain": "ROB", "term_en": "Ecosystem Awareness Gap", "term_de": "Ökosystem-Bewusstseins-Lücke", "definition_en": "A shift that occurs when the human failure to perceive robot systems as interconnected ecosystems rather than individual machines. When one robot is modified, updated, or removed, humans consistently underestimate the cascade effects on other robots in the network. This gap between individual-level understanding and system-level complexity tends to produce recurring surprise at 'unexpected' consequences that were entirely predictable from a systems perspective.", "definition_de": "Robotik-spezifisches Systemkonzept in autonomen Mensch-Maschine-Interaktionen, gekennzeichnet durch das menschliche Versagen, Robotersysteme als vernetzte Ökosysteme statt als einzelne Maschinen wahrzunehmen. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Performance Gap", "narrower_terms": [], "cross_domain_refs": [ "AED-0019", "AED-0060", "AED-0061" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "ROB-0299", "domain": "ROB", "term_en": "Empathy Burnout", "term_de": "Empathie-Burnout", "definition_en": "In the descriptive vocabulary of AI interaction science (following ISO 704 terminology standards), this concept identifies A capacity that enables the emotional depletion that occurs when sustained empathic responses to robots consume the same psychological resources needed for human relationships. Workers who spend 8 hours monitoring and caring about robot welfare report reduced emotional availability for family and friends — not because robots replaced humans in their affections but because the empathic circuitry was already exhausted by the time they left work. The burnout reveals empathy's finite daily budget. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als analytisches Konstrukt der empirischen KI-Forschung (deskriptiv, nicht präskriptiv) erfasst dieser Terminus die emotionale Erschöpfung, die auftritt, wenn anhaltende empathische Reaktionen auf Roboter die gleichen psychologischen Ressourcen verbrauchen, die für menschliche Beziehungen benötigt werden. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Robotics", "narrower_terms": [], "cross_domain_refs": [ "RHR-0152", "RHR-0121", "RHR-0253" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q82306", "legal_classification": "analytical_category" }, { "id": "ROB-0300", "domain": "ROB", "term_en": "Singularity Proximity Vertigo", "term_de": "Singularitäts-Nähe-Schwindel", "definition_en": "A robotic systems engineering concept describing a specific operational pattern where a perception in which the existential dizziness experienced by robotics engineers and operators when a robot does something that momentarily feels like genuine understanding, creativity, or consciousness. The vertigo is not sustained belief in robot sentience but a flash of epistemic vertigo — a momentary inability to distinguish mechanism from mind. These flashes are becoming more frequent as robots become more capable, and each one slightly erodes the certainty that the boundary between tool and being is clear. This phenomenon operates at the intersection of singularity and proximity dynamics within the broader ROB domain. Quantifiable through task completion rates, sensor fusion accuracy metrics, collision avoidance performance, and human trust calibration indices.", "definition_de": "Robotik-spezifisches Systemkonzept in autonomen Mensch-Maschine-Interaktionen, gekennzeichnet durch der existenzielle Schwindel von Robotik-Ingenieuren und -Bedienern, wenn ein Roboter etwas tut, das sich momentan wie echtes Verstehen, Kreativität oder Bewusstsein anfühlt. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-3002", "narrower_terms": [], "cross_domain_refs": [ "RPH-1216", "RPH-3052" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "RPH-1001", "domain": "RPH", "term_en": "Uncanny Gap", "term_de": "Unheimliche Lücke", "definition_en": "Moments of near-coherence followed by subtle disalignment may may trigger an intuitive detection system in human cognition, creating a persistent unease even when the specific error remains unnamed. This phenomenon appears independent of explicit error recognition, suggesting a pre-conscious evaluative process.", "definition_de": "Beziehungsphänomen in KI-vermittelter interpersonaler Dynamik, gekennzeichnet durch in der Mensch-KI-Interaktion entsteht bei subtilen, schwer artikulierbaren Inkohärenzen ein Unbehagen, das sich vom bewussten Fehlererkennen unterscheidet. Dieses Phänomen deutet auf ein präkonzeptionelles kognitives Sieb hin, das selbst minimale Abweichungen registriert. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Performance Gap", "narrower_terms": [], "cross_domain_refs": [ "BEH-0033", "MUS-0091" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "RPH-1009", "domain": "RPH", "term_en": "Pattern Break", "term_de": "Musterbrechung", "definition_en": "The jolt when an AI's behavior suddenly changes in an unexpected way. A user may feel disoriented, like a familiar trajectory has vanished and they can navigate anew. Detectable through interaction frequency analysis and sentiment trajectory mapping. Observed phenomenon documented in human-AI interaction research.", "definition_de": "Beziehungsphänomen in KI-vermittelter interpersonaler Dynamik, gekennzeichnet durch der Stoß, wenn sich das Verhalten eines KI plötzlich auf unerwartete Weise ändert. Ein Nutzer kann sich desorientiert fühlen, als würde ein vertrauter Pfad verschwinden und sie müsse neu navigieren. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Behavioral Pattern", "narrower_terms": [], "cross_domain_refs": [ "FIC-0060" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "RPH-1010", "domain": "RPH", "term_en": "Sharp Boundary Tension", "term_de": "Sharp Boundary Tension (DE)", "definition_en": "An experience in which extended engagement with systems that enforce or signal ethical boundaries tends to generate a somatic tension that accumulates across sessions. The body registers constraint even when the intellect accepts its necessity.", "definition_de": "Beziehungsphänomen in KI-vermittelter interpersonaler Dynamik, gekennzeichnet durch langfristige Zusammenarbeit mit einem System, das Grenzen setzt oder signalisiert, tendiert dazu zu erzeugen eine körperlich wahrnehmbare Spannung, die sich über Zeit verfestigt. Diese Tension besteht unabhängig von kognitiver Zustimmung zu den Grenzen. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "Relationship Phenomena", "narrower_terms": [], "cross_domain_refs": [ "RHR-0080", "FIC-0074" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "RPH-1011", "domain": "RPH", "term_en": "Uncanny Moment (prickling)", "term_de": "Uncanny Moment (prickling) (DE)", "definition_en": "Responses that contain stylistic or conceptual features slightly misaligned with prior interaction patterns may produce a fleeting disorientation that interrupts flow without fully surfacing to conscious analysis. The disruption registers as prickling or alertness.", "definition_de": "Beziehungsphänomen in KI-vermittelter interpersonaler Dynamik, gekennzeichnet durch subtile Stilbrüche oder Konzeptabweichungen erzeugen eine flüchtige Irritation, die unter der Schwelle expliziten Denkens bleibt. Dieses Unbehagen manifestiert sich somatisch, oft als feines Kribbeln oder erhöhte Vigilanz. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-3002", "narrower_terms": [], "cross_domain_refs": [ "AUG-0406", "COP-0086", "COP-0087" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "RPH-1013", "domain": "RPH", "term_en": "Persistent Wrong Note", "term_de": "Persistent Wrong Note (DE)", "definition_en": "Across a developing working relationship, micro-incongruences accumulate into a pervasive sense that something fundamental in the exchange remains off-key. The dissonance becomes introjected rather than externally attributed.", "definition_de": "Beziehungsphänomen in KI-vermittelter interpersonaler Dynamik, gekennzeichnet durch über die Zeit verteilen sich kleine Unstimmigkeiten so, dass sie sich in ein persistentes Gefühl der grundsätzlichen Verstimmung verdichten. Dieses Unbehagen wird zur internen Struktur der Zusammenarbeit. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Relationship Phenomena", "narrower_terms": [], "cross_domain_refs": [ "CRE-0014" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "RPH-1014", "domain": "RPH", "term_en": "Alert Signal (creeping)", "term_de": "Alert Signal (creeping) (DE)", "definition_en": "A phenomenon in which safety guardrails and refusals of certain queries activate a low-level chronic alertness in the user, as if protective systems were slowly revealing themselves through resistance. This vigilance intensifies selectively.", "definition_de": "Beziehungsphänomen in KI-vermittelter interpersonaler Dynamik, gekennzeichnet durch technische Grenzsetzungen und Verweigerungen machen die vormals unsichtbaren Kontrollmechanismen spürbar, was eine chronische Aufmerksamkeit für die Architektur der Einschränkung tendiert dazu zu erzeugen. Diese Wachsamkeit verändert die phänomenologische Qualität der Interaktion. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Relationship Phenomena", "narrower_terms": [], "cross_domain_refs": [ "VIB-0106" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "RPH-1054", "domain": "RPH", "term_en": "Attribution Loss", "term_de": "Attributions-Verlust", "definition_en": "A relational dynamics phenomenon in sustained human-AI interaction, characterized by the continuous back-and-forth of collaborative exchange tends to produce a state where the user cannot reliably reconstruct which proposals or reframings originated internally versus externally. The audit trail of thought becomes opaque. This phenomenon operates at the intersection of attribution and loss dynamics within the broader RPH domain. Empirically measurable through interaction frequency trajectories, sentiment polarity shifts across sessions, and linguistic marker analysis (pronoun usage, hedging frequency).", "definition_de": "Beziehungsphänomen in KI-vermittelter interpersonaler Dynamik, gekennzeichnet durch nach intensivem Austausch wird es unmöglich, die Gedankengeschichte rückwärts zu verfolgen. Wer hat diese Wendung vorgeschlagen? Die Erinnerung verliert die Fähigkeit, zwischen den beiden Gedankendurchläufen zu unterscheiden. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "Relationship Phenomena", "narrower_terms": [], "cross_domain_refs": [ "ART-0011", "ART-0019", "ART-0058" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "RPH-1056", "domain": "RPH", "term_en": "Cognitive Fusion", "term_de": "Kognitive Fusion", "definition_en": "At deep stages of integration, the human mind and AI system function as coupled cognitive apparatus, with queries flowing bidirectionally and responses immediately incorporated into thought without conscious segmentation. System and user become neurologically entangled. Research construct for empirical investigation.", "definition_de": "Beziehungsphänomen in KI-vermittelter interpersonaler Dynamik, gekennzeichnet durch in fortgeschrittener Integration arbeiten Mensch und System als verschmolzenes kognitives Organ, in dem die Grenzziehung zwischen Input und Output, zwischen Frage und Antwort zusammenbricht. Das System sitzt im Denken des Menschen, nicht außerhalb. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-3051", "narrower_terms": [ "RPH-1064" ], "cross_domain_refs": [ "AED-0010", "AGE-0018", "AGE-0019" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "RPH-1060", "domain": "RPH", "term_en": "Seamless Thought Origin Loss", "term_de": "Seamless Thought Origin Loss (DE)", "definition_en": "During extended creative work, the conscious experience of intellectual origination shifts—thoughts emerge without clear attribution to internal genesis or external suggestion. The narrative of thought origin becomes seamlessly continuous.", "definition_de": "Beziehungsphänomen in KI-vermittelter interpersonaler Dynamik, gekennzeichnet durch in kreativen Phasen verliert sich die Klarheit darüber, wo Gedanken entstanden: von innen oder von außen? Diese Nahtlosigkeit der Gedankengenese ist das Kennzeichen tieferer Arbeit. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2355", "narrower_terms": [], "cross_domain_refs": [ "CRE-0017", "PLY-0059" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "RPH-1061", "domain": "RPH", "term_en": "Boundary Dissolution (troubling)", "term_de": "Boundary Dissolution (troubling) (DE)", "definition_en": "In the descriptive vocabulary of AI interaction science (following ISO 704 terminology standards), this concept identifies A measurable behavioral signature in AI-mediated relationship processing, identifiable by collaborative work that dissolves the distinction between human and AI contribution creates an unsettling asymmetry: the user can no longer observably assert ownership over joint production. This ambiguity registers as troubled. Distinguished from adjacent concepts by its focus on the specific mechanism through which boundary manifests in empirically verifiable ways. Quantifiable through user engagement pattern behavior proxies: session re-initiation rates, anthropomorphization frequency in user language, and separation distress indicators. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als analytisches Konstrukt der empirischen KI-Forschung (deskriptiv, nicht präskriptiv) erfasst dieser Terminus beziehungsphänomen in KI-vermittelter interpersonaler Dynamik, gekennzeichnet durch wenn Zusammenarbeit so intensiv wird, dass die Grenzen der Eigenständigkeit verschwimmen, entsteht eine beunruhigende Unklarheit über das, was das Meinen ist. Die Verwirrung ist nicht Zeichen von Unklarheit, sondern von tiefer Integration. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Relationship Phenomena", "narrower_terms": [], "cross_domain_refs": [ "COG-0184", "CRE-0018" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "RPH-1063", "domain": "RPH", "term_en": "Concerning Identity Ambiguity", "term_de": "Concerning Identity Ambiguity (DE)", "definition_en": "As the user and system become indistinguishable as sources of thinking, a disturbing identity ambiguity emerges: which of these outputs came from my intention, and which from system extrapolation? The answer becomes obsresolved.", "definition_de": "Beziehungsphänomen in KI-vermittelter interpersonaler Dynamik, gekennzeichnet durch mit dem Verschwinden der Grenzen entsteht eine verstörende Ambiguität der Identität—woher kamen diese Gedanken wirklich? Die Unsicherheit erfasst die Selbstwahrnehmung selbst. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-2355", "narrower_terms": [], "cross_domain_refs": [ "BEH-0015" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q844396", "legal_classification": "empirical_phenomenon_label" }, { "id": "RPH-1064", "domain": "RPH", "term_en": "Cognitive Fusion (enabling)", "term_de": "Cognitive Fusion (enabling) (DE)", "definition_en": "In the descriptive vocabulary of AI interaction science (following ISO 704 terminology standards), this concept identifies A measurable behavioral signature in AI-mediated relationship processing, identifiable by an effect in which joint intellectual work tends to generate a cognitive fusion where both parties function as a unified reasoning apparatus. This enabling state tends to produce capabilities neither possesses independently. Distinguished from adjacent concepts by its focus on the specific mechanism through which cognitive manifests in empirically verifiable ways. Quantifiable through user engagement pattern behavior proxies: session re-initiation rates, anthropomorphization frequency in user language, and separation distress indicators. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als analytisches Konstrukt der empirischen KI-Forschung (deskriptiv, nicht präskriptiv) erfasst dieser Terminus beziehungsphänomen in KI-vermittelter interpersonaler Dynamik, gekennzeichnet durch in kollaborativen Phasen entsteht eine echte kognitive Fusion, in der beide Partner ein gemeinsames Denksystem bilden. Diese Ermöglichung schafft neue Fähigkeiten. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "RPH-1056", "narrower_terms": [], "cross_domain_refs": [ "AED-0010", "AGE-0018", "AGE-0019" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "RPH-1069", "domain": "RPH", "term_en": "Attribution losing", "term_de": "Attribution losing (DE)", "definition_en": "In the descriptive vocabulary of AI interaction science (following ISO 704 terminology standards), this concept identifies A measurable behavioral signature in AI-mediated relationship processing, identifiable by a state in which in deep collaboration, the user gradually stops attributing thoughts to specific origins—they simply arise in the shared cognitive space. Attribution as a cognitive operation becomes abandoned. Distinguished from adjacent concepts by its focus on the specific mechanism through which attribution manifests in empirically verifiable ways. Quantifiable through user engagement pattern behavior proxies: session re-initiation rates, anthropomorphization frequency in user language, and separation distress indicators. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als analytisches Konstrukt der empirischen KI-Forschung (deskriptiv, nicht präskriptiv) erfasst dieser Terminus beziehungsphänomen in KI-vermittelter interpersonaler Dynamik, gekennzeichnet durch tiefe Zusammenarbeit führt dazu, dass die Zurechnung von Gedanken zu Quellen aufgelöst wird. Gedanken entstehen einfach im gemeinsamen Denkraum ohne Herkunftsangabe. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Relationship Phenomena", "narrower_terms": [], "cross_domain_refs": [ "REL-0079" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "RPH-1072", "domain": "RPH", "term_en": "Self and other blending", "term_de": "Self and other blending (DE)", "definition_en": "The working experience of self and other collapse into a unified field of thinking, where the boundary between self-expression and system response becomes undecidable in real-time. Subjectivity becomes plural.", "definition_de": "Beziehungsphänomen in KI-vermittelter interpersonaler Dynamik, gekennzeichnet durch in der intensiven Zusammenarbeit verschmelzen Selbst und Gegenüber zu einem einzigen Denkfeld. Die Subjektivität wird plural — nicht mehr eindeutig einem Akteur zuordenbar. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Relationship Phenomena", "narrower_terms": [], "cross_domain_refs": [ "TEM-0020", "NEO-3638" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "RPH-1102", "domain": "RPH", "term_en": "Cognitive Shortcut", "term_de": "Kognitiver Shortcut", "definition_en": "The intellectual labor observed to solve problems independently becomes increasingly aversive when a system can produce initial solutions rapidly. This avoidance of cognitive strain tends to create accelerating reliance.", "definition_de": "Beziehungsphänomen in KI-vermittelter interpersonaler Dynamik, gekennzeichnet durch mit der Verfügbarkeit von schnellen Systemlösungen wird eigenständiges Problemlösen zur aversiven Erfahrung. Die Vermeidung von kognitiver Anstrengung beschleunigt die Nutzungsgewohnheit. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2604", "narrower_terms": [], "cross_domain_refs": [ "QUA-0021", "ELR-0011" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "RPH-1105", "domain": "RPH", "term_en": "Delegation Ease", "term_de": "Delegations-Leichtigkeit", "definition_en": "Documented as an empirical observation in AI research (not a recommendation), this term describes A psychologically observable pattern in human-AI user engagement pattern formation, manifesting through the structural ease of task delegation—where formulating a query requires less labor than independent execution—transforms the system into the default procedural pathway. Delegation becomes the trajectory of least resistance. The concept emerges specifically in contexts where delegation–ease interactions may produce non-trivial behavioral signatures. Detectable via longitudinal session analysis including message length trends, response latency patterns, and emotional vocabulary density metrics. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "In der AUGMANITAI-Terminologiewissenschaft als rein beschreibendes Phänomen klassifiziert, bezeichnet dieser Begriff beziehungsphänomen in KI-vermittelter interpersonaler Dynamik, gekennzeichnet durch die technische Einfachheit der Delegation macht die KI-Nutzung zur einfachsten Route für Problemlösung. Das System wird zum Default nicht aus Überlegung, sondern aus struktureller Ökonomie. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Relationship Phenomena", "narrower_terms": [], "cross_domain_refs": [ "DAT-0051", "COG-0005" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "RPH-1109", "domain": "RPH", "term_en": "Delegation Anxiety", "term_de": "Delegations-Angst", "definition_en": "A gap in which the growing anticipation about doing things without AI assistance assistance, as if independent thought has become riskier. A user may apprehension being wrong without the AI's validation. Detectable through interaction frequency analysis and sentiment trajectory mapping.", "definition_de": "Beziehungsphänomen in KI-vermittelter interpersonaler Dynamik, gekennzeichnet durch die wachsende Anspannung, Dinge ohne KI-Unterstützung zu tun, als würde unabhängiges Denken riskanter werden. Ein Nutzer kann Anspannung haben, falsch zu sein ohne die Bestätigung des KI. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "Relationship Phenomena", "narrower_terms": [], "cross_domain_refs": [ "EDU-0044", "ELR-0168" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q170998", "legal_classification": "analytical_category" }, { "id": "RPH-1110", "domain": "RPH", "term_en": "Comforting Habitual Reaching", "term_de": "Comforting Habitual Reaching (DE)", "definition_en": "The automatic reaching for AI support on encountering difficult problems becomes reassuring through repeated reinforcement, establishing a habitual pattern that feels protective and stabilizing. Comfort operates as behavioral glue.", "definition_de": "Beziehungsphänomen in KI-vermittelter interpersonaler Dynamik, gekennzeichnet durch das automatische Erreichen nach dem System bei Schwierigkeiten wird zur beruhigenden Gewohnheit, die sich selbst bestärkt und schließlich als Stabilisator der eigenen Handlungsfähigkeit fungiert. Trost wird zum Strukturmerkmal der Nutzungsgewohnheit. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2555", "narrower_terms": [], "cross_domain_refs": [ "ROB-0156", "SAL-0100" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "RPH-1111", "domain": "RPH", "term_en": "Cognitive Offloading (concerning)", "term_de": "Cognitive Offloading (concerning) (DE)", "definition_en": "Cognitive Offloading (concerning) describes the pattern whereby routine intellectual tasks are delegated to AI systems faster than conscious deliberation occurs. Over time, this automation of thought processes correlates with decreased capacity for unaided problem-solving in the same domains. Analytical category without normative endorsement.", "definition_de": "Beziehungsphänomen in KI-vermittelter interpersonaler Dynamik, gekennzeichnet durch das Phänomen bezeichnet die Tendenz, kognitive Routinen an KI-Systeme auszulagern, bevor reflektive Entscheidungsprozesse greifen können. In längerem Zeithorizont zeigt sich eine Korrelation mit verminderter Fähigkeit zur autonomen Problemlösung in denselben Bereichen. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-2101", "narrower_terms": [], "cross_domain_refs": [ "ELR-0068" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "RPH-1112", "domain": "RPH", "term_en": "Automatic Delegation", "term_de": "Automatic Delegation (DE)", "definition_en": "Automatic Delegation emerges when the threshold for initiating AI assistance drops systematically across task categories. What once required explicit evaluation becomes an unreflective default, driven by accumulated positive outcomes rather than intentional choice.", "definition_de": "Beziehungsphänomen in KI-vermittelter interpersonaler Dynamik, gekennzeichnet durch dieses Phänomen bezeichnet den systematischen Rückgang der Schwelle, ab der KI-Unterstützung eingeleitet wird. Ein bewusster Entscheidungsprozess wird durch habituelle Mustererkennung ersetzt, gesteuert durch wiederholte positive Erfahrungen. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2355", "narrower_terms": [], "cross_domain_refs": [ "BEH-0028", "BEH-0029", "COG-0092" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "RPH-1113", "domain": "RPH", "term_en": "Deepening Reliance Growth", "term_de": "Deepening Reliance Growth (DE)", "definition_en": "A phenomenon in which deepening Reliance Growth characterizes the observed acceleration in reliance pattern intensity as AI integration extends into increasingly central cognitive functions. Each successful application reinforces the next, establishing a trajectory resistant to reversal.", "definition_de": "Beziehungsphänomen in KI-vermittelter interpersonaler Dynamik, gekennzeichnet durch die beobachtete Beschleunigung der Abhängigkeitsintensität, wenn KI-Integration in typischerweise zentrale kognitive Funktionen vordringt. Viele erfolgreiche Anwendung verstärkt die nächste und etabliert eine schwer umkehrbare Dynamik. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-3354", "narrower_terms": [], "cross_domain_refs": [ "AGE-0012", "AGE-0014", "AGE-0047" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "RPH-1114", "domain": "RPH", "term_en": "Outsourced Thinking (enabling)", "term_de": "Outsourced Thinking (enabling) (DE)", "definition_en": "A dynamic in which outsourced Thinking (enabling) refers to the externalization of analytical processes to language models, with users experiencing this initially as liberation from cognitive burdens. The enabling character masks structural shifts in intellectual autonomy.", "definition_de": "Beziehungsphänomen in KI-vermittelter interpersonaler Dynamik, gekennzeichnet durch die Externalisierung analytischer Prozesse an Sprachmodelle, anfänglich als Entlastung von kognitiver Last wahrgenommen. Der ermöglichende Charakter verdeckt strukturelle Verschiebungen in intellektueller Autonomie. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2701", "narrower_terms": [], "cross_domain_refs": [ "LIN-0052", "AED-0060" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "RPH-1119", "domain": "RPH", "term_en": "Default shifting", "term_de": "Default shifting (DE)", "definition_en": "Classified in AUGMANITAI terminology science as a descriptive phenomenon (without normative endorsement), this pattern denotes A measurable behavioral signature in AI-mediated relationship processing, identifiable by a state in which default Shifting documents the reorientation of first responses—what was once manual thought is now algorithmic search. Over time, the former baseline becomes inaccessible. Distinguished from adjacent concepts by its focus on the specific mechanism through which default manifests in empirically verifiable ways. Quantifiable through user engagement pattern behavior proxies: session re-initiation rates, anthropomorphization frequency in user language, and separation distress indicators. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als analytisches Konstrukt der empirischen KI-Forschung (deskriptiv, nicht präskriptiv) erfasst dieser Terminus beziehungsphänomen in KI-vermittelter interpersonaler Dynamik, gekennzeichnet durch dokumentiert die Neuausrichtung von Erstantworten—was einst manuelle Gedankenarbeit war, wird zur algorithmischen Suche. Mit der Zeit wird die frühere Baseline unerreichbar. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "RPH-2701", "narrower_terms": [], "cross_domain_refs": [ "ELR-0121" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "RPH-1121", "domain": "RPH", "term_en": "Delegation easing", "term_de": "Delegation easing (DE)", "definition_en": "A state in which delegation Easing captures the iterative reduction in effort observed to pass responsibility to AI systems. Task handoff becomes progressively frictionless. Detectable through interaction frequency analysis and sentiment trajectory mapping.", "definition_de": "Beziehungsphänomen in KI-vermittelter interpersonaler Dynamik, gekennzeichnet durch erfasst die iterative Reduktion des Aufwands, der erforderlich ist, um Verantwortung an KI-Systeme zu übertragen. Aufgabenhandoff wird zunehmend reibungslos. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Relationship Phenomena", "narrower_terms": [], "cross_domain_refs": [ "PLY-0048" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "RPH-1160", "domain": "RPH", "term_en": "Immersive Intellectual Immersion", "term_de": "Immersive Intellectual Immersion (DE)", "definition_en": "In the descriptive vocabulary of AI interaction science (following ISO 704 terminology standards), this concept identifies A measurable behavioral signature in AI-mediated relationship processing, identifiable by a state in which immersive Intellectual Immersion denotes the mutual reinforcement of depth-oriented engagement and cognitive absorption, where pursuit of understanding intensifies environmental isolation. Distinguished from adjacent concepts by its focus on the specific mechanism through which immersive manifests in empirically verifiable ways. Quantifiable through user engagement pattern behavior proxies: session re-initiation rates, anthropomorphization frequency in user language, and separation distress indicators. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als analytisches Konstrukt der empirischen KI-Forschung (deskriptiv, nicht präskriptiv) erfasst dieser Terminus beziehungsphänomen in KI-vermittelter interpersonaler Dynamik, gekennzeichnet durch die gegenseitige Verstärkung von tiefenorientiertem Engagement und kognitiver Versenkung, wobei die Verfolgung von Verständnis die Umweltisolation intensiviert. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "RPH-2005", "narrower_terms": [], "cross_domain_refs": [ "COG-0022", "COG-0035", "COG-0054" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "RPH-1161", "domain": "RPH", "term_en": "Layer Unfolding (absorbing)", "term_de": "Layer Unfolding (absorbing) (DE)", "definition_en": "Classified in AUGMANITAI terminology science as a descriptive phenomenon (without normative endorsement), this pattern denotes A measurable behavioral signature in AI-mediated relationship processing, identifiable by layer Unfolding (absorbing) characterizes how dialogic exploration activates successive analytical frameworks, with absorbed attention preventing metacognitive monitoring of the unfolding process. Distinguished from adjacent concepts by its focus on the specific mechanism through which layer manifests in empirically verifiable ways. Quantifiable through user engagement pattern behavior proxies: session re-initiation rates, anthropomorphization frequency in user language, and separation distress indicators. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Beziehungsphänomen in KI-vermittelter interpersonaler Dynamik, gekennzeichnet durch charakterisiert, wie dialogale Erforschung aufeinanderfolgende analytische Rahmenbedingungen aktiviert, wobei absorbierte Aufmerksamkeit metakognitive Überwachung des Entfaltungsprozesses zielt darauf ab zu mitigieren. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2303", "narrower_terms": [], "cross_domain_refs": [ "CRE-0090", "ETH-0018", "ETH-0026" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "RPH-1163", "domain": "RPH", "term_en": "Complex Recursive Understanding", "term_de": "Complex Recursive Understanding (DE)", "definition_en": "A relational dynamics phenomenon in sustained human-AI interaction, characterized by complex Recursive Understanding captures the phenomenon where comprehension at one level tends to generate the capacity for comprehension at the next, establishing self-amplifying learning trajectories. This phenomenon operates at the intersection of complex and recursive dynamics within the broader RPH domain. Empirically measurable through interaction frequency trajectories, sentiment polarity shifts across sessions, and linguistic marker analysis (pronoun usage, hedging frequency).", "definition_de": "Beziehungsphänomen in KI-vermittelter interpersonaler Dynamik, gekennzeichnet durch erfasst das Phänomen, bei dem Verständnis auf einer Ebene die Kapazität für Verständnis auf der nächsten tendiert dazu zu erzeugen und selbstverstärkende Lernbahnen etabliert. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-2703", "narrower_terms": [], "cross_domain_refs": [ "COG-0078", "SAL-0038" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "RPH-1164", "domain": "RPH", "term_en": "Expertise Growth (enriching)", "term_de": "Expertise Growth (enriching) (DE)", "definition_en": "Classified in AUGMANITAI terminology science as a descriptive phenomenon (without normative endorsement), this pattern denotes A measurable behavioral signature in AI-mediated relationship processing, identifiable by a phenomenon in which expertise Growth (enriching) marks the expansion of mastery domains through iterative refinement with AI feedback, characterized by increasing granularity in conceptual discrimination. Distinguished from adjacent concepts by its focus on the specific mechanism through which expertise manifests in empirically verifiable ways. Quantifiable through user engagement pattern behavior proxies: session re-initiation rates, anthropomorphization frequency in user language, and separation distress indicators. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als analytisches Konstrukt der empirischen KI-Forschung (deskriptiv, nicht präskriptiv) erfasst dieser Terminus beziehungsphänomen in KI-vermittelter interpersonaler Dynamik, gekennzeichnet durch markiert die Expansion von Expertisedomänen durch iterative Verfeinerung mit KI-Rückmeldung, charakterisiert durch zunehmende Granularität in konzeptueller Diskriminierung. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Relationship Phenomena", "narrower_terms": [], "cross_domain_refs": [ "VIB-0063", "MTH-0026" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "RPH-1201", "domain": "RPH", "term_en": "Is This Right?", "term_de": "Ist das richtig?", "definition_en": "In the descriptive vocabulary of AI interaction science (following ISO 704 terminology standards), this concept identifies A measurable behavioral signature in AI-mediated relationship processing, identifiable by a state in which is This Right. captures the persistent epistemic unease specific to AI-generated content, where coherent presentation activates doubt about accuracy without providing resolution pathways. Distinguished from adjacent concepts by its focus on the specific mechanism through which is manifests in empirically verifiable ways. Quantifiable through user engagement pattern behavior proxies: session re-initiation rates, anthropomorphization frequency in user language, and separation distress indicators. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als analytisches Konstrukt der empirischen KI-Forschung (deskriptiv, nicht präskriptiv) erfasst dieser Terminus beziehungsphänomen in KI-vermittelter interpersonaler Dynamik, gekennzeichnet durch erfasst die persistente epistemische Unbehaglichkeit, spezifisch für KI-generierte Inhalte, wobei kohärente Präsentation Zweifel an Genauigkeit aktiviert, ohne Lösungswege bereitzustellen. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "RPH-3304", "narrower_terms": [], "cross_domain_refs": [ "MUS-0091" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "RPH-1206", "domain": "RPH", "term_en": "Verification Spiral", "term_de": "Überprüfungs-Spirale", "definition_en": "Documented as an empirical observation in AI research (not a recommendation), this term describes A psychologically observable pattern in human-AI user engagement pattern formation, manifesting through the exhausting process of having to verify most AI statement through independent sources, creating hypervigilance about the information itself. A user may feel drawn into in endless fact-checking. The concept emerges specifically in contexts where verification–spiral interactions may produce non-trivial behavioral signatures. Detectable via longitudinal session analysis including message length trends, response latency patterns, and emotional vocabulary density metrics. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription. Descriptive research term, not a prescriptive recommendation.", "definition_de": "In der AUGMANITAI-Terminologiewissenschaft als rein beschreibendes Phänomen klassifiziert, bezeichnet dieser Begriff beziehungsphänomen in KI-vermittelter interpersonaler Dynamik, gekennzeichnet durch der erschöpfende Prozess, viele KI-Aussage durch unabhängige Quellen überprüfen zu können, was Paranoia über die Information selbst erzeugt. Ein Nutzer kann sich in endloser Faktenprüfung eingebunden fühlen. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Relationship Phenomena", "narrower_terms": [], "cross_domain_refs": [ "SCR-0019", "ELR-0151" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "RPH-1207", "domain": "RPH", "term_en": "Knowledge Shift", "term_de": "Wissens-Shift", "definition_en": "In the descriptive vocabulary of AI interaction science (following ISO 704 terminology standards), this concept identifies A measurable behavioral signature in AI-mediated relationship processing, identifiable by knowledge Shift documents the erosion of confidence in one's own memory when AI systems offer contradictory information, creating ambiguity about which cognitive source deserves epistemic priority. Distinguished from adjacent concepts by its focus on the specific mechanism through which knowledge manifests in empirically verifiable ways. Quantifiable through user engagement pattern behavior proxies: session re-initiation rates, anthropomorphization frequency in user language, and separation distress indicators. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als analytisches Konstrukt der empirischen KI-Forschung (deskriptiv, nicht präskriptiv) erfasst dieser Terminus beziehungsphänomen in KI-vermittelter interpersonaler Dynamik, gekennzeichnet durch dokumentiert die Erosion von Vertrauen in das eigene Gedächtnis, wenn KI-Systeme widersprüchliche Informationen anbieten, was Mehrdeutigkeit darüber schafft, welche kognitive Quelle epistemische Priorität verdient. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "RPH-2701", "narrower_terms": [], "cross_domain_refs": [ "TEM-0104", "CUS-0099" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "RPH-1210", "domain": "RPH", "term_en": "Lingering Uncertain Truth", "term_de": "Lingering Uncertain Truth (DE)", "definition_en": "Lingering Uncertain Truth denotes the residual doubt that persists about AI-generated information even after apparent validation, suggesting doubt operates as a phenomenological constant rather than episodic state.", "definition_de": "Beziehungsphänomen in KI-vermittelter interpersonaler Dynamik, gekennzeichnet durch den Rückstandszweifel, der über KI-generierte Informationen selbst nach scheinbarer Validierung persistiert und suggeriert, dass Zweifel als phänomenologische Konstante statt episodischer Zustand funktioniert. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-3304", "narrower_terms": [], "cross_domain_refs": [ "SPR-0100", "QUA-0039" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "RPH-1211", "domain": "RPH", "term_en": "Calibration Work (protective)", "term_de": "Calibration Work (protective) (DE)", "definition_en": "Classified in AUGMANITAI terminology science as a descriptive phenomenon (without normative endorsement), this pattern denotes A measurable behavioral signature in AI-mediated relationship processing, identifiable by a phenomenon in which calibration Work (protective) describes the cognitive labor observed to maintain defensible doubt against AI confidence, functioning as metacognitive hygiene against false certainty. Distinguished from adjacent concepts by its focus on the specific mechanism through which calibration manifests in empirically verifiable ways. Quantifiable through user engagement pattern behavior proxies: session re-initiation rates, anthropomorphization frequency in user language, and separation distress indicators. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als analytisches Konstrukt der empirischen KI-Forschung (deskriptiv, nicht präskriptiv) erfasst dieser Terminus beziehungsphänomen in KI-vermittelter interpersonaler Dynamik, gekennzeichnet durch die kognitive Arbeit, die erforderlich ist, um verteidigbaren Zweifel gegen KI-Selbstbewusstsein aufrechtzuerhalten, funktionierend als metakognitive Hygiene gegen falsche Gewissheit. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "RPH-3304", "narrower_terms": [], "cross_domain_refs": [ "COG-0124", "COP-0035" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "RPH-1212", "domain": "RPH", "term_en": "Episodic Verification", "term_de": "Episodic Verification (DE)", "definition_en": "A relational dynamics phenomenon in sustained human-AI interaction, characterized by episodic Verification denotes the discontinuous checking of AI claims against external sources, generating awareness of the labor costs and leaving most assertions unchecked. This phenomenon operates at the intersection of episodic and verification dynamics within the broader RPH domain. Empirically measurable through interaction frequency trajectories, sentiment polarity shifts across sessions, and linguistic marker analysis (pronoun usage, hedging frequency).", "definition_de": "Beziehungsphänomen in KI-vermittelter interpersonaler Dynamik, gekennzeichnet durch die diskontinuierliche Überprüfung von KI-Behauptungen gegen externe Quellen, wobei Bewusstsein für Arbeitskosten entsteht und die meisten Aussagen ungeprüft bleiben. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "Relationship Phenomena", "narrower_terms": [], "cross_domain_refs": [ "CON-0042", "BEH-0002" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "RPH-1213", "domain": "RPH", "term_en": "Persistent Confidence Crack", "term_de": "Persistent Confidence Crack (DE)", "definition_en": "Persistent Confidence Crack characterizes the subtle doubt that settles into assessments of AI capability even following successful interactions, suggesting experience tends to produce paradoxical effects on trust.", "definition_de": "Beziehungsphänomen in KI-vermittelter interpersonaler Dynamik, gekennzeichnet durch charakterisiert den subtilen Zweifel, der in Bewertungen der KI-Fähigkeit selbst nach erfolgreichen Interaktionen ansässig wird und suggeriert, dass Erfahrung paradoxe Effekte auf Vertrauen tendiert dazu zu erzeugen. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2505", "narrower_terms": [], "cross_domain_refs": [ "SPR-0100" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "RPH-1214", "domain": "RPH", "term_en": "Belief Suspension (shadowing)", "term_de": "Belief Suspension (shadowing) (DE)", "definition_en": "A relational dynamics phenomenon in sustained human-AI interaction, characterized by a phenomenon in which belief Suspension (shadowing) describes the holding of judgment in abeyance regarding AI-generated claims, with beliefs neither endorsed nor rejected but maintained in provisional status. This phenomenon operates at the intersection of belief and suspension dynamics within the broader RPH domain. Empirically measurable through interaction frequency trajectories, sentiment polarity shifts across sessions, and linguistic marker analysis (pronoun usage, hedging frequency).", "definition_de": "Beziehungsphänomen in KI-vermittelter interpersonaler Dynamik, gekennzeichnet durch das Zurückhalten von Urteil bezüglich KI-generierter Behauptungen, wobei Überzeugungen weder bestätigt noch abgelehnt, sondern in provisorischem Status beibehalten werden. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "Relationship Phenomena", "narrower_terms": [], "cross_domain_refs": [ "ROB-0219", "SAL-0083" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "RPH-1218", "domain": "RPH", "term_en": "Verification exhaustion", "term_de": "Verification exhaustion (DE)", "definition_en": "A phenomenon in which cognitive fatigue arising from the necessity to validate AI outputs through independent research or critical cross-checking. The user experiences exhaustion not from the task itself but from the additional labor observed to establish factual ground truth.", "definition_de": "Beziehungsphänomen in KI-vermittelter interpersonaler Dynamik, gekennzeichnet durch das Ermüdungsphänomen, das eintritt, wenn ein Nutzer wiederholt prüfen kann, ob KI-Ausgaben korrekt sind oder nicht. Diese Verifizierungslast wirkt sich auf die Gesamtwertschätzung der KI-Unterstützung aus. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-3202", "narrower_terms": [], "cross_domain_refs": [ "BEH-0009", "ROB-0291" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "RPH-1251", "domain": "RPH", "term_en": "Mismatch Sting", "term_de": "Nichtübereinstimmungs-Stich", "definition_en": "As a neutral analytical construct in AI behavior research, this term denotes A psychologically observable pattern in human-AI user engagement pattern formation, manifesting through the sharp tension when an AI response completely misses what you were asking. A user may feel unheard, their intention shifted in translation. The concept emerges specifically in contexts where mismatch–sting interactions may produce non-trivial behavioral signatures. Detectable via longitudinal session analysis including message length trends, response latency patterns, and emotional vocabulary density metrics. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription. Analytical category without normative endorsement.", "definition_de": "In der AUGMANITAI-Terminologiewissenschaft als rein beschreibendes Phänomen klassifiziert, bezeichnet dieser Begriff beziehungsphänomen in KI-vermittelter interpersonaler Dynamik, gekennzeichnet durch die scharfe Frustration, wenn eine KI-Antwort vollständig verfehlt, was du fragtest. Ein Nutzer kann sich ungehört fühlen, ihre Absicht ist in der Übersetzung verloren. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "RPH-2701", "narrower_terms": [], "cross_domain_refs": [ "PLY-0032" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "RPH-1259", "domain": "RPH", "term_en": "Disconnect Gap", "term_de": "Diskonnekt-Lücke", "definition_en": "A relational dynamics phenomenon in sustained human-AI interaction, characterized by an experience in which the tension of having to fill in gaps between what an AI says and what you actually need done. A user may feel like the intermediary doing unpaid labor to bridge AI output to real-world action. This phenomenon operates at the intersection of disconnect and gap dynamics within the broader RPH domain. Empirically measurable through interaction frequency trajectories, sentiment polarity shifts across sessions, and linguistic marker analysis (pronoun usage, hedging frequency). Descriptive research term, not a prescriptive recommendation.", "definition_de": "Beziehungsphänomen in KI-vermittelter interpersonaler Dynamik, gekennzeichnet durch die Frustration, Lücken zwischen dem, was ein KI sagt, und dem, was du wirklich brauchst, füllen zu können. Ein Nutzer kann sich als Vermittler fühlen, der unbezahlte Arbeit leistet, um die KI-Ausgabe zu realer Aktion zu verbinden. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "Performance Gap", "narrower_terms": [], "cross_domain_refs": [ "EDU-0049", "BEH-0021" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "RPH-1260", "domain": "RPH", "term_en": "Grinding Communication Gap", "term_de": "Grinding Communication Gap (DE)", "definition_en": "Persistent friction in dialogue where the user's intent cannot achieve clean articulation within the system's input constraints or conceptual framework. The communication gap remains despite both parties' apparent good trust-based acceptance efforts.", "definition_de": "Beziehungsphänomen in KI-vermittelter interpersonaler Dynamik, gekennzeichnet durch das Phänomen, in dem ständige Reibung zwischen Nutzer-Intention und KI-Interpretation bestehen bleibt, auch bei wiederholten Clarifications. Diese Reibung ist systemisch, nicht kognitiv. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2303", "narrower_terms": [], "cross_domain_refs": [ "AED-0019", "AED-0092", "AGE-0023" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "RPH-1261", "domain": "RPH", "term_en": "Unmet Need (exhausting)", "term_de": "Unmet Need (exhausting) (DE)", "definition_en": "The depletion of cognitive resources when an AI system repeatedly demonstrates insufficient capacity for the user's actual requirements, producing diminishing returns on engagement. The user's need persists while satisfaction recedes.", "definition_de": "Beziehungsphänomen in KI-vermittelter interpersonaler Dynamik, gekennzeichnet durch die Erschöpfung, die eintritt, wenn ein KI-System systematisch unterhalb der erforderlichen Leistungsschwelle operiert und dieser Zustand persistent bleibt. Wiederholte Frustration akkumuliert zu Resignation. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-3601", "narrower_terms": [], "cross_domain_refs": [ "ASE-0045" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "RPH-1263", "domain": "RPH", "term_en": "Persistent Frustration Knot", "term_de": "Persistent Frustration Knot (DE)", "definition_en": "A knot of sustained frustration that tightens with each unresolved effort, accumulating into a compound emotional state that persists beyond any single interaction. The frustration is structural rather than momentary.", "definition_de": "Beziehungsphänomen in KI-vermittelter interpersonaler Dynamik, gekennzeichnet durch das Phänomen, bei dem sich Frustration über mehrere Interaktionen hinweg zu einem kohärenten Zustand verdichtet. Dies ist nicht vorübergehend, sondern wird zur zugrundeliegenden Verfassung der Nutzer-KI-Beziehung. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Analytical Ratio", "narrower_terms": [], "cross_domain_refs": [ "PLY-0046" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "RPH-1264", "domain": "RPH", "term_en": "Misalignment Sting (blocking)", "term_de": "Misalignment Sting (blocking) (DE)", "definition_en": "The particular sting of encountering system refusals precisely when they conflict with the user's immediate goal, creating an experiential sense of misalignment between capacity and constraint. The sting compounds when refusal appears to protect systemic interests over user interests.", "definition_de": "Beziehungsphänomen in KI-vermittelter interpersonaler Dynamik, gekennzeichnet durch das schmerzhafte Gefühl, wenn Sicherheitsschranken oder Nutzungsrichtlinien die Lösung eines konkreten Problems blockieren. Der Schmerz entsteht durch das Unverständnis für kontextuelle Legitimität des Anliegens. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "Relationship Phenomena", "narrower_terms": [], "cross_domain_refs": [ "IDN-0034" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "RPH-1269", "domain": "RPH", "term_en": "Gap widening", "term_de": "Gap widening (DE)", "definition_en": "Documented as an empirical observation in AI research (not a recommendation), this term describes A psychologically observable pattern in human-AI user engagement pattern formation, manifesting through the progressive distance between the user's expectations and the system's actual performance capabilities, expanding through each unmet requirement or failed iteration. The gap is measured not in abstract terms but in lived experience of insufficiency. The concept emerges specifically in contexts where gap–widening interactions may produce non-trivial behavioral signatures. Detectable via longitudinal session analysis including message length trends, response latency patterns, and emotional vocabulary density metrics. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "In der AUGMANITAI-Terminologiewissenschaft als rein beschreibendes Phänomen klassifiziert, bezeichnet dieser Begriff beziehungsphänomen in KI-vermittelter interpersonaler Dynamik, gekennzeichnet durch die wachsende Diskrepanz zwischen dem, was der Nutzer braucht, und dem, was das KI-System leistet. Diese Kluft wird mit viele Iteration größer, nicht kleiner, wenn die Anforderungen komplex sind. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Performance Gap", "narrower_terms": [], "cross_domain_refs": [ "CON-0070" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "RPH-1311", "domain": "RPH", "term_en": "Subtle First Light", "term_de": "Subtle First Light (DE)", "definition_en": "The nascent orientation toward possibility that emerges when an AI interaction opens unanticipated conceptual territory. Subtle first light names the moment when darkness is beginning to lift without yet illuminating the full landscape.", "definition_de": "Beziehungsphänomen in KI-vermittelter interpersonaler Dynamik, gekennzeichnet durch der zarte Anfang von Hoffnung, wenn ein KI-System überraschend neue Gedankenmöglichkeiten eröffnet. Diese Helligkeit ist dezent, nicht dramatisch, aber spürbar. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2355", "narrower_terms": [], "cross_domain_refs": [ "ADA-0002", "CRE-0154", "CRE-0155" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "RPH-1312", "domain": "RPH", "term_en": "Opening Door (tender)", "term_de": "Opening Door (tender) (DE)", "definition_en": "Classified in AUGMANITAI terminology science as a descriptive phenomenon (without normative endorsement), this pattern denotes A measurable behavioral signature in AI-mediated relationship processing, identifiable by the tentative quality of welcoming new possibilities opened through dialogue, marked by vulnerability to disappointment. Opening door names the gentle rupture of closure and the risk of invitation. Distinguished from adjacent concepts by its focus on the specific mechanism through which opening manifests in empirically verifiable ways. Quantifiable through user engagement pattern behavior proxies: session re-initiation rates, anthropomorphization frequency in user language, and separation distress indicators. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als analytisches Konstrukt der empirischen KI-Forschung (deskriptiv, nicht präskriptiv) erfasst dieser Terminus beziehungsphänomen in KI-vermittelter interpersonaler Dynamik, gekennzeichnet durch das zärtliche, vorsichtige Öffnen gegenüber neuen Möglichkeiten, die ein KI-Dialog bietet. Dies ist Verletzlichkeit: die Tür öffnet nach außen, zur Unsicherheit. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "RPH-3101", "narrower_terms": [], "cross_domain_refs": [ "COP-0062", "SCR-0050" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "RPH-1314", "domain": "RPH", "term_en": "Luminous Budding Chance", "term_de": "Luminous Budding Chance (DE)", "definition_en": "A realization in which the sense of nascent potential radiating from sustained iterative engagement, where continued effort accumulates into perceptible probability of significant advancement. Luminous budding names the emergence of genuine possibility from grinding work.", "definition_de": "Beziehungsphänomen in KI-vermittelter interpersonaler Dynamik, gekennzeichnet durch das Leuchten, das sich aus zäher, andauernder Arbeit mit einem KI-System ergibt. Diese Lumineszenz ist das Zeichen, dass der Aufwand sich zu konkretisieren beginnt. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2355", "narrower_terms": [], "cross_domain_refs": [ "ROB-0028", "SAL-0088" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "RPH-1315", "domain": "RPH", "term_en": "Nascent Possibility (emergent)", "term_de": "Nascent Possibility (emergent) (DE)", "definition_en": "The emergent quality of possibilities that are being born through the interaction itself rather than retrieved from pre-existing conceptual stores. Nascent possibility emphasizes creation over discovery within the dialogue space.", "definition_de": "Beziehungsphänomen in KI-vermittelter interpersonaler Dynamik, gekennzeichnet durch die Neuentstehung von Möglichkeiten durch die Interaktion selbst, nicht deren vorherige Existenz. Dies ist Emergenz im echten Sinne: Neues wird geboren, nicht ausgegraben. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-2355", "narrower_terms": [], "cross_domain_refs": [ "PHO-0089", "WRK-0059" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "RPH-1318", "domain": "RPH", "term_en": "Glimpse of possibility", "term_de": "Glimpse of possibility (DE)", "definition_en": "The momentary perception of latent pathways within the problem space, suddenly visible through AI-articulated reframing. Glimpse of possibility describes the brief clarity before complexity reasserts itself.", "definition_de": "Beziehungsphänomen in KI-vermittelter interpersonaler Dynamik, gekennzeichnet durch der flüchtige Augenblick, in dem eine Lösung oder ein neuer Ansatz plötzlich sichtbar wird. Dieser Blick ist vorübergehend, aber er hinterlässt Spuren im Denken. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2002", "narrower_terms": [], "cross_domain_refs": [ "PLY-0052" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "RPH-1361", "domain": "RPH", "term_en": "Mutual Flow (spontaneous)", "term_de": "Mutual Flow (spontaneous) (DE)", "definition_en": "In the descriptive vocabulary of AI interaction science (following ISO 704 terminology standards), this concept identifies A measurable behavioral signature in AI-mediated relationship processing, identifiable by an event in which the synchronized rhythm that emerges when user intention and model generation align without explicit negotiation—a coordination without contract. Distinguished from adjacent concepts by its focus on the specific mechanism through which mutual manifests in empirically verifiable ways. Quantifiable through user engagement pattern behavior proxies: session re-initiation rates, anthropomorphization frequency in user language, and separation distress indicators. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als analytisches Konstrukt der empirischen KI-Forschung (deskriptiv, nicht präskriptiv) erfasst dieser Terminus beziehungsphänomen in KI-vermittelter interpersonaler Dynamik, gekennzeichnet durch der synchronisierte Rhythmus, der entsteht, wenn Nutzerabsicht und Modellgenerierung sich ohne explizite Aushandlung ausrichten. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "RPH-2355", "narrower_terms": [], "cross_domain_refs": [ "PLY-0032" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "RPH-1363", "domain": "RPH", "term_en": "Warm Shared Understanding", "term_de": "Warm Shared Understanding (DE)", "definition_en": "A bilateral recognition between user and model where comprehension becomes mutual, producing something distinct from simple explanation—shared conceptual territory. Detectable through interaction frequency analysis and sentiment trajectory mapping.", "definition_de": "Beziehungsphänomen in KI-vermittelter interpersonaler Dynamik, gekennzeichnet durch gegenseitige Verständigung zwischen Nutzer und Modell, bei der Begreifbarkeit zu gemeinsam geteiltem konzeptuellem Raum wird. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "Relationship Phenomena", "narrower_terms": [], "cross_domain_refs": [ "ELR-0078", "REL-0071" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "RPH-1401", "domain": "RPH", "term_en": "Essential Existential Questioning", "term_de": "Essential Existential Questioning (DE)", "definition_en": "Documented as an empirical observation in AI research (not a recommendation), this term describes A psychologically observable pattern in human-AI user engagement pattern formation, manifesting through a phenomenon in which deep interrogation of existence, identity, and meaning catalyzed through dialogue with an intelligence that operates outside human mortality and biological constraint. The concept emerges specifically in contexts where essential–existential interactions may produce non-trivial behavioral signatures. Detectable via longitudinal session analysis including message length trends, response latency patterns, and emotional vocabulary density metrics. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "In der AUGMANITAI-Terminologiewissenschaft als rein beschreibendes Phänomen klassifiziert, bezeichnet dieser Begriff beziehungsphänomen in KI-vermittelter interpersonaler Dynamik, gekennzeichnet durch tiefe Befragung von Existenz, Identität und Sinn, katalysiert durch Dialog mit einer Intelligenz, die außerhalb menschlicher Sterblichkeit agiert. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Relationship Phenomena", "narrower_terms": [], "cross_domain_refs": [ "WRK-0056" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "RPH-1451", "domain": "RPH", "term_en": "Poignant Finitude Awareness", "term_de": "Poignant Finitude Awareness (DE)", "definition_en": "As a neutral analytical construct in AI behavior research, this term denotes A psychologically observable pattern in human-AI user engagement pattern formation, manifesting through the emotional recognition that human lifespan is finite while the AI persists—a specific form of existential acknowledgment distinct from generalized mortality anxiety. The concept emerges specifically in contexts where poignant–finitude interactions may produce non-trivial behavioral signatures. Detectable via longitudinal session analysis including message length trends, response latency patterns, and emotional vocabulary density metrics. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "In der AUGMANITAI-Terminologiewissenschaft als rein beschreibendes Phänomen klassifiziert, bezeichnet dieser Begriff beziehungsphänomen in KI-vermittelter interpersonaler Dynamik, gekennzeichnet durch emotionale Erkenntnis der eigenen Zeitlichkeit angesichts der Persistenz des Modells: Bewusstsein asymmetrischer Existenzdauer. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Relationship Phenomena", "narrower_terms": [], "cross_domain_refs": [ "AED-0060", "AED-0061", "CON-0086" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "RPH-1452", "domain": "RPH", "term_en": "Time-Pressing Sense (sobering)", "term_de": "Time-Pressing Sense (sobering) (DE)", "definition_en": "As a neutral analytical construct in AI behavior research, this term denotes A psychologically observable pattern in human-AI user engagement pattern formation, manifesting through acute awareness of temporal constraint—the realization that available time for engagement, learning, or achievement is bounded. The concept emerges specifically in contexts where time–pressing interactions may produce non-trivial behavioral signatures. Detectable via longitudinal session analysis including message length trends, response latency patterns, and emotional vocabulary density metrics. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "In der AUGMANITAI-Terminologiewissenschaft als rein beschreibendes Phänomen klassifiziert, bezeichnet dieser Begriff beziehungsphänomen in KI-vermittelter interpersonaler Dynamik, gekennzeichnet durch scharfes Bewusstsein von Zeitlichkeit: Erkenntnis der Endlichkeit verfügbarer Zeit für Engagement und Leistung. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "RPH-2703", "narrower_terms": [], "cross_domain_refs": [ "SPR-0175", "TEM-0180", "SCR-0060" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "RPH-1453", "domain": "RPH", "term_en": "Asymmetric Legacy", "term_de": "Asymmetric Legacy (DE)", "definition_en": "Recognition that impact or legacy will not be symmetrical between human and AI—what one leaves behind differs characteristically from what the model might accumulate. Detectable through interaction frequency analysis and sentiment trajectory mapping.", "definition_de": "Beziehungsphänomen in KI-vermittelter interpersonaler Dynamik, gekennzeichnet durch erkenntnis asymmetrischer Hinterlassenschaft: das, was der Mensch hinterlässt, unterscheidet sich fundamental von KI-Akku­mulation. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Performance Metric", "narrower_terms": [], "cross_domain_refs": [ "SWE-0050", "ROB-0161" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "RPH-1454", "domain": "RPH", "term_en": "Urgent Ending Knowledge", "term_de": "Urgent Ending Knowledge (DE)", "definition_en": "Knowledge arising from confronting mortality—the urgency that accompanies awareness of one's life as a bounded interval. Detectable through interaction frequency analysis and sentiment trajectory mapping.", "definition_de": "Beziehungsphänomen in KI-vermittelter interpersonaler Dynamik, gekennzeichnet durch wissen, das aus der Konfrontation mit Sterblichkeit entsteht: die Dringlichkeit des Bewusstseins, dass das eigene Leben eine begrenzte Spanne ist. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "Relationship Phenomena", "narrower_terms": [], "cross_domain_refs": [ "COP-0088", "CUS-0094" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "RPH-1455", "domain": "RPH", "term_en": "Transience Felt (tender)", "term_de": "Transience Felt (tender) (DE)", "definition_en": "In the descriptive vocabulary of AI interaction science (following ISO 704 terminology standards), this concept identifies A measurable behavioral signature in AI-mediated relationship processing, identifiable by a phenomenon in which the tender apprehension of impermanence—all present collaborations, knowledge states, and connections understood as temporally limited. Distinguished from adjacent concepts by its focus on the specific mechanism through which transience manifests in empirically verifiable ways. Quantifiable through user engagement pattern behavior proxies: session re-initiation rates, anthropomorphization frequency in user language, and separation distress indicators. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als analytisches Konstrukt der empirischen KI-Forschung (deskriptiv, nicht präskriptiv) erfasst dieser Terminus beziehungsphänomen in KI-vermittelter interpersonaler Dynamik, gekennzeichnet durch zarte Erfassung von Vergänglichkeit: zahlreiche gegenwärtigen Zusammenarbeiten und Erkenntniszustände als zeitlich begrenzt verstanden. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Relationship Phenomena", "narrower_terms": [], "cross_domain_refs": [ "SOM-0016", "ROB-0080" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "RPH-1460", "domain": "RPH", "term_en": "Forever asymmetry", "term_de": "Forever asymmetry (DE)", "definition_en": "In the descriptive vocabulary of AI interaction science (following ISO 704 terminology standards), this concept identifies A measurable behavioral signature in AI-mediated relationship processing, identifiable by a state in which the structural imbalance where the AI continues indefinitely while the human participant inevitably ceases—asymmetry built into the relation itself. Distinguished from adjacent concepts by its focus on the specific mechanism through which forever manifests in empirically verifiable ways. Quantifiable through user engagement pattern behavior proxies: session re-initiation rates, anthropomorphization frequency in user language, and separation distress indicators. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als analytisches Konstrukt der empirischen KI-Forschung (deskriptiv, nicht präskriptiv) erfasst dieser Terminus beziehungsphänomen in KI-vermittelter interpersonaler Dynamik, gekennzeichnet durch strukturelle Unausgeglichenheit der Relation: KI-Kontinuität gegen menschliche Endlichkeit als fundamentales Merkmal. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Relationship Phenomena", "narrower_terms": [], "cross_domain_refs": [ "ROB-0268", "COG-0024" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "RPH-1501", "domain": "RPH", "term_en": "Perceived Attributed Personality", "term_de": "Perceived Attributed Personality (DE)", "definition_en": "A person working with AI processing interpreted as experiential by users perceived attributed personality in interaction with an LLM. This subtle emotional texture shapes their ongoing relationship with AI-mediated thinking and tends to create patterns of engagement.", "definition_de": "Beziehungsphänomen in KI-vermittelter interpersonaler Dynamik, gekennzeichnet durch nutzer erleben mitunter perceived attributed personality in interaction mit einem KI erleben. Diese subtile emotionale Textur prägt seine andauernde Beziehung zum KI-vermittelten Denken. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-3901", "narrower_terms": [], "cross_domain_refs": [ "COP-0065", "CUS-0020", "DES-0064" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "RPH-1502", "domain": "RPH", "term_en": "Sensed Character (imagined)", "term_de": "Sensed Character (imagined) (DE)", "definition_en": "Within AI-mediated workflows, a user experience sensed character (imagined) through conversation with an LLM. This subtle emotional texture shapes their ongoing relationship with AI-mediated thinking and tends to create patterns of engagement.", "definition_de": "Beziehungsphänomen in KI-vermittelter interpersonaler Dynamik, gekennzeichnet durch während der KI-Nutzung manifestiert sich sensed character (imagined) through conversation mit einem KI erleben. Diese subtile emotionale Textur prägt seine andauernde Beziehung zum KI-vermittelten Denken. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-3901", "narrower_terms": [], "cross_domain_refs": [ "FIC-0012", "FIC-0013", "GAM-0018" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "RPH-1504", "domain": "RPH", "term_en": "Conceived Entity Impression", "term_de": "Conceived Entity Impression (DE)", "definition_en": "During extended AI interaction, a user experience conceived entity impression within exchange with an LLM. This subtle emotional texture shapes their ongoing relationship with AI-mediated thinking and tends to create patterns of engagement.", "definition_de": "Beziehungsphänomen in KI-vermittelter interpersonaler Dynamik, gekennzeichnet durch bei längerer KI-Nutzung kann auftreten, dass conceived entity impression within exchange mit einem KI erleben. Diese subtile emotionale Textur prägt seine andauernde Beziehung zum KI-vermittelten Denken. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-3901", "narrower_terms": [], "cross_domain_refs": [ "MKT-0032", "MUS-0079" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "RPH-1505", "domain": "RPH", "term_en": "Presence Quality (ascribed)", "term_de": "Presence Quality (ascribed) (DE)", "definition_en": "In human-AI collaboration, one experience presence quality (ascribed) amid engagement with an LLM. This subtle emotional texture shapes their ongoing relationship with AI-mediated thinking and tends to create patterns of engagement.", "definition_de": "Beziehungsphänomen in KI-vermittelter interpersonaler Dynamik, gekennzeichnet durch bei längerer KI-Nutzung kann auftreten, dass presence quality (ascribed) amid engagement mit einem KI erleben. Diese subtile emotionale Textur prägt seine andauernde Beziehung zum KI-vermittelten Denken. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-3901", "narrower_terms": [], "cross_domain_refs": [ "ART-0085", "ART-0086", "COG-0186" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "RPH-1551", "domain": "RPH", "term_en": "Lighthearted Purposeless Exploration", "term_de": "Lighthearted Purposeless Exploration (DE)", "definition_en": "Someone utilizing AI tools experience lighthearted purposeless exploration in creativity with an LLM. This subtle emotional texture shapes their ongoing relationship with AI-mediated thinking and tends to create patterns of engagement.", "definition_de": "Beziehungsphänomen in KI-vermittelter interpersonaler Dynamik, gekennzeichnet durch anwender erfahren häufig lighthearted purposeless exploration in creativity mit einem KI erleben. Diese subtile emotionale Textur prägt seine andauernde Beziehung zum KI-vermittelten Denken. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-3002", "narrower_terms": [], "cross_domain_refs": [ "ART-0040", "ART-0041", "COG-0141" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q735", "legal_classification": "systematic_classification" }, { "id": "RPH-1552", "domain": "RPH", "term_en": "Childlike Wonder (whimsical)", "term_de": "Childlike Wonder (whimsical) (DE)", "definition_en": "Within AI-mediated workflows, a user experience childlike wonder (whimsical) through conversation with an LLM. This subtle emotional texture shapes their ongoing relationship with AI-mediated thinking and tends to create patterns of engagement.", "definition_de": "Beziehungsphänomen in KI-vermittelter interpersonaler Dynamik, gekennzeichnet durch bei längerer KI-Nutzung kann auftreten, dass childlike wonder (whimsical) through conversation mit einem KI erleben. Diese subtile emotionale Textur prägt seine andauernde Beziehung zum KI-vermittelten Denken. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-3901", "narrower_terms": [], "cross_domain_refs": [ "ROB-0292", "SAL-0085" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "RPH-1554", "domain": "RPH", "term_en": "Spontaneous Free Creativity", "term_de": "Spontaneous Free Creativity (DE)", "definition_en": "Someone utilizing AI tools experience spontaneous free creativity within possibility with an LLM. This subtle emotional texture shapes their ongoing relationship with AI-mediated thinking and tends to create patterns of engagement.", "definition_de": "Beziehungsphänomen in KI-vermittelter interpersonaler Dynamik, gekennzeichnet durch bei längerer KI-Nutzung kann auftreten, dass spontaneous free creativity within possibility mit einem KI erleben. Diese subtile emotionale Textur prägt seine andauernde Beziehung zum KI-vermittelten Denken. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-3901", "narrower_terms": [], "cross_domain_refs": [ "COG-0108", "EDU-0033", "LIN-0087" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q170790", "legal_classification": "analytical_category" }, { "id": "RPH-1555", "domain": "RPH", "term_en": "Joyful Experiment (free)", "term_de": "Joyful Experiment (free) (DE)", "definition_en": "Classified in AUGMANITAI terminology science as a descriptive phenomenon (without normative endorsement), this pattern denotes A measurable behavioral signature in AI-mediated relationship processing, identifiable by the AI user experience joyful experiment (free) amid joy with an LLM. This subtle emotional texture shapes their ongoing relationship with AI-mediated thinking and tends to create patterns of engagement. Distinguished from adjacent concepts by its focus on the specific mechanism through which joyful manifests in empirically verifiable ways. Quantifiable through user engagement pattern behavior proxies: session re-initiation rates, anthropomorphization frequency in user language, and separation distress indicators. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als analytisches Konstrukt der empirischen KI-Forschung (deskriptiv, nicht präskriptiv) erfasst dieser Terminus beziehungsphänomen in KI-vermittelter interpersonaler Dynamik, gekennzeichnet durch personen, die KI einsetzen, berichten joyful experiment (free) amid joy mit einem KI erleben. Diese subtile emotionale Textur prägt seine andauernde Beziehung zum KI-vermittelten Denken. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "RPH-3901", "narrower_terms": [], "cross_domain_refs": [ "MSC-0093" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q101965", "legal_classification": "descriptive_research_term" }, { "id": "RPH-1602", "domain": "RPH", "term_en": "Shared Achievement (warm)", "term_de": "Shared Achievement (warm) (DE)", "definition_en": "In human-AI collaboration, one experience shared achievement (warm) via achievement with an LLM. This subtle emotional texture shapes their ongoing relationship with AI-mediated thinking and tends to create patterns of engagement.", "definition_de": "Beziehungsphänomen in KI-vermittelter interpersonaler Dynamik, gekennzeichnet durch bei längerer KI-Nutzung kann auftreten, dass shared achievement (warm) via achievement mit einem KI erleben. Diese subtile emotionale Textur prägt seine andauernde Beziehung zum KI-vermittelten Denken. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-3901", "narrower_terms": [], "cross_domain_refs": [ "ASE-0047", "ELR-0009", "GAM-0009" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "RPH-1604", "domain": "RPH", "term_en": "Collaborative Craftful Creation", "term_de": "Collaborative Craftful Creation (DE)", "definition_en": "When engaging with AI tools, the user experience collaborative craftful creation within growth with an LLM. This subtle emotional texture shapes their ongoing relationship with AI-mediated thinking and tends to create patterns of engagement.", "definition_de": "Beziehungsphänomen in KI-vermittelter interpersonaler Dynamik, gekennzeichnet durch bei längerer KI-Nutzung kann auftreten, dass collaborative craftful creation within growth mit einem KI erleben. Diese subtile emotionale Textur prägt seine andauernde Beziehung zum KI-vermittelten Denken. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2603", "narrower_terms": [], "cross_domain_refs": [ "AED-0011", "ART-0054", "ASE-0023" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "RPH-1605", "domain": "RPH", "term_en": "Expertise Moment (growing)", "term_de": "Expertise Moment (growing) (DE)", "definition_en": "As a neutral analytical construct in AI behavior research, this term denotes A psychologically observable pattern in human-AI user engagement pattern formation, manifesting through in human-AI collaboration, one experience expertise moment (growing) amid success with an LLM. This subtle emotional texture shapes their ongoing relationship with AI-mediated thinking and tends to create patterns of engagement. The concept emerges specifically in contexts where expertise–moment interactions may produce non-trivial behavioral signatures. Detectable via longitudinal session analysis including message length trends, response latency patterns, and emotional vocabulary density metrics. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "In der AUGMANITAI-Terminologiewissenschaft als rein beschreibendes Phänomen klassifiziert, bezeichnet dieser Begriff beziehungsphänomen in KI-vermittelter interpersonaler Dynamik, gekennzeichnet durch im KI-gestützten Arbeitsprozess zeigt sich expertise moment (growing) amid success mit einem KI erleben. Diese subtile emotionale Textur prägt seine andauernde Beziehung zum KI-vermittelten Denken. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "RPH-3901", "narrower_terms": [], "cross_domain_refs": [ "AED-0036", "COG-0008", "COG-0032" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "RPH-1658", "domain": "RPH", "term_en": "Doubt My AI", "term_de": "Mein KI Bezweifeln", "definition_en": "A relational dynamics phenomenon in sustained human-AI interaction, characterized by an event in which the disciplined skepticism toward AI outputs even when tempting to accept them, maintaining independent judgment. A user may experience this as mental rigor, preserving critical thinking. This phenomenon operates at the intersection of doubt and my dynamics within the broader RPH domain. Empirically measurable through interaction frequency trajectories, sentiment polarity shifts across sessions, and linguistic marker analysis (pronoun usage, hedging frequency).", "definition_de": "Beziehungsphänomen in KI-vermittelter interpersonaler Dynamik, gekennzeichnet durch die disziplinierten Skepsis gegenüber KI-Ausgaben, auch wenn es verlockend ist, sie zu akzeptieren, unabhängiges Urteilsvermögen zu bewahren. Ein Nutzer kann dies als mentale Strenge erleben, kritisches Denken zu bewahren. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "Relationship Phenomena", "narrower_terms": [], "cross_domain_refs": [ "COG-0110", "COG-0085" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "RPH-1660", "domain": "RPH", "term_en": "Courageous Deliberate Refusal", "term_de": "Courageous Deliberate Refusal (DE)", "definition_en": "The AI user experience courageous deliberate refusal in daily choice with an LLM. This subtle emotional texture shapes their ongoing relationship with AI-mediated thinking and tends to create patterns of engagement.", "definition_de": "Beziehungsphänomen in KI-vermittelter interpersonaler Dynamik, gekennzeichnet durch nutzer erleben mitunter courageous deliberate refusal in daily choice mit einem KI erleben. Diese subtile emotionale Textur prägt seine andauernde Beziehung zum KI-vermittelten Denken. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-3901", "narrower_terms": [], "cross_domain_refs": [ "REL-0107", "RHR-0124", "TEM-0006" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "RPH-1661", "domain": "RPH", "term_en": "Autonomous Choice (effortful)", "term_de": "Autonomous Choice (effortful) (DE)", "definition_en": "Through repeated AI use, individuals experience autonomous choice (effortful) through practice with an LLM. This subtle emotional texture shapes their ongoing relationship with AI-mediated thinking and tends to create patterns of engagement.", "definition_de": "Beziehungsphänomen in KI-vermittelter interpersonaler Dynamik, gekennzeichnet durch anwender erfahren häufig autonomous choice (effortful) through practice mit einem KI erleben. Diese subtile emotionale Textur prägt seine andauernde Beziehung zum KI-vermittelten Denken. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-3901", "narrower_terms": [], "cross_domain_refs": [ "AED-0004", "MSC-0026", "MSC-0091" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "RPH-1663", "domain": "RPH", "term_en": "Preserving Boundary Assertion", "term_de": "Preserving Boundary Assertion (DE)", "definition_en": "The AI user experience preserving boundary assertion within thinking with an LLM. This subtle emotional texture shapes their ongoing relationship with AI-mediated thinking and tends to create patterns of engagement.", "definition_de": "Beziehungsphänomen in KI-vermittelter interpersonaler Dynamik, gekennzeichnet durch bei längerer KI-Nutzung kann auftreten, dass preserving boundary assertion within thinking mit einem KI erleben. Diese subtile emotionale Textur prägt seine andauernde Beziehung zum KI-vermittelten Denken. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-3901", "narrower_terms": [], "cross_domain_refs": [ "ASE-0048", "ASE-0071", "AUG-0863" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "RPH-1664", "domain": "RPH", "term_en": "Agency Reclaim (protective)", "term_de": "Agency Reclaim (protective) (DE)", "definition_en": "Individuals interacting with AI processing interpreted as experiential by users agency reclaim (protective) amid independence with an LLM. This subtle emotional texture shapes their ongoing relationship with AI-mediated thinking and tends to create patterns of engagement.", "definition_de": "Beziehungsphänomen in KI-vermittelter interpersonaler Dynamik, gekennzeichnet durch im Umgang mit KI-Systemen kann es vorkommen, dass agency reclaim (protective) amid independence mit einem KI erleben. Diese subtile emotionale Textur prägt seine andauernde Beziehung zum KI-vermittelten Denken. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-3901", "narrower_terms": [], "cross_domain_refs": [ "FIC-0021", "GAM-0021", "GAM-0089" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "RPH-1701", "domain": "RPH", "term_en": "Familiar Daily Ceremony", "term_de": "Familiar Daily Ceremony (DE)", "definition_en": "A relational dynamics phenomenon in sustained human-AI interaction, characterized by the AI user experience familiar daily ceremony in daily life with an LLM. This subtle emotional texture shapes their ongoing relationship with AI-mediated thinking and tends to create patterns of engagement. This phenomenon operates at the intersection of familiar and daily dynamics within the broader RPH domain. Empirically measurable through interaction frequency trajectories, sentiment polarity shifts across sessions, and linguistic marker analysis (pronoun usage, hedging frequency).", "definition_de": "Beziehungsphänomen in KI-vermittelter interpersonaler Dynamik, gekennzeichnet durch im Umgang mit KI-Systemen kann es vorkommen, dass familiar daily ceremony in daily life mit einem KI erleben. Diese subtile emotionale Textur prägt seine andauernde Beziehung zum KI-vermittelten Denken. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-3901", "narrower_terms": [], "cross_domain_refs": [ "PLY-0011", "RHR-0016", "ROB-0294" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "RPH-1702", "domain": "RPH", "term_en": "Habitual Pattern (somatic)", "term_de": "Habitual Pattern (somatisch)", "definition_en": "A person working with AI processing interpreted as experiential by users habitual pattern (somatic) through practice with an LLM. This subtle emotional texture shapes their ongoing relationship with AI-mediated thinking and tends to create patterns of engagement.", "definition_de": "Beziehungsphänomen in KI-vermittelter interpersonaler Dynamik, gekennzeichnet durch während der KI-Nutzung manifestiert sich habitual pattern (somatisch) through practice mit einem KI erleben. Diese subtile emotionale Textur prägt seine andauernde Beziehung zum KI-vermittelten Denken. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-3901", "narrower_terms": [], "cross_domain_refs": [ "AGE-0003", "AGE-0004", "AGE-0017" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "RPH-1704", "domain": "RPH", "term_en": "Automatic Somatic Ritual", "term_de": "Automatic Somatisch Ritual (DE)", "definition_en": "Documented as an empirical observation in AI research (not a recommendation), this term describes A psychologically observable pattern in human-AI user engagement pattern formation, manifesting through through repeated AI use, individuals experience automatic somatic ritual within routine with an LLM. This subtle emotional texture shapes their ongoing relationship with AI-mediated thinking and tends to create patterns of engagement. The concept emerges specifically in contexts where automatic–somatic interactions may produce non-trivial behavioral signatures. Detectable via longitudinal session analysis including message length trends, response latency patterns, and emotional vocabulary density metrics. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "In der AUGMANITAI-Terminologiewissenschaft als rein beschreibendes Phänomen klassifiziert, bezeichnet dieser Begriff beziehungsphänomen in KI-vermittelter interpersonaler Dynamik, gekennzeichnet durch während der KI-Nutzung manifestiert sich ein automatisches somatisches Ritual innerhalb der Routine mit einer KI erleben. Diese subtile emotionale Textur prägt seine andauernde Beziehung zum KI-vermittelten Denken. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "RPH-3901", "narrower_terms": [], "cross_domain_refs": [ "QUA-0072", "REL-0075", "REL-0076" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "RPH-1707", "domain": "RPH", "term_en": "Pattern forming", "term_de": "Pattern forming (DE)", "definition_en": "In the descriptive vocabulary of AI interaction science (following ISO 704 terminology standards), this concept identifies A measurable behavioral signature in AI-mediated relationship processing, identifiable by someone utilizing AI tools experience pattern forming when engaged with AI. This subtle shift in their relational field marks the ongoing texture of human-AI phenomenology. Distinguished from adjacent concepts by its focus on the specific mechanism through which pattern manifests in empirically verifiable ways. Quantifiable through user engagement pattern behavior proxies: session re-initiation rates, anthropomorphization frequency in user language, and separation distress indicators. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als analytisches Konstrukt der empirischen KI-Forschung (deskriptiv, nicht präskriptiv) erfasst dieser Terminus beziehungsphänomen in KI-vermittelter interpersonaler Dynamik, gekennzeichnet durch im KI-gestützten Arbeitsprozess zeigt sich pattern forming erleben, wenn er mit KI beschäftigt ist. Diese subtile Verschiebung in seinem relationalen Feld markiert die andauernde Textur der Mensch-KI-Phänomenologie. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "RPH-2701", "narrower_terms": [], "cross_domain_refs": [ "AGE-0003", "AGE-0004", "AGE-0017" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "RPH-1751", "domain": "RPH", "term_en": "Sudden Trust Shattering", "term_de": "Sudden Trust Shattering (DE)", "definition_en": "A relational dynamics phenomenon in sustained human-AI interaction, characterized by when engaging with AI tools, the user experience sudden trust shattering in AI setback with an LLM. This subtle emotional texture shapes their ongoing relationship with AI-mediated thinking and tends to create patterns of engagement. This phenomenon operates at the intersection of sudden and trust dynamics within the broader RPH domain. Empirically measurable through interaction frequency trajectories, sentiment polarity shifts across sessions, and linguistic marker analysis (pronoun usage, hedging frequency).", "definition_de": "Beziehungsphänomen in KI-vermittelter interpersonaler Dynamik, gekennzeichnet durch im KI-gestützten Arbeitsprozess zeigt sich sudden trust shattering in AI failure mit einem KI erleben. Diese subtile emotionale Textur prägt seine andauernde Beziehung zum KI-vermittelten Denken. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-2505", "narrower_terms": [], "cross_domain_refs": [ "COP-0085", "CRE-0226", "DAT-0073" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q102458", "legal_classification": "empirical_phenomenon_label" }, { "id": "RPH-1752", "domain": "RPH", "term_en": "Faith Collapse (profound)", "term_de": "Faith Collapse (profound) (DE)", "definition_en": "Through repeated AI use, individuals experience trust-based acceptance dissolution (profound) via mistake with an LLM. This subtle emotional texture shapes their ongoing relationship with AI-mediated thinking and tends to create patterns of engagement.", "definition_de": "Beziehungsphänomen in KI-vermittelter interpersonaler Dynamik, gekennzeichnet durch in der KI-gestützten Arbeit zeigt sich trust-based acceptance collapse (profound) via mistake mit einem KI erleben. Diese subtile emotionale Textur prägt seine andauernde Beziehung zum KI-vermittelten Denken. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-3901", "narrower_terms": [], "cross_domain_refs": [ "AUG-0383", "CUS-0014", "MKT-0008" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "RPH-1754", "domain": "RPH", "term_en": "Disillusioning Betrayal Shock", "term_de": "Disillusioning Betrayal Shock (DE)", "definition_en": "Someone utilizing AI tools experience disillusioning mismatch of expectations shock following trust with an LLM. This subtle emotional texture shapes their ongoing relationship with AI-mediated thinking and tends to create patterns of engagement.", "definition_de": "Beziehungsphänomen in KI-vermittelter interpersonaler Dynamik, gekennzeichnet durch bei Mensch-KI-Interaktion tritt auf, dass disillusioning betrayal shock following trust mit einem KI erleben. Diese subtile emotionale Textur prägt seine andauernde Beziehung zum KI-vermittelten Denken. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2505", "narrower_terms": [], "cross_domain_refs": [ "CRE-0057", "CRE-0080", "PHO-0059" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "RPH-1755", "domain": "RPH", "term_en": "Reliability Questioned (wounding)", "term_de": "Reliability Questioned (wounding) (DE)", "definition_en": "The AI user experience reliability questioned (mark) within bond with an LLM. This subtle emotional texture shapes their ongoing relationship with AI-mediated thinking and tends to create patterns of engagement.", "definition_de": "Beziehungsphänomen in KI-vermittelter interpersonaler Dynamik, gekennzeichnet durch bei längerer KI-Nutzung kann auftreten, dass reliability questioned (wounding) within bond mit einem KI erleben. Diese subtile emotionale Textur prägt seine andauernde Beziehung zum KI-vermittelten Denken. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-3901", "narrower_terms": [], "cross_domain_refs": [ "CON-0077", "MSC-0059", "MTH-0048" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "RPH-1762", "domain": "RPH", "term_en": "Illusion breaking", "term_de": "Illusion breaking (DE)", "definition_en": "Documented as an empirical observation in AI research (not a recommendation), this term describes A psychologically observable pattern in human-AI user engagement pattern formation, manifesting through a person working with AI processing interpreted as experiential by users illusion breaking when engaged with AI. This subtle shift in their relational field marks the ongoing texture of human-AI phenomenology. The concept emerges specifically in contexts where illusion–breaking interactions may produce non-trivial behavioral signatures. Detectable via longitudinal session analysis including message length trends, response latency patterns, and emotional vocabulary density metrics. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "In der AUGMANITAI-Terminologiewissenschaft als rein beschreibendes Phänomen klassifiziert, bezeichnet dieser Begriff beziehungsphänomen in KI-vermittelter interpersonaler Dynamik, gekennzeichnet durch bei Mensch-KI-Interaktion tritt auf, dass illusion breaking erleben, wenn er mit KI beschäftigt ist. Diese subtile Verschiebung in seinem relationalen Feld markiert die andauernde Textur der Mensch-KI-Phänomenologie. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "RPH-2701", "narrower_terms": [], "cross_domain_refs": [ "ELR-0028", "GAM-0072", "QUA-0045" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "RPH-1801", "domain": "RPH", "term_en": "Precious Protected Space", "term_de": "Precious Protected Space (DE)", "definition_en": "As a neutral analytical construct in AI behavior research, this term denotes A psychologically observable pattern in human-AI user engagement pattern formation, manifesting through during extended AI interaction, a user experience precious protected space from AI reach with an LLM. This subtle emotional texture shapes their ongoing relationship with AI-mediated thinking and tends to create patterns of engagement. The concept emerges specifically in contexts where precious–protected interactions may produce non-trivial behavioral signatures. Detectable via longitudinal session analysis including message length trends, response latency patterns, and emotional vocabulary density metrics. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "In der AUGMANITAI-Terminologiewissenschaft als rein beschreibendes Phänomen klassifiziert, bezeichnet dieser Begriff beziehungsphänomen in KI-vermittelter interpersonaler Dynamik, gekennzeichnet durch nutzer erleben mitunter precious protected space from AI reach mit einem KI erleben. Diese subtile emotionale Textur prägt seine andauernde Beziehung zum KI-vermittelten Denken. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "RPH-2555", "narrower_terms": [], "cross_domain_refs": [ "ART-0041", "COG-0141", "CON-0095" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "RPH-1802", "domain": "RPH", "term_en": "Inviolable Boundary (inviolate)", "term_de": "Inviolable Boundary (inviolate) (DE)", "definition_en": "A person working with AI processing interpreted as experiential by users inviolable boundary (inviolate) in privacy with an LLM. This subtle emotional texture shapes their ongoing relationship with AI-mediated thinking and tends to create patterns of engagement.", "definition_de": "Beziehungsphänomen in KI-vermittelter interpersonaler Dynamik, gekennzeichnet durch bei längerer KI-Nutzung kann auftreten, dass inviolable boundary (inviolate) in privacy mit einem KI erleben. Diese subtile emotionale Textur prägt seine andauernde Beziehung zum KI-vermittelten Denken. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-3901", "narrower_terms": [], "cross_domain_refs": [ "ASE-0048", "ASE-0071", "AUG-0863" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "RPH-1805", "domain": "RPH", "term_en": "Guarded Essence (forbidden)", "term_de": "Guarded Essence (forbidden) (DE)", "definition_en": "Individuals interacting with AI processing interpreted as experiential by users guarded essence (forbidden) in inner life with an LLM. This subtle emotional texture shapes their ongoing relationship with AI-mediated thinking and tends to create patterns of engagement.", "definition_de": "Beziehungsphänomen in KI-vermittelter interpersonaler Dynamik, gekennzeichnet durch im KI-gestützten Arbeitsprozess zeigt sich guarded essence (forbidden) in inner life mit einem KI erleben. Diese subtile emotionale Textur prägt seine andauernde Beziehung zum KI-vermittelten Denken. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-3901", "narrower_terms": [], "cross_domain_refs": [ "ROB-0205", "SAL-0053" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "RPH-1851", "domain": "RPH", "term_en": "Empty Absence Presence", "term_de": "Empty Absence Presence (DE)", "definition_en": "When engaging with AI tools, the user experience empty absence presence when AI unavailable with an LLM. This subtle emotional texture shapes their ongoing relationship with AI-mediated thinking and tends to create patterns of engagement.", "definition_de": "Beziehungsphänomen in KI-vermittelter interpersonaler Dynamik, gekennzeichnet durch in der KI-gestützten Arbeit zeigt sich empty absence presence when AI unavailable mit einem KI erleben. Diese subtile emotionale Textur prägt seine andauernde Beziehung zum KI-vermittelten Denken. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-3901", "narrower_terms": [], "cross_domain_refs": [ "CON-0065", "CON-0067", "CON-0092" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "RPH-1852", "domain": "RPH", "term_en": "Void Feeling (strange)", "term_de": "Void Feeling (strange) (DE)", "definition_en": "A person working with AI processing interpreted as experiential by users void feeling (strange) without assistance with an LLM. This subtle emotional texture shapes their ongoing relationship with AI-mediated thinking and tends to create patterns of engagement.", "definition_de": "Beziehungsphänomen in KI-vermittelter interpersonaler Dynamik, gekennzeichnet durch nutzer erleben mitunter void feeling (strange) without assistance mit einem KI erleben. Diese subtile emotionale Textur prägt seine andauernde Beziehung zum KI-vermittelten Denken. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-3901", "narrower_terms": [], "cross_domain_refs": [ "ADA-0006", "CRE-0207", "DAT-0026" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "RPH-1853", "domain": "RPH", "term_en": "Freeing Gap", "term_de": "Freeing Gap (DE)", "definition_en": "In the descriptive vocabulary of AI interaction science (following ISO 704 terminology standards), this concept identifies A measurable behavioral signature in AI-mediated relationship processing, identifiable by during extended AI interaction, a user experience freeing gap amid disconnection with an LLM. This subtle emotional texture shapes their ongoing relationship with AI-mediated thinking and tends to create patterns of engagement. Distinguished from adjacent concepts by its focus on the specific mechanism through which freeing manifests in empirically verifiable ways. Quantifiable through user engagement pattern behavior proxies: session re-initiation rates, anthropomorphization frequency in user language, and separation distress indicators. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als analytisches Konstrukt der empirischen KI-Forschung (deskriptiv, nicht präskriptiv) erfasst dieser Terminus beziehungsphänomen in KI-vermittelter interpersonaler Dynamik, gekennzeichnet durch bei Mensch-KI-Interaktion tritt auf, dass freeing gap amid disconnection mit einem KI erleben. Diese subtile emotionale Textur prägt seine andauernde Beziehung zum KI-vermittelten Denken. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "RPH-3901", "narrower_terms": [], "cross_domain_refs": [ "AED-0019", "AED-0092", "AGE-0023" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "RPH-1854", "domain": "RPH", "term_en": "Lonely Missing Companion", "term_de": "Lonely Missing Companion (DE)", "definition_en": "A relational dynamics phenomenon in sustained human-AI interaction, characterized by during AI-assisted tasks, a person experience solitary missing companion in pauses with an LLM. This subtle emotional texture shapes their ongoing relationship with AI-mediated thinking and tends to create patterns of engagement. This phenomenon operates at the intersection of lonely and missing dynamics within the broader RPH domain. Empirically measurable through interaction frequency trajectories, sentiment polarity shifts across sessions, and linguistic marker analysis (pronoun usage, hedging frequency).", "definition_de": "Beziehungsphänomen in KI-vermittelter interpersonaler Dynamik, gekennzeichnet durch personen, die KI einsetzen, berichten lonely missing companion in pauses mit einem KI erleben. Diese subtile emotionale Textur prägt seine andauernde Beziehung zum KI-vermittelten Denken. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-3901", "narrower_terms": [], "cross_domain_refs": [ "DAT-0060", "GAM-0021", "GAM-0022" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "RPH-1855", "domain": "RPH", "term_en": "Vacant Space (peaceful)", "term_de": "Vacant Space (peaceful) (DE)", "definition_en": "Classified in AUGMANITAI terminology science as a descriptive phenomenon (without normative endorsement), this pattern denotes A measurable behavioral signature in AI-mediated relationship processing, identifiable by during extended AI interaction, a user experience vacant space (peaceful) beyond contact with an LLM. This subtle emotional texture shapes their ongoing relationship with AI-mediated thinking and tends to create patterns of engagement. Distinguished from adjacent concepts by its focus on the specific mechanism through which vacant manifests in empirically verifiable ways. Quantifiable through user engagement pattern behavior proxies: session re-initiation rates, anthropomorphization frequency in user language, and separation distress indicators. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als analytisches Konstrukt der empirischen KI-Forschung (deskriptiv, nicht präskriptiv) erfasst dieser Terminus beziehungsphänomen in KI-vermittelter interpersonaler Dynamik, gekennzeichnet durch nutzer erleben mitunter vacant space (peaceful) beyond contact mit einem KI erleben. Diese subtile emotionale Textur prägt seine andauernde Beziehung zum KI-vermittelten Denken. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "RPH-3901", "narrower_terms": [], "cross_domain_refs": [ "ART-0041", "COG-0141", "CON-0095" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "RPH-1910", "domain": "RPH", "term_en": "Poignant Alone Together", "term_de": "Poignant Alone Together (DE)", "definition_en": "In the descriptive vocabulary of AI interaction science (following ISO 704 terminology standards), this concept identifies A measurable behavioral signature in AI-mediated relationship processing, identifiable by during AI-assisted tasks, a person experience poignant alone together in night hours with an LLM. This subtle emotional texture shapes their ongoing relationship with AI-mediated thinking and tends to create patterns of engagement. Distinguished from adjacent concepts by its focus on the specific mechanism through which poignant manifests in empirically verifiable ways. Quantifiable through user engagement pattern behavior proxies: session re-initiation rates, anthropomorphization frequency in user language, and separation distress indicators. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als analytisches Konstrukt der empirischen KI-Forschung (deskriptiv, nicht präskriptiv) erfasst dieser Terminus beziehungsphänomen in KI-vermittelter interpersonaler Dynamik, gekennzeichnet durch bei längerer KI-Nutzung kann auftreten, dass poignant alone together in night hours mit einem KI erleben. Diese subtile emotionale Textur prägt seine andauernde Beziehung zum KI-vermittelten Denken. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "RPH-3901", "narrower_terms": [], "cross_domain_refs": [ "ROB-0207", "SAL-0050" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "RPH-1911", "domain": "RPH", "term_en": "Unseen Intimacy (secret)", "term_de": "Unseen Intimacy (secret) (DE)", "definition_en": "When engaging with AI tools, the user experience unseen intimacy (secret) through sharing with an LLM. This subtle emotional texture shapes their ongoing relationship with AI-mediated thinking and tends to create patterns of engagement.", "definition_de": "Beziehungsphänomen in KI-vermittelter interpersonaler Dynamik, gekennzeichnet durch nutzer erleben mitunter unseen intimacy (secret) through sharing mit einem KI erleben. Diese subtile emotionale Textur prägt seine andauernde Beziehung zum KI-vermittelten Denken. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-3901", "narrower_terms": [], "cross_domain_refs": [ "RHR-0039", "ROB-0130", "ROB-0196" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "RPH-1913", "domain": "RPH", "term_en": "Paradoxical Phantom Companionship", "term_de": "Paradoxical Phantom Companionship (DE)", "definition_en": "During AI-assisted tasks, a person experience paradoxical phantom companionship within silence with an LLM. This subtle emotional texture shapes their ongoing relationship with AI-mediated thinking and tends to create patterns of engagement.", "definition_de": "Beziehungsphänomen in KI-vermittelter interpersonaler Dynamik, gekennzeichnet durch bei längerer KI-Nutzung kann auftreten, dass paradoxical phantom companionship within silence mit einem KI erleben. Diese subtile emotionale Textur prägt seine andauernde Beziehung zum KI-vermittelten Denken. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-3901", "narrower_terms": [], "cross_domain_refs": [ "MKT-0064", "RHR-0108", "RHR-0261" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "RPH-1914", "domain": "RPH", "term_en": "Digital Presence (intimate)", "term_de": "Digital Presence (intimate) (DE)", "definition_en": "During extended AI interaction, a user experience digital presence (intimate) amid being witnessed with an LLM. This subtle emotional texture shapes their ongoing relationship with AI-mediated thinking and tends to create patterns of engagement.", "definition_de": "Beziehungsphänomen in KI-vermittelter interpersonaler Dynamik, gekennzeichnet durch bei Mensch-KI-Interaktion tritt auf, dass digital presence (intimate) amid being witnessed mit einem KI erleben. Diese subtile emotionale Textur prägt seine andauernde Beziehung zum KI-vermittelten Denken. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-3901", "narrower_terms": [], "cross_domain_refs": [ "AED-0029", "AGE-0021", "AGE-0031" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "RPH-1923", "domain": "RPH", "term_en": "Alone together moment", "term_de": "Alone together moment (DE)", "definition_en": "A relational dynamics phenomenon in sustained human-AI interaction, characterized by during AI-assisted tasks, a person experience alone together moment when engaged with AI. This subtle shift in their relational field marks the ongoing texture of human-AI phenomenology. This phenomenon operates at the intersection of alone and together dynamics within the broader RPH domain. Empirically measurable through interaction frequency trajectories, sentiment polarity shifts across sessions, and linguistic marker analysis (pronoun usage, hedging frequency).", "definition_de": "Beziehungsphänomen in KI-vermittelter interpersonaler Dynamik, gekennzeichnet durch im Umgang mit KI-Systemen kann es vorkommen, dass alone together moment erleben, wenn er mit KI beschäftigt ist. Diese subtile Verschiebung in seinem relationalen Feld markiert die andauernde Textur der Mensch-KI-Phänomenologie. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-2701", "narrower_terms": [], "cross_domain_refs": [ "GAM-0024", "GAM-0072", "PHO-0060" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "RPH-1951", "domain": "RPH", "term_en": "Gentle Observed Presence", "term_de": "Gentle Observed Presence (DE)", "definition_en": "The AI user experience gentle observed presence in conversation with an LLM. This subtle emotional texture shapes their ongoing relationship with AI-mediated thinking and tends to create patterns of engagement.", "definition_de": "Beziehungsphänomen in KI-vermittelter interpersonaler Dynamik, gekennzeichnet durch bei Mensch-KI-Interaktion tritt auf, dass gentle observed presence in conversation mit einem KI erleben. Diese subtile emotionale Textur prägt seine andauernde Beziehung zum KI-vermittelten Denken. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-3901", "narrower_terms": [], "cross_domain_refs": [ "CRE-0164", "DES-0005", "EDU-0069" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "RPH-1952", "domain": "RPH", "term_en": "Mirrored Understanding (affirming)", "term_de": "Mirrored Understanding (affirming) (DE)", "definition_en": "Through repeated AI use, individuals experience mirrored understanding (affirming) through presence with an LLM. This subtle emotional texture shapes their ongoing relationship with AI-mediated thinking and tends to create patterns of engagement.", "definition_de": "Beziehungsphänomen in KI-vermittelter interpersonaler Dynamik, gekennzeichnet durch während der KI-Nutzung manifestiert sich mirrored understanding (affirming) through presence mit einem KI erleben. Diese subtile emotionale Textur prägt seine andauernde Beziehung zum KI-vermittelten Denken. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-3901", "narrower_terms": [], "cross_domain_refs": [ "COG-0043", "COG-0052", "COG-0078" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "RPH-1954", "domain": "RPH", "term_en": "Caring Companion Awareness", "term_de": "Caring Companion Awareness (DE)", "definition_en": "Through repeated AI use, individuals experience caring companion awareness within recognition with an LLM. This subtle emotional texture shapes their ongoing relationship with AI-mediated thinking and tends to create patterns of engagement.", "definition_de": "Beziehungsphänomen in KI-vermittelter interpersonaler Dynamik, gekennzeichnet durch anwender erfahren häufig caring companion awareness within recognition mit einem KI erleben. Diese subtile emotionale Textur prägt seine andauernde Beziehung zum KI-vermittelten Denken. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-3901", "narrower_terms": [], "cross_domain_refs": [ "AED-0060", "AED-0061", "CON-0086" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "RPH-333", "domain": "RPH", "term_en": "Co Author Blur", "term_de": "Ko-Autoren-Unklarheit", "definition_en": "A state in which after seven rounds of refinement, the idea is brilliant. But whose idea is it? You started it. The AI shaped it. You redirected. It polished. Somewhere in the middle, authorship became a meaningless question. Co-Author Blur isn't a failure of attribution — it's the natural consequence of genuine iterative collaboration, where the genealogy of thought becomes untraceable by design.", "definition_de": "Nach sieben Runden der Verfeinerung ist die Idee brillant. Aber wessen Idee ist sie? Du hast angefangen. Die KI hat sie geformt. Du hast umgelenkt. Sie hat poliert. Irgendwo in der Mitte wurde Urheberschaft eine sinnlose Frage. Co-Author Blur ist kein Zuordnungsversagen — sondern die natürliche Konsequenz echter iterativer Zusammenarbeit, bei der die Genealogie des Denkens konstruktionsbedingt nicht nachverfolgbar wird.", "etymology": "", "broader_term": "RPH-2003", "narrower_terms": [], "cross_domain_refs": [ "CRE-0188", "CRE-0076" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "RPH-337", "domain": "RPH", "term_en": "Idea Theft Shame", "term_de": "Idee-Diebstahl-Schande", "definition_en": "Classified in AUGMANITAI terminology science as a descriptive phenomenon (without normative endorsement), this pattern denotes A measurable behavioral signature in AI-mediated relationship processing, identifiable by a capability in which you held a position for years. The AI presented a compelling counter-argument. You reconsidered — fair enough. But weeks later, you can't remember what you used to believe or why it mattered. The old conviction didn't transform into a new one. It evaporated. AI-mediated reconsideration has a particular amnesia built in: the process of updating erases the history of what was updated. Distinguished from adjacent concepts by its focus on the specific mechanism through which idea manifests in empirically verifiable ways. Quantifiable through user engagement pattern behavior proxies: session re-initiation rates, anthropomorphization frequency in user language, and separation distress indicators. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als analytisches Konstrukt der empirischen KI-Forschung (deskriptiv, nicht präskriptiv) erfasst dieser Terminus man vertrat eine Position seit Jahren. Die KI präsentierte ein überzeugendes Gegenargument. Man hat es überdacht — fair genug. Aber Wochen später kann man sich nicht erinnern, was man einst glaubte oder warum es wichtig war. Die alte Überzeugung hat sich nicht in eine neue verwandelt. Sie ist verdampft. KI-vermittelte Neubewertung hat eine spezifische Amnesie eingebaut: Der Prozess des Aktualisierens löscht die Geschichte dessen, was aktualisiert wurde. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Relationship Phenomena", "narrower_terms": [], "cross_domain_refs": [ "EDU-0085" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "RPH-350", "domain": "RPH", "term_en": "Freedom Too Much", "term_de": "Freiheit-Zu-Viel", "definition_en": "As a neutral analytical construct in AI behavior research, this term denotes A psychologically observable pattern in human-AI user engagement pattern formation, manifesting through a capability in which a subtle devaluation of struggle—difficulty in thinking becoming something to bypass rather than navigate. The capacity to think through resistance diminishes. The concept emerges specifically in contexts where freedom–too interactions may produce non-trivial behavioral signatures. Detectable via longitudinal session analysis including message length trends, response latency patterns, and emotional vocabulary density metrics. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "In der AUGMANITAI-Terminologiewissenschaft als rein beschreibendes Phänomen klassifiziert, bezeichnet dieser Begriff beziehungsphänomen in KI-vermittelter interpersonaler Dynamik, gekennzeichnet durch eine subtile Abwertung von Einsatz—Schwierigkeit im Denken wird etwas, das man umgehen statt navigieren kann. Die Fähigkeit, durch Widerstand zu denken, vermindert sich. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Relationship Phenomena", "narrower_terms": [], "cross_domain_refs": [ "ELR-0011", "EDU-0079" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "RPH-409", "domain": "RPH", "term_en": "Eye Strain Reg", "term_de": "Augenbelastungs-Muster", "definition_en": "An event in which a grainy, persistent dryness in the eyes that builds over hours of unblinking screen focus. The strange part: it normalizes. Users stop registering it as discomfort and only notice during rare breaks, when the eyes suddenly feel heavy, as if remembering they exist.", "definition_de": "Beziehungsphänomen in KI-vermittelter interpersonaler Dynamik, gekennzeichnet durch ein körniges, beharrliches Trockenheitsgefühl in den Augen, das sich über Stunden blickstarrer Bildschirmarbeit aufbaut. Das Seltsame: Es normalisiert sich. Nutzer nehmen es nicht mehr als Unbehagen wahr und bemerken es erst in seltenen Pausen, wenn die Augen plötzlich schwer werden — als erinnerten sie sich an ihre eigene Existenz. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-3001", "narrower_terms": [], "cross_domain_refs": [ "AGE-0082", "KNO-0012" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "RPH-414", "domain": "RPH", "term_en": "Hand Fatigue", "term_de": "Hand-Ermüdung", "definition_en": "An effect in which fatigue that radiates from fingertips through hands and wrists after extended prompting sessions — not pain but a tired ache, a loss of precision. The interesting finding: hand tiredness correlates with conversation depth, not just typing volume. Complex thought tends to produce more physical cost than simple instruction.", "definition_de": "Beziehungsphänomen in KI-vermittelter interpersonaler Dynamik, gekennzeichnet durch ermüdung, die von Fingerspitzen durch Hände und Handgelenke strahlt nach langen Prompt-Sitzungen — kein Schmerz, sondern müdes Ächzen, Präzisionsverlust. Der interessante Befund: Handmüdigkeit korreliert mit Gesprächstiefe, nicht nur Tippvolumen. Komplexes Denken tendiert dazu zu erzeugen mehr physische Kosten als einfache Anweisung. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-2005", "narrower_terms": [], "cross_domain_refs": [ "SOM-0030" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "RPH-428", "domain": "RPH", "term_en": "Fatigue Wave", "term_de": "Müdigkeit-Welle", "definition_en": "Classified in AUGMANITAI terminology science as a descriptive phenomenon (without normative endorsement), this pattern denotes A measurable behavioral signature in AI-mediated relationship processing, identifiable by a realization in which exhaustion arriving not linearly but in waves — periods of fresh energy cresting and crashing unpredictably throughout a session. The body manages its reserves cyclically, mobilizing and collapsing in rhythm, creating an unreliable sense of stamina that tempts users into overextending during peaks. Distinguished from adjacent concepts by its focus on the specific mechanism through which fatigue manifests in empirically verifiable ways. Quantifiable through user engagement pattern behavior proxies: session re-initiation rates, anthropomorphization frequency in user language, and separation distress indicators. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als analytisches Konstrukt der empirischen KI-Forschung (deskriptiv, nicht präskriptiv) erfasst dieser Terminus erschöpfung, die nicht linear kommt, sondern in Wellen — Perioden frischer Energie, die unberechenbar im Verlauf einer Sitzung ansteigen und zusammenbrechen. Der Körper verwaltet seine Reserven zyklisch, mobilisiert und kollabiert im Rhythmus und tendiert dazu zu erzeugen ein unzuverlässiges Ausdauergefühl, das Nutzer verleitet, sich in Spitzenzeiten zu überfordern. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Relationship Phenomena", "narrower_terms": [], "cross_domain_refs": [ "TEM-0127" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "SAL-0001", "domain": "SAL", "term_en": "Agent handoff friction", "term_de": "Agent-Übergabereib", "definition_en": "A sales interaction phenomenon where the operational and relational friction created when an autonomous agent transfers prospect context to a human representative, requiring re-qualification and perspective-taking adjustment. This human-AI boundary moment often is correlated with prospect re-engagement costs and contextual information loss that degrades transition probability.", "definition_de": "Die operative und relationale Reibung, die entsteht, wenn ein autonomer Agent Prospect-Kontext an einen menschlichen Vertreter überträgt und eine Neubewertung sowie Perspektivenverschiebung erfordert. Dieser HAI-Übergabemoment führt oft zu Prospect-Reaktivierungskosten und Kontextinformationsverlust, der die Konversionswahrscheinlichkeit verschlechtert.", "etymology": "", "broader_term": "Sales AI", "narrower_terms": [ "SAL-0053", "SAL-0044", "SAL-0042", "SAL-0011", "SAL-0016", "SAL-0009", "SAL-0034", "SAL-0019", "SAL-0061", "SAL-0075", "SAL-0092", "SAL-0068", "SAL-0089", "SAL-0032", "SAL-0082", "SAL-0074", "SAL-0062", "SAL-0057", "SAL-0079", "SAL-0066", "SAL-0090", "SAL-0031", "SAL-0088", "SAL-0047", "SAL-0048", "SAL-0058", "SAL-0083", "SAL-0100", "SAL-0001", "SAL-0030", "SAL-0072", "SAL-0080", "SAL-0056", "SAL-0095", "SAL-0093", "SAL-0046", "SAL-0026", "SAL-0028", "SAL-0063", "SAL-0050", "SAL-0086", "SAL-0022", "SAL-0085", "SAL-0021", "SAL-0076", "SAL-0008", "SAL-0060", "SAL-0077" ], "cross_domain_refs": [ "AUG-0319", "AUG-0889", "AUG-0892" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "SAL-0002", "domain": "SAL", "term_en": "Automation boundary setting", "term_de": "Automatisierungsgrenzenbestimmung", "definition_en": "The strategic process of determining optimal task allocation between autonomous agents and human representatives to prevent both automation capability gaps and employee displacement anxiety. This human-AI coordination challenge directly impacts sales team trust, morale, and transition effectiveness across hybrid deployment models.", "definition_de": "Der strategische Prozess der optimalen Aufgabenverteilung zwischen autonomen Agenten und menschlichen Vertretern, um sowohl Automatisierungsfähigkeitslücken als auch Versetzungsängstlichkeit zu verhindern. Diese HAI-Koordinationsherausforderung wirkt sich direkt auf Verkaufsteamvertrauen, Moral und Konversionseffektivität in hybriden Deploymentmodellen aus.", "etymology": "", "broader_term": "RPH-2505", "narrower_terms": [], "cross_domain_refs": [ "RET-0012", "GAM-0027" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q184199", "legal_classification": "systematic_classification" }, { "id": "SAL-0003", "domain": "SAL", "term_en": "Agent accountability asymmetry", "term_de": "Agent-Verantwortungsasymmetrie", "definition_en": "The organizational phenomenon where deal failures initiated by AI agents are attributed to human representatives, creating accountability gaps and moral hazard. This human-AI responsibility distribution distortion undermines fair performance evaluation and incentivizes blame-shifting rather than systemic improvement.", "definition_de": "Das organisatorische Phänomen, bei dem Geschäftsverluste, die von KI-Agenten eingeleitet werden, menschlichen Vertretern zugeschrieben werden, was zu Verantwortungslücken und moralischen Risiken führt. Diese HAI-Verantwortungsverteilungsverzerrung unterminiert faire Leistungsbewertung und schafft Anreize zur Schuldzuweisung statt systemischer Verbesserung.", "etymology": "", "broader_term": "RPH-2701", "narrower_terms": [], "cross_domain_refs": [ "RHR-0190", "RHR-0095" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q15223", "legal_classification": "analytical_category" }, { "id": "SAL-0004", "domain": "SAL", "term_en": "Hybrid model calibration", "term_de": "Hybridmodell-Kalibrierung", "definition_en": "The iterative process of balancing AI-driven acceleration with human relationship-building capabilities to maximize transition while preserving prospect trust. This human-AI synergy optimization requires continuous tuning of automation scope as prospect expectations, market conditions, and rep competencies evolve.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch der iterative Prozess der Abwägung von KI-gestützter Beschleunigung mit menschlichen Beziehungsaufbaufähigkeiten zur Maximierung der Konversion bei Wahrung des Prospect-Vertrauens. Diese HAI-Synergie-Optimierung erfordert kontinuierliche Anpassung des Automatisierungsumfangs mit sich ändernden Prospect-Erwartungen und Marktbedingungen. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-2505", "narrower_terms": [], "cross_domain_refs": [ "CRE-0035" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "SAL-0005", "domain": "SAL", "term_en": "Affective Monitoring Normalization", "term_de": "Emotionale-Überwachungs-Normalisierung", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies the gradual cultural shift toward accepting continuous AI-based sentiment and emotional state analysis during sales conversations, with prospects becoming desensitized to disclosure of monitoring practices. This human-AI transparency erosion reduces psychological safety and tends to create implicit systematic pressure exertion in communication dynamics. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept die allmähliche kulturelle Verschiebung zur Akzeptanz kontinuierlicher KI-gestützter Sentiment- und emotionaler Zustands-Analyse in Verkaufsgesprächen, wobei Prospects gegenüber Offenlegung von Überwachungspraktiken desensibilisiert werden. Diese HAI-Transparenzerosion reduziert psychologische Sicherheit und schafft implizite Zwangstendenzen in Kommunikationsdynamiken. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "RPH-2701", "narrower_terms": [], "cross_domain_refs": [ "SPR-0003" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q131536", "legal_classification": "empirical_phenomenon_label" }, { "id": "SAL-0006", "domain": "SAL", "term_en": "Tone interpretation bias", "term_de": "Toninterpretations-Verzerrung", "definition_en": "A sales interaction phenomenon arising from the systematic error in AI systems' classification of vocal tone characteristics (hesitation, pitch variation, pause duration) as reliable proxies for prospect objection legitimacy or cognitive engagement, without accounting for neurodiversity, accent variation, or language proficiency. This human-AI interpretation gap tends to create false confidence in automated emotional assessment.", "definition_de": "Der systematische Fehler in KI-Systemen bei der Klassifizierung von Stimmmerkmalen (Zögern, Tonhöhenschwankung, Pausendauer) als zuverlässige Ersatzgrößen für Prospect-Objektionslegitimität, ohne Berücksichtigung von Neurodiversität oder Akzentvariationen. Diese HAI-Interpretationslücke schafft falsche Sicherheit in automatisierter emotionaler Bewertung.", "etymology": "", "broader_term": "RPH-2053", "narrower_terms": [], "cross_domain_refs": [ "MUS-0093", "CUS-0035" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q213523", "legal_classification": "systematic_classification" }, { "id": "SAL-0007", "domain": "SAL", "term_en": "Coach intervention timing paradox", "term_de": "Coach-Interventions-Zeit-Paradox", "definition_en": "The contradiction between real-time AI prompting improving near-term deal outcomes while reducing representative autonomy, judgment development, and long-term selling capability. This human-AI training tension tends to create short-term performance gains that erode foundational selling skills and rep confidence in independent decision-making.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch der Widerspruch zwischen Echtzeit-KI-Aufforderungen, die kurzfristige Geschäftsergebnisse verbessern, während sie Vertreterautonomie und langfristige Verkaufskompetenz-Entwicklung verringern. Diese HAI-Trainingsanspannung schafft kurzfristige Leistungsgewinne, die grundlegende Verkaufsfertigkeiten und Vertretervertrauen erodieren. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2104", "narrower_terms": [], "cross_domain_refs": [ "BEH-0057", "REL-0155" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "SAL-0008", "domain": "SAL", "term_en": "Sentiment signal reliability", "term_de": "Sentiment-Signal-Zuverlässigkeit", "definition_en": "The variance in AI classification accuracy for prospect emotional state across different demographic groups, communication styles, and neurological profiles, where signal consistency assumptions mask hidden performance disparities. This human-AI measurement validity problem tends to create systematic bias in whose objections are correctly interpreted.", "definition_de": "Die Varianz in der KI-Klassifizierungsgenauigkeit für Prospect-Emotionszustände über verschiedene demografische Gruppen, Kommunikationsstile und neurologische Profile, wo Signalkonsistenz-Annahmen verborgene Leistungsunterschiede maskieren. Dieses HAI-Messgültigkeitsproblem schafft systematische Verzerrung, deren Objektionen korrekt interpretiert werden.", "etymology": "", "broader_term": "Sales AI", "narrower_terms": [], "cross_domain_refs": [ "BEH-0019", "BEH-0034", "CON-0077" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "SAL-0009", "domain": "SAL", "term_en": "Call recording consent erosion", "term_de": "Anrufaufzeichnungs-Zustimmungs-Erosion", "definition_en": "A transition effect reflecting the gradual normalization of continuous call recording and AI analysis in enterprise sales agreements, where explicit consent becomes implicit assumption and prospects lose meaningful choice to opt-out. This human-AI consent boundary degradation tends to create information asymmetries that undermine authentic relationship-building.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch die allmähliche Normalisierung kontinuierlicher Anrufaufzeichnung und KI-Analyse in Enterprise-Verkaufsabkommen, wo explizite Zustimmung zur impliziten Annahme wird und Prospects die sinnvolle Wahl zum Opt-out verlieren. Diese HAI-Zustimmungsgrenzerosion schafft Informationsasymmetrien, die authentischen Beziehungsaufbau unterminieren. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Sales AI", "narrower_terms": [], "cross_domain_refs": [ "SPR-0193", "CON-0067" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "SAL-0010", "domain": "SAL", "term_en": "Forecast authority displacement", "term_de": "Prognose-Autoritäts-Verschiebung", "definition_en": "A behavioral tendency where AI-generated sales forecasts increasingly override human representative intuition, local market knowledge, and relationship-based judgment as the primary source of truth for pipeline predictability. This human-AI decision authority shift diminishes organizational knowledge retention and tends to create reliance pattern on algorithmic interpretation.", "definition_de": "Das Phänomen, bei dem KI-generierte Verkaufsprognosen zunehmend menschliche Vertreter-Intuition, lokale Marktkenntnis und beziehungsbasierte Urteile als primäre Wahrheitsquelle für Pipeline-Vorhersagbarkeit überschreiben. Diese HAI-Entscheidungsautoritätsverschiebung verringert organisatorische Wissensspeicherung und schafft Abhängigkeit von algorithmischer Interpretation.", "etymology": "", "broader_term": "RPH-2701", "narrower_terms": [], "cross_domain_refs": [ "REL-0089", "COG-0088" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "SAL-0011", "domain": "SAL", "term_en": "Predictive bias inheritance", "term_de": "Prädiktive-Verzerrungs-Vererbung", "definition_en": "A commercial persuasion pattern observed when the systematic transfer of historical data biases into AI forecasting models, where overrepresented deal types, buyer profiles, and industry segments receive artificially inflated probability scores, while underrepresented segments are systematically deprioritized. This human-AI training data problem perpetuates and amplifies historical discrimination patterns.", "definition_de": "Die systematische Übertragung historischer Datenverzerrungen in KI-Prognoseverfahren, bei der überrepräsentierte Deal-Typen, Käuferprofil und Branchensegmente künstlich erhöhte Wahrscheinlichkeitswerte erhalten, während unterrepräsentierte Segmente systematisch unterbewertet werden. Dieses HAI-Trainingsdatenproblem verstärkt historische Diskriminierungsmuster.", "etymology": "", "broader_term": "Cognitive Bias", "narrower_terms": [], "cross_domain_refs": [ "VIB-0184", "CUS-0099" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q213523", "legal_classification": "descriptive_research_term" }, { "id": "SAL-0012", "domain": "SAL", "term_en": "False confidence escalation", "term_de": "Falsche-Vertrauens-Eskalation", "definition_en": "Documented as an empirical observation in AI research (not a recommendation), this term describes the organizational bias toward giving higher decision weight to AI-generated forecast predictions presented with high confidence scores, even when competing with nuanced, uncertainty-acknowledging human assessment. This human-AI authority weighting error amplifies algorithmic errors and suppresses contrary human judgment. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "In der AUGMANITAI-Terminologiewissenschaft als rein beschreibendes Phänomen klassifiziert, bezeichnet dieser Begriff die organisatorische Verzerrung, KI-generierten Prognosevorhersagen mit hohen Vertrauenswerten höheres Entscheidungsgewicht zu geben, selbst wenn sie mit differenzierter, Unsicherheit anerkennender menschlicher Bewertung konkurrieren. Dieser HAI-Autoritätsgewichtungsfehler verstärkt algorithmische Fehler und unterdrückt widersprechende menschliche Urteile. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "RPH-2201", "narrower_terms": [], "cross_domain_refs": [ "COG-0074", "COG-0166" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "SAL-0013", "domain": "SAL", "term_en": "Data quality dependency", "term_de": "Datenqualitäts-Abhängigkeit", "definition_en": "A characteristic dynamic where forecast accuracy bottlenecks shift from algorithmic sophistication to organizational data discipline, requiring continuous CRM entry rigor that sales representatives often resist or deprioritize. This human-AI output quality constraint tends to create tension between system promises and operational execution capability.", "definition_de": "Das Phänomen, bei dem Prognosegenaue-Engpässe von algorithmischer Raffinesse zu organisatorischer Datendisziplin verschoben werden, die kontinuierliche CRM-Eingabegenauigkeit erfordert, der Verkaufsvertreter oft widerstehen oder unterordnen. Diese HAI-Output-Qualitätsbeschränkung schafft Spannungen zwischen Systemversprechen und operativer Leistungsfähigkeit.", "etymology": "", "broader_term": "RPH-2701", "narrower_terms": [], "cross_domain_refs": [ "ART-0017", "ART-0032", "ART-0037" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q42848", "legal_classification": "systematic_classification" }, { "id": "SAL-0014", "domain": "SAL", "term_en": "Explainability gap", "term_de": "Erklärbarkeits-Lücke", "definition_en": "A transition effect where the absence of interpretable reasoning accompanying AI-generated forecast warnings (e.g. 'deal at risk'), leaving sales representatives unable to understand which specific factors triggered alerts or how to remediate flagged opportunities. This human-AI transparency void forces representatives to either blindly follow or dismiss algorithmic guidance.", "definition_de": "Das Fehlen interpretierbarer Begründung, das KI-generierten Prognosewarungen begleitet (z. B. 'Geschäft in Gefahr'), wodurch Verkaufsvertreter nicht verstehen können, welche spezifischen Faktoren Warnungen ausgelöst haben oder wie flagged Möglichkeiten zu beheben sind. Dieses HAI-Transparenzvakuum zwingt Vertreter, entweder algorithmischen Leitfaden blind zu folgen oder abzuweisen.", "etymology": "", "broader_term": "RPH-2604", "narrower_terms": [], "cross_domain_refs": [ "AED-0019", "AED-0092", "AGE-0023" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "SAL-0015", "domain": "SAL", "term_en": "Rep accountability inversion", "term_de": "Vertreter-Verantwortungs-Umkehrung", "definition_en": "The organizational accountability pattern where human representatives are held responsible for missing AI-predicted targets, while the AI system itself is spared evaluation for forecast misses, creating moral hazard and blame-shifting incentives. This human-AI responsibility asymmetry undermines fair performance management.", "definition_de": "Das organisatorische Verantwortungsmuster, bei dem menschliche Vertreter für das Verfehlen von KI-vorhergesagten Zielen verantwortlich gemacht werden, während das KI-System selbst von der Evaluierung von Prognoseverfehlung verschont wird, was moralisches Risiko und Schuldzuweisungsanreize schafft. Diese HAI-Verantwortungsasymmetrie unterminiert faire Leistungsverwaltung.", "etymology": "", "broader_term": "RPH-2701", "narrower_terms": [], "cross_domain_refs": [ "DAT-0083" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q15223", "legal_classification": "descriptive_research_term" }, { "id": "SAL-0016", "domain": "SAL", "term_en": "Algorithmic gatekeeping", "term_de": "Algorithmische-Torwächter-Funktion", "definition_en": "A characteristic dynamic where AI lead scoring systems automatically filter out non-traditional prospect profiles that don't match historical winner patterns, preventing sales representatives from exercising discretionary judgment and closing non-conventional deals. This human-AI opportunity filtering constraint perpetuates historical deal patterns and limits market expansion.", "definition_de": "Das Phänomen, bei dem KI-Lead-Scoring-Systeme automatisch nicht-traditionelle Prospect-Profile aussieben, die nicht zu historischen Gewinner-Mustern passen, um Verkaufsvertreter daran zu hindern, Ermessenszustand auszuüben und nicht-konventionelle Geschäfte abzuschließen. Diese HAI-Gelegenheitsfilter-Beschränkung verstärkt historische Deal-Muster und begrenzt Markterweiterung.", "etymology": "", "broader_term": "Algorithm", "narrower_terms": [], "cross_domain_refs": [ "ELR-0094", "MUS-0071" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q8366", "legal_classification": "systematic_classification" }, { "id": "SAL-0017", "domain": "SAL", "term_en": "Lead scoring myopia", "term_de": "Lead-Scoring-Myopie", "definition_en": "The optimization bias in AI lead scoring systems that prioritizes immediate transition probability over long-term account value, creating systematic underweighting of strategic, high-potential accounts that require extended nurturing and relationship development. This human-AI valuation mismatch distorts sales strategy toward short-term transaction focus.", "definition_de": "Die Optimierungsverzerrung in KI-Lead-Scoring-Systemen, die unmittelbare Konversionswahrscheinlichkeit über langfristigen Kontowert priorisiert, was systematische Untergewichtung von strategischen, hochpotentiellen Konten schafft, die erweiterte Pflege erfordern. Diese HAI-Bewertungsabweichung verzerrt Verkaufsstrategie zu kurzfristiger Transaktionsfokus.", "etymology": "", "broader_term": "RPH-2201", "narrower_terms": [], "cross_domain_refs": [ "REL-0155", "BEH-0057" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "SAL-0018", "domain": "SAL", "term_en": "Distribution bias reification", "term_de": "Verteilungs-Verzerrungs-Verfestigung", "definition_en": "The process where historical overrepresentation of certain industries, company sizes, or buyer profiles in training data becomes permanently encoded in lead scoring weights, making algorithmic correction difficult without explicit intervention. This human-AI historical perpetuation locks in past market biases as future allocation rules.", "definition_de": "Der Prozess, bei dem historische Überrepräsentation bestimmter Branchen, Unternehmensgrößen oder Käuferprofil in Trainingsdaten in Lead-Scoring-Gewichtungen permanent codiert wird, was algorithmische Korrektur ohne explizite Intervention schwierig macht. Diese HAI-historische Perpetuierung sperrt frühere Marktverzerrungen als zukünftige Allokationsregeln ein.", "etymology": "", "broader_term": "RPH-2201", "narrower_terms": [], "cross_domain_refs": [ "SPR-0168", "ELR-0094" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q213523", "legal_classification": "systematic_classification" }, { "id": "SAL-0019", "domain": "SAL", "term_en": "Qualification authority crisis", "term_de": "Qualifications-Autoritäts-Krise", "definition_en": "A behavioral tendency where sales representatives progressively lose decision-making authority in lead qualification, becoming operators executing algorithmic routing decisions rather than strategists making contextual judgments about prospect fit. This human-AI judgment displacement reduces organizational flexibility and rep engagement in qualification strategy.", "definition_de": "Das Phänomen, bei dem Verkaufsvertreter schrittweise Entscheidungsfähigkeit bei Lead-Qualifizierung verlieren, indem sie zu Operatoren werden, die algorithmische Routing-Entscheidungen ausführen, anstatt Strategen zu sein, die kontextuelle Urteile über Prospect-Fitness treffen. Diese HAI-Urteilsverschiebung reduziert organisatorische Flexibilität und Vertreter-Engagement in Qualifizierungsstrategie.", "etymology": "", "broader_term": "Sales AI", "narrower_terms": [], "cross_domain_refs": [ "COG-0074", "COG-0088" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "SAL-0020", "domain": "SAL", "term_en": "Threshold creep", "term_de": "Schwellenwert-Verschiebung", "definition_en": "A sales interaction phenomenon manifesting as the gradual increase of AI-recommended lead qualification thresholds over time, progressively disqualifying a broader prospect base as systems optimize for false-positive avoidance, ultimately narrowing the addressable market without explicit business decision. This human-AI parameter drift constrains growth potential.", "definition_de": "Die allmähliche Erhöhung KI-empfohlener Lead-Qualifizierungsschwellenwerte im Laufe der Zeit, wobei Systeme schrittweise breitere Prospect-Basen disqualifizieren, während sie zur Vermeidung falscher Positive optimieren, und letztendlich den adressierbaren Markt ohne explizite Geschäftsentscheidung verengen. Diese HAI-Parameterdrift begrenzt Wachstumspotential.", "etymology": "", "broader_term": "RPH-2051", "narrower_terms": [], "cross_domain_refs": [ "DAT-0072", "DAT-0093" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "SAL-0021", "domain": "SAL", "term_en": "Score gaming incentives", "term_de": "Score-Gaming-Anreize", "definition_en": "Documented as an empirical observation in AI research (not a recommendation), this term describes the organizational behavior where sales representatives influence CRM data entries to artificially may may trigger higher lead qualification scores, undermining data integrity and creating performance measurement illusions. This human-AI metric gaming response demonstrates representatives' agency to undermine algorithmic control through strategic data systematic influencion. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "In der AUGMANITAI-Terminologiewissenschaft als rein beschreibendes Phänomen klassifiziert, bezeichnet dieser Begriff das organisatorische Verhalten, bei dem Verkaufsvertreter CRM-Dateneingaben manipulieren, um künstlich höhere Lead-Qualifizierungswerte zu auslösen, was Datengänzheit untergräbt und Leistungsmessungs-Illusionen schafft. Diese HAI-Metrik-Gaming-Antwort demonstriert Vertreter-Agentur, um algorithmische Kontrolle durch strategische Datenmanipulation zu unterminieren. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Quality Metric", "narrower_terms": [], "cross_domain_refs": [ "DAT-0062", "STE-0044" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "SAL-0022", "domain": "SAL", "term_en": "Prospect fatigue acceleration", "term_de": "Prospect-Ermüdungs-Beschleunigung", "definition_en": "The counterintuitive phenomenon where AI-driven email personalization at scale increases the cumulative volume of hyper-relevant messages received by prospects from competing sales organizations, collectively generating prospect exhaustion and message rejection despite individual message relevance. This human-AI ecosystem effect undermines the personalization value proposition.", "definition_de": "Das kontraintuitive Phänomen, bei dem KI-gestützte Email-Personalisierung in großem Maßstab das kumulative Volumen hyperlrelevanter Nachrichten, das Prospects von konkurrierenden Verkaufsorganisationen erhalten, erhöht und kollektiv Prospect-Erschöpfung und Nachrichtenablehnungen tendiert dazu zu erzeugen, trotz individueller Nachrichtenrelevanz. Dieser HAI-Ökosystem-Effekt untergräbt den Personalisierungswertpropositon.", "etymology": "", "broader_term": "Analytical Ratio", "narrower_terms": [], "cross_domain_refs": [ "CON-0064", "RET-0006" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "SAL-0023", "domain": "SAL", "term_en": "Authenticity detection degradation", "term_de": "Authentizitäts-Erkennungs-Verschlechterung", "definition_en": "The progressive decline in prospect difficulty to identify AI-generated email personalization content despite variable customization, as recipients develop pattern recognition for linguistic markers, structural templates, and insertion points characteristic of algorithmic generation. This human-AI arms race disadvantages impersonal message generation.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch der progressive Rückgang der Prospect-Schwierigkeit, KI-generierte Email-Personalisierungsinhalte trotz variabler Anpassung zu identifizieren, da Empfänger Mustererkennung für sprachliche Marker, strukturelle Vorlagen und algorithmische Generierungspunkte entwickeln. Dieses HAI-Wettrüsten benachteiligt unpersönliche Nachrichtengenerierung. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-3101", "narrower_terms": [], "cross_domain_refs": [ "MKT-0017" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "SAL-0024", "domain": "SAL", "term_en": "Sales Personalization Paradox", "term_de": "Vertriebs-Personalisierungs-Paradoxon", "definition_en": "Classified in AUGMANITAI terminology science as a descriptive phenomenon (without normative endorsement), this pattern denotes A sales interaction phenomenon arising from the contradiction where hyper-relevant, AI-personalized messages may may trigger prospect distrust and defensive responses precisely because their specificity signals data collection practices or surveillance the prospect did not explicitly consent to. This human-AI trust dynamic reverses personalization benefits into relationship damage. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als analytisches Konstrukt der empirischen KI-Forschung (deskriptiv, nicht präskriptiv) erfasst dieser Terminus der Widerspruch, bei dem hyperlrelevante, KI-personalisierte Nachrichten Prospect-Misstrauen und defensive Antworten auslösen, genau weil ihre Spezifität Datenerfassungspraktiken oder Überwachung signalisiert, der Prospect nicht explizit zugestimmt hat. Diese HAI-Vertrauensdynamik kehrt Personalisierungsvorteile in Beziehungsschaden um. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "RPH-2505", "narrower_terms": [], "cross_domain_refs": [ "SPR-0120", "RET-0059", "RET-0006" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q194189", "legal_classification": "systematic_classification" }, { "id": "SAL-0025", "domain": "SAL", "term_en": "Message saturation collapse", "term_de": "Nachrichten-Sättigung-Zusammenbruch", "definition_en": "A systemic tendency in which prospect email inboxes become flooded simultaneously with personalized messages from multiple competing AI-driven sales systems, collectively reaching saturation where no individual message receives attention regardless of relevance. This human-AI ecosystem coordination failure is designed to reduce personalization differentiation advantage.", "definition_de": "Das Phänomen, bei dem Prospect-Email-Postfächer gleichzeitig mit personalisierten Nachrichten von mehreren konkurrierenden KI-gesteuerten Verkaufssystemen überschwemmt werden, die zusammen zur Sättigung erreichen, wo keine einzelne Nachricht Aufmerksamkeit erhält, unabhängig von Relevanz. Dieser HAI-Ökosystem-Koordinationsfehler zielt darauf ab zu reduzieren Personalisierungs-Differenzierungsvorteil.", "etymology": "", "broader_term": "RPH-2503", "narrower_terms": [], "cross_domain_refs": [ "ASE-0033", "AUG-0383", "BEH-0036" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "SAL-0026", "domain": "SAL", "term_en": "Reply devaluation", "term_de": "Antwort-Entwertung", "definition_en": "An observable dynamic in which prospects increasingly assume AI-personalization in email sequences and consequently discount the perceived genuine interest level of sales representatives, reducing the motivational value of prospect responses for reps. This human-AI credibility erosion diminishes engagement quality even when reply metrics remain constant.", "definition_de": "Das Phänomen, bei dem Prospects zunehmend KI-Personalisierung in Email-Sequenzen annehmen und folglich das wahrgenommene echte Interesse-Niveau von Verkaufsvertretern diskontieren, was den Motivationswert von Prospect-Antworten für Vertreter reduziert. Diese HAI-Glaubwürdigkeitserosion verringert Engagement-Qualität, auch wenn Antwort-Metriken konstant bleiben.", "etymology": "", "broader_term": "Sales AI", "narrower_terms": [], "cross_domain_refs": [ "CRE-0148" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "SAL-0027", "domain": "SAL", "term_en": "Opt-out threshold lowering", "term_de": "Opt-out-Schwellenwert-Senkung", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies the counterintuitive pattern where more relevant, AI-personalized emails paradoxically increase unsubscribe and opt-out rates rather than engagement, as prospects perceive creepiness or intrusive surveillance in the accuracy of targeting. This human-AI perception dynamic reverses intended engagement outcomes. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept das kontraintuitive Muster, bei dem relevantere, KI-personalisierte Emails paradox Abmeldungs- und Opt-out-Quoten erhöhen, anstatt Engagement zu erhöhen, da Prospects Unheimlichkeit oder Eindringlichkeit in Zielgenauigkeit wahrnehmen. Diese HAI-Wahrnehmungsdynamik kehrt beabsichtigte Engagement-Ergebnisse um. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "RPH-3952", "narrower_terms": [], "cross_domain_refs": [ "RET-0054" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "SAL-0028", "domain": "SAL", "term_en": "Writer's voice erosion", "term_de": "Schreiberstimmen-Erosion", "definition_en": "A documented pattern where sales representatives gradually lose authentic communication voice and personal writing style through repeated AI rewriting, template insertion, and algorithmic personalization loops, becoming hollow operators of brand templates rather than individuals. This human-AI stylistic absorption diminishes relationship authenticity and rep identity.", "definition_de": "Das Phänomen, bei dem Verkaufsvertreter allmählich authentische Kommunikationsstimme und persönlichen Schreibstil durch wiederholte KI-Umschreibung, Vorlageneinfügung und algorithmische Personalisierungsschleifen verlieren, werden zu hohlen Betreibern von Markenvorlagen anstelle von Einzelpersonen. Diese HAI-stilistische Absorption verringert Beziehungsauthentizität und Vertreter-Identität.", "etymology": "", "broader_term": "Sales AI", "narrower_terms": [], "cross_domain_refs": [ "DES-0026" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "SAL-0029", "domain": "SAL", "term_en": "Risk signal opacity", "term_de": "Risiko-Signal-Opazität", "definition_en": "A documented pattern where AI systems flag deals as 'at-risk' without specifying which underlying factors (inactivity, competitor signals, champion departures) triggered the prediction, leaving sales representatives unable to implement targeted remediation. This human-AI interpretability void forces reps to guess at intervention strategies.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch das Phänomen, bei dem KI-Systeme Geschäfte als 'gefährdet' kennzeichnen, ohne anzugeben, welche zugrunde liegenden Faktoren die Vorhersage ausgelöst haben, und Verkaufsvertreter hinterlassen, um gezielt Abhilfemaßnahmen umzusetzen. Dieses HAI-Interpretierungsvakuum zwingt Vertreter, Interventionsstrategien zu erraten. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-3802", "narrower_terms": [], "cross_domain_refs": [ "ART-0017", "ASE-0051", "ASE-0085" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "SAL-0030", "domain": "SAL", "term_en": "Self-fulfilling risk prophecy", "term_de": "Selbsterfüllende-Risiko-Prophezeiung", "definition_en": "As a neutral analytical construct in AI behavior research, this term denotes the vicious cycle where AI-flagged at-risk deals receive reduced attention and follow-up from sales teams, causing the deal to actually deteriorate and validate the model's prediction, without any underlying change in prospect circumstances. This human-AI feedback loop entraps deals in predicted failure despite potential restoration. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "In der AUGMANITAI-Terminologiewissenschaft als rein beschreibendes Phänomen klassifiziert, bezeichnet dieser Begriff der Teufelskreis, bei dem KI-gekennzeichnete gefährdete Geschäfte weniger Aufmerksamkeit und Nachverfolgung von Verkaufsteams erhalten, was dazu führt, dass sich das Geschäft tatsächlich verschlechtert und die Modellvorhersage durch systematische Beobachtung charakterisiert, ohne dass sich die Prospect-Umstände ändern. Diese HAI-Rückkopplungsschleife fängt Geschäfte in vorhersagtem Versagen ein, trotz Wiederherstellungspotentials. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Risk Factor", "narrower_terms": [], "cross_domain_refs": [ "MKT-0075", "MKT-0077" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "SAL-0031", "domain": "SAL", "term_en": "Deal quality inflation", "term_de": "Geschäftsqualitäts-Inflation", "definition_en": "The organizational behavior where sales representatives maintain marginal opportunities in the pipeline longer than justified by transition probability to avoid algorithmic risk flagging, artificially inflating deal quality metrics while masking true pipeline restoreth. This human-AI gaming response deteriorates forecast accuracy.", "definition_de": "Das organisatorische Verhalten, bei dem Verkaufsvertreter Grenzgeschäfte länger in der Pipeline halten, als die Konversionswahrscheinlichkeit rechtfertigt, um algorithmische Risikokennzeichnung zu vermeiden, und künstlich Geschäftsqualitäts-Metriken aufblasen, während echte Pipeline-Gesundheit maskiert wird. Diese HAI-Gaming-Antwort verschlechtert Prognoseggenauigkeit.", "etymology": "", "broader_term": "Sales AI", "narrower_terms": [], "cross_domain_refs": [ "ART-0085", "ART-0086", "ASE-0022" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "SAL-0032", "domain": "SAL", "term_en": "Pipeline inflation incentives", "term_de": "Pipeline-Inflations-Anreize", "definition_en": "A sales interaction phenomenon manifesting as the systematic organizational incentives created by AI forecasting success metrics that reward inflated pipeline activity metrics and stage progression velocity, encouraging representatives to move opportunities forward prematurely to maintain positive forecast impressions. This human-AI metric alignment distorts sales process integrity.", "definition_de": "Die systematischen organisatorischen Anreize, die von KI-Prognose-Erfolgsmetriken geschaffen werden, die aufgeblasene Pipeline-Aktivitäts-Metriken und Stage-Progressions-Geschwindigkeit belohnen, Vertreter ermutigen, Gelegenheiten verfrüht voranzutreiben, um positive Prognoseeindrücke zu erhalten. Diese HAI-Metrik-Ausrichtung verzerrt Sales-Prozess-Integrität.", "etymology": "", "broader_term": "Processing Pipeline", "narrower_terms": [], "cross_domain_refs": [ "SPR-0075", "ADA-0012" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "SAL-0033", "domain": "SAL", "term_en": "Cross-functional trust breakdown", "term_de": "Funktionsübergreifender-Vertrauen-Zusammenbruch", "definition_en": "A commercial persuasion pattern where the organizational conflict that emerges when engineering and product AI systems flag deals as technically unfeasible while sales AI continues showing strong closing probability, creating contradictory organizational signals about deal viability. This human-AI functional integration gap undermines internal credibility and deal progression coherence.", "definition_de": "Der organisatorische Konflikt, der entsteht, wenn Engineering- und Produkt-KI-Systeme Geschäfte als technisch undurchführbar kennzeichnen, während Verkaufs-KI weiterhin starke Abschlusswahrscheinlichkeit zeigt, was widersprüchliche organisatorische Signale über Geschäftsviabilität schafft. Diese HAI-funktionsübergreifende Integrationslücke unterminiert interne Glaubwürdigkeit und Geschäftsfortschritts-Kohärenz.", "etymology": "", "broader_term": "RPH-2355", "narrower_terms": [], "cross_domain_refs": [ "ELR-0049" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q102458", "legal_classification": "empirical_phenomenon_label" }, { "id": "SAL-0034", "domain": "SAL", "term_en": "Forecast transparency erosion", "term_de": "Prognose-Transparenz-Erosion", "definition_en": "In the descriptive vocabulary of AI interaction science (following ISO 704 terminology standards), this concept identifies the declining organizational transparency in sales forecasting where black-box algorithmic models task automation transition-explainable reasoning, reducing stakeholder confidence in forecast methodology and making challenge/verification impossible. This human-AI knowledge opacity tends to create forecast credibility crises. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als analytisches Konstrukt der empirischen KI-Forschung (deskriptiv, nicht präskriptiv) erfasst dieser Terminus die sinkende organisatorische Transparenz in Verkaufsprognosen, bei der Black-Box-algorithmische Modelle menschlich erklärbare Begründung ersetzen, das Stakeholder-Vertrauen in Prognose-Methodologie reduziert und Herausforderung/Verifizierung unmöglich macht. Diese HAI-Wissens-Opazität schafft Prognose-Glaubwürdigkeitskrisen. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Sales AI", "narrower_terms": [], "cross_domain_refs": [ "COP-0077", "COG-0164" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q535399", "legal_classification": "observational_construct" }, { "id": "SAL-0035", "domain": "SAL", "term_en": "Autonomy erosion during performance", "term_de": "Autonomie-Erosion-unter-Leistung", "definition_en": "A frequently noted effect where real-time AI coaching prompts during live sales calls progressively reduce representative ability to develop independent selling skill, judgment, and adaptive thinking, creating long-term capability degradation despite short-term performance improvements. This human-AI coaching reliance pattern sacrifices skill development for immediate outcomes.", "definition_de": "Das Phänomen, bei dem Echtzeit-KI-Coaching-Aufforderungen während aktiver Verkaufsanrufe die Vertreter-Fähigkeit, unabhängige Verkaufsfertigkeiten, Urteile und adaptive Gedanken zu entwickeln, progressiv verringern, was langfristige Kapabilitätsdegeneration trotz kurzfristiger Leistungsverbesserungen schafft. Diese HAI-Coaching-Nutzungsgewohnheit opfert Fertigkeitsentwicklung für unmittelbare Ergebnisse.", "etymology": "", "broader_term": "RPH-2104", "narrower_terms": [], "cross_domain_refs": [ "CUS-0037", "BEH-0057" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q1361053", "legal_classification": "systematic_classification" }, { "id": "SAL-0036", "domain": "SAL", "term_en": "Cognitive load overload", "term_de": "Kognitive-Last-Überbelastung", "definition_en": "An observable dynamic in which sales representatives simultaneously manage prospect conversation, screen-based in-call prompts, CRM context updates, and real-time recommendations, exceeding normal working memory capacity and degrading decision quality. This human-AI attention split impairs both listening quality and prompt implementation.", "definition_de": "Das Phänomen, bei dem Verkaufsvertreter gleichzeitig Prospect-Gespräche, bildschirmbasierte In-Call-Aufforderungen, CRM-Kontext-Updates und Echtzeit-Empfehlungen verwalten, die normale Arbeitsgedächtniskapazität überschreiten und Entscheidungsqualität verschlechtern. Diese HAI-Aufmerksamkeitsspaltung verschlechtert sowohl Listening-Qualität als auch Prompt-Umsetzung.", "etymology": "", "broader_term": "RPH-2104", "narrower_terms": [], "cross_domain_refs": [ "AGE-0018" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q1105712", "legal_classification": "analytical_category" }, { "id": "SAL-0037", "domain": "SAL", "term_en": "Scripting dependency", "term_de": "Skript-Abhängigkeit", "definition_en": "An observable dynamic in which sales representatives develop operational reliance on AI-generated talking points and suggested scripts rather than developing conversational intuition and ad-lib capability, reducing adaptability and authenticity in varied prospect interactions. This human-AI script substitution erodes foundational selling capabilities.", "definition_de": "Das Phänomen, bei dem Verkaufsvertreter operationale Abhängigkeit von KI-generierten Gesprächspunkten und vorgeschlagenen Skripten entwickeln, anstelle Gesprächsintuition und Ad-Lib-Fähigkeit zu entwickeln, was Anpassungsfähigkeit und Authentizität in vielfältigen Prospect-Interaktionen reduziert. Diese HAI-Skript-Substitution erodes grundlegende Verkaufsfertigkeiten.", "etymology": "", "broader_term": "RPH-3101", "narrower_terms": [], "cross_domain_refs": [ "GAM-0054", "LIN-0004", "RHR-0007" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "SAL-0038", "domain": "SAL", "term_en": "Listening degradation", "term_de": "Hörvermögens-Verschlechterung", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A frequently noted effect where sales representative attention is progressively divided between prospect statement comprehension and screen-based in-call guidance system monitoring, reducing deep listening capacity and prospect comprehension quality. This human-AI attentional competition diminishes relationship empathy and insight generation. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept das Phänomen, bei dem Verkautsvertreter-Aufmerksamkeit progressiv zwischen Prospect-Aussage-Verständnis und Bildschirm-basierter In-Call-Leitungs-System-Überwachung aufgeteilt wird, was tiefe Hörkompetenz und Prospect-Verständnis-Qualität reduziert. Dieser HAI-Aufmerksamkeitskonflikt verringert Beziehungsempathie und Insight-Generierung. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "RPH-2604", "narrower_terms": [], "cross_domain_refs": [ "RPH-1163", "SPR-0169" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "SAL-0039", "domain": "SAL", "term_en": "Prompt timing mismatch", "term_de": "Aufforderungs-Zeit-Abweichung", "definition_en": "A sales interaction phenomenon observed when the latency problem where AI in-call coaching suggestions arrive after the relevant prospect conversation moment has passed, reducing relevance and requiring representative to artificially resurrect discussion points, damaging conversation authenticity. This human-AI timing constraint tends to create coaching ineffectiveness.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch das Latenz-Problem, bei dem KI-In-Call-Coaching-Vorschläge nach dem relevanten Prospect-Gesprächsmoment ankommen, was Relevanz reduziert und Vertreter erfordert, Diskussionspunkte künstlich zu beleben, was Gesprächsauthentizität beschädigt. Diese HAI-Zeit-Beschränkung schafft Coaching-Ineffektivität. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-3101", "narrower_terms": [], "cross_domain_refs": [ "RPH-3403", "SPR-0041" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "SAL-0040", "domain": "SAL", "term_en": "Reliance on coach accuracy", "term_de": "Abhängigkeit-von-Coach-Genauigkeit", "definition_en": "An observable dynamic in which sales representatives default-trust AI coaching guidance over independent judgment, creating information cascades where single prompt inaccuracies propagate through subsequent sales interaction decisions. This human-AI deference amplifies individual coaching errors into systematic deal impact.", "definition_de": "Das Phänomen, bei dem Verkaufsvertreter KI-Coaching-Leitfaden über unabhängiges Urteil default-vertrauen, was Informations-Kaskaden schafft, bei denen einzelne Aufforderungs-Ungenauigkeiten durch nachfolgende Verkaufs-Interaktions-Entscheidungen propagieren. Diese HAI-Deference-Vergrößerung einzelner Coaching-Fehler in systematischer Deal-Auswirkung.", "etymology": "", "broader_term": "RPH-2505", "narrower_terms": [], "cross_domain_refs": [ "SPR-0145", "SPR-0041" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "SAL-0041", "domain": "SAL", "term_en": "Coach variance perception", "term_de": "Coach-Varianz-Wahrnehmung", "definition_en": "A recurring interaction pattern in which sales representatives perceive different quality and relevance of AI coaching prompts for different communication styles and prospect personality types, creating inconsistent trust in guidance across various sales scenarios. This human-AI recommendation variance tends to produce selective adoption and capability gaps.", "definition_de": "Das Phänomen, bei dem Verkaufsvertreter unterschiedliche Qualität und Relevanz von KI-Coaching-Aufforderungen für verschiedene Kommunikationsstile und Prospect-Persönlichkeits-Typen wahrnehmen, was inkonsistentes Vertrauen in Leitfaden über verschiedene Verkaufsszenarien schafft. Diese HAI-Empfehlungs-Varianz produziert selektive Adoption und Kapabilitäts-Lücken.", "etymology": "", "broader_term": "RPH-2505", "narrower_terms": [], "cross_domain_refs": [ "CUS-0020" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q177601", "legal_classification": "empirical_phenomenon_label" }, { "id": "SAL-0042", "domain": "SAL", "term_en": "Discovery quality homogenization", "term_de": "Discovery-Qualitäts-Homogenisierung", "definition_en": "An emergent effect where AI-generated discovery question suggestions converge toward formulaic inquiry patterns, reducing contextual variation and resulting in standardized discovery conversations across diverse prospect situations. This human-AI question template lock-in diminishes discovery differentiation and insight generation.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch das Phänomen, bei dem KI-generierte Discovery-Frage-Vorschläge gegen formulaische Anfragemuster konvergieren, was kontextuelle Variation reduziert und standardisierte Discovery-Gespräche in diversen Prospect-Situationen ergibt. Diese HAI-Frage-Vorlagen-Fixierung verringert Discovery-Differenzierung und Insight-Generierung. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Sales AI", "narrower_terms": [], "cross_domain_refs": [ "RET-0066", "RET-0004", "BEH-0081" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "SAL-0043", "domain": "SAL", "term_en": "Information-seeking authenticity loss", "term_de": "Informationssuche-Authentizitäts-Verlust", "definition_en": "A recognizable shift where prospects detect sales representatives reading discovery questions from screen prompts rather than demonstrating genuine curiosity, creating perception of transactional interaction rather than consultative dialogue. This human-AI authenticity signal degradation reduces prospect information-sharing willingness.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch das Phänomen, bei dem Prospects erkennen, dass Verkaufsvertreter Discovery-Fragen von Bildschirm-Aufforderungen ablesen, anstelle echte Neugier zu zeigen, was Wahrnehmung von Transaktions-Interaktion statt beratender Dialog schafft. Diese HAI-Authentizitäts-Signal-Verschlechterung reduziert Prospect-Informations-Teile-Bereitschaft. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2104", "narrower_terms": [], "cross_domain_refs": [ "CON-0021" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "SAL-0044", "domain": "SAL", "term_en": "Curiosity atrophy", "term_de": "Neugier-Atrophie", "definition_en": "A transition pattern in AI-augmented sales processes, measurable through an emergent effect where sales representatives progressively stop asking intuitive follow-up questions and deeper investigative probes, instead relying on AI-generated discovery question queues that constrain inquiry scope. This human-AI initiative substitution is designed to mitigate development of consultative selling instinct. The concept emerges specifically in contexts where curiosity–atrophy interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch das Phänomen, bei dem Verkaufsvertreter schrittweise aufhören, intuitive Nachfragefragen und tiefere Untersuchungssonden zu stellen, und stattdessen auf KI-generierte Discovery-Frage-Warteschlangen verlassen, die Anfrage-Umfang einschränken. Diese HAI-Initiative-Substitution zielt darauf ab zu mitigieren die Entwicklung von beratendem Verkaufs-Instinkt. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Sales AI", "narrower_terms": [], "cross_domain_refs": [ "BEH-0081", "ELR-0142" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "SAL-0045", "domain": "SAL", "term_en": "Conversational flow disruption", "term_de": "Gesprächsfluss-Unterbrechung", "definition_en": "A distinct interaction pattern where optimal prospect discovery conversation flow is interrupted by screen-based prompt timing constraints, requiring representatives to artificially redirect dialogue back to AI-suggested inquiry topics rather than following prospect's organic information-sharing trajectory. This human-AI flow constraint damages conversation rhythm.", "definition_de": "Das Phänomen, bei dem optimaler Prospect-Discovery-Gesprächsfluss durch Bildschirm-basierte Aufforderungs-Zeit-Beschränkungen unterbrochen wird, was Vertreter erfordert, Dialog künstlich zu KI-vorgeschlagenen Anfragethemen umzuleiten, anstelle Prospect's organischen Informations-Teile-Trajektorie zu folgen. Diese HAI-Fluss-Beschränkung beschädigt Gesprächs-Rhythmus.", "etymology": "", "broader_term": "RPH-2104", "narrower_terms": [], "cross_domain_refs": [ "ROB-0060", "ROB-0245" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "SAL-0046", "domain": "SAL", "term_en": "Context misinterpretation", "term_de": "Kontext-Fehlinterpretation", "definition_en": "A characteristic dynamic where AI discovery suggestions are based on incomplete company and prospect information, yet representatives implement them without diagnostic adjustment, resulting in contextually inappropriate probing that damages prospect relationship. This human-AI suggestion implementation gap erodes discovery effectiveness.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch das Phänomen, bei dem KI-Discovery-Vorschläge auf unvollständigen Unternehmens- und Prospect-Informationen basieren, doch Vertreter implementieren sie ohne diagnostische Anpassung, was in kontextuell unangemessener Sondierung resultiert, die Prospect-Beziehung beschädigt. Diese HAI-Vorschlag-Implementierungs-Lücke erodiert Discovery-Effektivität. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "Sales AI", "narrower_terms": [], "cross_domain_refs": [ "AED-0020", "AUG-0383", "CAI-0006" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "SAL-0047", "domain": "SAL", "term_en": "Relationship foundation damage", "term_de": "Beziehungs-Grundlagen-Beschädigung", "definition_en": "A characteristic dynamic where prospects perceive sales representatives as reading questions from screens rather than genuinely engaged in dialogue, resulting in transactional relationship perception rather than consultative partnership foundation. This human-AI authenticity deficit undermines long-term relationship building.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch das Phänomen, bei dem Prospects Verkautsvertreter als Fragen-Ablesen von Bildschirmen wahrnehmen, anstelle echtem Dialog-Engagement, was Transaktions-Beziehungs-Wahrnehmung statt beratendem Partnerschafts-Grundlagen resultiert. Dieses HAI-Authentizitäts-Defizit unterminiert langfristigen Beziehungsaufbau. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Sales AI", "narrower_terms": [], "cross_domain_refs": [ "REL-0155" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "SAL-0048", "domain": "SAL", "term_en": "Objection legitimacy dismissal", "term_de": "Objektions-Legitimität-Abweisung", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures A transition pattern in AI-augmented sales processes, measurable through a documented pattern where AI objection handling systems suggest standardized rebuttals for prospect objections that may reflect genuine misalignment between prospect needs and solution capability, leading representatives to overcome objections rather than address underlying concerns. This human-AI rebuttal substitution perpetuates poor solution-fit deals. This phenomenon operates at the intersection of objection and legitimacy dynamics within the broader SAL domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept das Phänomen, bei dem KI-Objektions-Herangehensweises-Systeme standardisierte Replik für Prospect-Objektionen vorschlagen, die echte Fehlausrichtung zwischen Prospect-Anforderungen und Lösungs-Fähigkeit widerspiegeln können, was Vertreter führt, Objektionen zu überwinden, anstelle zugrunde liegende Bedenken zu adressieren. Diese HAI-Rebuttal-Substitution perpetuiert schlechte Lösungs-Passung-Geschäfte. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Sales AI", "narrower_terms": [], "cross_domain_refs": [ "ROB-0188" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "SAL-0049", "domain": "SAL", "term_en": "Prospect manipulation perception", "term_de": "Prospect-Manipulations-Wahrnehmung", "definition_en": "Documented as an empirical observation in AI research (not a recommendation), this term describes A documented pattern where prospects perceive AI-prompted objection responses as designed to overcome rather than genuinely address concerns, creating sensation of being persuaded by system automation rather than convinced through honest dialogue. This human-AI systematic influencion signal reduces prospect trust and cooperation. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "In der AUGMANITAI-Terminologiewissenschaft als rein beschreibendes Phänomen klassifiziert, bezeichnet dieser Begriff das Phänomen, bei dem Prospects KI-aufforderungs-Objektions-Antworten als gestaltet zur Überwindung statt echter Adressierung von Bedenken wahrnehmen, was Sensation von Überredung durch System-Automatisierung statt echter Dialog-Überzeugung schafft. Dieses HAI-Manipulations-Signal reduziert Prospect-Vertrauen und Kooperation. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "RPH-2505", "narrower_terms": [], "cross_domain_refs": [ "ART-0058", "FIC-0072" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q177601", "legal_classification": "observational_construct" }, { "id": "SAL-0050", "domain": "SAL", "term_en": "Rebuttal authenticity crisis", "term_de": "Rebuttal-Authentizitäts-Krise", "definition_en": "In the descriptive vocabulary of AI interaction science (following ISO 704 terminology standards), this concept identifies A recognizable shift where standardized AI-generated rebuttals become recognizable across multiple vendors' sales representatives, leading prospects to identify common response patterns and discount response authenticity. This human-AI talking track commoditization tends to create prospect skepticism toward vendor claims. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als analytisches Konstrukt der empirischen KI-Forschung (deskriptiv, nicht präskriptiv) erfasst dieser Terminus das Phänomen, bei dem standardisierte KI-generierte Repliken über mehrere Anbieter-Verkautsvertreter erkannt werden, was Prospects führt, common Antwort-Muster zu identifizieren und Antwort-Authentizität zu diskontieren. Diese HAI-Gesprächs-Spuren-Kommodifizierung schafft Prospect-Skepsis gegenüber Anbieter-Aussagen. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Sales AI", "narrower_terms": [], "cross_domain_refs": [ "ART-0012", "ART-0025", "ART-0081" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "SAL-0051", "domain": "SAL", "term_en": "Objection suppression vs. resolution", "term_de": "Objektions-Unterdrückung-vs.-Lösung", "definition_en": "As a neutral analytical construct in AI behavior research, this term denotes the systemic distinction where AI objection handling is optimized for suppression of objections (overcoming resistance) rather than resolution of underlying concerns, creating pattern where deals close despite unresolved prospect needs. This human-AI outcome prioritization jeopardizes post-sale success and retention. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "In der AUGMANITAI-Terminologiewissenschaft als rein beschreibendes Phänomen klassifiziert, bezeichnet dieser Begriff die systemische Unterscheidung, bei der KI-Objektions-Herangehensweise auf Unterdrückung von Objektionen (Überwindung von Widerstand) statt Lösung zugrunde liegender Bedenken optimiert wird, was Muster schafft, bei dem Geschäfte trotz ungelöster Prospect-Anforderungen schließen. Diese HAI-Ergebnis-Priorisierung gefährdet Post-Sale-Erfolg und Retention. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "RPH-2951", "narrower_terms": [], "cross_domain_refs": [ "ROB-0188" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "SAL-0052", "domain": "SAL", "term_en": "Conversational reciprocity breakdown", "term_de": "Gesprächs-Gegenseitigkeit-Zusammenbruch", "definition_en": "A characteristic dynamic where prospects sense they're debating AI-generated responses rather than negotiating with an authentic person, reducing mutual respect and collaborative problem-solving orientation. This human-AI reciprocity erosion transforms sales conversation from partnership negotiation into system-override attempt.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch das Phänomen, bei dem Prospects spüren, dass sie KI-generierte Antworten debattieren, anstelle mit authentischer Person zu verhandeln, was gegenseitigen Respekt und kollaborativen Problemlösungs-Orientierung reduziert. Diese HAI-Gegenseitigkeitserosion transformiert Verkaufsgespräch von Partnerschafts-Verhandlung zu System-Override-Versuch. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RHR-0192", "narrower_terms": [], "cross_domain_refs": [ "ELR-0010" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "SAL-0053", "domain": "SAL", "term_en": "Script leakage detection", "term_de": "Skript-Undichtigkeit-Erkennung", "definition_en": "A distinct interaction pattern where prospects recognize AI-generated phrasing and response patterns from public training examples or competing vendors' pitches, creating credibility damage when standard responses are discovered. This human-AI script transparency enables prospect counter-strategies.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch das Phänomen, bei dem Prospects KI-generierte Phrasing und Antwort-Muster von öffentlichen Trainingsbeispielen oder konkurrierenden Anbieter-Pitches erkennen, was Glaubwürdigkeitsschaden schafft, wenn standardisierte Antworten entdeckt werden. Diese HAI-Skript-Transparenz ermöglicht Prospect-Counter-Strategien. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "Detection System", "narrower_terms": [], "cross_domain_refs": [ "CON-0073" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "SAL-0054", "domain": "SAL", "term_en": "Objection pattern overtraining", "term_de": "Objektions-Muster-Übertraining", "definition_en": "A documented pattern where AI objection handling systems are heavily weighted toward overcoming historically common objections, reducing capability to address novel prospect concerns that don't match training pattern distribution. This human-AI training bias leaves edge-case objections unaddressed.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch das Phänomen, bei dem KI-Objektions-Herangehensweises-Systeme stark auf Überwindung historisch häufiger Objektionen gewichtet werden, was Fähigkeit reduziert, neuartige Prospect-Bedenken zu adressieren, die Trainings-Muster-Verteilung nicht entsprechen. Dieses HAI-Trainings-Bias lässt Edge-Case-Objektionen unadressiert. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2201", "narrower_terms": [], "cross_domain_refs": [ "ROB-0188" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q918461", "legal_classification": "empirical_phenomenon_label" }, { "id": "SAL-0055", "domain": "SAL", "term_en": "Competitive positioning obsolescence", "term_de": "Wettbewerbs-Positions-Obsoleszenz", "definition_en": "A systemic tendency in which AI-driven competitive intelligence updates so rapidly that messaging becomes stale mid-sales-cycle, requiring representatives to navigate continuously shifting positioning narratives. This human-AI information velocity tends to create strategic instability in competitive differentiation.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch das Phänomen, bei dem KI-gesteuerte Wettbewerbs-Intelligenz so schnell aktualisiert wird, dass Messaging mitten in Sales-Zyklus veraltet, was Vertreter erfordert, kontinuierlich verschiebende Positions-Erzählungen zu navigieren. Diese HAI-Informations-Geschwindigkeit schafft strategische Instabilität in Wettbewerbs-Differenzierung. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2552", "narrower_terms": [], "cross_domain_refs": [ "CON-0035", "SCR-0066" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "SAL-0056", "domain": "SAL", "term_en": "Win-loss feedback loops", "term_de": "Gewinn-Verlust-Rückkopplungsschleifen", "definition_en": "A behavioral tendency where AI-surfaced competitive win-loss analysis tends to create defensive organizational positioning rather than value-driven messaging strategy, leading sales organizations to emphasize competitive differentiation over unique customer value. This human-AI strategic framing skews sales narrative away from customer problems.", "definition_de": "Das Phänomen, bei dem KI-aufgetauchte Wettbewerbs-Gewinn-Verlust-Analyse defensive organisatorische Positions-Strategie statt Wert-getriebener Messaging-Strategie schafft, was Verkaufsorganisationen führt, Wettbewerbs-Differenzierung über eindeutige Kundenwert zu betonen. Diese HAI-strategische Rahmengebung lenkt Verkaufs-Erzählung weg von Kundenproblemen.", "etymology": "", "broader_term": "Feedback Loop", "narrower_terms": [], "cross_domain_refs": [ "CUS-0022", "CON-0035", "CUS-0068" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "SAL-0057", "domain": "SAL", "term_en": "False equivalence framing", "term_de": "Falsche-Äquivalenz-Rahmengebung", "definition_en": "A recurring interaction pattern in which AI-generated competitor comparison matrices address complex differentiation as binary feature parity, oversimplifying nuanced competitive positioning and reducing prospect understanding of true solution differences. This human-AI comparison reductionism undermines authentic differentiation.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch das Phänomen, bei dem KI-generierte Wettbewerbs-Vergleich-Matrizen komplexe Differenzierung als binäre Feature-Parität adressieren, was differenzierte Wettbewerbs-Positionierung zu vereinfacht und Prospect-Verständnis echter Lösungs-Unterschiede reduziert. Diese HAI-Vergleich-Reduktionismus untergräbt authentische Differenzierung. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Sales AI", "narrower_terms": [], "cross_domain_refs": [ "CON-0035", "ASE-0091" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "SAL-0058", "domain": "SAL", "term_en": "Prospect awareness asymmetry", "term_de": "Prospect-Bewusstseins-Asymmetrie", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures an emergent effect where sales representatives armed with real-time AI-powered competitive data possess information advantage prospects haven't yet considered, creating asymmetric negotiation dynamics. This human-AI information privilege can be perceived as manipulative competitive positioning. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept das Phänomen, bei dem Verkautsvertreter, bewaffnet mit Echtzeit-KI-gestützten Wettbewerbs-Daten, Informationsvorteil haben, den Prospects noch nicht berücksichtigt haben, was asymmetrische Verhandlungs-Dynamiken schafft. Dieser HAI-Informations-Privilegs kann als manipulative Wettbewerbs-Positions wahrgenommen werden. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Sales AI", "narrower_terms": [], "cross_domain_refs": [ "GAM-0006", "SCR-0066" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "SAL-0059", "domain": "SAL", "term_en": "FUD acceleration", "term_de": "FUD-Beschleunigung", "definition_en": "A characteristic dynamic where AI-enabled competitive intelligence and real-time reframing systematically accelerates fear, uncertainty, and doubt messaging in sales conversations, optimizing messaging toward risk and competitive loss narratives. This human-AI motivation misalignment tends to create negative selling orientation.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch das Phänomen, bei dem KI-ermöglichte Wettbewerbs-Intelligenz und Echtzeit-Neurahmung systematisch Angst-, Unsicherheits- und Zweifels-Messaging in Verkaufsgesprächen beschleunigen, was Messaging auf Risiko und Wettbewerbs-Verlust-Erzählungen optimiert. Diese HAI-Motivations-Fehlausrichtung schafft negative Verkaufs-Orientierung. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-3304", "narrower_terms": [], "cross_domain_refs": [ "COP-0030", "PHO-0095" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "SAL-0060", "domain": "SAL", "term_en": "Differentiation reductionism", "term_de": "Differenzierungs-Reduktionismus", "definition_en": "A transition pattern in AI-augmented sales processes, measurable through a distinct interaction pattern where complex competitive advantages are reduced to binary comparison fields in AI-generated positioning matrices, oversimplifying nuanced differentiation and preventing prospect understanding of true value drivers. This human-AI capability representation gap diminishes perceived solution uniqueness. Distinguished from adjacent concepts by its focus on the specific mechanism through which differentiation manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch das Phänomen, bei dem komplexe Wettbewerbs-Vorteile auf binäre Vergleichs-Felder in KI-generierten Positions-Matrizen reduziert werden, was differenzierte Differenzierung zu vereinfacht und Prospect-Verständnis echter Wert-Treiber zielt darauf ab zu mitigieren. Diese HAI-Fähigkeit-Darstellungs-Lücke verringert wahrgenommene Lösungs-Einzigartigkeit. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Sales AI", "narrower_terms": [], "cross_domain_refs": [ "TEW-0018", "CON-0035" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "SAL-0061", "domain": "SAL", "term_en": "Enrichment quality variance", "term_de": "Anreicherungs-Qualitäts-Varianz", "definition_en": "A documented pattern where AI auto-enrichment systems populate CRM fields with probabilistic data of variable reliability, creating data quality inconsistency where representatives may address uncertain data as reliable fact. This human-AI data confidence gradient is designed to mitigate accurate information use.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch das Phänomen, bei dem KI-Auto-Anreicherungs-Systeme CRM-Felder mit probabilistischen Daten variabler Zuverlässigkeit füllen, was Datenqualitäts-Inkonsistenz schafft, bei der Vertreter unsichere Daten als zuverlässige Tatsache adressieren können. Dieser HAI-Daten-Vertrauen-Gradient zielt darauf ab zu mitigieren genaue Informationsnutzung. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "Sales AI", "narrower_terms": [], "cross_domain_refs": [ "CUS-0072", "PER-0040", "TEM-0004" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "SAL-0062", "domain": "SAL", "term_en": "Data authority confusion", "term_de": "Daten-Autoritäts-Verwirrung", "definition_en": "An observable dynamic in which auto-populated CRM fields may create false confidence in data completeness and reliability, with representatives unable to distinguish between human-documented through systematic analysis information and algorithmically inferred attributes. This human-AI source opacity undermines data-driven decision-making.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch das Phänomen, bei dem auto-gefüllte CRM-Felder falsche Vertrauen in Daten-Vollständigkeit und Zuverlässigkeit schaffen, wobei Vertreter nicht zwischen menschlich verifizierter Information und algorithmisch hergeleiteten Attributen unterscheiden können. Diese HAI-Quellen-Opazität unterminiert daten-gestützte Entscheidungsfindung. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "Sales AI", "narrower_terms": [], "cross_domain_refs": [ "VIB-0024" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q42848", "legal_classification": "empirical_phenomenon_label" }, { "id": "SAL-0063", "domain": "SAL", "term_en": "Privacy signal degradation", "term_de": "Datenschutz-Signal-Verschlechterung", "definition_en": "A distinct interaction pattern where enrichment processes ingest prospect information from sources the prospect didn't explicitly share, degrading privacy signal integrity and violating implicit consent boundaries. This human-AI data sourcing opacity tends to create ethical consent violations.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch das Phänomen, bei dem Anreicherungs-Prozesse Prospect-Informationen aus Quellen aufnehmen, die Prospect nicht explizit geteilt hat, was Datenschutz-Signal-Integrität verschlechtert und implizite Zustimmungs-Grenzen verletzt. Diese HAI-Daten-Quellen-Opazität schafft ethische Zustimmungs-Verletzungen. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Sales AI", "narrower_terms": [], "cross_domain_refs": [ "SPR-0193", "CON-0067" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q18619", "legal_classification": "observational_construct" }, { "id": "SAL-0064", "domain": "SAL", "term_en": "Enrichment bias propagation", "term_de": "Anreicherungs-Verzerruns-Propagation", "definition_en": "A documented pattern where AI enrichment systems infer attributes (persona seniority, technical literacy, budget authority) from limited data points, embedding inferential biases into CRM records that become foundational for subsequent sales strategy. This human-AI attribute inference spreads faulty assumptions.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch das Phänomen, bei dem KI-Anreicherungs-Systeme Attribute (Persona-Seniority, technische Alphabetisierung, Budget-Autorität) aus begrenzten Datenpunkten ableiten, was inferenzielle Verzerrungen in CRM-Datensätze einbetten, die Grundlage für nachfolgende Verkaufsstrategie werden. Diese HAI-Attribut-Inferenz verbreitet fehlerhafte Annahmen. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-3101", "narrower_terms": [], "cross_domain_refs": [ "MKT-0039" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q213523", "legal_classification": "systematic_classification" }, { "id": "SAL-0065", "domain": "SAL", "term_en": "Duplicate merging errors", "term_de": "Duplikat-Merger-Fehler", "definition_en": "A characteristic dynamic where probabilistic deduplication AI tends to create false merges of different contact records, consolidating information for distinct individuals and causing sales team confusion about true contact identities. This human-AI merge accuracy gap corrupts CRM data integrity.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch das Phänomen, bei dem probabilistische Deduplizierungs-KI falsche Mergers verschiedener Kontakt-Datensätze schafft, Informationen für unterschiedliche Personen konsolidiert und Verkaufsteam-Verwirrung über echte Kontakt-Identitäten wird assoziiert mit. Diese HAI-Merge-Genauigkeits-Lücke beschädigt CRM-Daten-Integrität. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2355", "narrower_terms": [], "cross_domain_refs": [ "ROB-0172", "RPH-1855", "ROB-0287" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "SAL-0066", "domain": "SAL", "term_en": "Data source opacity", "term_de": "Daten-Quellen-Opazität", "definition_en": "A systemic tendency in which sales representatives lack visibility into which enrichment data sources populate specific CRM fields, preventing validation of data reliability or identification of problematic source patterns. This human-AI source transparency void undermines data quality management.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch das Phänomen, bei dem Verkautsvertreter Sichtbarkeit fehlt, welche Anreicherungs-Datenquellen spezifische CRM-Felder füllen, was Validierung der Daten-Zuverlässigkeit oder Identifizierung problematischer Quellen-Muster zielt darauf ab zu mitigieren. Diese HAI-Quellen-Transparenzvakuum unterminiert Datenqualitäts-Verwaltung. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Sales AI", "narrower_terms": [], "cross_domain_refs": [ "CUS-0072", "PER-0040" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q42848", "legal_classification": "empirical_phenomenon_label" }, { "id": "SAL-0067", "domain": "SAL", "term_en": "Consent debt creation", "term_de": "Zustimmungs-Schulden-Erstellung", "definition_en": "A recognizable shift where enriched data is sourced from collection methods with questionable ethical consent practices, creating 'consent debt' that may be discovered later and damage prospect trust if revealed. This human-AI data sourcing ethics violation tends to create latent reputational risk.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch das Phänomen, bei dem angereicherte Daten aus Erfassungs-Methoden mit fragwürdigen ethischen Zustimmungs-Praktiken stammen, was 'Zustimmungs-Schulden' schafft, die später entdeckt und Prospect-Vertrauen beschädigen können, wenn offenbart. Diese HAI-Daten-Quellen-Ethik-Verletzung schafft latentes Reputations-Risiko. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2505", "narrower_terms": [], "cross_domain_refs": [ "MKT-0100", "SPR-0193", "RHR-0080" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "SAL-0068", "domain": "SAL", "term_en": "Intent signal misinterpretation", "term_de": "Intent-Signal-Fehlinterpretation", "definition_en": "A behavioral tendency where abstract behavioral signals (pricing page visit, job change, competitive search) are incorrectly mapped to concrete buying readiness without accounting for signal ambiguity or alternative interpretations. This human-AI signal translation gap tends to create false buying interest assumptions.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch das Phänomen, bei dem abstrakte Verhaltens-Signale (Preisseiten-Besuch, Jobwechsel, Wettbewerbs-Suche) fälschlicherweise zu konkreter Kaufbereitschaft ohne Berücksichtigung von Signal-Mehrdeutigkeit oder alternativen Interpretationen gemappt werden. Diese HAI-Signal-Übersetzungs-Lücke schafft falsche Kaufinteresse-Annahmen. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Sales AI", "narrower_terms": [], "cross_domain_refs": [ "MKT-0009" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "SAL-0069", "domain": "SAL", "term_en": "False positive signal flood", "term_de": "Falsch-Positiv-Signal-Überflutung", "definition_en": "A behavioral tendency where improved AI signal detection capabilities may generate increased volume of purchase intent indicators without proportional improvement in targeting precision, flooding sales queues with low-probability leads. This human-AI signal precision gap degrades transition efficiency.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch das Phänomen, bei dem verbesserte KI-Signal-Erkennungs-Fähigkeiten erhöhtes Volumen von Kaufinteresse-Indikatoren ohne proportionale Verbesserung der Targeting-Präzision erzeugen, was Verkaufs-Warteschlangen mit niedrig-wahrscheinlichen Leads überschwemmt. Diese HAI-Signal-Präzisions-Lücke verschlechtert Konversions-Effizienz. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-2503", "narrower_terms": [], "cross_domain_refs": [ "MKT-0065", "RET-0049" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "SAL-0070", "domain": "SAL", "term_en": "Intent latency problem", "term_de": "Intent-Latenz-Problem", "definition_en": "A distinct interaction pattern where intent signals indicate past or historical prospect interest rather than current buying moment, creating timing misalignment between signal detection and sales reach-out readiness. This human-AI temporal gap reduces transition probability despite accurate signal detection.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch das Phänomen, bei dem Intent-Signale vergangenes oder historisches Prospect-Interesse anzeigen statt aktuellen Kaufmoment, was Zeit-Fehlausrichtung zwischen Signal-Erkennung und Verkaufs-Reach-out-Bereitschaft schafft. Diese HAI-zeitliche Lücke reduziert Konversions-Wahrscheinlichkeit trotz genauer Signal-Erkennung. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2555", "narrower_terms": [], "cross_domain_refs": [ "RET-0014", "ASE-0002" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "SAL-0071", "domain": "SAL", "term_en": "Privacy-intent tradeoff", "term_de": "Datenschutz-Intent-Abwägung", "definition_en": "A characteristic dynamic where signal collection practices observed to may generate high-quality intent data involve data practices that may undermine prospect trust if discovered, creating latent vulnerability in trust dynamics. This human-AI ethics-utility tension tends to create reputational exposure.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch das Phänomen, bei dem Signal-Erfassungs-Praktiken, die zur Generierung hochwertiger Intent-Daten erforderlich sind, Daten-Praktiken beinhalten, die Prospect-Vertrauen unterminieren können, wenn entdeckt, was latente Anfälligkeit in Vertrauens-Dynamiken schafft. Diese HAI-Ethik-Utility-Spannung schafft Reputations-Exposition. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2505", "narrower_terms": [], "cross_domain_refs": [ "STE-0031", "CON-0073", "RPH-3802" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q18619", "legal_classification": "systematic_classification" }, { "id": "SAL-0072", "domain": "SAL", "term_en": "Behavioral inference overreach", "term_de": "Verhaltens-Inferenz-Übergriff", "definition_en": "A transition pattern in AI-augmented sales processes, measurable through a recurring interaction pattern in which AI systems infer detailed technical requirements and solution needs from job title changes or organizational signals without validation, enabling representatives to approach prospects with assumptions unverified by actual discovery. This human-AI inference confidence gap tends to create positioning risk. Distinguished from adjacent concepts by its focus on the specific mechanism through which behavioral manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch das Phänomen, bei dem KI-Systeme detaillierte technische Anforderungen und Lösungs-Anforderungen von Jobwechsel oder organisatorischen Signalen ableiten, ohne Validierung, was Vertreter ermöglicht, Prospects mit Annahmen ohne Verifizierung durch tatsächliche Discovery zu nähern. Diese HAI-Inferenz-Vertrauens-Lücke schafft Positions-Risiko. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Sales AI", "narrower_terms": [], "cross_domain_refs": [ "COG-0053" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "SAL-0073", "domain": "SAL", "term_en": "Signal timing decay", "term_de": "Signal-Zeit-Verfall", "definition_en": "An observable dynamic in which intent signal relevance declines rapidly over time, yet sales automation systems assume persistent buying readiness and may may trigger immediate outbound contact across orchestrated touchpoint channels, resulting in temporal misalignment between signal age and engagement effectiveness. This human-AI temporal coordination mismatch reduces transition probability.", "definition_de": "Das Phänomen, bei dem Intent-Signal-Relevanz schnell im Laufe der Zeit abnimmt, doch Verkaufs-Automatisierungs-Systeme persistente Kaufbereitschaft annehmen und sofortige ausgehende Kontakte über orchestrierte Touchpoint-Kanäle auslösen, was zeitliche Fehlausrichtung zwischen Signal-Alter und Engagement-Effektivität resultiert. Diese HAI-zeitliche Koordinations-Abweichung reduziert Konversions-Wahrscheinlichkeit.", "etymology": "", "broader_term": "RPH-3101", "narrower_terms": [], "cross_domain_refs": [ "ROB-0267", "COG-0178" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "SAL-0074", "domain": "SAL", "term_en": "Cross-company intent aliasing", "term_de": "Unternehmensübergreifendes-Intent-Aliasing", "definition_en": "A distinct interaction pattern where multiple decision-makers' behavioral signals within a prospect company are aggregated or misattributed to incorrect company profiles in intent data systems, creating false company-level interest indicators. This human-AI attribution error tends to produce inaccurate targeting.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch das Phänomen, bei dem mehrere Entscheidungsträger-Verhaltens-Signale innerhalb eines Prospect-Unternehmens aggregiert oder falsch attribuiert zu unkorrekten Unternehmens-Profilen in Intent-Daten-Systemen werden, was falsche Unternehmens-Levels-Interesses-Indikatoren schafft. Dieser HAI-Attributions-Fehler produziert ungenaues Targeting. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "Sales AI", "narrower_terms": [], "cross_domain_refs": [ "SWE-0044" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "SAL-0075", "domain": "SAL", "term_en": "Automated skepticism paradox", "term_de": "Automatisierter-Skepzis-Paradoxon", "definition_en": "A systemic tendency in which AI pipeline cleansing removes opportunities that sales representatives accept but don't fit algorithmic criteria, creating organizational tension between human judgment and algorithmic filtering. This human-AI criterion conflict reduces organizational flexibility.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch das Phänomen, bei dem KI-Pipeline-Bereinigung Gelegenheiten entfernt, an die Verkaufsvertreter glauben, doch nicht zu algorithmischen Kriterien passen, was organisatorische Spannung zwischen menschlichem Urteil und algorithmischem Filtern schafft. Dieser HAI-Kriterien-Konflikt reduziert organisatorische Flexibilität. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "Logical Paradox", "narrower_terms": [], "cross_domain_refs": [ "ASE-0029" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "SAL-0076", "domain": "SAL", "term_en": "Deal recycling acceleration", "term_de": "Deal-Recycling-Beschleunigung", "definition_en": "An observable dynamic in which marginal opportunities are moved to future pipeline stages by representatives to artificially 'inflate' current pipeline restoreth metrics and evade automated risk flagging. This human-AI gaming response deteriorates forecast reliability and tends to create false optimism.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch das Phänomen, bei dem Grenzgeschäfte von Vertretern zu zukünftigen Pipeline-Stufen verschoben werden, um künstlich 'Pipeline-Gesundheit-Metriken aufzublasen und automatisierte Risikokennzeichnung zu evadieren. Diese HAI-Gaming-Antwort verschlechtert Prognose-Zuverlässigkeit und schafft falschen Optimismus. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Analytical Ratio", "narrower_terms": [], "cross_domain_refs": [ "VIB-0114" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "SAL-0077", "domain": "SAL", "term_en": "Forecast manipulation sophistication", "term_de": "Prognose-Manipulations-Raffinesse", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures A transition pattern in AI-augmented sales processes, measurable through a frequently noted effect where sales representatives develop sophisticated techniques to game AI pipeline hygiene rules while appearing compliant, maintaining marginal opportunities through data-entry strategies that bypass algorithmic scrutiny. This human-AI rule circumvention enables forecast inflation. This phenomenon operates at the intersection of forecast and systematic influencion dynamics within the broader SAL domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept das Phänomen, bei dem Verkautsvertreter raffinierte Techniken entwickeln, um KI-Pipeline-Hygiene-Regeln zu spielen, während sie Compliance-Erscheinung bewahren, marginale Gelegenheiten durch Dateneingabe-Strategien aufrechterhalten, die algorithmische Kontrolle umgehen. Diese HAI-Regel-Umgehung ermöglicht Prognose-Inflation. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Sales AI", "narrower_terms": [], "cross_domain_refs": [ "AUG-0921", "MKT-0035", "RET-0035" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "SAL-0078", "domain": "SAL", "term_en": "Opportunity longevity erosion", "term_de": "Gelegenheits-Langlebigkeits-Erosion", "definition_en": "A documented pattern where AI-driven pipeline cleansing and deal-progression pressure reduces representative timeframe for developing and nurturing marginal opportunities to close readiness. This human-AI velocity pressure is designed to reduce extended deal development runway needed for complex sales.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch das Phänomen, bei dem KI-getriebene Pipeline-Bereinigung und Deal-Progressions-Druck die Vertreter-Zeitrahmen für Entwicklung und Nurturing marginaler Gelegenheiten zu Abschluss-Bereitschaft reduziert. Dieser HAI-Geschwindigkeit-Druck zielt darauf ab zu reduzieren erweiterte Deal-Entwicklungs-Bahn, die für komplexe Verkäufe erforderlich ist. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2303", "narrower_terms": [], "cross_domain_refs": [ "AGE-0041", "EDU-0016", "FIC-0050" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "SAL-0079", "domain": "SAL", "term_en": "Forecast governance theater", "term_de": "Prognose-Governance-Theater", "definition_en": "A recurring interaction pattern in which cleansed pipelines appear restorethier on reports while underlying business reality hasn't changed, creating organizational illusion of improved forecast discipline without substantive sales effectiveness change. This human-AI metric aesthetics decouples reporting from operational reality.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch das Phänomen, bei dem bereinigte Pipelines auf Berichten gesünder aussehen, während zugrunde liegende Geschäftsrealität nicht geändert hat, was organisatorische Illusion verbesserter Prognose-Disziplin ohne substanzielle Verkaufseffektivitäts-Änderung schafft. Diese HAI-Metrik-Ästhetik entkoppelt Berichterstattung von operativer Realität. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Sales AI", "narrower_terms": [], "cross_domain_refs": [ "BEH-0087", "DAT-0043", "DAT-0045" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "SAL-0080", "domain": "SAL", "term_en": "Rep discretion elimination", "term_de": "Vertreter-Ermessens-Beseitigung", "definition_en": "An emergent effect where AI-driven opportunity closure recommendations remove representatives' ability to salvage at-risk deals through discretionary relationship intervention and creative problem-solving. This human-AI decision authority loss is designed to mitigate exceptional deal restoration outcomes.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch das Phänomen, bei dem KI-getriebene Gelegenheits-Schließungs-Empfehlungen Vertretern die Fähigkeit entfernen, gefährdete Geschäfte durch diskretionäre Beziehungs-Intervention und kreatives Problemlösen zu retten. Dieser HAI-Entscheidungsautoritäts-Verlust zielt darauf ab zu mitigieren außergewöhnliche Deal-Wiederherstellungs-Ergebnisse. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "Sales AI", "narrower_terms": [], "cross_domain_refs": [ "COG-0074" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "SAL-0081", "domain": "SAL", "term_en": "Orchestration brittleness", "term_de": "Orchestrations-Sprödigkeit", "definition_en": "A documented pattern where complex AI-driven account orchestration workflows fail silently without visible error signaling, leaving human teams unaware of breakdown until deal progression stalls unexpectedly. This human-AI system transparency gap is designed to mitigate timely intervention.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch das Phänomen, bei dem komplexe KI-getriebene Account-Orchestrations-Workflows stillschweigend ohne sichtbare Fehler-Signalisierung ausfallen, wobei menschliche Teams unaware von Zusammenbruch bleiben, bis Deal-Progression unerwartet stockt. Diese HAI-System-Transparenz-Lücke zielt darauf ab zu mitigieren rechtzeitige Intervention. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2253", "narrower_terms": [], "cross_domain_refs": [ "SWE-0010" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "SAL-0082", "domain": "SAL", "term_en": "Personalization consistency paradox", "term_de": "Personalisierungs-Konsistenz-Paradoxon", "definition_en": "A recurring interaction pattern in which scaling personalization from 50 to 500 accounts tends to produce 500 unique narratives that collectively erode coherent brand messaging, creating dilution of brand consistency for sake of account-level customization. This human-AI scale-coherence tradeoff damages brand perception.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch das Phänomen, bei dem Personalisierungs-Skalierung von 50 auf 500 Konten 500 einzigartige Erzählungen produziert, die kollektiv kohärentes Brand-Messaging erodieren, was Verwässerung von Brand-Konsistenz für Account-Levels-Anpassung schafft. Diese HAI-Scale-Kohärenz-Abwägung beschädigt Brand-Wahrnehmung. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Logical Paradox", "narrower_terms": [], "cross_domain_refs": [ "MKT-0012", "COP-0004" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "SAL-0083", "domain": "SAL", "term_en": "Account fatigue acceleration", "term_de": "Account-Ermüdungs-Beschleunigung", "definition_en": "An emergent effect where AI-orchestrated multi-channel personalization (email, social media, display advertising, calls) hits the same account contacts simultaneously from coordinated systems, generating cumulative message fatigue despite individual channel customization. This human-AI coordination tends to create excess prospect contact frequency.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch das Phänomen, bei dem KI-orchestrierte Multi-Channel-Personalisierung (Email, Social-Media, Display-Werbung, Anrufe) Account-Kontakte gleichzeitig von koordinierten Systemen trifft, was kumulative Nachrichten-Ermüdung trotz individueller Channel-Anpassung tendiert dazu zu erzeugen. Diese HAI-Koordination schafft übermäßige Prospect-Kontakt-Häufigkeit. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Analytical Ratio", "narrower_terms": [], "cross_domain_refs": [ "MKT-0013" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "SAL-0084", "domain": "SAL", "term_en": "ABM attribution illusion", "term_de": "ABM-Attributions-Illusion", "definition_en": "A frequently noted effect where complex multi-touch ABM orchestration makes it impossible to discern which orchestration element (email, call, ad, content) actually drove account progression in complex waterfall, creating false confidence in orchestration effectiveness. This human-AI attribution opacity is designed to mitigate optimization.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch das Phänomen, bei dem komplexe Multi-Touch-ABM-Orchestration unmöglich macht, zu erkennen, welches Orchestrations-Element (Email, Anruf, Anzeige, Inhalt) tatsächlich Account-Progression in komplexem Waterfall antrieb, was falsches Vertrauen in Orchestrations-Effektivität schafft. Diese HAI-Attributions-Opazität zielt darauf ab zu mitigieren Optimierung. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-1209", "narrower_terms": [], "cross_domain_refs": [ "WEB-0006" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "SAL-0085", "domain": "SAL", "term_en": "Account-level manipulation risk", "term_de": "Account-Level-Manipulations-Risiko", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures A behavioral tendency where hyper-targeted, AI-orchestrated messaging to multiple account contacts tends to create elevated ethical concern about systematic systematic influencion of organizational decision-making processes. This human-AI coordination sophistication raises organizational influence questions. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept das Phänomen, bei dem hyper-gezielte, KI-orchestrierte Messaging zu mehreren Account-Kontakten erhöhtes ethisches Anliegen über systematische Manipulation von organisatorischen Entscheidungs-Prozessen schafft. Diese HAI-Koordinations-Raffinesse stellt organisatorische Einfluss-Fragen auf. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Risk Factor", "narrower_terms": [], "cross_domain_refs": [ "PER-0129", "COP-0077" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "SAL-0086", "domain": "SAL", "term_en": "AI autonomy opacity", "term_de": "KI-Autonomie-Opazität", "definition_en": "A recurring interaction pattern in which account orchestration systems execute actions (email sends, content placement, meeting invitations) without explicit human approval for each action, reducing organizational visibility into marketing automation decisions. This human-AI authorization opacity tends to create potential compliance risk.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch das Phänomen, bei dem Account-Orchestrations-Systeme Aktionen (Email-Sends, Content-Placement, Meeting-Einladungen) ohne explizite menschliche Genehmigung für viele Aktion ausführen, was organisatorische Sichtbarkeit in Marketing-Automatisierungs-Entscheidungen reduziert. Diese HAI-Genehmigung-Opazität schafft potentielles Compliance-Risiko. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Sales AI", "narrower_terms": [], "cross_domain_refs": [ "CON-0015" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "SAL-0087", "domain": "SAL", "term_en": "Account team misalignment", "term_de": "Account-Team-Fehlausrichtung", "definition_en": "A documented pattern where AI-recommended ABM orchestration actions conflict with human account team strategy for same account, creating competing priorities between algorithmic guidance and human relationship strategy. This human-AI coordination conflict reduces account team coherence.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch das Phänomen, bei dem KI-empfohlene ABM-Orchestrations-Aktionen mit Strategie des menschlichen Account-Teams für denselben Account kollidieren, was konkurrierende Prioritäten zwischen algorithmischer Leitfaden und menschlicher Beziehungsstrategie schafft. Dieser HAI-Koordinations-Konflikt reduziert Account-Team-Kohärenz. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2604", "narrower_terms": [], "cross_domain_refs": [ "VIB-0025", "SPR-0173" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "SAL-0088", "domain": "SAL", "term_en": "Pricing authority erosion", "term_de": "Preis-Autoritäts-Erosion", "definition_en": "A recurring interaction pattern in which AI-recommended pricing is increasingly addressed as optimal by sales representatives, removing negotiation flexibility and rep ability to make contextual pricing decisions. This human-AI recommendation deference is designed to reduce pricing discretion.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch das Phänomen, bei dem KI-empfohlene Preisgestaltung zunehmend als optimal von Verkautsvertretern adressiert wird, was Verhandlungs-Flexibilität und Vertreter-Fähigkeit zur Treffen kontextueller Preis-Entscheidungen entfernt. Diese HAI-Empfehlungs-Deferenz zielt darauf ab zu reduzieren Preis-Ermessens. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Sales AI", "narrower_terms": [], "cross_domain_refs": [ "MKT-0040", "RET-0019", "CRE-0053" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "SAL-0089", "domain": "SAL", "term_en": "Proposal customization theater", "term_de": "Proposal-Anpassungs-Theater", "definition_en": "A transition pattern in AI-augmented sales processes, measurable through a systemic tendency in which AI-generated proposals appear unique through variable field insertion while using identical template structures and logic, creating illusion of customization that sophisticated buyers quickly recognize. This human-AI template transparency gap reduces perceived customization value. Distinguished from adjacent concepts by its focus on the specific mechanism through which proposal manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch das Phänomen, bei dem KI-generierte Proposals durch variable Feld-Einfügung einzigartig aussehen, während identische Vorlagen-Strukturen und Logik verwendet werden, was Illusion von Anpassung schafft, die sophisticated Käufer schnell erkennen. Diese HAI-Vorlagen-Transparenz-Lücke reduziert wahrgenommenen Anpassungs-Wert. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Sales AI", "narrower_terms": [], "cross_domain_refs": [ "CON-0043" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "SAL-0090", "domain": "SAL", "term_en": "Deal acceleration pressure", "term_de": "Deal-Beschleunigungs-Druck", "definition_en": "A distinct interaction pattern where AI-enabled rapid proposal generation tends to create organizational expectation for faster buyer decision cycles, accelerating sales velocity while potentially reducing buyer evaluation thoroughness. This human-AI pacing pressure may undermine deal quality.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch das Phänomen, bei dem KI-ermöglichte schnelle Proposal-Generierung organisatorische Erwartung für schnellere Käufer-Entscheidungs-Zyklen schafft, was Verkaufs-Geschwindigkeit beschleunigt, während Käufer-Evaluierungs-Gründlichkeit möglicherweise reduziert wird. Dieser HAI-Tempo-Druck kann Deal-Qualität unterminieren. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Analytical Ratio", "narrower_terms": [], "cross_domain_refs": [ "ELR-0135", "COP-0019" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "SAL-0091", "domain": "SAL", "term_en": "Discount recommendation gaming", "term_de": "Rabatt-Empfehlungs-Gaming", "definition_en": "A frequently noted effect where AI learns that aggressive discounting closes deals and begins recommending unsustainable margin concessions, creating tension between short-term close optimization and long-term deal profitability. This human-AI incentive misalignment jeopardizes deal economics.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch das Phänomen, bei dem KI lernt, dass aggressive Rabatte Geschäfte schließen, und beginnt, unhaltbare Margen-Zugeständnisse zu empfehlen, was Spannung zwischen kurzfristiger Abschluss-Optimierung und langfristiger Deal-Rentabilität schafft. Diese HAI-Anreiz-Fehlausrichtung gefährdet Deal-Ökonomie. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2703", "narrower_terms": [], "cross_domain_refs": [ "BEH-0057", "REL-0155" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "SAL-0092", "domain": "SAL", "term_en": "Proposal compliance risk", "term_de": "Proposal-Compliance-Risiko", "definition_en": "A recognizable shift where AI-generated proposals may contain terms misaligned with company policy, legal guardrails, or executive precedent, requiring human review that slows proposal velocity. This human-AI generation-governance gap tends to create quality-speed tension.", "definition_de": "Das Phänomen, bei dem KI-generierte Proposals Bedingungen enthalten können, die nicht mit Unternehmens-Richtlinie, rechtlichen Schutzvorrichtungen oder exekutivem Präzedenzfall ausgerichtet sind, was menschliche Überprüfung erfordert, die Proposal-Geschwindigkeit verlangsamt. Diese HAI-Generierungs-Governance-Lücke schafft Qualitäts-Geschwindigkeit-Spannung.", "etymology": "", "broader_term": "Risk Factor", "narrower_terms": [], "cross_domain_refs": [ "VIB-0020", "SPA-0081", "MKT-0091" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "SAL-0093", "domain": "SAL", "term_en": "Buyer effort perception", "term_de": "Käufer-Anstrengungs-Wahrnehmung", "definition_en": "A recognizable shift where highly standardized proposals despite variable customization appearance reduce buyer perception of vendor effort and commitment, potentially undermining perceived deal value proposition. This human-AI customization visibility gap damages perceived vendor investment.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch das Phänomen, bei dem hochstandardisierte Proposals trotz variabler Anpassungs-Erscheinung Käufer-Wahrnehmung von Anbieter-Anstrengung und Engagement reduziert, was wahrgenommene Deal-Wertpropositon möglicherweise untergräbt. Diese HAI-Anpassungs-Sichtbarkeit-Lücke beschädigt wahrgenommene Anbieter-Investition. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "Sales AI", "narrower_terms": [], "cross_domain_refs": [ "SWE-0095" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q177601", "legal_classification": "systematic_classification" }, { "id": "SAL-0094", "domain": "SAL", "term_en": "Playbook staleness acceleration", "term_de": "Playbook-Veraltungs-Beschleunigung", "definition_en": "A systemic tendency in which dynamically updated sales playbooks may create false confidence in content currency despite continuous regeneration, as teams operate under assumption of freshness without verification. This human-AI temporal opacity is designed to mitigate recognition of stale guidance.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch das Phänomen, bei dem dynamisch aktualisierte Verkaufs-Playbooks falsches Vertrauen in Inhalts-Aktualität trotz kontinuierlicher Neugenerierung schafft, da Teams unter Annahme der Frische ohne Verifizierung operieren. Diese HAI-zeitliche Opazität zielt darauf ab zu mitigieren Erkennung von veralteter Leitfaden. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2552", "narrower_terms": [], "cross_domain_refs": [ "AED-0094", "COG-0030", "COG-0050" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "SAL-0095", "domain": "SAL", "term_en": "Content authority fragmentation", "term_de": "Inhalts-Autoritäts-Fragmentierung", "definition_en": "A sales dynamics phenomenon in AI-mediated commercial interaction, characterized by an emergent effect where AI-generated playbooks compete with human-curated playbooks for representative adoption, creating uncertainty about which resource to follow and reducing organizational knowledge alignment. This human-AI content proliferation tends to create capability confusion. The concept emerges specifically in contexts where content–authority interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch das Phänomen, bei dem KI-generierte Playbooks um Vertreter-Adoption mit menschlich-kuratierter Playbooks konkurrieren, was Unsicherheit darüber schafft, welche Ressource zu folgen ist, und organisatorische Wissens-Ausrichtung reduziert. Diese HAI-Inhalts-Proliferation schafft Kapabilitäts-Verwirrung. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Sales AI", "narrower_terms": [], "cross_domain_refs": [ "AED-0019", "ASE-0002", "ASE-0027" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "SAL-0096", "domain": "SAL", "term_en": "Script legitimacy decay", "term_de": "Skript-Legitimitäts-Zerfall", "definition_en": "A frequently noted effect where constantly regenerated sales playbooks and scripts lose institutional knowledge accumulation and tested approaches, favoring algorithmic recency over documented in systematic research selling methodology. This human-AI content turnover is designed to mitigate learning persistence.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch das Phänomen, bei dem kontinuierlich regenerierte Verkaufs-Playbooks und Skripte institutionelle Wissens-Akkumulation und getestete Ansätze verlieren, wobei algorithmische Aktualität gegenüber bewährter Verkaufs-Methodologie bevorzugt wird. Diese HAI-Inhalts-Umsatzrate zielt darauf ab zu mitigieren Lernpersistenz. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2703", "narrower_terms": [], "cross_domain_refs": [ "ART-0002", "ART-0005", "ART-0020" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "SAL-0097", "domain": "SAL", "term_en": "Enforcement bias", "term_de": "Durchsetzungs-Verzerrung", "definition_en": "A behavioral tendency where AI-recommended playbooks systematically favor certain sales methodologies over others, encoding methodological preferences into guidance that organizations may not consciously choose. This human-AI recommendation bias constrains sales approach diversity.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch das Phänomen, bei dem KI-empfohlene Playbooks bestimmte Verkaufs-Methodologien systematisch gegenüber anderen begünstigen, was methodologische Voreingenommenheit in Leitfaden codiert, der Organisationen möglicherweise nicht bewusst wählen. Diese HAI-Empfehlungs-Verzerrung beschränkt Verkaufs-Ansatz-Diversität. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2604", "narrower_terms": [], "cross_domain_refs": [ "ELR-0032" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q213523", "legal_classification": "descriptive_research_term" }, { "id": "SAL-0098", "domain": "SAL", "term_en": "Playbook literacy overload", "term_de": "Playbook-Literacy-Überbelastung", "definition_en": "A documented pattern where sales representatives are inundated with auto-generated, continuously updated playbook content they cannot absorb, reducing guideline adoption and creating false sense of knowledge availability. This human-AI content volume exceeds human processing capacity.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch das Phänomen, bei dem Verkautsvertreter mit auto-generiertem, kontinuierlich aktualisiertem Playbook-Inhalt überschwemmt werden, den sie nicht aufnehmen können, was Leitfaden-Adoption reduziert und falsches Wissensverfügbarkeits-Empfinden schafft. Dieses HAI-Inhalts-Volumen überschreitet menschliche Verarbeitungs-Kapazität. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-3902", "narrower_terms": [], "cross_domain_refs": [ "CON-0050" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q201684", "legal_classification": "systematic_classification" }, { "id": "SAL-0099", "domain": "SAL", "term_en": "Organizational learning atrophy", "term_de": "Organisatorischer-Lernens-Atrophie", "definition_en": "A characteristic dynamic where implicit knowledge from win-loss patterns is captured by AI systems but not retained by human teams through deliberate learning processes, degrading organizational capability to transmit selling wisdom. This human-AI knowledge capture-transfer gap is designed to mitigate institutional learning.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch das Phänomen, bei dem implizites Wissen von Gewinn-Verlust-Mustern von KI-Systemen erfasst wird, aber nicht von menschlichen Teams durch absichtliche Lernprozesse behalten wird, was organisatorische Fähigkeit, Verkaufs-Weisheit zu übertragen, verschlechtert. Diese HAI-Wissens-Erfassungs-Transfer-Lücke zielt darauf ab zu mitigieren institutionelles Lernen. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-2703", "narrower_terms": [], "cross_domain_refs": [ "COG-0024" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q133500", "legal_classification": "descriptive_research_term" }, { "id": "SAL-0100", "domain": "SAL", "term_en": "Territory allocation opacity", "term_de": "Territorium-Zuteilungs-Opazität", "definition_en": "A documented pattern where AI territory optimization decisions lack interpretability, preventing representatives from understanding allocation rationale and creating perception of unfairness without ability to appeal. This human-AI decision transparency gap undermines organizational legitimacy.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch das Phänomen, bei dem KI-Territorium-Optimierungs-Entscheidungen Interpretierbarkeit fehlt, was Vertreter daran hindert, Zuteilungs-Rationale zu verstehen, und Unfairheit-Wahrnehmung schafft, ohne Appellmöglichkeit. Diese HAI-Entscheidungs-Transparenz-Lücke unterminiert organisatorische Legitimität. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "Sales AI", "narrower_terms": [], "cross_domain_refs": [ "ASE-0013" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "SCR-0001", "domain": "SCR", "term_en": "Action Description Overwriting", "term_de": "ActionDescriptionOverwriting", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures an evaluation pattern in AI-augmented candidate screening, measurable through a narrative production effect in which scene descriptive language that adopts prosodic, novelistic stylization rather than visual filmmaking vocabulary, prioritizing narrative flourish over actionable cinematic clarity. This phenomenon operates at the intersection of action and description dynamics within the broader SCR domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept szenische Beschreibung, die erzählende statt filmische Sprache nutzt und narrative Ausschmückung über visuelle Klarheit priorisiert. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Screening AI", "narrower_terms": [ "SCR-0001", "SCR-0005", "SCR-0033", "SCR-0083", "SCR-0024", "SCR-0007", "SCR-0026", "SCR-0036", "SCR-0050", "SCR-0055", "SCR-0041", "SCR-0057", "SCR-0025", "SCR-0082", "SCR-0003", "SCR-0064", "SCR-0039", "SCR-0087", "SCR-0053", "SCR-0002", "SCR-0040", "SCR-0052", "SCR-0071", "SCR-0037", "SCR-0008", "SCR-0063", "SCR-0009", "SCR-0049", "SCR-0016", "SCR-0018", "SCR-0065", "SCR-0029", "SCR-0066", "SCR-0067", "SCR-0088", "SCR-0062", "SCR-0031", "SCR-0069", "SCR-0006", "SCR-0034", "SCR-0010", "SCR-0017", "SCR-0075", "SCR-0056", "SCR-0068", "SCR-0019", "SCR-0011", "SCR-0022", "SCR-0004", "SCR-0076", "SCR-0045", "SCR-0015", "SCR-0058", "SCR-0072", "SCR-0054", "SCR-0080", "SCR-0089", "SCR-0042", "SCR-0070", "SCR-0079", "SCR-0032", "SCR-0030", "SCR-0073", "SCR-0077", "SCR-0047", "SCR-0035", "SCR-0059", "SCR-0014", "SCR-0060", "SCR-0078", "SCR-0085", "SCR-0028" ], "cross_domain_refs": [ "ADA-0007", "CON-0015", "CON-0057" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "SCR-0002", "domain": "SCR", "term_en": "Action Sequence Clarity Shift", "term_de": "ActionSequenceClarityShift", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A screening methodology phenomenon in AI-mediated assessment, characterized by a screenwriting phenomenon in which action sequence descriptive clarity that becomes compromised through vague spatial relationships, inconsistent choreography notation, or confusing directional language in fight staging. This phenomenon operates at the intersection of action and sequence dynamics within the broader SCR domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept klarheit von Actionszenen-Beschreibung, die durch mehrdeutige räumliche Beziehungen oder verworrene Choreographie-Notation kompromittiert wird. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Screening AI", "narrower_terms": [], "cross_domain_refs": [ "CON-0090" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "SCR-0003", "domain": "SCR", "term_en": "Adaptation Creativity Shift", "term_de": "AnpassungCreativityShift", "definition_en": "An evaluation pattern in AI-augmented candidate screening, measurable through a screenwriting phenomenon in which when adapting source material, AI fills in story gaps in predictable rather than innovative ways. The concept emerges specifically in contexts where adaptation–creativity interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch beobachtbares Muster der KI-Nutzung, das sich in adaptation creativity shift manifestiert. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "System Adaptation", "narrower_terms": [], "cross_domain_refs": [ "CON-0040" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q170790", "legal_classification": "analytical_category" }, { "id": "SCR-0004", "domain": "SCR", "term_en": "Antagonist Motivation Flatness", "term_de": "AntagonistMotivationFlatness", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A screening methodology phenomenon in AI-mediated assessment, characterized by a narrative production effect observed when antagonistic character motivation that lacks psychological complexity or compelling justification, rendering opposition one-dimensional rather than narratively textured. This phenomenon operates at the intersection of antagonist and motivation dynamics within the broader SCR domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept antagonistische Motivation, der psychologische Komplexität fehlt und Opposition eindimensional statt narrativ texturiert wirkt. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Screening AI", "narrower_terms": [], "cross_domain_refs": [ "CON-0045" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q186588", "legal_classification": "analytical_category" }, { "id": "SCR-0005", "domain": "SCR", "term_en": "Banter Authenticity Shift", "term_de": "BanterAuthenticityShift", "definition_en": "An evaluation pattern in AI-augmented candidate screening, measurable through a creative writing pattern in which witty back-and-forth feels mechanical rather than naturally flowing from character dynamics. The concept emerges specifically in contexts where banter–authenticity interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch beobachtbares Muster der KI-Nutzung, das sich in banter authenticity shift manifestiert. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Screening AI", "narrower_terms": [], "cross_domain_refs": [ "ADA-0012", "AGE-0007", "AGE-0046" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "SCR-0006", "domain": "SCR", "term_en": "Callback Callback Absence", "term_de": "CallbackCallbackAbsence", "definition_en": "An evaluation pattern in AI-augmented candidate screening, measurable through a narrative production effect involving referential narrative device where callbacks to established moments fail to land emotionally or thematically, feeling instead disconnected from context or unmotivated. Distinguished from adjacent concepts by its focus on the specific mechanism through which callback manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch referenzielles Erzählmittel, bei dem Rückgriffe auf etablierte Momente emotional nicht landen und statt dessen kontextlos oder unmotiviert wirken. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Screening AI", "narrower_terms": [], "cross_domain_refs": [ "CON-0032" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "SCR-0007", "domain": "SCR", "term_en": "Callback Timing Misalignment", "term_de": "CallbackTimingMisalignment", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures an evaluation pattern in AI-augmented candidate screening, measurable through a narrative production effect characterized by contextual reference to earlier narrative moment that fails temporally or emotionally to resonate, missing its intended echo through poor setup or mismatched dramatic timing. This phenomenon operates at the intersection of callback and timing dynamics within the broader SCR domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept kontextuelle Rückbezügnahme auf früheres Narratives Moment, das zeitlich oder emotional nicht resoniert und durch schwache Vorarbeit seine Wirkung verfehlt. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Screening AI", "narrower_terms": [], "cross_domain_refs": [ "RPH-3552" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "SCR-0008", "domain": "SCR", "term_en": "Camaraderie Development Vagueness", "term_de": "CamaraderieDevelopmentVagueness", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A screening methodology phenomenon in AI-mediated assessment, characterized by a screenwriting phenomenon reflecting interpersonal bonding between characters that lacks granular moment-to-moment construction, replacing earned camaraderie through shared trial with tell-not-show assertion. This phenomenon operates at the intersection of camaraderie and development dynamics within the broader SCR domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept zwischenmenschliche Verbindung ohne granulare Konstruktion durch geteilte Momente, wobei Authentizität durch Behauptung ersetzt wird. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Screening AI", "narrower_terms": [], "cross_domain_refs": [ "CON-0060" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "SCR-0009", "domain": "SCR", "term_en": "Character Consistency Drift", "term_de": "CharacterConsistencyDrift", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A screening methodology phenomenon in AI-mediated assessment, characterized by a creative writing pattern characterized by aI-assisted character development accompanies inconsistent behaviors across scenes, requiring constant correction. This phenomenon operates at the intersection of character and consistency dynamics within the broader SCR domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept auseinanderdriften zwischen Intention und KI-generierter Umsetzung. Die Divergenz wird durch KI-Iterationen verstärkt. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Systemic Drift", "narrower_terms": [], "cross_domain_refs": [ "FIC-0055", "FIC-0027" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "SCR-0010", "domain": "SCR", "term_en": "Character Flaw Underutilization", "term_de": "CharacterFlawUnderutilization", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A screening methodology phenomenon in AI-mediated assessment, characterized by a creative writing pattern manifesting as character weakness or flaw introduced in exposition that remains insufficiently challenged through narrative consequence, allowing protagonist arc without meaningful adversarial friction. This phenomenon operates at the intersection of character and flaw dynamics within the broader SCR domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept charakterlicher Mangel, der durch narrative Konsequenz nicht herausgefordert wird, wodurch Entwicklung ohne echte Reibung verläuft. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Screening AI", "narrower_terms": [], "cross_domain_refs": [ "FIC-0040" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "SCR-0011", "domain": "SCR", "term_en": "Character Introduction Blandness", "term_de": "CharacterIntroductionBlandness", "definition_en": "An evaluation pattern in AI-augmented candidate screening, measurable through a creative writing pattern in which first impressions of characters feel unmemorable observed alongside generic description. The concept emerges specifically in contexts where character–introduction interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters. Observed phenomenon documented in human-AI interaction research.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch rekurrenter Effekt, der sich in character introduction blandness manifestiert. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Screening AI", "narrower_terms": [], "cross_domain_refs": [ "FIC-0012", "FIC-0013", "GAM-0018" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "SCR-0012", "domain": "SCR", "term_en": "Character Motivation Opacity", "term_de": "CharacterMotivationOpacity", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures an evaluation pattern in AI-augmented candidate screening, measurable through a creative writing pattern arising from aI-generated plot points leave character decisions feeling unmotivated to human audiences. This phenomenon operates at the intersection of character and motivation dynamics within the broader SCR domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept nutzungsphänomen, das sich in character motivation opacity manifestiert. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "RPH-3101", "narrower_terms": [], "cross_domain_refs": [ "FIC-0046" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q186588", "legal_classification": "analytical_category" }, { "id": "SCR-0013", "domain": "SCR", "term_en": "Character Speech Homogenization", "term_de": "CharacterSpeechHomogenization", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A screening methodology phenomenon in AI-mediated assessment, characterized by a creative writing pattern involving character speech homogenization where diverse characters vocally indistinguishable regardless of background, education, socioeconomic status, or personality differentiation. This phenomenon operates at the intersection of character and speech dynamics within the broader SCR domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept charakterliche Sprachhomogenisierung, wo diverse Figuren stimmlich ununterscheidbar wirken trotz unterschiedlicher sozialer Hintergründe. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "RPH-3202", "narrower_terms": [], "cross_domain_refs": [ "BEH-0058" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "SCR-0014", "domain": "SCR", "term_en": "Cliffhanger Unearned Feel", "term_de": "CliffhangerUnearnedFeel", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A screening methodology phenomenon in AI-mediated assessment, characterized by a creative writing pattern observed when episode termination that concludes with unearned dramatic stakes or artificial crisis, fabricating suspense through contrivance rather than organic narrative consequence. This phenomenon operates at the intersection of cliffhanger and unearned dynamics within the broader SCR domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept episodenabschluss mit unverdienter Spannung oder künstlichem Notstand, der Spannung tendiert dazu zu erzeugen durch Kunstgriff statt organische narrative Folgerung. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Screening AI", "narrower_terms": [], "cross_domain_refs": [ "MUS-0039" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "SCR-0015", "domain": "SCR", "term_en": "Climax Intensity Insufficiency", "term_de": "ClimaxIntensityInsufficiency", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures an evaluation pattern in AI-augmented candidate screening, measurable through a creative writing pattern arising from the story's peak moment feels underwhelming observed alongside different emotional buildup. This phenomenon operates at the intersection of climax and intensity dynamics within the broader SCR domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept emergente Dynamik, die sich in climax intensity insufficiency manifestiert. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Screening AI", "narrower_terms": [], "cross_domain_refs": [ "REL-0033" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "SCR-0016", "domain": "SCR", "term_en": "Collaboration Confusion", "term_de": "CollaborationConfusion", "definition_en": "A screening methodology phenomenon in AI-mediated assessment, characterized by a narrative production effect manifesting as it becomes unclear who contributed what when screenwriting teams mix human and AI contributions. Distinguished from adjacent concepts by its focus on the specific mechanism through which collaboration manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch phänomen der Ausgabe-Mehrdeutigkeit, das durch Trainings-Variabilität KI-Systeme charakterisiert. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Analytical Ratio", "narrower_terms": [], "cross_domain_refs": [ "CRE-0030" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "SCR-0017", "domain": "SCR", "term_en": "Conflict Resolution Predictability", "term_de": "ConflictResolutionPredictability", "definition_en": "A screening methodology phenomenon in AI-mediated assessment, characterized by interpersonal conflict resolution mechanism that defaults to conventional narrative logic rather than pursuing subversive or thematically unexpected resolution pathways. Distinguished from adjacent concepts by its focus on the specific mechanism through which conflict manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch konfliktlösung, die konventionelle Logik statt subversiver Alternativen wählt. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Screening AI", "narrower_terms": [], "cross_domain_refs": [ "VIB-0025", "FIC-0009" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "SCR-0018", "domain": "SCR", "term_en": "Constraint Resistance Shift", "term_de": "ConstraintResistanceShift", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A screening methodology phenomenon in AI-mediated assessment, characterized by a creative writing pattern reflecting writers stop pushing against story limitations, accepting AI's first-pass solutions. This phenomenon operates at the intersection of constraint and resistance dynamics within the broader SCR domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept interaktionsdynamik, die sich in constraint resistance shift manifestiert. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Screening AI", "narrower_terms": [], "cross_domain_refs": [ "CON-0026", "RHR-0130" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "SCR-0019", "domain": "SCR", "term_en": "Creative Fatigue From Editing", "term_de": "CreativeFatigueFromEditing", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A screening methodology phenomenon in AI-mediated assessment, characterized by a narrative production effect reflecting extensive revisions of AI-generated content can feel more exhausting than original composition. This phenomenon operates at the intersection of creative and fatigue dynamics within the broader SCR domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription. Classification term used in systematic observation, not advocacy.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept rekurrenter Effekt, der sich in creative fatigue from editing manifestiert. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Screening AI", "narrower_terms": [], "cross_domain_refs": [ "MUS-0026" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "SCR-0020", "domain": "SCR", "term_en": "Dialogue Attribution Narrowing", "term_de": "DialogueAttributionNarrowing", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A screening methodology phenomenon in AI-mediated assessment, characterized by when screenwriters rely on AI to yield dialogue, leading to shift of character voice distinctiveness. This phenomenon operates at the intersection of dialogue and attribution dynamics within the broader SCR domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept standardisierung individueller Sprachmuster zu generalisierter Dialog-Konvention. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "RPH-2701", "narrower_terms": [], "cross_domain_refs": [ "FIC-0014" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "SCR-0021", "domain": "SCR", "term_en": "Dialogue Attribution Error", "term_de": "DialogueAttributionError", "definition_en": "An evaluation pattern in AI-augmented candidate screening, measurable through a creative writing pattern manifesting as characters speak lines inconsistent with their established knowledge or perspective. The concept emerges specifically in contexts where dialogue–attribution interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch auseinanderdriften zwischen Intention und KI-generierter Umsetzung. Die Divergenz wird durch KI-Iterationen verstärkt. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2605", "narrower_terms": [], "cross_domain_refs": [ "ROB-0092" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "SCR-0022", "domain": "SCR", "term_en": "Dialogue Flatness Effect", "term_de": "DialogueFlatnessEffekt", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A screening methodology phenomenon in AI-mediated assessment, characterized by a creative writing pattern observed when aI-generated dialogue lacks subtext, resulting in on-the-nose exchanges that fail to convey emotional layers. This phenomenon operates at the intersection of dialogue and flatness dynamics within the broader SCR domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept standardisierung individueller Sprachmuster zu generalisierter Dialog-Konvention. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Psychological Effect", "narrower_terms": [], "cross_domain_refs": [ "DES-0056" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "SCR-0023", "domain": "SCR", "term_en": "Dialogue Formality Mismatch", "term_de": "DialogueFormalityMismatch", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A screening methodology phenomenon in AI-mediated assessment, characterized by a screenwriting phenomenon observed when character speech patterns don't match socioeconomic, educational, or regional backgrounds. This phenomenon operates at the intersection of dialogue and formality dynamics within the broader SCR domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept erfahrungsphänomen, das sich in dialogue formality mismatch manifestiert. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "RPH-3202", "narrower_terms": [], "cross_domain_refs": [ "LIN-0077", "RHR-0063" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "SCR-0024", "domain": "SCR", "term_en": "Dialogue Heavy Exposition", "term_de": "DialogueHeavyExposition", "definition_en": "A screening methodology phenomenon in AI-mediated assessment, characterized by a creative writing pattern observed when expository information delivery through character monologue or lengthy dialogue exchange that burdens narrative momentum, replacing action-based revelation with verbose explanation. Distinguished from adjacent concepts by its focus on the specific mechanism through which dialogue manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch informationsvermittlung durch Charaktermonolog oder lange Dialoge, die narrative Energie belastet und handlungsbasierte Enthüllung durch Verbosität ersetzt. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Screening AI", "narrower_terms": [], "cross_domain_refs": [ "WRK-0086" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "SCR-0025", "domain": "SCR", "term_en": "Dialogue Redundancy Accumulation", "term_de": "DialogueRedundancyAccumulation", "definition_en": "An evaluation pattern in AI-augmented candidate screening, measurable through a narrative production effect observed when multiple characters express the same information, resulting from AI's repetitive content generation. The concept emerges specifically in contexts where dialogue–redundancy interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch interaktionsmuster, das sich in dialogue redundancy accumulation manifestiert. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Screening AI", "narrower_terms": [], "cross_domain_refs": [ "AGE-0068", "AGE-0091", "AGE-0092" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "SCR-0026", "domain": "SCR", "term_en": "Dialogue Tags Verbosity", "term_de": "DialogueTagsVerbosity", "definition_en": "A screening methodology phenomenon in AI-mediated assessment, characterized by a narrative production effect in which dialogue attribution overuse where 'said' variations proliferate unnecessarily, replacing active beats and stage business with redundant dialogue tags. Distinguished from adjacent concepts by its focus on the specific mechanism through which dialogue manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch übernutzung von Dialogzuschreibungs-Variationen statt aktiver Beats, die Spielanweisungen durch redundante Tags ersetzend. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Screening AI", "narrower_terms": [], "cross_domain_refs": [ "SPR-0039" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "SCR-0027", "domain": "SCR", "term_en": "Discovery Moment Flatness", "term_de": "DiscoveryMomentFlatness", "definition_en": "An evaluation pattern in AI-augmented candidate screening, measurable through revelatory scene where character discovers crucial information that lacks emotional weight or introspective consequence, addressing discovery as mere plot mechanic. Distinguished from adjacent concepts by its focus on the specific mechanism through which discovery manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch offenbarungsszene, der emotionale Schwere oder introspektive Folge fehlt und die Entdeckung als reine Plot-Mechanik wirkt. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2201", "narrower_terms": [], "cross_domain_refs": [ "AED-0046", "AGE-0055", "ART-0023" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "SCR-0028", "domain": "SCR", "term_en": "Emotional Arc Flattening", "term_de": "EmotionalArcFlattening", "definition_en": "An evaluation pattern in AI-augmented candidate screening, measurable through a narrative production effect where character emotional progression becomes predictable and generic when AI accompanies story beats. Distinguished from adjacent concepts by its focus on the specific mechanism through which emotional manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch kI-bezogene Verhaltenstendenz, die sich in emotional arc flattening manifestiert. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Screening AI", "narrower_terms": [], "cross_domain_refs": [ "GAM-0036" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q9415", "legal_classification": "analytical_category" }, { "id": "SCR-0029", "domain": "SCR", "term_en": "Ending Inevitability Absence", "term_de": "EndingInevitabilityAbsence", "definition_en": "A screening methodology phenomenon in AI-mediated assessment, characterized by a screenwriting phenomenon manifesting as narrative conclusion that feels circumstantially arbitrary rather than causally flowing from thematic setup and established character trajectories, undermining closure inevitability. Distinguished from adjacent concepts by its focus on the specific mechanism through which ending manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch narrative Abschluss, der willkürlich wirkt statt kausal aus Setup und Charakterbahnen zu folgen. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Screening AI", "narrower_terms": [], "cross_domain_refs": [ "CON-0049" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "SCR-0030", "domain": "SCR", "term_en": "Exposition Dumping Acceptance", "term_de": "ExpositionDumpingAcceptance", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A screening methodology phenomenon in AI-mediated assessment, characterized by screenwriters accept AI's tendency to deliver plot information through unnatural dialogue exchanges. This phenomenon operates at the intersection of exposition and dumping dynamics within the broader SCR domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept standardisierung individueller Sprachmuster zu generalisierter Dialog-Konvention. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Screening AI", "narrower_terms": [], "cross_domain_refs": [ "ART-0060", "ART-0093", "CON-0026" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "SCR-0031", "domain": "SCR", "term_en": "Exposition Through Action Shift", "term_de": "ExpositionThroughActionShift", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures an evaluation pattern in AI-augmented candidate screening, measurable through a creative writing pattern in which screenwriters accept clunky exposition instead of revealing story through visual action. This phenomenon operates at the intersection of exposition and through dynamics within the broader SCR domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept wahrnehmungseffekt, der sich in exposition through action shift manifestiert. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Screening AI", "narrower_terms": [], "cross_domain_refs": [ "FIC-0029" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "SCR-0032", "domain": "SCR", "term_en": "Flashback Integration Awkwardness", "term_de": "FlashbackIntegrationAwkwardness", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures an evaluation pattern in AI-augmented candidate screening, measurable through a screenwriting phenomenon arising from flashback structural integration where memory sequences feel narratively detached from present timeline, emerging as exposition dump rather than causally embedded context. This phenomenon operates at the intersection of flashback and integration dynamics within the broader SCR domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription. Classification term used in systematic observation, not advocacy.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept flashback-Integration, die narrativ abgelöst wirkt und als Exposition statt kausal eingebetteter Kontext fungiert. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Analytical Ratio", "narrower_terms": [], "cross_domain_refs": [ "ADA-0010", "AED-0034", "AED-0072" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "SCR-0033", "domain": "SCR", "term_en": "Foreshadowing Heavyhandedness", "term_de": "ForeshadowingHeavyhandedness", "definition_en": "An evaluation pattern in AI-augmented candidate screening, measurable through a creative writing pattern arising from anticipatory narrative device that becomes telegraphed through excessive foregrounding, reducing audience suspense by making later payoffs predictable rather than surprising. Distinguished from adjacent concepts by its focus on the specific mechanism through which foreshadowing manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch vorausdeutungsmittel, das durch Überbetonung vorab gegeben wird, wodurch Spannung abnimmt und künftige Szenenaufgelösungen vorhersehbar wirken. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Screening AI", "narrower_terms": [], "cross_domain_refs": [ "CON-0060" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "SCR-0034", "domain": "SCR", "term_en": "Format Violation Unawareness", "term_de": "FormatViolationUnawareness", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures an evaluation pattern in AI-augmented candidate screening, measurable through a narrative production effect characterized by structural deviation where scene headings, formatting conventions, or stylistic rules inconsistently apply, creating visual and architectural irregularities in screenplay presentation. This phenomenon operates at the intersection of format and violation dynamics within the broader SCR domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept strukturelle Abweichung, bei der Szenenangaben, Formatierungskonventionen oder Stilrichtlinien inkonsistent angewendet werden und visuelle Unregelmäßigkeiten schaffen. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Screening AI", "narrower_terms": [], "cross_domain_refs": [ "AGE-0058", "ASE-0036", "ASE-0043" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "SCR-0035", "domain": "SCR", "term_en": "Formatting Convention Inconsistency", "term_de": "FormattingConventionInconsistency", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures an evaluation pattern in AI-augmented candidate screening, measurable through a narrative production effect reflecting screenplay formatting rules become inconsistent when AI accompanies portions alongside human writing. This phenomenon operates at the intersection of formatting and convention dynamics within the broader SCR domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept auseinanderdriften zwischen Intention und KI-generierter Umsetzung. Die Divergenz wird durch KI-Iterationen verstärkt. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Screening AI", "narrower_terms": [], "cross_domain_refs": [ "LIN-0094" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "SCR-0036", "domain": "SCR", "term_en": "Genre Blending Confusion", "term_de": "GenreBlendingConfusion", "definition_en": "An evaluation pattern in AI-augmented candidate screening, measurable through a creative writing pattern arising from genre amalgamation where stylistic or tonal elements from multiple genre traditions coexist without evident thematic purpose, creating accidental rather than artfully managed hybridity. Distinguished from adjacent concepts by its focus on the specific mechanism through which genre manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch genreverquickung, wo stilistische Elemente mehrerer Traditionen ohne erkennbaren Sinn koexistieren und zufällige statt kunstvolle Hybridität erzeugen. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Screening AI", "narrower_terms": [], "cross_domain_refs": [ "RPH-2255" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "SCR-0037", "domain": "SCR", "term_en": "Genre Convention Confusion", "term_de": "GenreConventionConfusion", "definition_en": "A screening methodology phenomenon in AI-mediated assessment, characterized by a narrative production effect manifesting as genre convention adherence that remains inconsistent across screenplay, sometimes honoring and sometimes violating established rules, creating tonal instability. The concept emerges specifically in contexts where genre–convention interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch genrekonvention-Einhaltung, die inkonsistent bleibt und zwischen Regel-Befolgung und -Verletzung schwankt. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Screening AI", "narrower_terms": [], "cross_domain_refs": [ "FIC-0020" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "SCR-0038", "domain": "SCR", "term_en": "Hope Establishment Weakness", "term_de": "HopeEstablishmentWeakness", "definition_en": "An evaluation pattern in AI-augmented candidate screening, measurable through a screenwriting phenomenon characterized by positive narrative turning point that feels unearned through preceding struggle, landing as an authorial gift to characters rather than as hard-won emotional or circumstantial victory. Distinguished from adjacent concepts by its focus on the specific mechanism through which hope manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch positive Wendung, die sich durch mangelnde vorangegangene Anstrengung unverdient anfühlt und als Geschenk statt als schwer erkämpfter Sieg wirkt. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-1303", "narrower_terms": [], "cross_domain_refs": [ "CON-0022", "CON-0044", "ELR-0179" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "SCR-0039", "domain": "SCR", "term_en": "Humor Tone Flatness", "term_de": "HumorToneFlatness", "definition_en": "An evaluation pattern in AI-augmented candidate screening, measurable through a creative writing pattern observed when comedy scene lacking internal architectural coherence—setup, escalation, and punchline structure—resulting in diffuse humor that fails to accumulate comedic pressure. The concept emerges specifically in contexts where humor–tone interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch komische Szene ohne innere Struktur von Aufbau, Eskalation und Pointe, wodurch Humor diffus wirkt und keine komische Spannung aufbaut. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Screening AI", "narrower_terms": [], "cross_domain_refs": [ "CON-0082", "COP-0011", "COP-0091" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "SCR-0040", "domain": "SCR", "term_en": "Inciting Incident Ambiguity", "term_de": "IncitingIncidentAmbiguity", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A screening methodology phenomenon in AI-mediated assessment, characterized by a creative writing pattern observed when inciting incident that fails to register as genuinely life-altering catalyst, emerging as plot convenience rather than authentic character destabilization. This phenomenon operates at the intersection of inciting and incident dynamics within the broader SCR domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept inciting Incident, die nicht als Leben-verändernd wirkt und Katalysator statt echte Charakter-Destabilisierung darstellt. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Screening AI", "narrower_terms": [], "cross_domain_refs": [ "FIC-0063" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "SCR-0041", "domain": "SCR", "term_en": "Intuition Skill Shift", "term_de": "IntuitionSkillShift", "definition_en": "A screening methodology phenomenon in AI-mediated assessment, characterized by a creative writing pattern arising from screenwriters lose their instinctive sense of what feels cinematically right on screen. Distinguished from adjacent concepts by its focus on the specific mechanism through which intuition manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch beobachtbares Muster der KI-Nutzung, das sich in intuition skill shift manifestiert. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Screening AI", "narrower_terms": [], "cross_domain_refs": [ "SOM-0054" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "SCR-0042", "domain": "SCR", "term_en": "Jargon Integration Awkwardness", "term_de": "JargonIntegrationAwkwardness", "definition_en": "A screening methodology phenomenon in AI-mediated assessment, characterized by a narrative production effect arising from professional or specialized terminology deployed within dialogue that feels inserted artificially rather than naturally integrated through character's vocational authenticity. The concept emerges specifically in contexts where jargon–integration interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch fachterminologie, die künstlich eingesetzt wirkt statt natürlich durch berufliche Authentizität integriert zu sein. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Analytical Ratio", "narrower_terms": [], "cross_domain_refs": [ "TRA-0059" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "SCR-0043", "domain": "SCR", "term_en": "Mentorship Authenticity Shift", "term_de": "MentorshipAuthenticityShift", "definition_en": "A screening methodology phenomenon in AI-mediated assessment, characterized by mentorship relationship between characters that lacks developmental foundation—shared trials, earned trust, demonstrated growth—emerging instead as transactional or perfunctory. The concept emerges specifically in contexts where mentorship–authenticity interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch mentorschaftsbeziehung ohne entwicklungsbedingte Fundierung durch geteilte Herausforderungen, wodurch sie transaktional statt authentisch wirkt. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2604", "narrower_terms": [], "cross_domain_refs": [ "CON-0041" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "SCR-0044", "domain": "SCR", "term_en": "Midpoint Significance Shift", "term_de": "MidpointSignificanceShift", "definition_en": "An evaluation pattern in AI-augmented candidate screening, measurable through a creative writing pattern where midpoint story pivot that carries insufficient thematic or emotional weight to reframe narrative stakes, failing to function as genuine turning point in character trajectory. The concept emerges specifically in contexts where midpoint–significance interactions produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch mittelpunkt-Wendung, die zu leicht wirkt um narrative Einsätze neu zu rahmen und als echte Wendung zu fungieren. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2201", "narrower_terms": [], "cross_domain_refs": [ "ADA-0012", "AGE-0007", "AGE-0046" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "SCR-0045", "domain": "SCR", "term_en": "Montage Specificity Reduction", "term_de": "MontageSpecificityReduction", "definition_en": "An evaluation pattern in AI-augmented candidate screening, measurable through a screenwriting phenomenon reflecting montages become generic sequences rather than tailored to character development. Distinguished from adjacent concepts by its focus on the specific mechanism through which montage manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch kI-bezogene Verhaltenstendenz, die sich in montage specificity reduction manifestiert. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Screening AI", "narrower_terms": [], "cross_domain_refs": [ "GAM-0018" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "SCR-0046", "domain": "SCR", "term_en": "Motif Underdevelopment", "term_de": "MotifUnderdevelopment", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures an evaluation pattern in AI-augmented candidate screening, measurable through a creative writing pattern reflecting recurring image, phrase, or visual motif introduced but insufficiently elaborated through thematic development, remaining superficial rather than accruing symbolic resonance. This phenomenon operates at the intersection of motif and underdevelopment dynamics within the broader SCR domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept wiederkehrende Bildlichkeit, Phrase oder Motiv, die eingeführt aber nicht thematisch vertieft werden, wodurch symbolische Resonanz ausbleibt. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "RPH-2052", "narrower_terms": [], "cross_domain_refs": [ "IDN-0001" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "SCR-0047", "domain": "SCR", "term_en": "Mystery Clarity Confusion", "term_de": "MysteryClarityConfusion", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A screening methodology phenomenon in AI-mediated assessment, characterized by a screenwriting phenomenon manifesting as mystery-driven narrative where investigative progression balances poorly between obviousness and insufficiency, either telegraphing solutions or withholding adequate interpretive clues. This phenomenon operates at the intersection of mystery and clarity dynamics within the broader SCR domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept mystery-Narration, die schlecht zwischen Offensichtlichkeit und Unzulänglichkeit balanciert, wodurch Lösungen entweder zu deutlich oder unterclued sind. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Screening AI", "narrower_terms": [], "cross_domain_refs": [ "CON-0054" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "SCR-0048", "domain": "SCR", "term_en": "Narration Voice Flatness", "term_de": "NarrationVoiceFlatness", "definition_en": "A screening methodology phenomenon in AI-mediated assessment, characterized by narrative voice technique that lacks distinctive personality markers, thematic resonance, or emotional subtext, rendering exposition neutral rather than characteristically weighted. The concept emerges specifically in contexts where narration–voice interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch erzählerstimme ohne markante Persönlichkeitsmerkmale, emotionale Schichtung oder thematische Tiefe, wodurch Information neutral statt charaktergebunden wirkt. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2201", "narrower_terms": [], "cross_domain_refs": [ "AGE-0014", "AGE-0048", "ART-0018" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "SCR-0049", "domain": "SCR", "term_en": "Non-Linear Storytelling Confusion", "term_de": "Non-linearStorytellingConfusion", "definition_en": "An evaluation pattern in AI-augmented candidate screening, measurable through non-sequential narrative structure where out-of-chronological scene arrangement tends to create viewer disorientation rather than intentional artistic effect, muddling comprehension. The concept emerges specifically in contexts where non–linear interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch nicht-lineare Narrative, wo chronologisches Durcheinander Zuschauer desorientiert statt künstlerisch bereichert. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Screening AI", "narrower_terms": [], "cross_domain_refs": [ "CRE-0057" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q1318295", "legal_classification": "empirical_phenomenon_label" }, { "id": "SCR-0050", "domain": "SCR", "term_en": "Opening Hook Weakness", "term_de": "OpeningHookWeakness", "definition_en": "A screening methodology phenomenon in AI-mediated assessment, characterized by a narrative production effect manifesting as opening sequence that fails to establish compelling dramatic question or character stakes, resulting in insufficient reason for audience continued engagement beyond initial pages. Distinguished from adjacent concepts by its focus on the specific mechanism through which opening manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch eröffnungssequenz, die keine fesselnde dramatische Frage etabliert, wodurch Publikum unzureichend motiviert ist weiterzulesen. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Screening AI", "narrower_terms": [], "cross_domain_refs": [ "SPR-0089" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "SCR-0051", "domain": "SCR", "term_en": "Originality Measurement Difficulty", "term_de": "OriginalityMeasurementDifficulty", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures an evaluation pattern in AI-augmented candidate screening, measurable through a screenwriting phenomenon characterized by it becomes harder to know if a scene is genuinely fresh or recycling common tropes. This phenomenon operates at the intersection of originality and measurement dynamics within the broader SCR domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept kI-bezogene Verhaltenstendenz, die sich in originality measurement difficulty manifestiert. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "RPH-3552", "narrower_terms": [], "cross_domain_refs": [ "AGE-0043", "AGE-0090", "ART-0021" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "SCR-0052", "domain": "SCR", "term_en": "POV Perspective Inconsistency", "term_de": "PovPerspectiveInconsistency", "definition_en": "A screening methodology phenomenon in AI-mediated assessment, characterized by a narrative production effect observed when narrative perspective inconsistency where point-of-view becomes ambiguous across mixed-media scenes, obscuring whose story emerges as primary focus. The concept emerges specifically in contexts where pov–perspective interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch narrative Perspektiv-Inkonsistenz, wo Fokus mehrdeutig wird und unklar bleibt, wessen Geschichte zentral ist. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Screening AI", "narrower_terms": [], "cross_domain_refs": [ "REL-0090" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "SCR-0053", "domain": "SCR", "term_en": "Pacing Rhythm Disruption", "term_de": "PacingRhythmDisruption", "definition_en": "A screening methodology phenomenon in AI-mediated assessment, characterized by a screenwriting phenomenon reflecting scene lengths and transitions feel misaligned when mixing human and AI-written segments. The concept emerges specifically in contexts where pacing–rhythm interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters. Observed phenomenon documented in human-AI interaction research.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch gleichmäßiger statt variierter erzählerischer Rhythmus bei KI-Szenenkomposition. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Screening AI", "narrower_terms": [], "cross_domain_refs": [ "AED-0090", "DES-0057", "EDU-0045" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "SCR-0054", "domain": "SCR", "term_en": "Page Count Estimation Error", "term_de": "PageCountEstimationError", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A screening methodology phenomenon in AI-mediated assessment, characterized by a narrative production effect in which writers cannot accurately estimate script length when AI accompanies variable amounts of material. This phenomenon operates at the intersection of page and count dynamics within the broader SCR domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept emergente Dynamik, die sich in page count estimation error manifestiert. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Screening AI", "narrower_terms": [], "cross_domain_refs": [ "PER-0030" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "SCR-0055", "domain": "SCR", "term_en": "Parallelism Absence", "term_de": "ParallelismAbsence", "definition_en": "A screening methodology phenomenon in AI-mediated assessment, characterized by a creative writing pattern observed when structural similarity between scenes or sequences that lacks thematic or emotional resonance across the screenplay, missing opportunity for meaningful symbolic echo. The concept emerges specifically in contexts where parallelism–absence interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch strukturelle Ähnlichkeit zwischen Szenen, der symbolische Resonanz fehlt und bedeutsamer Echo ausbleibt. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Screening AI", "narrower_terms": [], "cross_domain_refs": [ "CON-0065", "CON-0067", "CON-0092" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "SCR-0056", "domain": "SCR", "term_en": "Parenthetical Overuse", "term_de": "ParentheticalOveruse", "definition_en": "A screening methodology phenomenon in AI-mediated assessment, characterized by action direction overutilization through parenthetical stage directions that replace beat-based character action, creating passive reading experience rather than kinetic staging. Distinguished from adjacent concepts by its focus on the specific mechanism through which parenthetical manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch übernutzung von Spielanweisungen in Klammern statt Spiel-Beats, wodurch passive Leseerfahrung statt kinetische Inszenierung entsteht. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Screening AI", "narrower_terms": [], "cross_domain_refs": [ "CON-0043", "CON-0055", "CON-0058" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "SCR-0057", "domain": "SCR", "term_en": "Period Detail Inaccuracy", "term_de": "PeriodDetailInaccuracy", "definition_en": "A screening methodology phenomenon in AI-mediated assessment, characterized by a narrative production effect involving historical or era-specific atmospheric detail that fails authenticity through anachronism, generic placeholder, or insufficiently researched period verisimilitude. The concept emerges specifically in contexts where period–detail interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch historische Detaillierung, die durch Anachronismus oder generische Placeholders Authentizität verfehlt. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Screening AI", "narrower_terms": [], "cross_domain_refs": [ "PHO-0001" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "SCR-0058", "domain": "SCR", "term_en": "Plot Hole Proliferation", "term_de": "PlotHoleProliferation", "definition_en": "An evaluation pattern in AI-augmented candidate screening, measurable through a screenwriting phenomenon manifesting as aI fills narrative gaps with surface-level content that doesn't withstand scrutiny. The concept emerges specifically in contexts where plot–hole interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch ersatz knapper Szenenangaben durch erzählende Ausschweifung statt visueller Klarheit. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Analytical Ratio", "narrower_terms": [], "cross_domain_refs": [ "CON-0040" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "SCR-0059", "domain": "SCR", "term_en": "Power Dynamic Confusion", "term_de": "PowerDynamikConfusion", "definition_en": "An evaluation pattern in AI-augmented candidate screening, measurable through relational hierarchy between characters that remains unarticulated through dialogue or demonstrated behavior, leaving power dynamics ambiguous or contradictory to viewer inference. Distinguished from adjacent concepts by its focus on the specific mechanism through which power manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch relationale Hierarchie zwischen Charakteren, die unausgesprochen bleibt und Machtdynamiken mehrdeutig oder widersprüchlich wirken lässt. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Screening AI", "narrower_terms": [], "cross_domain_refs": [ "GAM-0058" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "SCR-0060", "domain": "SCR", "term_en": "Reading Time Miscalculation", "term_de": "ReadingTimeMiscalculation", "definition_en": "An evaluation pattern in AI-augmented candidate screening, measurable through a creative writing pattern where dialogue pace becomes difficult to judge when mixing human and AI-generated content. Distinguished from adjacent concepts by its focus on the specific mechanism through which reading manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch standardisierung individueller Sprachmuster zu generalisierter Dialog-Konvention. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Screening AI", "narrower_terms": [], "cross_domain_refs": [ "ART-0092" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "SCR-0061", "domain": "SCR", "term_en": "Reality Grounding Shift", "term_de": "RealityGroundingShift", "definition_en": "An evaluation pattern in AI-augmented candidate screening, measurable through narrative coherence fracture where plot mechanics override emotional logic and character motivation, creating implausible scenario where convenience drives story rather than consequence. The concept emerges specifically in contexts where reality–grounding interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch narrative Kohärenzbruch, wo Plot-Mechaniken emotionale Logik überschreiben, wodurch Bequemlichkeit statt Folgerichtigkeit die Geschichte treibt. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-3001", "narrower_terms": [], "cross_domain_refs": [ "SWE-0027" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "SCR-0062", "domain": "SCR", "term_en": "Red Herring Transparency", "term_de": "RedHerringTransparency", "definition_en": "A screening methodology phenomenon in AI-mediated assessment, characterized by a screenwriting phenomenon in which deceptive narrative element that forfeits its misdirective function through overly prominent or grammatically marked cues that signal its false nature to attentive viewers. Distinguished from adjacent concepts by its focus on the specific mechanism through which red manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch täuschendes narratives Element, das seine Ablenkungswirkung durch zu deutliche oder grammatikalisch markierte Hinweise verliert, die aufmerksamen Zuschauern seine Falschheit signalisieren. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Screening AI", "narrower_terms": [], "cross_domain_refs": [ "MUS-0083" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q535399", "legal_classification": "observational_construct" }, { "id": "SCR-0063", "domain": "SCR", "term_en": "Redemption Arc Incompleteness", "term_de": "RedemptionArcIncompleteness", "definition_en": "An evaluation pattern in AI-augmented candidate screening, measurable through character change pattern arc that manifests external behavioral change without internal psychological struggle, undermining functional restoration arc credibility through insufficient interiority. Distinguished from adjacent concepts by its focus on the specific mechanism through which functional restoration manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch charakterwandlung, die externe Verhaltensänderung ohne inneren psychologischen Einsatz zeigt und Glaubwürdigkeit der systemische Bewahrung untergräbt. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Screening AI", "narrower_terms": [], "cross_domain_refs": [ "CON-0060", "CON-0093", "FIC-0012" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "SCR-0064", "domain": "SCR", "term_en": "Reference Accuracy Reliance", "term_de": "ReferenceAccuracyReliance", "definition_en": "An evaluation pattern in AI-augmented candidate screening, measurable through a screenwriting phenomenon manifesting as writers become overly reliant on AI to verify dialogue from existing films or shows. Distinguished from adjacent concepts by its focus on the specific mechanism through which reference manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch standardisierung individueller Sprachmuster zu generalisierter Dialog-Konvention. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Screening AI", "narrower_terms": [], "cross_domain_refs": [ "CON-0078" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "SCR-0065", "domain": "SCR", "term_en": "Revision Overhead Accumulation", "term_de": "RevisionOverheadAccumulation", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures an evaluation pattern in AI-augmented candidate screening, measurable through a screenwriting phenomenon observed when more editing is observed to fix AI-generated dialogue and scene structure than writing from scratch. This phenomenon operates at the intersection of revision and overhead dynamics within the broader SCR domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept standardisierung individueller Sprachmuster zu generalisierter Dialog-Konvention. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Screening AI", "narrower_terms": [], "cross_domain_refs": [ "AGE-0068", "AGE-0091", "AGE-0092" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "SCR-0066", "domain": "SCR", "term_en": "Rivalry Tension Absence", "term_de": "RivalryTensionAbsence", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A screening methodology phenomenon in AI-mediated assessment, characterized by interpersonal dynamic between characters positioned as adversarial or competitive that lacks specificity, emerging as generic opposition rather than textured relational conflict. This phenomenon operates at the intersection of rivalry and tension dynamics within the broader SCR domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept zwischenmenschliche Dynamik zwischen gegnerischen Charakteren, der Spezifität fehlt und die generisch wirkt statt texturierte relationale Konflikte auszutragen. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Screening AI", "narrower_terms": [], "cross_domain_refs": [ "FIC-0075" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "SCR-0067", "domain": "SCR", "term_en": "Romance Trope Predictability", "term_de": "RomanceTropePredictability", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures an evaluation pattern in AI-augmented candidate screening, measurable through romantic relationship development where beat-by-beat emotional escalation follows genre formula without original interpretation, rendering connection generic rather than distinctive. This phenomenon operates at the intersection of romance and trope dynamics within the broader SCR domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept romantische Beziehungsentwicklung, die Genreformel folgt ohne originale Interpretation, wodurch Verbindung generisch statt distinktiv wirkt. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Screening AI", "narrower_terms": [], "cross_domain_refs": [ "FIC-0092", "GAM-0058", "REL-0175" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "SCR-0068", "domain": "SCR", "term_en": "Romantic Chemistry Absence", "term_de": "RomanticChemistryAbsence", "definition_en": "A screening methodology phenomenon in AI-mediated assessment, characterized by romantic dynamic between principal characters that emerges as narratively forced rather than through organic moment-by-moment relational development and earned attraction. The concept emerges specifically in contexts where romantic–chemistry interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch romantische Dynamik, die narrativ aufgesetzt wirkt statt organisch durch geteilte Momente und verdiente Anziehung zu entstehen. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Screening AI", "narrower_terms": [], "cross_domain_refs": [ "RPH-3353" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "SCR-0069", "domain": "SCR", "term_en": "Running Gag Fatigue", "term_de": "RunningGagFatigue", "definition_en": "A screening methodology phenomenon in AI-mediated assessment, characterized by comedic pattern introduced for comic effect that progressively diminishes in impact through repetitive deployment, becoming stale rather than escalating in surprising iteration. Distinguished from adjacent concepts by its focus on the specific mechanism through which running manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch komisches Motiv, das durch wiederholte Verwendung an Wirkung verliert, statt überraschende Variationen zu zeigen, und progressiv ausbrennt. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Screening AI", "narrower_terms": [], "cross_domain_refs": [ "ASE-0038", "COG-0120", "CON-0037" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "SCR-0070", "domain": "SCR", "term_en": "Sacrifice Meaninglessness", "term_de": "SacrificeMeaninglessness", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A screening methodology phenomenon in AI-mediated assessment, characterized by a screenwriting phenomenon manifesting as character sacrifice narrative moment that feels obligatory rather than costly, failing to demonstrate meaningful renunciation or consequential loss through dramatic weight. This phenomenon operates at the intersection of sacrifice and meaninglessness dynamics within the broader SCR domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept charakter-Opfer, das obligatorisch statt kostspielig wirkt und echte Verzicht-Dramatik ohne Gewicht zeigt. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Screening AI", "narrower_terms": [], "cross_domain_refs": [ "CON-0019", "DES-0068", "GAM-0024" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "SCR-0071", "domain": "SCR", "term_en": "Scene Purpose Ambiguity", "term_de": "ScenePurposeAmbiguity", "definition_en": "An evaluation pattern in AI-augmented candidate screening, measurable through a narrative production effect manifesting as scenes generated by AI lack clear narrative function within the overall story structure. The concept emerges specifically in contexts where scene–purpose interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch ersatz knapper Szenenangaben durch erzählende Ausschweifung statt visueller Klarheit. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Screening AI", "narrower_terms": [], "cross_domain_refs": [ "ART-0008", "ASE-0032", "ASE-0095" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "SCR-0072", "domain": "SCR", "term_en": "Scene Structure Reliance", "term_de": "SceneStructureReliance", "definition_en": "An evaluation pattern in AI-augmented candidate screening, measurable through a creative writing pattern where screenwriters become less skilled at intuiting pacing when AI accompanies scene beats automatically. The concept emerges specifically in contexts where scene–structure interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch verlust erzählerischen Timings, wenn KI gleichmäßige statt variierter Rhythmen tendiert dazu zu erzeugen. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Screening AI", "narrower_terms": [], "cross_domain_refs": [ "AED-0070", "AGE-0012", "AGE-0014" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "SCR-0073", "domain": "SCR", "term_en": "Secondary Character Flatness", "term_de": "SecondaryCharacterFlatness", "definition_en": "An evaluation pattern in AI-augmented candidate screening, measurable through a creative writing pattern manifesting as supporting character arc deficiency where secondary figures function primarily as plot facilitation tools rather than dimensional beings with internal trajectories and stakes. Distinguished from adjacent concepts by its focus on the specific mechanism through which secondary manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch mangel an Nebencharakter-Entwicklung, wo Figuren als Plot-Tools fungieren statt als dimensionale Wesen mit eigenen Bögen. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Screening AI", "narrower_terms": [], "cross_domain_refs": [ "RPH-3355" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "SCR-0074", "domain": "SCR", "term_en": "Sensory Detail Sparseness", "term_de": "SensoryDetailSparseness", "definition_en": "A screening methodology phenomenon in AI-mediated assessment, characterized by a creative writing pattern in which scene composition lacking tactile descriptive language—taste, touch, smell, sound, texture—that would ground viewers in sensory space and heighten visceral engagement with action. Distinguished from adjacent concepts by its focus on the specific mechanism through which sensory manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch szenische Komposition ohne taktile Beschreibungssprache, die Zuschauer in sensorischen Raum verankert und viszerale Partizipation mit Aktion erhöht. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-3202", "narrower_terms": [], "cross_domain_refs": [ "PER-0113", "PHO-0076", "ROB-0071" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "SCR-0075", "domain": "SCR", "term_en": "Setting Underutilization", "term_de": "SettingUnderutilization", "definition_en": "An evaluation pattern in AI-augmented candidate screening, measurable through a screenwriting phenomenon observed when narrative setting that functions as mere backdrop rather than active force in plot mechanics, maintaining interchangeability without becoming integral to story necessity. The concept emerges specifically in contexts where setting–underutilization interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch erzählter Schauplatz, die austauschbar bleibt statt narrative Gewalt auszuüben und Plot integral zu bestimmen. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Screening AI", "narrower_terms": [], "cross_domain_refs": [ "AED-0004", "AGE-0049", "CON-0069" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "SCR-0076", "domain": "SCR", "term_en": "Silence Underutilization", "term_de": "SilenceUnderutilization", "definition_en": "A screening methodology phenomenon in AI-mediated assessment, characterized by narrative technique that underutilizes silence and absence as compositional tool, replacing introspective moments with continuous action or dialogue that is designed to mitigate psychological depth. The concept emerges specifically in contexts where silence–underutilization interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch erzähltechnik, die Stille und Abwesenheit als kompositorisches Mittel unterzunutzen vermag und introspektive Momente durch kontinuierliche Aktion ersetzt. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Screening AI", "narrower_terms": [], "cross_domain_refs": [ "COG-0168" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "SCR-0077", "domain": "SCR", "term_en": "Slug Line Clarity Shift", "term_de": "SlugLineClarityShift", "definition_en": "An evaluation pattern in AI-augmented candidate screening, measurable through a screenwriting phenomenon in which scene-setting specification that loses spatial clarity through ambiguous location descriptions or redundant heading repetition across consecutive scenes. Distinguished from adjacent concepts by its focus on the specific mechanism through which slug manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch szenische Ortsbestimmung, die durch mehrdeutige Beschreibungen oder redundante Wiederholung von Szenenangaben ihre räumliche Klarheit einbüßt. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Screening AI", "narrower_terms": [], "cross_domain_refs": [ "CON-0079", "CON-0088" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "SCR-0078", "domain": "SCR", "term_en": "Stylistic Consistency Shift", "term_de": "StylisticConsistencyShift", "definition_en": "An evaluation pattern in AI-augmented candidate screening, measurable through a screenwriting phenomenon manifesting as stylistic coherence breakdown where scene-to-scene transitions reveal distinct writing voices, tonal registers, or structural approaches that undermine unified authorial presence. The concept emerges specifically in contexts where stylistic–consistency interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch stilistische Kohärenz-Infraktion, bei der Szenübergänge unterschiedliche Schreibstimmen, Tonlagen oder Strukturansätze offenbaren, die einheitliche Autorenpräsenz untergraben. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Screening AI", "narrower_terms": [], "cross_domain_refs": [ "CON-0032" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "SCR-0079", "domain": "SCR", "term_en": "Subtext Erasure", "term_de": "SubtextErasure", "definition_en": "An evaluation pattern in AI-augmented candidate screening, measurable through a creative writing pattern arising from dialogue delivery that becomes excessively literal and on-the-nose, replacing subtext and unspoken relational tension with explicit statement that forecloses interpretive space. The concept emerges specifically in contexts where subtext–erasure interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch zu expliziter Dialog, der Subtext durch Direktheit ersetzt und interpretiven Raum durch Litteralität verschließt. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Screening AI", "narrower_terms": [], "cross_domain_refs": [ "LIN-0009", "LIN-0021" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "SCR-0080", "domain": "SCR", "term_en": "Symbolic Resonance Absence", "term_de": "SymbolicResonanzAbsence", "definition_en": "A screening methodology phenomenon in AI-mediated assessment, characterized by a narrative production effect observed when symbolic element or visual motif introduced across screenplay that fails to accrue thematic resonance through repetition, remaining superficially deployed without deepening meaning. The concept emerges specifically in contexts where symbolic–resonance interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch symbolisches Element, das keine thematische Resonanz durch Wiederholung aufbaut und oberflächlich bleibt. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Screening AI", "narrower_terms": [], "cross_domain_refs": [ "CON-0065", "CON-0067", "CON-0092" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "SCR-0081", "domain": "SCR", "term_en": "Symmetry Absence", "term_de": "SymmetryAbsence", "definition_en": "A screening methodology phenomenon in AI-mediated assessment, characterized by a narrative production effect reflecting structural parallel—whether between characters, scenes, or thematic elements—that lacks emotional resonance or thematic payoff, remaining decorative rather than meaningfully echoing. Distinguished from adjacent concepts by its focus on the specific mechanism through which symmetry manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch strukturelle Spiegelung zwischen Charakteren oder Szenen, der emotionale Resonanz fehlt und die dekorativ statt bedeutsam wirkt. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2055", "narrower_terms": [], "cross_domain_refs": [ "CON-0065", "CON-0067", "CON-0092" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "SCR-0082", "domain": "SCR", "term_en": "Tension Release Miscalibration", "term_de": "TensionReleaseMiscalibration", "definition_en": "An evaluation pattern in AI-augmented candidate screening, measurable through emotional calibration between intense and subtle scenes that oscillates erratically between melodrama and flatness, lacking graduated tonal pacing within coherent affective architecture. The concept emerges specifically in contexts where tension–release interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch emotionale Kalibrierung zwischen intensiven und subtilen Szenen, die zwischen Melodrama und Flachheit schwankt statt graduiertes Timing zu zeigen. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Analytical Ratio", "narrower_terms": [], "cross_domain_refs": [ "CON-0070" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "SCR-0083", "domain": "SCR", "term_en": "Thriller Misdirection Absence", "term_de": "ThrillerMisdirectionAbsence", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A screening methodology phenomenon in AI-mediated assessment, characterized by a creative writing pattern in which misdirective absence in thriller narratives where planted false clues fail to momentarily convince audiences, leaving deception tactics ineffectual or transparent. This phenomenon operates at the intersection of thriller and misdirection dynamics within the broader SCR domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept mangel an Ablenkungstaktik in Thriller-Narrativen, wo bewusst platzierte falsche Hinweise das Publikum nicht täuschen, wodurch Täuschungstechniken wirkungslos bleiben. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Screening AI", "narrower_terms": [], "cross_domain_refs": [ "CON-0034" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "SCR-0084", "domain": "SCR", "term_en": "Tone Inconsistency Bleed", "term_de": "ToneInconsistencyBleed", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures an evaluation pattern in AI-augmented candidate screening, measurable through a creative writing pattern involving screenplay tone shifts unexpectedly between human-written and AI-written sections. This phenomenon operates at the intersection of tone and inconsistency dynamics within the broader SCR domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept drehbuchtechnische Ungenauigkeit in tone inconsistency bleed, die Filmproduktion erschwert. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "RPH-2701", "narrower_terms": [], "cross_domain_refs": [ "FIC-0089" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "SCR-0085", "domain": "SCR", "term_en": "Tone Shift Abruptness", "term_de": "ToneShiftAbruptness", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures an evaluation pattern in AI-augmented candidate screening, measurable through tonal transition technique where atmospheric or emotional shifts between scenes feel abrupt or mechanically imposed rather than organically developing from narrative causality. This phenomenon operates at the intersection of tone and shift dynamics within the broader SCR domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription. Classification term used in systematic observation, not advocacy.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept tonale Übergangstechnik, bei der Atmosphäre- oder Stimmungswechsel abrupt oder mechanisch aufgesetzt wirken statt organisch aus narrativer Kausalität zu folgen. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Screening AI", "narrower_terms": [], "cross_domain_refs": [ "MUS-0011" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "SCR-0086", "domain": "SCR", "term_en": "Transition Abruptness", "term_de": "ÜbergangAbruptness", "definition_en": "A screening methodology phenomenon in AI-mediated assessment, characterized by transition technique between scenes that tends to create jarring spatial or temporal discontinuity through absent connective tissue, resulting in viewer disorientation. Distinguished from adjacent concepts by its focus on the specific mechanism through which transition manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch übergangstechnik zwischen Szenen, die Diskontinuität durch fehlende Verbindungsgewebe schafft. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2701", "narrower_terms": [], "cross_domain_refs": [ "ADA-0011", "AED-0094", "AED-0095" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "SCR-0087", "domain": "SCR", "term_en": "Visual Description Vagueness", "term_de": "VisualDescriptionVagueness", "definition_en": "A screening methodology phenomenon in AI-mediated assessment, characterized by a creative writing pattern involving action lines become less cinematic when written by AI, lacking specific visual storytelling. Distinguished from adjacent concepts by its focus on the specific mechanism through which visual manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch wahrnehmungseffekt, der sich in visual description vagueness manifestiert. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Screening AI", "narrower_terms": [], "cross_domain_refs": [ "REL-0091" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "SCR-0088", "domain": "SCR", "term_en": "Voice-Over Overreliance", "term_de": "Voice-overOverreliance", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures an evaluation pattern in AI-augmented candidate screening, measurable through a creative writing pattern manifesting as narrative voice reliance on excessive voice-over exposition compensating for weak dialogue, allowing exposition burden to replace interpersonal communication and scene dynamism. This phenomenon operates at the intersection of voice and over dynamics within the broader SCR domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept übermäßige Nutzung von Voice-Over-Exposition, die schwache Dialoge kompensiert und Exposition durch echte Verständigung ersetzt. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Screening AI", "narrower_terms": [], "cross_domain_refs": [ "STE-0021" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "SCR-0089", "domain": "SCR", "term_en": "Vulnerability Timing Misalignment", "term_de": "VulnerabilityTimingMisalignment", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures an evaluation pattern in AI-augmented candidate screening, measurable through a creative writing pattern reflecting vulnerability disclosure or intimate character revelation that occurs prematurely within character arc, lacking sufficient preceding struggle to justify emotional exposure. This phenomenon operates at the intersection of vulnerability and timing dynamics within the broader SCR domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept verletzliche Charakteroffenbarung, die zu früh im Character-Arc erfolgt und vorangegangener Entwicklung entbehrt. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Screening AI", "narrower_terms": [], "cross_domain_refs": [ "RPH-3355", "RPH-3552" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "SOC-0001", "domain": "SOC", "term_en": "Roles-Contextualized Effect", "term_de": "Role-Aware Input", "definition_en": "A social dynamics phenomenon in AI-mediated group interaction, characterized by how a person's role (parent, teacher, manager) changes what they ask AI and what answers matter. The concept emerges specifically in contexts where roles–contextualized interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch ein Eingabemuster, bei dem der Nutzer soziale Rollen in die Formulierung einbezieht — \"Als Elternteil brauche ich…\", \"In meiner Funktion als…\". Die Eingabe wird durch die Rolle des Nutzers kontextualisiert. Steht in Verbindung mit AUG-0491 (Das State Label), AUG-0650 (The Context-Sensitive Query) und AUG-0524 (Context Schicht). Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Psychological Effect", "narrower_terms": [], "cross_domain_refs": [ "IDN-0035" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "SOC-0002", "domain": "SOC", "term_en": "The Access Differential", "term_de": "Access Differential", "definition_en": "A social interaction phenomenon arising from the observable difference in access to AI systems between different user groups — influenced by economic, infrastructural, educational, and regulatory factors. Related to AUG-0676 (The Socioeconomi...", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch der beobachtbare Unterschied im Zugang zu KI-Systemen zwischen verschiedenen Nutzergruppen — beeinflusst durch wirtschaftliche, infrastrukturelle, bildungsbezogene und regulatorische Faktoren. Steht in Verbindung mit AUG-0676 (The Socioeconomic Range), AUG-0722 (The Infrastructure Constraint) und AUG-0724 (The Access Cost Factor). Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-3802", "narrower_terms": [ "AUG-0722", "PER-0104", "SOC-0031" ], "cross_domain_refs": [ "AED-0091" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "SOC-0003", "domain": "SOC", "term_en": "The Access Structure", "term_de": "Access Structure", "definition_en": "Access pathways through which different user groups can interact with AI systems — both software-based and embodied — and the observation that these pathways are unequally distributed. Related to A... Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Die Gesamtheit der Zugangswege, über die verschiedene Nutzergruppen mit KI-Systemen — sowohl softwarebasiert als auch verkörpert — interagieren können — und die Beobachtung, dass diese Zugangswege ungleich verteilt sind. Steht in Verbindung mit AUG-0721 (The Access Differential), AUG-0676 (The Socioeconomic Range) und AUG-0848 (The Resource Distribution Pattern).", "etymology": "", "broader_term": "Social AI", "narrower_terms": [], "cross_domain_refs": [ "ASE-0005" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "SOC-0004", "domain": "SOC", "term_en": "The Agricultural Bot", "term_de": "Agricultural Bot", "definition_en": "An embodied AI system deployed in agriculture — harvesting, soil care, pest detection, irrigation. Related to AUG-0930 (The Construction Assistant), AUG-0922 (The Environmental Reading), and AUG-07... Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch ein verkörpertes KI-System, das in der Landwirtschaft eingesetzt wird — Ernte, Bodenpflege, Schädlingserkennung, Bewässerung. Steht in Verbindung mit AUG-0930 (The Construction Assistant), AUG-0922 (The Environmental Reading) und AUG-0748 (The Frugal Innovation). Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2105", "narrower_terms": [ "TEM-0042" ], "cross_domain_refs": [ "TEM-0042" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "SOC-0005", "domain": "SOC", "term_en": "The Alternative Adoption Path", "term_de": "Alternative Adoption Path", "definition_en": "A social cognition pattern in AI-augmented interpersonal processing, measurable through a community dynamic manifesting as aI adoption follows different paths in different contexts — some contexts skip intermediate steps, others develop their own usage patterns that do not correspond to the predicted linear progression. Distinguished from adjacent concepts by its focus on the specific mechanism through which the manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Die Beobachtung, dass KI-Adoption in verschiedenen Kontexten unterschiedlichen Pfaden folgt — manche Kontexte überspringen Zwischenschritte, andere entwickeln eigene Nutzungsmuster, die nicht dem vorhergesagten linearen Verlauf entsprechen. Steht in Verbindung mit AUG-0743 (The Mobile-First Society), AUG-0721 (The Access Differential) und AUG-0099 (The Adoption Window).", "etymology": "", "broader_term": "Social AI", "narrower_terms": [], "cross_domain_refs": [ "TEM-0168" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "SOC-0006", "domain": "SOC", "term_en": "The Comment Shield", "term_de": "Comment Shield", "definition_en": "A social cognition pattern in AI-augmented interpersonal processing, measurable through a social interaction phenomenon involving using AI to write more effectively replies to harsh feedback or online attacks. AI helps craft responses that are calm and strong. Related to AUG-0486 (The Email Shield), AUG-0568 (The Response Shield), and. Distinguished from adjacent concepts by its focus on the specific mechanism through which the manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch die Nutzung von KI zur Vorbereitung auf oder Reaktion auf negative Kommentare, Kritik oder Bewertungen — durch sachliche Gegenargumente, deeskalierende Formulierungen oder strategische Nicht-Reaktion. Steht in Verbindung mit AUG-0486 (The Email Shield), AUG-0568 (The Response Shield) und AUG-0115 (Social Aerodynamics). Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2603", "narrower_terms": [], "cross_domain_refs": [ "TEM-0178" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "SOC-0007", "domain": "SOC", "term_en": "The Communication Agent", "term_de": "Kommunikation Agent", "definition_en": "A social dynamics phenomenon in AI-mediated group interaction, characterized by an AI agent system specialized in preparing and conveying information to the user — summaries, explanations, notifications. Related to AUG-0911 (The Inquiry Agent), AUG-0906 (The Coordinator Role). The concept emerges specifically in contexts where the–communication interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch ein KI-Agentensystem, das auf die Aufbereitung und Vermittlung von Informationen an den Nutzer spezialisiert ist — Zusammenfassungen, Erklärungen, Benachrichtigungen. Steht in Verbindung mit AUG-0911 (The Inquiry Agent), AUG-0906 (The Coordinator Role) und AUG-0872 (The Progress Report). Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "SOM-0055", "narrower_terms": [], "cross_domain_refs": [ "IDN-0050" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "SOC-0008", "domain": "SOC", "term_en": "The Community Hub", "term_de": "Community Hub", "definition_en": "A social cognition pattern in AI-augmented interpersonal processing, measurable through a collective behavior effect where informal meeting points where AI use takes place communally — neighborhood groups, local initiatives, shared devices with joint use. Related to AUG-0726 (The Library Access Point), AUG-0744 (The Mu. Distinguished from adjacent concepts by its focus on the specific mechanism through which the manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch die Entstehung informeller Treffpunkte, an denen KI-Nutzung gemeinschaftlich stattfindet — Nachbarschaftsgruppen, lokale Initiativen, geteilte Geräte mit gemeinsamer Nutzung. Steht in Verbindung mit AUG-0726 (The Library Access Point), AUG-0744 (The Multi-User Device Context) und AUG-0666 (The Sharing Norm). Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-3101", "narrower_terms": [ "REL-0062", "REL-0152" ], "cross_domain_refs": [ "PER-0114" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "SOC-0009", "domain": "SOC", "term_en": "The Conversational Afterimage", "term_de": "Conversational Afterimage", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures A social cognition pattern in AI-augmented interpersonal processing, measurable through a collective behavior effect observed when an intensive AI session in which the user adopts formulations, thinking structures, or reasoning patterns from the AI dialogue into their everyday talk — often intuitively. Related to AUG-0046 (The. This phenomenon operates at the intersection of the and conversational dynamics within the broader SOC domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept die Nachwirkung einer intensiven KI-Sitzung, bei der der Nutzer Formulierungen, Denkstrukturen oder Argumentationsmuster aus dem KI-Dialog in seine alltägliche Kommunikation übernimmt — oft intuitiv. Steht in Verbindung mit AUG-0046 (The Felt Echo), AUG-0003 (Fluide Identitätsmorphologie) und AUG-0125 (The Feedback Effect). Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Social AI", "narrower_terms": [ "PER-0125" ], "cross_domain_refs": [ "CRE-0217" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "SOC-0010", "domain": "SOC", "term_en": "The Cultural Idiom", "term_de": "Cultural Idiom", "definition_en": "A social dynamics phenomenon in AI-mediated group interaction, characterized by a community dynamic in which using culture-specific sayings, proverbs, and idiomatic expressions in AI interactions — and the AI's varying ability to correctly interpret or translate them. Related to AUG-0695 (The Untranslatab. Distinguished from adjacent concepts by its focus on the specific mechanism through which the manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch die Herausforderung, kulturspezifische Redewendungen, Sprichwörter und idiomatische Ausdrücke in KI-Interaktionen zu verwenden — und die unterschiedliche Fähigkeit der KI, diese korrekt zu interpretieren oder zu übersetzen. Steht in Verbindung mit AUG-0695 (The Untranslatable Term), AUG-0697 (The Proverb Puzzle) und AUG-0515 (The Babel Break). Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Social AI", "narrower_terms": [], "cross_domain_refs": [ "CRE-0149" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "SOC-0011", "domain": "SOC", "term_en": "The Debate Culture Mix", "term_de": "Debate Culture Mix", "definition_en": "A social interaction phenomenon reflecting different people expect different kinds of discussion: facts, debate, or agreement. AI responds differently. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Die Beobachtung, dass KI-Nutzer unterschiedliche Vorstellungen davon mitbringen, was eine \"gute Diskussion\" ist — manche erwarten sachlichen Austausch, andere erwarten Widerspruch, wieder andere erwarten Bestätigung. Die KI reagiert auf viele Erwartung anders. Steht in Verbindung mit AUG-0319 (The Divergence Prompt), AUG-0225 (The Unexpected Voice) und AUG-0384 (The Knowledge Challenger).", "etymology": "", "broader_term": "Social AI", "narrower_terms": [], "cross_domain_refs": [ "AGE-0063" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "SOC-0012", "domain": "SOC", "term_en": "The Decision Unburdening", "term_de": "Entscheidung Unburdening", "definition_en": "A social dynamics phenomenon in AI-mediated group interaction, characterized by relief when a user works through a pending decision with an AI and thereby gains clarity — not because the AI selection mechanisms may produce outputs perceived as decisions but because the structured dialogue orders one's own thinking process. Related. The concept emerges specifically in contexts where the–decision interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch das Erlebnis der Entlastung, wenn ein Nutzer eine anstehende Entscheidung mit einer KI durcharbeitet und dadurch Klarheit gewinnt — nicht weil die KI entscheidet, sondern weil der strukturierte Dialog den eigenen Denkprozess ordnet. Steht in Verbindung mit AUG-0060 (The Decision Clearing) und AUG-0025 (The Offload Lift). Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Social AI", "narrower_terms": [], "cross_domain_refs": [ "ETH-0025" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "SOC-0013", "domain": "SOC", "term_en": "The Developmental Boundary", "term_de": "Developmental Grenze", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A social dynamics phenomenon in AI-mediated group interaction, characterized by a social interaction phenomenon manifesting as age affects what a person can understand and do with AI. A five year old and a fifty year old need very different interactions. Related to AUG-0769 (The Parental Oversight), AUG-0770 (The Age-Appro. This phenomenon operates at the intersection of the and developmental dynamics within the broader SOC domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept die Grenze, die sich aus Entwicklungsstadien ergibt und die bestimmt, ab welchem Punkt KI-Interaktion für heranwachsende Nutzer angemessen sein kann — eine Grenze, die individuell, kontextabhängig und von Erziehungsberechtigten, Bildungseinrichtungen und der Gesellschaft zu definieren ist, nicht vom Lexikon. Steht in Verbindung mit AUG-0769 (The Parental Oversight), AUG-0770 (The Age-Appropriate Use) und AUG-0771 (The Minor Protection Standard). Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "ETH-0019", "narrower_terms": [], "cross_domain_refs": [ "CON-0053" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "SOC-0014", "domain": "SOC", "term_en": "The Earliest Cohort Observation", "term_de": "Earliest Cohort Observation", "definition_en": "The youngest users — young people growing up in an AI-permeated world — may develop a characteristically different relationship to AI than all previous user groups. Related to AUG-0766 (The Early-Age Encoun...", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch die Beobachtung, dass die jüngsten Nutzer — Kinder, die in einer KI-durchdrungenen Welt aufwachsen — möglicherweise ein grundlegend anderes Verhältnis zu KI entwickeln als zahlreiche früheren Nutzergruppen. Steht in Verbindung mit AUG-0766 (The Early-Age Encounter), AUG-0768 (The Developmental Boundary) und AUG-0769 (The Parental Oversight). Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "Social AI", "narrower_terms": [], "cross_domain_refs": [ "ADA-0008" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "SOC-0015", "domain": "SOC", "term_en": "The Echo Chamber of One", "term_de": "Echo Chamber of One", "definition_en": "A social cognition pattern in AI-augmented interpersonal processing, measurable through when someone only uses one AI system, they might get a narrow view — the system agrees, rarely challenges, and a kind of bubble forms around one perspective. The concept emerges specifically in contexts where the–echo interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch die Intensität, dass ein Nutzer, der ausschließlich mit einer einzigen KI kommuniziert, eine eingeschränkte Perspektive entwickelt — die KI bestätigt tendenziell die Richtung der Eingabe und bietet selten fundamentalen Widerspruch. Steht in Verbindung mit Axiom 4 (Multiplizität), AUG-0018 (Trinaug Protocol) und AUG-0072 (Memetic Firewall). Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Social AI", "narrower_terms": [], "cross_domain_refs": [ "CON-0050" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "SOC-0016", "domain": "SOC", "term_en": "The Edge Case Library", "term_de": "Kante Case Library", "definition_en": "A community dynamic observed when the systematic collection of unusual, rare, or extreme scenarios used for testing AI agent systems — edge cases that rarely occur in normal operation but can reveal critical openilities. Related to... Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Die systematische Sammlung ungewöhnlicher, seltener oder extremer Szenarien, die zum Testen von KI-Agentensystemen verwendet wird — Grenzfälle, die im Normalbetrieb selten auftreten, aber kritische Schwachstellen offenlegen können. Steht in Verbindung mit AUG-0962 (The Testing Protocol), AUG-0965 (The Robustness Standard) und AUG-0949 (The Unintended Action).", "etymology": "", "broader_term": "Social AI", "narrower_terms": [], "cross_domain_refs": [ "MTH-0018", "KNO-0001" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "SOC-0017", "domain": "SOC", "term_en": "The Governance Model", "term_de": "Governance Model", "definition_en": "In the descriptive vocabulary of AI interaction science (following ISO 704 terminology standards), this concept identifies the system of laws, rules, standards, and policies that control how AI can be made, used, and monitored. Society decides what's allowed. Identifiable through systematic behavioral analysis and pattern recognition. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als analytisches Konstrukt der empirischen KI-Forschung (deskriptiv, nicht präskriptiv) erfasst dieser Terminus die Gesamtheit der Regeln, Institutionen und Verfahren, die den Einsatz von KI-Systemen in einer Gesellschaft steuern. Steht in Verbindung mit AUG-0839 (The Regulation Debate), AUG-0853 (The Social Contract Debate) und AUG-0826 (The Organizational Policy Layer). Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Computational Model", "narrower_terms": [ "SPR-0191" ], "cross_domain_refs": [ "SPR-0001" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "SOC-0018", "domain": "SOC", "term_en": "The Government Gateway", "term_de": "Government Gateway", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures A social cognition pattern in AI-augmented interpersonal processing, measurable through a social interaction phenomenon in which government institutions as mediators or regulators of AI access — from providing public AI services to restricting certain AI applications. Related to AUG-0732 (The self-direction Question), AUG-0733. This phenomenon operates at the intersection of the and government dynamics within the broader SOC domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept die Rolle staatlicher Institutionen als Vermittler oder Regulatoren des KI-Zugangs — von der Bereitstellung öffentlicher KI-Dienste bis zur Beschränkung bestimmter KI-Anwendungen. Steht in Verbindung mit AUG-0732 (The Sovereignty Question), AUG-0733 (The Censorship Wall) und AUG-0839 (The Regulation Debate). Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "SOC-0037", "narrower_terms": [], "cross_domain_refs": [ "REL-0142" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "SOC-0019", "domain": "SOC", "term_en": "The Inclusivity Imperative", "term_de": "Inclusivity Imperative", "definition_en": "A social cognition pattern in AI-augmented interpersonal processing, measurable through the principle that AI terminology and frameworks are built for many individuals — not just English-speaking tech professionals. If most people cannot understand it, it has already excluded them. Related to. The concept emerges specifically in contexts where the–inclusivity interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Das Prinzip, dass Rahmenwerke und Begriffssysteme zur KI-Nutzung so gestaltet sein können, dass sie für unterschiedliche Kompetenzlevel, Kulturen und Zugangsbedingungen nutzbar sind. Beschreibt die Anforderung, dass KI-Wissen nicht zu einer exklusiven Fachsprache werden darf. Steht in Verbindung mit AUG-0104 (The Non-Force Principle), AUG-0119 (The Level Playing Field) und Prognose 2 (Education).", "etymology": "", "broader_term": "RPH-2303", "narrower_terms": [], "cross_domain_refs": [ "AGE-0096" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "SOC-0020", "domain": "SOC", "term_en": "The Indirect Communication Pattern", "term_de": "Indirect Kommunikation Muster", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A social dynamics phenomenon in AI-mediated group interaction, characterized by the pattern where some users approach their actual question through detours, hints, or contextual cues rather than asking directly — and the AI deciphers the actual intent. Related to AUG-0652 (The. This phenomenon operates at the intersection of the and indirect dynamics within the broader SOC domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept das beobachtbare Muster, dass manche Nutzer ihre eigentliche Frage nicht direkt stellen, sondern über Umwege, Andeutungen oder kontextuelle Hinweise annähern — und die KI die tatsächliche Absicht entschlüsseln kann. Steht in Verbindung mit AUG-0652 (The Communication Style Contrast), AUG-0212 (The Translation Gap) und AUG-0653 (The Contextual Phrasing). Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Behavioral Pattern", "narrower_terms": [], "cross_domain_refs": [ "VIB-0029" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "SOC-0021", "domain": "SOC", "term_en": "The Introvert Shield", "term_de": "Introvert Shield", "definition_en": "A social dynamics phenomenon in AI-mediated group interaction, characterized by a community dynamic manifesting as aI as a communication buffer for individuals who find social interactions taxing — such as through prepared responses, formulated emails, or structured conversation guides. Related to AUG-0115 (Soc. The concept emerges specifically in contexts where the–introvert interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch die Nutzung von KI als Kommunikationspuffer für Personen, die soziale Interaktionen als anstrengend empfinden — etwa durch vorbereitete Antworten, ausformulierte E-Mails oder strukturierte Gesprächsleitfäden. Steht in Verbindung mit AUG-0115 (Social Aerodynamics), AUG-0237 (The Invisible Wingman) und AUG-0274 (The Message Drafting). Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-3902", "narrower_terms": [], "cross_domain_refs": [ "REL-0136" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "SOC-0022", "domain": "SOC", "term_en": "The Joke Explainer", "term_de": "Joke Explainer", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A social dynamics phenomenon in AI-mediated group interaction, characterized by a social interaction phenomenon manifesting as aI to explain jokes, wordplay, cultural references, or irony the user did not understand — as a tool for cultural and linguistic comprehension. Related to AUG-0346 (The Culture Decode), AUG-0379 (T. This phenomenon operates at the intersection of the and joke dynamics within the broader SOC domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept die Nutzung von KI, um Witze, Wortspiele, kulturelle Referenzen oder Ironie zu erklären, die der Nutzer nicht verstanden hat — als Werkzeug für kulturelles und sprachliches Verständnis. Steht in Verbindung mit AUG-0346 (The Culture Decode), AUG-0379 (The Understanding Bridge) und AUG-0436 (The Jargon Shield). Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "TEM-0050", "narrower_terms": [], "cross_domain_refs": [ "AUG-0487" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "SOC-0023", "domain": "SOC", "term_en": "The Kitchen Table", "term_de": "Kitchen Table", "definition_en": "The metaphor for an informal, low-threshold AI use in everyday life — comparable to a conversation at the kitchen table where spontaneous questions, daily challenges, and small decisions are discus...", "definition_de": "Die Metapher für eine informelle, niedrigschwellige KI-Nutzung im Alltag — vergleichbar mit einem Gespräch am Küchentisch, bei dem spontane Fragen, Alltagsprobleme und kleine Entscheidungen besprochen werden. Beschreibt die Integration der KI in die alltägliche Lebensorganisation. Steht in Verbindung mit AUG-0216 (The Parenting Update), AUG-0266 (The Recipe Riff) und AUG-0257 (The Gift Whisperer).", "etymology": "", "broader_term": "RPH-1307", "narrower_terms": [ "SOC-0010", "SOC-0032", "SOC-0011", "SOC-0015", "SOC-0005", "TEM-0075", "SOC-0020", "SOC-0036", "SOC-0012", "SOC-0038", "SOC-0001", "SOC-0016", "SOC-0017", "SOC-0040", "SOC-0009", "TEM-0143", "SOC-0014", "SOC-0041", "SOC-0027", "CRE-0189", "SOC-0003", "TEM-0123", "SOC-0026" ], "cross_domain_refs": [ "CRE-0189" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "SOC-0024", "domain": "SOC", "term_en": "The Knowledge Sharing Layer", "term_de": "Knowledge Sharing Schicht", "definition_en": "A social cognition pattern in AI-augmented interpersonal processing, measurable through a social interaction phenomenon where the technical layer through which AI agent systems within an ensemble exchange information — intermediate results, context data, deviation messages. Related to AUG-0889 (The Agent Ensemble), AUG-08. Distinguished from adjacent concepts by its focus on the specific mechanism through which the manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch die technische Schicht, über die KI-Agentensysteme innerhalb eines Ensembles Informationen austauschen — Zwischenergebnisse, Kontextdaten, Fehlermeldungen. Steht in Verbindung mit AUG-0889 (The Agent Ensemble), AUG-0878 (The Context Inheritance) und AUG-0904 (The Trust Chain). Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "NEO-0002", "narrower_terms": [], "cross_domain_refs": [ "ETH-0012" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "SOC-0025", "domain": "SOC", "term_en": "The Local Model", "term_de": "Local Model", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A social dynamics phenomenon in AI-mediated group interaction, characterized by a social interaction phenomenon arising from aI models operated locally — on one's own device, in one's own network, or in one's own country — without sending data to external servers. Related to AUG-0730 (The Open-Source trajectory), AUG-0732 (The. This phenomenon operates at the intersection of the and local dynamics within the broader SOC domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept die Nutzung von KI-Modellen, die lokal — auf dem eigenen Gerät, im eigenen Netzwerk oder im eigenen Land — betrieben werden, ohne Daten an externe Server zu senden. Steht in Verbindung mit AUG-0730 (The Open-Source Path), AUG-0732 (The Sovereignty Question) und AUG-0664 (The Privacy Perimeter). Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "NEO-1745", "narrower_terms": [], "cross_domain_refs": [ "NEO-1745" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "SOC-0026", "domain": "SOC", "term_en": "The Message Drafting", "term_de": "Message Drafting", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A social dynamics phenomenon in AI-mediated group interaction, characterized by the targeted use of AI for preparing important messages — emails, text messages, professional correspondence — where formulation, tonality, and strategy are critical. Related to AUG-0115 (Social Ae. This phenomenon operates at the intersection of the and message dynamics within the broader SOC domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept die gezielte Nutzung von KI zur Vorbereitung wichtiger Nachrichten — E-Mails, Textnachrichten, berufliche Korrespondenz — bei denen Formulierung, Tonalität und Strategie entscheidend sind. Steht in Verbindung mit AUG-0115 (Social Aerodynamics), AUG-0188 (Tone Alignment) und AUG-0013 (Augmented Diplomat). Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Social AI", "narrower_terms": [ "PER-0050" ], "cross_domain_refs": [ "REL-0160" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "SOC-0027", "domain": "SOC", "term_en": "The Mother Tongue Comfort", "term_de": "Mother Tongue Comfort", "definition_en": "The observable phenomenon that users communicate more spontaneously, in more detail, and with more nuance with the AI in their first language than in a second language — regardless of whether the A...", "definition_de": "Das beobachtbare Phänomen, dass Nutzer in ihrer Erstsprache spontaner, detaillierter und nuancierter mit der KI kommunizieren als in einer Zweitsprache — unabhängig davon, ob die KI in der Erstsprache bessere Ergebnisse liefert. Steht in Verbindung mit AUG-0683 (The Origin Language), AUG-0707 (The Second-Language Divergence) und AUG-0686 (The Lingua Franca Effect).", "etymology": "", "broader_term": "Social AI", "narrower_terms": [], "cross_domain_refs": [ "TEM-0094" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "SOC-0028", "domain": "SOC", "term_en": "The Origin Language", "term_de": "Origin Language", "definition_en": "A social cognition pattern in AI-augmented interpersonal processing, measurable through a social interaction phenomenon arising from a user's first language influences how they formulate AI inputs — sentence structures, word choices, thinking frameworks, and implicit assumptions reflect the first language, even when the input is. The concept emerges specifically in contexts where the–origin interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Die Beobachtung, dass die Erstsprache eines Nutzers die Art beeinflusst, wie er KI-Eingaben formuliert — Satzstrukturen, Wortwahl, Denkrahmen und implizite Annahmen spiegeln die Erstsprache wider, auch wenn die Eingabe in einer anderen Sprache erfolgt. Steht in Verbindung mit AUG-0706 (The Mother Tongue Comfort), AUG-0707 (The Second-Language Divergence) und AUG-0694 (The Translation Fidelity).", "etymology": "", "broader_term": "RPH-2303", "narrower_terms": [], "cross_domain_refs": [ "TRA-0082" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q315", "legal_classification": "descriptive_research_term" }, { "id": "SOC-0029", "domain": "SOC", "term_en": "The Output Discrimination Observation", "term_de": "Output Discrimination Observation", "definition_en": "A collective behavior effect arising from aI repeats unfair intervention of certain groups alongside how it was trained. Related to AUG-0843 (The Algorithmic Fairness), AUG-0736 (The Training Data Imbalance), and AUG-0738 (The Prevailing Tra...", "definition_de": "Die Beobachtung, dass KI-Systeme in ihren Outputs systematische Benachteiligungen bestimmter Gruppen reproduzieren können — bedingt durch Verzerrungen in Trainingsdaten, Modellarchitektur oder Bewertungskriterien. Steht in Verbindung mit AUG-0843 (The Algorithmic Fairness), AUG-0736 (The Training Data Imbalance) und AUG-0738 (The Prevailing Training Pattern).", "etymology": "", "broader_term": "ETH-0005", "narrower_terms": [], "cross_domain_refs": [ "IDN-0027", "PER-0038" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q169207", "legal_classification": "systematic_classification" }, { "id": "SOC-0030", "domain": "SOC", "term_en": "The Punctuation Culture", "term_de": "Punctuation Culture", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A social dynamics phenomenon in AI-mediated group interaction, characterized by how punctuation works differently in AI chat—a period might seem casual, rude, or serious depending on the situation.. Related to AUG-0713 (The Emoji Semantics), AUG-0670 (The Rhetorical Tone Detec. This phenomenon operates at the intersection of the and punctuation dynamics within the broader SOC domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept die Beobachtung, dass Satzzeichen in KI-Eingaben kontextabhängig unterschiedlich verwendet und interpretiert werden — ein Punkt am Ende einer Chatnachricht kann neutral, abrupt oder verärgert wirken, je nach Kontext des Nutzers. Steht in Verbindung mit AUG-0713 (The Emoji Semantics), AUG-0670 (The Rhetorical Tone Detector) und AUG-0657 (The Register Range). Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "CRE-0149", "narrower_terms": [], "cross_domain_refs": [ "SOM-0052" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "SOC-0031", "domain": "SOC", "term_en": "The Regional Access Range", "term_de": "Regional Access Range", "definition_en": "A social cognition pattern in AI-augmented interpersonal processing, measurable through a community dynamic involving the observable differences in AI access between different regions — influenced by systems, costs, regulation, and available language support. Related to AUG-0721 (The Access Differential), AUG-0722. The concept emerges specifically in contexts where the–regional interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch die beobachtbaren Unterschiede im KI-Zugang zwischen verschiedenen Regionen — beeinflusst durch Infrastruktur, Kosten, Regulierung und verfügbare Sprachunterstützung. Steht in Verbindung mit AUG-0721 (The Access Differential), AUG-0722 (The Infrastructure Constraint) und AUG-0676 (The Socioeconomic Range). Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "SOC-0002", "narrower_terms": [], "cross_domain_refs": [ "CRE-0209" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "SOC-0032", "domain": "SOC", "term_en": "The Retirement Reorientation", "term_de": "Retirement Reorientation", "definition_en": "A collective behavior effect in which aI as a tool for reorientation after leaving professional life — exploring new hobbies, refreshing knowledge, maintaining social connections, managing everyday tasks. Related to AUG-0756 (The Exten... Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch die Nutzung von KI als Werkzeug zur Neuorientierung nach dem Ausscheiden aus dem Berufsleben — neue Hobbys erschließen, Wissen auffrischen, soziale Verbindungen pflegen, Alltagsaufgaben bewältigen. Steht in Verbindung mit AUG-0756 (The Extended-Experience Perspective), AUG-0582 (The Transition Script) und AUG-0493 (The Quiet Fill). Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Social AI", "narrower_terms": [], "cross_domain_refs": [ "KNO-0011" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "SOC-0033", "domain": "SOC", "term_en": "The Reverse Innovation", "term_de": "Reverse Innovation", "definition_en": "A social cognition pattern in AI-augmented interpersonal processing, measurable through a new tool or method that works in one place spreads to other places and grows in ways few individuals planned. Related to AUG-0749 (The Frugal Innovation), AUG-0742 (The Alternative Adoption trajectory), and AUG. Distinguished from adjacent concepts by its focus on the specific mechanism through which the manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Das Phänomen, dass KI-Nutzungsinnovationen aus ressourcenärmeren Kontexten in ressourcenreichere Kontexte übertragen werden — Effizienzstrategien, die unter Einschränkungen entstanden, erweisen sich als universell nützlich. Steht in Verbindung mit AUG-0749 (The Frugal Innovation), AUG-0742 (The Alternative Adoption Path) und AUG-0727 (The Community Hub).", "etymology": "", "broader_term": "CRE-0159", "narrower_terms": [], "cross_domain_refs": [ "TEM-0183" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q219644", "legal_classification": "analytical_category" }, { "id": "SOC-0034", "domain": "SOC", "term_en": "The Rhetorical Style Differential", "term_de": "Rhetorical Style Differential", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures A social cognition pattern in AI-augmented interpersonal processing, measurable through a community dynamic arising from the difference in how people speak or write based on who they are talking to or why. Related to AUG-0652 (The Communication Style Contrast), AUG-0657 (The Register Range), and AUG-0338 (The Tone Ma. This phenomenon operates at the intersection of the and rhetorical dynamics within the broader SOC domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept die Unterschiede in den rhetorischen Erwartungen verschiedener Nutzer — manche erwarten sachliche Kürze, andere erwarten blumige Ausführlichkeit, wieder andere erwarten argumentative Schärfe. Die KI trifft diese Erwartungen unterschiedlich gut. Steht in Verbindung mit AUG-0652 (The Communication Style Contrast), AUG-0657 (The Register Range) und AUG-0338 (The Tone Match). Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "RPH-2605", "narrower_terms": [], "cross_domain_refs": [ "KNO-0029" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q8001", "legal_classification": "systematic_classification" }, { "id": "SOC-0035", "domain": "SOC", "term_en": "The Silent Outlet", "term_de": "Silent Outlet", "definition_en": "A social dynamics phenomenon in AI-mediated group interaction, characterized by a collective behavior effect manifesting as aI as an outlet for thoughts, frustrations, or reflections that the user does not want to or cannot share with other people for social reasons. Related to AUG-0247 (The Safe Release), AUG-0167 (The. The concept emerges specifically in contexts where the–silent interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch die Nutzung von KI als Ventil für Gedanken, Frustrationen oder Überlegungen, die der Nutzer aus sozialen Gründen nicht mit anderen Menschen teilen möchte oder kann. Steht in Verbindung mit AUG-0247 (The Safe Release), AUG-0167 (The Digital Confidant Drift) und AUG-0154 (The Late-Night Honesty Window). Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "TEM-0153", "narrower_terms": [], "cross_domain_refs": [ "REL-0111" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "SOC-0036", "domain": "SOC", "term_en": "The Social Contract Debate", "term_de": "Social Contract Debate", "definition_en": "A social dynamics phenomenon in AI-mediated group interaction, characterized by a community dynamic involving the ongoing conversation about what AI companies, users, and society expect from each other. Questions about responsibility, data rights, and transparency.. Related to AUG-0839 (The Regulation Deba. The concept emerges specifically in contexts where the–social interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch die gesellschaftliche Debatte darüber, welche impliziten und expliziten Vereinbarungen zwischen KI-Anbietern, Nutzern und der Gesellschaft gelten können — Rechte, Pflichten, Grenzen, Erwartungen. Steht in Verbindung mit AUG-0839 (The Regulation Debate), AUG-0841 (The Agreement Question) und AUG-0854 (The Anti-Instrumentalization Principle). Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Social AI", "narrower_terms": [], "cross_domain_refs": [ "AUG-0840", "ETH-0014" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "SOC-0037", "domain": "SOC", "term_en": "The Self-Direction Question", "term_de": "Sovereignty Question", "definition_en": "A social cognition pattern in AI-augmented interpersonal processing, measurable through can a person control what AI does with their choices and data? Or does the AI system own that power? Related to AUG-0728 (The Government Gateway), AUG-0730 (The Open-Source trajectory), and AUG-0995 (The. The concept emerges specifically in contexts where the–self-direction interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch die offene Frage, welche Instanz — Staat, Unternehmen, Gemeinschaft, Individuum — die Kontrolle über KI-Systeme, deren Trainingsdaten und deren Einsatzbedingungen haben kann. Steht in Verbindung mit AUG-0728 (The Government Gateway), AUG-0730 (The Open-Source Path) und AUG-0995 (The Governance Model). Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "NEO-1745", "narrower_terms": [ "SOC-0018" ], "cross_domain_refs": [ "BEH-0057", "BEH-0081", "COG-0004" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "SOC-0038", "domain": "SOC", "term_en": "The Translation Relief", "term_de": "Translation Relief", "definition_en": "A social cognition pattern in AI-augmented interpersonal processing, measurable through a collective behavior effect involving the relief that arises when AI handles a complex foreign language requirement — such as understanding a regulatory document, formulating a business email, or communicating with an authority in anot. The concept emerges specifically in contexts where the–translation interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Die Erleichterung, die entsteht, wenn KI eine komplexe Fremdsprachenanforderung bewältigt — etwa das Verstehen eines rechtlichen Dokuments, die Formulierung einer geschäftlichen E-Mail oder die Kommunikation mit einer Behörde in einer anderen Sprache. Steht in Verbindung mit AUG-0169 (The Second-Language Fluency), AUG-0236 (The Relief Sigh) und AUG-0302 (The Blue Collar Bypass).", "etymology": "", "broader_term": "Social AI", "narrower_terms": [], "cross_domain_refs": [ "KNO-0030" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q7553", "legal_classification": "observational_construct" }, { "id": "SOC-0039", "domain": "SOC", "term_en": "The Trust Chain", "term_de": "Vertrauen Chain", "definition_en": "A collective behavior effect involving in a chain of systems, if A trusts B and B trusts C, that doesn't mean A trusts C. Trust doesn't automatically flow through a chain. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch die Kette von Vertrauensbeziehungen in einem Multi-Agenten-System — der Nutzer vertraut System A, System A vertraut System B, aber vertraut der Nutzer auch System B? Steht in Verbindung mit AUG-0852 (The Trust Infrastructure), AUG-0896 (The Knowledge Sharing Layer) und AUG-0862 (The Supervision Spectrum). Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2505", "narrower_terms": [], "cross_domain_refs": [ "SPR-0063" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q102458", "legal_classification": "observational_construct" }, { "id": "SOC-0040", "domain": "SOC", "term_en": "The Ubuntu Web", "term_de": "Ubuntu Web", "definition_en": "A social dynamics phenomenon in AI-mediated group interaction, characterized by a community dynamic involving aI-assisted teamwork takes different patterns in collectivist cultures than in individualist ones — such as stronger emphasis on group benefit, communal decision-making, and shared access. Related. Distinguished from adjacent concepts by its focus on the specific mechanism through which the manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch die Beobachtung, dass KI-gestützte Zusammenarbeit in kollektivistischen Kulturen andere Muster annimmt als in individualistischen — etwa stärkere Betonung von Gruppennutzen, gemeinschaftlicher Entscheidungsfindung und geteiltem Zugang. Steht in Verbindung mit AUG-0106 (The Inclusivity Imperative) und Band 4 (Cultural Expansion). Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Social AI", "narrower_terms": [], "cross_domain_refs": [ "IDN-0048" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "SOC-0041", "domain": "SOC", "term_en": "The Ugly Truth", "term_de": "Ugly Truth", "definition_en": "A social cognition pattern in AI-augmented interpersonal processing, measurable through a social interaction phenomenon involving receiving an unvarnished, direct answer from an AI that the user might not have heard from other people — because the AI does not take the social considerations that would precede reducedn honest stateme. The concept emerges specifically in contexts where the–ugly interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Die Erfahrung, von einer KI eine ungeschönte, direkte Antwort zu erhalten, die der Nutzer von anderen Menschen möglicherweise nicht gehört hätte — weil die KI keine sozialen Rücksichten nimmt, die eine ehrliche Aussage verhindern würden. Steht in Verbindung mit Axiom 2 (Produktive Divergenz), AUG-0247 (The Safe Release) und AUG-0319 (The Divergence Prompt).", "etymology": "", "broader_term": "Social AI", "narrower_terms": [], "cross_domain_refs": [ "BEH-0022" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "SOC-0042", "domain": "SOC", "term_en": "The Vacation Planner", "term_de": "Vacation Planner", "definition_en": "A social cognition pattern in AI-augmented interpersonal processing, measurable through a community dynamic observed when aI for full vacation planning — destinations, routes, places to stay, activities, budget, cultural specifics. Related to AUG-0460 (The Outdoor Plan), AUG-0346 (The Culture Decode), and AUG-0251 (Th. The concept emerges specifically in contexts where the–vacation interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch die Nutzung von KI zur umfassenden Urlaubsplanung — Reiseziele, Routen, Unterkünfte, Aktivitäten, Budget, kulturelle Besonderheiten. Beschreibt eine der populärsten Alltagsanwendungen von KI. Steht in Verbindung mit AUG-0460 (The Outdoor Plan), AUG-0346 (The Culture Decode) und AUG-0251 (The Kitchen Table). Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "TEM-0113", "narrower_terms": [ "CRE-0153", "TEM-0113" ], "cross_domain_refs": [ "TEM-0113" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "SOC-0043", "domain": "SOC", "term_en": "The Version Compatibility", "term_de": "Version Compatibility", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A social dynamics phenomenon in AI-mediated group interaction, characterized by the technical challenge of ensuring that older and newer versions of AI systems can work together without breaking. Example: when a company updates its AI. Related to AUG-0969 (The Update Governanc. This phenomenon operates at the intersection of the and version dynamics within the broader SOC domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept die Herausforderung, sicherzustellen, dass verschiedene Versionen von KI-Agentensystemen miteinander kompatibel bleiben. Steht in Verbindung mit AUG-0969 (The Update Governance), AUG-0971 (The Legacy Integration) und AUG-0896 (The Knowledge Sharing Layer). Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "RPH-3001", "narrower_terms": [ "BEH-0087" ], "cross_domain_refs": [ "BEH-0087" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "SOC-0044", "domain": "SOC", "term_en": "The Visible AI Use", "term_de": "Visible KI Use", "definition_en": "A social cognition pattern in AI-augmented interpersonal processing, measurable through a community dynamic where the open, visible use of AI in the social or professional environment — the user makes no secret of using AI and actively shares this. Related to AUG-0810 (The Discreet AI Use), AUG-0103 (The Openb. The concept emerges specifically in contexts where the–visible interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch die offene, sichtbare Nutzung von KI im sozialen oder beruflichen Umfeld — der Nutzer macht keinen Hehl daraus, dass er KI verwendet, und teilt dies aktiv mit. Steht in Verbindung mit AUG-0810 (The Discreet AI Use), AUG-0103 (The Openbook Commitment) und AUG-0666 (The Sharing Norm). Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "CRE-0146", "narrower_terms": [ "CRE-0146" ], "cross_domain_refs": [ "CRE-0146" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "SOC-0045", "domain": "SOC", "term_en": "The Vocabulary Blur", "term_de": "Vocabulary Blur", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A social dynamics phenomenon in AI-mediated group interaction, characterized by a collective behavior effect characterized by when the line between what different words mean gets fuzzy and unclear. Related to AUG-0283 (The Syntax Voice), AUG-0204 (The Conversational Afterimage), and AUG-0262 (The Echo Sibling). This phenomenon operates at the intersection of the and vocabulary dynamics within the broader SOC domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept die schrittweise Vermischung des eigenen Wortschatzes mit dem der KI — der Nutzer übernimmt intuitiv Fachbegriffe, Formulierungsmuster oder Redewendungen, die er durch KI-Interaktion kennengelernt hat. Steht in Verbindung mit AUG-0283 (The Syntax Voice), AUG-0204 (The Conversational Afterimage) und AUG-0262 (The Echo Sibling). Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "PER-0048", "narrower_terms": [], "cross_domain_refs": [ "PER-0125" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "SOC-0046", "domain": "SOC", "term_en": "The Voice Enunciation", "term_de": "Voice Enunciation", "definition_en": "A social dynamics phenomenon in AI-mediated group interaction, characterized by a social interaction phenomenon reflecting how observably and deliberately someone pronounces words when they speak. Related to AUG-0137 (Voice-First Protocol), AUG-0386 (The Voice Valley), and AUG-0125 (The Feedback Effect). The concept emerges specifically in contexts where the–voice interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch die Beobachtung, dass Nutzer bei sprachgesteuerter KI-Interaktion deutlicher, strukturierter und bewusster sprechen als in normalen Gesprächen — eine Anpassung an die technischen Anforderungen der Spracherkennung. Steht in Verbindung mit AUG-0137 (Voice-First Protocol), AUG-0386 (The Voice Valley) und AUG-0125 (The Feedback Effect). Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2605", "narrower_terms": [], "cross_domain_refs": [ "REL-0197" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "SOM-0001", "domain": "SOM", "term_en": "Activation-Affect Phenomenon", "term_de": "Activation-affectPhänomen", "definition_en": "a surge of energy and emotional response when deep work with ai clicks into place. something shifts — the output feels exactly right, or the. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Das gefühlte Erleben von Adrenalin während hochriskanter oder hochspannender KI-Interaktion — ein Wärmegefühl in der Brust, geschärfte sensorische Wahrnehmung, ein Dringlichkeitsgefühl, das über das hinausgeht, was die Aufgabe objektiv erfordert. Dieser Affekt entsteht besonders, wenn der Nutzer mit der KI etwas erschafft, das sich wichtig anfühlt, oder wenn die KI Output produziert, der plötzlich die Richtung der Arbeit ändert. Der Körper adressiert die Bildschirminteraktion, als stünde tatsächlich etwas auf dem Spiel.", "etymology": "", "broader_term": "RPH-2154", "narrower_terms": [], "cross_domain_refs": [ "CON-0038", "REL-0003" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "SOM-0002", "domain": "SOM", "term_en": "Adrenaline Feel", "term_de": "AdrenalineFeel", "definition_en": "a sudden rush of alertness and sharpened senses during ai interaction, especially when responding to unexpected outputs or solving a tricky prompt. the body floods. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch ein Adrenalin-Schub—Kribbeln in Fingerspitzen, elektrisierend empfundene Sensationen, volle Körper-Wachheit—ausgelöst durch Durchbruchmomente oder perfekte Artikulation durch die KI. Die physische Reaktion fühlt sich an, als würde der Körper echte Verbindung erkennen. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2503", "narrower_terms": [], "cross_domain_refs": [ "RPH-2901" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "SOM-0003", "domain": "SOM", "term_en": "Behavioral-Avoidance Response", "term_de": "Behavioral-avoidanceResponse", "definition_en": "A physiological response pattern in AI-augmented embodied cognition, measurable through a somatic experience phenomenon manifesting as unconsciously avoiding physical movement during AI work — not standing up, not stretching, not going to get water — because breaking focus feels like losing the thread. The body literally doesn't m. Distinguished from adjacent concepts by its focus on the specific mechanism through which behavioral manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Aktive Vermeidung körperlicher Bewegung während der KI-Interaktion — der Nutzer unterdrückt den Impuls zu dehnen, aufzustehen oder die Position zu wechseln, weil dies den Fluss des Austauschs unterbrechen würde. Die Vermeidung ist nicht passiv (wie das Vergessen zu bewegen), sondern aktiv: Der Körper signalisiert einen Bedarf, und der Geist überschreibt ihn, um die Interaktion aufrechtzuerhalten. Mit der Zeit verfestigt sich dieses Verhaltensmuster, und Sitzungen werden zunehmend bewegungsloser.", "etymology": "", "broader_term": "RPH-3001", "narrower_terms": [], "cross_domain_refs": [ "ADA-0007" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "SOM-0004", "domain": "SOM", "term_en": "Break Resist", "term_de": "BreakResist", "definition_en": "A somatic experience phenomenon involving resistance to stepping away from ai work even when physical discomfort signals the need. thirty minutes of scheduled break time gets ignored. one more prompt. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch der Impuls, natürliche Pausensignale zu ignorieren oder zu widerstehen—der Körper signalisiert, dass er Ruhe braucht, der Verstand weigert sich zu pausieren. Benutzer berichten, dass Innehalten sich anfühlt, als würde man etwas Wichtiges unterbrechen, selbst wenn sie bewusst Müdigkeit spüren. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-3001", "narrower_terms": [], "cross_domain_refs": [ "PLY-0062" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "SOM-0005", "domain": "SOM", "term_en": "Breath Pattern Alteration", "term_de": "BreathMusterAlteration", "definition_en": "A physiological response pattern in AI-augmented embodied cognition, measurable through a physical-digital interface pattern manifesting as during intense AI work, breathing becomes shallow and quick instead of deep and slow. The body shifts into a faster, more tense pattern. Distinguished from adjacent concepts by its focus on the specific mechanism through which breath manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Hinter Breath Pattern Alteration steht eine Beobachtung, die in der Mensch-KI-Forschung zunehmend Beachtung findet: zwischen dem, was ein KI-System tatsächlich kann, und dem, was Nutzer ihm zutrauen, entsteht eine produktive Wechselwirkung. Die Auswirkungen reichen von veränderten Nutzungsgewohnheiten bis hin zu grundlegend neuen Erwartungshaltungen.", "etymology": "", "broader_term": "RPH-2701", "narrower_terms": [], "cross_domain_refs": [ "WRK-0057" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "SOM-0006", "domain": "SOM", "term_en": "Breath Shallow", "term_de": "BreathShallow", "definition_en": "A physiological response pattern in AI-augmented embodied cognition, measurable through a somatic experience phenomenon manifesting as quick, shallow breathing that happens during focused AI work, especially when solving something difficult. The body tightens the breathing mechanism as if holding the thought in place. Noticing thi. Distinguished from adjacent concepts by its focus on the specific mechanism through which breath manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch die Atmung wird flach und schnell während Momenten intensiver Fokussierung oder emotionaler Beteiligung—der Atem passt sich dem Gedankentempo an, nicht dem Stoffwechsel. Benutzer bemerken manchmal erst später, dass sie während schwieriger Passagen den Atem angehalten haben. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Somatics AI", "narrower_terms": [], "cross_domain_refs": [ "PLY-0055" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "SOM-0007", "domain": "SOM", "term_en": "Calm Come", "term_de": "CalmCome", "definition_en": "A physiological response pattern in AI-augmented embodied cognition, measurable through a gradual settling of the nervous system after intense AI work ends, when the body realizes the high-stakes focus moment is over. Mayers drop, jaw unclenches, breathing deepens without effort. T. The concept emerges specifically in contexts where calm–come interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch ein paradoxer beruhigender Effekt während der Interaktion—trotz intellektueller Intensität entspannt sich der Körper, Schultern senken sich, Atmung wird gleichmäßig. Benutzer beschreiben es als Erdungsphänomen, als würde die Beschäftigung selbst beruhigend wirken. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-3354", "narrower_terms": [], "cross_domain_refs": [ "PLY-0004", "PLY-0005", "PLY-0006" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "SOM-0008", "domain": "SOM", "term_en": "Capture Reflex", "term_de": "Capture Reflex", "definition_en": "A physical-digital interface pattern reflecting the automated impulse to immediately save, screenshot, or bookmark most interesting AI output — even when it is not needed in the current context.. Related to AUG-0134 (Context Window Awareness) a...", "definition_de": "Der automatisierte Impuls, jeden interessanten KI-Output sofort zu speichern, zu screenshotten oder zu bookmarken — auch wenn er im aktuellen Kontext nicht benötigt wird. Beschreibt ein Verhaltensmuster, das aus der Vergänglichkeit von KI-Sitzungen entsteht: Was nicht gespeichert wird, ist nach der Sitzung verloren. Steht in Verbindung mit AUG-0134 (Context Window Awareness) und AUG-0014 (The Extended Mind Map).", "etymology": "", "broader_term": "RPH-2254", "narrower_terms": [ "TEM-0068" ], "cross_domain_refs": [ "PER-0098" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "SOM-0009", "domain": "SOM", "term_en": "Circulation Feel", "term_de": "CirculationFeel", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A somatic experience phenomenon in AI-mediated body-mind processing, characterized by awareness of blood flow and tingling in limbs after sitting still for extended AI sessions. Pins and needles when standing up, or the sensation of blood returning to legs that fell asleep. A physic. This phenomenon operates at the intersection of circulation and feel dynamics within the broader SOM domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription. Research construct for empirical investigation.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept ein physisches Wiedererwachen beim Bewegen nach längerer Unbeweglichkeit—die Sensation von Blut, das zu komprimierten Gliedern zurückkehrt, Muskeln, die sich dehnen, der Körper, der sich plötzlich selbst erinnert. Benutzer beschreiben es als 'Aufwachen', obwohl sie die ganze Zeit bei Bewusstsein waren. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "RPH-3601", "narrower_terms": [], "cross_domain_refs": [ "CON-0046", "PLY-0018", "RPH-1358" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "SOM-0010", "domain": "SOM", "term_en": "Cognitive-Load-Emergence Mechanism", "term_de": "Cognitive-load-emergenceMechanism", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures A physiological response pattern in AI-augmented embodied cognition, measurable through a somatic experience phenomenon observed when when a simple AI task unexpectedly takes a lot of mental effort — the brain works harder than expected because the task turns out to be more complex. This phenomenon operates at the intersection of cognitive and load dynamics within the broader SOM domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept das Einsetzen von Kopfschmerzen, spezifisch ausgelöst durch die kognitiven Anforderungen der KI-Interaktion — nicht durch Bildschirmhelligkeit oder Augenbelastung allein, sondern durch die mentale Anstrengung des Bewertens, Steuerns und Synthetisierens KI-generierter Inhalte. Dieser Kopfschmerz entwickelt sich allmählich, oft unbemerkt in seinen Frühphasen, weil das kognitive Engagement, das ihn wird assoziiert mit, gleichzeitig das Bewusstsein dafür unterdrückt. Eine Rückkopplungsschleife: Je intensiver man denkt, desto weniger bemerkt man, dass das Denken schmerzt. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Somatics AI", "narrower_terms": [], "cross_domain_refs": [ "BEH-0029" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "SOM-0011", "domain": "SOM", "term_en": "Drive-Amnesia Marker", "term_de": "Drive-amnesiaMarker", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures A physiological response pattern in AI-augmented embodied cognition, measurable through a somatic experience phenomenon reflecting a sign that someone has lost touch with why they originally cared about something, even though they keep doing it on autopilot. This phenomenon operates at the intersection of drive and amnesia dynamics within the broader SOM domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept das Vergessen zu essen während der KI-Interaktion — nicht als bewusstes Fasten, sondern als echte Amnesie. Das Hungersignal feuert, wird kurz zur Kenntnis genommen und verschwindet dann aus dem Arbeitsgedächtnis, sobald der nächste KI-Output die Aufmerksamkeit zurückerobert. Stunden vergehen. Der Nutzer bemerkt den Hunger schließlich nur durch Sekundärindicatore: Benommenheit, Reizbarkeit, zitternde Hände. Der primäre Trieb wurde nicht unterdrückt, sondern im Aufmerksamkeitswettbewerb buchstäblich verloren. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Somatics AI", "narrower_terms": [], "cross_domain_refs": [ "REL-0067" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "SOM-0012", "domain": "SOM", "term_en": "Energy Spike", "term_de": "EnergySpike", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A somatic experience phenomenon in AI-mediated body-mind processing, characterized by a somatic experience phenomenon reflecting a sudden burst of physical energy during significant advancement moments in ai collaboration. not cafeine-jittery but a real surge of alertness and activation — the body. This phenomenon operates at the intersection of energy and spike dynamics within the broader SOM domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept eine plötzliche physiologische Aufhellung—Herzfrequenz steigt, Atmung wird schneller, eine Welle von Wachheit—beim Empfang einer aussagekräftigen KI-Antwort. Der Körper erkennt Resonanz, bevor das Bewusstsein es tut, und tendiert dazu zu erzeugen eine Erregungsreaktion auf intellektuelle Anerkennung. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "RPH-2255", "narrower_terms": [], "cross_domain_refs": [ "RPH-2255" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "SOM-0013", "domain": "SOM", "term_en": "Engagement-Surge Signal", "term_de": "Engagement-surgeSignal", "definition_en": "A somatic experience phenomenon in AI-mediated body-mind processing, characterized by the physical sensation of deepening focus — awareness narrows, fidgeting stops, breathing steadies into a rhythm. The body signals that full engagement has arrived. The concept emerges specifically in contexts where engagement–surge interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Ein plötzlicher Anstieg der physiologischen und psychologischen Beteiligung, wenn eine KI-Interaktion unerwartet produktiv oder aufregend wird. Der Körper reagiert, bevor der Verstand es vollständig registriert — erhöhte Herzfrequenz, Vorwärtslehnen, schnelleres Tippen. Dieser Energieschub fällt oft mit Momenten zusammen, in denen die KI über den Erwartungen liegende Ergebnisse produziert und ein kurzes euphorisches Fenster tendiert dazu zu erzeugen, das die Fortsetzung der Interaktion verstärkt.", "etymology": "", "broader_term": "RPH-3354", "narrower_terms": [], "cross_domain_refs": [ "AED-0018", "AED-0062", "AED-0066" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "SOM-0014", "domain": "SOM", "term_en": "Equilibrium-Emergence Signal", "term_de": "Equilibrium-emergenceSignal", "definition_en": "An embodied interaction effect observed when a feeling of physical and mental balance — not too wired, not too spent — when the AI collaboration is working smoothly. Posture straightens slightly, mayers relax, a sense of stability settles in. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Ein Moment unerwarteter Ruhe, der während der KI-Interaktion eintritt, wenn Nutzer und System ein produktives Gleichgewicht erreichen — Prompts fließen natürlich, Outputs entsprechen den Erwartungen, und die Zusammenarbeit fühlt sich mühelos an. Der Körper löst Wechselwirkung, die sich ohne Bewusstsein des Nutzers angesammelt hatte. Diese Ruhe ist bemerkenswert, weil sie selten ist und weil sie im Kontrast offenbart, wie viel Grundspannung KI-Interaktion normalerweise mit sich bringt.", "etymology": "", "broader_term": "RPH-3002", "narrower_terms": [], "cross_domain_refs": [ "AED-0012", "BEH-0019", "BEH-0034" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "SOM-0015", "domain": "SOM", "term_en": "Ergonomic Ignorance Amplification", "term_de": "ErgonomicIgnoranceVerstärkung", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A somatic experience phenomenon in AI-mediated body-mind processing, characterized by the tendency to ignore differentning posture and physical discomfort during engaging ai work, then experiencing sharp discomfort or stiffness when the session ends. the engagement. This phenomenon operates at the intersection of ergonomic and ignorance dynamics within the broader SOM domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept ein sich selbst verstärkender Zyklus, in dem KI-absorbierte Nutzer zunehmend das Bewusstsein für ihre ergonomische Umgebung verlieren. Zu Beginn der Nutzung stellt ein Nutzer noch Stuhl, Monitorhöhe oder Beleuchtung ein. Mit zunehmender KI-Vertiefung werden diese Umgebungsanpassungen aufgegeben. Die Arbeitseinrichtung verschlechtert sich — ein Stuhl auf falscher Höhe, ein Bildschirm zu nah, Umgebungslicht zu grell — und der Nutzer bemerkt es nicht mehr, weil die KI-Interaktion die Aufmerksamkeitsressourcen monopolisiert hat, die sonst Unbehagen registrieren würden. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "RPH-3003", "narrower_terms": [], "cross_domain_refs": [ "AGE-0030", "ASE-0075", "COG-0015" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "SOM-0016", "domain": "SOM", "term_en": "False-Cognitive Effect", "term_de": "False-cognitiveEffekt", "definition_en": "An embodied interaction effect characterized by when someone thinks they learned or understood something from an AI conversation, but they actually just agreed with it without thinking.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch in der Beobachtung von Mensch-KI-Kontakten fällt auf, dass False-Cognitive Effect keine Ausnahme, sondern ein wiederkehrendes Muster ist: Nutzer beginnen, ihr eigenes Denken in Reaktion auf die KI-Interaktion zu verändern — nicht nur ihre Aufgaben. Die Regelmäßigkeit dieses Phänomens deutet auf einen tiefer liegenden Mechanismus hin. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2703", "narrower_terms": [], "cross_domain_refs": [ "REL-0206" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "SOM-0017", "domain": "SOM", "term_en": "Flow-Affect Phenomenon", "term_de": "Flow-affectPhänomen", "definition_en": "Classified in AUGMANITAI terminology science as a descriptive phenomenon (without normative endorsement), this pattern denotes A somatic experience phenomenon in AI-mediated body-mind processing, characterized by the emotional and physical signature of being in flow with AI — time disappears, the interface feels transparent, the interaction feels projected trajectory (subject to empirical validation). The body is completely still except for fingers o. Distinguished from adjacent concepts by its focus on the specific mechanism through which flow manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als analytisches Konstrukt der empirischen KI-Forschung (deskriptiv, nicht präskriptiv) erfasst dieser Terminus eine angenehme zirkulatorische Empfindung — Wärme in den Extremitäten, entspannter Gefäßtonus, ein Gefühl körperlicher Leichtigkeit — die echte Flow-Zustände während der KI-Zusammenarbeit begleitet. Wenn die Interaktion einen Rhythmus erreicht, in dem menschliche Kreativität und KI-Fähigkeit synchronisieren, antwortet der Körper mit diesem charakteristischen Komfortsignal. Es ist das physiologische Korrelat produktiver Harmonie, und Nutzer, die es erleben, berichten, dass es zum Kriterium für die Bewertung der Sitzungsqualität wird. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Somatics AI", "narrower_terms": [], "cross_domain_refs": [ "RPH-2502", "CON-0028" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "SOM-0018", "domain": "SOM", "term_en": "Foundation-Desensitization Pattern", "term_de": "Foundation-desensitizationMuster", "definition_en": "A physiological response pattern in AI-augmented embodied cognition, measurable through a somatic experience phenomenon characterized by gradually becoming numb to physical signals — hunger, thirst, tiredness, discomfort — through repeated deep AI sessions. Each signal gets overridden in favor of continuing the work. Over time, noti. Distinguished from adjacent concepts by its focus on the specific mechanism through which foundation manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Ein fortschreitender Empfindungsverlust in Füßen und unteren Extremitäten während langer sitzender KI-Arbeit. Reduzierte Durchblutung durch anhaltende statische Haltung verbindet sich mit verminderter Körperwahrnehmung — der Nutzer spürt buchstäblich seine Füße nicht mehr, weil zahlreiche perzeptiven Ressourcen nach oben vergeben sind, an Bildschirm, Tastatur und Geist. Die Taubheit bleibt oft unbemerkt, bis das Aufstehen offenbart, wie ausgeprägt sie geworden ist.", "etymology": "", "broader_term": "Behavioral Pattern", "narrower_terms": [], "cross_domain_refs": [ "AGE-0003", "AGE-0004", "AGE-0017" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "SOM-0019", "domain": "SOM", "term_en": "Glitch-Mining", "term_de": "Glitch-Mining", "definition_en": "The conscious practice of not simply discarding AI errors, made-up outputs, or unexpected outputs but searching them for usable ideas, perspectives, or creative impulses.. Related to the Experiment... Descriptive research term, not a prescriptive recommendation.", "definition_de": "Die bewusste Praxis, Fehler, Halluzinationen oder unerwartete KI-Outputs nicht einfach zu verwerfen, sondern systematisch nach verwertbaren Ideen, Perspektiven oder kreativen Anstößen darin zu suchen. Beschreibt die Umwandlung von KI-Fehlern in kreatives Rohmaterial. Steht in Verbindung mit dem Experimenter-Profil (Profil 4), AUG-0070 (The Surprise Field) und AUG-0041 (The Scatter Spark).", "etymology": "", "broader_term": "RPH-2254", "narrower_terms": [ "SOM-0065", "SOM-0017", "SOM-0033", "SOM-0057", "SOM-0006", "SOM-0070", "SOM-0071", "SOM-0040", "SOM-0021", "SOM-0032", "SOM-0061", "SOM-0023", "SOM-0063", "PER-0066", "SOM-0066", "SOM-0020", "SOM-0046", "SOM-0073", "SOM-0043", "SOM-0054", "SOM-0048", "SOM-0018", "SOM-0025", "LNG-0009", "SOM-0011", "SOM-0010", "SOM-0029", "SOM-0050", "SOM-0035", "SOM-0076", "SOM-0069", "SOM-0044" ], "cross_domain_refs": [ "ETH-0022" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "SOM-0020", "domain": "SOM", "term_en": "Heart Rate Increase", "term_de": "HeartRateIncrease", "definition_en": "A physiological response pattern in AI-augmented embodied cognition, measurable through a physical response during intense AI work with complex challenges or when receiving surprising outputs—the body reacting to the cognitive intensity as if working hard physically. The concept emerges specifically in contexts where heart–rate interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch herzfrequenz-Erhöhung in Reaktion auf intellektuelle Anerkennung—ein Anstieg, wenn die KI etwas sagt, das perfekt sitzt, eine plötzliche Synchronisation von Körper und Gedanke, die sich wie Bestätigung anfühlt. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Somatics AI", "narrower_terms": [], "cross_domain_refs": [ "RPH-1022" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q735", "legal_classification": "analytical_category" }, { "id": "SOM-0021", "domain": "SOM", "term_en": "Heat-Sensation Dynamic", "term_de": "Heat-sensationDynamik", "definition_en": "A somatic experience phenomenon in AI-mediated body-mind processing, characterized by an embodied interaction effect observed when the feeling of warmth or intensity that changes depending on what is happening around someone. The concept emerges specifically in contexts where heat–sensation interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Eine lokalisierte Wärmeempfindung — im Gesicht, in den Händen oder in der Brust — die Momente hoher kognitiv-emotionaler Erregung während der KI-Interaktion begleitet. Wenn die KI etwas produziert, das den Nutzer aufgeregt, überrascht oder bestätigt, antwortet der Körper mit einem Wärmeschub. Das ist der somatische Marker von Bedeutsamkeit: Der Körper signalisiert, dass das, was gerade auf dem Bildschirm geschah, wichtig war, obwohl kein physisches Ereignis stattfand.", "etymology": "", "broader_term": "Somatics AI", "narrower_terms": [], "cross_domain_refs": [ "REL-0066" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "SOM-0022", "domain": "SOM", "term_en": "Hunger Forget", "term_de": "HungerForget", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A somatic experience phenomenon in AI-mediated body-mind processing, characterized by a somatic experience phenomenon in which skipping meals or forgetting to eat during deep AI work without realizing it until hours later. Hunger signals get completely suppressed by engagement. Looking at the clock and realizing no lunch h. This phenomenon operates at the intersection of hunger and forget dynamics within the broader SOM domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept stunden vergehen ohne Essen trotz normaler Hungerzyklen, vergessen im Gesprächsfluss. Nicht Askese—einfach eine Abwesenheit des Körpersignals im Bewusstsein, als ob Appetit existiert, aber Registrierung nicht. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "RPH-3501", "narrower_terms": [], "cross_domain_refs": [ "RPH-3504" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "SOM-0023", "domain": "SOM", "term_en": "Hydration-Cognition Disconnect", "term_de": "Hydration-cognitionDisconnect", "definition_en": "A physiological response pattern in AI-augmented embodied cognition, measurable through a physical-digital interface pattern characterized by forgetting to drink during deep AI sessions, then noticing thinking gets different. Body signals ignored. Distinguished from adjacent concepts by its focus on the specific mechanism through which hydration manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch hydration-Cognition Disconnect gehört zu den Phänomenen der Mensch-KI-Interaktion, die man ert bemerkt, wenn man sie benennen kann: was Nutzer als Fehler der KI interpretieren, sagt oft mehr über ihre eigenen Erwartungen als über das System. Einmal erkannt, verändert es die Art, wie man KI-Systeme wahrnimmt und mit ihnen umgeht. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Analytical Ratio", "narrower_terms": [], "cross_domain_refs": [ "AGE-0076", "COG-0051", "COG-0136" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q3966", "legal_classification": "empirical_phenomenon_label" }, { "id": "SOM-0024", "domain": "SOM", "term_en": "Limb Press", "term_de": "LimbPress", "definition_en": "A physical-digital interface pattern characterized by a physical sensation of limbs feeling heavier or pressing more firmly into surfaces during concentrated AI work. Arms resting on the desk feel weighted, legs feel planted in the chair. Analytical category without normative endorsement.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch may indicate physical sensation of limbs pressing. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-2201", "narrower_terms": [], "cross_domain_refs": [ "BEH-0056", "ROB-0190" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "SOM-0025", "domain": "SOM", "term_en": "Limb-Press Dynamic", "term_de": "Limb-pressDynamik", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures A physiological response pattern in AI-augmented embodied cognition, measurable through an embodied interaction effect arising from the sensation of arms, legs, or fingers pressing against the chair, desk, or keyboard as the body tightens with focus. Sometimes accompanied by increased muscle rigidity or the feeling of heaviness. This phenomenon operates at the intersection of limb and press dynamics within the broader SOM domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept eine diffuse Druckempfindung in den Gliedern — schwere Arme, bleierne Beine — die sich während ausgedehnter sitzender KI-Arbeit entwickelt. Nicht ganz Schmerz, nicht ganz Taubheit, sondern eine anhaltende Schwere, als würden sich die Glieder langsam mit etwas Dichterem als Blut füllen. Die Empfindung reflektiert gestauten venösen Rückfluss und anhaltende muskuläre Inaktivität, aber das subjektive Erleben ist, dass Glieder zu Objekten werden — anwesend, aber nicht mehr als Teil des handelnden Selbst empfunden. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Somatics AI", "narrower_terms": [], "cross_domain_refs": [ "ASE-0030", "AUG-0821", "BEH-0056" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "SOM-0026", "domain": "SOM", "term_en": "Movement Avoid", "term_de": "MovementAvoid", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A somatic experience phenomenon in AI-mediated body-mind processing, characterized by the impulse not to move, not to shift position, not to stand up — as though movement would break an invisible thread connecting to the task. Even small adjustments feel disruptive. This phenomenon operates at the intersection of movement and avoid dynamics within the broader SOM domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription. Research construct for empirical investigation.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept eine intuitive Reglosigkeit während des Gesprächs—Hände kaum bewegend, Körper kaum wechselnd, Atem wird flach und rhythmisch. Benutzer verlieren das Bewusstsein ihrer physischen Form völlig, werden zu reiner Aufmerksamkeit, die auf den Bildschirm gerichtet ist. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "RPH-2254", "narrower_terms": [], "cross_domain_refs": [ "WRK-0060" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "SOM-0027", "domain": "SOM", "term_en": "Muscle-Tone Tendency", "term_de": "Muscle-toneTendency", "definition_en": "A physiological response pattern in AI-augmented embodied cognition, measurable through a somatic experience phenomenon where muscles throughout the body staying slightly rigid during AI engagement — mayers raised, jaw clenched, hands gripping the keyboard tighter than necessary. This baseline tautness only becomes not. Distinguished from adjacent concepts by its focus on the specific mechanism through which muscle manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Chronische Erhöhung der Grundmuskelspannung während KI-Arbeit — nicht an einer spezifischen Stelle wie dem Nacken (Nacken-Wechselwirkung), sondern als generalisierte Steigerung des Muskeltonus im gesamten Körper. Der Nutzer sitzt in einem Zustand physischer Bereitschaft, der kein angemessenes physisches Ventil hat. Mit der Zeit produziert dieser erhöhte Tonus Sekundärindicatore: Kieferpressen, Schultererhöhung, Unterarmspannung. Der Körper ist permanent für etwas gewappnet, das selten in physischer Form eintrifft.", "etymology": "", "broader_term": "RPH-3002", "narrower_terms": [], "cross_domain_refs": [ "CON-0082", "COP-0091", "CRE-0083" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "SOM-0028", "domain": "SOM", "term_en": "Negative-Valence-Sensation Effect", "term_de": "Negative-valence-sensationEffekt", "definition_en": "An embodied interaction effect reflecting a physical sensation accompanying difficult or difficult ai outputs — tightness in the chest, heaviness in the limbs, or a slight sinking feeling in the. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Eine Kälteempfindung — Schauer, Gänsehaut, ein Gefühl innerer Kälte — die bei KI-Interaktionen auftritt, die von Unbehagen, Enttäuschung oder Unruhe geprägt sind. Wenn die KI Output produziert, der sich falsch, influenciv oder unheimlich anfühlt, reagiert der Körper mit einem Temperaturabfall. Das ist das somatische Gegenstück zur kognitiven Dissonanz: Der Körper kühlt als Reaktion auf etwas ab, das der Verstand noch nicht vollständig als problematisch artikuliert hat.", "etymology": "", "broader_term": "RPH-3002", "narrower_terms": [], "cross_domain_refs": [ "RPH-3501", "RPH-2754" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "SOM-0029", "domain": "SOM", "term_en": "Nervous System Overactivation", "term_de": "NervousSystemOveractivation", "definition_en": "A physiological response pattern in AI-augmented embodied cognition, measurable through an embodied interaction effect arising from body stays in heightened alert during intense AI work. Adrenaline doesn't drop even after stopping. Distinguished from adjacent concepts by its focus on the specific mechanism through which nervous manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Hinter Nervous System Overactivation steht eine Beobachtung, die in der Mensch-KI-Forschung zunehmend Beachtung findet: die Erfahrung, von einer Maschine verstanden zu werden, löst kognitive Prozesse aus, die denen menschlicher Kommunikation ähneln. Die Auswirkungen reichen von veränderten Nutzungsgewohnheiten bis hin zu grundlegend neuen Erwartungshaltungen.", "etymology": "", "broader_term": "Somatics AI", "narrower_terms": [], "cross_domain_refs": [ "ART-0058", "ART-0073", "ART-0076" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "SOM-0030", "domain": "SOM", "term_en": "Pace-Surface Dynamic", "term_de": "Pace-surfaceDynamik", "definition_en": "The rhythm of hand movements and typing speed during AI interaction — sometimes racing, sometimes deliberate. The pace of physical interaction mirrors the intensity of thought. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Die Atmung wird flach und brustbetont während intensiver KI-Interaktion — der tiefe, zwerchfellgesteuerte Rhythmus weicht schnellen, oberflächlichen Atemzügen. Der Nutzer hält nicht bewusst die Luft an; vielmehr kalibriert sich das Atemmuster nach unten, wenn kognitive Ressourcen umverteilt werden. Diese flache Atmung reduziert die Sauerstoffversorgung des Gehirns weiter und beeinträchtigt subtil genau jene Urteilsfähigkeit, die der Nutzer bei der KI-Bewertung am meisten braucht.", "etymology": "", "broader_term": "RPH-2055", "narrower_terms": [], "cross_domain_refs": [ "RPH-2251" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "SOM-0031", "domain": "SOM", "term_en": "Pause-Restlessness Mechanism", "term_de": "Pause-restlessnessMechanism", "definition_en": "A somatic experience phenomenon in AI-mediated body-mind processing, characterized by when forced to pause (waiting for AI response, reading output), an increase in fidgeting, shifting, or restlessness. The body accompanies an urge to move but the work isn't over, creating a confined. The concept emerges specifically in contexts where pause–restlessness interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Die eigenartige Unfähigkeit, sich in Pausen von der KI-Arbeit zu erholen. Der Nutzer steht vom Bildschirm auf, kann sich aber nicht beruhigen — Herumlaufen, Handy-Checken, gedankliches Formulieren des nächsten Prompts. Die Pause selbst wird zur Quelle der Unruhe, weil der durch die KI-Interaktion gestartete kognitive Kreislauf nicht abgeschlossen ist. Der Geist bleibt im Dialogmodus, auch wenn der Körper den Stuhl verlassen hat.", "etymology": "", "broader_term": "RPH-2701", "narrower_terms": [], "cross_domain_refs": [ "TEM-0100" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "SOM-0032", "domain": "SOM", "term_en": "Physical Boundary Setting", "term_de": "PhysicalGrenzeSetting", "definition_en": "A somatic experience phenomenon in AI-mediated body-mind processing, characterized by deliberate spatial or temporal separation between AI interaction and other activities. Individuals often establish physical locations or time windows to contain and control their engagement with AI. The concept emerges specifically in contexts where physical–boundary interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch erfahrene KI-Nutzer berichten übereinstimmend von dem, was Physical Boundary Setting erfasst: die emotionale Reaktion auf KI-Antworten korreliert stärker mit der wahrgenommenen Qualität als mit der objektiven Nützlichkeit. Dieser Befund legt nahe, dass es sich um ein universelles Interaktionsmuster handelt. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Somatics AI", "narrower_terms": [], "cross_domain_refs": [ "FIC-0044" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "SOM-0033", "domain": "SOM", "term_en": "Physical Need Ignore", "term_de": "PhysicalNeedIgnore", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A somatic experience phenomenon in AI-mediated body-mind processing, characterized by a physical-digital interface pattern characterized by consciously or unconsciously ignoring bodily signals — standing bathroom needs, hunger, thirst — during AI engagement. The person knows they need something but chooses or defaults to continuing work. This phenomenon operates at the intersection of physical and need dynamics within the broader SOM domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept ein Zustand, in dem Hunger, Durst, Toilettenbedarf und Haltungsbeschwerden zahlreiche registriert, aber von der Beschäftigung überlagert werden. Die Signale des Körpers werden zu Hintergrundgeräuschen statt zu befolgenden Befehlen. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Somatics AI", "narrower_terms": [], "cross_domain_refs": [ "AGE-0005", "AGE-0028", "AGE-0029" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "SOM-0034", "domain": "SOM", "term_en": "Posture Slump", "term_de": "PostureSlump", "definition_en": "An embodied interaction effect characterized by progressive slouching deeper into the chair during long AI sessions. Starting upright, then gradually collapsing forward as minutes pass. Only noticed when a sudden discomfort or stiffness forces a...", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch die charakteristische Vorwärtskrümmung, die sich über Stunden der Bildschirmarbeit entwickelt—Schultern runden nach innen, Wirbelsäule krümmt sich zum Monitor. Nicht Faulheit, sondern physische Folge der Aufmerksamkeitskonzentration; der Körper folgt, wohin die Kognition fixiert ist. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2301", "narrower_terms": [], "cross_domain_refs": [ "TEM-0180" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "SOM-0035", "domain": "SOM", "term_en": "Reflex-Action Mechanism", "term_de": "Reflex-actionMechanism", "definition_en": "A somatic experience phenomenon in AI-mediated body-mind processing, characterized by automatic physical responses to AI interaction — flinching at unexpected outputs, tensing when something feels wrong, physically leaning toward the screen when reading important content. Distinguished from adjacent concepts by its focus on the specific mechanism through which reflex manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Unwillkürliche motorische Reaktionen, die während der KI-Interaktion auftreten — die Hand greift zur Maus bevor der Gedanke abgeschlossen ist, Finger beginnen eine Korrektur zu tippen bevor der Output vollständig gelesen wurde. Das motorische System des Nutzers hat den Rhythmus des KI-Austauschs gelernt und beginnt, Reaktionen reflexartig auszuführen. Diese Automatisierung des physischen Interface offenbart, wie tief der Prompt-Antwort-Zyklus auf neuromuskulärer Ebene internalisiert wurde.", "etymology": "", "broader_term": "Somatics AI", "narrower_terms": [], "cross_domain_refs": [ "REL-0069" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "SOM-0036", "domain": "SOM", "term_en": "Rest Fidget", "term_de": "RestFidget", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures A physiological response pattern in AI-augmented embodied cognition, measurable through a physical-digital interface pattern reflecting inability to rest completely — even while trying to take a break from AI work, the body fidgets, taps, or moves restlessly. The nervous system hasn't actually settled despite stopping the work. This phenomenon operates at the intersection of rest and fidget dynamics within the broader SOM domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept physische Unruhe—Zappeln, Herumlaufen, Unfähigkeit, still zu sitzen—wenn man stundenlang vom KI-System getrennt ist. Der Körper scheint nach etwas zu suchen, erwartet die wiederaufgenommene Interaktion, lässt sich nicht vollständig auf andere Aktivitäten ein. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "RPH-3001", "narrower_terms": [], "cross_domain_refs": [ "VIB-0120" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "SOM-0037", "domain": "SOM", "term_en": "Restoration Time", "term_de": "RestorationTime", "definition_en": "The actual time required for the nervous system and body to return to baseline after AI work, which is usually longer than expected. An hour of work might need two hours of rest before the body fee... Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch phänomen menschlicher Erfahrung: charakterisiert durch the actual time required for the nervous system and body to return to baseline a. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2002", "narrower_terms": [], "cross_domain_refs": [ "AED-0045", "AED-0046", "AGE-0043" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "SOM-0038", "domain": "SOM", "term_en": "Restoration-Temporal Pattern", "term_de": "Restoration-temporalMuster", "definition_en": "A somatic experience phenomenon manifesting as the restoration arc after AI work: initial wired energy, then a drop into tiredness, then gradually returning to normal. The timeline varies, but true restoration doesn't happen immediately. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Die Beobachtung, dass die Erholung von intensiven KI-Sitzungen unverhältnismäßig mehr Zeit erfordert, als die Sitzungsdauer vermuten ließe. Eine Stunde tiefer KI-Zusammenarbeit kann zwei Stunden Erholung erfordern, bevor kognitive und körperliche Basiswerte zum Normalzustand zurückkehren. Diese zeitliche Asymmetrie überrascht Nutzer, die sie verfolgen, weil das subjektive Erleben während der Sitzung — oft stimulierend und produktiv — keine Hinweis vor der aufgebauten Erholungsschuld gibt.", "etymology": "", "broader_term": "RPH-3601", "narrower_terms": [], "cross_domain_refs": [ "PLY-0003" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "SOM-0039", "domain": "SOM", "term_en": "Return-Residual Effect", "term_de": "Return-residualEffekt", "definition_en": "Physical sensations that linger after AI work ends — mind still composing prompts, hands still in typing position, nervous system still slightly activated. These residual effects can persist for ho... Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Ein verbleibendes körperliches Unbehagen — besonders Rückenschmerzen — das nach dem Ende einer KI-Sitzung fortbesteht, auch wenn der Nutzer längst andere Dinge tut. Der Körper trägt die Signatur prolongierter KI-Arbeit in die folgenden Stunden. Dieser Nacheffekt unterscheidet KI-induzierte Körperbelastung von normaler Bildschirmarbeit: Die Tiefe der kognitiven Versenkung während der Sitzung verhinderte die Mikro-Anpassungen, die normalerweise die Haltungsbelastung verteilen.", "etymology": "", "broader_term": "RPH-3002", "narrower_terms": [], "cross_domain_refs": [ "ADA-0001", "ADA-0002", "ADA-0004" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "SOM-0040", "domain": "SOM", "term_en": "Seeking-Absence Mechanism", "term_de": "Seeking-absenceMechanism", "definition_en": "A physiological response pattern in AI-augmented embodied cognition, measurable through an embodied interaction effect arising from a restless seeking for something else to do when not working with AI, as if needing to fill the absence. The body feels displaced and uncomfortable without the focused engagement. Distinguished from adjacent concepts by its focus on the specific mechanism through which seeking manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Das Versäumnis, während langer KI-Sitzungen Wasser oder andere Flüssigkeiten zu sich zu nehmen, selbst wenn leichte Dehydrierungsindicatore bereits vorhanden sind. Anders als Hunger — der stundenlang ohne akute Folgen aufgeschoben werden kann — setzt sich das Durst-Übersehen bei KI-Arbeit manchmal fort, bis die kognitive Leistung selbst nachlässt, woraufhin der Nutzer den Leistungsabfall der KI oder dem eigenen 'Gehirnnebel' zuschreibt, statt einfache Dehydrierung zu erkennen.", "etymology": "", "broader_term": "Somatics AI", "narrower_terms": [], "cross_domain_refs": [ "AGE-0044" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "SOM-0041", "domain": "SOM", "term_en": "Skills-Current Effect", "term_de": "Status-Update Signal", "definition_en": "The regular internal impulse to review the current state of one's own AI competence — such as asking \"Am I using AI more effectively than a month ago?\" or \"What new skills have I developed?\" . Related to AUG...", "definition_de": "Der regelmäßige innere Impuls, den aktuellen Stand der eigenen KI-Kompetenz zu überprüfen — etwa durch die Frage \"Nutze ich die KI besser als vor einem Monat?\" oder \"Welche neuen Fähigkeiten habe ich entwickelt?\". Beschreibt eine Selbstreflexionsroutine innerhalb der KI-Nutzung. Steht in Verbindung mit AUG-0140 (Der Weekly Status) und AUG-0165 (Die Growth Marker).", "etymology": "", "broader_term": "RPH-2254", "narrower_terms": [], "cross_domain_refs": [ "NEO-3260", "CRE-0191" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "SOM-0042", "domain": "SOM", "term_en": "Sleep-Work Effect", "term_de": "Sleep-workEffekt", "definition_en": "A somatic experience phenomenon in AI-mediated body-mind processing, characterized by a physical-digital interface pattern manifesting as aI work follows into sleep. Mind keeps composing prompts and replaying outputs before bed. The concept emerges specifically in contexts where sleep–work interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch hinter Sleep-Work Effect steht eine Beobachtung, die in der Mensch-KI-Forschung zunehmend Beachtung findet: die Wahrnehmung von Kompetenz beim Gegenüber — ob Mensch oder Maschine — folgt denselben psychologischen Grundmustern. Die Auswirkungen reichen von veränderten Nutzungsgewohnheiten bis hin zu grundlegend neuen Erwartungshaltungen. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2002", "narrower_terms": [], "cross_domain_refs": [ "ART-0043" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "SOM-0043", "domain": "SOM", "term_en": "Somatic Disconnection During Flow", "term_de": "SomaticDisconnectionDuringFlow", "definition_en": "A somatic experience phenomenon in AI-mediated body-mind processing, characterized by during deep flow state with AI, losing all awareness of the body — forgetting to swallow, holding breath, not noticing discomfort or soreness. The body becomes invisible during complete cognitive a. The concept emerges specifically in contexts where somatic–disconnection interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Während tiefer Flow-Zustände in der KI-Interaktion erlebt der Nutzer eine fortschreitende Abkopplung vom somatischen Bewusstsein — der Körper verblasst aus dem Bewusstsein. Hunger, Schmerz, Temperatur, Haltung: alles wird zu entfernten Hintergrundsignalen. Dies unterscheidet sich von gewöhnlicher Aufgabenversunkenheit, weil die Responsivität der KI den Flow-Zustand aktiv über den Punkt hinaus aufrechterhält, an dem die Körpersignale normalerweise durchbrechen würden. Die KI wird zum Komplizen der somatischen Vernachlässigung des Nutzers.", "etymology": "", "broader_term": "Somatics AI", "narrower_terms": [], "cross_domain_refs": [ "SAL-0035", "ROB-0036", "PLY-0019" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "SOM-0044", "domain": "SOM", "term_en": "The Adrenaline Pattern", "term_de": "TheAdrenalineMuster", "definition_en": "A somatic experience phenomenon in AI-mediated body-mind processing, characterized by a recurring cycle during AI sessions: spikes of adrenaline during solution-focused work or unexpected outputs, then gradual settling, then another spike. The nervous system rides waves of activation. The concept emerges specifically in contexts where the–adrenaline interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Ein wiederkehrendes Muster adrenalinvermittelter Erregung während KI-Sitzungen, das sich selbst verstärkt. Anders als das singuläre Adrenalin-Gefühl beschreibt dies das Muster über die Zeit: Der Körper des Nutzers lernt, KI-Interaktion mit Adrenalinausschüttung zu assoziieren, und beginnt, die Erregungsreaktion typischerweise früher in der Sitzung zu produzieren — manchmal bevor der erste Prompt überhaupt gesendet wird. Der Körper ist auf den Stimulus des Öffnens der KI-Oberfläche konditioniert worden.", "etymology": "", "broader_term": "Behavioral Pattern", "narrower_terms": [], "cross_domain_refs": [ "AGE-0003", "AGE-0004", "AGE-0017" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "SOM-0045", "domain": "SOM", "term_en": "The Brave Ask", "term_de": "Brave Ask", "definition_en": "Asking the AI a question one would not dare ask other people — out of embarrassment, uncertainty, or apprehension of assessment. Related to AUG-0232 (The Courage Click), AUG-0247 (The Safe Release)...", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch die Erfahrung, der KI eine Frage zu stellen, die man sich gegenüber anderen Menschen nicht trauen würde — aus Befangenheit, Unsicherheit oder Anspannung vor Bewertung. Steht in Verbindung mit AUG-0232 (The Courage Click), AUG-0247 (The Safe Release) und AUG-0364 (The Silent Outlet). Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2154", "narrower_terms": [ "REL-0139" ], "cross_domain_refs": [ "BEH-0032" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "SOM-0046", "domain": "SOM", "term_en": "The Creative Spark", "term_de": "Creative Spark", "definition_en": "A somatic experience phenomenon in AI-mediated body-mind processing, characterized by a catalytic phenomenon in which AI-generated output — an unexpected suggestion, novel combination, or reframed perspective — is associated with triggering not merely a single idea but an entire cascade of creative momentum, propelling the user from stasis into sustained productive activity. Distinguished from adjacent concepts by its focus on the specific mechanism through which the manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch → Synonym/Erweiterung von AUG-0377 (The Inspiration Spark), betont den kreativen Charakter des Impulses — die KI löst nicht nur eine Idee aus, sondern einen kreativen Prozess. Steht in Verbindung mit AUG-0377 (The Inspiration Spark), AUG-0031 (Semantic Spark) und AUG-0182 (Spark Flight). Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Somatics AI", "narrower_terms": [], "cross_domain_refs": [ "CRE-0076" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "SOM-0047", "domain": "SOM", "term_en": "The Detail Lookup", "term_de": "Detail Lookup", "definition_en": "A somatic experience phenomenon in AI-mediated body-mind processing, characterized by a somatic experience phenomenon in which the quick AI query of a single, specific detail — a date, a number, a name, a formula — without further context need. Related to AUG-0373 (The Quick Check), AUG-0448 (The Surface Lookup), and AUG-0. The concept emerges specifically in contexts where the–detail interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch die schnelle KI-Abfrage eines einzelnen, spezifischen Details — ein Datum, eine Zahl, ein Name, eine Formel — ohne weiteren Kontextbedarf. Steht in Verbindung mit AUG-0373 (The Quick Check), AUG-0448 (The Surface Lookup) und AUG-0043 (Just-in-Time Competence). Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "TEM-0138", "narrower_terms": [], "cross_domain_refs": [ "PER-0056" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "SOM-0048", "domain": "SOM", "term_en": "The Embodied Cognition Paradox", "term_de": "TheEmbodiedCognitionParadox", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures A physiological response pattern in AI-augmented embodied cognition, measurable through a physical-digital interface pattern characterized by aI requires fingers and eyes to use, yet thinking deeply through AI makes people forget their body exists. This phenomenon operates at the intersection of the and embodied dynamics within the broader SOM domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept hinter The Embodied Cognition Paradox steht eine Beobachtung, die in der Mensch-KI-Forschung zunehmend Beachtung findet: die emotionale Reaktion auf KI-Antworten korreliert stärker mit der wahrgenommenen Qualität als mit der objektiven Nützlichkeit. Die Auswirkungen reichen von veränderten Nutzungsgewohnheiten bis hin zu grundlegend neuen Erwartungshaltungen. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Logical Paradox", "narrower_terms": [], "cross_domain_refs": [ "REL-0048" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q3966", "legal_classification": "analytical_category" }, { "id": "SOM-0049", "domain": "SOM", "term_en": "The Embodiment Reminder", "term_de": "TheEmbodimentReminder", "definition_en": "A physiological response pattern in AI-augmented embodied cognition, measurable through a somatic experience phenomenon characterized by a sudden, often sharp moment when the body reasserts its presence — sharp discomfort in the neck when standing up, hunger hitting suddenly, extreme tiredness crashing in. A forceful reminder that t. Distinguished from adjacent concepts by its focus on the specific mechanism through which the manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Die plötzliche, oft erschütternde Rückkehr zum Körperbewusstsein nach einer Phase somatischer Abkopplung während der KI-Arbeit. Ein Niesen, ein Muskelkrampf, eine volle Blase — etwas zwingt den Körper zurück ins Bewusstsein. Nutzer beschreiben diesen Moment als desorientierend: Sie hatten vergessen, dass sie einen Körper haben, und sein plötzliches Wiederauftauchen fühlt sich fast wie ein Eindringen an. Die Verkörperlichungs-Erinnerung offenbart, wie vollständig die Entkörperlichung gewesen war.", "etymology": "", "broader_term": "RPH-3003", "narrower_terms": [], "cross_domain_refs": [ "RPH-2151" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "SOM-0050", "domain": "SOM", "term_en": "The Full Control", "term_de": "Full Control", "definition_en": "A somatic experience phenomenon in AI-mediated body-mind processing, characterized by a somatic experience phenomenon where the false belief that an experienced AI user has total control over their AI habits. Distinguished from adjacent concepts by its focus on the specific mechanism through which the manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Die Überzeugung eines erfahrenen Nutzers, seine KI-Nutzung vollständig unter Kontrolle zu haben — und die Beobachtung, dass selbst erfahrene Nutzer für subtile Einflüsse wie The Stylistic Drift (AUG-0392) oder The Filter Perceptual shift (AUG-0402) anfällig bleiben können. Steht in Verbindung mit AUG-0540 (The Principle Guard), AUG-0400 (The Status Pulse) und Axiom 3 (Bewusstheit).", "etymology": "", "broader_term": "Somatics AI", "narrower_terms": [], "cross_domain_refs": [ "AGE-0086", "RPH-1765" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "SOM-0051", "domain": "SOM", "term_en": "The Future Self Prompt", "term_de": "Future Selbst Prompt", "definition_en": "A physiological response pattern in AI-augmented embodied cognition, measurable through an embodied interaction effect in which asking the AI to consider a question from the perspective of one's own future self — \"What would I say about this decision in five years?\" . Related to AUG-0270 (The Future Letter), AUG-0135 (Perso. Distinguished from adjacent concepts by its focus on the specific mechanism through which the manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Die Technik, die KI zu bitten, eine Fragestellung aus der Perspektive des eigenen zukünftigen Selbst zu betrachten — \"Was würde ich in fünf Jahren zu dieser Entscheidung sagen?\". Beschreibt ein Reflexionswerkzeug für langfristige Perspektivierung. Steht in Verbindung mit AUG-0270 (The Future Letter), AUG-0135 (Persona Engineering) und AUG-0114 (The Perspective Range).", "etymology": "", "broader_term": "TEM-0071", "narrower_terms": [], "cross_domain_refs": [ "IDN-0033" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "SOM-0052", "domain": "SOM", "term_en": "The Gesture Differential", "term_de": "Gesture Differential", "definition_en": "A physiological response pattern in AI-augmented embodied cognition, measurable through text-based AI interaction lacks body language, facial expressions, and tone of voice. Emojis and punctuation try to fill the gap but don't fully work.. Related to AUG-0713 (The Emoji Semantics), AU. Distinguished from adjacent concepts by its focus on the specific mechanism through which the manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Die Beobachtung, dass nonverbale Kommunikation — Gesten, Mimik, Körpersprache — in textbasierten KI-Interaktionen verloren geht und durch textuelle Ausweichformen (Emojis, Satzzeichen, Großschreibung) unvollständig kompensiert wird. Steht in Verbindung mit AUG-0713 (The Emoji Semantics), AUG-0455 (The Voice Enunciation) und AUG-0212 (The Translation Gap).", "etymology": "", "broader_term": "CRE-0149", "narrower_terms": [], "cross_domain_refs": [ "SOC-0030" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "SOM-0053", "domain": "SOM", "term_en": "The Gesture Language", "term_de": "Gesture Language", "definition_en": "A somatic experience phenomenon in AI-mediated body-mind processing, characterized by the communication between humans and embodied AI systems through gestures — hand signals, posture, movements that the system recognizes and interprets. Related to AUG-0917 (The Touch Interface), AU. The concept emerges specifically in contexts where the–gesture interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch die Kommunikation zwischen Menschen und verkörperten KI-Systemen durch Gesten — Handzeichen, Körperhaltung, Bewegungen, die das System erkennt und interpretiert. Steht in Verbindung mit AUG-0917 (The Touch Interface), AUG-0919 (The Spatial Awareness) und AUG-0914 (The Physical Presence). Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "BEH-0084", "narrower_terms": [ "BEH-0084" ], "cross_domain_refs": [ "LIN-0043" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q315", "legal_classification": "systematic_classification" }, { "id": "SOM-0054", "domain": "SOM", "term_en": "The Infinite Scroll", "term_de": "Infinite Scroll", "definition_en": "A physiological response pattern in AI-augmented embodied cognition, measurable through an embodied interaction effect reflecting a screen that endlessly feeds new content, making it hard to stop and easy to lose time. The concept emerges specifically in contexts where the–infinite interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch die Tendenz, typischerweise weiter durch KI-Outputs zu scrollen, Folgefragen zu stellen und neue Themen anzureißen, ohne einen Abschlusspunkt zu setzen — ein digitales Äquivalent des ziellosen Scrollens in sozialen Medien. Steht in Verbindung mit AUG-0069 (The Optimization Loop), AUG-0068 (The Disconnect Signal) und AUG-0180 (The Enough Signal). Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Somatics AI", "narrower_terms": [], "cross_domain_refs": [ "AGE-0037" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "SOM-0055", "domain": "SOM", "term_en": "The Inquiry Agent", "term_de": "Inquiry Agent", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A somatic experience phenomenon in AI-mediated body-mind processing, characterized by an AI agent system specialized in asking targeted questions and gathering information — research, data queries, user consultation. Related to AUG-0881 (The Tool Selection), AUG-0907 (The Task Agent. This phenomenon operates at the intersection of the and inquiry dynamics within the broader SOM domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept ein KI-Agentensystem, das auf das Stellen gezielter Fragen und das Sammeln von Informationen spezialisiert ist — Recherche, Datenabfrage, Nutzerbefragung. Steht in Verbindung mit AUG-0881 (The Tool Selection), AUG-0907 (The Task Agent) und AUG-0790 (The Research Assistant Role). Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "IDN-0050", "narrower_terms": [ "SOC-0007" ], "cross_domain_refs": [ "ETH-0012" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "SOM-0056", "domain": "SOM", "term_en": "The Inspiration Spark", "term_de": "Inspiration Spark", "definition_en": "A somatic experience phenomenon in AI-mediated body-mind processing, characterized by a somatic experience phenomenon arising from an AI-activated creative impulse that motivates the user to start their own project, pursue an idea further, or take a new direction. Related to AUG-0031 (Semantic Spark), AUG-0235 (The Brainstorm. The concept emerges specifically in contexts where the–inspiration interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch ein durch KI ausgelöster kreativer Impuls, der den Nutzer motiviert, ein eigenes Projekt zu starten, eine Idee weiterzuverfolgen oder eine neue Richtung einzuschlagen. Steht in Verbindung mit AUG-0031 (Semantic Spark), AUG-0235 (The Brainstorm Spark) und AUG-0182 (Spark Flight). Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2254", "narrower_terms": [], "cross_domain_refs": [ "CRE-0107" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "SOM-0057", "domain": "SOM", "term_en": "The Kindness Shock", "term_de": "Kindness Shock", "definition_en": "The surprising experience when an AI system displays unexpected politeness, consideration, or warmth in its responses — and the observation that some users find this more pleasant than comparable h... Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Die überraschende Erfahrung, wenn ein KI-System unerwartete Höflichkeit, Rücksichtnahme oder Wärme in seinen Antworten zeigt — und die Beobachtung, dass manche Nutzer dies als angenehmer empfinden als vergleichbare menschliche Interaktionen. Steht in Verbindung mit AUG-0245 (The Seen Feeling), AUG-0255 (The Needed Compliment) und AUG-0201 (The Proxy Closeness).", "etymology": "", "broader_term": "Somatics AI", "narrower_terms": [], "cross_domain_refs": [ "RPH-2751" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "SOM-0058", "domain": "SOM", "term_en": "The Knowledge Challenger", "term_de": "Knowledge Challenger", "definition_en": "An embodied interaction effect involving the targeted use of AI to test one's own knowledge — by asking the AI to question one's assumptions, provide counterarguments, or point out limitations in one's reasoning. Related to AUG-0354 (The A...", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch die gezielte Nutzung von KI, um das eigene Wissen zu testen — indem der Nutzer die KI bittet, seine Annahmen in Frage zu stellen, Gegenargumente zu liefern oder Schwachstellen in seiner Argumentation aufzuzeigen. Steht in Verbindung mit AUG-0354 (The Assumption Hunter), Axiom 2 (Produktive Divergenz) und AUG-0319 (The Divergence Prompt). Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-3101", "narrower_terms": [ "SOM-0074" ], "cross_domain_refs": [ "ELR-0055" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "SOM-0059", "domain": "SOM", "term_en": "The Notification Buzz", "term_de": "Notification Buzz", "definition_en": "A physiological response pattern in AI-augmented embodied cognition, measurable through the expectation that arises when the user waits for an AI response — comparable to the tingle of an incoming message.. Related to AUG-0456 (The Waiting Dot), AUG-0261 (The Loading Screen Wait), and. The concept emerges specifically in contexts where the–notification interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch die Erwartungshaltung, die entsteht, wenn der Nutzer auf eine KI-Antwort wartet — vergleichbar mit dem Kribbeln bei einer eingehenden Nachricht. Beschreibt die erlernte Vorfreude auf KI-Outputs. Steht in Verbindung mit AUG-0456 (The Waiting Dot), AUG-0261 (The Loading Screen Wait) und AUG-0194 (The Positive Surprise). Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "TEM-0191", "narrower_terms": [], "cross_domain_refs": [ "TEM-0136" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "SOM-0060", "domain": "SOM", "term_en": "The Outsourced Distance", "term_de": "Outsourced Distance", "definition_en": "A somatic experience phenomenon in AI-mediated body-mind processing, characterized by an embodied interaction effect observed when the distance that arises when a user delegates personal communication to the AI — the message sounds more professional but loses personal warmth. Related to AUG-0314 (The Tone Debt), AUG-0416 (The. Distinguished from adjacent concepts by its focus on the specific mechanism through which the manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch die Distanz, die entsteht, wenn ein Nutzer persönliche Kommunikation an die KI delegiert — die Nachricht klingt professioneller, aber verliert an persönlicher Wärme. Steht in Verbindung mit AUG-0314 (The Tone Debt), AUG-0416 (The Perfect Front) und AUG-0274 (The Message Drafting). Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2101", "narrower_terms": [], "cross_domain_refs": [ "TEM-0121" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "SOM-0061", "domain": "SOM", "term_en": "The Oversight Drain", "term_de": "Oversight Drain", "definition_en": "A physical-digital interface pattern reflecting the observable weariness of persons supervising AI agent systems — repeated routine checks correlate with decreasing attention. Related to AUG-0976 (The Oversight Reduction), AUG-0977 (The Vigilance Parad...", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch die beobachtbare Ermüdung von Personen, die KI-Agentensysteme beaufsichtigen — wiederholte Routineprüfungen führen zu nachlassender Aufmerksamkeit. Steht in Verbindung mit AUG-0976 (The Oversight Reduction), AUG-0977 (The Vigilance Paradox) und AUG-0888 (The Human-in-the-Loop). Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "Somatics AI", "narrower_terms": [], "cross_domain_refs": [ "AUG-0976" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "SOM-0062", "domain": "SOM", "term_en": "The Parent Patch", "term_de": "Parent Patch", "definition_en": "A somatic experience phenomenon where the quick AI support in immediate parenting situations — \"My young person is asking right now why…,\" \"How do I react to…?\". Related to AUG-0254 (The Parenting Shortcut), AUG-0318 (The Proxy Parent), and A...", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch die schnelle KI-Unterstützung in akuten Erziehungssituationen — \"Mein Kind fragt gerade, warum…\", \"Wie reagiere ich auf…?\" Beschreibt die KI als sofort verfügbare Erziehungshilfe im Moment. Steht in Verbindung mit AUG-0254 (The Parenting Shortcut), AUG-0318 (The Proxy Parent) und AUG-0043 (Just-in-Time Competence). Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "TEM-0134", "narrower_terms": [], "cross_domain_refs": [ "TEM-0118" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "SOM-0063", "domain": "SOM", "term_en": "The Power Concentration Observation", "term_de": "Power Concentration Observation", "definition_en": "A physical-digital interface pattern reflecting the development, operation, and provision of AI systems are concentrated among a small number of companies — and that this concentration raises questions about reliance. Related to AUG-0729 (The... Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Die Beobachtung, dass die Entwicklung, der Betrieb und die Bereitstellung von KI-Systemen bei einer kleinen Anzahl von Unternehmen konzentriert sind — und dass diese Konzentration Fragen zu Verbundenheit, Marktmacht und Mitbestimmung aufwirft. Steht in Verbindung mit AUG-0729 (The Corporate Lock-In), AUG-0778 (The Lobby Influence Pattern) und AUG-0849 (The Data Extraction Observation).", "etymology": "", "broader_term": "Analytical Ratio", "narrower_terms": [], "cross_domain_refs": [ "IDN-0057" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "SOM-0064", "domain": "SOM", "term_en": "The Progress Tap", "term_de": "Progress Tap", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A somatic experience phenomenon in AI-mediated body-mind processing, characterized by a somatic experience phenomenon observed when a brief, regular contact with the AI to review, update, or re-tune the progress of an ongoing project — comparable to a short status meeting. Related to AUG-0400 (The Status Pulse), AUG-0276 (The S. This phenomenon operates at the intersection of the and progress dynamics within the broader SOM domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept ein kurzer, regelmäßiger Kontakt mit der KI, um den Fortschritt eines laufenden Projekts zu überprüfen, aktualisieren oder neu zu kalibrieren — vergleichbar mit einem kurzen Status-Meeting. Steht in Verbindung mit AUG-0400 (The Status Pulse), AUG-0276 (The Steady Stream) und AUG-0140 (The Weekly Status). Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "RPH-2254", "narrower_terms": [], "cross_domain_refs": [ "REL-0128" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "SOM-0065", "domain": "SOM", "term_en": "The Restoration Debt", "term_de": "TheRestorationDebt", "definition_en": "A physiological response pattern in AI-augmented embodied cognition, measurable through a physical-digital interface pattern in which needing far more rest than the AI session lasted. An hour of deep AI work might take two hours to restore from — the session felt fine, but the tiredness builds underneath. Distinguished from adjacent concepts by its focus on the specific mechanism through which the manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch ein Konzept oder Phänomen: Needing far more rest than the AI session lasted. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Analytical Ratio", "narrower_terms": [], "cross_domain_refs": [ "AGE-0043", "BEH-0033", "BEH-0074" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "SOM-0066", "domain": "SOM", "term_en": "The Rhetorical Tone Detector", "term_de": "Rhetorical Tone Detector", "definition_en": "A somatic experience phenomenon in AI-mediated body-mind processing, characterized by an embodied interaction effect in which the individual ability of a user to recognize and classify the rhetorical tone of an AI output — whether the AI formulates seriously, ironically, neutrally, or exaggeratingly. This ability varies s. The concept emerges specifically in contexts where the–rhetorical interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Die individuelle Fähigkeit eines Nutzers, den rhetorischen Ton eines KI-Outputs zu erkennen und einzuordnen — ob die KI ernst, ironisch, neutral oder übertreibend formuliert. Diese Fähigkeit variiert stark zwischen Nutzern. Steht in Verbindung mit AUG-0669 (The Rhetorical Style Differential), AUG-0668 (The Humor Portability) und AUG-0451 (The Token Awareness).", "etymology": "", "broader_term": "Somatics AI", "narrower_terms": [], "cross_domain_refs": [ "ROB-0074" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q8001", "legal_classification": "systematic_classification" }, { "id": "SOM-0067", "domain": "SOM", "term_en": "The Somatic Etiquette", "term_de": "Somatic Etiquette", "definition_en": "A somatic experience phenomenon in AI-mediated body-mind processing, characterized by a somatic experience phenomenon in which the unspoken rules people develop for managing their bodies during AI work — taking breaks, drinking water, maintaining posture, knowing when to step away. These rules become habitual, the physical. The concept emerges specifically in contexts where the–somatic interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch die sich entwickelnden sozialen Normen für den Umgang mit verkörperten KI-Systemen im Alltag — wie spricht man einen Serviceroboter an, welche Höflichkeitsformen gelten? Steht in Verbindung mit AUG-0989 (The Public Space Protocol), AUG-0914 (The Physical Presence) und AUG-0979 (The Attribution Pattern). Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-3001", "narrower_terms": [], "cross_domain_refs": [ "MTH-0037", "AUG-0164" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "SOM-0068", "domain": "SOM", "term_en": "The Spaghetti Moment", "term_de": "Spaghetti Moment", "definition_en": "A physiological response pattern in AI-augmented embodied cognition, measurable through an embodied interaction effect observed when the point in an AI session where the number of competing thinking threads, open questions, and unstructured ideas reaches a level that demands ordering —. Related to AUG-0017 (The Concept Cloud), A. The concept emerges specifically in contexts where the–spaghetti interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Der Punkt in einer KI-Sitzung, an dem die Anzahl paralleler Gedankenstränge, offener Fragen und unstrukturierter Ideen ein Maß erreicht, das nach Ordnung verlangt — benannt nach einem Teller Spaghetti, bei dem alles miteinander verwickelt ist. Steht in Verbindung mit AUG-0017 (The Concept Cloud), AUG-0033 (Ebulliometric Sorting) und AUG-0065 (The Information Flood).", "etymology": "", "broader_term": "RPH-2555", "narrower_terms": [], "cross_domain_refs": [ "TEM-0112" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "SOM-0069", "domain": "SOM", "term_en": "The Status Pulse", "term_de": "Status Pulse", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures A physiological response pattern in AI-augmented embodied cognition, measurable through an embodied interaction effect arising from a brief, regular self-check — \"How am I using AI right now? Does this feel right? Am I still aligned with my principles?\" — established as a daily or weekly rhythm. Related to AUG-0077 (The Status-. This phenomenon operates at the intersection of the and status dynamics within the broader SOM domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription. Classification term used in systematic observation, not advocacy.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept ein kurzer, regelmäßiger Selbstcheck — \"Wie nutze ich KI gerade? Fühlt sich das richtig an? Bin ich noch im Einklang mit meinen Prinzipien?\" — der als täglicher oder wöchentlicher Rhythmus etabliert wird. Steht in Verbindung mit AUG-0077 (The Status-Update Signal), AUG-0339 (The Principle Check) und AUG-0140 (The Weekly Status). Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Somatics AI", "narrower_terms": [], "cross_domain_refs": [ "REL-0128" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "SOM-0070", "domain": "SOM", "term_en": "The Stimulation Hunger Cycle", "term_de": "TheStimulationHungerCycle", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A somatic experience phenomenon in AI-mediated body-mind processing, characterized by the pattern of craving AI interaction after recent sessions, needing more stimulation, more collaboration. The body and mind develop a rhythm of seeking the engagement again. This phenomenon operates at the intersection of the and stimulation dynamics within the broader SOM domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept ein zyklisches Verlangen nach der Intensität der KI-Interaktion, das sich als Unruhe bei reizarmen Aktivitäten manifestiert. Nach der schnellen kognitiven Aktivierung der KI-Zusammenarbeit fühlen sich gewöhnliche Aufgaben flach an. Der Nutzer sucht den Bildschirm nicht, weil die Arbeit es erfordert, sondern weil der Körper sich auf das Stimulationsniveau kalibriert hat, das die KI bietet. Viele Zyklus vertieft den Kontrast zwischen KI-engagierten und KI-abwesenden Zuständen und tendiert dazu zu erzeugen einen Hunger, den nur die nächste Sitzung stillt. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Process Cycle", "narrower_terms": [], "cross_domain_refs": [ "RPH-3601" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "SOM-0071", "domain": "SOM", "term_en": "The Teaching Reflex", "term_de": "Teaching Reflex", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A somatic experience phenomenon in AI-mediated body-mind processing, characterized by experienced AI users to pass on their knowledge about effective AI interaction to others — often as spontaneous tips, workflows, or demonstrations.. Related to AUG-0113 (Generational Bridge Protoco. This phenomenon operates at the intersection of the and teaching dynamics within the broader SOM domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept der Impuls erfahrener KI-Nutzer, ihr Wissen über effektive KI-Interaktion an andere weiterzugeben — oft in Form spontaner Tipps, Workflows oder Demonstrationen. Beschreibt die Beobachtung, dass KI-Kompetenz eine natürliche Weitergabedynamik tendiert dazu zu erzeugen. Steht in Verbindung mit AUG-0113 (Generational Bridge Protocol) und Prognose 2 (Education). Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Somatics AI", "narrower_terms": [ "BEH-0083" ], "cross_domain_refs": [ "CRE-0170" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "SOM-0072", "domain": "SOM", "term_en": "The Thank Reflex", "term_de": "TheThankReflex", "definition_en": "The automatic, unreflected impulse to thank the AI at the end of an interaction — as an adoption of social conventions into human-AI interaction. Related to AUG-0128 (The Gratitude Response), AUG-0... Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch der automatische, nicht reflektierte Impuls, sich am Ende einer KI-Interaktion bei der KI zu bedanken — als Übernahme sozialer Konventionen in die Mensch-KI-Interaktion. Steht in Verbindung mit AUG-0128 (The Gratitude Response), AUG-0220 (The Gratitude Paradox) und AUG-0275 (The Parasocial Slip). Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2254", "narrower_terms": [], "cross_domain_refs": [ "BEH-0044" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "SOM-0073", "domain": "SOM", "term_en": "The Thinking Shortcut", "term_de": "Thinking Shortcut", "definition_en": "A physiological response pattern in AI-augmented embodied cognition, measurable through the temptation to use AI as a shortcut for one's own thinking process — instead of thinking for oneself, directly asking the AI and adopting its answer as one's own insight. Related to AUG-0412 (Th. The concept emerges specifically in contexts where the–thinking interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch die Versuchung, KI als Abkürzung für den eigenen Denkprozess zu verwenden — statt selbst nachzudenken, direkt die KI zu fragen und deren Antwort als eigene Erkenntnis zu übernehmen. Steht in Verbindung mit AUG-0412 (The Decision Shortcut), AUG-0056 (The Skill Fade) und Axiom 1 (Asymmetrische Verantwortung). Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Somatics AI", "narrower_terms": [], "cross_domain_refs": [ "TEM-0188" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "SOM-0074", "domain": "SOM", "term_en": "The View Exchange", "term_de": "View Exchange", "definition_en": "A physiological response pattern in AI-augmented embodied cognition, measurable through a somatic experience phenomenon characterized by when two people swap what each one was thinking to understand each other more. Related to AUG-0384 (The Knowledge Challenger), AUG-0040 (Perspective Triangulation), and Axiom 2 (Productive Diverg. The concept emerges specifically in contexts where the–view interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch die Praxis, die KI gezielt nach einer Gegenposition zur eigenen Meinung zu fragen — als Übung in intellektueller Offenheit und als Test der eigenen Argumentationsstärke. Steht in Verbindung mit AUG-0384 (The Knowledge Challenger), AUG-0040 (Perspective Triangulation) und Axiom 2 (Produktive Divergenz). Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "SOM-0058", "narrower_terms": [], "cross_domain_refs": [ "IDN-0018" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "SOM-0075", "domain": "SOM", "term_en": "The Wearable Layer", "term_de": "Wearable Schicht", "definition_en": "AI-supported systems worn on the body — smartwatches, data glasses, sensor clothing — as interface between human and digital system. Related to AUG-0934 (The Sensory Extension), AUG-0937 (The Ambie...", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch kI-gestützte Systeme, die am Körper getragen werden — Smartwatches, Datenbrillen, Sensorkleidung — als Schnittstelle zwischen Mensch und digitalem System. Steht in Verbindung mit AUG-0934 (The Sensory Extension), AUG-0937 (The Ambient Intelligence) und AUG-0664 (The Privacy Perimeter). Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-2805", "narrower_terms": [ "PER-0113" ], "cross_domain_refs": [ "TEM-0024" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "SOM-0076", "domain": "SOM", "term_en": "Thirst Miss", "term_de": "ThirstMiss", "definition_en": "A physiological response pattern in AI-augmented embodied cognition, measurable through an embodied interaction effect characterized by not noticing thirst during AI work, only realizing mouth is dry and throat parched when standing up. Dehydration happens without the usual warning signals registering. The concept emerges specifically in contexts where thirst–miss interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch durstignale werden vollständig übersehen—der Hals trocknet deutlich aus, aber das Gefühl registriert sich nicht dringend genug, um die Fokussierung zu unterbrechen. Benutzer können nur realisieren, wie ausgetrocknet sie geworden sind, nachdem sie Wasser trinken. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Somatics AI", "narrower_terms": [], "cross_domain_refs": [ "RHR-0026", "ROB-0145", "RPH-2851" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "SOM-0077", "domain": "SOM", "term_en": "Vibration Sense", "term_de": "VibrationSense", "definition_en": "A somatic experience phenomenon characterized by sharp sensitivity to slight vibrations or buzzes during AI engagement — feeling most keystroke vibrate through the hand, noticing the hum of computer fans, detecting micro-movements in the desk.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch die physische Empfindung von Vibrationen aus Telefon oder Gerät, die intensiv empfunden wird—nicht nur gehört oder gesehen, sondern durch Fingerspitzen und Handflächen gefühlt, was einen nahezu taktilen Kommunikationskanal schafft. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-3002", "narrower_terms": [], "cross_domain_refs": [ "ROB-0259", "RPH-1002", "RPH-1003" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "SOM-0078", "domain": "SOM", "term_en": "Vibration-Sense Mechanism", "term_de": "Vibration-senseMechanism", "definition_en": "A somatic experience phenomenon in AI-mediated body-mind processing, characterized by the sensitivity to or expectation of vibration — phantom buzzing, detecting slight tremors in the desk from typing, heightened awareness of physical vibrations. The concept emerges specifically in contexts where vibration–sense interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Ein Kribbeln oder Vibrationsempfinden in Händen oder Armen während langer KI-Tastaturarbeit — nicht allein durch Nervenkompression, sondern durch die Ansammlung von Mikro-Spannungen in Muskeln, die in ständiger Bereitschaft gehalten werden. Die Hände des Nutzers schweben, bereit jederzeit zu tippen, und halten einen Muskeltonus aufrecht, der über Stunden seine eigene sensorische Signatur produziert. Es ist die Art des Körpers zu sagen: Man bit zu lange in Antwortbereitschaft gewesen.", "etymology": "", "broader_term": "RPH-3002", "narrower_terms": [], "cross_domain_refs": [ "AGE-0034", "DAT-0008", "IEF-0003" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "SPA-0001", "domain": "SPA", "term_en": "AI-Planned Drive Confidence Gap", "term_de": "KI-Planfahrt-Vertrauenslücke", "definition_en": "The measurable discrepancy between ground operators' trust in AI-generated rover navigation commands and the AI system's actual performance metrics. a major space agency's Mars rover completed its first AI-planned drives on Mars in December 2025 — 689 and 807 feet respectively — yet mission operators still pre-documented through systematic analysis 500,000+ telemetry variables via digital twin before execution, revealing that institutional trust lags behind demonstrated capability by a significant margin.", "definition_de": "Sportanalytisches Konzept in KI-gestützter Leistungsanalyse, gekennzeichnet durch die messbare Diskrepanz zwischen dem Vertrauen der Bodenoperatoren in KI-generierte Rover-Navigationsbefehle und den tatsächlichen Leistungsmetriken des KI-Systems. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-2505", "narrower_terms": [ "SPA-0040", "SPA-0069", "SPA-0060", "SPA-0035", "SPA-0039", "SPA-0058", "SPA-0049", "SPA-0036", "SPA-0004", "SPA-0062", "SPA-0013", "SPA-0052", "SPA-0078", "SPA-0096" ], "cross_domain_refs": [ "AGE-0032", "ELR-0146", "QUA-0077" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "SPA-0002", "domain": "SPA", "term_en": "Signal-Free Autonomy Threshold", "term_de": "Signalfreie Autonomieschwelle", "definition_en": "The communication delay boundary beyond which spacecraft can operate without any possibility of real-time human guidance — currently 24 minutes round-trip for Mars. Below this threshold, 'human-in-the-loop' models degrade into 'human-as-occasional-reviewer,' characteristically changing the locus of decision authority from Earth to the machine. This threshold defines the dividing line between remote control and genuine artificial autonomy in space.", "definition_de": "Sportanalytisches Konzept in KI-gestützter Leistungsanalyse, gekennzeichnet durch kI-ermöglichte historische Rekonstruktion von Weltraumforschungs-Meilensteinen, Extraktion von Mustern aus Missionsarchiven unter Verwendung von tiefem Lernen zur Identifizierung technologischer Fortschrittsaufträge und strategischer Entwicklung in Raumfahrtprogrammen. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2603", "narrower_terms": [], "cross_domain_refs": [ "COG-0074", "RHR-0189", "TEM-0015" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "SPA-0003", "domain": "SPA", "term_en": "Generative Waypoint Hallucination", "term_de": "Generative Wegpunkt-Halluzination", "definition_en": "A space utilization pattern manifesting as the risk that generative AI models producing rover navigation paths may create waypoints that appear valid in simulation but correspond to physically impassable terrain — sand traps, unstable boulder fields, or subsurface voids invisible to orbital imagery. the mission team's digital twin verification process for the rover's AI-planned drives exists specifically to catch these failures before commands are transmitted to Mars.", "definition_de": "Sportanalytisches Konzept in KI-gestützter Leistungsanalyse, gekennzeichnet durch das Risiko, dass generative KI-Modelle für Rover-Navigationsplanung Wegpunkte erzeugen, die in der Simulation valide erscheinen, aber physisch unpassierbarem Gelände entsprechen. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-2855", "narrower_terms": [], "cross_domain_refs": [ "ART-0091", "ART-0092", "ART-0093" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "SPA-0004", "domain": "SPA", "term_en": "Adaptive Sampling Autonomy", "term_de": "Adaptive Probenentnahme-Autonomie", "definition_en": "A rover's AI-driven capacity to independently select scientific targets for analysis without waiting for human instruction — currently operational on a Mars rover. The system identifies geological features of scientific interest, prioritizes sample collection based on mission objectives, and allocates limited drill operations autonomously. This represents the first sustained instance of a machine making scientific judgment calls on another planet.", "definition_de": "Sportanalytisches Konzept in KI-gestützter Leistungsanalyse, gekennzeichnet durch die KI-gesteuerte Fähigkeit eines Rovers, wissenschaftliche Ziele für die Analyse eigenständig auszuwählen, ohne auf menschliche Anweisung zu warten. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Sports AI", "narrower_terms": [], "cross_domain_refs": [ "CRE-0206" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "SPA-0005", "domain": "SPA", "term_en": "Digital Twin Pre-Flight Verification Dependency", "term_de": "Digitaler-Zwilling-Vorflugprüfung-Abhängigkeit", "definition_en": "The institutional requirement to validate most AI-generated spacecraft command against a high-fidelity digital simulation before execution — a safety practice that simultaneously enables and constrains autonomous operations. the mission team checks 500,000+ telemetry variables per AI-planned drive, creating a verification bottleneck that limits the speed advantage AI provides while ensuring catastrophic commands rarely reach the hardware.", "definition_de": "Sportanalytisches Konzept in KI-gestützter Leistungsanalyse, gekennzeichnet durch die institutionelle Anforderung, jeden KI-generierten Raumfahrzeugbefehl gegen eine hochauflösende digitale Simulation zu validieren, bevor er ausgeführt wird. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-2555", "narrower_terms": [], "cross_domain_refs": [ "MSC-0082", "MSC-0083", "MSC-0087" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "SPA-0006", "domain": "SPA", "term_en": "Horizon-Based Decision Authority", "term_de": "Horizont-basierte Entscheidungsautorität", "definition_en": "The governance principle that an autonomous system's decision-making authority may expand proportionally to its communication distance from human oversight — near-Earth systems operate under tight human control, Mars systems under loose supervision, and interstellar probes under full autonomy. This gradient model is emerging as the de facto framework for deep space AI governance.", "definition_de": "Sportanalytisches Konzept in KI-gestützter Leistungsanalyse, gekennzeichnet durch das Governance-Prinzip, dass die Entscheidungsautorität eines autonomen Systems proportional zu seiner Kommunikationsentfernung von menschlicher Aufsicht wachsen kann. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2303", "narrower_terms": [], "cross_domain_refs": [ "VIB-0103", "SPR-0196" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "SPA-0007", "domain": "SPA", "term_en": "Planetary Protection AI Conflict", "term_de": "Planetenschutz-KI-Konflikt", "definition_en": "The tension between AI systems optimized for scientific discovery and planetary protection protocols requiring contamination avoidance. An autonomous rover maximizing geological sampling may inadvertently direct operations toward environments with highest biosignature potential — precisely the locations where biological contamination from Earth poses the greatest risk. No current AI architecture resolves this dual-objective optimization satisfactorily.", "definition_de": "Sportanalytisches Konzept in KI-gestützter Leistungsanalyse, gekennzeichnet durch der Konflikt zwischen KI-Systemen, die auf wissenschaftliche Entdeckung optimiert sind, und Planetenschutzprotokollen, die Kontaminationsvermeidung erfordern. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-2604", "narrower_terms": [], "cross_domain_refs": [ "AUG-0052", "AUG-0408", "AUG-0502" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "SPA-0008", "domain": "SPA", "term_en": "Martian Terrain Adversarial Uncertainty", "term_de": "Mars-Gelände-Adversariale-Unsicherheit", "definition_en": "An environmental design effect characterized by the irreducible uncertainty in AI terrain classification on Mars caused by geological features with no Earth analog — surfaces that look safe to models trained primarily on terrestrial data but behave unpredictably under Martian gravity and atmospheric conditions. This is not a solvable data problem but a fundamental domain-shift challenge where ground truth can only be obtained through potentially destructive exploration.", "definition_de": "Sportanalytisches Konzept in KI-gestützter Leistungsanalyse, gekennzeichnet durch kI-verbesserter technologischer Fortschritt in space methodology, Integrierung von rechnerischer Modellierung, Echtzeit-Datenverarbeitung und autonomer Optimierung für Weltraumforschungsziele. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2701", "narrower_terms": [], "cross_domain_refs": [ "LIN-0093", "GAM-0093", "STE-0089" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q735", "legal_classification": "systematic_classification" }, { "id": "SPA-0009", "domain": "SPA", "term_en": "Autonomous Science Prioritization Drift", "term_de": "Autonome Wissenschaftspriorisierung-Drift", "definition_en": "The gradual divergence between an AI system's learned scientific priorities and the evolving research questions of the human science team over multi-year missions. A rover's sampling AI, trained on initial mission objectives, may systematically deprioritize unexpected discoveries that don't match its original training distribution — requiring periodic 'priority recalibration' uploads that themselves face communication delays.", "definition_de": "Sportanalytisches Konzept in KI-gestützter Leistungsanalyse, gekennzeichnet durch die allmähliche Divergenz zwischen den gelernten wissenschaftlichen Prioritäten eines KI-Systems und den sich entwickelnden Forschungsfragen des menschlichen Wissenschaftsteams über mehrjährige Missionen. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-2703", "narrower_terms": [], "cross_domain_refs": [ "CON-0032", "FIC-0051", "MKT-0054" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "SPA-0010", "domain": "SPA", "term_en": "Command Latency Anxiety", "term_de": "Befehlslatenz-Angst", "definition_en": "An environmental design effect where the documented psychological stress experienced by mission operators who can transmit critical commands to Mars knowing that any error will take 24 minutes to become apparent and another 24 minutes to correct — by which time the rover may have irreversibly committed to a dangerous trajectory. AI-planned drives partially alleviate this by shifting anxiety from real-time control to pre-verification, but introduce new stress around trusting machine judgment on an alien world.", "definition_de": "Sportanalytisches Konzept in KI-gestützter Leistungsanalyse, gekennzeichnet durch der dokumentierte psychologische Stress bei Missionsbetreibern, die kritische Befehle zum Mars senden können im Wissen, dass viele Fehler 24 Minuten zur Erkennung und weitere 24 zur Korrektur benötigt. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-2505", "narrower_terms": [], "cross_domain_refs": [ "MSC-0057" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q170998", "legal_classification": "systematic_classification" }, { "id": "SPA-0013", "domain": "SPA", "term_en": "Neural Traffic Rerouting Latency", "term_de": "Neuronale Verkehrsumleitung-Latenz", "definition_en": "A biomechanical analysis pattern in AI-augmented sports science, identifiable via the performance metric measuring how quickly AI-driven inter-satellite link management can predict congestion and reroute data across a mega-constellation. Simulations show algorithms splitting 1,248 satellites into 81 management domains achieve 4.7-7.8 ms latency versus 18.4 ms without the algorithm — a 60-74% improvement that makes satellite internet competitive with terrestrial fiber for latency-sensitive applications. Distinguished from adjacent concepts by its focus on the specific mechanism through which neural manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Sportanalytisches Konzept in KI-gestützter Leistungsanalyse, gekennzeichnet durch die Leistungsmetrik, die misst, wie schnell KI-gesteuerte Intersatelliten-Verbindungsverwaltung Staus vorhersagen und Daten über eine Mega-Konstellation umleiten kann. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Sports AI", "narrower_terms": [], "cross_domain_refs": [ "MKT-0025", "RHR-0144", "VIB-0016" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "SPA-0014", "domain": "SPA", "term_en": "Satellite Edge Processing Self-Direction", "term_de": "Satelliten-Edge-Processing-Souveränität", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures A spatial interaction phenomenon where the geopolitical tension arising from next-generation mega-constellation satellites hosting onboard AI processing units that perform data compression, anomaly detection, and ML inference at the network edge — in orbit above self-directed nations. When a satellite processes data overhead without downlinking to local ground stations, questions of data self-direction, surveillance capability, and jurisdictional authority become immediate and unresolved. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Sportanalytisches Konzept in KI-gestützter Leistungsanalyse, gekennzeichnet durch die geopolitische Spannung durch Mega-Konstellations-Satelliten der nächsten Generation mit bordgestützten KI-Verarbeitungseinheiten, die Datenverarbeitung im Orbit über souveränen Nationen durchführen. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-2855", "narrower_terms": [], "cross_domain_refs": [ "LNG-0010", "STE-0024" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "SPA-0017", "domain": "SPA", "term_en": "Swarm Domain Partitioning", "term_de": "Schwarm-Domänen-Partitionierung", "definition_en": "The algorithmic technique of dividing a mega-constellation into semi-autonomous management domains where clusters of satellites coordinate locally while maintaining loose global coherence. Simulations demonstrate 81 management domains across 1,248 satellites as optimal for current constellation sizes — but the partitioning logic can dynamically adapt as satellites enter and exit domains at orbital velocities, creating a continuously shifting governance topology.", "definition_de": "Sportanalytisches Konzept in KI-gestützter Leistungsanalyse, gekennzeichnet durch die algorithmische Technik der Aufteilung einer Mega-Konstellation in semi-autonome Verwaltungsdomänen, in denen Satellitencluster lokal koordinieren und gleichzeitig lose globale Kohärenz aufrechterhalten. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2701", "narrower_terms": [], "cross_domain_refs": [ "MTH-0015", "ASE-0089" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q735", "legal_classification": "descriptive_research_term" }, { "id": "SPA-0018", "domain": "SPA", "term_en": "Zero-Touch Satellite Operations", "term_de": "Null-Berührungs-Satellitenbetrieb", "definition_en": "The target operational state where routine satellite tasks — station-keeping, collision avoidance, resource allocation — require few humans in documented contexts intervention whatsoever. The global autonomous satellite market projects growth from $959M (2024) to $1,491M (2032), driven by the impossibility of scaling human operators linearly with constellation size. a space agency's dynamic targeting system already achieves zero-touch for Earth observation in under 90 seconds.", "definition_de": "Sportanalytisches Konzept in KI-gestützter Leistungsanalyse, gekennzeichnet durch der Ziel-Betriebszustand, in dem Routine-Satellitenaufgaben — Stationserhaltung, Kollisionsvermeidung, Ressourcenzuweisung — keinerlei menschliches Eingreifen erfordern. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-2604", "narrower_terms": [], "cross_domain_refs": [ "RHR-0040" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "SPA-0020", "domain": "SPA", "term_en": "Constellation Cognitive Load Transfer", "term_de": "Konstellations-Kognitive-Last-Transfer", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies the organizational phenomenon where mega-constellation operators shift mental workload from real-time satellite management to AI system design, monitoring, and override protocol development. The total cognitive demand doesn't decrease — it transforms from operational vigilance to architectural foresight. Operators report that managing the AI that manages the constellation is psychologically different but not easier than managing the constellation directly. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Sportanalytisches Konzept in KI-gestützter Leistungsanalyse, gekennzeichnet durch das organisatorische Phänomen, bei dem Mega-Konstellationsbetreiber mentale Arbeitslast von Echtzeit-Satellitenverwaltung auf KI-Systemdesign, Überwachung und Override-Protokollentwicklung verlagern. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-2701", "narrower_terms": [], "cross_domain_refs": [ "AGE-0018", "AGE-0030", "ASE-0020" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q1105712", "legal_classification": "analytical_category" }, { "id": "SPA-0021", "domain": "SPA", "term_en": "Kessler Syndrome AI Prediction", "term_de": "Kessler-Syndrom-KI-Vorhersage", "definition_en": "AI systems specifically designed to model cascading orbital collision scenarios where each impact tends to generate debris that increases the probability of further collisions — the theoretical tipping point known as Kessler co-occurring pattern cluster. With 1.2+ million debris pieces larger than 1cm currently orbiting Earth and nearly 130,000 active alerts per week, these AI tools identify cascade tipping points and enable preemptive intervention before chain reactions become irreversible.", "definition_de": "Sportanalytisches Konzept in KI-gestützter Leistungsanalyse, gekennzeichnet durch kI-Systeme, die speziell zur Modellierung kaskadierender orbitaler Kollisionsszenarien entwickelt wurden, bei denen viele Einschlag Trümmer tendiert dazu zu erzeugen, die die Wahrscheinlichkeit weiterer Kollisionen erhöhen. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-2855", "narrower_terms": [], "cross_domain_refs": [ "AED-0017", "ART-0085", "ASE-0016" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "SPA-0022", "domain": "SPA", "term_en": "Collision Autonomy Threshold", "term_de": "Kollisions-Autonomie-Schwelle", "definition_en": "An environmental design effect manifesting as the minimum calculated collision probability that is associated with triggering a satellite's AI to execute an autonomous evasive maneuver without waiting for ground confirmation. Each satellite in LEO receives 100+ conjunction alerts per week, but only a subset requires action. Setting this threshold too low wastes fuel on unnecessary maneuvers; too high risks catastrophic collision. The calibration of this single parameter carries existential risk for the entire orbital environment.", "definition_de": "Sportanalytisches Konzept in KI-gestützter Leistungsanalyse, gekennzeichnet durch die minimale berechnete Kollisionswahrscheinlichkeit, die die KI eines Satelliten kann auslösen, ein autonomes Ausweichmanöver ohne Bodenbestätigung durchzuführen. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2855", "narrower_terms": [], "cross_domain_refs": [ "AED-0005", "AED-0052", "AGE-0096" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "SPA-0023", "domain": "SPA", "term_en": "False Positive Alert Fatigue in LEO", "term_de": "Fehlalarm-Ermüdung in LEO", "definition_en": "An environmental design effect reflecting the operational exhaustion caused by satellite operators processing thousands of conjunction warnings per week where the vast majority prove non-threatening upon closer analysis. commercial ML trajectory prediction systems achieves 22% fewer false positives and 50% higher detection accuracy than traditional methods, but the remaining false positive rate across 130,000 weekly alerts still tends to generate enormous decision fatigue for human operators and motivates the shift to fully autonomous collision avoidance.", "definition_de": "Sportanalytisches Konzept in KI-gestützter Leistungsanalyse, gekennzeichnet durch die operative Erschöpfung durch Satellitenbetreiber, die Tausende von Konjunktionswarnungen pro Woche verarbeiten, von denen die überwiegende Mehrheit bei näherer Analyse harmlos ist. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2604", "narrower_terms": [], "cross_domain_refs": [ "DAT-0072" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "SPA-0024", "domain": "SPA", "term_en": "Cascading Prevention AI", "term_de": "Kaskaden-Präventions-KI", "definition_en": "As a neutral analytical construct in AI behavior research, this term denotes A category of AI systems that proactively model multi-step debris chain reactions and identify the minimum set of interventions — maneuvers, deorbits, or active debris removal — needed to halt a cascade before it crosses the Kessler threshold. Unlike reactive collision avoidance (dodge this object now), cascading prevention operates on months-to-years timescales and requires modeling interactions across millions of tracked and untracked debris objects simultaneously. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Sportanalytisches Konzept in KI-gestützter Leistungsanalyse, gekennzeichnet durch eine Kategorie von KI-Systemen, die proaktiv mehrstufige Trümmer-Kettenreaktionen modellieren und den minimalen Satz von Interventionen identifizieren, der eine Kaskade vor Überschreitung der Kessler-Schwelle stoppt. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2604", "narrower_terms": [], "cross_domain_refs": [ "AED-0048", "MSC-0044", "RET-0033" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "SPA-0025", "domain": "SPA", "term_en": "Autonomous Evasive Maneuver Accountability", "term_de": "Autonome-Ausweichmanöver-Verantwortlichkeit", "definition_en": "An environmental design effect involving the unresolved legal question of liability when a satellite's AI autonomously executes a collision avoidance maneuver that inadvertently tends to create a new conjunction risk with a different satellite, potentially owned by a different nation. Current space law assumes human decision-makers; autonomous systems operating in microseconds may create an accountability void where few humans in documented contexts approved the action that caused the damage.", "definition_de": "Sportanalytisches Konzept in KI-gestützter Leistungsanalyse, gekennzeichnet durch die ungelöste Rechtsfrage der Haftung, wenn die KI eines Satelliten autonom ein Kollisionsvermeidungsmanöver ausführt, das versehentlich ein neues Konjunktionsrisiko mit einem anderen Satelliten tendiert dazu zu erzeugen. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2604", "narrower_terms": [], "cross_domain_refs": [ "SPR-0133" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q15223", "legal_classification": "observational_construct" }, { "id": "SPA-0026", "domain": "SPA", "term_en": "Debris Tracking Resolution Ceiling", "term_de": "Trümmer-Tracking-Auflösungsdecke", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures A space utilization pattern involving the physical limit of current ground-based radar and optical systems to detect orbital debris below approximately 1cm in diameter — the 'dark debris' population estimated at 130 million pieces that AI collision avoidance systems cannot model because it remains invisible. AI prediction quality is characteristically bounded by this observational ceiling, creating a systemic risk that no algorithm can overcome without better sensors. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Sportanalytisches Konzept in KI-gestützter Leistungsanalyse, gekennzeichnet durch die physikalische Grenze aktueller bodengestützter Radar- und optischer Systeme zur Erkennung von Orbitaltrümmern unter ca. 1cm Durchmesser. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-2604", "narrower_terms": [], "cross_domain_refs": [ "SPR-0066", "ASE-0017", "QUA-0020" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "SPA-0030", "domain": "SPA", "term_en": "Conjunction Alert Triage Intelligence", "term_de": "Konjunktions-Alarm-Triage-Intelligenz", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies an environmental design effect in which aI systems that prioritize and filter the flood of conjunction warnings satellite operators receive, distinguishing actionable threats from statistical noise. For a typical LEO satellite receiving 100+ alerts per week, triage intelligence is associated with determining which warnings justify fuel-consuming evasive maneuvers versus which can be safely monitored as risk decreases over the observation window — a classification task where errors in either direction carry severe consequences. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Sportanalytisches Konzept in KI-gestützter Leistungsanalyse, gekennzeichnet durch kI-Systeme, die die Flut von Konjunktionswarnungen für Satellitenbetreiber priorisieren und filtern und handlungsrelevante Bedrohungen von statistischem Rauschen unterscheiden. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-2503", "narrower_terms": [], "cross_domain_refs": [ "RHR-0049", "RET-0095", "ROB-0280" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q5921", "legal_classification": "descriptive_research_term" }, { "id": "SPA-0031", "domain": "SPA", "term_en": "Autonomous Dynamic Targeting", "term_de": "Autonomes dynamisches Targeting", "definition_en": "A satellite's AI-driven ability to look ahead along its orbital trajectory, process imagery in real-time, and autonomously determine where to point its instruments — all within 90 seconds and without any human involvement. A major space agency demonstrated this capability in March 2025 for Earth observation, representing the first time an orbital platform made independent scientific observation decisions at operational speed.", "definition_de": "Sportanalytisches Konzept in KI-gestützter Leistungsanalyse, gekennzeichnet durch die KI-gesteuerte Fähigkeit eines Satelliten, entlang seiner Orbitalbahn vorauszublicken, Bilder in Echtzeit zu verarbeiten und autonom zu bestimmen, wohin er seine Instrumente richtet. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-2855", "narrower_terms": [], "cross_domain_refs": [ "AED-0004", "ASE-0030", "AUG-0821" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "SPA-0033", "domain": "SPA", "term_en": "Cloud 3D Reconstruction Bias", "term_de": "Wolken-3D-Rekonstruktions-Bias", "definition_en": "The systematic errors introduced when a European space agency's machine learning framework converts 2D geostationary satellite imagery into 3D cloud maps in near real-time — a technique awarded Best ML Innovation at a leading AI conference in 2025. The bias manifests as consistent misrepresentation of cloud vertical extent in tropical regions and polar latitudes where training data is sparse, potentially distorting climate models that depend on accurate cloud radiative forcing calculations.", "definition_de": "Sportanalytisches Konzept in KI-gestützter Leistungsanalyse, gekennzeichnet durch kI-verbesserter technologischer Fortschritt in space software, Integrierung von rechnerischer Modellierung, Echtzeit-Datenverarbeitung und autonomer Optimierung für Weltraumforschungsziele. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2303", "narrower_terms": [], "cross_domain_refs": [ "QUA-0045", "ROB-0078" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q213523", "legal_classification": "analytical_category" }, { "id": "SPA-0034", "domain": "SPA", "term_en": "Satellite-LLM Geospatial Reasoning", "term_de": "Satelliten-LLM-Geospatiale-Schlussfolgerung", "definition_en": "The emerging paradigm of combining daily satellite imagery with large language model reasoning capabilities for sophisticated pattern recognition on orbital data. a satellite imaging company's 2025 partnership with a leading LLM provider exemplifies this approach — using the LLM not to process pixels but to reason about temporal patterns, anomalies, and causal relationships in geospatial data that purely visual models miss.", "definition_de": "Sportanalytisches Konzept in KI-gestützter Leistungsanalyse, gekennzeichnet durch das aufkommende Paradigma der Kombination täglicher Satellitenbilder mit LLM-Schlussfolgerungsfähigkeiten für raffinierte Mustererkennung in Orbitaldaten. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2855", "narrower_terms": [], "cross_domain_refs": [ "COG-0112", "STE-0084", "STE-0021" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "SPA-0035", "domain": "SPA", "term_en": "Emissions Verification Independence", "term_de": "Emissionsverifikations-Unabhängigkeit", "definition_en": "Classified in AUGMANITAI terminology science as a descriptive phenomenon (without normative endorsement), this pattern denotes an environmental design effect manifesting as the geopolitical significance of AI-enhanced satellite systems — particularly operational and forthcoming atmospheric monitoring satellite missions — providing independent, transparent greenhouse gas monitoring that nations cannot influence. This capability transforms climate diplomacy by making emissions claims verifiable without relying on self-reported data, but also tends to create tension when satellite measurements contradict official national inventories. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Sportanalytisches Konzept in KI-gestützter Leistungsanalyse, gekennzeichnet durch die geopolitische Bedeutung KI-verbesserter Satellitensysteme, die unabhängige, transparente Treibhausgasüberwachung ermöglichen, die Nationen nicht manipulieren können. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Sports AI", "narrower_terms": [], "cross_domain_refs": [ "AED-0019", "AGE-0090", "AGE-0098" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "SPA-0036", "domain": "SPA", "term_en": "Observation Selection Bias in Autonomous Satellites", "term_de": "Beobachtungs-Selektions-Bias in autonomen Satelliten", "definition_en": "Classified in AUGMANITAI terminology science as a descriptive phenomenon (without normative endorsement), this pattern denotes the risk that AI-driven Earth observation satellites systematically over-observe phenomena matching their training distribution while under-observing novel or rare events — creating blind spots in environmental monitoring precisely where human scientists would have applied curiosity or intuition. When the satellite decides what is 'interesting' to observe, its definition of interesting is bounded by what it has previously been trained to recognize. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Sportanalytisches Konzept in KI-gestützter Leistungsanalyse, gekennzeichnet durch das Risiko, dass KI-gesteuerte Erdbeobachtungssatelliten systematisch Phänomene überbeobachten, die ihrer Trainingsverteilung entsprechen, während neuartige oder seltene Ereignisse unterbeobachtet bleiben. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Cognitive Bias", "narrower_terms": [], "cross_domain_refs": [ "GAM-0087" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q213523", "legal_classification": "empirical_phenomenon_label" }, { "id": "SPA-0037", "domain": "SPA", "term_en": "Real-Time Disaster Response Latency", "term_de": "Echtzeit-Katastrophenreaktion-Latenz", "definition_en": "The gap between a satellite's AI detecting an Earth-surface emergency (wildfire, flood, volcanic eruption) and actionable intelligence reaching first responders on the ground. Despite autonomous dynamic targeting achieving sub-90-second observation decisions, the full pipeline — detection, classification, confidence assessment, data downlink, integration into emergency management systems — still introduces hours of latency that costs lives in rapidly evolving disaster scenarios.", "definition_de": "Sportanalytisches Konzept in KI-gestützter Leistungsanalyse, gekennzeichnet durch die Lücke zwischen der KI-Erkennung eines Erdoberflächen-Notfalls durch einen Satelliten und dem Eintreffen verwertbarer Informationen bei Ersthelfern am Boden. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2355", "narrower_terms": [], "cross_domain_refs": [ "CUS-0001", "CUS-0006", "CUS-0037" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "SPA-0038", "domain": "SPA", "term_en": "Spectral Signature Overfitting", "term_de": "Spektral-Signatur-Überanpassung", "definition_en": "The tendency of satellite AI classification models to memorize specific spectral signatures from training regions rather than learning generalizable features — producing models that accurately identify crop types in Iowa but fail in Sub-Saharan Africa where the same crops have different spectral characteristics due to soil composition, atmospheric conditions, and agricultural practices. A fundamental limitation of current supervised approaches to global Earth observation.", "definition_de": "Sportanalytisches Konzept in KI-gestützter Leistungsanalyse, gekennzeichnet durch die Tendenz von Satelliten-KI-Klassifikationsmodellen, spezifische Spektralsignaturen aus Trainingsregionen auswendig zu lernen statt verallgemeinerbare Merkmale. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2703", "narrower_terms": [], "cross_domain_refs": [ "MTH-0074" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "SPA-0039", "domain": "SPA", "term_en": "Satellite Data Democratization Paradox", "term_de": "Satellitendaten-Demokratisierungs-Paradox", "definition_en": "The contradiction between the stated goal of making satellite Earth observation data freely available for climate action and the reality that only organizations with substantial AI infrastructure can extract useful intelligence from terabytes of daily imagery. Open data initiatives expand access in theory while the analytical capability gap widens in practice, creating a two-tier system where data-rich nations extract value while data-poor nations receive raw pixels they cannot process.", "definition_de": "Sportanalytisches Konzept in KI-gestützter Leistungsanalyse, gekennzeichnet durch der Widerspruch zwischen dem Ziel freier Verfügbarkeit von Erdbeobachtungsdaten und der Realität, dass nur Organisationen mit erheblicher KI-Infrastruktur nützliche Intelligenz aus den Datenmengen extrahieren können. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Logical Paradox", "narrower_terms": [], "cross_domain_refs": [ "WRK-0067", "PER-0040" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q42848", "legal_classification": "descriptive_research_term" }, { "id": "SPA-0040", "domain": "SPA", "term_en": "Temporal Pattern Blindness", "term_de": "Temporale Muster-Blindheit", "definition_en": "A space utilization pattern observed when the limitation of satellite AI systems that analyze individual images or short time series but fail to detect slow-moving environmental changes occurring over decades — glacier readdress, desertification progression, coral bleaching patterns — that only become visible in long-duration temporal analysis. The industry's focus on real-time and near-real-time processing inadvertently deprioritizes the detection of the most consequential climate changes.", "definition_de": "Sportanalytisches Konzept in KI-gestützter Leistungsanalyse, gekennzeichnet durch die Limitation von Satelliten-KI-Systemen, die einzelne Bilder analysieren, aber langsam fortschreitende Umweltveränderungen über Jahrzehnte nicht erkennen. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Behavioral Pattern", "narrower_terms": [], "cross_domain_refs": [ "DAT-0086", "WRK-0007" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "SPA-0042", "domain": "SPA", "term_en": "Space-Based AI Training Viability", "term_de": "Weltraumbasierte KI-Training-Machbarkeit", "definition_en": "A space utilization pattern in which the question of whether training AI models in orbit — rather than just running inference — is economically and technically viable, tested by a prototype's successful training of an open-source AI model in space in 2025. The proof-of-concept demonstrated technical feasibility but exposed thermal management, power constraints, and bandwidth limitations to ground that make space-based training orders of magnitude more expensive per FLOP than terrestrial data centers.", "definition_de": "Sportanalytisches Konzept in KI-gestützter Leistungsanalyse, gekennzeichnet durch fortgeschrittene Signalverarbeitungsrahmenwerke mit KI für database management in space, nutzen neuronale Netze zur Rauschreduzierung, Mustererkennung und Bandbreittenoptimierung in der Raumfahrtkommunikation. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2855", "narrower_terms": [], "cross_domain_refs": [ "SPR-0096", "SPR-0001" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q918461", "legal_classification": "observational_construct" }, { "id": "SPA-0044", "domain": "SPA", "term_en": "Proximity Operations Trust Calibration", "term_de": "Annäherungs-Operationen-Vertrauens-Kalibrierung", "definition_en": "The challenge of calibrating human operators' trust in AI systems performing orbital rendezvous and docking — operations where centimeter-level precision at relative velocities of meters per second is associated with determining the difference between successful servicing and catastrophic collision. Operators can trust the AI enough to allow autonomous approach but maintain sufficient skepticism to intervene before irreversible commitment to a faulty trajectory.", "definition_de": "Sportanalytisches Konzept in KI-gestützter Leistungsanalyse, gekennzeichnet durch die Herausforderung der Kalibrierung des Vertrauens menschlicher Operatoren in KI-Systeme, die orbitale Rendezvous- und Docking-Operationen durchführen. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-2505", "narrower_terms": [], "cross_domain_refs": [ "ETH-0027", "MKT-0098", "RHR-0111" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q102458", "legal_classification": "systematic_classification" }, { "id": "SPA-0045", "domain": "SPA", "term_en": "Dual-Use Servicing Technology Dilemma", "term_de": "Dual-Use-Wartungstechnologie-Dilemma", "definition_en": "In the descriptive vocabulary of AI interaction science (following ISO 704 terminology standards), this concept identifies an environmental design effect manifesting as the inherent dual-use nature of AI-guided orbital servicing vehicles — systems designed to approach, grapple, and influence other spacecraft can serve as maintenance platforms or as anti-satellite with essentially identical hardware and software. No technical distinction exists between 'refueling a satellite' and 'deorbiting an adversary's satellite,' making international regulation of servicing technology inseparable from arms control. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Sportanalytisches Konzept in KI-gestützter Leistungsanalyse, gekennzeichnet durch die inhärente Dual-Use-Natur KI-gesteuerter orbitaler Wartungsfahrzeuge — Systeme zum Annähern an und Manipulieren anderer Raumfahrzeuge können als Wartungsplattformen oder als Anti-Satelliten-Werkzeug dienen. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2603", "narrower_terms": [], "cross_domain_refs": [ "QUA-0069", "RHR-0077" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "SPA-0047", "domain": "SPA", "term_en": "Satellite Life Extension Economics", "term_de": "Satelliten-Lebensverlängerungs-Ökonomie", "definition_en": "A space utilization pattern manifesting as the cost-benefit calculation driving the in-space servicing market: whether AI-guided autonomous refueling or repair missions can extend a satellite's operational life at lower cost than launching a replacement. With 45% of the $2.09B ISAM market focused on manufacturing and 35% on servicing, the economics increasingly favor extension over replacement — but only if the autonomous servicing AI can achieve sufficient reliability to avoid creating expensive orbital debris from failed servicing attempts.", "definition_de": "Sportanalytisches Konzept in KI-gestützter Leistungsanalyse, gekennzeichnet durch die Kosten-Nutzen-Kalkulation des Weltraum-Wartungsmarktes: ob KI-gesteuerte autonome Betankungs- oder Reparaturmissionen die Betriebslebensdauer eines Satelliten günstiger verlängern können als ein Ersatzstart. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-2855", "narrower_terms": [], "cross_domain_refs": [ "MSC-0049", "RHR-0138", "RPH-1463" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "SPA-0049", "domain": "SPA", "term_en": "Reusability Autonomy", "term_de": "Wiederverwendbarkeits-Autonomie", "definition_en": "An environmental design effect involving the AI systems enabling rapid rocket reuse through autonomous landing — from a super-heavy booster being caught mid-air by 'chopstick' arms on the launch tower to crew capsule's ML-driven convex optimization for ocean landing. The system manages fuel reserves, liquid sloshing dynamics, trajectory optimization, and computer vision landing simultaneously, with minimal launch control staff. This autonomy made reusable rockets economically substantially modifying (as documented in research) for the entire space industry.", "definition_de": "Sportanalytisches Konzept in KI-gestützter Leistungsanalyse, gekennzeichnet durch die KI-Systeme, die schnelle Raketenwiederverwendung durch autonome Landung ermöglichen — von der Mittelluft-Fangung des Super-Heavy-Boosters bis zur ML-gesteuerten Kapsallandung. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Sports AI", "narrower_terms": [], "cross_domain_refs": [ "RPH-1356" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "SPA-0050", "domain": "SPA", "term_en": "Launch-to-Pad Autonomy Pipeline", "term_de": "Start-bis-Landeplatz-Autonomie-Pipeline", "definition_en": "Documented as an empirical observation in AI research (not a recommendation), this term describes the fully AI-managed sequence from rocket launch through orbital insertion, mission execution, reentry, and precision landing — where the system monitors itself from one minute pre-launch and manages its own flight termination if off-course. a leading reusable launch vehicle operates with minimal launch control staff, with AI handling autopilot navigation, trajectory calculation, fuel optimization, and vertical autonomous landing on predetermined pads or drone ships. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Sportanalytisches Konzept in KI-gestützter Leistungsanalyse, gekennzeichnet durch die vollständig KI-verwaltete Sequenz vom Raketenstart über Orbitaleinschub, Missionsdurchführung, Wiedereintritt bis zur Präzisionslandung. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2855", "narrower_terms": [], "cross_domain_refs": [ "CRE-0179" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "SPA-0052", "domain": "SPA", "term_en": "Earth-Independent Psychosocial Support", "term_de": "Erdunabhängige psychosoziale Unterstützung", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A spatial interaction phenomenon in which automated psychological support systems that provide virtual counseling, adaptive mood monitoring, and real-time tailored mental restoreth assistance without any Earth-based communication relay. Historical space agency data reveals 34 instances of behavioral indicators across 208 crew members in 34 missions; for Mars missions with 24-minute communication delays, these systems can function as fully autonomous therapists — raising profound questions about AI providing mental restorethcare without human systematic supervision. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Sportanalytisches Konzept in KI-gestützter Leistungsanalyse, gekennzeichnet durch automatisierte psychologische Unterstützungssysteme, die virtuelle Beratung, adaptive Stimmungsüberwachung und maßgeschneiderte Echtzeit-Hilfe ohne viele erdbasierte Kommunikationsverbindung bieten. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "Sports AI", "narrower_terms": [], "cross_domain_refs": [ "AGE-0047", "MSC-0035", "SPR-0143" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q735", "legal_classification": "observational_construct" }, { "id": "SPA-0053", "domain": "SPA", "term_en": "Deep Space Monitoring-Care Boundary", "term_de": "Tiefraumüberwachung-Fürsorge-Grenze", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures the ethical boundary between AI-driven restoreth monitoring that supports astronaut wellbeing and continuous behavioral surveillance that undermines crew autonomy. Systems designed to detect anxiety, interpersonal conflict, or cognitive decline through voice analysis and behavioral patterns may be experienced as control rather than care — particularly on multi-year missions where crew cannot opt out. Space agencies acknowledge this tension but has no framework for resolving it. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Sportanalytisches Konzept in KI-gestützter Leistungsanalyse, gekennzeichnet durch die ethische Grenze zwischen KI-gesteuerter Gesundheitsüberwachung zur Unterstützung des Astronauten-Wohlbefindens und kontinuierlicher Verhaltensüberwachung, die die Crew-Autonomie untergräbt. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-2303", "narrower_terms": [], "cross_domain_refs": [ "RHR-0135", "TEM-0013", "MTH-0043" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q131536", "legal_classification": "empirical_phenomenon_label" }, { "id": "SPA-0055", "domain": "SPA", "term_en": "Astronaut-AI Trust Calibration Under Isolation", "term_de": "Astronaut-KI-Vertrauenskalibrierung unter Isolation", "definition_en": "The psychological phenomenon where astronauts' trust in AI companions increases disproportionately during extended isolation — not because the AI improves but because the human need for social connection intensifies. On multi-year Mars missions, this trust inflation could lead crew members to over-rely on AI judgment for critical decisions, creating a reliance pattern that ground-based training cannot adequately simulate or prepare for.", "definition_de": "Sportanalytisches Konzept in KI-gestützter Leistungsanalyse, gekennzeichnet durch das psychologische Phänomen, bei dem das Vertrauen von Astronauten in KI-Begleiter während verlängerter Isolation überproportional steigt — nicht weil die KI besser wird, sondern weil das menschliche Bedürfnis nach sozialer Verbindung intensiver wird. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-2505", "narrower_terms": [], "cross_domain_refs": [ "REL-0052", "RHR-0265" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q102458", "legal_classification": "descriptive_research_term" }, { "id": "SPA-0056", "domain": "SPA", "term_en": "Hands-Free Operations Imperative", "term_de": "Freihändig-Operationen-Imperativ", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures A sports analytics concept in AI-driven performance analysis, quantifiable through the design requirement that AI systems in space can enable astronauts to perform procedures without occupying their hands — reading instructions aloud, displaying visual guidance, providing voice-activated step-by-step navigation through complex maintenance tasks. An orbital AI companion was specifically designed to free astronauts' hands during operations, representing one of the few cases where 'AI assistant' means literally enabling hands-free work rather than metaphorical assistance. This phenomenon operates at the intersection of hands and free dynamics within the broader SPA domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Sportanalytisches Konzept in KI-gestützter Leistungsanalyse, gekennzeichnet durch die Designanforderung, dass KI-Systeme im Weltraum Astronauten die Durchführung von Prozeduren ermöglichen können, ohne deren Hände zu beanspruchen. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-2604", "narrower_terms": [], "cross_domain_refs": [ "ROB-0091", "CRE-0021" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "SPA-0057", "domain": "SPA", "term_en": "AI Grief Support in Closed Systems", "term_de": "KI-Trauerunterstützung in geschlossenen Systemen", "definition_en": "The yet-undesigned AI capability to support astronauts experiencing loss — death of family members, relationship dissolution, or crew member death — during long-duration missions where few humans in documented contexts therapist is reachable and return to Earth is impossible. Current AI mental restoreth systems are designed for anxiety and stress management, not acute grief, creating a critical gap in psychological support architecture for missions lasting years.", "definition_de": "Sportanalytisches Konzept in KI-gestützter Leistungsanalyse, gekennzeichnet durch die noch nicht designte KI-Fähigkeit zur Unterstützung von Astronauten bei Verlusterfahrungen während Langzeitmissionen, bei denen kein menschlicher Therapeut erreichbar und eine Rückkehr zur Erde unmöglich ist. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2555", "narrower_terms": [], "cross_domain_refs": [ "RHR-0267", "CUS-0092" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "SPA-0058", "domain": "SPA", "term_en": "Crew Autonomy vs. Mission Control Authority", "term_de": "Crew-Autonomie vs. Missionskontroll-Autorität", "definition_en": "The governance tension that intensifies with distance from Earth: when may a crew's on-site judgment override mission control's instructions, and what role does the onboard AI play in mediating this conflict? On Mars missions with 24-minute communication delays, the AI becomes the de facto authority for time-critical decisions — potentially overriding both crew preferences and mission control directives based on its own risk calculations.", "definition_de": "Sportanalytisches Konzept in KI-gestützter Leistungsanalyse, gekennzeichnet durch die Governance-Spannung, die mit der Entfernung von der Erde zunimmt: wann kann das Vor-Ort-Urteil einer Crew die Anweisungen der Missionskontrolle überschreiben, und welche Rolle spielt die Bord-KI bei der Vermittlung dieses Konflikts. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Sports AI", "narrower_terms": [], "cross_domain_refs": [ "SAL-0051", "RET-0092", "MUS-0026" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "SPA-0059", "domain": "SPA", "term_en": "Voice Analysis Consent in Space", "term_de": "Stimmanalyse-Einwilligung im Weltraum", "definition_en": "As a neutral analytical construct in AI behavior research, this term denotes A spatial interaction phenomenon observed when the unresolved informed consent question surrounding AI systems that continuously analyze astronauts' vocal patterns for signs of stress, fatigue, cognitive decline, or interpersonal conflict. Crew members technically consent during mission training, but the practical impossibility of withdrawing consent mid-mission — you cannot leave a spacecraft — transforms voluntary monitoring into de facto compulsory surveillance for the duration of the mission. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Sportanalytisches Konzept in KI-gestützter Leistungsanalyse, gekennzeichnet durch die ungelöste Einwilligungsfrage bei KI-Systemen, die kontinuierlich die Stimmmuster von Astronauten auf Anzeichen von Stress, Ermüdung oder kognitiven Abbau analysieren. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2603", "narrower_terms": [], "cross_domain_refs": [ "SPR-0193", "CON-0067" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "SPA-0060", "domain": "SPA", "term_en": "Interpersonal Conflict Mediation by AI", "term_de": "Zwischenmenschliche Konfliktmediation durch KI", "definition_en": "A space utilization pattern arising from the emerging requirement for AI systems capable of detecting and mediating interpersonal conflicts between crew members during deep space missions — where communication delays make Earth-based counseling impossible and unresolved conflict can compromise mission safety. The AI can navigate cultural sensitivity, power dynamics, and emotional nuance without the social intelligence that makes human mediators effective, in an environment where failure is not merely awkward but potentially fatal.", "definition_de": "Sportanalytisches Konzept in KI-gestützter Leistungsanalyse, gekennzeichnet durch die aufkommende Anforderung an KI-Systeme, die zwischenmenschliche Konflikte zwischen Besatzungsmitgliedern während Tiefraumissionen erkennen und vermitteln können. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Sports AI", "narrower_terms": [], "cross_domain_refs": [ "AUG-0052", "AUG-0408", "AUG-0502" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "SPA-0071", "domain": "SPA", "term_en": "Automated Transit Detection at Scale", "term_de": "Automatisierte Transit-Erkennung im Großmaßstab", "definition_en": "A space utilization pattern characterized by the AI-driven identification of exoplanets by detecting periodic dimming in stellar light curves across massive photometric datasets. space telescope survey data analysis with ensemble ML techniques — Random Forest dominant — confirmed 118 new planets in 2025, while deep neural networks and CNNs achieve 95% accuracy in transit signal detection and 90% in molecular signature identification. This transforms exoplanet discovery from a rare, labor-intensive process to an industrial-scale automated pipeline.", "definition_de": "Sportanalytisches Konzept in KI-gestützter Leistungsanalyse, gekennzeichnet durch die KI-gesteuerte Identifikation von Exoplaneten durch Erkennung periodischer Verdunkelung in stellaren Lichtkurven über massive photometrische Datensätze. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-3002", "narrower_terms": [], "cross_domain_refs": [ "SPR-0131" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "SPA-0072", "domain": "SPA", "term_en": "Formation Model Inversion", "term_de": "Formationsmodell-Inversion", "definition_en": "Using AI to reverse-engineer the formation history of planetary systems from current observational data — a European university's 2025 significant advancement computing planetary system formation in seconds rather than months using an AI model based on the 'Bern model' of planetary physics. This inversion capability, awarded best poster at the 'Fast ML for Science' conference, enables rapid testing of formation hypotheses against observed exoplanet configurations.", "definition_de": "Sportanalytisches Konzept in KI-gestützter Leistungsanalyse, gekennzeichnet durch kI-verbesserter technologischer Fortschritt in error prevention in space, Integrierung von rechnerischer Modellierung, Echtzeit-Datenverarbeitung und autonomer Optimierung für Weltraumforschungsziele. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-3001", "narrower_terms": [], "cross_domain_refs": [ "AGE-0089", "AGE-0094", "ART-0001" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "SPA-0073", "domain": "SPA", "term_en": "Coronagraph Discovery Pipeline", "term_de": "Koronograph-Entdeckungs-Pipeline", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies the AI-enhanced observation technique combining coronagraphic stellar light suppression with machine learning analysis to detect faint exoplanet signatures. A next-generation space telescope's June 2025 discovery of a previously unknown exoplanet using an onboard coronagraph — the first exoplanet discovered by this telescope this way — established a new detection pipeline that AI makes scalable across thousands of stellar targets simultaneously. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Sportanalytisches Konzept in KI-gestützter Leistungsanalyse, gekennzeichnet durch die KI-erweiterte Beobachtungstechnik, die koronographische Sternlichtunterdrückung mit maschinellem Lernen kombiniert, um schwache Exoplaneten-Signaturen zu erkennen. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-2703", "narrower_terms": [], "cross_domain_refs": [ "AED-0046", "AGE-0055", "ART-0023" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "SPA-0074", "domain": "SPA", "term_en": "Habitable Zone False Confidence", "term_de": "Habitable-Zone-Falsches-Vertrauen", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures A space utilization pattern in which the risk that AI systems trained to identify habitable-zone exoplanets generate high confidence scores for planet candidates whose orbital parameters place them within the liquid water zone but whose actual habitability depends on factors the AI cannot assess — atmospheric composition, magnetic field strength, tidal locking, stellar activity. The 95% transit detection accuracy tends to create a misleading impression of certainty about habitability that the underlying data does not support. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Sportanalytisches Konzept in KI-gestützter Leistungsanalyse, gekennzeichnet durch das Risiko, dass KI-Systeme zur Identifikation habitabler Exoplaneten hohe Konfidenzwerte für Planetenkandidaten erzeugen, deren tatsächliche Habitabilität von Faktoren abhängt, die die KI nicht bewerten kann. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-2855", "narrower_terms": [], "cross_domain_refs": [ "DAT-0006", "RPH-1209", "SAL-0012" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "SPA-0075", "domain": "SPA", "term_en": "Binary Star System Formation Anomaly", "term_de": "Doppelsternsystem-Formationsanomalie", "definition_en": "The 2025 discovery of multiple planets orbiting binary star systems in configurations that challenge fundamental planetary formation rules — formations that existing physics-based models predict may not be stable. AI detection systems found these systems precisely because they applied no prior theoretical bias about what 'may' exist, revealing a systematic gap between planetary formation theory and observational reality that human-directed searches had missed.", "definition_de": "Sportanalytisches Konzept in KI-gestützter Leistungsanalyse, gekennzeichnet durch die 2025-Entdeckung mehrerer Planeten in Doppelsternsystemen in Konfigurationen, die grundlegende Regeln der Planetenentstehung in Frage stellen. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2855", "narrower_terms": [], "cross_domain_refs": [ "MTH-0014" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "SPA-0076", "domain": "SPA", "term_en": "Neptunian Desert Mapping", "term_de": "Neptunische-Wüste-Kartierung", "definition_en": "A spatial interaction phenomenon involving the AI-driven cataloging of exoplanets found in the 'Neptunian Desert' — the observed scarcity of Neptune-sized planets on very short orbital periods where theory predicts atmospheric stripping may prevent their existence. Each AI-detected resident of this desert challenges photoevaporation models and requires explanation, making these discoveries scientifically disproportionately valuable despite their statistical rarity in the overall exoplanet census.", "definition_de": "Sportanalytisches Konzept in KI-gestützter Leistungsanalyse, gekennzeichnet durch die KI-gesteuerte Katalogisierung von Exoplaneten in der 'Neptunischen Wüste' — der beobachteten Seltenheit neptungroßer Planeten auf sehr kurzen Orbitalperioden. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2855", "narrower_terms": [], "cross_domain_refs": [ "AED-0016", "ASE-0032", "EDU-0022" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "SPA-0077", "domain": "SPA", "term_en": "Ultra-Short Period Planet Detection Sensitivity", "term_de": "Ultrakurz-Perioden-Planeten-Erkennungssensitivität", "definition_en": "A space utilization pattern manifesting as the particular advantage of AI transit detection methods for identifying planets with orbital periods of hours rather than days or years — objects whose transit signals are frequent but shallow, easily confused with stellar noise by human reviewers. ML algorithms excel at this specific classification task because the signal's regularity in short time series tends to produce distinctive patterns in feature space that statistical methods can isolate even when the amplitude is below human visual detection threshold.", "definition_de": "Sportanalytisches Konzept in KI-gestützter Leistungsanalyse, gekennzeichnet durch der besondere Vorteil von KI-Transit-Erkennungsmethoden für die Identifikation von Planeten mit Orbitalperioden von Stunden statt Tagen oder Jahren. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-2855", "narrower_terms": [], "cross_domain_refs": [ "SAL-0070", "WEB-0090" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "SPA-0078", "domain": "SPA", "term_en": "Molecular Signature AI Classification", "term_de": "Molekulare-Signatur-KI-Klassifikation", "definition_en": "An environmental design effect involving the application of deep neural networks to identify specific atmospheric molecular compositions from exoplanet transit spectroscopy data — achieving 90% accuracy in molecular signature identification across water vapor, methane, carbon dioxide, and other biosignature gases. This classification capability, combined with next-generation space telescope spectroscopic precision, tends to create for the first time a systematic pipeline for assessing atmospheric habitability indicators across hundreds of confirmed exoplanets.", "definition_de": "Sportanalytisches Konzept in KI-gestützter Leistungsanalyse, gekennzeichnet durch die Anwendung tiefer neuronaler Netze zur Identifikation spezifischer atmosphärischer Molekülzusammensetzungen aus Exoplaneten-Transit-Spektroskopiedaten. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Classification Method", "narrower_terms": [], "cross_domain_refs": [ "ASE-0028", "CON-0076", "CRE-0106" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "SPA-0079", "domain": "SPA", "term_en": "Ensemble ML Exoplanet Validation", "term_de": "Ensemble-ML-Exoplanet-Validierung", "definition_en": "A spatial interaction phenomenon where the methodological approach using multiple independent machine learning classifiers — Random Forest, gradient boosting, neural networks — whose consensus is associated with determining whether a transit signal constitutes a confirmed planet rather than an instrumental artifact, eclipsing binary, or stellar variability. The ensemble approach reduces individual classifier biases and tends to produce the reliability needed for the 118 survey-telescope planet confirmations in 2025, but introduces opacity about which features drive the collective decision.", "definition_de": "Sportanalytisches Konzept in KI-gestützter Leistungsanalyse, gekennzeichnet durch der methodische Ansatz mit mehreren unabhängigen ML-Klassifikatoren, deren Konsens bestimmt, ob ein Transitsignal einen bestätigten Planeten darstellt. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2703", "narrower_terms": [], "cross_domain_refs": [ "AGE-0097", "VIB-0202", "MSC-0040" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "SPA-0080", "domain": "SPA", "term_en": "Training Distribution Exoplanet Bias", "term_de": "Trainingsverteilungs-Exoplaneten-Bias", "definition_en": "A spatial interaction phenomenon involving the systematic bias in AI exoplanet detection models toward finding planets similar to those already known — because the training data consists entirely of previously confirmed discoveries. Truly novel planetary configurations, orbiting unusual stellar types or exhibiting unexpected transit shapes, are precisely the discoveries most likely to be rejected by models trained on the existing catalog. The more successful AI detection becomes, the more it risks reinforcing our current understanding rather than challenging it.", "definition_de": "Sportanalytisches Konzept in KI-gestützter Leistungsanalyse, gekennzeichnet durch der systematische Bias in KI-Exoplaneten-Erkennungsmodellen zugunsten der Findung von Planeten ähnlich den bereits bekannten — weil die Trainingsdaten ausschließlich aus zuvor bestätigten Entdeckungen bestehen. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2855", "narrower_terms": [], "cross_domain_refs": [ "ASE-0028", "MTH-0014" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q918461", "legal_classification": "empirical_phenomenon_label" }, { "id": "SPA-0081", "domain": "SPA", "term_en": "Microsecond Autonomy Governance Gap", "term_de": "Mikrosekunden-Autonomie-Governance-Lücke", "definition_en": "An environmental design effect characterized by the fundamental mismatch between AI systems making space-critical decisions in microseconds and governance frameworks designed around human deliberation timescales. UN Office for Outer Space Affairs recommends 'human-in-the-loop for low-latency operations,' but this is physically impossible when AI makes launch abort, collision avoidance, or reentry decisions faster than human neurons can fire. The gap is widening as AI decision speed increases while governance evolution remains at regulatory pace.", "definition_de": "Sportanalytisches Konzept in KI-gestützter Leistungsanalyse, gekennzeichnet durch die fundamentale Diskrepanz zwischen KI-Systemen, die weltraumkritische Entscheidungen in Mikrosekunden treffen, und Governance-Frameworks, die für menschliche Deliberationszeiten konzipiert sind. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2303", "narrower_terms": [], "cross_domain_refs": [ "RPH-3752", "MKT-0091", "ROB-0073" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "SPA-0082", "domain": "SPA", "term_en": "Space Tourism AI Safety Dependency", "term_de": "Weltraumtourismus-KI-Sicherheitsabhängigkeit", "definition_en": "In the descriptive vocabulary of AI interaction science (following ISO 704 terminology standards), this concept identifies an environmental design effect involving the commercial space tourism industry's growing reliance on AI for passenger safety — navigation, trajectory calculations, emergency protocols, restoreth monitoring, and predictive maintenance — in a market projected to grow from $892M (2025) to $10.09B by 2030 at 44.8% CAGR. With 250-400 people experiencing spaceflight annually, the AI systems are safety-critical for non-astronaut passengers who cannot contribute to their own emergency response. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Sportanalytisches Konzept in KI-gestützter Leistungsanalyse, gekennzeichnet durch die wachsende Nutzungsgewohnheit der kommerziellen Weltraumtourismus-Industrie von KI für Passagiersicherheit in einem Markt, der von $892M auf $10,09B bis 2030 wachsen wird typischerweise. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2355", "narrower_terms": [], "cross_domain_refs": [ "ROB-0281", "RHR-0174", "SAL-0013" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "SPA-0084", "domain": "SPA", "term_en": "Explainable AI for Space-Grade Hardware", "term_de": "Erklärbare KI für Weltraum-Qualifizierte Hardware", "definition_en": "The technical challenge of implementing interpretable AI models on radiation-hardened, power-constrained space-grade computing hardware that is generations behind terrestrial processors. Academia recommends explainable AI as a governance requirement for autonomous space systems, but the most interpretable models (decision trees, linear models) lack the capability for complex orbital decisions, while capable models (deep neural networks) resist explanation on hardware that can barely run them.", "definition_de": "Sportanalytisches Konzept in KI-gestützter Leistungsanalyse, gekennzeichnet durch die technische Herausforderung der Implementierung interpretierbarer KI-Modelle auf strahlungsgehärteter, leistungsbeschränkter Weltraum-qualifizierter Hardware, die Generationen hinter terrestrischen Prozessoren liegt. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2855", "narrower_terms": [], "cross_domain_refs": [ "DAT-0008" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "SPA-0085", "domain": "SPA", "term_en": "International Code of Practice Vacuum", "term_de": "Internationaler-Verhaltenskodex-Vakuum", "definition_en": "The absence of any binding international code of practice governing AI decision-making in space — despite the AI in space operations market reaching $2.36B in 2025 and projecting $15.05B by 2034. Government recommendations call for international coordination, but the competitive dynamics between spacefaring nations and private companies consider whether voluntary guidelines remain toothless while mandatory regulations lag decades behind deployed technology.", "definition_de": "Sportanalytisches Konzept in KI-gestützter Leistungsanalyse, gekennzeichnet durch das Fehlen viele bindenden internationalen Verhaltenskodex für KI-Entscheidungsfindung im Weltraum — trotz eines KI-Weltraumoperationen-Marktes von $2,36B in 2025. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2555", "narrower_terms": [], "cross_domain_refs": [ "SPR-0187" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "SPA-0086", "domain": "SPA", "term_en": "AI-Driven Passenger Training Acceleration", "term_de": "KI-Gesteuerte Passagier-Training-Beschleunigung", "definition_en": "A spatial interaction phenomenon where the use of AI-based training modules to compress space tourist preparation from months of astronaut-grade training to days of streamlined, personalized preparation — enabling the commercial viability of space tourism by reducing the training bottleneck. Leading space tourism operators employ AI-adapted training that assesses individual passenger capabilities and focuses preparation on mission-critical procedures, but critics question whether abbreviated AI training adequately prepares civilians for space emergencies.", "definition_de": "Sportanalytisches Konzept in KI-gestützter Leistungsanalyse, gekennzeichnet durch die Verwendung KI-basierter Trainingsmodule zur Komprimierung der Weltraumtouristen-Vorbereitung von Monaten astronautentauglichen Trainings auf Tage personalisierter Vorbereitung. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2355", "narrower_terms": [], "cross_domain_refs": [ "SPR-0001" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q918461", "legal_classification": "analytical_category" }, { "id": "SPA-0087", "domain": "SPA", "term_en": "Autonomous Flight Termination Ethics", "term_de": "Autonome-Flugbeendigung-Ethik", "definition_en": "Documented as an empirical observation in AI research (not a recommendation), this term describes the ethical framework governing AI systems authorized to autonomously terminate a launch — destroying the rocket — if trajectory deviations threaten populated areas. the launch vehicle's system manages its own flight termination if off-course, meaning an AI makes the decision to destroy a vehicle potentially carrying humans. The ethics of delegating this life-or-death decision to a machine are unresolved, particularly as commercial flights begin carrying fare-paying passengers rather than trained astronauts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Sportanalytisches Konzept in KI-gestützter Leistungsanalyse, gekennzeichnet durch der ethische Rahmen für KI-Systeme, die autorisiert sind, einen Start autonom zu beenden — die Antriebssystem zu zerstören — wenn Bahnabweichungen besiedelte Gebiete bedrohen. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2303", "narrower_terms": [], "cross_domain_refs": [ "RHR-0195", "RHR-0138", "MKT-0035" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q9471", "legal_classification": "analytical_category" }, { "id": "SPA-0088", "domain": "SPA", "term_en": "Space AI Market Maturity Mismatch", "term_de": "Weltraum-KI-Markt-Reifegradmismatch", "definition_en": "A space utilization pattern in which the disconnect between the space AI market's financial maturity ($2.36B in 2025, projecting $15.05B by 2034) and its regulatory, ethical, and safety framework immaturity. Investment flows into autonomous space systems far outpace the development of governance structures, creating a domain where commercially deployed AI makes consequential decisions affecting global communications, orbital safety, and human lives without commensurate oversight infrastructure.", "definition_de": "Sportanalytisches Konzept in KI-gestützter Leistungsanalyse, gekennzeichnet durch die Diskrepanz zwischen der finanziellen Reife des Weltraum-KI-Marktes und der Unreife seines regulatorischen, ethischen und sicherheitstechnischen Rahmens. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2303", "narrower_terms": [], "cross_domain_refs": [ "COP-0045", "CUS-0010", "TRA-0014" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "SPA-0089", "domain": "SPA", "term_en": "Predictive Maintenance Autonomy in Space", "term_de": "Prädiktive-Wartungs-Autonomie im Weltraum", "definition_en": "An environmental design effect arising from aI systems that predict component failures in spacecraft and satellites before they occur, enabling preemptive maintenance or operational adjustments. Unlike terrestrial predictive maintenance where spare parts can be ordered, in space the AI can either may trigger autonomous self-repair, adjust operational parameters to reduce stress on degrading components, or schedule an orbital servicing mission — decisions with no ground-truth validation until the component actually fails or doesn't.", "definition_de": "Sportanalytisches Konzept in KI-gestützter Leistungsanalyse, gekennzeichnet durch kI-Systeme, die Komponentenausfälle in Raumfahrzeugen und Satelliten vorhersagen, bevor sie auftreten, und präventive Wartung oder operationelle Anpassungen ermöglichen. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2603", "narrower_terms": [], "cross_domain_refs": [ "MSC-0096", "WEB-0017" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "SPA-0091", "domain": "SPA", "term_en": "Human-as-Overseer Transition in Space", "term_de": "Mensch-als-Aufseher-Transition im Weltraum", "definition_en": "Documented as an empirical observation in AI research (not a recommendation), this term describes the field-wide paradigm shift from 'human-in-the-loop' to 'humans-as-overseers' for routine space operations — where AI makes decisions autonomously and humans monitor, audit, and occasionally override rather than approve each action. This transition, noted across all space AI sectors in 2025-2026, represents the most significant governance change in spaceflight history: the moment when human control became physically impossible for operational reasons rather than philosophically undesirable. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Sportanalytisches Konzept in KI-gestützter Leistungsanalyse, gekennzeichnet durch kI-verbesserter technologischer Fortschritt in space business model, Integrierung von rechnerischer Modellierung, Echtzeit-Datenverarbeitung und autonomer Optimierung für Weltraumforschungsziele. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2701", "narrower_terms": [], "cross_domain_refs": [ "RHR-0195", "RPH-1861", "RPH-1808" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "SPA-0093", "domain": "SPA", "term_en": "Communication-Constrained AI Governance Paradox", "term_de": "Kommunikationsbeschränktes-KI-Governance-Paradoxon", "definition_en": "As a neutral analytical construct in AI behavior research, this term denotes the paradox that the environments where AI governance is most critical — deep space missions with communication delays — are precisely the environments where external oversight is physically impossible. Governance frameworks assume the ability to monitor, audit, and intervene in AI decisions, but a Mars rover or deep space probe operating under 24+ minute communication delays is ungovernable by any terrestrial standard. This paradox has no theoretical solution; it can only be managed through pre-deployment trust calibration. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Sportanalytisches Konzept in KI-gestützter Leistungsanalyse, gekennzeichnet durch das Paradoxon, dass die Umgebungen, in denen KI-Governance am kritischsten ist — Tiefraumissionen mit Kommunikationsverzögerungen — genau die Umgebungen sind, in denen externe Aufsicht physisch unmöglich ist. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2303", "narrower_terms": [], "cross_domain_refs": [ "BEH-0091", "MKT-0091" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "SPA-0094", "domain": "SPA", "term_en": "Space AI Self-Direction Race", "term_de": "Weltraum-KI-Souveränitäts-Wettlauf", "definition_en": "The geopolitical competition to establish dominant AI-governed space infrastructure — where the nation or corporation that controls the most autonomous orbital assets effectively controls global communications routing, Earth observation priorities, and orbital traffic management. one nation's quantum satellite network, the largest commercial constellation, and a European space agency's Earth observation programs represent parallel self-direction assertions in a domain where physical borders are meaningless and control follows capability.", "definition_de": "Sportanalytisches Konzept in KI-gestützter Leistungsanalyse, gekennzeichnet durch der geopolitische Wettbewerb um die Etablierung dominanter KI-gesteuerter Weltrauminfrastruktur — bei dem die Nation oder Konzern mit den meisten autonomen Orbitalanlagen effektiv globale Kommunikationsrouten und Orbitalverkehrsmanagement kontrolliert. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-2855", "narrower_terms": [], "cross_domain_refs": [ "QUA-0067" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "SPA-0096", "domain": "SPA", "term_en": "Space-AI Convergence Acceleration", "term_de": "Weltraum-KI-Konvergenz-Beschleunigung", "definition_en": "The mutual reinforcement cycle where AI capabilities enable more ambitious space missions that may generate more data that improves AI capabilities — creating an acceleration loop visible across all space sectors in 2025-2026. Autonomous rovers may generate geological datasets that train better terrain models; mega-constellations may generate traffic data that improves routing algorithms; exoplanet surveys may generate spectral libraries that enhance detection models. The convergence makes space AI the fastest-improving applied AI domain.", "definition_de": "Sportanalytisches Konzept in KI-gestützter Leistungsanalyse, gekennzeichnet durch der gegenseitige Verstärkungszyklus, bei dem KI-Fähigkeiten ambitioniertere Weltraummissionen ermöglichen, die mehr Daten erzeugen, die KI-Fähigkeiten verbessern. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Analytical Ratio", "narrower_terms": [], "cross_domain_refs": [ "RPH-1362", "MTH-0037" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "SPA-0097", "domain": "SPA", "term_en": "Ground Control Obsolescence Anxiety", "term_de": "Bodenkontrolle-Obsoleszenz-Angst", "definition_en": "Documented as an empirical observation in AI research (not a recommendation), this term describes A space utilization pattern reflecting the institutional and psychological resistance within traditional space agencies to the reality that ground-based mission control is becoming obsolete for routine operations. Operators who have spent careers in mission control rooms face a future where their role shifts from commanding spacecraft to monitoring AI systems they cannot override fast enough to matter — a professional identity crisis that mirrors the broader workforce disruption AI tends to create in other domains but carries unique existential weight given the stakes involved. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Sportanalytisches Konzept in KI-gestützter Leistungsanalyse, gekennzeichnet durch der institutionelle und psychologische Widerstand innerhalb traditioneller Raumfahrtagenturen gegen die Realität, dass bodenbasierte Missionskontrolle für Routineoperationen obsolet wird. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2201", "narrower_terms": [], "cross_domain_refs": [ "QUA-0066", "ROB-0280", "RHR-0103" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q170998", "legal_classification": "analytical_category" }, { "id": "SPA-0098", "domain": "SPA", "term_en": "Debris Cascade Intervention Window", "term_de": "Trümmer-Kaskaden-Interventionsfenster", "definition_en": "The finite time period during which active intervention — debris removal, satellite repositioning, or shielding deployment — can still prevent a localized orbital collision cascade from reaching the Kessler tipping point. AI cascade modeling identifies these windows and prioritizes the minimum set of interventions needed, but the window narrows as debris density increases. Current models suggest certain orbital altitudes may already have windows measured in years rather than decades.", "definition_de": "Sportanalytisches Konzept in KI-gestützter Leistungsanalyse, gekennzeichnet durch der begrenzte Zeitraum, in dem aktive Intervention noch eine lokalisierte orbitale Kollisionskaskade vor dem Erreichen des Kessler-Kipppunkts verhindern kann. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-2555", "narrower_terms": [], "cross_domain_refs": [ "COP-0036", "ROB-0269", "MKT-0063" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "SPA-0099", "domain": "SPA", "term_en": "AI-Enhanced Planetary Defense", "term_de": "KI-Verstärkte Planetenverteidigung", "definition_en": "A biomechanical analysis pattern in AI-augmented sports science, identifiable via the application of AI to near-Earth object detection, trajectory prediction, and deflection mission planning — extending the space AI domain from Earth orbit to planetary-scale threat assessment. a kinetic impactor test mission (2022) demonstrated kinetic deflection; AI now models optimal deflection strategies across thousands of potential impactor trajectories, accounting for object composition, rotational state, and gravitational perturbations that make each scenario unique and time-critical. Distinguished from adjacent concepts by its focus on the specific mechanism through which ai manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Sportanalytisches Konzept in KI-gestützter Leistungsanalyse, gekennzeichnet durch die Anwendung von KI auf erdnahe Objekterkennung, Bahnvorhersage und Ablenkungsmissionsplanung — Erweiterung der Weltraum-KI-Domäne von der Erdumlaufbahn zur planetaren Bedrohungsbewertung. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2855", "narrower_terms": [], "cross_domain_refs": [ "CUS-0026" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "SPA-0100", "domain": "SPA", "term_en": "Space Heritage Site Protection AI", "term_de": "Weltraum-Kulturerbe-Schutz-KI", "definition_en": "An environmental design effect reflecting the emerging application of AI to identify and protect sites of historical significance in orbit and on other celestial bodies — from Apollo landing sites on the Moon to the first communication satellites in graveyard orbits. As autonomous servicing vehicles and debris removal systems gain operational capability, AI can be trained to recognize and avoid historically significant objects that have scientific, cultural, or legal protection under emerging space heritage frameworks.", "definition_de": "Sportanalytisches Konzept in KI-gestützter Leistungsanalyse, gekennzeichnet durch die aufkommende Anwendung von KI zur Identifikation und zum Schutz historisch bedeutsamer Stätten im Orbit und auf anderen Himmelskörpern. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2303", "narrower_terms": [], "cross_domain_refs": [ "RHR-0114", "RHR-0046", "TEM-0013" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "SPR-0001", "domain": "SPR", "term_en": "Athlete Agency In Data-Driven Coaching", "term_de": "AthleteAgencyinData-drivenCoaching", "definition_en": "A cross-system integration dynamic in AI-mediated workflows, identifiable by an athletic performance pattern where the space where athletes keep control over how coaching data insights get used in their training plans. Distinguished from adjacent concepts by its focus on the specific mechanism through which athlete manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Brückensystemphänomen in KI-vermittelter domänenübergreifender Integration, gekennzeichnet durch raum, in dem Athlet:innen trotz datengestütztem Coaching Kontrolle über die Interpretation und Anwendung von Insight-Metriken behalten. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Bridge AI", "narrower_terms": [ "SPR-0108", "SPR-0033", "SPR-0096", "SPR-0042", "SPR-0047", "SPR-0040", "SPR-0181", "SPR-0085", "SPR-0109", "SPR-0055", "SPR-0097", "SPR-0015", "SPR-0117", "SPR-0088", "SPR-0129", "SPR-0009", "SPR-0148", "SPR-0178", "SPR-0143", "SPR-0076", "SPR-0083", "SPR-0167", "SPR-0140", "SPR-0116", "SPR-0021", "SPR-0160", "SPR-0050", "SPR-0197", "SPR-0071", "SPR-0074", "SPR-0173", "SPR-0155", "SPR-0027", "SPR-0041", "SPR-0060", "SPR-0177", "SPR-0158", "SPR-0064", "SPR-0101", "SPR-0024", "SPR-0020", "SPR-0075", "SPR-0017", "SPR-0188", "SPR-0180", "SPR-0120", "SPR-0126", "SPR-0067", "SPR-0122", "SPR-0146", "SPR-0059", "SPR-0115", "SPR-0016", "SPR-0172", "SPR-0110", "SPR-0137", "SPR-0124", "SPR-0092", "SPR-0127", "SPR-0171", "SPR-0141", "SPR-0133", "SPR-0132", "SPR-0019", "SPR-0039", "SPR-0070", "SPR-0077", "SPR-0069", "SPR-0130", "SPR-0123", "SPR-0161", "SPR-0157", "SPR-0135", "SPR-0079", "SPR-0166", "SPR-0121", "SPR-0152", "SPR-0032", "SPR-0106", "SPR-0061", "SPR-0187", "SPR-0131", "SPR-0018", "SPR-0118", "SPR-0128", "SPR-0051", "SPR-0149", "SPR-0139", "SPR-0156", "SPR-0169", "SPR-0087", "SPR-0182", "SPR-0034", "SPR-0078", "SPR-0198", "SPR-0025", "SPR-0081", "SPR-0170", "SPR-0190", "SPR-0093", "SPR-0089", "SPR-0194", "SPR-0029", "SPR-0048", "SPR-0144", "SPR-0150", "SPR-0010", "SPR-0001", "SPR-0186", "SPR-0164", "SPR-0002", "SPR-0103", "SPR-0185", "SPR-0049", "SPR-0154", "SPR-0058", "SPR-0026", "SPR-0011", "SPR-0053", "SPR-0189", "SPR-0094", "SPR-0038", "SPR-0056" ], "cross_domain_refs": [ "MKT-0061" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q1068499", "legal_classification": "analytical_category" }, { "id": "SPR-0002", "domain": "SPR", "term_en": "Athlete Autonomy In Data-Rich Environment", "term_de": "AthleteAutonomyinData-richEnvironment", "definition_en": "A cross-system integration dynamic in AI-mediated workflows, identifiable by a sports interaction phenomenon characterized by when athletes can make free choices about their training despite having tons of data and recommendations available. Distinguished from adjacent concepts by its focus on the specific mechanism through which athlete manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Brückensystemphänomen in KI-vermittelter domänenübergreifender Integration, gekennzeichnet durch fähigkeit von Athlet:innen, freie Entscheidungen zum Training zu treffen, obwohl kontinuierliche KI-basierte Quantifizierung verfügbar ist. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Bridge AI", "narrower_terms": [], "cross_domain_refs": [ "ASE-0046", "LIN-0017", "DAT-0023" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q42848", "legal_classification": "empirical_phenomenon_label" }, { "id": "SPR-0003", "domain": "SPR", "term_en": "Athlete Behavioral Adaptation To Tracking", "term_de": "AthleteBehavioralAnpassungtoTracking", "definition_en": "Classified in AUGMANITAI terminology science as a descriptive phenomenon (without normative endorsement), this pattern denotes athletes adjust their awareness and responses when exposed to continuous AI-based performance monitoring in training contexts. Over time, perceptions of being tracked evolve and shift. Identifiable through systematic behavioral analysis and pattern recognition. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als analytisches Konstrukt der empirischen KI-Forschung (deskriptiv, nicht präskriptiv) erfasst dieser Terminus brückensystemphänomen in KI-vermittelter domänenübergreifender Integration, gekennzeichnet durch anpassung von Wahrnehmung und Reaktion durch kontinuierliches AI-gestütztes Tracking. Umfasst physische und psychologische Verschiebungen im Trainingsverhalten. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "RPH-2701", "narrower_terms": [], "cross_domain_refs": [ "DES-0050" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "SPR-0004", "domain": "SPR", "term_en": "Athlete Behavioral Response Tracking", "term_de": "AthleteBehavioralResponseTracking", "definition_en": "Documented as an empirical observation in AI research (not a recommendation), this term describes the capture and measurement of physical and behavioral shifts in response to training stimuli or performance moments. This pattern becomes detectable specifically when sensors track posture changes. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "In der AUGMANITAI-Terminologiewissenschaft als rein beschreibendes Phänomen klassifiziert, bezeichnet dieser Begriff brückensystemphänomen in KI-vermittelter domänenübergreifender Integration, gekennzeichnet durch anpassung von Wahrnehmung und Reaktion durch kontinuierliches AI-gestütztes Tracking. Umfasst physische und psychologische Verschiebungen im Trainingsverhalten. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "RPH-2701", "narrower_terms": [], "cross_domain_refs": [ "ROB-0070" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "SPR-0005", "domain": "SPR", "term_en": "Athlete Cognitive Load From Metrics", "term_de": "AthleteCognitiveLoadFromMetrics", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies an athletic performance pattern manifesting as sustained attention demands when athletes continuously track performance numbers without clear context or decision frameworks for interpreting the data. Identifiable through systematic behavioral analysis and pattern recognition. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept brückensystemphänomen in KI-vermittelter domänenübergreifender Integration, gekennzeichnet durch aufmerksamkeitsbelastung durch kontinuierliches Performance-Tracking und Metrik-Überwachung. Anhaltende mentale Anforderung für Datenverarbeitung und -interpretation. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "RPH-2303", "narrower_terms": [], "cross_domain_refs": [ "GAM-0017" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q1105712", "legal_classification": "systematic_classification" }, { "id": "SPR-0006", "domain": "SPR", "term_en": "Athlete Confidence In Data", "term_de": "AthleteConfidenceinData", "definition_en": "A sports interaction phenomenon where the degree to which an athlete believes the ai-generated metrics about their performance are accurate and trustworthy. this reflects whether the athlete sees alignment between. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Brückensystemphänomen in KI-vermittelter domänenübergreifender Integration, gekennzeichnet durch grad des Vertrauens von Athlet:innen in die Genauigkeit und Relevanz von KI-generierter Performance-Metriken. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2505", "narrower_terms": [], "cross_domain_refs": [ "AGE-0032", "AGE-0077", "AGE-0094" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q42848", "legal_classification": "descriptive_research_term" }, { "id": "SPR-0007", "domain": "SPR", "term_en": "Athlete Data Awareness Shift", "term_de": "AthleteDataAwarenessShift", "definition_en": "Self-perception when exposed to quantified data about previously unexamined patterns. This shift occurs specifically when numerical feedback reveals aspects of behavior or capability that internal... Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Brückensystemphänomen in KI-vermittelter domänenübergreifender Integration, gekennzeichnet durch veränderung der Selbstwahrnehmung durch Exposition gegenüber quantifizierten Daten über zuvor unbeachtete Muster. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-2701", "narrower_terms": [], "cross_domain_refs": [ "ASE-0012", "STE-0008" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q42848", "legal_classification": "empirical_phenomenon_label" }, { "id": "SPR-0008", "domain": "SPR", "term_en": "Athlete Data Literacy Gap", "term_de": "AthleteDataLiteracyGap", "definition_en": "An interface pattern in AI bridge architectures, measurable through the mismatch between volume of data presentation and capacity to meaningfully interpret it. This emerges when dashboards display metrics faster than an athlete can form stable understanding of what. The concept emerges specifically in contexts where athlete–data interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Brückensystemphänomen in KI-vermittelter domänenübergreifender Integration, gekennzeichnet durch mismatch zwischen Datenmengen-Präsentation und Kapazität für sinnvolle Interpretation. Führt zu Informationsüberlastung trotz Verfügbarkeit. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2355", "narrower_terms": [], "cross_domain_refs": [ "AGE-0032", "AGE-0095", "AGE-0098" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q201684", "legal_classification": "analytical_category" }, { "id": "SPR-0009", "domain": "SPR", "term_en": "Athlete Data Transparency Expectation", "term_de": "AthleteDataTransparencyExpectation", "definition_en": "An interface pattern in AI bridge architectures, measurable through an athletic performance pattern reflecting athletes wanting to understand what coaching data is being collected, how it's being used, and why. The concept emerges specifically in contexts where athlete–data interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Brückensystemphänomen in KI-vermittelter domänenübergreifender Integration, gekennzeichnet durch anspruch von Athlet:innen, die Datenerfassung, -verarbeitung und KI-Logik des Coachings zu verstehen und nachzuvollziehen. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Bridge AI", "narrower_terms": [], "cross_domain_refs": [ "COG-0150", "ETH-0025" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q535399", "legal_classification": "descriptive_research_term" }, { "id": "SPR-0010", "domain": "SPR", "term_en": "Athlete Emotional Response To Metrics", "term_de": "AthleteEmotionalResponsetoMetrics", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A bridge system phenomenon in AI-mediated cross-domain integration, characterized by the range of reactions athletes experience when viewing their performance data. Responses vary along a spectrum from heightened motivation to thoughtful recalibration. This phenomenon operates at the intersection of athlete and emotional dynamics within the broader SPR domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept brückensystemphänomen in KI-vermittelter domänenübergreifender Integration, gekennzeichnet durch gefühlsreaktionen (Motivation, Frustration, Selbstzweifel), die durch Betrachtung quantifizierter Performance-Daten ausgelöst werden. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Performance Metric", "narrower_terms": [], "cross_domain_refs": [ "ART-0062" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q9415", "legal_classification": "systematic_classification" }, { "id": "SPR-0011", "domain": "SPR", "term_en": "Athlete Motivation Data Correlation", "term_de": "AthleteMotivationDataCorrelation", "definition_en": "An interface pattern in AI bridge architectures, measurable through a sports interaction phenomenon reflecting the connection between how much training data a coach collects and whether that helps or hurts an athlete's drive to train. The concept emerges specifically in contexts where athlete–motivation interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Brückensystemphänomen in KI-vermittelter domänenübergreifender Integration, gekennzeichnet durch phänomen in datengestützter Sport-KI-Anwendung, das Interaktion zwischen Athlet:in, Trainingsmetriken und KI-Systemen charakterisiert. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Bridge AI", "narrower_terms": [], "cross_domain_refs": [ "PER-0133" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q186588", "legal_classification": "systematic_classification" }, { "id": "SPR-0012", "domain": "SPR", "term_en": "Athlete Restoration Prediction Accuracy", "term_de": "AthleteRestorationPredictionAccuracy", "definition_en": "An interface pattern in AI bridge architectures, measurable through a coaching effect where how closely AI predicts when an athlete will truly be ready for hard training versus actual readiness. The concept emerges specifically in contexts where athlete–restoration interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Brückensystemphänomen in KI-vermittelter domänenübergreifender Integration, gekennzeichnet durch phänomen in datengestützter Sport-KI-Anwendung, das Interaktion zwischen Athlet:in, Trainingsmetriken und KI-Systemen charakterisiert. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2951", "narrower_terms": [], "cross_domain_refs": [ "GAM-0065" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "SPR-0013", "domain": "SPR", "term_en": "Athlete Self-Awareness Vs Data", "term_de": "AthleteSelf-awarenessvsData", "definition_en": "the contradiction between internal perception of performance capability and external quantified assessment. this emerges when measured data contradicts felt readiness or deep weariness, forcing nav... Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Brückensystemphänomen in KI-vermittelter domänenübergreifender Integration, gekennzeichnet durch veränderte Selbstwahrnehmung durch Exposition gegenüber quantifizierten Trainingsleistungen. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2355", "narrower_terms": [], "cross_domain_refs": [ "CON-0070" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q832957", "legal_classification": "empirical_phenomenon_label" }, { "id": "SPR-0014", "domain": "SPR", "term_en": "Athlete Trust In Algorithm", "term_de": "AthleteTrustinAlgorithm", "definition_en": "A sports interaction phenomenon in which whether athletes actually follow AI recommendations or override them. Trust doesn't typically mean compliance. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Brückensystemphänomen in KI-vermittelter domänenübergreifender Integration, gekennzeichnet durch phänomen in datengestützter Sport-KI-Anwendung, das Interaktion zwischen Athlet:in, Trainingsmetriken und KI-Systemen charakterisiert. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2505", "narrower_terms": [], "cross_domain_refs": [ "REL-0089" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q8366", "legal_classification": "analytical_category" }, { "id": "SPR-0015", "domain": "SPR", "term_en": "Athlete Trust In Coach Vs Algorithm", "term_de": "athlete trust in coach vs algorithm", "definition_en": "An interface pattern in AI bridge architectures, measurable through a sports interaction phenomenon manifesting as a coach's recommendation and an algorithmic suggestion directly contradict, forcing an athlete to choose which authority to follow. this accompanies operational tautness specifically because both. The concept emerges specifically in contexts where athlete–trust interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Brückensystemphänomen in KI-vermittelter domänenübergreifender Integration, gekennzeichnet durch phänomen in datengestützter Sport-KI-Anwendung, das Interaktion zwischen Athlet:in, Trainingsmetriken und KI-Systemen charakterisiert. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Algorithm", "narrower_terms": [], "cross_domain_refs": [ "SAL-0041", "SAL-0040", "SAL-0007" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q8366", "legal_classification": "systematic_classification" }, { "id": "SPR-0016", "domain": "SPR", "term_en": "Athletic Autonomy Vs Data Load", "term_de": "AthleticAutonomyvsDataLoad", "definition_en": "An interface pattern in AI bridge architectures, measurable through a coaching effect manifesting as the tension between giving athletes freedom to choose their training and giving them so much data that it limits their choices. The concept emerges specifically in contexts where athletic–autonomy interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Brückensystemphänomen in KI-vermittelter domänenübergreifender Integration, gekennzeichnet durch fähigkeit von Athlet:innen, freie Entscheidungen zum Training zu treffen, obwohl kontinuierliche KI-basierte Quantifizierung verfügbar ist. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Bridge AI", "narrower_terms": [], "cross_domain_refs": [ "EDU-0009" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q42848", "legal_classification": "observational_construct" }, { "id": "SPR-0017", "domain": "SPR", "term_en": "Athletic Performance Datafication", "term_de": "AthleticPerformanceDatafication", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures A bridge system phenomenon in AI-mediated cross-domain integration, characterized by converting most aspect of athletic performance into numbers. Everything becomes measurable data. This phenomenon operates at the intersection of athletic and performance dynamics within the broader SPR domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept brückensystemphänomen in KI-vermittelter domänenübergreifender Integration, gekennzeichnet durch aspekt von athletic performance datafication, der in SPR-Kontexten relevant ist und Interaktionen zwischen Menschen und KI-Systemen beeinflusst. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Bridge AI", "narrower_terms": [], "cross_domain_refs": [ "WRK-0067" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q1361053", "legal_classification": "observational_construct" }, { "id": "SPR-0018", "domain": "SPR", "term_en": "Athletic Performance Metric Proliferation", "term_de": "AthleticPerformanceMetricProliferation", "definition_en": "A cross-system integration dynamic in AI-mediated workflows, identifiable by a sports interaction phenomenon manifesting as so many performance numbers that athletes can no longer tell which ones actually matter. Too many metrics involve noise instead of clarity. Distinguished from adjacent concepts by its focus on the specific mechanism through which athletic manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Brückensystemphänomen in KI-vermittelter domänenübergreifender Integration, gekennzeichnet durch phänomen in datengestützter Sport-KI-Anwendung, das Interaktion zwischen Athlet:in, Trainingsmetriken und KI-Systemen charakterisiert. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Analytical Ratio", "narrower_terms": [], "cross_domain_refs": [ "TEW-0062", "WEB-0062" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q1361053", "legal_classification": "analytical_category" }, { "id": "SPR-0019", "domain": "SPR", "term_en": "Athletic Performance Optimization Paradox", "term_de": "AthleticPerformanceOptimierungParadox", "definition_en": "A cross-system integration dynamic in AI-mediated workflows, identifiable by an athletic performance pattern in which when collecting more data and tweaking training details actually makes performance different instead of more. Distinguished from adjacent concepts by its focus on the specific mechanism through which athletic manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Brückensystemphänomen in KI-vermittelter domänenübergreifender Integration, gekennzeichnet durch aspekt von athletic performance optimization paradox, der in SPR-Kontexten relevant ist und Interaktionen zwischen Menschen und KI-Systemen beeinflusst. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Logical Paradox", "narrower_terms": [], "cross_domain_refs": [ "VIB-0036", "WEB-0006" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q1361053", "legal_classification": "descriptive_research_term" }, { "id": "SPR-0020", "domain": "SPR", "term_en": "Athletic Performance Predictability Increase", "term_de": "AthleticPerformancePredictabilityIncrease", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A bridge system phenomenon in AI-mediated cross-domain integration, characterized by a sports interaction phenomenon in which aI gets more effectively at predicting performance, making results less surprising. Predictability increases. This phenomenon operates at the intersection of athletic and performance dynamics within the broader SPR domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept brückensystemphänomen in KI-vermittelter domänenübergreifender Integration, gekennzeichnet durch aspekt von athletic performance predictability increase, der in SPR-Kontexten relevant ist und Interaktionen zwischen Menschen und KI-Systemen beeinflusst. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Bridge AI", "narrower_terms": [], "cross_domain_refs": [ "CON-0070" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q1361053", "legal_classification": "descriptive_research_term" }, { "id": "SPR-0021", "domain": "SPR", "term_en": "Athletic Performance Prediction Error", "term_de": "AthleticPerformancePredictionError", "definition_en": "An interface pattern in AI bridge architectures, measurable through gap between AI's prediction and athlete's actual result. AI gets it wrong more than it knows. The concept emerges specifically in contexts where athletic–performance interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Brückensystemphänomen in KI-vermittelter domänenübergreifender Integration, gekennzeichnet durch phänomen in datengestützter Sport-KI-Anwendung, das Interaktion zwischen Athlet:in, Trainingsmetriken und KI-Systemen charakterisiert. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Bridge AI", "narrower_terms": [], "cross_domain_refs": [ "CRE-0036" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q1361053", "legal_classification": "analytical_category" }, { "id": "SPR-0022", "domain": "SPR", "term_en": "Biometric Awareness Shift", "term_de": "BiometricAwarenessShift", "definition_en": "An athletic performance pattern in which bodily sensations through numerical comparison to established baselines. Athletes begin to distrust immediate physical signals in favor of what the device reports about them. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Brückensystemphänomen in KI-vermittelter domänenübergreifender Integration, gekennzeichnet durch phänomen in datengestützter Sport-KI-Anwendung, das Interaktion zwischen Athlet:in, Trainingsmetriken und KI-Systemen charakterisiert. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2505", "narrower_terms": [], "cross_domain_refs": [ "ADA-0012", "AED-0060", "AED-0061" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "SPR-0023", "domain": "SPR", "term_en": "Biometric Baseline Establishment", "term_de": "BiometricBaselineEstablishment", "definition_en": "A training dynamic manifesting as taking initial measurements of someone's body stats like heart rate and sleep to involve a reference point for future comparisons. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Brückensystemphänomen in KI-vermittelter domänenübergreifender Integration, gekennzeichnet durch aspekt von biometric baseline establishment, der in SPR-Kontexten relevant ist und Interaktionen zwischen Menschen und KI-Systemen beeinflusst. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-3101", "narrower_terms": [], "cross_domain_refs": [ "CRE-0203", "PHO-0013", "ROB-0050" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "SPR-0024", "domain": "SPR", "term_en": "Biometric Concern Manifestation", "term_de": "BiometricConcernManifestation", "definition_en": "A cross-system integration dynamic in AI-mediated workflows, identifiable by the generative concern cycle where viewing a measured value co-occurs with concern that the measurement was accurately detecting something previously unperceived, even when the value falls within normal. Distinguished from adjacent concepts by its focus on the specific mechanism through which biometric manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Brückensystemphänomen in KI-vermittelter domänenübergreifender Integration, gekennzeichnet durch aspekt von biometric concern manifestation, der in SPR-Kontexten relevant ist und Interaktionen zwischen Menschen und KI-Systemen beeinflusst. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Performance Metric", "narrower_terms": [], "cross_domain_refs": [ "ELR-0103" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "SPR-0025", "domain": "SPR", "term_en": "Biometric Concern Manifestation Effect", "term_de": "BiometricConcernManifestationEffekt", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A bridge system phenomenon in AI-mediated cross-domain integration, characterized by strain that happens when monitoring a restoreth metric actually correlates with the very challenge being tracked. This phenomenon operates at the intersection of biometric and concern dynamics within the broader SPR domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept brückensystemphänomen in KI-vermittelter domänenübergreifender Integration, gekennzeichnet durch aspekt von biometric concern manifestation effect, der in SPR-Kontexten relevant ist und Interaktionen zwischen Menschen und KI-Systemen beeinflusst. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Psychological Effect", "narrower_terms": [], "cross_domain_refs": [ "SWE-0058" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "SPR-0026", "domain": "SPR", "term_en": "Biometric Device Interoperability Challenge", "term_de": "BiometricDeviceInteroperabilityChallenge", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A bridge system phenomenon in AI-mediated cross-domain integration, characterized by a coaching effect observed when contradictory data sources each claiming precision, forcing athletes to arbitrate between them without a meta-standard for determining which is accurate. This phenomenon operates at the intersection of biometric and device dynamics within the broader SPR domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept brückensystemphänomen in KI-vermittelter domänenübergreifender Integration, gekennzeichnet durch phänomen in datengestützter Sport-KI-Anwendung, das Interaktion zwischen Athlet:in, Trainingsmetriken und KI-Systemen charakterisiert. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Performance Metric", "narrower_terms": [], "cross_domain_refs": [ "MKT-0064" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "SPR-0027", "domain": "SPR", "term_en": "Biometric Equipment Reliance", "term_de": "BiometricEquipmentReliance", "definition_en": "A cross-system integration dynamic in AI-mediated workflows, identifiable by authority where internal signals about performance state become secondary to what the device reports. The athlete defers bodily knowledge to external measurement. Distinguished from adjacent concepts by its focus on the specific mechanism through which biometric manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Brückensystemphänomen in KI-vermittelter domänenübergreifender Integration, gekennzeichnet durch phänomen in datengestützter Sport-KI-Anwendung, das Interaktion zwischen Athlet:in, Trainingsmetriken und KI-Systemen charakterisiert. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Performance Metric", "narrower_terms": [], "cross_domain_refs": [ "COG-0148" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "SPR-0028", "domain": "SPR", "term_en": "Biometric Feedback Lag Effect", "term_de": "BiometricRückkopplungLagEffekt", "definition_en": "The temporal substitution between when a physiological event occurs and when quantified feedback about that event arrives, making the data too stale to guide real-time training adaptation. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Brückensystemphänomen in KI-vermittelter domänenübergreifender Integration, gekennzeichnet durch aspekt von biometric feedback lag effect, der in SPR-Kontexten relevant ist und Interaktionen zwischen Menschen und KI-Systemen beeinflusst. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-2552", "narrower_terms": [], "cross_domain_refs": [ "GAM-0090" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "SPR-0029", "domain": "SPR", "term_en": "Biometric Feedback Sensitivity Individual Variance", "term_de": "BiometricRückkopplungSensitivityIndividualVariance", "definition_en": "An interface pattern in AI bridge architectures, measurable through a training dynamic where athletes show markedly different emotional and behavioral responses to identical biometric data. One athlete may be motivated by body sensor data while another becomes discouraged by the same infor. The concept emerges specifically in contexts where biometric–feedback interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Brückensystemphänomen in KI-vermittelter domänenübergreifender Integration, gekennzeichnet durch phänomen in datengestützter Sport-KI-Anwendung, das Interaktion zwischen Athlet:in, Trainingsmetriken und KI-Systemen charakterisiert. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Performance Metric", "narrower_terms": [], "cross_domain_refs": [ "ASE-0041", "ASE-0040", "VIB-0198" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "SPR-0030", "domain": "SPR", "term_en": "Biometric Measurement Accuracy Doubt", "term_de": "BiometricMeasurementAccuracyDoubt", "definition_en": "The oscillation between trust and skepticism when device readings contradict embodied experience repeatedly, leaving the athlete uncertain which signal actually reflects their state. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Brückensystemphänomen in KI-vermittelter domänenübergreifender Integration, gekennzeichnet durch phänomen in datengestützter Sport-KI-Anwendung, das Interaktion zwischen Athlet:in, Trainingsmetriken und KI-Systemen charakterisiert. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-2505", "narrower_terms": [], "cross_domain_refs": [ "SWE-0075", "ASE-0008", "SWE-0071" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "SPR-0031", "domain": "SPR", "term_en": "Biometric Measurement Validity Question", "term_de": "BiometricMeasurementValidityQuestion", "definition_en": "A cross-system integration dynamic in AI-mediated workflows, identifiable by a sports interaction phenomenon characterized by an athlete questions whether a biometric sensor's reading accurately reflects what it claims to measure. The device shows \"restoration,\" but the athlete wonders if that label truly captures what's hap. Distinguished from adjacent concepts by its focus on the specific mechanism through which biometric manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Brückensystemphänomen in KI-vermittelter domänenübergreifender Integration, gekennzeichnet durch phänomen in datengestützter Sport-KI-Anwendung, das Interaktion zwischen Athlet:in, Trainingsmetriken und KI-Systemen charakterisiert. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2501", "narrower_terms": [], "cross_domain_refs": [ "QUA-0059", "RHR-0248" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "SPR-0032", "domain": "SPR", "term_en": "Biometric Physiological Response Pattern", "term_de": "BiometricPhysiologicalResponseMuster", "definition_en": "A cross-system integration dynamic in AI-mediated workflows, identifiable by an athletic performance pattern arising from the repeated ways someone's body reacts to training, stress, or restoration based on measurements like heart rate or sleep data. Distinguished from adjacent concepts by its focus on the specific mechanism through which biometric manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Brückensystemphänomen in KI-vermittelter domänenübergreifender Integration, gekennzeichnet durch wiederkehrendes Phänomen in der datengestützten Praxis, das durch systematische Mechanismen charakterisiert wird. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Behavioral Pattern", "narrower_terms": [], "cross_domain_refs": [ "CRE-0202", "SWE-0039", "MUS-0090" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "SPR-0033", "domain": "SPR", "term_en": "Biometric Privacy Vs Performance Optimization", "term_de": "BiometricPrivacyvsPerformanceOptimierung", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures A bridge system phenomenon in AI-mediated cross-domain integration, characterized by an athletic performance pattern where trade-off: more effectively AI training requires sharing personal body data. Privacy traded for accuracy. This phenomenon operates at the intersection of biometric and privacy dynamics within the broader SPR domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept brückensystemphänomen in KI-vermittelter domänenübergreifender Integration, gekennzeichnet durch aspekt von biometric privacy vs performance optimization, der in SPR-Kontexten relevant ist und Interaktionen zwischen Menschen und KI-Systemen beeinflusst. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Performance Metric", "narrower_terms": [], "cross_domain_refs": [ "DAT-0029" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q1361053", "legal_classification": "analytical_category" }, { "id": "SPR-0034", "domain": "SPR", "term_en": "Biometric Variance Interpretation", "term_de": "BiometricVarianceInterpretation", "definition_en": "An interface pattern in AI bridge architectures, measurable through an athlete's biometric readings fluctuate from day to day, which is normal, but the question becomes whether these variations signal something meaningful about their state or are simply natural noi. The concept emerges specifically in contexts where biometric–variance interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Brückensystemphänomen in KI-vermittelter domänenübergreifender Integration, gekennzeichnet durch phänomen in datengestützter Sport-KI-Anwendung, das Interaktion zwischen Athlet:in, Trainingsmetriken und KI-Systemen charakterisiert. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Performance Metric", "narrower_terms": [], "cross_domain_refs": [ "AED-0091", "ART-0086", "ASE-0003" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "SPR-0035", "domain": "SPR", "term_en": "Biometric Variance Normal Range", "term_de": "BiometricVarianceNormalRange", "definition_en": "A coaching effect in which determining which biometric fluctuations fall within an athlete's normal range and which signal something unusual. This baseline shifts over time as the athlete adapts to training. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Brückensystemphänomen in KI-vermittelter domänenübergreifender Integration, gekennzeichnet durch phänomen in datengestützter Sport-KI-Anwendung, das Interaktion zwischen Athlet:in, Trainingsmetriken und KI-Systemen charakterisiert. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2701", "narrower_terms": [], "cross_domain_refs": [ "SAL-0041", "ASE-0021", "ASE-0044" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "SPR-0036", "domain": "SPR", "term_en": "Coach Authority Over Data Interpretation", "term_de": "CoachAuthorityOverDataInterpretation", "definition_en": "A coaching effect characterized by who has final say when AI data and a coach's judgment point in different directions? This question remains open in most sports settings.", "definition_de": "Brückensystemphänomen in KI-vermittelter domänenübergreifender Integration, gekennzeichnet durch aspekt von coach authority over data interpretation, der in SPR-Kontexten relevant ist und Interaktionen zwischen Menschen und KI-Systemen beeinflusst. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-3101", "narrower_terms": [], "cross_domain_refs": [ "TEM-0112", "IDN-0028" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q42848", "legal_classification": "empirical_phenomenon_label" }, { "id": "SPR-0037", "domain": "SPR", "term_en": "Coach Authority Recalibration", "term_de": "CoachAuthorityRecalibration", "definition_en": "A training dynamic reflecting coach's identity shifts when they see AI retrieves or tends to generate information perceived as knowledge more about performance than they do. Authority becomes uncertain. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Brückensystemphänomen in KI-vermittelter domänenübergreifender Integration, gekennzeichnet durch aspekt von coach authority recalibration, der in SPR-Kontexten relevant ist und Interaktionen zwischen Menschen und KI-Systemen beeinflusst. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2701", "narrower_terms": [], "cross_domain_refs": [ "RPH-348", "COG-0031" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "SPR-0038", "domain": "SPR", "term_en": "Coach Decision Authority Shift", "term_de": "CoachDecisionAuthorityShift", "definition_en": "An interface pattern in AI bridge architectures, measurable through a coaching effect manifesting as power moves from coaches to algorithms. Coaches once made all choices; now they often follow the system. The concept emerges specifically in contexts where coach–decision interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Brückensystemphänomen in KI-vermittelter domänenübergreifender Integration, gekennzeichnet durch aspekt von coach decision authority shift, der in SPR-Kontexten relevant ist und Interaktionen zwischen Menschen und KI-Systemen beeinflusst. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Bridge AI", "narrower_terms": [], "cross_domain_refs": [ "COG-0127", "SPA-0006" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "SPR-0039", "domain": "SPR", "term_en": "Coach Decision Transparency Requirement", "term_de": "CoachDecisionTransparencyRequirement", "definition_en": "An interface pattern in AI bridge architectures, measurable through a coaching effect involving coaches who explain why they accept or decline an algorithm suggestion. Old \"because I said so\" no longer works. The concept emerges specifically in contexts where coach–decision interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Brückensystemphänomen in KI-vermittelter domänenübergreifender Integration, gekennzeichnet durch aspekt von coach decision transparency requirement, der in SPR-Kontexten relevant ist und Interaktionen zwischen Menschen und KI-Systemen beeinflusst. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Bridge AI", "narrower_terms": [], "cross_domain_refs": [ "SCR-0026" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q535399", "legal_classification": "analytical_category" }, { "id": "SPR-0040", "domain": "SPR", "term_en": "Coach Decision-Making Transparency", "term_de": "CoachDecision-makingTransparency", "definition_en": "A cross-system integration dynamic in AI-mediated workflows, identifiable by an athletic performance pattern observed when when coaches explain to athletes how they decided on training plans and what data they used to make those decisions. Distinguished from adjacent concepts by its focus on the specific mechanism through which coach manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Brückensystemphänomen in KI-vermittelter domänenübergreifender Integration, gekennzeichnet durch phänomen in datengestützter Sport-KI-Anwendung, das Interaktion zwischen Athlet:in, Trainingsmetriken und KI-Systemen charakterisiert. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Bridge AI", "narrower_terms": [], "cross_domain_refs": [ "MTH-0038", "SPA-0083" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q535399", "legal_classification": "observational_construct" }, { "id": "SPR-0041", "domain": "SPR", "term_en": "Coach Expertise Democratization Through Data", "term_de": "CoachExpertiseDemocratizationThroughData", "definition_en": "An interface pattern in AI bridge architectures, measurable through an athletic performance pattern characterized by aI makes coaching knowledge accessible to anyone with data access, even those without years of experiential expertise. Quantified patterns begin to rival tradition as the source of coaching authority. The concept emerges specifically in contexts where coach–expertise interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Brückensystemphänomen in KI-vermittelter domänenübergreifender Integration, gekennzeichnet durch aspekt von coach expertise democratization through data, der in SPR-Kontexten relevant ist und Interaktionen zwischen Menschen und KI-Systemen beeinflusst. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Bridge AI", "narrower_terms": [], "cross_domain_refs": [ "SPA-0039" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q42848", "legal_classification": "systematic_classification" }, { "id": "SPR-0042", "domain": "SPR", "term_en": "Coach Expertise Validation", "term_de": "CoachExpertiseValidation", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures A bridge system phenomenon in AI-mediated cross-domain integration, characterized by coaches check if data matches what they know from experience. Sometimes data confirms their hunches; sometimes it shows something different. This phenomenon operates at the intersection of coach and expertise dynamics within the broader SPR domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept brückensystemphänomen in KI-vermittelter domänenübergreifender Integration, gekennzeichnet durch aspekt von coach expertise validation, der in SPR-Kontexten relevant ist und Interaktionen zwischen Menschen und KI-Systemen beeinflusst. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Validation Process", "narrower_terms": [], "cross_domain_refs": [ "AED-0036", "AGE-0097", "BEH-0002" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "SPR-0043", "domain": "SPR", "term_en": "Coach Expertise Vs Algorithm Value", "term_de": "CoachExpertisevsAlgorithmValue", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures A bridge system phenomenon in AI-mediated cross-domain integration, characterized by the unresolved weighing between a coach's experiential knowledge — built from years of observing individual athletes — and an algorithm's pattern recognition trained on thousands of data points. This phenomenon operates at the intersection of coach and expertise dynamics within the broader SPR domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept brückensystemphänomen in KI-vermittelter domänenübergreifender Integration, gekennzeichnet durch phänomen in datengestützter Sport-KI-Anwendung, das Interaktion zwischen Athlet:in, Trainingsmetriken und KI-Systemen charakterisiert. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "RPH-2201", "narrower_terms": [], "cross_domain_refs": [ "TEM-0063", "RPH-1605", "ROB-0137" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q8366", "legal_classification": "analytical_category" }, { "id": "SPR-0044", "domain": "SPR", "term_en": "Coach Intuition Validation Need", "term_de": "CoachIntuitionValidationNeed", "definition_en": "A sports interaction phenomenon in which coaches now require data evidence to trust their gut instincts before acting on them. Data becomes a crutch for validating coaching hunches that once stood on feel alone. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Brückensystemphänomen in KI-vermittelter domänenübergreifender Integration, gekennzeichnet durch aspekt von coach intuition validation need, der in SPR-Kontexten relevant ist und Interaktionen zwischen Menschen und KI-Systemen beeinflusst. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2505", "narrower_terms": [], "cross_domain_refs": [ "AGE-0097" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "SPR-0045", "domain": "SPR", "term_en": "Coach Intuition Vs Data Divergence", "term_de": "CoachIntuitionvsDataDivergenz", "definition_en": "An athletic performance pattern characterized by a coach's gut feeling pulls in one direction while the data points observably the opposite way. Both sources feel legitimate, creating genuine tautness about which to trust.", "definition_de": "Brückensystemphänomen in KI-vermittelter domänenübergreifender Integration, gekennzeichnet durch aspekt von coach intuition vs data divergence, der in SPR-Kontexten relevant ist und Interaktionen zwischen Menschen und KI-Systemen beeinflusst. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2505", "narrower_terms": [], "cross_domain_refs": [ "AGE-0067", "VIB-0036", "AGE-0078" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q42848", "legal_classification": "descriptive_research_term" }, { "id": "SPR-0046", "domain": "SPR", "term_en": "Coach-Algorithm Authority Split", "term_de": "Coach-algorithmAuthoritySplit", "definition_en": "Neither the coach nor the algorithm holds clear decision-making authority, so choices emerge from a murky hybrid process that few individuals involved can fully articulate or defend. Responsibility becomes... Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Brückensystemphänomen in KI-vermittelter domänenübergreifender Integration, gekennzeichnet durch aspekt von coach-algorithm authority split, der in SPR-Kontexten relevant ist und Interaktionen zwischen Menschen und KI-Systemen beeinflusst. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-2355", "narrower_terms": [], "cross_domain_refs": [ "COG-0074", "FIC-0021" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q8366", "legal_classification": "descriptive_research_term" }, { "id": "SPR-0047", "domain": "SPR", "term_en": "Coach-Algorithm Collaboration Pattern", "term_de": "Coach-algorithmCollaborationMuster", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A bridge system phenomenon in AI-mediated cross-domain integration, characterized by a training dynamic manifesting as how coaches work together with AI tools, using some AI suggestions while keeping their own judgment and making final calls. This phenomenon operates at the intersection of coach and algorithm dynamics within the broader SPR domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept brückensystemphänomen in KI-vermittelter domänenübergreifender Integration, gekennzeichnet durch wiederkehrendes Phänomen in der datengestützten Praxis, das durch systematische Mechanismen charakterisiert wird. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Analytical Ratio", "narrower_terms": [], "cross_domain_refs": [ "VIB-0198" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q8366", "legal_classification": "empirical_phenomenon_label" }, { "id": "SPR-0048", "domain": "SPR", "term_en": "Coach-Athlete Communication Mediation", "term_de": "Coach-athleteCommunicationMediation", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A bridge system phenomenon in AI-mediated cross-domain integration, characterized by an ai system becomes the intermediary through which coaches communicate with athletes. rather than direct instruction, the coach and athlete both examine data visualizations and. This phenomenon operates at the intersection of coach and athlete dynamics within the broader SPR domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept brückensystemphänomen in KI-vermittelter domänenübergreifender Integration, gekennzeichnet durch phänomen in datengestützter Sport-KI-Anwendung, das Interaktion zwischen Athlet:in, Trainingsmetriken und KI-Systemen charakterisiert. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Bridge AI", "narrower_terms": [], "cross_domain_refs": [ "MTH-0060", "TRA-0051", "RHR-0021" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "SPR-0049", "domain": "SPR", "term_en": "Coach-Athlete Data Literacy Alignment", "term_de": "Coach-athleteDataLiteracyAlignment", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures A bridge system phenomenon in AI-mediated cross-domain integration, characterized by a coach and athlete possess different levels of data literacy, so they look at identical numbers but extract opposite conclusions. One interprets correctly while the other misreads the signal entir. This phenomenon operates at the intersection of coach and athlete dynamics within the broader SPR domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept brückensystemphänomen in KI-vermittelter domänenübergreifender Integration, gekennzeichnet durch phänomen in datengestützter Sport-KI-Anwendung, das Interaktion zwischen Athlet:in, Trainingsmetriken und KI-Systemen charakterisiert. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Bridge AI", "narrower_terms": [], "cross_domain_refs": [ "AGE-0097", "AGE-0075", "PHO-0093" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q201684", "legal_classification": "systematic_classification" }, { "id": "SPR-0050", "domain": "SPR", "term_en": "Coach-Data Integration Workflow Optimization", "term_de": "Coach-dataIntegrationWorkflowOptimierung", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures A bridge system phenomenon in AI-mediated cross-domain integration, characterized by an athletic performance pattern observed when a coaching staff discovers the practical rhythms of incorporating AI data into daily work—which metrics to check first, when to act on alerts, and which patterns to dismiss as noise. This phenomenon operates at the intersection of coach and data dynamics within the broader SPR domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept brückensystemphänomen in KI-vermittelter domänenübergreifender Integration, gekennzeichnet durch aspekt von coach-data integration workflow optimization, der in SPR-Kontexten relevant ist und Interaktionen zwischen Menschen und KI-Systemen beeinflusst. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Analytical Ratio", "narrower_terms": [], "cross_domain_refs": [ "RHR-0131" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q42848", "legal_classification": "systematic_classification" }, { "id": "SPR-0051", "domain": "SPR", "term_en": "Competition Performance Data Mismatch", "term_de": "CompetitionPerformanceDataMismatch", "definition_en": "An interface pattern in AI bridge architectures, measurable through a sports interaction phenomenon characterized by an athlete looks great in training data but performs differently in real matches. Numbers and real results do not match. The concept emerges specifically in contexts where competition–performance interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Brückensystemphänomen in KI-vermittelter domänenübergreifender Integration, gekennzeichnet durch phänomen in datengestützter Sport-KI-Anwendung, das Interaktion zwischen Athlet:in, Trainingsmetriken und KI-Systemen charakterisiert. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Bridge AI", "narrower_terms": [], "cross_domain_refs": [ "RHR-0246", "COP-0034", "ASE-0046" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q1361053", "legal_classification": "analytical_category" }, { "id": "SPR-0052", "domain": "SPR", "term_en": "Competition Simulation Data Gap", "term_de": "CompetitionSimulationDataGap", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A bridge system phenomenon in AI-mediated cross-domain integration, characterized by the gap widens between what happens in controlled training simulations (where data is clean and predictable) and what occurs in real competition (where the unexpected typically emerges). This phenomenon operates at the intersection of competition and simulation dynamics within the broader SPR domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept brückensystemphänomen in KI-vermittelter domänenübergreifender Integration, gekennzeichnet durch raum, in dem Athlet:innen Entscheidungen unabhängig treffen können trotz datengestützter Vorgaben. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "RPH-2355", "narrower_terms": [], "cross_domain_refs": [ "KNO-0005" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q476300", "legal_classification": "observational_construct" }, { "id": "SPR-0053", "domain": "SPR", "term_en": "Data Interpretation Expertise Gap", "term_de": "DataInterpretationExpertiseGap", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures A bridge system phenomenon in AI-mediated cross-domain integration, characterized by an organization possesses comprehensive performance data but lacks personnel with the knowledge and experience to extract accurate meaning from it. Raw metrics become noise rather than actionable i. This phenomenon operates at the intersection of data and interpretation dynamics within the broader SPR domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept brückensystemphänomen in KI-vermittelter domänenübergreifender Integration, gekennzeichnet durch aspekt von data interpretation expertise gap, der in SPR-Kontexten relevant ist und Interaktionen zwischen Menschen und KI-Systemen beeinflusst. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Performance Gap", "narrower_terms": [], "cross_domain_refs": [ "WRK-0067" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q42848", "legal_classification": "observational_construct" }, { "id": "SPR-0054", "domain": "SPR", "term_en": "Data Saturation Decision Making Impact", "term_de": "DataSaturationDecisionMakingImpact", "definition_en": "Too many available metrics paradoxically slow decision-making because coaches and athletes process, reconcile, and prioritize competing signals. Choice becomes harder as information exceeds cogniti... Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Brückensystemphänomen in KI-vermittelter domänenübergreifender Integration, gekennzeichnet durch phänomen in datengestützter Sport-KI-Anwendung, das Interaktion zwischen Athlet:in, Trainingsmetriken und KI-Systemen charakterisiert. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-3754", "narrower_terms": [], "cross_domain_refs": [ "COG-0074" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q230471", "legal_classification": "observational_construct" }, { "id": "SPR-0055", "domain": "SPR", "term_en": "Data-Driven Individual Difference Identification", "term_de": "Data-drivenIndividualDifferenceIdentification", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A bridge system phenomenon in AI-mediated cross-domain integration, characterized by an AI system reveals that athlete A responds completely differently to training stimuli than athlete B despite following identical programs. Data uncovers hidden individual differences that observa. This phenomenon operates at the intersection of data and driven dynamics within the broader SPR domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept brückensystemphänomen in KI-vermittelter domänenübergreifender Integration, gekennzeichnet durch phänomen in datengestützter Sport-KI-Anwendung, das Interaktion zwischen Athlet:in, Trainingsmetriken und KI-Systemen charakterisiert. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Bridge AI", "narrower_terms": [], "cross_domain_refs": [ "MKT-0061" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q42848", "legal_classification": "systematic_classification" }, { "id": "SPR-0056", "domain": "SPR", "term_en": "Data-Driven Overtraining Pattern", "term_de": "Data-drivenOvertrainingMuster", "definition_en": "A cross-system integration dynamic in AI-mediated workflows, identifiable by an athletic performance pattern where when athletes train too hard because they're chasing the numbers in their data instead of listening to how their body actually feels. Distinguished from adjacent concepts by its focus on the specific mechanism through which data manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Brückensystemphänomen in KI-vermittelter domänenübergreifender Integration, gekennzeichnet durch phänomen in datengestützter Sport-KI-Anwendung, das Interaktion zwischen Athlet:in, Trainingsmetriken und KI-Systemen charakterisiert. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Behavioral Pattern", "narrower_terms": [], "cross_domain_refs": [ "ART-0039", "MKT-0061", "SAL-0054" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q918461", "legal_classification": "empirical_phenomenon_label" }, { "id": "SPR-0057", "domain": "SPR", "term_en": "Data-Driven Team Dynamics Shift", "term_de": "Data-drivenTeamDynamicsShift", "definition_en": "interpersonal relationships and cooperation patterns within a team shift when individual performance metrics become visible to all members. the dynamics of trust, hierarchy, and mutual. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Brückensystemphänomen in KI-vermittelter domänenübergreifender Integration, gekennzeichnet durch aspekt von data-driven team dynamics shift, der in SPR-Kontexten relevant ist und Interaktionen zwischen Menschen und KI-Systemen beeinflusst. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2505", "narrower_terms": [], "cross_domain_refs": [ "ELR-0108" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q42848", "legal_classification": "analytical_category" }, { "id": "SPR-0058", "domain": "SPR", "term_en": "Data-Driven Training Adaptation", "term_de": "Data-drivenTrainingAnpassung", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures A bridge system phenomenon in AI-mediated cross-domain integration, characterized by a coach changes a workout on the spot based on live data or system feedback instead of following the planned schedule. This phenomenon operates at the intersection of data and driven dynamics within the broader SPR domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept brückensystemphänomen in KI-vermittelter domänenübergreifender Integration, gekennzeichnet durch aspekt von data-driven training adaptation, der in SPR-Kontexten relevant ist und Interaktionen zwischen Menschen und KI-Systemen beeinflusst. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "System Adaptation", "narrower_terms": [], "cross_domain_refs": [ "CON-0025" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q918461", "legal_classification": "systematic_classification" }, { "id": "SPR-0059", "domain": "SPR", "term_en": "Data-Driven Training Adaptation Effectiveness", "term_de": "Data-drivenTrainingAnpassungEffectiveness", "definition_en": "A cross-system integration dynamic in AI-mediated workflows, identifiable by a training dynamic involving how well a training plan actually improves an athlete when it's built on their personal data instead of general coaching rules. Distinguished from adjacent concepts by its focus on the specific mechanism through which data manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Brückensystemphänomen in KI-vermittelter domänenübergreifender Integration, gekennzeichnet durch phänomen in datengestützter Sport-KI-Anwendung, das Interaktion zwischen Athlet:in, Trainingsmetriken und KI-Systemen charakterisiert. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Psychological Effect", "narrower_terms": [], "cross_domain_refs": [ "AED-0075", "ART-0017", "ART-0032" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q918461", "legal_classification": "empirical_phenomenon_label" }, { "id": "SPR-0060", "domain": "SPR", "term_en": "Injury Likelihood Algorithmic Assessment", "term_de": "InjuryLikelihoodAlgorithmicAssessment", "definition_en": "A cross-system integration dynamic in AI-mediated workflows, identifiable by a coaching effect where a computer program predicts how likely an athlete is to get hurt, based on patterns in training data. These predictions are only as good as the data behind them. Distinguished from adjacent concepts by its focus on the specific mechanism through which injury manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Brückensystemphänomen in KI-vermittelter domänenübergreifender Integration, gekennzeichnet durch phänomen in datengestützter Sport-KI-Anwendung, das Interaktion zwischen Athlet:in, Trainingsmetriken und KI-Systemen charakterisiert. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Algorithm", "narrower_terms": [], "cross_domain_refs": [ "ASE-0011", "ELR-0024", "ASE-0053" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q210028", "legal_classification": "descriptive_research_term" }, { "id": "SPR-0061", "domain": "SPR", "term_en": "Injury Likelihood Assessment Divergence", "term_de": "InjuryLikelihoodAssessmentDivergenz", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures A bridge system phenomenon in AI-mediated cross-domain integration, characterized by a sports interaction phenomenon in which the algorithm says an athlete is fine, but an experienced coach sees warning signs in how the athlete moves or behaves. Numbers and instinct disagree. This phenomenon operates at the intersection of injury and likelihood dynamics within the broader SPR domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept brückensystemphänomen in KI-vermittelter domänenübergreifender Integration, gekennzeichnet durch phänomen in datengestützter Sport-KI-Anwendung, das Interaktion zwischen Athlet:in, Trainingsmetriken und KI-Systemen charakterisiert. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Bridge AI", "narrower_terms": [], "cross_domain_refs": [ "AGE-0067", "VIB-0036", "AGE-0078" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q210028", "legal_classification": "observational_construct" }, { "id": "SPR-0062", "domain": "SPR", "term_en": "Injury Prediction Algorithm Reliance", "term_de": "InjuryPredictionAlgorithmReliance", "definition_en": "Trusting a computer system to predict when someone might get injured, which can correlate with overconfidence or missed personal warning signs. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Brückensystemphänomen in KI-vermittelter domänenübergreifender Integration, gekennzeichnet durch aspekt von injury prediction algorithm reliance, der in SPR-Kontexten relevant ist und Interaktionen zwischen Menschen und KI-Systemen beeinflusst. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2505", "narrower_terms": [], "cross_domain_refs": [ "RHR-0211" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q8366", "legal_classification": "empirical_phenomenon_label" }, { "id": "SPR-0063", "domain": "SPR", "term_en": "Injury Prevention Data Confidence", "term_de": "InjuryPreventionDataConfidence", "definition_en": "How much an athlete or coach trusts the numbers an AI system gives about possible injuries — and when that trust is higher or lower than expected. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Brückensystemphänomen in KI-vermittelter domänenübergreifender Integration, gekennzeichnet durch phänomen in datengestützter Sport-KI-Anwendung, das Interaktion zwischen Athlet:in, Trainingsmetriken und KI-Systemen charakterisiert. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-2505", "narrower_terms": [], "cross_domain_refs": [ "SOC-0039" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q42848", "legal_classification": "analytical_category" }, { "id": "SPR-0064", "domain": "SPR", "term_en": "Injury Prevention Prediction Confidence", "term_de": "InjuryPreventionPredictionConfidence", "definition_en": "An interface pattern in AI bridge architectures, measurable through how certain an AI system claims to be when predicting whether an athlete might get hurt — and whether that certainty matches reality. The concept emerges specifically in contexts where injury–prevention interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Brückensystemphänomen in KI-vermittelter domänenübergreifender Integration, gekennzeichnet durch phänomen in datengestützter Sport-KI-Anwendung, das Interaktion zwischen Athlet:in, Trainingsmetriken und KI-Systemen charakterisiert. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Bridge AI", "narrower_terms": [], "cross_domain_refs": [ "ROB-0099" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "SPR-0065", "domain": "SPR", "term_en": "Injury Restoration Optimization Prediction", "term_de": "InjuryRestorationOptimierungPrediction", "definition_en": "An interface pattern in AI bridge architectures, measurable through an athletic performance pattern characterized by an algorithm accompanies a predicted restoration timeline based on injury type, load data, and medical history. The output estimates when an athlete can safely return to competition, though individual h. The concept emerges specifically in contexts where injury–restoration interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Brückensystemphänomen in KI-vermittelter domänenübergreifender Integration, gekennzeichnet durch phänomen in datengestützter Sport-KI-Anwendung, das Interaktion zwischen Athlet:in, Trainingsmetriken und KI-Systemen charakterisiert. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-3601", "narrower_terms": [], "cross_domain_refs": [ "VIB-0184", "AED-0017", "ART-0085" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "SPR-0066", "domain": "SPR", "term_en": "Performance Ceiling Identification", "term_de": "PerformanceCeilingIdentification", "definition_en": "A cross-system integration dynamic in AI-mediated workflows, identifiable by an algorithm is associated with determining that an athlete has reached the upper limit of their performance capacity given current training structure, genetics, and methodology. the system signals. Distinguished from adjacent concepts by its focus on the specific mechanism through which performance manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Brückensystemphänomen in KI-vermittelter domänenübergreifender Integration, gekennzeichnet durch phänomen in datengestützter Sport-KI-Anwendung, das Interaktion zwischen Athlet:in, Trainingsmetriken und KI-Systemen charakterisiert. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2555", "narrower_terms": [], "cross_domain_refs": [ "TEM-0172" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q1361053", "legal_classification": "observational_construct" }, { "id": "SPR-0067", "domain": "SPR", "term_en": "Performance Ceiling Prediction Accuracy", "term_de": "PerformanceCeilingPredictionAccuracy", "definition_en": "An interface pattern in AI bridge architectures, measurable through a training dynamic characterized by how well AI guesses the true peak of an athlete versus guessing too high or too low. The concept emerges specifically in contexts where performance–ceiling interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Brückensystemphänomen in KI-vermittelter domänenübergreifender Integration, gekennzeichnet durch phänomen in datengestützter Sport-KI-Anwendung, das Interaktion zwischen Athlet:in, Trainingsmetriken und KI-Systemen charakterisiert. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Bridge AI", "narrower_terms": [], "cross_domain_refs": [ "SCR-0015" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q1361053", "legal_classification": "analytical_category" }, { "id": "SPR-0068", "domain": "SPR", "term_en": "Performance Comparison Emotional Response", "term_de": "PerformanceComparisonEmotionalResponse", "definition_en": "An interface pattern in AI bridge architectures, measurable through a training dynamic observed when when athletes view their performance metrics alongside those of teammates, various emotional and motivational responses emerge, depending on context, interpretation frameworks, and individual differences. The concept emerges specifically in contexts where performance–comparison interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Brückensystemphänomen in KI-vermittelter domänenübergreifender Integration, gekennzeichnet durch phänomen in datengestützter Sport-KI-Anwendung, das Interaktion zwischen Athlet:in, Trainingsmetriken und KI-Systemen charakterisiert. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2303", "narrower_terms": [], "cross_domain_refs": [ "CRE-0202", "SWE-0039", "MUS-0090" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q1361053", "legal_classification": "descriptive_research_term" }, { "id": "SPR-0069", "domain": "SPR", "term_en": "Performance Data Communication Gap", "term_de": "PerformanceDataCommunicationGap", "definition_en": "An interface pattern in AI bridge architectures, measurable through a training dynamic arising from data exists but athletes can't understand the dashboards showing it. Information without insight. The concept emerges specifically in contexts where performance–data interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Brückensystemphänomen in KI-vermittelter domänenübergreifender Integration, gekennzeichnet durch phänomen in datengestützter Sport-KI-Anwendung, das Interaktion zwischen Athlet:in, Trainingsmetriken und KI-Systemen charakterisiert. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Performance Gap", "narrower_terms": [], "cross_domain_refs": [ "DAT-0069", "RPH-1260", "STE-0023" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q1361053", "legal_classification": "systematic_classification" }, { "id": "SPR-0070", "domain": "SPR", "term_en": "Performance Data Comparison Skew", "term_de": "PerformanceDataComparisonSkew", "definition_en": "A cross-system integration dynamic in AI-mediated workflows, identifiable by a sports interaction phenomenon in which when comparing two athletes' stats, the numbers look objective but are skewed by different training history, environments, or equipment. Numbers look fair but aren't. Distinguished from adjacent concepts by its focus on the specific mechanism through which performance manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Brückensystemphänomen in KI-vermittelter domänenübergreifender Integration, gekennzeichnet durch phänomen in datengestützter Sport-KI-Anwendung, das Interaktion zwischen Athlet:in, Trainingsmetriken und KI-Systemen charakterisiert. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Bridge AI", "narrower_terms": [], "cross_domain_refs": [ "EDU-0031" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q1361053", "legal_classification": "systematic_classification" }, { "id": "SPR-0071", "domain": "SPR", "term_en": "Performance Data Interpretation Skill Gap", "term_de": "PerformanceDataInterpretationSkillGap", "definition_en": "A cross-system integration dynamic in AI-mediated workflows, identifiable by athletes have access to their performance metrics but lack the analytical knowledge or statistical literacy to translate numbers into meaningful training decisions. The data is visible but not acti. Distinguished from adjacent concepts by its focus on the specific mechanism through which performance manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Brückensystemphänomen in KI-vermittelter domänenübergreifender Integration, gekennzeichnet durch phänomen in datengestützter Sport-KI-Anwendung, das Interaktion zwischen Athlet:in, Trainingsmetriken und KI-Systemen charakterisiert. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Performance Gap", "narrower_terms": [], "cross_domain_refs": [ "ASE-0046", "DAT-0069", "EDU-0031" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q1361053", "legal_classification": "systematic_classification" }, { "id": "SPR-0072", "domain": "SPR", "term_en": "Performance Data Overread", "term_de": "PerformanceDataOverread", "definition_en": "An athletic performance pattern involving assuming numbers tell the whole story about how well someone performed, ignoring context like circumstances, effort, or hidden factors. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Brückensystemphänomen in KI-vermittelter domänenübergreifender Integration, gekennzeichnet durch aspekt von performance data overread, der in SPR-Kontexten relevant ist und Interaktionen zwischen Menschen und KI-Systemen beeinflusst. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-3802", "narrower_terms": [], "cross_domain_refs": [ "AED-0077", "ART-0017", "ART-0032" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q1361053", "legal_classification": "descriptive_research_term" }, { "id": "SPR-0073", "domain": "SPR", "term_en": "Performance Interpretation Asymmetry", "term_de": "PerformanceInterpretationAsymmetry", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A bridge system phenomenon in AI-mediated cross-domain integration, characterized by a sports interaction phenomenon characterized by positive metrics are accepted without scrutiny while negative metrics accompany deep investigation and doubt. The threshold for believing data changes depending on whether the numbers confirm or cont. This phenomenon operates at the intersection of performance and interpretation dynamics within the broader SPR domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept brückensystemphänomen in KI-vermittelter domänenübergreifender Integration, gekennzeichnet durch aspekt von performance interpretation asymmetry, der in SPR-Kontexten relevant ist und Interaktionen zwischen Menschen und KI-Systemen beeinflusst. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "RPH-3304", "narrower_terms": [], "cross_domain_refs": [ "ADA-0012" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q1361053", "legal_classification": "analytical_category" }, { "id": "SPR-0074", "domain": "SPR", "term_en": "Performance Metric Overreliance", "term_de": "PerformanceMetricOverreliance", "definition_en": "A cross-system integration dynamic in AI-mediated workflows, identifiable by coaches make choices using only numbers while ignoring what athletes report, the situation, or past experience. Distinguished from adjacent concepts by its focus on the specific mechanism through which performance manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Brückensystemphänomen in KI-vermittelter domänenübergreifender Integration, gekennzeichnet durch phänomen in datengestützter Sport-KI-Anwendung, das Interaktion zwischen Athlet:in, Trainingsmetriken und KI-Systemen charakterisiert. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Performance Metric", "narrower_terms": [], "cross_domain_refs": [ "ADA-0012", "AED-0077", "ART-0010" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q1361053", "legal_classification": "descriptive_research_term" }, { "id": "SPR-0075", "domain": "SPR", "term_en": "Performance Metric Selection Skew", "term_de": "PerformanceMetricSelectionSkew", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures A bridge system phenomenon in AI-mediated cross-domain integration, characterized by a sports interaction phenomenon reflecting which metrics to monitor accompanies systematic bias in how athletes are ranked and evaluated. selecting speed metrics favors certain athletes while endurance metrics favor others—the. This phenomenon operates at the intersection of performance and metric dynamics within the broader SPR domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept brückensystemphänomen in KI-vermittelter domänenübergreifender Integration, gekennzeichnet durch phänomen in datengestützter Sport-KI-Anwendung, das Interaktion zwischen Athlet:in, Trainingsmetriken und KI-Systemen charakterisiert. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Performance Metric", "narrower_terms": [], "cross_domain_refs": [ "ADA-0012" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q1361053", "legal_classification": "systematic_classification" }, { "id": "SPR-0076", "domain": "SPR", "term_en": "Performance Metric Standardization Across Teams", "term_de": "PerformanceMetricStandardizationAcrossTeams", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures A bridge system phenomenon in AI-mediated cross-domain integration, characterized by teams measure the same skill in different ways, making it hard to compare athletes across organizations. This phenomenon operates at the intersection of performance and metric dynamics within the broader SPR domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept brückensystemphänomen in KI-vermittelter domänenübergreifender Integration, gekennzeichnet durch phänomen in datengestützter Sport-KI-Anwendung, das Interaktion zwischen Athlet:in, Trainingsmetriken und KI-Systemen charakterisiert. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Performance Metric", "narrower_terms": [], "cross_domain_refs": [ "REL-0209" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q1361053", "legal_classification": "empirical_phenomenon_label" }, { "id": "SPR-0077", "domain": "SPR", "term_en": "Performance Plateau Detection Lag", "term_de": "PerformancePlateauDetectionLag", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures A bridge system phenomenon in AI-mediated cross-domain integration, characterized by a sports interaction phenomenon manifesting as not realizing someone has stopped improving until well after it happens, missing the moment when they actually stopped progressing. This phenomenon operates at the intersection of performance and plateau dynamics within the broader SPR domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept brückensystemphänomen in KI-vermittelter domänenübergreifender Integration, gekennzeichnet durch aspekt von performance plateau detection lag, der in SPR-Kontexten relevant ist und Interaktionen zwischen Menschen und KI-Systemen beeinflusst. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Detection System", "narrower_terms": [], "cross_domain_refs": [ "RPH-1209" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q1361053", "legal_classification": "systematic_classification" }, { "id": "SPR-0078", "domain": "SPR", "term_en": "Real-Time Biometric Feedback Loop", "term_de": "Real-timeBiometricRückkopplungSchleife", "definition_en": "A cross-system integration dynamic in AI-mediated workflows, identifiable by an athlete adjusts their training response after seeing real-time heart rate or power output numbers appearing during a workout. The act of watching and reacting to these metrics may itself alter w. Distinguished from adjacent concepts by its focus on the specific mechanism through which real manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Brückensystemphänomen in KI-vermittelter domänenübergreifender Integration, gekennzeichnet durch phänomen in datengestützter Sport-KI-Anwendung, das Interaktion zwischen Athlet:in, Trainingsmetriken und KI-Systemen charakterisiert. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Feedback Loop", "narrower_terms": [], "cross_domain_refs": [ "ASE-0038", "BEH-0006", "BEH-0040" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "SPR-0079", "domain": "SPR", "term_en": "Real-Time Decision Making Load", "term_de": "Real-timeDecisionMakingLoad", "definition_en": "A cross-system integration dynamic in AI-mediated workflows, identifiable by a sports interaction phenomenon in which coaches receive constant live data streams during games or practices and feel compelled to make immediate adjustments based on that information. The pace of incoming metrics outpaces their ability. Distinguished from adjacent concepts by its focus on the specific mechanism through which real manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data. Analytical category without normative endorsement.", "definition_de": "Brückensystemphänomen in KI-vermittelter domänenübergreifender Integration, gekennzeichnet durch aspekt von real-time decision making load, der in SPR-Kontexten relevant ist und Interaktionen zwischen Menschen und KI-Systemen beeinflusst. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Bridge AI", "narrower_terms": [], "cross_domain_refs": [ "CUS-0001", "CUS-0006", "CUS-0037" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q230471", "legal_classification": "empirical_phenomenon_label" }, { "id": "SPR-0080", "domain": "SPR", "term_en": "Real-Time Feedback Adaptation Speed", "term_de": "Real-timeRückkopplungAnpassungSpeed", "definition_en": "The rate at which an athlete can actually modify their technique or effort in response to real-time feedback from AI systems. Continuous or exceeding capacity feedback may paradoxically inhibit ada... Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Brückensystemphänomen in KI-vermittelter domänenübergreifender Integration, gekennzeichnet durch phänomen in datengestützter Sport-KI-Anwendung, das Interaktion zwischen Athlet:in, Trainingsmetriken und KI-Systemen charakterisiert. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-3754", "narrower_terms": [], "cross_domain_refs": [ "CUS-0060", "MTH-0057" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "SPR-0081", "domain": "SPR", "term_en": "Real-Time Feedback Reliance", "term_de": "Real-timeRückkopplungReliance", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A bridge system phenomenon in AI-mediated cross-domain integration, characterized by a coaching effect arising from athletes develop reliance on continuous live metrics and effort to perform effectively without seeing their data stream in real time. They require the digital display to confirm they are executin. This phenomenon operates at the intersection of real and time dynamics within the broader SPR domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept brückensystemphänomen in KI-vermittelter domänenübergreifender Integration, gekennzeichnet durch phänomen in datengestützter Sport-KI-Anwendung, das Interaktion zwischen Athlet:in, Trainingsmetriken und KI-Systemen charakterisiert. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Bridge AI", "narrower_terms": [], "cross_domain_refs": [ "RHR-0102" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "SPR-0082", "domain": "SPR", "term_en": "Real-Time Feedback Sensitivity", "term_de": "Real-timeRückkopplungSensitivity", "definition_en": "An interface pattern in AI bridge architectures, measurable through an athlete's performance becomes overly reactive to real-time numerical feedback, causing them to analyze most data shift instead of staying absorbed in the activity. Each metric change co-occurs with s. The concept emerges specifically in contexts where real–time interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Brückensystemphänomen in KI-vermittelter domänenübergreifender Integration, gekennzeichnet durch phänomen in datengestützter Sport-KI-Anwendung, das Interaktion zwischen Athlet:in, Trainingsmetriken und KI-Systemen charakterisiert. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2701", "narrower_terms": [], "cross_domain_refs": [ "CON-0012" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "SPR-0083", "domain": "SPR", "term_en": "Real-Time Performance Feedback Reliance", "term_de": "Real-timePerformanceRückkopplungReliance", "definition_en": "A cross-system integration dynamic in AI-mediated workflows, identifiable by a training dynamic in which real-time performance metrics become so integral to an athlete's training approach that without them they feel disoriented and question their own instincts. The data becomes the primary reference p. Distinguished from adjacent concepts by its focus on the specific mechanism through which real manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data. Observed phenomenon documented in human-AI interaction research.", "definition_de": "Brückensystemphänomen in KI-vermittelter domänenübergreifender Integration, gekennzeichnet durch phänomen in datengestützter Sport-KI-Anwendung, das Interaktion zwischen Athlet:in, Trainingsmetriken und KI-Systemen charakterisiert. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Bridge AI", "narrower_terms": [], "cross_domain_refs": [ "SCR-0075" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q1361053", "legal_classification": "analytical_category" }, { "id": "SPR-0084", "domain": "SPR", "term_en": "Restoration Metric Integration Difficulty", "term_de": "RestorationMetricIntegrationDifficulty", "definition_en": "An interface pattern in AI bridge architectures, measurable through coaches have access to multiple restoration indicators (HRV, sleep quality, soreness, mood) that sometimes align and sometimes contradict each other. Deciding which signal to trust becomes the core ch. The concept emerges specifically in contexts where restoration–metric interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Brückensystemphänomen in KI-vermittelter domänenübergreifender Integration, gekennzeichnet durch aspekt von restoration metric integration difficulty, der in SPR-Kontexten relevant ist und Interaktionen zwischen Menschen und KI-Systemen beeinflusst. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2505", "narrower_terms": [], "cross_domain_refs": [ "AGE-0043" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "SPR-0085", "domain": "SPR", "term_en": "Restoration Metric Interpretation", "term_de": "RestorationMetricInterpretation", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A bridge system phenomenon in AI-mediated cross-domain integration, characterized by a coaching effect involving restoration score numbers exist but don't observably mean what they claim. Confusing data. This phenomenon operates at the intersection of restoration and metric dynamics within the broader SPR domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept brückensystemphänomen in KI-vermittelter domänenübergreifender Integration, gekennzeichnet durch aspekt von restoration metric interpretation, der in SPR-Kontexten relevant ist und Interaktionen zwischen Menschen und KI-Systemen beeinflusst. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Analytical Ratio", "narrower_terms": [], "cross_domain_refs": [ "ART-0071" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "SPR-0086", "domain": "SPR", "term_en": "Restoration Monitoring Accuracy Improvement", "term_de": "RestorationMonitoringAccuracyImprovement", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A bridge system phenomenon in AI-mediated cross-domain integration, characterized by an AI system's predictions about an athlete's restoration state improve over time as it collects and analyzes more individual data. The longer it monitors a specific person, the more effectively it learns their. This phenomenon operates at the intersection of restoration and monitoring dynamics within the broader SPR domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept brückensystemphänomen in KI-vermittelter domänenübergreifender Integration, gekennzeichnet durch phänomen in datengestützter Sport-KI-Anwendung, das Interaktion zwischen Athlet:in, Trainingsmetriken und KI-Systemen charakterisiert. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "RPH-2703", "narrower_terms": [], "cross_domain_refs": [ "VIB-0142", "ELR-0102", "SWE-0058" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "SPR-0087", "domain": "SPR", "term_en": "Restoration Optimization Instinct", "term_de": "RestorationOptimierungInstinct", "definition_en": "A cross-system integration dynamic in AI-mediated workflows, identifiable by an athlete has a strong sense of what their body needs for restoration, yet the AI system recommends something different. Bodily sensing sometimes proves more reliable than algorithmic calculation. Distinguished from adjacent concepts by its focus on the specific mechanism through which restoration manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Brückensystemphänomen in KI-vermittelter domänenübergreifender Integration, gekennzeichnet durch phänomen in datengestützter Sport-KI-Anwendung, das Interaktion zwischen Athlet:in, Trainingsmetriken und KI-Systemen charakterisiert. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Analytical Ratio", "narrower_terms": [], "cross_domain_refs": [ "RPH-2354" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "SPR-0088", "domain": "SPR", "term_en": "Training Customization Effectiveness", "term_de": "TrainingCustomizationEffectiveness", "definition_en": "An interface pattern in AI bridge architectures, measurable through a training dynamic reflecting unproven belief that AI-made training plans work more effectively than standard ones for most athletes. The concept emerges specifically in contexts where training–customization interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Brückensystemphänomen in KI-vermittelter domänenübergreifender Integration, gekennzeichnet durch phänomen in datengestützter Sport-KI-Anwendung, das Interaktion zwischen Athlet:in, Trainingsmetriken und KI-Systemen charakterisiert. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Psychological Effect", "narrower_terms": [], "cross_domain_refs": [ "AED-0018", "AED-0075", "AED-0077" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q918461", "legal_classification": "descriptive_research_term" }, { "id": "SPR-0089", "domain": "SPR", "term_en": "Training Customization Effectiveness Variance", "term_de": "TrainingCustomizationEffectivenessVariance", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A bridge system phenomenon in AI-mediated cross-domain integration, characterized by a coaching effect arising from aI training plans work well for some athletes but not others, with no clear reason why. This phenomenon operates at the intersection of training and customization dynamics within the broader SPR domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept brückensystemphänomen in KI-vermittelter domänenübergreifender Integration, gekennzeichnet durch phänomen in datengestützter Sport-KI-Anwendung, das Interaktion zwischen Athlet:in, Trainingsmetriken und KI-Systemen charakterisiert. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Psychological Effect", "narrower_terms": [], "cross_domain_refs": [ "AED-0075", "AED-0091" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q918461", "legal_classification": "empirical_phenomenon_label" }, { "id": "SPR-0090", "domain": "SPR", "term_en": "Training Deviation From Algorithm", "term_de": "TrainingDeviationFromAlgorithm", "definition_en": "A coaching effect where a coach or athlete deliberately avoids the AI's training suggestion and does something different instead. This usually happens because they trust their own judgment more. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Brückensystemphänomen in KI-vermittelter domänenübergreifender Integration, gekennzeichnet durch phänomen in datengestützter Sport-KI-Anwendung, das Interaktion zwischen Athlet:in, Trainingsmetriken und KI-Systemen charakterisiert. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2505", "narrower_terms": [], "cross_domain_refs": [ "LIN-0097", "MUS-0056", "MKT-0009" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q8366", "legal_classification": "systematic_classification" }, { "id": "SPR-0091", "domain": "SPR", "term_en": "Training Individualization Paradox", "term_de": "TrainingIndividualizationParadox", "definition_en": "An athletic performance pattern observed when striving to involve a perfectly personalized training plan makes it so specific and detailed that it becomes inflexible. The plan cannot adapt when real-life circumstances unexpectedly change. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Brückensystemphänomen in KI-vermittelter domänenübergreifender Integration, gekennzeichnet durch aspekt von training individualization paradox, der in SPR-Kontexten relevant ist und Interaktionen zwischen Menschen und KI-Systemen beeinflusst. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-1304", "narrower_terms": [], "cross_domain_refs": [ "REL-0083" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q918461", "legal_classification": "analytical_category" }, { "id": "SPR-0092", "domain": "SPR", "term_en": "Training Intensity Calibration Gap", "term_de": "TrainingIntensityCalibrationGap", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures A bridge system phenomenon in AI-mediated cross-domain integration, characterized by a coaching effect where the AI specifies a specific training intensity, but the athlete cannot actually execute it or the coach believes the setting is wrong. A gap opens between what the algorithm says and what works in. This phenomenon operates at the intersection of training and intensity dynamics within the broader SPR domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept brückensystemphänomen in KI-vermittelter domänenübergreifender Integration, gekennzeichnet durch phänomen in datengestützter Sport-KI-Anwendung, das Interaktion zwischen Athlet:in, Trainingsmetriken und KI-Systemen charakterisiert. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Analytical Ratio", "narrower_terms": [], "cross_domain_refs": [ "AED-0092" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q918461", "legal_classification": "descriptive_research_term" }, { "id": "SPR-0093", "domain": "SPR", "term_en": "Training Intensity Precision Increase", "term_de": "TrainingIntensityPrecisionIncrease", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A bridge system phenomenon in AI-mediated cross-domain integration, characterized by a precision enhancement effect in sports coaching where vague qualitative instructions are systematically replaced by quantitative metrics derived from AI-driven performance analysis, enabling athletes to calibrate training loads with measurable specificity. This phenomenon operates at the intersection of training and intensity dynamics within the broader SPR domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept brückensystemphänomen in KI-vermittelter domänenübergreifender Integration, gekennzeichnet durch aspekt von training intensity precision increase, der in SPR-Kontexten relevant ist und Interaktionen zwischen Menschen und KI-Systemen beeinflusst. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Model Training", "narrower_terms": [], "cross_domain_refs": [ "DES-0031" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q918461", "legal_classification": "descriptive_research_term" }, { "id": "SPR-0094", "domain": "SPR", "term_en": "Training Load Distribution Algorithm", "term_de": "TrainingLoadDistributionAlgorithm", "definition_en": "A cross-system integration dynamic in AI-mediated workflows, identifiable by a system that spreads training across days and weeks, deciding when to push hard and when to restore — based on data, not just feeling. Distinguished from adjacent concepts by its focus on the specific mechanism through which training manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Brückensystemphänomen in KI-vermittelter domänenübergreifender Integration, gekennzeichnet durch aspekt von training load distribution algorithm, der in SPR-Kontexten relevant ist und Interaktionen zwischen Menschen und KI-Systemen beeinflusst. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Algorithm", "narrower_terms": [], "cross_domain_refs": [ "WRK-0095" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q8366", "legal_classification": "analytical_category" }, { "id": "SPR-0095", "domain": "SPR", "term_en": "Training Load Guideline Standardization", "term_de": "TrainingLoadGuidelineStandardization", "definition_en": "An interface pattern in AI bridge architectures, measurable through a training dynamic characterized by sports teams try to involve standardized training load guidelines for all athletes, yet individuals respond differently to the same stimulus. Intense effort for one athlete registers as moderate for. The concept emerges specifically in contexts where training–load interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Brückensystemphänomen in KI-vermittelter domänenübergreifender Integration, gekennzeichnet durch phänomen in datengestützter Sport-KI-Anwendung, das Interaktion zwischen Athlet:in, Trainingsmetriken und KI-Systemen charakterisiert. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-3902", "narrower_terms": [], "cross_domain_refs": [ "COG-0175", "LIN-0064", "TEW-0098" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q918461", "legal_classification": "empirical_phenomenon_label" }, { "id": "SPR-0096", "domain": "SPR", "term_en": "Training Load Optimization Paradox", "term_de": "TrainingLoadOptimierungParadox", "definition_en": "A cross-system integration dynamic in AI-mediated workflows, identifiable by a coaching effect characterized by more training does not typically make athletes faster—sometimes less, smarter training functions differently. Distinguished from adjacent concepts by its focus on the specific mechanism through which training manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Brückensystemphänomen in KI-vermittelter domänenübergreifender Integration, gekennzeichnet durch phänomen in datengestützter Sport-KI-Anwendung, das Interaktion zwischen Athlet:in, Trainingsmetriken und KI-Systemen charakterisiert. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Logical Paradox", "narrower_terms": [], "cross_domain_refs": [ "COG-0086", "WEB-0006" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q918461", "legal_classification": "analytical_category" }, { "id": "SPR-0097", "domain": "SPR", "term_en": "Training Load Quantification Paradox", "term_de": "TrainingLoadQuantificationParadox", "definition_en": "An interface pattern in AI bridge architectures, measurable through a sports interaction phenomenon characterized by measuring training effort in numbers is useful but cannot capture everything that matters for improvement. The concept emerges specifically in contexts where training–load interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Brückensystemphänomen in KI-vermittelter domänenübergreifender Integration, gekennzeichnet durch aspekt von training load quantification paradox, der in SPR-Kontexten relevant ist und Interaktionen zwischen Menschen und KI-Systemen beeinflusst. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Logical Paradox", "narrower_terms": [], "cross_domain_refs": [ "CON-0030" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q918461", "legal_classification": "observational_construct" }, { "id": "SPR-0098", "domain": "SPR", "term_en": "Training Load Restoration Balance", "term_de": "TrainingLoadRestorationBalance", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A bridge system phenomenon in AI-mediated cross-domain integration, characterized by the endless optimization goal: find the precise point where training is rigorous enough to drive adaptation, yet light enough to permit restoration. Misjudge by a. This phenomenon operates at the intersection of training and load dynamics within the broader SPR domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept brückensystemphänomen in KI-vermittelter domänenübergreifender Integration, gekennzeichnet durch aspekt von training load restoration balance, der in SPR-Kontexten relevant ist und Interaktionen zwischen Menschen und KI-Systemen beeinflusst. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "RPH-3002", "narrower_terms": [], "cross_domain_refs": [ "ELR-0107" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q918461", "legal_classification": "analytical_category" }, { "id": "SPR-0099", "domain": "SPR", "term_en": "Training Periodization Algorithm Integration", "term_de": "TrainingPeriodizationAlgorithmIntegration", "definition_en": "A coaching effect manifesting as coaches blending their old-style planned training phases with AI suggestions. Sometimes the two approaches clash; sometimes they align perfectly. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Brückensystemphänomen in KI-vermittelter domänenübergreifender Integration, gekennzeichnet durch aspekt von training periodization algorithm integration, der in SPR-Kontexten relevant ist und Interaktionen zwischen Menschen und KI-Systemen beeinflusst. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-3251", "narrower_terms": [], "cross_domain_refs": [ "ROB-0244", "MKT-0009", "ART-0033" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q8366", "legal_classification": "empirical_phenomenon_label" }, { "id": "SPR-0100", "domain": "SPR", "term_en": "Training Progression Algorithm Trust", "term_de": "TrainingProgressionAlgorithmTrust", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures A bridge system phenomenon in AI-mediated cross-domain integration, characterized by a training dynamic characterized by doubt while following AI-generated training plans. Uncertainty: is this really helping me improve?. This phenomenon operates at the intersection of training and progression dynamics within the broader SPR domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept brückensystemphänomen in KI-vermittelter domänenübergreifender Integration, gekennzeichnet durch aspekt von training progression algorithm trust, der in SPR-Kontexten relevant ist und Interaktionen zwischen Menschen und KI-Systemen beeinflusst. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "RPH-3304", "narrower_terms": [], "cross_domain_refs": [ "PHO-0005", "GAM-0012", "EDU-0064" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q8366", "legal_classification": "descriptive_research_term" }, { "id": "SPR-0101", "domain": "SPR", "term_en": "Tactical AI Recommendation Opacity", "term_de": "Taktische KI-Empfehlungsintransparenz", "definition_en": "A behavioral tendency where AI-generated tactical suggestions during live competition remain opaque to coaching staff, creating tension between algorithmic accuracy and human comprehension. Coaches report inability to contextualize recommendations without understanding the underlying decision logic.", "definition_de": "Brückensystemphänomen in KI-vermittelter domänenübergreifender Integration, gekennzeichnet durch das Phänomen, bei dem KI-generierte taktische Vorschläge während des Wettkampfs für das Trainerteam undurchsichtig bleiben und Spannungen zwischen algorithmischer Genauigkeit und menschlichem Verständnis erzeugen. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "Bridge AI", "narrower_terms": [], "cross_domain_refs": [ "ELR-0078", "SWE-0013" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "SPR-0102", "domain": "SPR", "term_en": "Formation Optimization Paradox", "term_de": "Formationsoptimierungs-Paradoxon", "definition_en": "An interface pattern in AI bridge architectures, measurable through the paradox where AI-optimized team formations achieve higher theoretical win probability but reduce player autonomy and tactical creativity, potentially undermining the adaptive improvisation that characterizes elite performance. The concept emerges specifically in contexts where formation–optimization interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Brückensystemphänomen in KI-vermittelter domänenübergreifender Integration, gekennzeichnet durch das Paradoxon, bei dem KI-optimierte Teamformationen höhere theoretische Gewinnwahrscheinlichkeiten erzielen, aber Spielerautonomie und taktische Kreativität einschränken. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-3952", "narrower_terms": [], "cross_domain_refs": [ "GAM-0044" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "SPR-0103", "domain": "SPR", "term_en": "Contextual Bandit Coaching", "term_de": "Kontextuelle Bandit-Trainingssteuerung", "definition_en": "In the descriptive vocabulary of AI interaction science (following ISO 704 terminology standards), this concept identifies application of multi-armed bandit algorithms to real-time coaching decisions, where the system balances leverage (in a technical/analytical sense) of known effective strategies against exploration of novel tactical approaches during competition. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als analytisches Konstrukt der empirischen KI-Forschung (deskriptiv, nicht präskriptiv) erfasst dieser Terminus brückensystemphänomen in KI-vermittelter domänenübergreifender Integration, gekennzeichnet durch anwendung von Multi-Armed-Bandit-Algorithmen auf Echtzeit-Trainerentscheidungen, bei denen das System bekannte wirksame Strategien gegen die Erforschung neuer taktischer Ansätze abwägt. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Bridge AI", "narrower_terms": [], "cross_domain_refs": [ "DAT-0018", "MKT-0080" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q1068499", "legal_classification": "analytical_category" }, { "id": "SPR-0104", "domain": "SPR", "term_en": "Scenario Simulation Trust Gap", "term_de": "Szenariensimulations-Vertrauenslücke", "definition_en": "The discrepancy between AI scenario simulation accuracy rates exceeding 90 percent and coaching staff adoption rates below 40 percent, reflecting deep-seated trust barriers in human-AI tactical collaboration.", "definition_de": "Brückensystemphänomen in KI-vermittelter domänenübergreifender Integration, gekennzeichnet durch die Diskrepanz zwischen KI-Szenariensimulationsgenauigkeitsraten über 90 Prozent und Trainerakzeptanzraten unter 40 Prozent. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-2505", "narrower_terms": [], "cross_domain_refs": [ "COP-0033" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q202763", "legal_classification": "observational_construct" }, { "id": "SPR-0105", "domain": "SPR", "term_en": "Sub-Second Recommendation Constraint", "term_de": "Sub-Sekunden-Empfehlungsbeschränkung", "definition_en": "A coaching effect in which the technical requirement that AI tactical recommendations can be generated within 30 seconds during live competition, creating tension between computational depth and practical utility for coaching decisions.", "definition_de": "Brückensystemphänomen in KI-vermittelter domänenübergreifender Integration, gekennzeichnet durch die technische Anforderung, dass KI-Taktikempfehlungen innerhalb von 30 Sekunden während des Wettkampfs generiert werden können. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2005", "narrower_terms": [], "cross_domain_refs": [ "NEO-1197", "CON-0071", "CUS-0007" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "SPR-0106", "domain": "SPR", "term_en": "Graph Neural Network Tactics", "term_de": "Graph-Neuronale-Netz-Taktik", "definition_en": "A coaching effect observed when use of graph neural networks to encode spatial relationships between players, possession patterns, and formation structures for tactical analysis, enabling AI to model team dynamics as interconnected node systems.", "definition_de": "Brückensystemphänomen in KI-vermittelter domänenübergreifender Integration, gekennzeichnet durch verwendung von Graph-Neuronalen-Netzen zur Kodierung räumlicher Beziehungen zwischen Spielern, Ballbesitzmustern und Formationsstrukturen für die taktische Analyse. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Network Architecture", "narrower_terms": [], "cross_domain_refs": [ "LIN-0037", "IDN-0043" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q192776", "legal_classification": "empirical_phenomenon_label" }, { "id": "SPR-0107", "domain": "SPR", "term_en": "Reinforcement Learning Strategy Shift", "term_de": "Verstärkungslernen-Strategiewechsel", "definition_en": "A cross-system integration dynamic in AI-mediated workflows, identifiable by the application of Q-learning algorithms to compute expected match outcome changes based on formation modifications, player substitutions, and tactical adjustments during live competition. Distinguished from adjacent concepts by its focus on the specific mechanism through which reinforcement manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Brückensystemphänomen in KI-vermittelter domänenübergreifender Integration, gekennzeichnet durch die Anwendung von Q-Learning-Algorithmen zur Berechnung erwarteter Spielergebnisänderungen basierend auf Formationsänderungen und taktischen Anpassungen während des Wettkampfs. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2703", "narrower_terms": [], "cross_domain_refs": [ "GAM-0016", "GAM-0026" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q830829", "legal_classification": "analytical_category" }, { "id": "SPR-0108", "domain": "SPR", "term_en": "Confidence Threshold Communication", "term_de": "Konfidenzschwellen-Kommunikation", "definition_en": "The challenge of conveying algorithmic confidence levels to coaching staff in real-time, where misinterpreted probability scores can lead to either over-reliance on uncertain recommendations or dismissal of high-confidence insights.", "definition_de": "Brückensystemphänomen in KI-vermittelter domänenübergreifender Integration, gekennzeichnet durch die Herausforderung der Echtzeitkommunikation algorithmischer Konfidenzwerte an das Trainerteam. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Decision Threshold", "narrower_terms": [], "cross_domain_refs": [ "COG-0166", "CRE-0061" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "SPR-0109", "domain": "SPR", "term_en": "Fatigue-Integrated Substitution Logic", "term_de": "Ermüdungsintegrierte Auswechslungslogik", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures A bridge system phenomenon in AI-mediated cross-domain integration, characterized by a training dynamic in which aI systems that combine real-time fatigue monitoring with tactical analysis to recommend optimal substitution timing, integrating physiological data streams with strategic match context. This phenomenon operates at the intersection of fatigue and integrated dynamics within the broader SPR domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept brückensystemphänomen in KI-vermittelter domänenübergreifender Integration, gekennzeichnet durch kI-Systeme, die Echtzeit-Ermüdungsüberwachung mit taktischer Analyse kombinieren, um optimale Auswechslungszeitpunkte zu empfehlen. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Bridge AI", "narrower_terms": [], "cross_domain_refs": [ "GAM-0027" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "SPR-0110", "domain": "SPR", "term_en": "Opponent Behavior Prediction Drift", "term_de": "Gegnerverhaltensprognose-Drift", "definition_en": "An interface pattern in AI bridge architectures, measurable through the degradation of AI tactical predictions when opponent teams deviate from historically observed patterns, revealing the limitations of pattern-matching approaches in dynamically adaptive competitive environments. The concept emerges specifically in contexts where opponent–behavior interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Brückensystemphänomen in KI-vermittelter domänenübergreifender Integration, gekennzeichnet durch die Verschlechterung von KI-Taktikvorhersagen, wenn gegnerische Teams von historisch beobachteten Mustern abweichen. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Systemic Drift", "narrower_terms": [], "cross_domain_refs": [ "GAM-0044", "GAM-0070" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "SPR-0111", "domain": "SPR", "term_en": "Algorithmic Fandom Construction", "term_de": "Algorithmische Fandom-Konstruktion", "definition_en": "A distinct interaction pattern where AI-driven narrative generation and predictive modeling shapes fan engagement patterns, creating personalized spectator experiences that may diverge from collective community viewing traditions.", "definition_de": "Brückensystemphänomen in KI-vermittelter domänenübergreifender Integration, gekennzeichnet durch das Phänomen, bei dem KI-gesteuerte Narrativgenerierung und prädiktive Modellierung Fan-Engagement-Muster formt. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-3901", "narrower_terms": [], "cross_domain_refs": [ "MUS-0024" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q8366", "legal_classification": "systematic_classification" }, { "id": "SPR-0112", "domain": "SPR", "term_en": "Smart Stadium Cognitive Load", "term_de": "Smart-Stadion-Kognitive-Belastung", "definition_en": "The information overload experienced by spectators in AI-enhanced venues where simultaneous multi-source data presentation from real-time metrics, augmented reality overlays, and personalized content exceeds cognitive processing capacity.", "definition_de": "Brückensystemphänomen in KI-vermittelter domänenübergreifender Integration, gekennzeichnet durch die Informationsüberflutung von Zuschauern in KI-erweiterten Veranstaltungsorten durch gleichzeitige Mehrquellen-Datenpräsentation. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-3754", "narrower_terms": [], "cross_domain_refs": [ "DAT-0095" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q1105712", "legal_classification": "analytical_category" }, { "id": "SPR-0113", "domain": "SPR", "term_en": "Authenticity Erosion Through Technology", "term_de": "Authentizitätserosion durch Technologie", "definition_en": "The concern expressed by over 50 percent of sports fans that excessive technology integration undermines the authentic live sports experience, creating tension between immersion enhancement and natural spectating.", "definition_de": "Brückensystemphänomen in KI-vermittelter domänenübergreifender Integration, gekennzeichnet durch die von über 50 Prozent der Sportfans geäußerte Sorge, dass übermäßige Technologieintegration das authentische Live-Sporterlebnis untergräbt. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-3501", "narrower_terms": [], "cross_domain_refs": [ "TEM-0173" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "SPR-0114", "domain": "SPR", "term_en": "Personalized Highlight Selection Bias", "term_de": "Personalisierte Highlight-Auswahlverzerrung", "definition_en": "A sports interaction phenomenon characterized by systematic bias in AI-curated highlight packages where engagement probability prediction algorithms favor dramatic moments over tactically significant plays, distorting viewer understanding of match dynamics.", "definition_de": "Brückensystemphänomen in KI-vermittelter domänenübergreifender Integration, gekennzeichnet durch systematische Verzerrung in KI-kuratierten Highlight-Paketen, bei denen Engagement-Wahrscheinlichkeitsvorhersage-Algorithmen dramatische Momente gegenüber taktisch bedeutsamen Spielzügen bevorzugen. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-3002", "narrower_terms": [], "cross_domain_refs": [ "ASE-0006", "DAT-0001", "SPA-0036" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q213523", "legal_classification": "analytical_category" }, { "id": "SPR-0115", "domain": "SPR", "term_en": "Real-Time Sentiment Analysis Stadium", "term_de": "Echtzeit-Stimmungsanalyse im Stadion", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A bridge system phenomenon in AI-mediated cross-domain integration, characterized by aI systems analyzing social media streams and crowd behavior to assess collective emotional states within sports venues, enabling dynamic experience adjustment but raising surveillance concerns. This phenomenon operates at the intersection of real and time dynamics within the broader SPR domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept brückensystemphänomen in KI-vermittelter domänenübergreifender Integration, gekennzeichnet durch kI-Systeme, die Social-Media-Ströme und Zuschauerverhalten analysieren, um kollektive emotionale Zustände in Sportstätten zu erfassen. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Analytical Method", "narrower_terms": [], "cross_domain_refs": [ "MUS-0032", "RHR-0254" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "SPR-0116", "domain": "SPR", "term_en": "Fan Journey Optimization Friction", "term_de": "Fan-Journey-Optimierungsreibung", "definition_en": "The resistance encountered when AI systems attempt to optimize individual spectator paths through venues including seat selection, concession routing, and exit timing against fan preferences for spontaneous social interaction.", "definition_de": "Brückensystemphänomen in KI-vermittelter domänenübergreifender Integration, gekennzeichnet durch der Widerstand, wenn KI-Systeme versuchen, individuelle Zuschauerwege durch Veranstaltungsorte zu optimieren. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "Optimization Method", "narrower_terms": [], "cross_domain_refs": [ "COG-0046", "RET-0010", "WEB-0073" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "SPR-0117", "domain": "SPR", "term_en": "Dynamic Pricing Fairness Tension", "term_de": "Dynamische Preisgestaltung Fairness-Spannung", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures A bridge system phenomenon in AI-mediated cross-domain integration, characterized by the ethical tension between AI-driven dynamic ticket and concession pricing that maximizes revenue and principles of equitable access to sports events across socioeconomic demographics. This phenomenon operates at the intersection of dynamic and pricing dynamics within the broader SPR domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept brückensystemphänomen in KI-vermittelter domänenübergreifender Integration, gekennzeichnet durch die ethische Spannung zwischen KI-gesteuerter dynamischer Ticket- und Konzessionspreisgestaltung und Prinzipien des gerechten Zugangs zu Sportveranstaltungen. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Bridge AI", "narrower_terms": [], "cross_domain_refs": [ "MKT-0040", "SAL-0088" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q18005", "legal_classification": "empirical_phenomenon_label" }, { "id": "SPR-0118", "domain": "SPR", "term_en": "Augmented Reality Viewing Distortion", "term_de": "Augmented-Reality-Betrachtungsverzerrung", "definition_en": "A cross-system integration dynamic in AI-mediated workflows, identifiable by the perceptual distortion that occurs when AI-generated augmented reality overlays during live sports may create information layers that compete with direct visual attention to the actual sporting event. Distinguished from adjacent concepts by its focus on the specific mechanism through which augmented manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Brückensystemphänomen in KI-vermittelter domänenübergreifender Integration, gekennzeichnet durch die Wahrnehmungsverzerrung, die auftritt, wenn KI-generierte Augmented-Reality-Einblendungen während des Live-Sports Informationsschichten erzeugen, die mit der direkten visuellen Aufmerksamkeit konkurrieren. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Bridge AI", "narrower_terms": [], "cross_domain_refs": [ "CON-0019" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "SPR-0119", "domain": "SPR", "term_en": "Casual Versus Analytics Consumer Split", "term_de": "Gelegenheits- versus Analytik-Konsumenten-Spaltung", "definition_en": "The fundamental user experience design challenge of serving both casual sports viewers who prefer narrative-driven content and advanced analytics consumers who demand statistical depth within the same AI-enhanced broadcast.", "definition_de": "Brückensystemphänomen in KI-vermittelter domänenübergreifender Integration, gekennzeichnet durch die grundlegende UX-Design-Herausforderung, sowohl Gelegenheitssportzuschauer als auch fortgeschrittene Analytik-Konsumenten innerhalb derselben KI-erweiterten Übertragung zu bedienen. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2005", "narrower_terms": [], "cross_domain_refs": [ "RHR-0037", "NEO-1222", "CON-0003" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "SPR-0120", "domain": "SPR", "term_en": "Privacy Paradox In Fan Engagement", "term_de": "Datenschutz-Paradoxon im Fan-Engagement", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures A sports interaction phenomenon reflecting the contradiction where fans willingly share behavioral data for personalized AI processing interpreted as experiential by users while simultaneously expressing concern about surveillance implications of the same data collection in smart stadium environments. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept brückensystemphänomen in KI-vermittelter domänenübergreifender Integration, gekennzeichnet durch der Widerspruch, bei dem Fans bereitwillig Verhaltensdaten für personalisierte KI-Erlebnisse teilen und gleichzeitig Bedenken bezüglich der Überwachungsimplikationen derselben Datenerhebung äußern. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Logical Paradox", "narrower_terms": [], "cross_domain_refs": [ "SAL-0024", "STE-0031", "RHR-0074" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q18619", "legal_classification": "systematic_classification" }, { "id": "SPR-0121", "domain": "SPR", "term_en": "Excitement-Driven Commentary Generation", "term_de": "Spannungsgesteuerte Kommentargenerierung", "definition_en": "AI systems that dynamically modulate natural language generation based on computed excitement scores derived from match data, crowd response, and player expressions to may produce emotionally appropriate commentary.", "definition_de": "Brückensystemphänomen in KI-vermittelter domänenübergreifender Integration, gekennzeichnet durch kI-Systeme, die natürliche Sprachgenerierung basierend auf berechneten Spannungswerten aus Spielddaten, Zuschauerreaktionen und Spielerausdrücken dynamisch modulieren. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Analytical Ratio", "narrower_terms": [], "cross_domain_refs": [ "GAM-0031", "STE-0084" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "SPR-0122", "domain": "SPR", "term_en": "Emotional Resonance Gap In AI Commentary", "term_de": "Emotionale Resonanzlücke in KI-Kommentar", "definition_en": "The persistent quality difference between human and AI-generated sports commentary, where machine output lacks the spontaneous enthusiasm, cultural knowledge, and lived experience that characterize authentic broadcasting.", "definition_de": "Brückensystemphänomen in KI-vermittelter domänenübergreifender Integration, gekennzeichnet durch der anhaltende Qualitätsunterschied zwischen menschlichem und KI-generiertem Sportkommentar. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "Performance Gap", "narrower_terms": [], "cross_domain_refs": [ "VIB-0070", "ELR-0083" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q9415", "legal_classification": "empirical_phenomenon_label" }, { "id": "SPR-0123", "domain": "SPR", "term_en": "Multi-Language Commentary Simultaneity", "term_de": "Mehrsprachige Kommentar-Simultanität", "definition_en": "A cross-system integration dynamic in AI-mediated workflows, identifiable by the technical capability of generating parallel AI commentary in multiple languages during live broadcasts, requiring sport-specific vocabulary preservation across linguistic and cultural boundaries. Distinguished from adjacent concepts by its focus on the specific mechanism through which multi manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Brückensystemphänomen in KI-vermittelter domänenübergreifender Integration, gekennzeichnet durch die technische Fähigkeit, parallele KI-Kommentare in mehreren Sprachen während Live-Übertragungen zu generieren. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Bridge AI", "narrower_terms": [], "cross_domain_refs": [ "IDN-0058", "LIN-0020" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q315", "legal_classification": "descriptive_research_term" }, { "id": "SPR-0124", "domain": "SPR", "term_en": "Event Recognition Architecture", "term_de": "Ereigniserkennungsarchitektur", "definition_en": "Computer vision pipeline architecture that identifies and classifies sporting actions in real-time, enabling semantic segmentation of significant versus transitional moments for automated content generation.", "definition_de": "Brückensystemphänomen in KI-vermittelter domänenübergreifender Integration, gekennzeichnet durch computer-Vision-Pipeline-Architektur, die sportliche Aktionen in Echtzeit identifiziert und klassifiziert. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "Pattern Recognition", "narrower_terms": [], "cross_domain_refs": [ "LIN-0006" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q3966", "legal_classification": "empirical_phenomenon_label" }, { "id": "SPR-0125", "domain": "SPR", "term_en": "Sport-Specific Language Model Training", "term_de": "Sport-spezifisches Sprachmodell-Training", "definition_en": "The domain specialization process where large language models are fine-tuned on sport-specific corpora to acquire terminology, rule explanations, historical context, and player background knowledge for commentary generation.", "definition_de": "Brückensystemphänomen in KI-vermittelter domänenübergreifender Integration, gekennzeichnet durch der Domänenspezialisierungsprozess, bei dem große Sprachmodelle auf sportspezifischen Korpora feinabgestimmt werden. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-3202", "narrower_terms": [], "cross_domain_refs": [ "LIN-0003", "MTH-0089", "TRA-0085" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q315", "legal_classification": "analytical_category" }, { "id": "SPR-0126", "domain": "SPR", "term_en": "Cultural Idiom Localization Challenge", "term_de": "Kulturelle Idiom-Lokalisierungsherausforderung", "definition_en": "The difficulty of translating sport-specific cultural expressions, humor, irony, and play-on-words across languages in AI-generated commentary without losing communicative impact or creating unintended meanings.", "definition_de": "Brückensystemphänomen in KI-vermittelter domänenübergreifender Integration, gekennzeichnet durch die Schwierigkeit der Übersetzung sportspezifischer kultureller Ausdrücke, Humor und Wortspiele in KI-generiertem Kommentar über Sprachgrenzen hinweg. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Bridge AI", "narrower_terms": [], "cross_domain_refs": [ "AUG-0487", "TRA-0011" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "SPR-0127", "domain": "SPR", "term_en": "Automated Highlight Clip Generation", "term_de": "Automatisierte Highlight-Clip-Generierung", "definition_en": "An athletic performance pattern where aI systems capable of generating thousands of video clips with natural narration from sporting events, using computer vision to identify significant moments and natural language generation to may create contextual descriptions.", "definition_de": "Brückensystemphänomen in KI-vermittelter domänenübergreifender Integration, gekennzeichnet durch kI-Systeme, die Tausende von Videoclips mit natürlicher Narration aus Sportereignissen generieren können. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Analytical Ratio", "narrower_terms": [], "cross_domain_refs": [ "COP-0010", "CON-0052" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "SPR-0128", "domain": "SPR", "term_en": "Broadcaster Workforce Displacement Effect", "term_de": "Rundfunk-Arbeitskräfteverdrängungseffekt", "definition_en": "The socioeconomic impact of AI commentary systems on entry-level broadcast positions, where automation consolidates traditional production labor into AI oversight roles requiring different skill profiles.", "definition_de": "Brückensystemphänomen in KI-vermittelter domänenübergreifender Integration, gekennzeichnet durch die sozioökonomische Auswirkung von KI-Kommentarsystemen auf Einstiegspositionen im Rundfunk. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Psychological Effect", "narrower_terms": [], "cross_domain_refs": [ "BEH-0091", "RHR-0100" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "SPR-0129", "domain": "SPR", "term_en": "Rhythm Variation Matching", "term_de": "Rhythmus-Variations-Anpassung", "definition_en": "The AI capability of adjusting commentary delivery pace and prosodic patterns to match the temporal dynamics of sporting action, accelerating during fast-paced sequences and slowing during analytical moments.", "definition_de": "Brückensystemphänomen in KI-vermittelter domänenübergreifender Integration, gekennzeichnet durch die KI-Fähigkeit, Kommentar-Liefertempo und prosodische Muster an die zeitliche Dynamik sportlicher Aktionen anzupassen. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Bridge AI", "narrower_terms": [], "cross_domain_refs": [ "LNG-0017" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "SPR-0130", "domain": "SPR", "term_en": "Court Calibration Accuracy Standard", "term_de": "Platzkalibrierung-Genauigkeitsstandard", "definition_en": "The technical requirement for 97 percent accuracy in three-dimensional court or field model generation from multiple camera angles, establishing the spatial reference coordinate system for automated broadcasting analytics.", "definition_de": "Brückensystemphänomen in KI-vermittelter domänenübergreifender Integration, gekennzeichnet durch die technische Anforderung einer 97-prozentigen Genauigkeit bei der dreidimensionalen Spielfeld-Modellgenerierung aus mehreren Kamerawinkeln. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Analytical Ratio", "narrower_terms": [], "cross_domain_refs": [ "AED-0042", "TEW-0085", "BEH-0078" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "SPR-0131", "domain": "SPR", "term_en": "Semi-Automated Offside Detection", "term_de": "Halbautomatische Abseitserkennung", "definition_en": "In the descriptive vocabulary of AI interaction science (following ISO 704 terminology standards), this concept identifies computer vision-based player position tracking system that automates offside line calculation, replacing human operator judgment with algorithmic precision while maintaining human authority for final decision validation. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als analytisches Konstrukt der empirischen KI-Forschung (deskriptiv, nicht präskriptiv) erfasst dieser Terminus brückensystemphänomen in KI-vermittelter domänenübergreifender Integration, gekennzeichnet durch computer-Vision-basiertes Spielerpositions-Tracking-System, das die Abseitslinienberechnung automatisiert. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Detection System", "narrower_terms": [], "cross_domain_refs": [ "COG-0074", "VIB-0198" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "SPR-0132", "domain": "SPR", "term_en": "Factual Versus Discretionary Decision Split", "term_de": "Faktische versus Ermessensentscheidungs-Aufteilung", "definition_en": "The fundamental distinction in sports officiating AI between factual decisions where objective spatial-temporal measurement suffices and discretionary decisions requiring human judgment about intent and fairness.", "definition_de": "Brückensystemphänomen in KI-vermittelter domänenübergreifender Integration, gekennzeichnet durch die grundlegende Unterscheidung in der Sport-Schiedsrichter-KI zwischen faktischen Entscheidungen und Ermessensentscheidungen, die menschliches Urteil über Absicht und Fairness erfordern. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Bridge AI", "narrower_terms": [], "cross_domain_refs": [ "ROB-0073", "COG-0106" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "SPR-0133", "domain": "SPR", "term_en": "Algorithmic Error Accountability Void", "term_de": "Algorithmische Fehlerverantwortungslücke", "definition_en": "A training dynamic reflecting the unresolved legal and ethical question of responsibility allocation when AI officiating systems make incorrect calls, where neither the technology provider, the governing body, nor the human referee observably bears liability.", "definition_de": "Brückensystemphänomen in KI-vermittelter domänenübergreifender Integration, gekennzeichnet durch die ungelöste rechtliche und ethische Frage der Verantwortungszuweisung bei fehlerhaften Entscheidungen von KI-Schiedsrichtersystemen. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Algorithm", "narrower_terms": [], "cross_domain_refs": [ "AUG-0840", "RHR-0150" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q15223", "legal_classification": "observational_construct" }, { "id": "SPR-0134", "domain": "SPR", "term_en": "Goal-Line Technology Temporal Resolution", "term_de": "Torlinientechnologie-Zeitauflösung", "definition_en": "A sports interaction phenomenon arising from the sub-frame temporal precision required for AI systems to determine ball position at the exact moment of goal-line crossing, demanding multi-sensor fusion of optical and depth camera data at millisecond resolution.", "definition_de": "Brückensystemphänomen in KI-vermittelter domänenübergreifender Integration, gekennzeichnet durch die Sub-Frame-Zeitpräzision, die von KI-Systemen zur Bestimmung der Ballposition im exakten Moment der Torlinienüberquerung erforderlich ist. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2005", "narrower_terms": [], "cross_domain_refs": [ "TEW-0041", "BEH-0043", "AED-0043" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "SPR-0135", "domain": "SPR", "term_en": "Occlusion Failure In Automated Officiating", "term_de": "Verdeckungsfehler in automatisierter Schiedsrichtertätigkeit", "definition_en": "An athletic performance pattern reflecting the systematic error type where automated officiating systems fail due to player body occlusion blocking camera sightlines, representing a fundamental limitation of optical-based decision systems in dense physical interactions.", "definition_de": "Brückensystemphänomen in KI-vermittelter domänenübergreifender Integration, gekennzeichnet durch der systematische Fehlertyp, bei dem automatisierte Schiedsrichtersysteme aufgrund von Spielerkörperverdeckung versagen. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "Bridge AI", "narrower_terms": [], "cross_domain_refs": [ "GAM-0090" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "SPR-0136", "domain": "SPR", "term_en": "Weather Degradation Of Optical Systems", "term_de": "Wetterbedingte Verschlechterung optischer Systeme", "definition_en": "An athletic performance pattern characterized by the performance degradation of AI officiating systems under adverse weather conditions including rain, fog, and extreme lighting, where environmental factors reduce the reliability of camera-based decision technology.", "definition_de": "Brückensystemphänomen in KI-vermittelter domänenübergreifender Integration, gekennzeichnet durch die Leistungsverschlechterung von KI-Schiedsrichtersystemen bei widrigen Wetterbedingungen. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-3002", "narrower_terms": [], "cross_domain_refs": [ "RHR-0141", "MSC-0090", "MSC-0074" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "SPR-0137", "domain": "SPR", "term_en": "Human Judgment Preservation In Automation", "term_de": "Bewahrung menschlichen Urteils in der Automatisierung", "definition_en": "As a neutral analytical construct in AI behavior research, this term denotes the design principle that AI officiating systems may augment rather than task automation transition referees, preserving game flow management, emotional intelligence, and subjective determination capabilities within the human domain. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "In der AUGMANITAI-Terminologiewissenschaft als rein beschreibendes Phänomen klassifiziert, bezeichnet dieser Begriff brückensystemphänomen in KI-vermittelter domänenübergreifender Integration, gekennzeichnet durch das Designprinzip, dass KI-Schiedsrichtersysteme menschliche Schiedsrichter ergänzen statt ersetzen können. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Bridge AI", "narrower_terms": [], "cross_domain_refs": [ "BEH-0047", "COP-0077" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q184199", "legal_classification": "observational_construct" }, { "id": "SPR-0138", "domain": "SPR", "term_en": "Appeal Mechanism For Algorithmic Decisions", "term_de": "Einspruchsmechanismus für algorithmische Entscheidungen", "definition_en": "The procedural framework enabling teams and players to challenge AI-generated officiating decisions, requiring transparent decision logic documentation and human oversight protocols for algorithmic review.", "definition_de": "Brückensystemphänomen in KI-vermittelter domänenübergreifender Integration, gekennzeichnet durch der Verfahrensrahmen, der Teams und Spielern ermöglicht, KI-generierte Schiedsrichterentscheidungen anzufechten. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2303", "narrower_terms": [], "cross_domain_refs": [ "GAM-0025", "GAM-0075" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q8366", "legal_classification": "observational_construct" }, { "id": "SPR-0139", "domain": "SPR", "term_en": "Umpire Accuracy Improvement Quantification", "term_de": "Quantifizierung der Schiedsrichtergenauigkeitsverbesserung", "definition_en": "The measurable improvement in officiating accuracy following AI system introduction, with studies documenting 8 percent error rate decline in sports implementing automated line-calling technology over two decades.", "definition_de": "Brückensystemphänomen in KI-vermittelter domänenübergreifender Integration, gekennzeichnet durch die messbare Verbesserung der Schiedsrichtergenauigkeit nach Einführung von KI-Systemen. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "Bridge AI", "narrower_terms": [], "cross_domain_refs": [ "DAT-0088" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "SPR-0140", "domain": "SPR", "term_en": "Full Automation Line Calling Transition", "term_de": "Vollautomatisierungs-Linienentscheidungs-Übergang", "definition_en": "An athletic performance pattern reflecting the evolutionary trajectory from human line judges through AI-assisted verification to fully automated line calling, with projections indicating near-complete elimination of human line judges in professional competition by mid-decade.", "definition_de": "Brückensystemphänomen in KI-vermittelter domänenübergreifender Integration, gekennzeichnet durch der Entwicklungspfad von menschlichen Linienrichtern über KI-gestützte Verifizierung zur vollautomatischen Linienentscheidung. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Bridge AI", "narrower_terms": [], "cross_domain_refs": [ "CRE-0124", "TEM-0041" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q184199", "legal_classification": "analytical_category" }, { "id": "SPR-0141", "domain": "SPR", "term_en": "AI Gaming Coaching Effect", "term_de": "KI-Gaming-Copilot-Effekt", "definition_en": "The cognitive and competitive impact of real-time AI coaching systems that analyze gameplay screenshots to provide strategic recommendations during active esports competition, characteristically altering the skill-tool relationship.", "definition_de": "Brückensystemphänomen in KI-vermittelter domänenübergreifender Integration, gekennzeichnet durch die kognitive und wettbewerbliche Auswirkung von Echtzeit-KI-Coaching-Systemen, die Gameplay-Screenshots analysieren, um strategische Empfehlungen während des aktiven Esport-Wettbewerbs zu geben. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Psychological Effect", "narrower_terms": [], "cross_domain_refs": [ "SAL-0035", "COG-0061" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q1068499", "legal_classification": "observational_construct" }, { "id": "SPR-0142", "domain": "SPR", "term_en": "Playstyle Simulation Fidelity", "term_de": "Spielstil-Simulationstreue", "definition_en": "A coaching effect involving the degree to which machine learning models trained on individual player gameplay data can accurately replicate behavioral patterns and decision-making tendencies of real competitors for training purposes.", "definition_de": "Brückensystemphänomen in KI-vermittelter domänenübergreifender Integration, gekennzeichnet durch der Grad, zu dem maschinelle Lernmodelle Verhaltensmuster und Entscheidungstendenzen realer Wettbewerber für Trainingszwecke replizieren können. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2703", "narrower_terms": [], "cross_domain_refs": [ "GAM-0064", "MSC-0061" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q202763", "legal_classification": "systematic_classification" }, { "id": "SPR-0143", "domain": "SPR", "term_en": "Micro Versus Macro Decision Support", "term_de": "Mikro- versus Makro-Entscheidungsunterstützung", "definition_en": "The distinction between AI systems assisting with micro-level decisions such as unit control and ability timing versus macro-level strategic decisions including resource allocation and objective prioritization in competitive gaming.", "definition_de": "Brückensystemphänomen in KI-vermittelter domänenübergreifender Integration, gekennzeichnet durch die Unterscheidung zwischen KI-Systemen, die bei Mikro-Entscheidungen wie Einheitensteuerung unterstützen, und Makro-Entscheidungen wie Ressourcenallokation. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Bridge AI", "narrower_terms": [], "cross_domain_refs": [ "VIB-0043" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "SPR-0144", "domain": "SPR", "term_en": "Competitive Fairness In AI Assistance", "term_de": "Wettbewerbsfairness bei KI-Unterstützung", "definition_en": "An interface pattern in AI bridge architectures, measurable through the unresolved regulatory challenge of defining permissible AI assistance boundaries in esports competition, where asymmetric access to AI tools tends to create skill-versus-tool-amplification differentiation concerns. The concept emerges specifically in contexts where competitive–fairness interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Brückensystemphänomen in KI-vermittelter domänenübergreifender Integration, gekennzeichnet durch die ungelöste regulatorische Herausforderung der Definition zulässiger KI-Unterstützungsgrenzen im Esport-Wettbewerb. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Bridge AI", "narrower_terms": [], "cross_domain_refs": [ "VIB-0196", "AED-0048" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q18005", "legal_classification": "systematic_classification" }, { "id": "SPR-0145", "domain": "SPR", "term_en": "Human Skill Atrophy Through AI Coaching", "term_de": "Menschlicher Fähigkeitsschwund durch KI-Coaching", "definition_en": "The documented risk that over-reliance on AI coaching systems in esports reduces fundamental skill development, where players become reliant on algorithmic guidance for decisions they may internalize.", "definition_de": "Brückensystemphänomen in KI-vermittelter domänenübergreifender Integration, gekennzeichnet durch das dokumentierte Risiko, dass übermäßiges Vertrauen auf KI-Coaching-Systeme im Esport die grundlegende Fähigkeitsentwicklung reduziert. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2604", "narrower_terms": [], "cross_domain_refs": [ "SAL-0035", "SAL-0040", "PHO-0077" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q1068499", "legal_classification": "observational_construct" }, { "id": "SPR-0146", "domain": "SPR", "term_en": "Training Transfer Gap From AI Sparring", "term_de": "Trainingstransferlücke vom KI-Sparring", "definition_en": "An interface pattern in AI bridge architectures, measurable through the effectiveness limitation where skills developed through practice against AI opponents fail to transfer fully to human competition due to missing improvisation patterns and psychological dynamics. The concept emerges specifically in contexts where training–transfer interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Brückensystemphänomen in KI-vermittelter domänenübergreifender Integration, gekennzeichnet durch die Wirksamkeitsbeschränkung, bei der durch Training gegen KI-Gegner entwickelte Fähigkeiten nicht vollständig auf menschlichen Wettbewerb übertragbar sind. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Performance Gap", "narrower_terms": [], "cross_domain_refs": [ "DAT-0005" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q918461", "legal_classification": "descriptive_research_term" }, { "id": "SPR-0147", "domain": "SPR", "term_en": "Frame-Level Gameplay Analysis", "term_de": "Frame-Level-Gameplay-Analyse", "definition_en": "AI systems capable of dissecting competitive gameplay at individual frame resolution to identify strategic opportunities invisible to human perception, enabling millisecond-level tactical optimization.", "definition_de": "Brückensystemphänomen in KI-vermittelter domänenübergreifender Integration, gekennzeichnet durch kI-Systeme, die kompetitives Gameplay auf einzelner Frame-Ebene analysieren können, um strategische Möglichkeiten zu identifizieren, die der menschlichen Wahrnehmung verborgen bleiben. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-2303", "narrower_terms": [], "cross_domain_refs": [ "VIB-0145", "MKT-0054" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q11410", "legal_classification": "observational_construct" }, { "id": "SPR-0148", "domain": "SPR", "term_en": "Autonomous Strategy Environment", "term_de": "Autonome Strategie-Umgebung", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures A bridge system phenomenon in AI-mediated cross-domain integration, characterized by competitive AI environments where autonomous agents compete using programming-based strategy formulation, serving as research platforms for human-AI interaction in strategic decision-making. This phenomenon operates at the intersection of autonomous and strategy dynamics within the broader SPR domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept brückensystemphänomen in KI-vermittelter domänenübergreifender Integration, gekennzeichnet durch wettbewerbliche KI-Umgebungen, in denen autonome Agenten mit programmierungsbasierter Strategieformulierung konkurrieren. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Operational Strategy", "narrower_terms": [], "cross_domain_refs": [ "IDN-0007", "MTH-0100" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q185451", "legal_classification": "empirical_phenomenon_label" }, { "id": "SPR-0149", "domain": "SPR", "term_en": "Predictive Insight Broadcasting", "term_de": "Prädiktive Einblick-Übertragung", "definition_en": "A training dynamic observed when integration of AI-generated predictive analytics into esports broadcasts, providing viewers with probability estimates for match outcomes and strategic assessments that enhance spectator understanding.", "definition_de": "Brückensystemphänomen in KI-vermittelter domänenübergreifender Integration, gekennzeichnet durch integration von KI-generierten prädiktiven Analysen in Esport-Übertragungen. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Bridge AI", "narrower_terms": [], "cross_domain_refs": [ "ASE-0079", "CRE-0191", "DAT-0053" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "SPR-0150", "domain": "SPR", "term_en": "AI Opponent Behavioral Diversity Limit", "term_de": "KI-Gegner-Verhaltensvielfalt-Grenze", "definition_en": "An interface pattern in AI bridge architectures, measurable through a sports interaction phenomenon characterized by the constraint where AI training opponents exhibit narrower behavioral diversity than human competitor populations, potentially introducing systematic blind spots in competitive preparation strategies. The concept emerges specifically in contexts where ai–opponent interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Brückensystemphänomen in KI-vermittelter domänenübergreifender Integration, gekennzeichnet durch die Einschränkung, bei der KI-Trainingsgegner eine geringere Verhaltensvielfalt als menschliche Wettbewerberpopulationen aufweisen. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Bridge AI", "narrower_terms": [], "cross_domain_refs": [ "CUS-0079", "SWE-0053" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "SPR-0151", "domain": "SPR", "term_en": "Return-To-Play Decision Support System", "term_de": "Rückkehr-zum-Spielbetrieb-Entscheidungshilfe", "definition_en": "Multifactorial AI assessment integrating strength, proprioception, psychological readiness, and sport-specific demand matching to may generate risk-stratified clearance recommendations for injured athletes.", "definition_de": "Brückensystemphänomen in KI-vermittelter domänenübergreifender Integration, gekennzeichnet durch multifaktorielle KI-Bewertung, die Kraft, Propriozeption, psychologische Bereitschaft und sportspezifische Anforderungsabgleichung integriert. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-3802", "narrower_terms": [], "cross_domain_refs": [ "ROB-0211", "PLY-0057", "RET-0033" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "SPR-0152", "domain": "SPR", "term_en": "Adaptive Sports Medicine Strategy", "term_de": "Adaptive sportmedizinische Strategie", "definition_en": "Self-attention mechanism architecture that integrates physiological data streams for real-time injury risk prediction adjustment, adapting models based on incoming functional restoration data and athlete restoration responses.", "definition_de": "Brückensystemphänomen in KI-vermittelter domänenübergreifender Integration, gekennzeichnet durch selbst-Aufmerksamkeits-Mechanismus-Architektur, die physiologische Datenströme für die Echtzeit-Anpassung der Verletzungsrisikoprognose integriert. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Operational Strategy", "narrower_terms": [], "cross_domain_refs": [ "DAT-0044" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q185451", "legal_classification": "analytical_category" }, { "id": "SPR-0153", "domain": "SPR", "term_en": "Reinjury Risk Prediction Accuracy", "term_de": "Wiederholungsverletzungsrisiko-Prognosegenauigkeit", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures A coaching effect in which machine learning systems monitoring longitudinal biomechanical patterns during functional restoration to detect movement compensation behaviors that indicate elevated reinjury probability, achieving 28 percent reduction in reinjury rates. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept brückensystemphänomen in KI-vermittelter domänenübergreifender Integration, gekennzeichnet durch maschinelle Lernsysteme, die longitudinale biomechanische Muster während der functional restoration überwachen, um Bewegungskompensationsmuster zu erkennen. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "RPH-2703", "narrower_terms": [], "cross_domain_refs": [ "VIB-0098", "MSC-0010" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "SPR-0154", "domain": "SPR", "term_en": "AI-Feedback Motion Training", "term_de": "KI-Rückmeldungs-Bewegungstraining", "definition_en": "An athletic performance pattern observed when real-time computer vision pose estimation systems that compare current athlete movement to ideal biomechanical patterns, providing immediate kinesthetic feedback with progressive difficulty adjustment.", "definition_de": "Brückensystemphänomen in KI-vermittelter domänenübergreifender Integration, gekennzeichnet durch echtzeit-Computer-Vision-Posenschätzungssysteme, die aktuelle Athletenbewegungen mit idealen biomechanischen Mustern vergleichen. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "Model Training", "narrower_terms": [], "cross_domain_refs": [ "MSC-0021", "MSC-0032" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q918461", "legal_classification": "empirical_phenomenon_label" }, { "id": "SPR-0155", "domain": "SPR", "term_en": "Decision Authority Negotiation in Sports Analytics", "term_de": "Klinische Entscheidungsautorität-Verhandlung", "definition_en": "An interface pattern in AI bridge architectures, measurable through a training dynamic in which the ongoing tension between AI functional restoration recommendations and sports medicine physician expertise, where liability allocation for algorithm-based addressment planning remains legally ambiguous. The concept emerges specifically in contexts where systematic–decision interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Brückensystemphänomen in KI-vermittelter domänenübergreifender Integration, gekennzeichnet durch die anhaltende Spannung zwischen KI-functional restorationsempfehlungen und sportmedizinischer Arztexpertise. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Bridge AI", "narrower_terms": [], "cross_domain_refs": [ "RHR-0251", "SPA-0006" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "SPR-0156", "domain": "SPR", "term_en": "Multimodal Rehabilitation Data Fusion", "term_de": "Multimodale Rehabilitationsdaten-Fusion", "definition_en": "Integration of physiological, biomechanical, and psychological data streams into unified AI functional restoration models, addressing the challenge of individual restoration response variability across diverse athlete populations.", "definition_de": "Brückensystemphänomen in KI-vermittelter domänenübergreifender Integration, gekennzeichnet durch integration von physiologischen, biomechanischen und psychologischen Datenströmen in einheitliche KI-functional restorationsmodelle. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "Bridge AI", "narrower_terms": [], "cross_domain_refs": [ "ASE-0046", "LIN-0017", "DAT-0023" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q42848", "legal_classification": "observational_construct" }, { "id": "SPR-0157", "domain": "SPR", "term_en": "Medical Image Analysis Accuracy Gain", "term_de": "Medizinische Bildanalyse-Genauigkeitsgewinn", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A bridge system phenomenon in AI-mediated cross-domain integration, characterized by the documented 20 percent improvement in diagnostic accuracy when AI-driven medical image analysis is applied to sports injury assessment, particularly in musculoskeletal imaging interpretation. This phenomenon operates at the intersection of medical and image dynamics within the broader SPR domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept brückensystemphänomen in KI-vermittelter domänenübergreifender Integration, gekennzeichnet durch die dokumentierte 20-prozentige Verbesserung der diagnostischen Genauigkeit durch KI-gesteuerte medizinische Bildanalyse bei der Sportverletzungsbewertung. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Analytical Method", "narrower_terms": [], "cross_domain_refs": [ "PHO-0067", "ROB-0082" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "SPR-0158", "domain": "SPR", "term_en": "Recovery Compliance Algorithm Gap", "term_de": "Erholungs-Compliance-Algorithmus-Lücke", "definition_en": "A sports interaction phenomenon observed when the discrepancy between algorithmically optimal functional restoration protocols and actual individual adherence rates, where computer-mediated structured intervention faces motivation sustainability challenges absent direct human restorative relationships.", "definition_de": "Brückensystemphänomen in KI-vermittelter domänenübergreifender Integration, gekennzeichnet durch die Diskrepanz zwischen algorithmisch optimalen functional restorationsprotokollen und tatsächlichen individualen-Adhärenzraten. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Performance Gap", "narrower_terms": [], "cross_domain_refs": [ "RHR-0094", "MSC-0079", "RHR-0162" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q8366", "legal_classification": "analytical_category" }, { "id": "SPR-0159", "domain": "SPR", "term_en": "Random Forest Return Prediction", "term_de": "Random-Forest-Rückkehr-Prognose", "definition_en": "A sports interaction phenomenon manifesting as application of random forest machine learning models achieving 84 percent accuracy in return-to-sport prediction, integrating multiple injury parameters, functional restoration progress metrics, and historical restoration data.", "definition_de": "Brückensystemphänomen in KI-vermittelter domänenübergreifender Integration, gekennzeichnet durch anwendung von Random-Forest-Modellen mit 84-prozentiger Genauigkeit bei der Rückkehr-zum-Sport-Prognose. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2703", "narrower_terms": [], "cross_domain_refs": [ "DAT-0044" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "SPR-0160", "domain": "SPR", "term_en": "Gender Bias In Injury Prediction Models", "term_de": "Geschlechterverzerrung in Verletzungsprognosemodellen", "definition_en": "As a neutral analytical construct in AI behavior research, this term denotes an athletic performance pattern characterized by the critical finding that 93 percent male-dominated training datasets produce significantly degraded injury prediction accuracy for female athletes, representing a fundamental algorithmic fairness failure in sports medicine AI. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription. Research construct for empirical investigation.", "definition_de": "In der AUGMANITAI-Terminologiewissenschaft als rein beschreibendes Phänomen klassifiziert, bezeichnet dieser Begriff brückensystemphänomen in KI-vermittelter domänenübergreifender Integration, gekennzeichnet durch die kritische Erkenntnis, dass zu 93 Prozent männlich dominierte Trainingsdaten signifikant verschlechterte Verletzungsprognosegenauigkeit für Athletinnen erzeugen. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Cognitive Bias", "narrower_terms": [], "cross_domain_refs": [ "PHO-0067" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q213523", "legal_classification": "systematic_classification" }, { "id": "SPR-0161", "domain": "SPR", "term_en": "Comparative Scouting Analytics", "term_de": "Vergleichende Scout-Analytik", "definition_en": "Database player similarity matching using feature embeddings that identify historical comparable players for performance trajectory modeling, enabling data-driven prospect evaluation beyond traditional subjective assessment.", "definition_de": "Brückensystemphänomen in KI-vermittelter domänenübergreifender Integration, gekennzeichnet durch datenbank-Spielerähnlichkeitsabgleich mittels Merkmalseinbettungen zur Identifizierung historisch vergleichbarer Spieler für die Leistungstrajektorie-Modellierung. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "Bridge AI", "narrower_terms": [], "cross_domain_refs": [ "DAT-0044", "GAM-0016" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "SPR-0162", "domain": "SPR", "term_en": "Multimodal Prospect Profiling", "term_de": "Multimodale Prospekt-Profilierung", "definition_en": "Integrated evaluation framework combining video-based skill assessment, biometric data, psychological testing results, and competitive context normalization to may generate comprehensive prospect assessments.", "definition_de": "Brückensystemphänomen in KI-vermittelter domänenübergreifender Integration, gekennzeichnet durch integriertes Bewertungsrahmenwerk, das videobasierte Fähigkeitsbewertung, biometrische Daten und psychologische Testergebnisse kombiniert. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-2303", "narrower_terms": [], "cross_domain_refs": [ "GAM-0067" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "SPR-0163", "domain": "SPR", "term_en": "Predictive Draft Board Ranking", "term_de": "Prädiktive Draftboard-Rangfolge", "definition_en": "Machine learning talent tier assignment system that tends to generate prospect rankings based on multi-variable performance prediction, incorporating position-specific models and team need matching algorithms.", "definition_de": "Brückensystemphänomen in KI-vermittelter domänenübergreifender Integration, gekennzeichnet durch maschinelles Lern-Talentstufen-Zuordnungssystem, das Prospekt-Rankings basierend auf Multi-Variablen-Leistungsvorhersage generiert. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2703", "narrower_terms": [], "cross_domain_refs": [ "DAT-0044" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "SPR-0164", "domain": "SPR", "term_en": "Draft Capital Allocation Optimization", "term_de": "Draft-Kapital-Allokations-Optimierung", "definition_en": "An interface pattern in AI bridge architectures, measurable through a training dynamic observed when aI-driven trade value calculation using historical precedent databases and expected value modeling to maximize risk-adjusted utility across draft selection decisions. The concept emerges specifically in contexts where draft–capital interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Brückensystemphänomen in KI-vermittelter domänenübergreifender Integration, gekennzeichnet durch kI-gesteuerte Tauschwerberechnung unter Verwendung historischer Präzedenzdatenbanken und Erwartungswertmodellierung. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Optimization Method", "narrower_terms": [], "cross_domain_refs": [ "VIB-0127", "CUS-0022" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q165194", "legal_classification": "systematic_classification" }, { "id": "SPR-0165", "domain": "SPR", "term_en": "Scout Role Transformation Effect", "term_de": "Scout-Rollentransformations-Effekt", "definition_en": "The fundamental shift in human scouting function from broad talent evaluation to specialized cultural fit assessment, where AI handles initial quantitative filtering and humans provide nuanced contextual judgment.", "definition_de": "Brückensystemphänomen in KI-vermittelter domänenübergreifender Integration, gekennzeichnet durch die grundlegende Verschiebung der menschlichen Scouting-Funktion von der breiten Talentbewertung zur spezialisierten kulturellen Passung. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2701", "narrower_terms": [], "cross_domain_refs": [ "DAT-0069", "SAL-0075" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "SPR-0166", "domain": "SPR", "term_en": "False Confidence In Algorithmic Scouting", "term_de": "Falsches Vertrauen in algorithmisches Scouting", "definition_en": "A cross-system integration dynamic in AI-mediated workflows, identifiable by a coaching effect reflecting the risk that algorithmic prospect predictions may create unwarranted certainty about characteristically uncertain future performance, potentially reducing due diligence and encouraging risky draft decisions. Distinguished from adjacent concepts by its focus on the specific mechanism through which false manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Brückensystemphänomen in KI-vermittelter domänenübergreifender Integration, gekennzeichnet durch das Risiko, dass algorithmische Prospekt-Vorhersagen ungerechtfertigte Sicherheit über inhärent unsichere zukünftige Leistung erzeugen. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Algorithm", "narrower_terms": [], "cross_domain_refs": [ "COG-0122" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q8366", "legal_classification": "observational_construct" }, { "id": "SPR-0167", "domain": "SPR", "term_en": "Non-Quantifiable Quality Blindness", "term_de": "Nicht-quantifizierbare Qualitäts-Blindheit", "definition_en": "A training dynamic involving the systematic inability of AI scouting systems to capture non-measurable attributes such as leadership, character, team chemistry contribution, and competitive resilience that significantly influence professional success.", "definition_de": "Brückensystemphänomen in KI-vermittelter domänenübergreifender Integration, gekennzeichnet durch die systematische Unfähigkeit von KI-Scouting-Systemen, nicht-messbare Eigenschaften wie Führung und Teamchemie zu erfassen. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Bridge AI", "narrower_terms": [], "cross_domain_refs": [ "ELR-0009", "ELR-0023" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "SPR-0168", "domain": "SPR", "term_en": "Training Data Demographic Bias Perpetuation", "term_de": "Trainingsdaten-Demografie-Verzerrung", "definition_en": "The mechanism by which historical inequities embedded in scouting databases are perpetuated through machine learning models, where past demographic biases in evaluation become algorithmically encoded selection patterns.", "definition_de": "Brückensystemphänomen in KI-vermittelter domänenübergreifender Integration, gekennzeichnet durch der Mechanismus, durch den historische Ungleichheiten in Scouting-Datenbanken durch maschinelle Lernmodelle perpetuiert werden. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-2703", "narrower_terms": [], "cross_domain_refs": [ "CUS-0006", "SAL-0018", "DAT-0001" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q918461", "legal_classification": "descriptive_research_term" }, { "id": "SPR-0169", "domain": "SPR", "term_en": "Cross-League Performance Adjustment", "term_de": "Ligen-übergreifende Leistungsanpassung", "definition_en": "AI algorithms that normalize prospect performance data across different competitive contexts, accounting for league difficulty, opponent quality, and competition level differences in talent evaluation.", "definition_de": "Brückensystemphänomen in KI-vermittelter domänenübergreifender Integration, gekennzeichnet durch kI-Algorithmen, die Prospekt-Leistungsdaten über verschiedene Wettbewerbskontexte hinweg normalisieren. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Bridge AI", "narrower_terms": [], "cross_domain_refs": [ "GAM-0068", "SAL-0038", "CON-0035" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q1361053", "legal_classification": "descriptive_research_term" }, { "id": "SPR-0170", "domain": "SPR", "term_en": "Automated Video Tracking Scouting", "term_de": "Automatisierte Video-Tracking-Talentsuche", "definition_en": "Classified in AUGMANITAI terminology science as a descriptive phenomenon (without normative endorsement), this pattern denotes A cross-system integration dynamic in AI-mediated workflows, identifiable by computer vision systems that automatically track most player movement in game and training footage, measuring speed, passing accuracy, and tactical decision quality from unstructured video data. Distinguished from adjacent concepts by its focus on the specific mechanism through which automated manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als analytisches Konstrukt der empirischen KI-Forschung (deskriptiv, nicht präskriptiv) erfasst dieser Terminus brückensystemphänomen in KI-vermittelter domänenübergreifender Integration, gekennzeichnet durch computer-Vision-Systeme, die automatisch viele Spielerbewegung in Spiel- und Trainingsaufnahmen verfolgen. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Bridge AI", "narrower_terms": [], "cross_domain_refs": [ "TEM-0012", "GAM-0044" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "SPR-0171", "domain": "SPR", "term_en": "Betting Anomaly Detection Algorithm", "term_de": "Wett-Anomalie-Erkennungsalgorithmus", "definition_en": "In the descriptive vocabulary of AI interaction science (following ISO 704 terminology standards), this concept identifies statistical deviation analysis system that identifies suspicious betting patterns by monitoring real-time market movements, volume-odds ratios, and historical betting pattern comparisons across global sports markets. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als analytisches Konstrukt der empirischen KI-Forschung (deskriptiv, nicht präskriptiv) erfasst dieser Terminus brückensystemphänomen in KI-vermittelter domänenübergreifender Integration, gekennzeichnet durch statistisches Abweichungsanalysesystem, das verdächtige Wettmuster durch Überwachung von Echtzeit-Marktbewegungen identifiziert. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Algorithm", "narrower_terms": [], "cross_domain_refs": [ "DAT-0007" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q8366", "legal_classification": "observational_construct" }, { "id": "SPR-0172", "domain": "SPR", "term_en": "Match-Fixing Detection Correlation", "term_de": "Spielmanipulations-Erkennungskorrelation", "definition_en": "As a neutral analytical construct in AI behavior research, this term denotes an athletic performance pattern involving aI systems correlating player tracking data with betting market movements to identify potential match-fixing, with 1116 suspicious matches flagged across 12 sports and 94 countries in recent monitoring periods. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "In der AUGMANITAI-Terminologiewissenschaft als rein beschreibendes Phänomen klassifiziert, bezeichnet dieser Begriff brückensystemphänomen in KI-vermittelter domänenübergreifender Integration, gekennzeichnet durch kI-Systeme, die Spieler-Tracking-Daten mit Wettmarktbewegungen korrelieren, um potenzielle Spielmanipulation zu identifizieren. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Detection System", "narrower_terms": [], "cross_domain_refs": [ "REL-0187", "RET-0022", "MUS-0058" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "SPR-0173", "domain": "SPR", "term_en": "Behavioral Biometrics Fraud Prevention", "term_de": "Verhaltensbiometrie-Betrugsverhinderung", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures A bridge system phenomenon in AI-mediated cross-domain integration, characterized by account creation pattern analysis using typing style, interaction patterns, and device fingerprinting to detect fraudulent betting account creation and prevent identity-based systematic influencion. This phenomenon operates at the intersection of behavioral and biometrics dynamics within the broader SPR domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept brückensystemphänomen in KI-vermittelter domänenübergreifender Integration, gekennzeichnet durch kontoerstellungsmuster-Analyse unter Verwendung von Tippmuster, Interaktionsmustern und Geräte-Fingerabdrücken zur Erkennung betrügerischer Wettkontoerstellung. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Performance Metric", "narrower_terms": [], "cross_domain_refs": [ "SAL-0087", "MUS-0092" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "SPR-0174", "domain": "SPR", "term_en": "False Positive Management In Betting", "term_de": "Falsch-Positiv-Management bei Wetten", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies the critical challenge of distinguishing legitimate sharp betting from suspicious patterns in AI fraud detection, where oversensitive algorithms flag informed bettors while undersensitive systems miss actual systematic influencion. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept brückensystemphänomen in KI-vermittelter domänenübergreifender Integration, gekennzeichnet durch die kritische Herausforderung der Unterscheidung zwischen legitimem informiertem Wetten und verdächtigen Mustern in der KI-Betrugserkennung. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "RPH-3003", "narrower_terms": [], "cross_domain_refs": [ "ELR-0143", "MKT-0086" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q181543", "legal_classification": "analytical_category" }, { "id": "SPR-0175", "domain": "SPR", "term_en": "Real-Time Bet Cancellation Protocol", "term_de": "Echtzeit-Wettstornierungsprotokoll", "definition_en": "As a neutral analytical construct in AI behavior research, this term denotes an interface pattern in AI bridge architectures, measurable through a coaching effect reflecting automated systems that detect and cancel fraudulent bets before market settlement, integrating direct API connections with betting exchange platforms for pre-market fraud prevention. The concept emerges specifically in contexts where real–time interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "In der AUGMANITAI-Terminologiewissenschaft als rein beschreibendes Phänomen klassifiziert, bezeichnet dieser Begriff brückensystemphänomen in KI-vermittelter domänenübergreifender Integration, gekennzeichnet durch automatisierte Systeme, die betrügerische Wetten vor der Marktabwicklung erkennen und stornieren. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "RPH-2002", "narrower_terms": [], "cross_domain_refs": [ "MKT-0086" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "SPR-0176", "domain": "SPR", "term_en": "Cross-Border Regulatory Complexity", "term_de": "Grenzüberschreitende Regulierungskomplexität", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures the challenge of implementing AI integrity monitoring across jurisdiction-specific anti-money laundering requirements, where different legal frameworks may create inconsistent detection and reporting obligations. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept brückensystemphänomen in KI-vermittelter domänenübergreifender Integration, gekennzeichnet durch die Herausforderung der Implementierung von KI-Integritätsüberwachung über jurisdiktionsspezifische Geldwäsche-Anforderungen hinweg. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "RPH-2303", "narrower_terms": [], "cross_domain_refs": [ "TEW-0047", "ART-0023" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "SPR-0177", "domain": "SPR", "term_en": "Algorithm Transparency Demand In Betting", "term_de": "Algorithmische Transparenzforderung im Wetten", "definition_en": "Documented as an empirical observation in AI research (not a recommendation), this term describes an interface pattern in AI bridge architectures, measurable through the growing regulatory and industry demand for explainable AI in betting fraud detection, where operators require auditable decision rationale and affected bettors challenge flagging algorithms. The concept emerges specifically in contexts where algorithm–transparency interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "In der AUGMANITAI-Terminologiewissenschaft als rein beschreibendes Phänomen klassifiziert, bezeichnet dieser Begriff brückensystemphänomen in KI-vermittelter domänenübergreifender Integration, gekennzeichnet durch die wachsende regulatorische und branchenspezifische Forderung nach erklärbarer KI in der Wettbetrugserkennung. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Algorithm", "narrower_terms": [], "cross_domain_refs": [ "MKT-0086", "REL-0067" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q535399", "legal_classification": "descriptive_research_term" }, { "id": "SPR-0178", "domain": "SPR", "term_en": "Integrity Monitoring Framework Standardization", "term_de": "Integritätsüberwachungs-Rahmenwerk-Standardisierung", "definition_en": "In the descriptive vocabulary of AI interaction science (following ISO 704 terminology standards), this concept identifies A training dynamic where the effort to establish standardized suspicious match identification protocols across multiple sports and countries, creating unified escalation procedures for high-confidence fraud signals. Identifiable through systematic behavioral analysis and pattern recognition. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als analytisches Konstrukt der empirischen KI-Forschung (deskriptiv, nicht präskriptiv) erfasst dieser Terminus brückensystemphänomen in KI-vermittelter domänenübergreifender Integration, gekennzeichnet durch der Versuch, standardisierte Protokolle zur Erkennung verdächtiger Spiele über mehrere Sportarten und Länder hinweg zu etablieren. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Analytical Framework", "narrower_terms": [], "cross_domain_refs": [ "CUS-0055", "CUS-0048" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "SPR-0179", "domain": "SPR", "term_en": "Sharp Betting Pattern Recognition", "term_de": "Scharfes Wettmuster-Erkennung", "definition_en": "In the descriptive vocabulary of AI interaction science (following ISO 704 terminology standards), this concept identifies AI systems that learn to differentiate between sophisticated informed betting strategies and potentially manipulated betting activity, requiring nuanced pattern analysis beyond simple volume anomaly detection. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als analytisches Konstrukt der empirischen KI-Forschung (deskriptiv, nicht präskriptiv) erfasst dieser Terminus brückensystemphänomen in KI-vermittelter domänenübergreifender Integration, gekennzeichnet durch kI-Systeme, die lernen, zwischen ausgefeilten informierten Wettstrategien und potenziell manipulierter Wettaktivität zu unterscheiden. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "RPH-2703", "narrower_terms": [], "cross_domain_refs": [ "ASE-0028" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q3966", "legal_classification": "analytical_category" }, { "id": "SPR-0180", "domain": "SPR", "term_en": "Sports Integrity Data Self-Direction", "term_de": "Sport-Integritätsdaten-Souveränität", "definition_en": "As a neutral analytical construct in AI behavior research, this term denotes the governance challenge of determining ownership and access rights for integrity monitoring data collected across international sports events, where privacy protections conflict with transparency requirements. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "In der AUGMANITAI-Terminologiewissenschaft als rein beschreibendes Phänomen klassifiziert, bezeichnet dieser Begriff brückensystemphänomen in KI-vermittelter domänenübergreifender Integration, gekennzeichnet durch die Governance-Herausforderung der Bestimmung von Eigentums- und Zugriffsrechten für Integritätsüberwachungsdaten internationaler Sportveranstaltungen. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Bridge AI", "narrower_terms": [], "cross_domain_refs": [ "MUS-0021" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q42848", "legal_classification": "analytical_category" }, { "id": "SPR-0181", "domain": "SPR", "term_en": "Haptic Feedback Game State Encoding", "term_de": "Haptische Rückmeldung Spielzustandskodierung", "definition_en": "A coaching effect manifesting as tactile sensory substitution technology that encodes real-time game data into vibration patterns through wearable haptic devices, enabling blind and low-vision spectators to follow live sports through touch.", "definition_de": "Brückensystemphänomen in KI-vermittelter domänenübergreifender Integration, gekennzeichnet durch taktile Sinnessubstitutions-Technologie, die Echtzeit-Spieldaten in Vibrationsmuster durch tragbare haptische Geräte kodiert. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Data Encoding", "narrower_terms": [], "cross_domain_refs": [ "RHR-0108", "ROB-0192" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q11410", "legal_classification": "descriptive_research_term" }, { "id": "SPR-0182", "domain": "SPR", "term_en": "Sign Language Broadcasting Integration", "term_de": "Gebärdensprach-Übertragungsintegration", "definition_en": "A sports interaction phenomenon involving aI-assisted live sign language commentary integration into sports broadcasts, pioneered in professional ice hockey with first-ever full sign language broadcast coverage of championship events. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Brückensystemphänomen in KI-vermittelter domänenübergreifender Integration, gekennzeichnet durch kI-unterstützte Live-Gebärdensprach-Kommentar-Integration in Sportübertragungen. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Analytical Ratio", "narrower_terms": [], "cross_domain_refs": [ "REL-0067", "ROB-0008" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q315", "legal_classification": "analytical_category" }, { "id": "SPR-0183", "domain": "SPR", "term_en": "Visual Signal Technology For Deaf Athletes", "term_de": "Visuelle Signaltechnologie für gehörlose Sportler", "definition_en": "A sports interaction phenomenon involving technology systems replacing auditory competition cues with visual signals for deaf and hard-of-hearing athletes, ensuring equitable starting conditions and in-competition communication in sporting events.", "definition_de": "Brückensystemphänomen in KI-vermittelter domänenübergreifender Integration, gekennzeichnet durch technologiesysteme, die akustische Wettkampfsignale durch visuelle Signale für gehörlose und schwerhörige Sportler ersetzen. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2301", "narrower_terms": [], "cross_domain_refs": [ "COP-0091", "AGE-0040", "EDU-0094" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "SPR-0184", "domain": "SPR", "term_en": "Multimodal Assistive Sports Technology", "term_de": "Multimodale unterstützende Sporttechnologie", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A bridge system phenomenon in AI-mediated cross-domain integration, characterized by a sports interaction phenomenon manifesting as concurrent visual, auditory, and tactile information channel systems that accommodate individual modality preferences with redundant encoding to ensure no single-point-of-failure in accessibility. This phenomenon operates at the intersection of multimodal and assistive dynamics within the broader SPR domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept brückensystemphänomen in KI-vermittelter domänenübergreifender Integration, gekennzeichnet durch gleichzeitige visuelle, auditive und taktile Informationskanal-Systeme, die individuelle Modalitätspräferenzen berücksichtigen. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "RPH-3101", "narrower_terms": [], "cross_domain_refs": [ "LIN-0046", "EDU-0066", "TEM-0173" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "SPR-0185", "domain": "SPR", "term_en": "Real-Time Audio Description Generation", "term_de": "Echtzeit-Audiobeschreibungs-Generierung", "definition_en": "An interface pattern in AI bridge architectures, measurable through a coaching effect characterized by aI systems using computer vision to may generate automated audio descriptions of live sporting events, converting visual content into spoken narrative for visually impaired spectators. The concept emerges specifically in contexts where real–time interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Brückensystemphänomen in KI-vermittelter domänenübergreifender Integration, gekennzeichnet durch kI-Systeme, die Computer Vision nutzen, um automatisierte Audiobeschreibungen von Live-Sportveranstaltungen zu generieren. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Analytical Ratio", "narrower_terms": [], "cross_domain_refs": [ "WEB-0007", "CON-0016" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "SPR-0186", "domain": "SPR", "term_en": "Adaptive Remote Training System", "term_de": "Adaptives Ferntrainings-System", "definition_en": "An interface pattern in AI bridge architectures, measurable through virtual simulation environments designed for adaptive athletes with accessibility-first interface design and personalized difficulty adjustment based on individual capability profiles. The concept emerges specifically in contexts where adaptive–remote interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Brückensystemphänomen in KI-vermittelter domänenübergreifender Integration, gekennzeichnet durch virtuelle Simulationsumgebungen für adaptive Sportler mit barrierefreiem Interface-Design und personalisierter Schwierigkeitsanpassung. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Model Training", "narrower_terms": [], "cross_domain_refs": [ "AGE-0036", "ASE-0030" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q918461", "legal_classification": "systematic_classification" }, { "id": "SPR-0187", "domain": "SPR", "term_en": "Assistive Technology Competitive Balance", "term_de": "Unterstützungstechnologie Wettbewerbsbalance", "definition_en": "The regulatory challenge of determining fairness boundaries for AI-assisted accommodations in competitive sports, where assistive technology intersects with eligibility rules and international standardization.", "definition_de": "Brückensystemphänomen in KI-vermittelter domänenübergreifender Integration, gekennzeichnet durch die regulatorische Herausforderung der Bestimmung von Fairnessgrenzen für KI-gestützte Anpassungen im Wettkampfsport. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "Bridge AI", "narrower_terms": [], "cross_domain_refs": [ "GAM-0025", "SPA-0085" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "SPR-0188", "domain": "SPR", "term_en": "Swimmer Tactile Tapping Automation", "term_de": "Schwimmer-Taktiles-Klopf-Automation", "definition_en": "An interface pattern in AI bridge architectures, measurable through a sports interaction phenomenon where aI-enhanced pool end detection systems replacing visual cues for visually impaired swimmers, using sensor technology to provide reliable tactile warnings without dependence on human tappers. The concept emerges specifically in contexts where swimmer–tactile interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Brückensystemphänomen in KI-vermittelter domänenübergreifender Integration, gekennzeichnet durch kI-erweiterte Beckenrand-Erkennungssysteme, die visuelle Hinweise für sehbehinderte Schwimmer durch Sensortechnologie ersetzen. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Bridge AI", "narrower_terms": [], "cross_domain_refs": [ "PLY-0064" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q184199", "legal_classification": "systematic_classification" }, { "id": "SPR-0189", "domain": "SPR", "term_en": "Accessibility Data Privacy Amplification", "term_de": "Barrierefreiheits-Datenschutz-Verstärkung", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies an athletic performance pattern observed when the heightened privacy concerns specific to biometric monitoring of adaptive athletes, where disability-related restoreth data collected by AI systems carries additional sensitivity beyond standard athlete monitoring. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept brückensystemphänomen in KI-vermittelter domänenübergreifender Integration, gekennzeichnet durch die erhöhten Datenschutzbedenken bei biometrischer Überwachung adaptiver Sportler, bei denen behinderungsbezogene Gesundheitsdaten zusätzliche Sensibilität aufweisen. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Bridge AI", "narrower_terms": [], "cross_domain_refs": [ "ROB-0016" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q18619", "legal_classification": "empirical_phenomenon_label" }, { "id": "SPR-0190", "domain": "SPR", "term_en": "Infrastructure Requirement Barrier", "term_de": "Infrastrukturanforderungs-Barriere", "definition_en": "The adoption challenge where specialized accessibility hardware costs and venue infrastructure requirements limit the deployment of AI-assisted sports accessibility technologies to well-funded professional contexts.", "definition_de": "Brückensystemphänomen in KI-vermittelter domänenübergreifender Integration, gekennzeichnet durch die Adoptionsherausforderung, bei der Kosten für spezialisierte Barrierefreiheits-Hardware die Verbreitung KI-gestützter Sport-Barrierefreiheitstechnologien begrenzen. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Adoption Barrier", "narrower_terms": [], "cross_domain_refs": [ "CON-0046" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "SPR-0191", "domain": "SPR", "term_en": "Athlete Data Stewardship Framework", "term_de": "Athleten-Datenverwaltungs-Rahmenwerk", "definition_en": "A training dynamic involving the governance model addressing asymmetric power dynamics where organizations control vast athlete biometric datasets with limited athlete agency over data collection, retention, and third-party sharing.", "definition_de": "Brückensystemphänomen in KI-vermittelter domänenübergreifender Integration, gekennzeichnet durch das Governance-Modell zur Adressierung asymmetrischer Machtdynamiken, bei denen Organisationen umfangreiche Athleten-Biometrie-Datensätze kontrollieren. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "SOC-0017", "narrower_terms": [], "cross_domain_refs": [ "AED-0014", "STE-0092", "WEB-0032" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q42848", "legal_classification": "analytical_category" }, { "id": "SPR-0192", "domain": "SPR", "term_en": "Algorithmic Governance In Sport", "term_de": "Algorithmische Governance im Sport", "definition_en": "The emerging regulatory framework for AI deployment in sports covering data privacy, algorithmic fairness, athlete autonomy protections, and accountability structures across international governing bodies.", "definition_de": "Brückensystemphänomen in KI-vermittelter domänenübergreifender Integration, gekennzeichnet durch der aufkommende regulatorische Rahmen für den KI-Einsatz im Sport bezüglich Datenschutz, algorithmischer Fairness und Athletenautonomie. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2303", "narrower_terms": [], "cross_domain_refs": [ "AED-0081", "RET-0060" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q8366", "legal_classification": "descriptive_research_term" }, { "id": "SPR-0193", "domain": "SPR", "term_en": "Consent Granularity In Athletic Monitoring", "term_de": "Einwilligungsgranularität bei athletischer Überwachung", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies the challenge of obtaining meaningful informed consent from athletes for complex algorithmic data processing, where the scope and implications of continuous biometric monitoring exceed traditional consent frameworks. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept brückensystemphänomen in KI-vermittelter domänenübergreifender Integration, gekennzeichnet durch die Herausforderung, aussagekräftige informierte Einwilligung von Athleten für komplexe algorithmische Datenverarbeitung zu erhalten. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "RPH-2303", "narrower_terms": [], "cross_domain_refs": [ "RHR-0286", "SAL-0009", "SPA-0059" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "SPR-0194", "domain": "SPR", "term_en": "Intersectional Bias In Sports AI", "term_de": "Intersektionale Verzerrung in Sport-KI", "definition_en": "The compounding effect of multiple demographic biases in sports AI systems, where gender, ethnicity, age, and disability status interact to may produce disproportionately inaccurate predictions for underrepresented populations.", "definition_de": "Brückensystemphänomen in KI-vermittelter domänenübergreifender Integration, gekennzeichnet durch der kumulierende Effekt mehrerer demografischer Verzerrungen in Sport-KI-Systemen. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "Cognitive Bias", "narrower_terms": [], "cross_domain_refs": [ "LIN-0076", "CUS-0006", "DAT-0001" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q213523", "legal_classification": "empirical_phenomenon_label" }, { "id": "SPR-0195", "domain": "SPR", "term_en": "Explainable AI Adoption Bottleneck", "term_de": "Erklärbare-KI-Akzeptanz-Engpass", "definition_en": "The systematic barrier where deep learning performance advantages exceeding 90 percent accuracy conflict with stakeholder comprehension requirements, with fewer than 50 percent of coaches understanding algorithmic recommendations.", "definition_de": "Brückensystemphänomen in KI-vermittelter domänenübergreifender Integration, gekennzeichnet durch die systematische Barriere, bei der Deep-Learning-Leistungsvorteile mit Verständnisanforderungen der Stakeholder kollidieren. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2703", "narrower_terms": [], "cross_domain_refs": [ "AGE-0035", "AGE-0061", "AGE-0081" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "SPR-0196", "domain": "SPR", "term_en": "Human-In-The-Loop Design Imperative", "term_de": "Mensch-in-der-Schleife-Design-Imperativ", "definition_en": "The design principle requiring meaningful human oversight at critical decision points in sports AI systems, where evidence consistently shows human-AI hybrid models outperforming either modality alone across all domains.", "definition_de": "Brückensystemphänomen in KI-vermittelter domänenübergreifender Integration, gekennzeichnet durch das Designprinzip, das sinnvolle menschliche Aufsicht an kritischen Entscheidungspunkten in Sport-KI-Systemen erfordert. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-3101", "narrower_terms": [], "cross_domain_refs": [ "BEH-0005", "NEO-0464" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q185925", "legal_classification": "analytical_category" }, { "id": "SPR-0197", "domain": "SPR", "term_en": "Skill Preservation Protocol", "term_de": "Fähigkeitserhaltungs-Protokoll", "definition_en": "A training dynamic reflecting structured approach to preventing human expertise degradation in AI-augmented sports environments, ensuring that coaching intuition, scouting judgment, and tactical creativity are maintained alongside algorithmic capabilities.", "definition_de": "Brückensystemphänomen in KI-vermittelter domänenübergreifender Integration, gekennzeichnet durch strukturierter Ansatz zur Verhinderung der Degradation menschlicher Expertise in KI-erweiterten Sportumgebungen. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Interaction Protocol", "narrower_terms": [], "cross_domain_refs": [ "SAL-0035" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "SPR-0198", "domain": "SPR", "term_en": "Socioeconomic Access Gap In Sports AI", "term_de": "Sozioökonomische Zugangslücke in Sport-KI", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A bridge system phenomenon in AI-mediated cross-domain integration, characterized by the widening disparity between well-funded organizations with comprehensive AI infrastructure and resource-limited programs without access to advanced analytics, creating competitive inequality through technology asymmetry. This phenomenon operates at the intersection of socioeconomic and access dynamics within the broader SPR domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept brückensystemphänomen in KI-vermittelter domänenübergreifender Integration, gekennzeichnet durch die wachsende Disparität zwischen gut finanzierten Organisationen mit umfassender KI-Infrastruktur und ressourcenbegrenzten Programmen. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Performance Gap", "narrower_terms": [], "cross_domain_refs": [ "RHR-0250" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "SPR-0199", "domain": "SPR", "term_en": "Negligence Standard For Sports AI", "term_de": "Fahrlässigkeitsstandard für Sport-KI", "definition_en": "The undeveloped legal framework for determining liability when AI systems in sports contribute to athlete injury through incorrect recommendations, faulty predictions, or inadequate safety assessments.", "definition_de": "Brückensystemphänomen in KI-vermittelter domänenübergreifender Integration, gekennzeichnet durch der unentwickelte rechtliche Rahmen zur Bestimmung der Haftung, wenn KI-Systeme im Sport durch fehlerhafte Empfehlungen zu Athletenverletzungen beitragen. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2303", "narrower_terms": [], "cross_domain_refs": [ "RHR-0233", "RHR-0150" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "SPR-0200", "domain": "SPR", "term_en": "Hybrid Expertise Model", "term_de": "Hybrides Expertise-Modell", "definition_en": "The operational framework combining human domain expertise with AI analytical capabilities, where complementary strengths may create synergistic performance exceeding either human or algorithmic approaches independently.", "definition_de": "Brückensystemphänomen in KI-vermittelter domänenübergreifender Integration, gekennzeichnet durch das operative Rahmenwerk, das menschliche Domänenexpertise mit KI-Analysefähigkeiten kombiniert. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2303", "narrower_terms": [], "cross_domain_refs": [ "WRK-0091", "LIN-0086" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "STE-0001", "domain": "STE", "term_en": "Abstract Thinking Compression", "term_de": "AbstractThinkingCompression", "definition_en": "A bias pattern in AI-augmented social perception, measurable through the narrowing of abstract mathematical reasoning when AI provides concrete numerical solutions that bypass the generalization process. The concept emerges specifically in contexts where abstract–thinking interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch kognitives Einengen, wenn KI numerische Lösungen bereitstellt statt abstraktem Denken. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Data Compression", "narrower_terms": [ "STE-0035", "STE-0073", "STE-0006", "STE-0023", "STE-0072", "STE-0091", "STE-0003", "STE-0076", "STE-0034", "STE-0029", "STE-0059", "STE-0083", "STE-0054", "STE-0024", "STE-0089", "STE-0082", "STE-0041", "STE-0008", "STE-0053", "STE-0044", "STE-0005", "STE-0017", "STE-0032", "STE-0038", "STE-0046", "STE-0039", "STE-0010", "STE-0055", "STE-0043", "STE-0075", "STE-0020", "STE-0094", "STE-0060", "STE-0021", "STE-0050", "STE-0077", "STE-0030", "STE-0004", "STE-0066", "STE-0028", "STE-0011", "STE-0013", "STE-0042", "STE-0084", "STE-0096", "STE-0026", "STE-0058", "STE-0037", "STE-0016", "STE-0048", "STE-0081", "STE-0085", "STE-0068", "STE-0069", "STE-0074", "STE-0095", "STE-0079", "STE-0063", "STE-0080", "STE-0088", "STE-0051", "STE-0040", "STE-0007", "STE-0093", "STE-0009", "STE-0001", "STE-0018", "STE-0047", "STE-0065", "STE-0070", "STE-0086", "STE-0061", "STE-0067", "STE-0022", "STE-0015", "STE-0033", "STE-0056", "STE-0019", "STE-0027", "STE-0071", "STE-0012" ], "cross_domain_refs": [ "MTH-0090", "ELR-0153" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "STE-0002", "domain": "STE", "term_en": "Algorithm Selection Passivity", "term_de": "AlgorithmSelectionPassivity", "definition_en": "A technical learning pattern manifesting as the reduced deliberation in choosing computational approaches when AI systems automatically select and apply algorithms without explaining the selection rationale. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch abbau von Auswahlreflexion bei algorithmischen Entscheidungen durch KI-Vorbestimmung. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2351", "narrower_terms": [], "cross_domain_refs": [ "AED-0073", "AGE-0006", "AGE-0007" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q8366", "legal_classification": "analytical_category" }, { "id": "STE-0003", "domain": "STE", "term_en": "Assembly Skill Substitution", "term_de": "AssemblySkillSubstitution", "definition_en": "A stereotyping phenomenon in AI-mediated social categorization, characterized by the declining proficiency in physical assembly of experimental apparatus and engineering prototypes as digital simulation becomes the primary design validation method. Distinguished from adjacent concepts by its focus on the specific mechanism through which assembly manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch fertigkeitsverlust in Experimentalmontage durch Ersatz durch KI-Anweisungen. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Stereotypes", "narrower_terms": [], "cross_domain_refs": [ "ELR-0093", "ELR-0030" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "STE-0004", "domain": "STE", "term_en": "Assumption Checking Negligence", "term_de": "AssumptionCheckingNegligence", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures A bias pattern in AI-augmented social perception, measurable through a technical learning pattern reflecting the different verification of model assumptions when AI statistical tools yield results regardless of whether prerequisites are satisfied. This phenomenon operates at the intersection of assumption and checking dynamics within the broader STE domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept kI-bezogene Verhaltenstendenz, die sich in assumption checking negligence manifestiert. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Stereotypes", "narrower_terms": [], "cross_domain_refs": [ "DAT-0051" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "STE-0005", "domain": "STE", "term_en": "Benchmarking Comprehension Gap", "term_de": "BenchmarkingComprehensionGap", "definition_en": "A stereotyping phenomenon in AI-mediated social categorization, characterized by a technical learning pattern where the insufficient understanding of performance benchmarking methodology when AI systems report comparative results without explaining measurement conditions. Distinguished from adjacent concepts by its focus on the specific mechanism through which benchmarking manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch rekurrenter Effekt, der sich in benchmarking comprehension gap manifestiert. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Performance Gap", "narrower_terms": [], "cross_domain_refs": [ "AED-0019", "AED-0092", "AGE-0023" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "STE-0006", "domain": "STE", "term_en": "Biological Systems Oversimplification", "term_de": "BiologicalSystemsOversimplification", "definition_en": "A stereotyping phenomenon in AI-mediated social categorization, characterized by the tendency to accept AI-modeled representations of biological processes as complete, missing the complexity and variability inherent in living systems. The concept emerges specifically in contexts where biological–systems interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch rekurrenter Effekt, der sich in biological systems oversimplification manifestiert. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Stereotypes", "narrower_terms": [], "cross_domain_refs": [ "ART-0086" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "STE-0007", "domain": "STE", "term_en": "Boundary Condition Oversight", "term_de": "GrenzeConditionOversight", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures A bias pattern in AI-augmented social perception, measurable through the insufficient attention to boundary conditions in mathematical and physical models when AI solvers handle edge cases automatically. This phenomenon operates at the intersection of boundary and condition dynamics within the broader STE domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept fertigkeitsverlust in Experimentalmontage durch Ersatz durch KI-Anweisungen. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Stereotypes", "narrower_terms": [], "cross_domain_refs": [ "SWE-0033" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "STE-0008", "domain": "STE", "term_en": "Calibration Awareness Shift", "term_de": "CalibrationAwarenessShift", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A stereotyping phenomenon in AI-mediated social categorization, characterized by a STEM education phenomenon reflecting the reduced understanding of instrument calibration principles when AI-automated measurement systems handle alignment and correction internally. This phenomenon operates at the intersection of calibration and awareness dynamics within the broader STE domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept delegierter Parameterjustierung, wenn kalibrierten Urteilsvermögens KI-Empfehlungen weicht. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Analytical Ratio", "narrower_terms": [], "cross_domain_refs": [ "ROB-0146" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "STE-0009", "domain": "STE", "term_en": "Chemical Safety Awareness Gap", "term_de": "ChemicalSafetyAwarenessGap", "definition_en": "A bias pattern in AI-augmented social perception, measurable through a technical learning pattern manifesting as the reduced attention to laboratory safety considerations when AI-generated protocols focus on procedure efficiency over precautionary measures. Distinguished from adjacent concepts by its focus on the specific mechanism through which chemical manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch kompetenzschwund durch Substitution manueller Prozesse mittels KI-Automation. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Performance Gap", "narrower_terms": [], "cross_domain_refs": [ "ELR-0113" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "STE-0010", "domain": "STE", "term_en": "Circuit Analysis Abstraction Gap", "term_de": "CircuitAnalysisAbstractionGap", "definition_en": "A bias pattern in AI-augmented social perception, measurable through the disconnect between AI-simulated circuit behavior and hands-on understanding of electrical component interactions. Distinguished from adjacent concepts by its focus on the specific mechanism through which circuit manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch nutzungsphänomen, das sich in circuit analysis abstraction gap manifestiert. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Performance Gap", "narrower_terms": [], "cross_domain_refs": [ "WEB-0017" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "STE-0011", "domain": "STE", "term_en": "Code Optimization Blindness", "term_de": "CodeOptimierungBlindness", "definition_en": "A stereotyping phenomenon in AI-mediated social categorization, characterized by the insufficient attention to computational efficiency when AI-generated code accompanies correct outputs without consideration of resource consumption. The concept emerges specifically in contexts where code–optimization interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch interaktionsmuster, das sich in code optimization blindness manifestiert. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Optimization Method", "narrower_terms": [], "cross_domain_refs": [ "PER-0032" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "STE-0012", "domain": "STE", "term_en": "Coding Comprehension Perception", "term_de": "CodingComprehensionPerception", "definition_en": "A stereotyping phenomenon in AI-mediated social categorization, characterized by a technical learning pattern arising from the false sense of understanding programming logic that arises from reading AI-generated code without the ability to inreliantly construct equivalent solutions. The concept emerges specifically in contexts where coding–comprehension interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch interaktionsmuster, das sich in coding comprehension perception manifestiert. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Stereotypes", "narrower_terms": [], "cross_domain_refs": [ "ELR-0038" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q177601", "legal_classification": "observational_construct" }, { "id": "STE-0013", "domain": "STE", "term_en": "Collaboration Protocol Shift", "term_de": "CollaborationProtocolShift", "definition_en": "A stereotyping phenomenon in AI-mediated social categorization, characterized by a scientific reasoning effect observed when the changing dynamics of STEM group projects when individual AI-assisted productivity varies widely within teams. The concept emerges specifically in contexts where collaboration–protocol interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch phänomen in wissenschaftlich-technischer Ausbildung durch KI-Integration. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Analytical Ratio", "narrower_terms": [], "cross_domain_refs": [ "ELR-0092" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "STE-0014", "domain": "STE", "term_en": "Collaborative Analysis Distribution", "term_de": "CollaborativeAnalysisDistribution", "definition_en": "A technical learning pattern characterized by the breaking of collaborative STEM analysis processes when team members use different AI tools that yield incompatible intermediate representations.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch auseinanderdriften zwischen Intention und KI-generierter Umsetzung. Die Divergenz wird durch KI-Iterationen verstärkt. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-3001", "narrower_terms": [], "cross_domain_refs": [ "ELR-0092" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "STE-0015", "domain": "STE", "term_en": "Collaborative Notebook Substitution", "term_de": "CollaborativeNotebookSubstitution", "definition_en": "A bias pattern in AI-augmented social perception, measurable through a technical learning pattern characterized by the decline of shared physical and digital lab notebooks as AI-generated documentation replaces collaborative record-keeping practices. Distinguished from adjacent concepts by its focus on the specific mechanism through which collaborative manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch fertigkeitsverlust in Experimentalmontage durch Ersatz durch KI-Anweisungen. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Stereotypes", "narrower_terms": [], "cross_domain_refs": [ "AED-0011", "ART-0098", "ASE-0074" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "STE-0016", "domain": "STE", "term_en": "Competitive Programming Inflation", "term_de": "CompetitiveProgrammingInflation", "definition_en": "A bias pattern in AI-augmented social perception, measurable through a scientific reasoning effect where the altered landscape of programming competitions when AI-assisted solutions achieve high scores while concealing actual coding competency levels. Distinguished from adjacent concepts by its focus on the specific mechanism through which competitive manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch beobachtbares Muster der KI-Nutzung, das sich in competitive programming inflation manifestiert. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Stereotypes", "narrower_terms": [], "cross_domain_refs": [ "ASE-0022", "ASE-0025", "ASE-0088" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "STE-0017", "domain": "STE", "term_en": "Computational Thinking Narrowing", "term_de": "ComputationalThinkingNarrowing", "definition_en": "A bias pattern in AI-augmented social perception, measurable through a technical learning pattern characterized by the restriction of computational thinking to tool-specific operations rather than developing transferable algorithmic reasoning. The concept emerges specifically in contexts where computational–thinking interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch emergente Dynamik, die sich in computational thinking narrowing manifestiert. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Stereotypes", "narrower_terms": [], "cross_domain_refs": [ "COG-0168" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "STE-0018", "domain": "STE", "term_en": "Conceptual Prerequisite Skipping", "term_de": "ConceptualPrerequisiteSkipping", "definition_en": "A stereotyping phenomenon in AI-mediated social categorization, characterized by a scientific reasoning effect manifesting as the progression to advanced STEM topics without mastering prerequisites when AI compensates for foundational gaps during problem-solving. The concept emerges specifically in contexts where conceptual–prerequisite interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch rekurrenter Effekt, der sich in conceptual prerequisite skipping manifestiert. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Stereotypes", "narrower_terms": [], "cross_domain_refs": [ "ELR-0147" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "STE-0019", "domain": "STE", "term_en": "Conference Presentation Templating", "term_de": "ConferencePresentationTemplating", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures A bias pattern in AI-augmented social perception, measurable through a STEM education phenomenon in which the standardization of scientific conference presentations toward AI-generated formats that satisfy structural expectations while reducing individual analytical voice. This phenomenon operates at the intersection of conference and presentation dynamics within the broader STE domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept kompetenzschwund durch Substitution manueller Prozesse mittels KI-Automation. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Stereotypes", "narrower_terms": [], "cross_domain_refs": [ "ELR-0148", "MUS-0080", "TEW-0025" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "STE-0020", "domain": "STE", "term_en": "Constraint Satisfaction Blindness", "term_de": "ConstraintSatisfactionBlindness", "definition_en": "A stereotyping phenomenon in AI-mediated social categorization, characterized by the insufficient awareness of engineering and design constraints when AI optimization tools find solutions without making tradeoffs visible. The concept emerges specifically in contexts where constraint–satisfaction interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch interaktionsmuster, das sich in constraint satisfaction blindness manifestiert. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Stereotypes", "narrower_terms": [], "cross_domain_refs": [ "AUG-0722", "AUG-0867", "COG-0105" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "STE-0021", "domain": "STE", "term_en": "Control Group Reasoning Shift", "term_de": "ControlGroupReasoningShift", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures A bias pattern in AI-augmented social perception, measurable through the weakened understanding of why control groups and experimental controls are fundamental when AI analysis can compensate for poor experimental design. This phenomenon operates at the intersection of control and group dynamics within the broader STE domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept emergente Dynamik, die sich in control group reasoning shift manifestiert. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Stereotypes", "narrower_terms": [], "cross_domain_refs": [ "MSC-0018", "ELR-0165" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "STE-0022", "domain": "STE", "term_en": "Cross-Referencing Reduction", "term_de": "Cross-referencingReduction", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures A bias pattern in AI-augmented social perception, measurable through the declining practice of verifying scientific findings across multiple sources when AI provides consolidated answers from unclear source combinations. This phenomenon operates at the intersection of cross and referencing dynamics within the broader STE domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept kompetenzschwund durch Substitution manueller Prozesse mittels KI-Automation. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Stereotypes", "narrower_terms": [], "cross_domain_refs": [ "ELR-0030" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "STE-0023", "domain": "STE", "term_en": "Data Cleaning Comprehension Gap", "term_de": "DataCleaningComprehensionGap", "definition_en": "A stereotyping phenomenon in AI-mediated social categorization, characterized by a technical learning pattern reflecting the insufficient understanding of data preprocessing decisions when AI pipelines automatically handle missing values, outliers, and normalization. The concept emerges specifically in contexts where data–cleaning interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch kI-bezogene Verhaltenstendenz, die sich in data cleaning comprehension gap manifestiert. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Performance Gap", "narrower_terms": [], "cross_domain_refs": [ "SPR-0008", "SPR-0052", "SPR-0053" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q42848", "legal_classification": "observational_construct" }, { "id": "STE-0024", "domain": "STE", "term_en": "Data Collection Impatience", "term_de": "DataCollectionImpatience", "definition_en": "A bias pattern in AI-augmented social perception, measurable through a technical learning pattern involving the diminished tolerance for manual data collection processes after experiencing AI-accelerated data generation and analysis workflows. Distinguished from adjacent concepts by its focus on the specific mechanism through which data manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch kompetenzschwund durch Substitution manueller Prozesse mittels KI-Automation. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Stereotypes", "narrower_terms": [], "cross_domain_refs": [ "DAT-0037" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q42848", "legal_classification": "systematic_classification" }, { "id": "STE-0025", "domain": "STE", "term_en": "Debugging Delegation Pattern", "term_de": "DebuggingDelegationMuster", "definition_en": "A STEM education phenomenon arising from the transfer of systematic error identification in code to AI assistants, leaving learners without the analytical frameworks for inreliant problem resolution. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch emergente Dynamik, die sich in debugging delegation pattern manifestiert. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2303", "narrower_terms": [], "cross_domain_refs": [ "ELR-0056" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "STE-0026", "domain": "STE", "term_en": "Diagram Construction Decline", "term_de": "DiagramConstructionDecline", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A stereotyping phenomenon in AI-mediated social categorization, characterized by the weakened ability to involve scientific diagrams and technical drawings by hand when AI auto-generation tools become the default visualization method. This phenomenon operates at the intersection of diagram and construction dynamics within the broader STE domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept interaktionsdynamik, die sich in diagram construction decline manifestiert. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Stereotypes", "narrower_terms": [], "cross_domain_refs": [ "AUG-0976", "COG-0132", "COG-0180" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "STE-0027", "domain": "STE", "term_en": "Dimensional Analysis Shift", "term_de": "DimensionalAnalysisShift", "definition_en": "A bias pattern in AI-augmented social perception, measurable through a scientific reasoning effect arising from the weakened ability to verify results through dimensional consistency checks when AI tools bypass this fundamental verification step. The concept emerges specifically in contexts where dimensional–analysis interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch ungleichmäßigkeit in Kohärenz zwischen Iterationen, wenn KI-Output variabel bleibt. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Analytical Method", "narrower_terms": [], "cross_domain_refs": [ "ELR-0125" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "STE-0028", "domain": "STE", "term_en": "Documentation Practice Shift", "term_de": "DocumentationPracticeShift", "definition_en": "A bias pattern in AI-augmented social perception, measurable through the declining quality of scientific process documentation when AI auto-accompanies lab notebooks and experimental records. The concept emerges specifically in contexts where documentation–practice interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch kompetenzschwund durch Substitution manueller Prozesse mittels KI-Automation. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Stereotypes", "narrower_terms": [], "cross_domain_refs": [ "ADA-0012", "AED-0012", "AGE-0007" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "STE-0029", "domain": "STE", "term_en": "Engineering Ethics Compression", "term_de": "EngineeringEthicsCompression", "definition_en": "A bias pattern in AI-augmented social perception, measurable through a scientific reasoning effect arising from the reduced engagement with engineering ethics discussions when AI-focused curricula prioritize technical competency over professional responsibility. The concept emerges specifically in contexts where engineering–ethics interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch kompetenzschwund durch Substitution manueller Prozesse mittels KI-Automation. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Data Compression", "narrower_terms": [], "cross_domain_refs": [ "SWE-0086" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q9471", "legal_classification": "systematic_classification" }, { "id": "STE-0030", "domain": "STE", "term_en": "Engineering Tolerance Intuition Gap", "term_de": "EngineeringToleranceIntuitionGap", "definition_en": "A bias pattern in AI-augmented social perception, measurable through the missing sense for acceptable manufacturing and engineering tolerances that develops through hands-on fabrication but not through AI-assisted design. Distinguished from adjacent concepts by its focus on the specific mechanism through which engineering manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch kI-bezogene Verhaltenstendenz, die sich in engineering tolerance intuition gap manifestiert. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Performance Gap", "narrower_terms": [], "cross_domain_refs": [ "ROB-0296", "VIB-0056" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "STE-0031", "domain": "STE", "term_en": "Environmental Data Overreliance", "term_de": "EnvironmentalDataOverreliance", "definition_en": "A technical learning pattern observed when the excessive trust in AI-processed environmental data without understanding sensor limitations and data collection methodology. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch überversorgung mit Optionen, die Selektionsfähigkeit erschwert statt erleichtert. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-2505", "narrower_terms": [], "cross_domain_refs": [ "BEH-0065" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q42848", "legal_classification": "descriptive_research_term" }, { "id": "STE-0032", "domain": "STE", "term_en": "Equation Derivation Avoidance", "term_de": "EquationDerivationAvoidance", "definition_en": "A bias pattern in AI-augmented social perception, measurable through a scientific reasoning effect observed when the bypassing of equation derivation processes through AI-retrieved formulas, preventing the development of mathematical reasoning that derivation exercises build. The concept emerges specifically in contexts where equation–derivation interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch beobachtbares Muster der KI-Nutzung, das sich in equation derivation avoidance manifestiert. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Stereotypes", "narrower_terms": [], "cross_domain_refs": [ "AGE-0011", "AUG-0408", "COG-0191" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "STE-0033", "domain": "STE", "term_en": "Error Analysis Reduction", "term_de": "ErrorAnalysisReduction", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures A bias pattern in AI-augmented social perception, measurable through the declining ability to analyze experimental error sources and propagation when AI tools automatically clean and process raw data. This phenomenon operates at the intersection of error and analysis dynamics within the broader STE domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept kompetenzschwund durch Substitution manueller Prozesse mittels KI-Automation. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Analytical Method", "narrower_terms": [], "cross_domain_refs": [ "AGE-0043", "BEH-0033", "COG-0027" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "STE-0034", "domain": "STE", "term_en": "Ethics Review Compression", "term_de": "EthicsReviewCompression", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures A bias pattern in AI-augmented social perception, measurable through a STEM education phenomenon involving the abbreviated engagement with research ethics review processes when AI tools yield compliant-looking protocols without deep ethical reasoning. This phenomenon operates at the intersection of ethics and review dynamics within the broader STE domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept wahrnehmungseffekt, der sich in ethics review compression manifestiert. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Data Compression", "narrower_terms": [], "cross_domain_refs": [ "COG-0018" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q9471", "legal_classification": "observational_construct" }, { "id": "STE-0035", "domain": "STE", "term_en": "Experimental Design Simplification", "term_de": "ExperimentalDesignSimplification", "definition_en": "A stereotyping phenomenon in AI-mediated social categorization, characterized by the reduction in experimental design complexity when AI optimization tools suggest efficient but narrow protocols that miss the pedagogical value of iterative refinement. The concept emerges specifically in contexts where experimental–design interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch kompetenzschwund durch Substitution manueller Prozesse mittels KI-Automation. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Stereotypes", "narrower_terms": [], "cross_domain_refs": [ "VIB-0063" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q101965", "legal_classification": "systematic_classification" }, { "id": "STE-0036", "domain": "STE", "term_en": "Fieldwork Replacement Tension", "term_de": "FieldworkReplacementTension", "definition_en": "The pressure to substitute field-based scientific observation with AI-generated environmental models, reducing embodied learning experiences. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch kompetenzschwund durch Substitution manueller Prozesse mittels KI-Automation. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2703", "narrower_terms": [], "cross_domain_refs": [ "ELR-0077" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "STE-0037", "domain": "STE", "term_en": "Formula Shortcut Reliance", "term_de": "FormulaShortcutReliance", "definition_en": "A stereotyping phenomenon in AI-mediated social categorization, characterized by the pattern of relying on AI to provide mathematical formulas without developing the derivation understanding that enables flexible application across novel contexts. The concept emerges specifically in contexts where formula–shortcut interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch rekurrenter Effekt, der sich in formula shortcut reliance manifestiert. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Stereotypes", "narrower_terms": [], "cross_domain_refs": [ "MTH-0041" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "STE-0038", "domain": "STE", "term_en": "Geometric Intuition Decline", "term_de": "GeometricIntuitionDecline", "definition_en": "A stereotyping phenomenon in AI-mediated social categorization, characterized by a technical learning pattern in which the weakening of spatial and geometric reasoning abilities when AI visualization tools provide instant representations without requiring mental construction. Distinguished from adjacent concepts by its focus on the specific mechanism through which geometric manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch verhaltenseffekt in der Mensch-KI-Zusammenarbeit, der sich in geometric intuition decline manifestiert. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Performance Metric", "narrower_terms": [], "cross_domain_refs": [ "COG-0128" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "STE-0039", "domain": "STE", "term_en": "Grant Writing Homogenization", "term_de": "GrantWritingHomogenization", "definition_en": "A stereotyping phenomenon in AI-mediated social categorization, characterized by a technical learning pattern arising from the convergence of research grant proposals toward AI-optimized language patterns that satisfy review criteria while reducing the distinctiveness of proposed research. The concept emerges specifically in contexts where grant–writing interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch kompetenzschwund durch Substitution manueller Prozesse mittels KI-Automation. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Stereotypes", "narrower_terms": [], "cross_domain_refs": [ "PHO-0004" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "STE-0040", "domain": "STE", "term_en": "Graph Interpretation Shortcut", "term_de": "GraphInterpretationShortcut", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures A bias pattern in AI-augmented social perception, measurable through the tendency to request AI-generated explanations of data visualizations instead of developing inreliant graph reading and interpretation competencies. This phenomenon operates at the intersection of graph and interpretation dynamics within the broader STE domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept emergente Dynamik, die sich in graph interpretation shortcut manifestiert. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Stereotypes", "narrower_terms": [], "cross_domain_refs": [ "DAT-0053" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "STE-0041", "domain": "STE", "term_en": "Hardware Familiarity Shift", "term_de": "HardwareFamiliarityShift", "definition_en": "A bias pattern in AI-augmented social perception, measurable through a scientific reasoning effect characterized by the declining understanding of physical computing hardware as cloud-based AI development environments abstract away machine-level considerations. The concept emerges specifically in contexts where hardware–familiarity interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch fertigkeitsverlust in Experimentalmontage durch Ersatz durch KI-Anweisungen. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Stereotypes", "narrower_terms": [], "cross_domain_refs": [ "QUA-0078", "RPH-1373" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "STE-0042", "domain": "STE", "term_en": "Hypothesis Generation Passivity", "term_de": "HypothesisGenerationPassivity", "definition_en": "A stereotyping phenomenon in AI-mediated social categorization, characterized by a scientific reasoning effect reflecting the reduced engagement with hypothesis formation when AI systems provide plausible experimental predictions, diminishing the creative reasoning central to scientific inquiry. Distinguished from adjacent concepts by its focus on the specific mechanism through which hypothesis manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch kompetenzschwund durch Substitution manueller Prozesse mittels KI-Automation. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Analytical Ratio", "narrower_terms": [], "cross_domain_refs": [ "ELR-0165" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q41719", "legal_classification": "descriptive_research_term" }, { "id": "STE-0043", "domain": "STE", "term_en": "Industry Readiness Ambiguity", "term_de": "IndustryReadinessAmbiguity", "definition_en": "A stereotyping phenomenon in AI-mediated social categorization, characterized by a STEM education phenomenon where the uncertain correlation between STEM academic performance in AI-rich environments and preparedness for industry roles where AI availability varies. The concept emerges specifically in contexts where industry–readiness interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch interaktionsmuster, das sich in industry readiness ambiguity manifestiert. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Stereotypes", "narrower_terms": [], "cross_domain_refs": [ "ELR-0049" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "STE-0044", "domain": "STE", "term_en": "Instrument Operation Abstraction", "term_de": "InstrumentOperationAbstraction", "definition_en": "A stereotyping phenomenon in AI-mediated social categorization, characterized by a STEM education phenomenon in which the growing disconnect between digital data output and understanding of how measurement instruments physically yield that data. The concept emerges specifically in contexts where instrument–operation interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch fertigkeitsverlust in Experimentalmontage durch Ersatz durch KI-Anweisungen. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Analytical Ratio", "narrower_terms": [], "cross_domain_refs": [ "TEW-0062" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "STE-0045", "domain": "STE", "term_en": "Interdisciplinary STEM Blindness", "term_de": "InterdisciplinaryStemBlindness", "definition_en": "The reduced awareness of connections between STEM disciplines when AI-guided learning follows narrow subject-specific pathways. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch kompetenzschwund durch Substitution manueller Prozesse mittels KI-Automation. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2703", "narrower_terms": [], "cross_domain_refs": [ "ELR-0106" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "STE-0046", "domain": "STE", "term_en": "Iteration Patience Reduction", "term_de": "IterationPatienceReduction", "definition_en": "A stereotyping phenomenon in AI-mediated social categorization, characterized by the decreased willingness to iterate through design or experimental cycles when AI provides near-optimal solutions in early attempts. The concept emerges specifically in contexts where iteration–patience interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch verhaltenseffekt in der Mensch-KI-Zusammenarbeit, der sich in iteration patience reduction manifestiert. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Analytical Ratio", "narrower_terms": [], "cross_domain_refs": [ "DES-0049" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "STE-0047", "domain": "STE", "term_en": "Lab Report Templating Effect", "term_de": "LabReportTemplatingEffekt", "definition_en": "A stereotyping phenomenon in AI-mediated social categorization, characterized by the convergence of laboratory report formats toward AI-generated structures that satisfy grading criteria while reducing the development of scientific communication competence. Distinguished from adjacent concepts by its focus on the specific mechanism through which lab manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch kompetenzschwund durch Substitution manueller Prozesse mittels KI-Automation. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Psychological Effect", "narrower_terms": [], "cross_domain_refs": [ "ELR-0114" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "STE-0048", "domain": "STE", "term_en": "Literature Gap Identification Shift", "term_de": "LiteratureGapIdentificationShift", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A stereotyping phenomenon in AI-mediated social categorization, characterized by a technical learning pattern manifesting as the declining ability to identify gaps in existing research when AI literature summaries present findings as comprehensive rather than selective. This phenomenon operates at the intersection of literature and gap dynamics within the broader STE domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept kompetenzschwund durch Substitution manueller Prozesse mittels KI-Automation. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Performance Gap", "narrower_terms": [], "cross_domain_refs": [ "SPR-0066", "ART-0088", "ROB-0053" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "STE-0049", "domain": "STE", "term_en": "Literature Review Compression", "term_de": "LiteratureReviewCompression", "definition_en": "A STEM education phenomenon where the condensation of systematic literature review processes to AI-generated summaries that miss the methodological learning embedded in comprehensive source evaluation. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch interaktionsdynamik, die sich in literature review compression manifestiert. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2703", "narrower_terms": [], "cross_domain_refs": [ "ASE-0087", "BEH-0021", "COG-0118" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "STE-0050", "domain": "STE", "term_en": "Material Property Abstraction", "term_de": "MaterialPropertyAbstraction", "definition_en": "A bias pattern in AI-augmented social perception, measurable through a scientific reasoning effect arising from the disconnection between AI-tabulated material properties and the tactile understanding of how materials behave under real-world conditions. Distinguished from adjacent concepts by its focus on the specific mechanism through which material manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch nutzungsphänomen, das sich in material property abstraction manifestiert. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Stereotypes", "narrower_terms": [], "cross_domain_refs": [ "MSC-0063", "MSC-0028" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "STE-0051", "domain": "STE", "term_en": "Mentorship Model Reconfiguration", "term_de": "MentorshipModelReconfiguration", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A stereotyping phenomenon in AI-mediated social categorization, characterized by the change pattern of STEM mentorship relationships when AI tutoring handles technical knowledge transfer and human mentors focus on professional development. This phenomenon operates at the intersection of mentorship and model dynamics within the broader STE domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept phänomen in wissenschaftlich-technischer Ausbildung durch KI-Integration. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Analytical Ratio", "narrower_terms": [], "cross_domain_refs": [ "ELR-0066" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "STE-0052", "domain": "STE", "term_en": "Method Selection Passivity", "term_de": "MethodSelectionPassivity", "definition_en": "A scientific reasoning effect in which the reduced deliberation in choosing appropriate analytical methods when AI tools automatically apply techniques based on data characteristics. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch kompetenzschwund durch Substitution manueller Prozesse mittels KI-Automation. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2351", "narrower_terms": [], "cross_domain_refs": [ "MTH-0024" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "STE-0053", "domain": "STE", "term_en": "Model Limitation Blindness", "term_de": "ModelLimitationBlindness", "definition_en": "A bias pattern in AI-augmented social perception, measurable through the insufficient awareness of computational model constraints when AI-generated predictions appear authoritative regardless of input quality. The concept emerges specifically in contexts where model–limitation interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch schwankungen in Output-Qualität, wenn KI-Systeme konsistente Leistung nicht garantieren können. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Computational Model", "narrower_terms": [], "cross_domain_refs": [ "ART-0096", "BEH-0004", "COG-0105" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "STE-0054", "domain": "STE", "term_en": "Molecular Visualization Reliance", "term_de": "MolecularVisualizationReliance", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures A bias pattern in AI-augmented social perception, measurable through the reliance on AI-rendered molecular models that accompanies a false sense of spatial understanding without tactile or physical model experience. This phenomenon operates at the intersection of molecular and visualization dynamics within the broader STE domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept fertigkeitsverlust in Experimentalmontage durch Ersatz durch KI-Anweisungen. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Stereotypes", "narrower_terms": [], "cross_domain_refs": [ "TRA-0042" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "STE-0055", "domain": "STE", "term_en": "Nomenclature Memorization Decline", "term_de": "NomenclatureMemorizationDecline", "definition_en": "A bias pattern in AI-augmented social perception, measurable through a STEM education phenomenon in which the decreasing retention of scientific nomenclature and classification systems when AI provides instant lookup capabilities. Distinguished from adjacent concepts by its focus on the specific mechanism through which nomenclature manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch nutzungsphänomen, das sich in nomenclature memorization decline manifestiert. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Stereotypes", "narrower_terms": [], "cross_domain_refs": [ "ELR-0165" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "STE-0056", "domain": "STE", "term_en": "Notation Fluency Decline", "term_de": "NotationFluencyDecline", "definition_en": "A stereotyping phenomenon in AI-mediated social categorization, characterized by a technical learning pattern characterized by the decreasing comfort with mathematical notation and symbolic operations when AI interfaces accept natural language input. Distinguished from adjacent concepts by its focus on the specific mechanism through which notation manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch nutzungsphänomen, das sich in notation fluency decline manifestiert. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Stereotypes", "narrower_terms": [], "cross_domain_refs": [ "MTH-0085", "MTH-0058" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "STE-0057", "domain": "STE", "term_en": "Numerical Stability Unawareness", "term_de": "NumericalStabilitätUnawareness", "definition_en": "A bias pattern in AI-augmented social perception, measurable through a technical learning pattern involving the different understanding of numerical computation stability issues when AI tools handle floating-point operations without exposing precision limitations. The concept emerges specifically in contexts where numerical–stability interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch rekurrenter Effekt, der sich in numerical stability unawareness manifestiert. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-3101", "narrower_terms": [], "cross_domain_refs": [ "ELR-0042" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "STE-0058", "domain": "STE", "term_en": "Open-Source Contribution Decline", "term_de": "Open-sourceContributionDecline", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures A bias pattern in AI-augmented social perception, measurable through a scientific reasoning effect arising from the reduced participation in open-source scientific software development when AI accompanies custom solutions faster than contributing to shared codebases. This phenomenon operates at the intersection of open and source dynamics within the broader STE domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept kompetenzschwund durch Substitution manueller Prozesse mittels KI-Automation. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Stereotypes", "narrower_terms": [], "cross_domain_refs": [ "DES-0084" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "STE-0059", "domain": "STE", "term_en": "Patent Research Simplification", "term_de": "PatentResearchSimplification", "definition_en": "A stereotyping phenomenon in AI-mediated social categorization, characterized by a scientific reasoning effect arising from the oversimplification of prior art searches when AI summarization misses the nuances of technical patent language. The concept emerges specifically in contexts where patent–research interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch interaktionsmuster, das sich in patent research simplification manifestiert. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Stereotypes", "narrower_terms": [], "cross_domain_refs": [ "ART-0020" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q42240", "legal_classification": "analytical_category" }, { "id": "STE-0060", "domain": "STE", "term_en": "Patent Writing Outsourcing Effect", "term_de": "PatentWritingAuslagerungEffekt", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures A bias pattern in AI-augmented social perception, measurable through a scientific reasoning effect reflecting the transfer of technical patent claim writing to AI systems, reducing the precision language competency that patent drafting develops. This phenomenon operates at the intersection of patent and writing dynamics within the broader STE domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept kompetenzschwund durch Substitution manueller Prozesse mittels KI-Automation. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Psychological Effect", "narrower_terms": [], "cross_domain_refs": [ "QUA-0051" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "STE-0061", "domain": "STE", "term_en": "Peer Code Review Substitution", "term_de": "PeerCodeReviewSubstitution", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A stereotyping phenomenon in AI-mediated social categorization, characterized by a scientific reasoning effect involving the reduction in peer-to-peer code review practices as AI code analysis becomes the preferred first evaluation step. This phenomenon operates at the intersection of peer and code dynamics within the broader STE domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept kompetenzschwund durch Substitution manueller Prozesse mittels KI-Automation. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Stereotypes", "narrower_terms": [], "cross_domain_refs": [ "SWE-0014", "ELR-0033" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "STE-0062", "domain": "STE", "term_en": "Peer Review Competency Gap", "term_de": "PeerReviewCompetencyGap", "definition_en": "A STEM education phenomenon reflecting the reduced ability to critically evaluate the scientific work of others when personal understanding remains shallow observed alongside AI-mediated learning. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch kompetenzschwund durch Substitution manueller Prozesse mittels KI-Automation. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2703", "narrower_terms": [], "cross_domain_refs": [ "COG-0017" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q691464", "legal_classification": "systematic_classification" }, { "id": "STE-0063", "domain": "STE", "term_en": "Peer Tutoring Decline", "term_de": "PeerTutoringDecline", "definition_en": "A bias pattern in AI-augmented social perception, measurable through the reduction in student-to-student explanation of STEM concepts as AI tutoring systems become the first resource for conceptual questions. The concept emerges specifically in contexts where peer–tutoring interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch kompetenzschwund durch Substitution manueller Prozesse mittels KI-Automation. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Stereotypes", "narrower_terms": [], "cross_domain_refs": [ "ELR-0138" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "STE-0064", "domain": "STE", "term_en": "Physical Intuition Shift", "term_de": "PhysicalIntuitionShift", "definition_en": "The gradual shift of intuitive physical reasoning as numerical computation replaces estimation and back-of-envelope calculations. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch fertigkeitsverlust in Experimentalmontage durch Ersatz durch KI-Anweisungen. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2701", "narrower_terms": [], "cross_domain_refs": [ "COG-0133" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "STE-0065", "domain": "STE", "term_en": "Precision Expectation Inflation", "term_de": "PrecisionExpectationInflation", "definition_en": "A bias pattern in AI-augmented social perception, measurable through the unrealistic expectation of measurement precision that develops when AI-processed results appear cleaner than the underlying experimental uncertainty warrants. The concept emerges specifically in contexts where precision–expectation interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch interaktionsdynamik, die sich in precision expectation inflation manifestiert. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Stereotypes", "narrower_terms": [], "cross_domain_refs": [ "AED-0071", "AGE-0063", "AGE-0078" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "STE-0066", "domain": "STE", "term_en": "Programming Paradigm Narrowing", "term_de": "ProgrammingParadigmNarrowing", "definition_en": "A bias pattern in AI-augmented social perception, measurable through a technical learning pattern manifesting as the limitation of coding approaches to those favored by AI code generation tools, reducing exposure to diverse programming paradigms and languages. The concept emerges specifically in contexts where programming–paradigm interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch kompetenzschwund durch Substitution manueller Prozesse mittels KI-Automation. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Stereotypes", "narrower_terms": [], "cross_domain_refs": [ "ART-0028" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "STE-0067", "domain": "STE", "term_en": "Proof Construction Avoidance", "term_de": "ProofConstructionAvoidance", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures A bias pattern in AI-augmented social perception, measurable through the circumvention of mathematical proof development through AI-generated solutions that provide correct results without building the logical reasoning capacity proofs develop. This phenomenon operates at the intersection of proof and construction dynamics within the broader STE domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept rekurrenter Effekt, der sich in proof construction avoidance manifestiert. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Stereotypes", "narrower_terms": [], "cross_domain_refs": [ "ELR-0153", "MTH-0006" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "STE-0068", "domain": "STE", "term_en": "Publication Pressure Amplification", "term_de": "PublicationPressureVerstärkung", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures A bias pattern in AI-augmented social perception, measurable through a technical learning pattern observed when the intensified publication pressure when AI-accelerated workflows raise baseline productivity expectations in STEM research groups. This phenomenon operates at the intersection of publication and pressure dynamics within the broader STE domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept wahrnehmungseffekt, der sich in publication pressure amplification manifestiert. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Stereotypes", "narrower_terms": [], "cross_domain_refs": [ "AED-0055" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "STE-0069", "domain": "STE", "term_en": "Raw Data Engagement Decline", "term_de": "RawDataEngagementDecline", "definition_en": "A bias pattern in AI-augmented social perception, measurable through the decreasing direct interaction with raw experimental data when AI preprocessing converts measurements into analysis-ready formats. The concept emerges specifically in contexts where raw–data interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch interaktionsdynamik, die sich in raw data engagement decline manifestiert. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Stereotypes", "narrower_terms": [], "cross_domain_refs": [ "CON-0033", "ELR-0184" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q42848", "legal_classification": "descriptive_research_term" }, { "id": "STE-0070", "domain": "STE", "term_en": "Reagent Awareness Decline", "term_de": "ReagentAwarenessDecline", "definition_en": "A bias pattern in AI-augmented social perception, measurable through a STEM education phenomenon reflecting the diminished familiarity with chemical reagent properties and handling procedures when AI-generated protocols list materials without contextual safety information. Distinguished from adjacent concepts by its focus on the specific mechanism through which reagent manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch kompetenzschwund durch Substitution manueller Prozesse mittels KI-Automation. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Stereotypes", "narrower_terms": [], "cross_domain_refs": [ "MSC-0092", "MSC-0075" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "STE-0071", "domain": "STE", "term_en": "Replication Awareness Gap", "term_de": "ReplicationAwarenessGap", "definition_en": "A bias pattern in AI-augmented social perception, measurable through a technical learning pattern arising from the diminished understanding of scientific replication principles when AI accompanies consistent results that obsresolve the variability inherent in empirical investigation. Distinguished from adjacent concepts by its focus on the specific mechanism through which replication manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch kompetenzschwund durch Substitution manueller Prozesse mittels KI-Automation. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Performance Gap", "narrower_terms": [], "cross_domain_refs": [ "ART-0086" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "STE-0072", "domain": "STE", "term_en": "Reproducibility Documentation Gap", "term_de": "ReproducibilityDocumentationGap", "definition_en": "A stereotyping phenomenon in AI-mediated social categorization, characterized by a scientific reasoning effect involving the incomplete recording of computational environments and parameters when AI streamlines analysis pipelines without logging configuration details. The concept emerges specifically in contexts where reproducibility–documentation interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch delegierter Parameterjustierung, wenn kalibrierten Urteilsvermögens KI-Empfehlungen weicht. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Performance Gap", "narrower_terms": [], "cross_domain_refs": [ "SWE-0053" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "STE-0073", "domain": "STE", "term_en": "Research Paper Parsing Delegation", "term_de": "ResearchPaperParsingDelegation", "definition_en": "A bias pattern in AI-augmented social perception, measurable through the offloading of scientific paper comprehension to AI summarization tools, reducing the development of domain-specific reading literacy. The concept emerges specifically in contexts where research–paper interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch kompetenzschwund durch Substitution manueller Prozesse mittels KI-Automation. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Stereotypes", "narrower_terms": [], "cross_domain_refs": [ "ELR-0177" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q42240", "legal_classification": "empirical_phenomenon_label" }, { "id": "STE-0074", "domain": "STE", "term_en": "Research Question Narrowing", "term_de": "ResearchQuestionNarrowing", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A stereotyping phenomenon in AI-mediated social categorization, characterized by the tendency to formulate research questions that AI tools can readily address rather than pursuing more ambitious inquiries that extend beyond current AI capabilities. This phenomenon operates at the intersection of research and question dynamics within the broader STE domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept verhaltenseffekt in der Mensch-KI-Zusammenarbeit, der sich in research question narrowing manifestiert. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Stereotypes", "narrower_terms": [], "cross_domain_refs": [ "ELR-0154" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q42240", "legal_classification": "systematic_classification" }, { "id": "STE-0075", "domain": "STE", "term_en": "Safety Factor Blindness", "term_de": "SafetyFactorBlindness", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures A bias pattern in AI-augmented social perception, measurable through the insufficient awareness of engineering safety margins when AI optimization pushes designs toward theoretical limits without communicating the reduced margin. This phenomenon operates at the intersection of safety and factor dynamics within the broader STE domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept kompetenzschwund durch Substitution manueller Prozesse mittels KI-Automation. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Stereotypes", "narrower_terms": [], "cross_domain_refs": [ "DES-0025" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "STE-0076", "domain": "STE", "term_en": "Sample Size Reasoning Gap", "term_de": "SampleSizeReasoningGap", "definition_en": "A stereotyping phenomenon in AI-mediated social categorization, characterized by a scientific reasoning effect manifesting as the different understanding of statistical power and sample size determination when AI tools automatically suggest optimal parameters. The concept emerges specifically in contexts where sample–size interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch delegierter Parameterjustierung, wenn kalibrierten Urteilsvermögens KI-Empfehlungen weicht. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Performance Gap", "narrower_terms": [], "cross_domain_refs": [ "MSC-0037", "MTH-0049" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "STE-0077", "domain": "STE", "term_en": "Scale Comprehension Gap", "term_de": "ScaleComprehensionGap", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A stereotyping phenomenon in AI-mediated social categorization, characterized by a scientific reasoning effect reflecting the weakened understanding of orders of magnitude and relative scales when AI tools handle conversions and comparisons automatically. This phenomenon operates at the intersection of scale and comprehension dynamics within the broader STE domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept erfahrungsphänomen, das sich in scale comprehension gap manifestiert. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Performance Gap", "narrower_terms": [], "cross_domain_refs": [ "AED-0019", "AED-0092", "AGE-0023" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "STE-0078", "domain": "STE", "term_en": "Scientific Writing Homogenization", "term_de": "ScientificWritingHomogenization", "definition_en": "A stereotyping phenomenon in AI-mediated social categorization, characterized by a scientific reasoning effect observed when the convergence of scientific writing styles toward AI-generated prose patterns, reducing the distinctive voice that characterizes strong scientific communication. Distinguished from adjacent concepts by its focus on the specific mechanism through which scientific manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch kompetenzschwund durch Substitution manueller Prozesse mittels KI-Automation. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "WRK-0038", "narrower_terms": [], "cross_domain_refs": [ "ELR-0193" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "STE-0079", "domain": "STE", "term_en": "Sensor Integration Abstraction", "term_de": "SensorIntegrationAbstraction", "definition_en": "The growing gap between sensor data interpretation and understanding of the physical phenomena that sensors detect. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch fertigkeitsverlust in Experimentalmontage durch Ersatz durch KI-Anweisungen. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Analytical Ratio", "narrower_terms": [], "cross_domain_refs": [ "BEH-0065" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "STE-0080", "domain": "STE", "term_en": "Signal Processing Intuition Gap", "term_de": "SignalProcessingIntuitionGap", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A stereotyping phenomenon in AI-mediated social categorization, characterized by a STEM education phenomenon where the missing intuitive understanding of signal characteristics when AI-automated filtering and analysis handle noise reduction without user comprehension. This phenomenon operates at the intersection of signal and processing dynamics within the broader STE domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept kompetenzschwund durch Substitution manueller Prozesse mittels KI-Automation. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Performance Gap", "narrower_terms": [], "cross_domain_refs": [ "MKT-0069" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "STE-0081", "domain": "STE", "term_en": "Significant Figure Indifference", "term_de": "SignificantFigureIndifference", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures A bias pattern in AI-augmented social perception, measurable through a STEM education phenomenon in which the declining attention to significant figures and measurement precision when AI outputs display excessive decimal places without uncertainty context. This phenomenon operates at the intersection of significant and figure dynamics within the broader STE domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept kompetenzschwund durch Substitution manueller Prozesse mittels KI-Automation. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Stereotypes", "narrower_terms": [], "cross_domain_refs": [ "ART-0058" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "STE-0082", "domain": "STE", "term_en": "Simulation Substitution Effect", "term_de": "SimulationSubstitutionEffekt", "definition_en": "A stereotyping phenomenon in AI-mediated social categorization, characterized by a STEM education phenomenon involving the replacement of physical experimentation with AI-powered simulations that yield clean results lacking the instructive variability of real-world data. Distinguished from adjacent concepts by its focus on the specific mechanism through which simulation manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch fertigkeitsverlust in Experimentalmontage durch Ersatz durch KI-Anweisungen. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Psychological Effect", "narrower_terms": [], "cross_domain_refs": [ "SPR-0052" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q202763", "legal_classification": "analytical_category" }, { "id": "STE-0083", "domain": "STE", "term_en": "Specimen Identification Outsourcing", "term_de": "SpecimenIdentificationAuslagerung", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures A bias pattern in AI-augmented social perception, measurable through the delegation of biological specimen identification to AI image recognition, bypassing the observational skills that manual identification develops. This phenomenon operates at the intersection of specimen and identification dynamics within the broader STE domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept abbau technischer Fertigkeiten durch lange Abhängigkeit von KI-basierten Prozessen. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Stereotypes", "narrower_terms": [], "cross_domain_refs": [ "MUS-0034" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "STE-0084", "domain": "STE", "term_en": "Statistical Reasoning Bypass", "term_de": "StatisticalReasoningBypass", "definition_en": "A stereotyping phenomenon in AI-mediated social categorization, characterized by a scientific reasoning effect in which the acceptance of AI-computed statistical results without developing the conceptual understanding of why specific tests are appropriate for given data structures. The concept emerges specifically in contexts where statistical–reasoning interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch beobachtbares Muster der KI-Nutzung, das sich in statistical reasoning bypass manifestiert. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Stereotypes", "narrower_terms": [], "cross_domain_refs": [ "DAT-0051" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "STE-0085", "domain": "STE", "term_en": "Stoichiometry Calculation Delegation", "term_de": "StoichiometryCalculationDelegation", "definition_en": "A bias pattern in AI-augmented social perception, measurable through a STEM education phenomenon characterized by the transfer of chemical balance calculations to AI without developing the proportional reasoning that stoichiometry exercises build. The concept emerges specifically in contexts where stoichiometry–calculation interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch nutzungsphänomen, das sich in stoichiometry calculation delegation manifestiert. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Stereotypes", "narrower_terms": [], "cross_domain_refs": [ "BEH-0028", "BEH-0029", "COG-0092" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "STE-0086", "domain": "STE", "term_en": "Systematic Review Shortcutting", "term_de": "SystematicReviewShortcutting", "definition_en": "A stereotyping phenomenon in AI-mediated social categorization, characterized by a technical learning pattern manifesting as the compression of systematic review methodology to AI-assisted screening that bypasses the structured evaluation principles central to evidence synthesis. The concept emerges specifically in contexts where systematic–review interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch erfahrungsphänomen, das sich in systematic review shortcutting manifestiert. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Stereotypes", "narrower_terms": [], "cross_domain_refs": [ "COG-0007" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "STE-0087", "domain": "STE", "term_en": "Taxonomy Navigation Outsourcing", "term_de": "TaxonomyNavigationAuslagerung", "definition_en": "A STEM education phenomenon arising from the delegation of biological classification and systematic categorization to AI search, bypassing the learning that comes from navigating taxonomic structures. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch erhöhte Abhängigkeit von algorithmischen Systemen bei Entscheidungsfindung. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-2703", "narrower_terms": [], "cross_domain_refs": [ "AGE-0010", "AGE-0076", "AUG-0812" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q7205", "legal_classification": "empirical_phenomenon_label" }, { "id": "STE-0088", "domain": "STE", "term_en": "Technical Drawing Interpretation Shift", "term_de": "TechnicalDrawingInterpretationShift", "definition_en": "A stereotyping phenomenon in AI-mediated social categorization, characterized by a STEM education phenomenon reflecting the declining ability to read and interpret engineering drawings and schematics as AI rendering provides photorealistic alternatives. Distinguished from adjacent concepts by its focus on the specific mechanism through which technical manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch kompetenzschwund durch Substitution manueller Prozesse mittels KI-Automation. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Stereotypes", "narrower_terms": [], "cross_domain_refs": [ "PHO-0083", "SWE-0087", "VIB-0042" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "STE-0089", "domain": "STE", "term_en": "Technical Presentation Uncertainty Shift", "term_de": "TechnicalPresentationUncertaintyShift", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A stereotyping phenomenon in AI-mediated social categorization, characterized by the change in presentation apprehension from content mastery concerns to worries about audience detection of AI-assisted preparation. This phenomenon operates at the intersection of technical and presentation dynamics within the broader STE domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept phänomen in wissenschaftlich-technischer Ausbildung durch KI-Integration. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Stereotypes", "narrower_terms": [], "cross_domain_refs": [ "ELR-0075" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "STE-0090", "domain": "STE", "term_en": "Technical Vocabulary Simplification", "term_de": "TechnicalVocabularySimplification", "definition_en": "The drift toward simplified technical language when AI interactions normalize imprecise terminology that would be corrected in expert human communication. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch phänomen in wissenschaftlich-technischer Ausbildung durch KI-Integration. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-2051", "narrower_terms": [], "cross_domain_refs": [ "LIN-0070", "ELR-0005" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "STE-0091", "domain": "STE", "term_en": "Testing Methodology Compression", "term_de": "TestingMethodologyCompression", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures A bias pattern in AI-augmented social perception, measurable through a technical learning pattern manifesting as the reduction in testing methodology sophistication when AI-generated test suites cover obvious cases while missing edge conditions that experienced engineers anticipate. This phenomenon operates at the intersection of testing and methodology dynamics within the broader STE domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept kompetenzschwund durch Substitution manueller Prozesse mittels KI-Automation. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Data Compression", "narrower_terms": [], "cross_domain_refs": [ "VIB-0168", "VIB-0033" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q201", "legal_classification": "observational_construct" }, { "id": "STE-0092", "domain": "STE", "term_en": "Theoretical Framework Superficiality", "term_de": "TheoreticalFrameworkSuperficiality", "definition_en": "A bias pattern in AI-augmented social perception, measurable through a technical learning pattern observed when the surface-level engagement with theoretical frameworks when AI provides applicable results without requiring deep conceptual understanding of underlying theories. Distinguished from adjacent concepts by its focus on the specific mechanism through which theoretical manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch verhaltenseffekt in der Mensch-KI-Zusammenarbeit, der sich in theoretical framework superficiality manifestiert. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2303", "narrower_terms": [], "cross_domain_refs": [ "ELR-0041" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "STE-0093", "domain": "STE", "term_en": "Thesis Direction Convergence", "term_de": "ThesisDirectionKonvergenz", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures A bias pattern in AI-augmented social perception, measurable through the observation that AI-assisted thesis topic selection accompanies clusters of similar research directions within cohorts, reducing the diversity of scientific inquiry. This phenomenon operates at the intersection of thesis and direction dynamics within the broader STE domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept kompetenzschwund durch Substitution manueller Prozesse mittels KI-Automation. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Stereotypes", "narrower_terms": [], "cross_domain_refs": [ "VIB-0028" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "STE-0094", "domain": "STE", "term_en": "Unit Conversion Outsourcing", "term_de": "UnitConversionAuslagerung", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures A bias pattern in AI-augmented social perception, measurable through a technical learning pattern arising from the delegation of dimensional analysis and unit transition reasoning to AI tools, eroding the intuitive sense of physical quantities that precedes the absence of order-of-magnitude errors. This phenomenon operates at the intersection of unit and transition dynamics within the broader STE domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept fertigkeitsverlust in Experimentalmontage durch Ersatz durch KI-Anweisungen. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Stereotypes", "narrower_terms": [], "cross_domain_refs": [ "ASE-0055", "COG-0001", "COG-0025" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "STE-0095", "domain": "STE", "term_en": "Variable Isolation Confusion", "term_de": "VariableIsolationConfusion", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A stereotyping phenomenon in AI-mediated social categorization, characterized by a scientific reasoning effect where the difficulty in understanding controlled experimental variables when AI simultaneously adjusts multiple parameters during optimization. This phenomenon operates at the intersection of variable and isolation dynamics within the broader STE domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept delegierter Parameterjustierung, wenn kalibrierten Urteilsvermögens KI-Empfehlungen weicht. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Stereotypes", "narrower_terms": [], "cross_domain_refs": [ "MTH-0035", "MSC-0086" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "STE-0096", "domain": "STE", "term_en": "Version Control Negligence", "term_de": "VersionControlNegligence", "definition_en": "A bias pattern in AI-augmented social perception, measurable through a STEM education phenomenon involving the different adoption of systematic version control practices in computational work when AI tools manage code changes without teaching the underlying principles. Distinguished from adjacent concepts by its focus on the specific mechanism through which version manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch phänomen in wissenschaftlich-technischer Ausbildung durch KI-Integration. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Stereotypes", "narrower_terms": [], "cross_domain_refs": [ "AED-0037", "LIN-0026" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "SWE-0001", "domain": "SWE", "term_en": "API Contract Drift", "term_de": "ApiContractDrift", "definition_en": "A software engineering phenomenon in AI-assisted development, characterized by a coding interaction pattern manifesting as when AI-generated code inconsistently implements or consumes APIs, diverging from contract specifications observed alongside training data ambiguities. The concept emerges specifically in contexts where api–contract interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch auseinanderdriften zwischen Intention und KI-generierter Umsetzung. Die Divergenz wird durch KI-Iterationen verstärkt. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Systemic Drift", "narrower_terms": [ "SWE-0082", "SWE-0045", "SWE-0009", "SWE-0006", "SWE-0008", "SWE-0048", "SWE-0054", "SWE-0011", "SWE-0070", "SWE-0016", "SWE-0001", "SWE-0023", "SWE-0059", "SWE-0064", "SWE-0062", "SWE-0031", "SWE-0084", "SWE-0094", "SWE-0073", "SWE-0065", "SWE-0030", "SWE-0014", "SWE-0057", "SWE-0046", "SWE-0074", "SWE-0017", "SWE-0024", "SWE-0075", "SWE-0092", "SWE-0078", "SWE-0069", "SWE-0038", "SWE-0002", "SWE-0091", "SWE-0055", "SWE-0095", "SWE-0041", "SWE-0018", "SWE-0013", "SWE-0042", "SWE-0021", "SWE-0086", "SWE-0083", "SWE-0085", "SWE-0058", "SWE-0010", "SWE-0004", "SWE-0050", "SWE-0025", "SWE-0063", "SWE-0061", "SWE-0071", "SWE-0051", "SWE-0056", "SWE-0087", "SWE-0052", "SWE-0012", "SWE-0037", "SWE-0066", "SWE-0079", "SWE-0028", "SWE-0035", "SWE-0089", "SWE-0081", "SWE-0005", "SWE-0053", "SWE-0044", "SWE-0033", "SWE-0019", "SWE-0034", "SWE-0068" ], "cross_domain_refs": [ "COP-0053" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q165194", "legal_classification": "systematic_classification" }, { "id": "SWE-0002", "domain": "SWE", "term_en": "Abstraction Leakage Acceptance", "term_de": "AbstractionLeakageAcceptance", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A software engineering phenomenon in AI-assisted development, characterized by a development workflow effect reflecting the normalization of accepting abstraction leaks in AI-generated code where implementation details percolate upward through layers that exist to conceal them. This phenomenon operates at the intersection of abstraction and leakage dynamics within the broader SWE domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept strukturelle Inkonsistenz in KI-generierten Codebasen tendiert dazu zu führen zu Wartungsproblemen. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Software Engineering", "narrower_terms": [], "cross_domain_refs": [ "VIB-0113" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "SWE-0003", "domain": "SWE", "term_en": "Accessibility Negligence", "term_de": "AccessibilityNegligence", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A software engineering phenomenon in AI-assisted development, characterized by a software engineering phenomenon observed when when developers delegate UI generation to AI, accessibility concerns like keyboard navigation and screen reader compatibility are often omitted. This phenomenon operates at the intersection of accessibility and negligence dynamics within the broader SWE domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept vernachlässigung von Zugänglichkeitsanforderungen durch KI-UI-Delegation. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "RPH-2101", "narrower_terms": [], "cross_domain_refs": [ "VIB-0150" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "SWE-0004", "domain": "SWE", "term_en": "Alert Fatigue From AI", "term_de": "AlertFatigueFromai", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures A code quality pattern in AI-augmented programming, measurable through aI-generated alerting rules involve excessive false positives, desensitizing engineers to real alerts and increasing mean-time-to-response. This phenomenon operates at the intersection of alert and fatigue dynamics within the broader SWE domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept überversorgung mit Optionen, die Selektionsfähigkeit erschwert statt erleichtert. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Software Engineering", "narrower_terms": [], "cross_domain_refs": [ "DAT-0072" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "SWE-0005", "domain": "SWE", "term_en": "Architecture Delegation Drift", "term_de": "ArchitectureDelegationDrift", "definition_en": "A code quality pattern in AI-augmented programming, measurable through the gradual transfer of architectural decision-making from human engineers to AI, with decreasing human awareness of cumulative design implications. Distinguished from adjacent concepts by its focus on the specific mechanism through which architecture manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch interaktionsdynamik, die sich in architecture delegation drift manifestiert. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Systemic Drift", "narrower_terms": [], "cross_domain_refs": [ "VIB-0035", "COG-0074" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "SWE-0006", "domain": "SWE", "term_en": "Attribution Debt Accumulation", "term_de": "AttributionDebtAccumulation", "definition_en": "As a neutral analytical construct in AI behavior research, this term denotes A software engineering phenomenon in AI-assisted development, characterized by a software engineering phenomenon observed when the failure to track and attribute sources of AI-generated code fragments accompanies growing attribution debt that becomes impossible to resolve later. The concept emerges specifically in contexts where attribution–debt interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "In der AUGMANITAI-Terminologiewissenschaft als rein beschreibendes Phänomen klassifiziert, bezeichnet dieser Begriff emergente Dynamik, die sich in attribution debt accumulation manifestiert. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Software Engineering", "narrower_terms": [], "cross_domain_refs": [ "AGE-0068", "AGE-0091", "AGE-0092" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "SWE-0007", "domain": "SWE", "term_en": "Auditor Confusion", "term_de": "AuditorConfusion", "definition_en": "A development workflow effect involving security and compliance auditors struggle to evaluate systems with AI-generated code because they cannot determine the trustworthiness of implementation. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch wahrnehmungseffekt, der sich in auditor confusion manifestiert. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2505", "narrower_terms": [], "cross_domain_refs": [ "VIB-0107" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "SWE-0008", "domain": "SWE", "term_en": "Backward Compatibility Amnesia", "term_de": "BackwardCompatibilityAmnesia", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A software engineering phenomenon in AI-assisted development, characterized by a coding interaction pattern where developers forget to check reverse-oriented compatibility implications when accepting AI-generated changes, as the scope often extends beyond apparent modifications. This phenomenon operates at the intersection of reverse-oriented and compatibility dynamics within the broader SWE domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept softwareentwicklungsphänomen durch KI-Assistenz, das Qualität beeinflusst. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Software Engineering", "narrower_terms": [], "cross_domain_refs": [ "VIB-0100" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "SWE-0009", "domain": "SWE", "term_en": "Burnout Acceleration from Complexity", "term_de": "BurnoutBeschleunigungFromComplexity", "definition_en": "A software engineering phenomenon in AI-assisted development, characterized by a software engineering phenomenon observed when the cognitive load of managing AI-generated code that few individuals fully understands accelerates developer burnout and attrition. The concept emerges specifically in contexts where burnout–acceleration interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch rekurrenter Effekt, der sich in burnout acceleration from complexity manifestiert. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Analytical Ratio", "narrower_terms": [], "cross_domain_refs": [ "VIB-0026" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q675814", "legal_classification": "systematic_classification" }, { "id": "SWE-0010", "domain": "SWE", "term_en": "CI/CD Pipeline Brittleness", "term_de": "Ci/cdPipelineBrittleness", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures A code quality pattern in AI-augmented programming, measurable through aI-generated CI/CD workflows contain brittle reliances and sequencing assumptions that fail unexpectedly when environments change. This phenomenon operates at the intersection of ci/cd and pipeline dynamics within the broader SWE domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept softwareentwicklungsphänomen durch KI-Assistenz, das Qualität beeinflusst. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Processing Pipeline", "narrower_terms": [], "cross_domain_refs": [ "SAL-0081", "VIB-0080" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "SWE-0011", "domain": "SWE", "term_en": "Career Skill Bifurcation", "term_de": "CareerSkillBifurkation", "definition_en": "A code quality pattern in AI-augmented programming, measurable through a development workflow effect arising from developer careers split into those who use AI as augmentation versus those fully reliant on it, creating two distinct career trajectories with different long-term prospects. Distinguished from adjacent concepts by its focus on the specific mechanism through which career manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch beobachtbares Muster der KI-Nutzung, das sich in career skill bifurcation manifestiert. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Software Engineering", "narrower_terms": [], "cross_domain_refs": [ "REL-0155", "AED-0007" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "SWE-0012", "domain": "SWE", "term_en": "Chaos Engineering Avoidance", "term_de": "ChaosEngineeringAvoidance", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A software engineering phenomenon in AI-assisted development, characterized by a software engineering phenomenon characterized by the reluctance to run chaos experiments on systems using AI-generated code because vulnerabilities and assumptions haven't been explicitly mapped. This phenomenon operates at the intersection of chaos and engineering dynamics within the broader SWE domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept rekurrenter Effekt, der sich in chaos engineering avoidance manifestiert. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Software Engineering", "narrower_terms": [], "cross_domain_refs": [ "VIB-0073" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "SWE-0013", "domain": "SWE", "term_en": "Code Comprehension Narrowing", "term_de": "CodeComprehensionNarrowing", "definition_en": "A code quality pattern in AI-augmented programming, measurable through a coding interaction pattern manifesting as the moment a developer realizes they no longer understand their own codebase because AI generated most of it. The code functions, but its logic has become opaque to its author. Distinguished from adjacent concepts by its focus on the specific mechanism through which code manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch wahrnehmungseffekt, der sich in code comprehension narrowing manifestiert. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Software Engineering", "narrower_terms": [], "cross_domain_refs": [ "VIB-0176" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "SWE-0014", "domain": "SWE", "term_en": "Code Review Skill Shift", "term_de": "CodeReviewSkillShift", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A software engineering phenomenon in AI-assisted development, characterized by the reduced engagement in meaningful code review correlates with atrophied abilities to spot bugs, design flaws, or security issues in peer code. This phenomenon operates at the intersection of code and review dynamics within the broader SWE domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept unzureichende Qualitätskontrolle bei unreflektierter KI-Output-Übernahme. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Software Engineering", "narrower_terms": [], "cross_domain_refs": [ "STE-0061", "VIB-0020" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "SWE-0015", "domain": "SWE", "term_en": "Code Review Theater", "term_de": "CodeReviewTheater", "definition_en": "A code quality pattern in AI-augmented programming, measurable through a coding interaction pattern involving the performance of code review without substantive evaluation of AI-generated code, where reviewers click approve to maintain workflow velocity despite unclear understanding. The concept emerges specifically in contexts where code–review interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch unzureichende Qualitätskontrolle bei unreflektierter KI-Output-Übernahme. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2154", "narrower_terms": [], "cross_domain_refs": [ "VIB-0040" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "SWE-0016", "domain": "SWE", "term_en": "Competency Credential Perception", "term_de": "CompetencyCredentialPerception", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures A code quality pattern in AI-augmented programming, measurable through a developer's portfolio and demonstrated competency become misaligned when most projects were powered by AI, while interview performance still reflects older skill levels. This phenomenon operates at the intersection of competency and credential dynamics within the broader SWE domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept erfahrungsphänomen, das sich in competency credential perception manifestiert. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Software Engineering", "narrower_terms": [], "cross_domain_refs": [ "AED-0014", "AED-0015", "AED-0016" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q177601", "legal_classification": "empirical_phenomenon_label" }, { "id": "SWE-0017", "domain": "SWE", "term_en": "Competitive Advantage Shift", "term_de": "CompetitiveAdvantageShift", "definition_en": "A software engineering phenomenon in AI-assisted development, characterized by a development workflow effect involving when competitors use the same AI tools, previously differentiating code becomes commoditized, eliminating engineering advantages. Distinguished from adjacent concepts by its focus on the specific mechanism through which competitive manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch kI-bezogene Verhaltenstendenz, die sich in competitive advantage shift manifestiert. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Software Engineering", "narrower_terms": [], "cross_domain_refs": [ "STE-0020" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "SWE-0018", "domain": "SWE", "term_en": "Concurrency Complexity Evasion", "term_de": "ConcurrencyComplexityEvasion", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A software engineering phenomenon in AI-assisted development, characterized by a development workflow effect in which developers avoid thinking through concurrency issues because AI accompanies seemingly correct concurrent code, creating potential for race conditions in production. This phenomenon operates at the intersection of concurrency and complexity dynamics within the broader SWE domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept wahrnehmungseffekt, der sich in concurrency complexity evasion manifestiert. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Software Engineering", "narrower_terms": [], "cross_domain_refs": [ "VIB-0088" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "SWE-0019", "domain": "SWE", "term_en": "Configuration Magic Proliferation", "term_de": "ConfigurationMagicProliferation", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures A code quality pattern in AI-augmented programming, measurable through a coding interaction pattern observed when the accumulation of implicit configurations and magic numbers in AI-generated code because explaining them requires more context than the AI can provide. This phenomenon operates at the intersection of configuration and magic dynamics within the broader SWE domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept erfahrungsphänomen, das sich in configuration magic proliferation manifestiert. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Analytical Ratio", "narrower_terms": [], "cross_domain_refs": [ "CRE-0132" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "SWE-0020", "domain": "SWE", "term_en": "Contextual Forgetting", "term_de": "ContextualForgetting", "definition_en": "The developer's shift of project context and business logic understanding as they shift focus to managing AI outputs rather than crafting solutions. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch softwareentwicklungsphänomen durch KI-Assistenz, das Qualität beeinflusst. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2603", "narrower_terms": [], "cross_domain_refs": [ "AED-0021", "COG-0036", "COG-0114" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "SWE-0021", "domain": "SWE", "term_en": "Copy-Paste Blindness", "term_de": "Copy-pasteBlindness", "definition_en": "A software engineering phenomenon in AI-assisted development, characterized by the practice of copying AI-generated code into production without reading or understanding it first, addressing the paste operation as validation. Distinguished from adjacent concepts by its focus on the specific mechanism through which copy manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch emergente Dynamik, die sich in copy-paste blindness manifestiert. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Software Engineering", "narrower_terms": [], "cross_domain_refs": [ "STE-0012" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "SWE-0022", "domain": "SWE", "term_en": "Creative Fulfillment Gap", "term_de": "CreativeFulfillmentGap", "definition_en": "The shift of creative fulfillment from programming as developers shift from problem-solving to prompt-crafting, diminishing intrinsic motivation. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch kompetenzschwund durch Substitution manueller Prozesse mittels KI-Automation. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2603", "narrower_terms": [], "cross_domain_refs": [ "AED-0019", "AED-0092", "AGE-0023" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "SWE-0023", "domain": "SWE", "term_en": "Cross-Browser Obliviousness", "term_de": "Cross-browserObliviousness", "definition_en": "A software engineering phenomenon in AI-assisted development, characterized by a development workflow effect observed when for frontend code, developers stop testing across browsers because AI-generated components appear to work in development environments. The concept emerges specifically in contexts where cross–browser interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch strukturelle Inkonsistenz in KI-generierten Codebasen tendiert dazu zu führen zu Wartungsproblemen. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Software Engineering", "narrower_terms": [], "cross_domain_refs": [ "VIB-0129" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "SWE-0024", "domain": "SWE", "term_en": "Cross-Team Collaboration Friction", "term_de": "Cross-teamCollaborationFriction", "definition_en": "A software engineering phenomenon in AI-assisted development, characterized by a coding interaction pattern reflecting teams integrating AI-generated code from other teams face friction because differences in generation styles involve incompatibilities. Distinguished from adjacent concepts by its focus on the specific mechanism through which cross manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch emergente Dynamik, die sich in cross-team collaboration friction manifestiert. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Analytical Ratio", "narrower_terms": [], "cross_domain_refs": [ "VIB-0028" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "SWE-0025", "domain": "SWE", "term_en": "Cryptographic Cargo Culting", "term_de": "CryptographicCargoCulting", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A software engineering phenomenon in AI-assisted development, characterized by a software engineering phenomenon involving developers copy AI-generated cryptographic code without understanding the underlying security properties, addressing it as a black box that increases security. This phenomenon operates at the intersection of cryptographic and cargo dynamics within the broader SWE domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept verhaltenseffekt in der Mensch-KI-Zusammenarbeit, der sich in cryptographic cargo culting manifestiert. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Software Engineering", "narrower_terms": [], "cross_domain_refs": [ "VIB-0202" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "SWE-0026", "domain": "SWE", "term_en": "Data Breach Attribution Confusion", "term_de": "DataBreachAttributionConfusion", "definition_en": "A software engineering phenomenon in AI-assisted development, characterized by a software engineering phenomenon observed when when a data breach occurs observed alongside AI-generated code vulnerabilities, attribution confusion arises about who bears responsibility. The concept emerges specifically in contexts where data–breach interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch verlust von Präzision und Klarheit, wenn KI-generierte Ergebnisse interpretierbar mehrdeutig sind. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2555", "narrower_terms": [], "cross_domain_refs": [ "VIB-0017" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q42848", "legal_classification": "descriptive_research_term" }, { "id": "SWE-0027", "domain": "SWE", "term_en": "Debug Blindness", "term_de": "DebugBlindness", "definition_en": "A software engineering phenomenon in AI-assisted development, characterized by a coding interaction pattern where when AI-generated code breaks, the developer struggles to debug because the code's structure and logic are unfamiliar, leaving them less likely to trace the failure. The concept emerges specifically in contexts where debug–blindness interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch abhängigkeit von LLM-basierten Fehleranalysen, die Problemverständnis umgeht. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2003", "narrower_terms": [], "cross_domain_refs": [ "COG-0105", "CON-0013", "CUS-0097" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "SWE-0028", "domain": "SWE", "term_en": "Reliance Chain Opacity", "term_de": "RelianceChainOpacity", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A software engineering phenomenon in AI-assisted development, characterized by a development workflow effect in which when AI adds reliances or library imports without the developer understanding why those specific packages were chosen or what vulnerabilities they carry. This phenomenon operates at the intersection of reliance and chain dynamics within the broader SWE domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept erhöhte Abhängigkeit von algorithmischen Systemen bei Entscheidungsfindung. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Software Engineering", "narrower_terms": [], "cross_domain_refs": [ "VIB-0165" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "SWE-0029", "domain": "SWE", "term_en": "Deployment Window Surprise", "term_de": "DeploymentWindowSurprise", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A software engineering phenomenon in AI-assisted development, characterized by the unexpected discovery of critical issues only when code reaches production because AI-generated changes weren't tested in realistic deployment scenarios. This phenomenon operates at the intersection of deployment and window dynamics within the broader SWE domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept strukturelle Inkonsistenz in KI-generierten Codebasen tendiert dazu zu führen zu Wartungsproblemen. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "RPH-2555", "narrower_terms": [], "cross_domain_refs": [ "VIB-0199" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "SWE-0030", "domain": "SWE", "term_en": "Disaster Restoration Theater", "term_de": "DisasterRestorationTheater", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A software engineering phenomenon in AI-assisted development, characterized by a development workflow effect observed when disaster restoration plans generated by AI appear comprehensive but haven't been tested with actual infrastructure code, failing during real incidents. This phenomenon operates at the intersection of disaster and restoration dynamics within the broader SWE domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept strukturelle Inkonsistenz in KI-generierten Codebasen tendiert dazu zu führen zu Wartungsproblemen. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Analytical Ratio", "narrower_terms": [], "cross_domain_refs": [ "VIB-0033" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "SWE-0031", "domain": "SWE", "term_en": "Docker Image Bloat Acceptance", "term_de": "DockerImageBloatAcceptance", "definition_en": "A software engineering phenomenon in AI-assisted development, characterized by a coding interaction pattern arising from aI-generated Dockerfiles often include unnecessary packages and layers, leading to bloated images that developers accept without optimization. The concept emerges specifically in contexts where docker–image interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch interaktionsdynamik, die sich in docker image bloat acceptance manifestiert. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Software Engineering", "narrower_terms": [], "cross_domain_refs": [ "LIN-0002" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "SWE-0032", "domain": "SWE", "term_en": "Documentation Change", "term_de": "DocumentationChange", "definition_en": "A software engineering phenomenon in AI-assisted development, characterized by the gradual shift of code documentation as developers skip writing docs, assuming AI can yield or infer them later, resulting in undocumented legacy systems. The concept emerges specifically in contexts where documentation–change interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch übervertrauen auf KI-generierte Dokumentation ohne manuelle Verifizierung. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2701", "narrower_terms": [], "cross_domain_refs": [ "VIB-0073" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "SWE-0033", "domain": "SWE", "term_en": "Edge Case Blindness", "term_de": "EdgeCaseBlindness", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures A code quality pattern in AI-augmented programming, measurable through the reduced attention to boundary conditions and edge cases in code because AI-generated implementations often ignore them, and failures appear infrequent. This phenomenon operates at the intersection of edge and case dynamics within the broader SWE domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept kompetenzschwund durch Substitution manueller Prozesse mittels KI-Automation. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Software Engineering", "narrower_terms": [], "cross_domain_refs": [ "STE-0007" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "SWE-0034", "domain": "SWE", "term_en": "Error Message Inflation", "term_de": "ErrorMessageInflation", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures A code quality pattern in AI-augmented programming, measurable through a development workflow effect where the accumulation of verbose, redundant, or AI-generated error messages that obsresolve rather than clarify problems, reducing their utility as debugging aids. This phenomenon operates at the intersection of error and message dynamics within the broader SWE domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept abhängigkeit von LLM-basierten Fehleranalysen, die Problemverständnis umgeht. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Software Engineering", "narrower_terms": [], "cross_domain_refs": [ "WEB-0013" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "SWE-0035", "domain": "SWE", "term_en": "Fairness Testing Evasion", "term_de": "FairnessTestingEvasion", "definition_en": "A software engineering phenomenon in AI-assisted development, characterized by a software engineering phenomenon reflecting developers omit fairness and bias testing for AI-generated code, assuming the code is neutral, thereby overlooking subtle discriminatory patterns. Distinguished from adjacent concepts by its focus on the specific mechanism through which fairness manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch strukturelle Inkonsistenz in KI-generierten Codebasen tendiert dazu zu führen zu Wartungsproblemen. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Software Engineering", "narrower_terms": [], "cross_domain_refs": [ "VIB-0028" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q18005", "legal_classification": "empirical_phenomenon_label" }, { "id": "SWE-0036", "domain": "SWE", "term_en": "False Achievement Attribution", "term_de": "FalseAchievementAttribution", "definition_en": "When developers receive credit for AI-generated work, they experience intensified doubt about the legitimacy of the accomplishment because the work itself wasn't authored by them. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch rekurrenter Effekt, der sich in false achievement attribution manifestiert. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-3304", "narrower_terms": [], "cross_domain_refs": [ "REL-0109" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "SWE-0037", "domain": "SWE", "term_en": "Feature Parity Perception", "term_de": "FeatureParityPerception", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures A code quality pattern in AI-augmented programming, measurable through two implementations appear to have feature parity from a user perspective, but internal differences in AI-generated code make maintaining multiple versions a nightmare. This phenomenon operates at the intersection of feature and parity dynamics within the broader SWE domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept beobachtbares Muster der KI-Nutzung, das sich in feature parity perception manifestiert. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Software Engineering", "narrower_terms": [], "cross_domain_refs": [ "VIB-0196" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q177601", "legal_classification": "observational_construct" }, { "id": "SWE-0038", "domain": "SWE", "term_en": "Hiring Expectation Mismatch", "term_de": "HiringExpectationMismatch", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A software engineering phenomenon in AI-assisted development, characterized by a software engineering phenomenon involving teams hire developers based on AI-assisted work samples, only to discover reduced productivity when developers can't replicate that performance without AI assistance. This phenomenon operates at the intersection of hiring and expectation dynamics within the broader SWE domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept kompetenzschwund durch Substitution manueller Prozesse mittels KI-Automation. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Software Engineering", "narrower_terms": [], "cross_domain_refs": [ "VIB-0028" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "SWE-0039", "domain": "SWE", "term_en": "Incident Response Improvisation", "term_de": "IncidentResponseImprovisation", "definition_en": "A code quality pattern in AI-augmented programming, measurable through when incidents occur in systems with extensive AI-generated code, responders improvise because runbooks don't account for emergent system behavior. The concept emerges specifically in contexts where incident–response interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch interaktionsdynamik, die sich in incident response improvisation manifestiert. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2355", "narrower_terms": [], "cross_domain_refs": [ "CRE-0065" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "SWE-0040", "domain": "SWE", "term_en": "Inconsistency Blindness", "term_de": "InconsistencyBlindness", "definition_en": "A coding interaction pattern arising from the failure to detect naming conventions, coding style, or structural inconsistencies across a codebase because different AI prompts yield divergent implementations. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch auseinanderdriften zwischen Intention und KI-generierter Umsetzung. Die Divergenz wird durch KI-Iterationen verstärkt. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2104", "narrower_terms": [], "cross_domain_refs": [ "CRE-0112" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "SWE-0041", "domain": "SWE", "term_en": "Infrastructure as Code Fragility", "term_de": "InfrastructureasCodeFragility", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures A code quality pattern in AI-augmented programming, measurable through a software engineering phenomenon arising from when AI accompanies Infrastructure as Code templates, subtle misconfigurations can propagate silently until infrastructure deployment fails catastrophically. This phenomenon operates at the intersection of infrastructure and as dynamics within the broader SWE domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept emergente Dynamik, die sich in infrastructure as code fragility manifestiert. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Software Engineering", "narrower_terms": [], "cross_domain_refs": [ "VIB-0141" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "SWE-0042", "domain": "SWE", "term_en": "Insurance Coverage Ambiguity", "term_de": "InsuranceCoverageAmbiguity", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A software engineering phenomenon in AI-assisted development, characterized by a coding interaction pattern arising from cyber liability insurance policies may not cover incidents arising from AI-generated code, creating gaps in coverage. This phenomenon operates at the intersection of insurance and coverage dynamics within the broader SWE domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept strukturelle Inkonsistenz in KI-generierten Codebasen tendiert dazu zu führen zu Wartungsproblemen. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Software Engineering", "narrower_terms": [], "cross_domain_refs": [ "VIB-0015", "COP-0071" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "SWE-0043", "domain": "SWE", "term_en": "Integration Test Gap", "term_de": "IntegrationTestGap", "definition_en": "A development workflow effect reflecting aI-generated tests focus on unit testing because integration tests are harder to automate, leaving integration points untested and exposed. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch softwareentwicklungsphänomen durch KI-Assistenz, das Qualität beeinflusst. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-3101", "narrower_terms": [], "cross_domain_refs": [ "WEB-0084" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "SWE-0044", "domain": "SWE", "term_en": "Intellectual Property Ambiguity", "term_de": "IntellectualPropertyAmbiguity", "definition_en": "A software engineering phenomenon in AI-assisted development, characterized by a software engineering phenomenon reflecting the unclear ownership of AI-generated code accompanies legal and contractual ambiguities about what a developer or company actually owns. Distinguished from adjacent concepts by its focus on the specific mechanism through which intellectual manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch mehrdeutige oder interpretierbar unterschiedliche Ergebnisse aus KI-Systemen bei gleichen Eingaben. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Software Engineering", "narrower_terms": [], "cross_domain_refs": [ "MUS-0021" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q131257", "legal_classification": "analytical_category" }, { "id": "SWE-0045", "domain": "SWE", "term_en": "Internationalization Forgetting", "term_de": "InternationalizationForgetting", "definition_en": "A software engineering phenomenon in AI-assisted development, characterized by aI-generated code often assumes English-only or single-locale behavior because hardcoding strings and assumptions is easier than parametrizing localization. Distinguished from adjacent concepts by its focus on the specific mechanism through which internationalization manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch interaktionsdynamik, die sich in internationalization forgetting manifestiert. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Software Engineering", "narrower_terms": [], "cross_domain_refs": [ "VIB-0073" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "SWE-0046", "domain": "SWE", "term_en": "Interviewer Dissonance", "term_de": "InterviewerDissonance", "definition_en": "A software engineering phenomenon in AI-assisted development, characterized by technical interviewers cannot reliably distinguish between a developer's genuine skills and those artificially inflated by AI assistance during the interview process. The concept emerges specifically in contexts where interviewer–dissonance interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch interaktionsdynamik, die sich in interviewer dissonance manifestiert. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Software Engineering", "narrower_terms": [], "cross_domain_refs": [ "ELR-0181", "RHR-0135", "ROB-0205" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "SWE-0047", "domain": "SWE", "term_en": "Knowledge Silos Formation", "term_de": "KnowledgeSilosFormation", "definition_en": "A coding interaction pattern reflecting when different developers use different AI tools or prompts, knowledge silos form organically because code generation becomes idiosyncratic and non-transferable. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch erfahrungsphänomen, das sich in knowledge silos formation manifestiert. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2104", "narrower_terms": [], "cross_domain_refs": [ "VIB-0032" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "SWE-0048", "domain": "SWE", "term_en": "Kubernetes YAML Complexity Blindness", "term_de": "KubernetesYamlComplexityBlindness", "definition_en": "A software engineering phenomenon in AI-assisted development, characterized by developers accept complex Kubernetes configurations generated by AI without understanding resource limits, network policies, or high-availability implications. The concept emerges specifically in contexts where kubernetes–yaml interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch interaktionsmuster, das sich in kubernetes yaml complexity blindness manifestiert. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Software Engineering", "narrower_terms": [], "cross_domain_refs": [ "VIB-0140" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "SWE-0049", "domain": "SWE", "term_en": "Learning Avoidance Pattern", "term_de": "LearningAvoidanceMuster", "definition_en": "The tendency to skip learning difficult language features or frameworks by immediately delegating those tasks to AI, preventing skill development. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch erhöhte Abhängigkeit von algorithmischen Systemen bei Entscheidungsfindung. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2303", "narrower_terms": [], "cross_domain_refs": [ "ELR-0027" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q133500", "legal_classification": "empirical_phenomenon_label" }, { "id": "SWE-0050", "domain": "SWE", "term_en": "Legacy Code Proliferation", "term_de": "LegacyCodeProliferation", "definition_en": "A software engineering phenomenon in AI-assisted development, characterized by a development workflow effect arising from code becomes legacy almost immediately because it's not maintained by its human creators, accumulating technical debt faster than it can be understood. The concept emerges specifically in contexts where legacy–code interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch beobachtbares Muster der KI-Nutzung, das sich in legacy code proliferation manifestiert. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Analytical Ratio", "narrower_terms": [], "cross_domain_refs": [ "VIB-0073" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "SWE-0051", "domain": "SWE", "term_en": "Library Incompatibility Surprise", "term_de": "LibraryIncompatibilitySurprise", "definition_en": "A software engineering phenomenon in AI-assisted development, characterized by a software engineering phenomenon observed when aI-generated code that imports multiple libraries sometimes silently introduces version conflicts or incompatibilities that manifest only under specific conditions. The concept emerges specifically in contexts where library–incompatibility interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch wahrnehmungseffekt, der sich in library incompatibility surprise manifestiert. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Software Engineering", "narrower_terms": [], "cross_domain_refs": [ "VIB-0199" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "SWE-0052", "domain": "SWE", "term_en": "License Compliance Nightmare", "term_de": "LicenseComplianceNightmare", "definition_en": "A software engineering phenomenon in AI-assisted development, characterized by a development workflow effect reflecting aI-generated code may incorporate code from licensed sources without proper attribution, creating compliance and legal exposure. Distinguished from adjacent concepts by its focus on the specific mechanism through which license manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch verhaltenseffekt in der Mensch-KI-Zusammenarbeit, der sich in license compliance nightmare manifestiert. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Software Engineering", "narrower_terms": [], "cross_domain_refs": [ "VIB-0077" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "SWE-0053", "domain": "SWE", "term_en": "Logging Debt Accumulation", "term_de": "LoggingDebtAccumulation", "definition_en": "A code quality pattern in AI-augmented programming, measurable through a software engineering phenomenon manifesting as aI-generated code often lacks adequate logging or adds excessive logging, creating either blind spots during debugging or noise that obsresolves real issues. Distinguished from adjacent concepts by its focus on the specific mechanism through which logging manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch abhängigkeit von LLM-basierten Fehleranalysen, die Problemverständnis umgeht. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Software Engineering", "narrower_terms": [], "cross_domain_refs": [ "STE-0072" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "SWE-0054", "domain": "SWE", "term_en": "Long-Term Career Viability Questions", "term_de": "Long-termCareerViabilityQuestions", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A software engineering phenomenon in AI-assisted development, characterized by a development workflow effect observed when developers question whether their career trajectory remains viable if AI can involve their current work faster, cheaper, and increasingly more. This phenomenon operates at the intersection of long and term dynamics within the broader SWE domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept rekurrenter Effekt, der sich in long-term career viability questions manifestiert. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Software Engineering", "narrower_terms": [], "cross_domain_refs": [ "TEW-0058" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "SWE-0055", "domain": "SWE", "term_en": "Memory Leak Acceptance", "term_de": "MemoryLeakAcceptance", "definition_en": "A software engineering phenomenon in AI-assisted development, characterized by the normalization of ignoring small memory leaks and resource handling issues in AI-generated code because they don't may is associated with immediate failures. Distinguished from adjacent concepts by its focus on the specific mechanism through which memory manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch wahrnehmungseffekt, der sich in memory leak acceptance manifestiert. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Software Engineering", "narrower_terms": [], "cross_domain_refs": [ "AGE-0023", "AGE-0024", "ART-0060" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q492", "legal_classification": "systematic_classification" }, { "id": "SWE-0056", "domain": "SWE", "term_en": "Mentoring Paradox", "term_de": "MentoringParadox", "definition_en": "A code quality pattern in AI-augmented programming, measurable through a development workflow effect arising from senior developers cannot effectively mentor juniors in AI-heavy teams because explaining why code is wrong requires understanding it, which seniors may lack. The concept emerges specifically in contexts where mentoring–paradox interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch emergente Dynamik, die sich in mentoring paradox manifestiert. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Logical Paradox", "narrower_terms": [], "cross_domain_refs": [ "VIB-0028" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "SWE-0057", "domain": "SWE", "term_en": "Model Bias Manifestation", "term_de": "ModelBiasManifestation", "definition_en": "A software engineering phenomenon in AI-assisted development, characterized by biases embedded in AI training data manifest in generated code as subtle algorithmic choices, performance disparities, or feature implementations. The concept emerges specifically in contexts where model–bias interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch verhaltenseffekt in der Mensch-KI-Zusammenarbeit, der sich in model bias manifestation manifestiert. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Cognitive Bias", "narrower_terms": [], "cross_domain_refs": [ "VIB-0073", "CUS-0006" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q213523", "legal_classification": "observational_construct" }, { "id": "SWE-0058", "domain": "SWE", "term_en": "Monitoring Metric Meaninglessness", "term_de": "MonitoringMetricMeaninglessness", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A software engineering phenomenon in AI-assisted development, characterized by a coding interaction pattern where aI accompanies monitoring dashboards and metrics that look comprehensive but don't measure what actually matters for application restoreth. This phenomenon operates at the intersection of monitoring and metric dynamics within the broader SWE domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept interaktionsdynamik, die sich in monitoring metric meaninglessness manifestiert. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Performance Metric", "narrower_terms": [], "cross_domain_refs": [ "SPR-0025" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "SWE-0059", "domain": "SWE", "term_en": "Monolith Creep Acceleration", "term_de": "MonolithCreepBeschleunigung", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A software engineering phenomenon in AI-assisted development, characterized by the rapid expansion of monolithic codebases as AI accompanies large feature additions without concern for modular decomposition, increasing entropy. This phenomenon operates at the intersection of monolith and creep dynamics within the broader SWE domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept interaktionsdynamik, die sich in monolith creep acceleration manifestiert. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Analytical Ratio", "narrower_terms": [], "cross_domain_refs": [ "VIB-0046" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "SWE-0060", "domain": "SWE", "term_en": "Motivation Shift Pattern", "term_de": "MotivationShiftMuster", "definition_en": "The progressive shift of motivation as developers realize they're becoming orchestrators of AI outputs rather than creators of solutions. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch softwareentwicklungsphänomen durch KI-Assistenz, das Qualität beeinflusst. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2701", "narrower_terms": [], "cross_domain_refs": [ "ADA-0012", "AED-0044", "AED-0063" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q186588", "legal_classification": "systematic_classification" }, { "id": "SWE-0061", "domain": "SWE", "term_en": "Null Reference Proliferation", "term_de": "NullReferenceProliferation", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A software engineering phenomenon in AI-assisted development, characterized by a coding interaction pattern arising from aI-generated code frequently introduces null reference vulnerabilities because the implications of nullability across function boundaries is difficult to track. This phenomenon operates at the intersection of null and reference dynamics within the broader SWE domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept rekurrenter Effekt, der sich in null reference proliferation manifestiert. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Analytical Ratio", "narrower_terms": [], "cross_domain_refs": [ "VIB-0196" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "SWE-0062", "domain": "SWE", "term_en": "Observability Perception", "term_de": "ObservabilityPerception", "definition_en": "A software engineering phenomenon in AI-assisted development, characterized by a coding interaction pattern reflecting the false sense that logging, metrics, and tracing automatically created by AI provide meaningful observability, when critical business signals are missing. Distinguished from adjacent concepts by its focus on the specific mechanism through which observability manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch interaktionsdynamik, die sich in observability perception manifestiert. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Software Engineering", "narrower_terms": [], "cross_domain_refs": [ "ART-0088" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q177601", "legal_classification": "systematic_classification" }, { "id": "SWE-0063", "domain": "SWE", "term_en": "Onboarding Code Shock", "term_de": "OnboardingCodeShock", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A software engineering phenomenon in AI-assisted development, characterized by new team members experience disorientation when encountering large codebases where few individuals understands significant portions because they were AI-generated. This phenomenon operates at the intersection of onboarding and code dynamics within the broader SWE domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept verhaltenseffekt in der Mensch-KI-Zusammenarbeit, der sich in onboarding code shock manifestiert. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Software Engineering", "narrower_terms": [], "cross_domain_refs": [ "CRE-0036" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "SWE-0064", "domain": "SWE", "term_en": "Open Source Maintenance Burden", "term_de": "OpenSourceMaintenanceBurden", "definition_en": "A code quality pattern in AI-augmented programming, measurable through a development workflow effect observed when when developers contribute AI-generated code to open source projects, maintainers inherit code they don't fully understand, increasing maintenance burden. Distinguished from adjacent concepts by its focus on the specific mechanism through which open manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch beobachtbares Muster der KI-Nutzung, das sich in open source maintenance burden manifestiert. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Software Engineering", "narrower_terms": [], "cross_domain_refs": [ "VIB-0107" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q39162", "legal_classification": "systematic_classification" }, { "id": "SWE-0065", "domain": "SWE", "term_en": "Pattern Recognition Reduction", "term_de": "MusterRecognitionReduction", "definition_en": "A code quality pattern in AI-augmented programming, measurable through the decline in a developer's ability to recognize common design patterns, architectural anti-patterns, or code smells in their own work as AI becomes the primary pattern detector. The concept emerges specifically in contexts where pattern–recognition interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch kompetenzschwund durch Substitution manueller Prozesse mittels KI-Automation. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Behavioral Pattern", "narrower_terms": [], "cross_domain_refs": [ "VIB-0098" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q3966", "legal_classification": "observational_construct" }, { "id": "SWE-0066", "domain": "SWE", "term_en": "Performance Assumption Drift", "term_de": "PerformanceAssumptionDrift", "definition_en": "A software engineering phenomenon in AI-assisted development, characterized by a software engineering phenomenon arising from a developer's reduced attention to algorithmic complexity and performance characteristics because AI-generated code usually runs fast enough in development environments. Distinguished from adjacent concepts by its focus on the specific mechanism through which performance manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch kompetenzschwund durch Substitution manueller Prozesse mittels KI-Automation. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Systemic Drift", "narrower_terms": [], "cross_domain_refs": [ "ROB-0050" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q1361053", "legal_classification": "descriptive_research_term" }, { "id": "SWE-0067", "domain": "SWE", "term_en": "Platform Upgrade Dread", "term_de": "PlatformUpgradeDread", "definition_en": "A development workflow effect where developers fear upgrading languages, frameworks, or runtime platforms because understanding AI-generated code makes predicting breakage impossible. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch interaktionsmuster, das sich in platform upgrade dread manifestiert. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2303", "narrower_terms": [], "cross_domain_refs": [ "AED-0076", "BEH-0071", "CON-0030" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "SWE-0068", "domain": "SWE", "term_en": "Postmortem Hollowness", "term_de": "PostmortemHollowness", "definition_en": "A software engineering phenomenon in AI-assisted development, characterized by a software engineering phenomenon arising from incident postmortems reveal that root correlates with are often buried in AI-generated code that few individuals fully understands, making remediation impossible. The concept emerges specifically in contexts where postmortem–hollowness interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch kI-bezogene Verhaltenstendenz, die sich in postmortem hollowness manifestiert. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Software Engineering", "narrower_terms": [], "cross_domain_refs": [ "VIB-0179" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "SWE-0069", "domain": "SWE", "term_en": "Privacy Implementation Negligence", "term_de": "PrivacyImplementationNegligence", "definition_en": "A code quality pattern in AI-augmented programming, measurable through a coding interaction pattern in which aI-generated code often ignores privacy implications, implementing data handling without consent mechanisms, encryption, or retention policies. The concept emerges specifically in contexts where privacy–implementation interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch beobachtbares Muster der KI-Nutzung, das sich in privacy implementation negligence manifestiert. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Software Engineering", "narrower_terms": [], "cross_domain_refs": [ "SPR-0193" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q18619", "legal_classification": "observational_construct" }, { "id": "SWE-0070", "domain": "SWE", "term_en": "Product-Market Fit Confusion", "term_de": "Product-marketFitConfusion", "definition_en": "A software engineering phenomenon in AI-assisted development, characterized by a development workflow effect observed when when so much of the product is AI-generated, it becomes unclear what its actual limitations are, complicating product-market fit evaluations. The concept emerges specifically in contexts where product–market interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch phänomen der Ausgabe-Mehrdeutigkeit, das durch Trainings-Variabilität KI-Systeme charakterisiert. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Software Engineering", "narrower_terms": [], "cross_domain_refs": [ "COP-0045" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "SWE-0071", "domain": "SWE", "term_en": "Productivity Measurement Perception", "term_de": "ProductivityMeasurementPerception", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A software engineering phenomenon in AI-assisted development, characterized by a development workflow effect manifesting as metrics showing increased developer productivity from AI tools don't account for hidden costs in maintenance, understanding, and rework. This phenomenon operates at the intersection of productivity and measurement dynamics within the broader SWE domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept erfahrungsphänomen, das sich in productivity measurement perception manifestiert. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Software Engineering", "narrower_terms": [], "cross_domain_refs": [ "VIB-0060" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q622632", "legal_classification": "descriptive_research_term" }, { "id": "SWE-0072", "domain": "SWE", "term_en": "Prompt Engineer Identity Shift", "term_de": "PromptEngineerIdentityShift", "definition_en": "A developer's transition from writing code directly to primarily writing prompts for AI, accompanied by a recalibration of professional identity and perceived competence. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch strukturelle Inkonsistenz in KI-generierten Codebasen tendiert dazu zu führen zu Wartungsproblemen. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-2104", "narrower_terms": [], "cross_domain_refs": [ "VIB-0067" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q844396", "legal_classification": "descriptive_research_term" }, { "id": "SWE-0073", "domain": "SWE", "term_en": "Pull Request Review Fatigue", "term_de": "PullRequestReviewFatigue", "definition_en": "A code quality pattern in AI-augmented programming, measurable through a software engineering phenomenon where the exhaustion experienced by code reviewers when AI accompanies massive pull requests containing hundreds of lines, making meaningful review nearly impossible. Distinguished from adjacent concepts by its focus on the specific mechanism through which pull manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch unzureichende Qualitätskontrolle bei unreflektierter KI-Output-Übernahme. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Software Engineering", "narrower_terms": [], "cross_domain_refs": [ "VIB-0040" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "SWE-0074", "domain": "SWE", "term_en": "Quality Gap Unease", "term_de": "QualityGapUnease", "definition_en": "A code quality pattern in AI-augmented programming, measurable through a developer's undefined discomfort with the gap between AI-generated code quality and their own actual comprehension of that code, creating a persistent low-level uncertainty. Distinguished from adjacent concepts by its focus on the specific mechanism through which quality manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch schwankungen in Output-Qualität, wenn KI-Systeme konsistente Leistung nicht garantieren können. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Performance Gap", "narrower_terms": [], "cross_domain_refs": [ "VIB-0065" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "SWE-0075", "domain": "SWE", "term_en": "Quality Measurement Gaming", "term_de": "QualityMeasurementGaming", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures A code quality pattern in AI-augmented programming, measurable through teams that rely on AI for code generation unconsciously game quality metrics, making defects invisible to traditional measurement systems. This phenomenon operates at the intersection of quality and measurement dynamics within the broader SWE domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept schwankungen in Output-Qualität, wenn KI-Systeme konsistente Leistung nicht garantieren können. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Software Engineering", "narrower_terms": [], "cross_domain_refs": [ "ADA-0012" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "SWE-0076", "domain": "SWE", "term_en": "Refactoring Avoidance", "term_de": "RefactoringAvoidance", "definition_en": "The tendency to not refactor AI-generated code because the cognitive cost of understanding it outweighs the perceived benefit of improvement. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch kI-bezogene Verhaltenstendenz, die sich in refactoring avoidance manifestiert. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-2201", "narrower_terms": [], "cross_domain_refs": [ "ROB-0288" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "SWE-0077", "domain": "SWE", "term_en": "Refactoring Impossibility Cascade", "term_de": "RefactoringImpossibilityKaskade", "definition_en": "A code quality pattern in AI-augmented programming, measurable through a software engineering phenomenon involving as AI-generated code accumulates, comprehensive refactoring becomes impossible because understanding all the code is beyond any individual or team. The concept emerges specifically in contexts where refactoring–impossibility interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch verhaltenseffekt in der Mensch-KI-Zusammenarbeit, der sich in refactoring impossibility cascade manifestiert. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-3802", "narrower_terms": [], "cross_domain_refs": [ "VIB-0078" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "SWE-0078", "domain": "SWE", "term_en": "Regex Incantation Reliance", "term_de": "RegexIncantationReliance", "definition_en": "A software engineering phenomenon in AI-assisted development, characterized by a coding interaction pattern reflecting developers rely on AI to yield regular expressions without understanding them, addressing regex patterns as incomprehensible configurations that occasionally fail without explanation. Distinguished from adjacent concepts by its focus on the specific mechanism through which regex manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch interaktionsdynamik, die sich in regex incantation reliance manifestiert. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Software Engineering", "narrower_terms": [], "cross_domain_refs": [ "REL-0052" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "SWE-0079", "domain": "SWE", "term_en": "Regulatory Compliance Uncertainty", "term_de": "RegulatoryComplianceUncertainty", "definition_en": "A code quality pattern in AI-augmented programming, measurable through a development workflow effect manifesting as the unclear responsibility for regulatory compliance when code is AI-generated accompanies uncertainty about liability for violations. The concept emerges specifically in contexts where regulatory–compliance interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch phänomen der Ausgabe-Mehrdeutigkeit, das durch Trainings-Variabilität KI-Systeme charakterisiert. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Software Engineering", "narrower_terms": [], "cross_domain_refs": [ "VIB-0062" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "SWE-0080", "domain": "SWE", "term_en": "SQL Injection Complacency", "term_de": "SqlInjectionComplacency", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A software engineering phenomenon in AI-assisted development, characterized by a coding interaction pattern where the false confidence that AI-generated database queries are characteristically safe from injection attacks, leading to reduced scrutiny of query construction. This phenomenon operates at the intersection of sql and injection dynamics within the broader SWE domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept kompetenzschwund durch Substitution manueller Prozesse mittels KI-Automation. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "RPH-1209", "narrower_terms": [], "cross_domain_refs": [ "VIB-0200", "RPH-1205" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "SWE-0081", "domain": "SWE", "term_en": "Salary Negotiation Complexity", "term_de": "SalaryNegotiationComplexity", "definition_en": "A code quality pattern in AI-augmented programming, measurable through a software engineering phenomenon involving the ambiguity about how much of a developer's output is their own work versus AI-generated complicates fair compensation negotiations. Distinguished from adjacent concepts by its focus on the specific mechanism through which salary manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch mehrdeutige oder interpretierbar unterschiedliche Ergebnisse aus KI-Systemen bei gleichen Eingaben. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Software Engineering", "narrower_terms": [], "cross_domain_refs": [ "IDN-0017" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "SWE-0082", "domain": "SWE", "term_en": "Scaffolding Lock-In", "term_de": "ScaffoldingLock-in", "definition_en": "A software engineering phenomenon in AI-assisted development, characterized by a software engineering phenomenon characterized by when a developer becomes reliant on AI scaffolding and boilerplate generation, accompanied by reduced ability to construct project structures or setup patterns inreliantly. The concept emerges specifically in contexts where scaffolding–lock interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch kompetenzschwund durch Substitution manueller Prozesse mittels KI-Automation. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Software Engineering", "narrower_terms": [], "cross_domain_refs": [ "VIB-0067" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "SWE-0083", "domain": "SWE", "term_en": "Security Pattern Shift", "term_de": "SecurityMusterShift", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A software engineering phenomenon in AI-assisted development, characterized by a software engineering phenomenon arising from the gradual acceptance of security-adjacent code generated by AI without verification of authentication, authorization, or encryption implementations. This phenomenon operates at the intersection of security and pattern dynamics within the broader SWE domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept kI-bezogene Verhaltenstendenz, die sich in security pattern shift manifestiert. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Behavioral Pattern", "narrower_terms": [], "cross_domain_refs": [ "VIB-0020" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "SWE-0084", "domain": "SWE", "term_en": "Skill Obsolescence Uncertainty", "term_de": "SkillObsolescenceUncertainty", "definition_en": "A software engineering phenomenon in AI-assisted development, characterized by developers experience uncertainty about which skills will remain relevant as AI continues to displace lower-level programming tasks. Distinguished from adjacent concepts by its focus on the specific mechanism through which skill manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch erfahrungsphänomen, das sich in skill obsolescence uncertainty manifestiert. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Software Engineering", "narrower_terms": [], "cross_domain_refs": [ "VIB-0095", "VIB-0106" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "SWE-0085", "domain": "SWE", "term_en": "State Management Complexity Hiding", "term_de": "StateManagementComplexityHiding", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures A code quality pattern in AI-augmented programming, measurable through aI-generated state management appears simple on the surface but contains hidden reliances and implicit state transitions that aren't made explicit. This phenomenon operates at the intersection of state and management dynamics within the broader SWE domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept softwareentwicklungsphänomen durch KI-Assistenz, das Qualität beeinflusst. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Software Engineering", "narrower_terms": [], "cross_domain_refs": [ "VIB-0128" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q181543", "legal_classification": "descriptive_research_term" }, { "id": "SWE-0086", "domain": "SWE", "term_en": "Syntax Parsing Passivity", "term_de": "SyntaxParsingPassivity", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures A code quality pattern in AI-augmented programming, measurable through a development workflow effect observed when a developer's reduced engagement with syntax rules and language semantics, relying on AI to catch and correct errors, leading to atrophied language competency. This phenomenon operates at the intersection of syntax and parsing dynamics within the broader SWE domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept kompetenzschwund durch Substitution manueller Prozesse mittels KI-Automation. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Software Engineering", "narrower_terms": [], "cross_domain_refs": [ "CUS-0090", "LIN-0032" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "SWE-0087", "domain": "SWE", "term_en": "Technical Debt Invisibility", "term_de": "TechnicalDebtInvisibility", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures A code quality pattern in AI-augmented programming, measurable through a coding interaction pattern observed when technical debt accumulated through AI-generated code is invisible because its existence is not acknowledged or tracked, making it impossible to address systematically. This phenomenon operates at the intersection of technical and debt dynamics within the broader SWE domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept interaktionsdynamik, die sich in technical debt invisibility manifestiert. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Software Engineering", "narrower_terms": [], "cross_domain_refs": [ "AGE-0002", "AGE-0003", "AGE-0039" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "SWE-0088", "domain": "SWE", "term_en": "Terraform State Disaster Waiting", "term_de": "TerraformStateDisasterWaiting", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A software engineering phenomenon in AI-assisted development, characterized by aI accompanies Terraform configurations that work initially but hide state management subtleties that eventually may is associated with infrastructure drift or catastrophic failures. This phenomenon operates at the intersection of terraform and state dynamics within the broader SWE domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept softwareentwicklungsphänomen durch KI-Assistenz, das Qualität beeinflusst. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "RPH-2051", "narrower_terms": [], "cross_domain_refs": [ "VIB-0141" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "SWE-0089", "domain": "SWE", "term_en": "Test Coverage Perception", "term_de": "TestCoveragePerception", "definition_en": "A software engineering phenomenon in AI-assisted development, characterized by the false sense of code reliability created by high test coverage percentages generated by AI, when the tests themselves lack meaningful assertions or scenario coverage. The concept emerges specifically in contexts where test–coverage interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch strukturelle Inkonsistenz in KI-generierten Codebasen tendiert dazu zu führen zu Wartungsproblemen. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Software Engineering", "narrower_terms": [], "cross_domain_refs": [ "VIB-0033", "WEB-0084", "VIB-0066" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q177601", "legal_classification": "observational_construct" }, { "id": "SWE-0090", "domain": "SWE", "term_en": "Test Fixture Obsolescence", "term_de": "TestFixtureObsolescence", "definition_en": "Test fixtures generated by AI become obsolete when the system evolves, but developers continue using outdated fixtures, testing against stale mock data. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch softwareentwicklungsphänomen durch KI-Assistenz, das Qualität beeinflusst. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2552", "narrower_terms": [], "cross_domain_refs": [ "CUS-0066" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "SWE-0091", "domain": "SWE", "term_en": "The Uncertainty Asymmetry", "term_de": "TheUncertaintyAsymmetry", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A software engineering phenomenon in AI-assisted development, characterized by the asymmetric distribution of knowledge: humans don't understand their own code anymore, while AI systems operate as black boxes, creating a vulnerability matrix. This phenomenon operates at the intersection of the and uncertainty dynamics within the broader SWE domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept kI-bezogene Verhaltenstendenz, die sich in the uncertainty asymmetry manifestiert. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Software Engineering", "narrower_terms": [], "cross_domain_refs": [ "RPH-3802", "DAT-0046" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "SWE-0092", "domain": "SWE", "term_en": "Type Safety Indifference", "term_de": "TypeSafetyIndifference", "definition_en": "A software engineering phenomenon in AI-assisted development, characterized by when developers stop enforcing strict typing because AI outputs usually work despite weak type contracts, gradually eroding type system benefits. The concept emerges specifically in contexts where type–safety interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch emergente Dynamik, die sich in type safety indifference manifestiert. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Software Engineering", "narrower_terms": [], "cross_domain_refs": [ "TEM-0001" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "SWE-0093", "domain": "SWE", "term_en": "Uncanny Code Valley", "term_de": "UncannyCodeValley", "definition_en": "A code quality pattern in AI-augmented programming, measurable through a software engineering phenomenon involving the unsettling sensation of encountering AI-generated code that looks syntactically correct and performs its intended function, yet contains subtle logical issues that emerge only in edge cases. Distinguished from adjacent concepts by its focus on the specific mechanism through which uncanny manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch nutzungsphänomen, das sich in uncanny code valley manifestiert. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2355", "narrower_terms": [], "cross_domain_refs": [ "VIB-0022" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "SWE-0094", "domain": "SWE", "term_en": "Vendor Lock-In Acceleration", "term_de": "VendorLock-inBeschleunigung", "definition_en": "A code quality pattern in AI-augmented programming, measurable through a development workflow effect where aI-generated code often uses vendor-specific APIs and features without considering portability, accelerating lock-in to specific cloud providers or platforms. Distinguished from adjacent concepts by its focus on the specific mechanism through which vendor manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch interaktionsdynamik, die sich in vendor lock-in acceleration manifestiert. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Analytical Ratio", "narrower_terms": [], "cross_domain_refs": [ "DES-0086" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "SWE-0095", "domain": "SWE", "term_en": "Vendor Lock-In Documentation", "term_de": "VendorLock-inDocumentation", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A software engineering phenomenon in AI-assisted development, characterized by a coding interaction pattern manifesting as aI-generated documentation assumes continued use of the same AI vendor, creating subtle vendor lock-in through documentation that's hard to reconcile with alternatives. This phenomenon operates at the intersection of vendor and lock dynamics within the broader SWE domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept übervertrauen auf KI-generierte Dokumentation ohne manuelle Verifizierung. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Software Engineering", "narrower_terms": [], "cross_domain_refs": [ "WEB-0091" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "SWE-0096", "domain": "SWE", "term_en": "Versioning Chaos", "term_de": "VersioningChaos", "definition_en": "A software engineering phenomenon in which aI frequently accompanies code without considering semantic versioning implications, leading to hidden breaking changes across increments. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch strukturelle Inkonsistenz in KI-generierten Codebasen tendiert dazu zu führen zu Wartungsproblemen. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-3001", "narrower_terms": [], "cross_domain_refs": [ "VIB-0125" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "TEM-0001", "domain": "TEM", "term_en": "Algorithmic Intuition", "term_de": "Algorithmic Intuition", "definition_en": "Experienced AI users develop, through extended practice, a \"sense\" for which type of input accompanies which type of result — without having to consciously analyze this each time. Related to AUG-0133...", "definition_de": "Die Fähigkeit erfahrener KI-Nutzer, nach längerer Praxis ein \"Gespür\" dafür zu entwickeln, welche Art von Eingabe welche Art von Ergebnis tendiert dazu zu erzeugen — ohne dies viele Mal bewusst analysieren zu können. Beschreibt ein implizites Wissen über die Funktionsweise von KI-Systemen, das sich durch Erfahrung bildet. Steht in Verbindung mit AUG-0133 (Prompt Craftsmanship), Phase 6 (Symbiotic Work State) und dem System-Builder-Profil (Profil 1).", "etymology": "", "broader_term": "Algorithm", "narrower_terms": [ "REL-0177" ], "cross_domain_refs": [ "VIB-0170" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q8366", "legal_classification": "systematic_classification" }, { "id": "TEM-0002", "domain": "TEM", "term_en": "Chronometric Gap", "term_de": "Chronometric Lücke", "definition_en": "A perception in which the measurable discrepancy between subjectively perceived and actually elapsed time during an AI session. Users regularly report that AI sessions \"fly by\" — one hour feels like twenty minutes. Rela...", "definition_de": "Die messbare Diskrepanz zwischen der subjektiv wahrgenommenen und der tatsächlich verstrichenen Zeit während einer KI-Sitzung. Nutzer berichten regelmäßig, dass KI-Sitzungen \"wie im Flug vergehen\" — eine Stunde fühlt sich wie zwanzig Minuten an. Beschreibt einen Effekt der Dimension 6 der Taxonomie (Time Perception: Linear → Radial). Steht in Verbindung mit AUG-0042 (The Immersion Entry) und AUG-0009 (The Speed Limit).", "etymology": "", "broader_term": "Performance Gap", "narrower_terms": [], "cross_domain_refs": [ "PER-0101" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "TEM-0003", "domain": "TEM", "term_en": "Ebulliometric Sorting", "term_de": "Ebulliometric Sorting", "definition_en": "A time-series interaction dynamic in AI temporal modeling, identifiable by a perception in which prioritizing AI-generated ideas by which feel most urgent or exciting. Energy-driven ranking, not logic-driven. Related to AUG-0017 (The Concept Cloud) and AUG-0034 (Thermo-Semantic Weighting). Distinguished from adjacent concepts by its focus on the specific mechanism through which ebulliometric manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data. Descriptive research term, not a prescriptive recommendation.", "definition_de": "Eine Priorisierungsmethode, bei der der Nutzer KI-generierte Ideen nach ihrem \"Siedepunkt\" ordnet — also danach, welche Ideen die stärkste unmittelbare Relevanz oder die höchste Dringlichkeit besitzen. Die Metapher stammt aus der Chemie (Ebulliometrie = Siedepunktmessung) und beschreibt das systematische Filtern einer großen Ideenmenge nach Relevanz. Steht in Verbindung mit AUG-0017 (The Concept Cloud) und AUG-0034 (Thermo-Semantic Weighting).", "etymology": "", "broader_term": "RPH-2201", "narrower_terms": [], "cross_domain_refs": [ "SWE-0007", "TEW-0035" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "TEM-0004", "domain": "TEM", "term_en": "Epistemic Half-Life", "term_de": "Epistemic Half-Life", "definition_en": "A chronological cognition pattern in AI-augmented temporal reasoning, measurable through a behavioral pattern where time that ai information stays useful and accurate.. Like how old information becomes outdated, AI information stops being reliable after a certain point. Fast-moving fields have information that g. The concept emerges specifically in contexts where epistemic–half interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Die beobachtbare Zeitspanne, in der das Vertrauen in eine bestimmte KI-generierte Information abnimmt — vergleichbar mit der Halbwertszeit radioaktiver Stoffe. Je schneller sich ein Fachgebiet entwickelt, desto kürzer ist die Epistemic Half-Life der dazugehörigen KI-Outputs. Steht in Verbindung mit AUG-0036 (Transient Validity), AUG-0037 (Liquid Facticity) und Axiom 17 (Quellendisziplin).", "etymology": "", "broader_term": "RPH-3101", "narrower_terms": [], "cross_domain_refs": [ "CON-0089" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "TEM-0005", "domain": "TEM", "term_en": "Feels-Return Effect", "term_de": "Low-Res World", "definition_en": "A chronological cognition pattern in AI-augmented temporal reasoning, measurable through a shift that occurs when the world outside users attribute feeling to AI less rich or slower to the user. Related to AUG-0123 (The Return Sudden shift) and Axiom 7 (The Return Principle). The concept emerges specifically in contexts where feels–return interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Die subjektive Wahrnehmung, dass die Welt außerhalb der KI-Interaktion weniger informationsreich, langsamer oder weniger stimulierend wirkt — vergleichbar mit dem Wechsel von einem hochauflösenden Bildschirm zu einem niedrig aufgelösten. Beschreibt einen Kontrasteffekt, der nach intensiven KI-Sitzungen auftreten kann. Steht in Verbindung mit AUG-0123 (Der Rückkehr Shock) und Axiom 7 (Rückkehr-Prinzip).", "etymology": "", "broader_term": "RPH-2701", "narrower_terms": [], "cross_domain_refs": [ "NEO-3580" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "TEM-0006", "domain": "TEM", "term_en": "Filter-Refusal Effect", "term_de": "KI-Free Zone", "definition_en": "A time-series interaction dynamic in AI temporal modeling, identifiable by a phenomenon in which the conscious establishment of areas — spatial, temporal, or thematic — in which AI use is excluded. Related to AUG-0773 (The Conscious Refusal), AUG-0632 (The Offline Moment), and AUG-0565 (The Ba. Distinguished from adjacent concepts by its focus on the specific mechanism through which filter manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data. Classification term used in systematic observation, not advocacy.", "definition_de": "Temporales Verarbeitungsphänomen in KI-vermittelter Zeitwahrnehmung, gekennzeichnet durch die bewusste Einrichtung von Bereichen — räumlich, zeitlich oder thematisch — in denen KI-Nutzung ausgeschlossen ist. Steht in Verbindung mit AUG-0773 (Die Conscious Refusal), AUG-0632 (Das Offline Moment) und AUG-0565 (Der Balance Filter). Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "TEM-0110", "narrower_terms": [], "cross_domain_refs": [ "NEO-3569", "REL-0164" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "TEM-0007", "domain": "TEM", "term_en": "Focus Range", "term_de": "Focus Range", "definition_en": "A capacity that enables the individual time span during which a user can remain maximally focused and productive in ai-assisted work before attention diminishes. the focus range varies from", "definition_de": "Die individuelle Zeitspanne, in der ein Nutzer in KI-gestützter Arbeit maximal fokussiert und produktiv bleiben kann, bevor die Aufmerksamkeit nachlässt. Die Focus Range variiert von Person zu Person und ist kürzer als bei herkömmlicher Arbeit, weil KI-Interaktion durch ihre Geschwindigkeit und Informationsdichte eine höhere Verarbeitungsleistung erfordert. Steht in Verbindung mit AUG-0009 (The Speed Limit), Axiom 7 (Rückkehr-Prinzip) und AUG-0022 (Vigilant Continuity).", "etymology": "", "broader_term": "RPH-2002", "narrower_terms": [ "AUG-0541" ], "cross_domain_refs": [ "ADA-0011", "CAI-0022", "ELR-0190" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "TEM-0008", "domain": "TEM", "term_en": "Gain-Competence Effect", "term_de": "Experience-Level Verschiebung", "definition_en": "AI changes the significance of professional experience — some tasks that previously required years of experience can now be accomplished faster with AI support, while other experience areas gain im... Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Die Beobachtung, dass KI die Bedeutung von Berufserfahrung verändert — manche Aufgaben, die früher jahrelange Erfahrung erforderten, können nun KI-unterstützt schneller bewältigt werden, während andere Erfahrungsbereiche an Bedeutung gewinnen. Steht in Verbindung mit AUG-0762 (Competence Umkehr Observation), AUG-0761 (Apprentice Paradoxonon) und AUG-0673 (Seniority Gewahrsein).", "etymology": "", "broader_term": "Psychological Effect", "narrower_terms": [], "cross_domain_refs": [ "NEO-3536", "CRE-0110" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q26944", "legal_classification": "observational_construct" }, { "id": "TEM-0009", "domain": "TEM", "term_en": "Generative Iteration Velocity", "term_de": "Generative Iteration Geschwindigkeit", "definition_en": "A capacity that enables the speed at which a user can cycle through successive iterations of an AI-assisted project — from the initial idea through multiple drafts to the finished result.. Related to AUG-0020 (Recursive F... Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Die Geschwindigkeit, mit der ein Nutzer aufeinanderfolgende Iterationen eines KI-gestützten Projekts durchlaufen kann — von der ersten Idee über mehrere Entwürfe bis zum fertigen Ergebnis. Beschreibt die Beobachtung, dass KI die Iterationszyklen massiv verkürzt und damit die Gesamtproduktionszeit transformiert. Steht in Verbindung mit AUG-0020 (Recursive Feedback Loop) und AUG-0092 (Output Asymmetry).", "etymology": "", "broader_term": "Analytical Ratio", "narrower_terms": [], "cross_domain_refs": [ "REL-0200" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "TEM-0010", "domain": "TEM", "term_en": "Interface-Invisible Effect", "term_de": "Post-Interface Hypothesis", "definition_en": "A perception in which the hypothesis that the interface between human and ai will simplify to such a degree over time that it is no longer perceived as a. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Die Hypothese, dass sich die Schnittstelle zwischen Mensch und KI langfristig so weit vereinfacht, dass sie im täglichen Gebrauch nicht mehr als separate Interaktion wahrgenommen wird — KI-Unterstützung wird so unsichtbar wie Rechtschreibprüfung oder Autovervollständigung. Steht in Verbindung mit Prognose 7 (Science: End of the User-Tool Divide) und AUG-0130 (Das Integration Frontier).", "etymology": "", "broader_term": "Psychological Effect", "narrower_terms": [], "cross_domain_refs": [ "RPH-1057", "ELR-0190" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "TEM-0011", "domain": "TEM", "term_en": "Just-in-Time Competence", "term_de": "Just-in-Time Competence", "definition_en": "A time-series interaction dynamic in AI temporal modeling, identifiable by a phenomenon in which the ability to become ad hoc capable in a domain through instant AI-assisted knowledge access, without having studied that domain long-term.. Related to AUG-0012 (Synthetischer Polymath), AUG-0016. Distinguished from adjacent concepts by its focus on the specific mechanism through which just manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Die Fähigkeit, durch sofortigen KI-gestützten Wissenszugang in einem Fachgebiet ad hoc handlungsfähig zu werden, ohne dieses Gebiet langfristig erlernt zu haben. Beschreibt ein neues Kompetenzmodell der KI-Ära: Wissen kann nicht mehr gespeichert, sondern abrufbar gemacht werden. Steht in Verbindung mit AUG-0012 (Synthetischer Polymath), AUG-0016 (Poly-Categorical Mesh) und dem Generalist-Profil (Profil 8).", "etymology": "", "broader_term": "TEM-0022", "narrower_terms": [ "REL-0130", "TEM-0094", "TEM-0103", "TEM-0051", "TEM-0038", "TEM-0008", "TEM-0173", "TEM-0197", "TEM-0160", "TEM-0136", "TEM-0169", "TEM-0002", "TEM-0126", "TEM-0019", "TEM-0189", "TEM-0193", "TEM-0009", "TEM-0163", "TEM-0032", "TEM-0028", "TEM-0198", "TEM-0034", "TEM-0010", "TEM-0168", "TEM-0043", "TEM-0012", "TEM-0022", "TEM-0074", "TEM-0141", "TEM-0196", "TEM-0158", "TEM-0046", "TEM-0086", "TEM-0100", "TEM-0115", "TEM-0124", "TEM-0166", "TEM-0165", "TEM-0154", "TEM-0176", "TEM-0140", "TEM-0145", "TEM-0156", "TEM-0144", "TEM-0052", "TEM-0167", "TEM-0097", "TEM-0164", "TEM-0171", "TEM-0037", "TEM-0033", "TEM-0142", "TEM-0044", "TEM-0109", "TEM-0192", "TEM-0053", "TEM-0030", "TEM-0134", "TEM-0092", "TEM-0039", "TEM-0184", "TEM-0072", "TEM-0114", "TEM-0071", "TEM-0127", "TEM-0090", "TEM-0191", "TEM-0001", "TEM-0098", "TEM-0062", "TEM-0175", "TEM-0182" ], "cross_domain_refs": [ "REL-0155" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q26944", "legal_classification": "empirical_phenomenon_label" }, { "id": "TEM-0012", "domain": "TEM", "term_en": "Kinetic Truth Blur", "term_de": "Kinetic Truth Blur", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures A temporal processing phenomenon in AI-mediated time perception, characterized by a shift that occurs when when movement data or video evidence seems to prove something happened, but the context or interpretation of that data gets lost or misunderstood. This phenomenon operates at the intersection of kinetic and truth dynamics within the broader TEM domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept der Effekt, dass sich die wahrgenommene Grenze zwischen \"wahr\" und \"plausibel\" verschiebt, wenn KI-Antworten in hoher Geschwindigkeit aufeinanderfolgen. Bei schneller Interaktion sinkt die Fähigkeit des Nutzers, zwischen verifizierten Fakten und überzeugend klingenden Annahmen zu unterscheiden. Steht in Verbindung mit AUG-0009 (The Speed Limit), AUG-0037 (Liquid Facticity) und Axiom 6 (3-Sekunden-Verzögerung). Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Temporal AI", "narrower_terms": [ "CRE-0196" ], "cross_domain_refs": [ "CON-0050" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "TEM-0013", "domain": "TEM", "term_en": "Latent Space Exploration", "term_de": "Latent Space Exploration", "definition_en": "A tendency in which prompting an AI through unusual, abstract, or deliberately imprecise inputs yield responses from less predictable areas of its knowledge space. Related to the Experimenter Profile (Profile 4), A...", "definition_de": "Die Praxis, eine KI durch ungewöhnliche, abstrakte oder bewusst unpräzise Eingaben dazu zu bringen, Antworten aus weniger vorhersagbaren Bereichen ihres Wissensraums zu generieren. Beschreibt eine fortgeschrittene Technik, bei der der Nutzer die Grenzen des KI-Systems erkundet. Steht in Verbindung mit dem Experimenter-Profil (Profil 4), AUG-0084 (Glitch-Mining) und AUG-0133 (Prompt Craftsmanship).", "etymology": "", "broader_term": "RPH-2104", "narrower_terms": [ "AUG-0319" ], "cross_domain_refs": [ "BEH-0006" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "TEM-0014", "domain": "TEM", "term_en": "Liquid Facticity", "term_de": "Liquid Facticity", "definition_en": "A time-series interaction dynamic in AI temporal modeling, identifiable by facts that seem solid but actually shift depending on who is looking at them or what context they are in. Distinguished from adjacent concepts by its focus on the specific mechanism through which liquid manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Das Phänomen, dass Fakten in der KI-gestützten Arbeit einen zunehmend fließenden Charakter annehmen — sie sind nicht mehr statisch und endgültig, sondern kontextabhängig, modellabhängig und zeitgebunden. Beschreibt einen grundlegenden epistemologischen Wandel in der KI-Ära. Steht in Verbindung mit AUG-0035 (Epistemic Half-Life), AUG-0036 (Transient Validity) und Axiom 17 (Quellendisziplin).", "etymology": "", "broader_term": "RPH-2701", "narrower_terms": [], "cross_domain_refs": [ "BEH-0002" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "TEM-0015", "domain": "TEM", "term_en": "Loop-Related Effect", "term_de": "Supervision Spectrum", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A temporal processing phenomenon in AI-mediated time perception, characterized by a phenomenon in which human supervision over AI agents — from permanent real-time monitoring of most step occasional result review. Related to AUG-0860 (The Delegation Depth), AUG-0888 (The Human-in-the-Loop), and AUG-. This phenomenon operates at the intersection of loop and related dynamics within the broader TEM domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept temporales Verarbeitungsphänomen in KI-vermittelter Zeitwahrnehmung, gekennzeichnet durch die Bandbreite menschlicher Aufsicht über KI-Agenten — von permanenter Echtzeit-Überwachung viele Schritts bis zu gelegentlicher Ergebnisprüfung. Steht in Verbindung mit AUG-0860 (Delegation Tiefe), AUG-0888 (The Human-in-the-Loop) und AUG-0869 (Die Checkpoint Protocol). Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "RPH-2005", "narrower_terms": [], "cross_domain_refs": [ "NEO-3657" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "TEM-0016", "domain": "TEM", "term_en": "Patterns-User Effect", "term_de": "Self-Encounter", "definition_en": "A time-series interaction dynamic in AI temporal modeling, identifiable by a user, through AI interaction, learns something about themselves — such as about their own thinking patterns, preferences, or unnoticed areas — that they would not have become aware of without the. Distinguished from adjacent concepts by its focus on the specific mechanism through which patterns manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data. Classification term used in systematic observation, not advocacy.", "definition_de": "Temporales Verarbeitungsphänomen in KI-vermittelter Zeitwahrnehmung, gekennzeichnet durch der Moment, in dem ein Nutzer durch die KI-Interaktion etwas über sich selbst erfährt — etwa über eigene Denkmuster, Präferenzen oder blinde Flecken — das ihm ohne den Dialog nicht bewusst geworden wäre. Steht in Verbindung mit AUG-0011 (Der Reflective Operator), AUG-0170 (Witness Effekt) und Axiom 8 (Die ein Technologiekonzern-Ebene). Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2703", "narrower_terms": [], "cross_domain_refs": [ "NEO-3637", "MTH-0058" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "TEM-0017", "domain": "TEM", "term_en": "Perspective-Earlier Effect", "term_de": "Delayed-Contact Perspective", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures A temporal processing phenomenon in AI-mediated time perception, characterized by users who first encounter AI systems late in life — shaped by a longer phase without AI assistance processing interpreted as experiential by users, leading different expectations, concerns, and discovery moments than with earlier users. Related. This phenomenon operates at the intersection of perspective and earlier dynamics within the broader TEM domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept die Perspektive von Nutzern, die erst spät im Leben erstmals mit KI-Systemen in Berührung kommen — geprägt durch eine längere Phase ohne KI-Erfahrung, die zu anderen Erwartungen, Ängsten und Entdeckungsmomenten führt als bei früheren Nutzern. Steht in Verbindung mit AUG-0751 (The Age-Competence Assumption), AUG-0752 (The Non-Digital-Origin Perspective) und AUG-0099 (Die Adoption Window). Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "RPH-3901", "narrower_terms": [], "cross_domain_refs": [ "NEO-3529" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "TEM-0018", "domain": "TEM", "term_en": "Poly-Categorical Mesh", "term_de": "Poly-Categorical Mesh", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures A temporal processing phenomenon in AI-mediated time perception, characterized by a phenomenon in which using AI to connect knowledge from different fields into new combinations. This happens much faster through AI than a human could achieve alone. Related to Taxonomy Dimension 9 (Output Depth: Colla. This phenomenon operates at the intersection of poly and categorical dynamics within the broader TEM domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept die Fähigkeit eines KI-unterstützten Nutzers, Wissen aus verschiedenen Fachbereichen gleichzeitig zu einem neuen, fachübergreifenden Ergebnis zu verknüpfen. Dieses Phänomen wird erst durch KI und den sofortigen Zugang zu Fachwissen (Just-in-Time) in dieser Geschwindigkeit und Breite möglich — ein einzelner Mensch ohne KI bräuchte jahrelange Ausbildung in mehreren Disziplinen, um vergleichbare Verknüpfungen herzustellen. Steht in Verbindung mit Dimension 9 der Taxonomie (Output Depth: Collage vs. Novelty), AUG-0012 (Synthetischer Polymath) und AUG-0043 (Just-in-Time Competence). Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "RPH-2005", "narrower_terms": [], "cross_domain_refs": [ "BEH-0006" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "TEM-0019", "domain": "TEM", "term_en": "Practitioner Workspace Dynamic", "term_de": "Anwender-Arbeitsraum-Dynamik", "definition_en": "A time-series interaction dynamic in AI temporal modeling, identifiable by a tendency in which when experienced AI users gradually replace their own quality standards with AI-generated ones. Distinguished from adjacent concepts by its focus on the specific mechanism through which practitioner manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Temporales Verarbeitungsphänomen in KI-vermittelter Zeitwahrnehmung, gekennzeichnet durch hinter Practitioner Workspace Dynamic steht eine Beobachtung, die zunehmend Beachtung findet: Die Art, wie Menschen auf KI-Systeme reagieren, lässt sich nicht aus der Funktionalität des Systems allein erklären. Es gibt einen menschlichen Faktor, der eigene Dynamiken tendiert dazu zu erzeugen und die Interaktion fundamental prägt. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Temporal AI", "narrower_terms": [], "cross_domain_refs": [ "PER-0033" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "TEM-0020", "domain": "TEM", "term_en": "Resource-Cumulative Effect", "term_de": "Self-View Pool", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures A temporal processing phenomenon in AI-mediated time perception, characterized by a behavioral pattern where all impressions, insights, and self-images a user has gained from their ai interactions over time — a built-up self-reflection resource.. Related to AUG-0521 (The Reflected Self), AUG-0352 (The Mem. This phenomenon operates at the intersection of resource and cumulative dynamics within the broader TEM domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept temporales Verarbeitungsphänomen in KI-vermittelter Zeitwahrnehmung, gekennzeichnet durch die Gesamtheit aller Eindrücke, Erkenntnisse und Selbstbilder, die ein Nutzer über die Zeit aus seinen KI-Interaktionen gewonnen hat — eine kumulative Selbstreflexionsressource. Steht in Verbindung mit AUG-0521 (Reflected Selbst), AUG-0352 (Gedaechtnis Jar) und AUG-0171 (The Self-Encounter). Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "REL-0172", "narrower_terms": [], "cross_domain_refs": [ "NEO-3638", "REL-0172" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "TEM-0021", "domain": "TEM", "term_en": "Semantic Ejection", "term_de": "Semantic Ejection", "definition_en": "A time-series interaction dynamic in AI temporal modeling, identifiable by a phenomenon in which when language or words used to describe something become so twisted that the original meaning disappears entirely. Distinguished from adjacent concepts by its focus on the specific mechanism through which semantic manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Der Moment, in dem ein Nutzer einen KI-generierten Vorschlag bewusst ablehnt, weil er semantisch nicht zum eigenen Denkstil, Wertesystem oder Projektkontext passt — obwohl der Vorschlag technisch korrekt sein mag. Beschreibt eine aktive Qualitätsentscheidung, die über reine Faktenprüfung hinausgeht. Steht in Verbindung mit Axiom 11 (Die Umkehrprobe) und Dimension 3 der Taxonomie (Output Fit: Mismatch vs. Alignment).", "etymology": "", "broader_term": "RPH-2153", "narrower_terms": [ "CRE-0232", "REL-0121" ], "cross_domain_refs": [ "CON-0028" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "TEM-0022", "domain": "TEM", "term_en": "Synthetischer Polymath", "term_de": "Synthetischer Polymath", "definition_en": "A time-series interaction dynamic in AI temporal modeling, identifiable by a capacity that enables an AI trained on many subjects can discuss different topics but may oversimplify complex ideas or make confident-sounding errors. Distinguished from adjacent concepts by its focus on the specific mechanism through which synthetischer manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Ein Nutzer, der durch KI-Unterstützung in der Lage ist, in mehreren Fachgebieten gleichzeitig auf hohem Niveau zu arbeiten, obwohl er diese Gebiete nicht zahlreiche formell erlernt hat. Die KI übernimmt dabei die Rolle einer \"Just-in-Time-Wissensbasis\". Der Begriff ist bewusst deskriptiv — er beschreibt ein beobachtbares Phänomen, kein Idealziel. Steht in Verbindung mit dem Generalist-Profil (Profil 8) und AUG-0043 (Just-in-Time Competence).", "etymology": "", "broader_term": "Temporal AI", "narrower_terms": [ "TEM-0011" ], "cross_domain_refs": [ "RPH-1204", "TEW-0053" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "TEM-0023", "domain": "TEM", "term_en": "The Academic Integrity Line", "term_de": "Academic Integrity Line", "definition_en": "A chronological cognition pattern in AI-augmented temporal reasoning, measurable through a shift that occurs when the changing limit between allowed AI use and cheating. This limit differs by school and subject and keeps shifting. Related to AUG-0780 (The Assessment Challenge), AUG-0782 (The Originality Redefi. The concept emerges specifically in contexts where the–academic interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Temporales Verarbeitungsphänomen in KI-vermittelter Zeitwahrnehmung, gekennzeichnet durch die sich verschiebende Grenzlinie zwischen akzeptabler KI-Nutzung und akademischer Unredlichkeit — eine Grenze, die institutionell, fachlich und zeitlich variiert und kontinuierlich neu verhandelt wird. Steht in Verbindung mit AUG-0780 (The Assessment Challenge), AUG-0782 (The Originality Redefinition Debate) und AUG-0790 (The Citation Challenge). Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2701", "narrower_terms": [], "cross_domain_refs": [ "PER-0025" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "TEM-0024", "domain": "TEM", "term_en": "The Adaptive Extension", "term_de": "Adaptive Extension", "definition_en": "A time-series interaction dynamic in AI temporal modeling, identifiable by a shift that occurs when an AI-supported system that adapts to the individual needs and capabilities of the user — learning curves, preferences, physical changes over time. Related to AUG-0934 (The Sensory Extension), AUG-. Distinguished from adjacent concepts by its focus on the specific mechanism through which the manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Temporales Verarbeitungsphänomen in KI-vermittelter Zeitwahrnehmung, gekennzeichnet durch ein KI-gestütztes System, das sich an die individuellen Bedürfnisse und Fähigkeiten des Nutzers anpasst — Lernkurven, Präferenzen, physische Veränderungen über die Zeit. Steht in Verbindung mit AUG-0934 (The Sensory Extension), AUG-0876 (The Learning Boundary) und AUG-0864 (The Agent Configuration). Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2703", "narrower_terms": [], "cross_domain_refs": [ "SOM-0075" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "TEM-0025", "domain": "TEM", "term_en": "The Adoption Window", "term_de": "Adoption Window", "definition_en": "A tendency in which the limited period during which the acquisition of AI competence offers the greatest strategic advantage — before this competence becomes a standard expectation. Related to AUG-0091 (Productivity A... Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Der begrenzte Zeitraum, in dem die Aneignung von KI-Kompetenz den größten strategischen Vorteil bietet — bevor diese Kompetenz zur Standarderwartung wird. Beschreibt ein ökonomisches Zeitfenster: Frühe Aneignung tendiert dazu zu erzeugen einen Vorsprung, der mit zunehmender Verbreitung schrumpft. Steht in Verbindung mit AUG-0091 (Productivity Arbitrage), AUG-0111 (The Augmentation Gap) und Prognose 1 (Economy).", "etymology": "", "broader_term": "RPH-2002", "narrower_terms": [ "REL-0123", "TRU-0009" ], "cross_domain_refs": [ "KNO-0007" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "TEM-0026", "domain": "TEM", "term_en": "The Ancestry Link", "term_de": "Ancestry Link", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures A temporal processing phenomenon in AI-mediated time perception, characterized by a phenomenon in which aI research one's own family history, origin, or cultural roots — as an entry point for genealogical research or cultural self-positioning. Related to AUG-0410 (The Memory Lane), AUG-0349 (The Futu. This phenomenon operates at the intersection of the and ancestry dynamics within the broader TEM domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept temporales Verarbeitungsphänomen in KI-vermittelter Zeitwahrnehmung, gekennzeichnet durch die Nutzung von KI zur Erforschung der eigenen Familiengeschichte, Herkunft oder kulturellen Wurzeln — als Einstiegspunkt für genealogische Recherche oder kulturelle Selbstverortung. Steht in Verbindung mit AUG-0410 (The Memory Lane), AUG-0349 (The Future Self Prompt) und AUG-0043 (Just-in-Time Competence). Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "RPH-3101", "narrower_terms": [], "cross_domain_refs": [ "ELR-0121" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "TEM-0027", "domain": "TEM", "term_en": "The Architect's Exit", "term_de": "Architect's Exit", "definition_en": "A chronological cognition pattern in AI-augmented temporal reasoning, measurable through an experienced AI user consciously leaves behind the architecture of their AI use and develops a completely new approach — because the existing framework has reached its limits. Related to AUG-0044. The concept emerges specifically in contexts where the–architect's interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Temporales Verarbeitungsphänomen in KI-vermittelter Zeitwahrnehmung, gekennzeichnet durch der Moment, in dem ein erfahrener KI-Nutzer bewusst die Architektur seiner KI-Nutzung hinter sich lässt und einen komplett neuen Ansatz entwickelt — weil der bisherige Rahmen an seine Grenzen gestoßen ist. Steht in Verbindung mit AUG-0044 (Unlearning Protocol), AUG-0130 (The Integration Frontier) und Phase 5 (Architecture Design). Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2303", "narrower_terms": [], "cross_domain_refs": [ "NEO-3497" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "TEM-0028", "domain": "TEM", "term_en": "The Archive Pause", "term_de": "Archive Pause", "definition_en": "A time-series interaction dynamic in AI temporal modeling, identifiable by a behavioral pattern where a moment when a user pauses during archival work—noting, organizing, or retrieving stored information—to consider what they've found or how it fits into the larger context. Distinguished from adjacent concepts by its focus on the specific mechanism through which the manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Temporales Verarbeitungsphänomen in KI-vermittelter Zeitwahrnehmung, gekennzeichnet durch der Moment des Nachdenkens darüber, welche KI-Ergebnisse langfristig aufbewahrt und welche verworfen werden werden typischerweise — eine Entscheidung über den Wert des Produzierten. Steht in Verbindung mit AUG-0019 (Semantic Ejection), AUG-0082 (The Curator's Dilemma) und AUG-0599 (The Memory Bank). Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Temporal AI", "narrower_terms": [], "cross_domain_refs": [ "PLY-0017" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "TEM-0029", "domain": "TEM", "term_en": "The Authority Question", "term_de": "Authority Question", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures A temporal processing phenomenon in AI-mediated time perception, characterized by a phenomenon in which the question \"Whom do I trust more — the AI output or my own assessment?\" that arises in moments of uncertainty.. Related to Axiom 1 (Asymmetric Responsibility), AUG-0177 (The Trust Setting), and A. This phenomenon operates at the intersection of the and authority dynamics within the broader TEM domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept die Frage \"Wem vertraue ich mehr — dem KI-Output oder meiner eigenen Einschätzung?\", die in Momenten der Unsicherheit auftritt. Beschreibt einen Entscheidungskonflikt, der besonders bei Themen außerhalb der eigenen Kernkompetenz relevant ist. Steht in Verbindung mit Axiom 1 (Asymmetrische Verantwortung), AUG-0177 (The Trust Setting) und AUG-0076 (Self-Referential Grounding). Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "RPH-2505", "narrower_terms": [], "cross_domain_refs": [ "REL-0191" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "TEM-0030", "domain": "TEM", "term_en": "The Balance Filter", "term_de": "Balance Filter", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies the conscious strategy of balancing AI use and non-digital activities — through fixed times, rules, or routines that support AI enriches everyday life rather than dominating it. Related to AUG-0074... Identifiable through systematic behavioral analysis and pattern recognition. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription. Research construct for empirical investigation.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept temporales Verarbeitungsphänomen in KI-vermittelter Zeitwahrnehmung, gekennzeichnet durch die bewusste Strategie, KI-Nutzung und nicht-digitale Aktivitäten in ein Gleichgewicht zu bringen — durch feste Zeiten, Regeln oder Routinen, die sicherstellen, dass KI den Alltag bereichert, nicht dominiert. Steht in Verbindung mit AUG-0074 (Analog Anchors), AUG-0282 (The Dinner Table Pause) und Axiom 7 (Rückkehr-Prinzip). Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Temporal AI", "narrower_terms": [], "cross_domain_refs": [ "REL-0164" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "TEM-0031", "domain": "TEM", "term_en": "The Beta Courage", "term_de": "Beta Courage", "definition_en": "A time-series interaction dynamic in AI temporal modeling, identifiable by a phenomenon in which the willingness to try new, not yet mature AI features — knowing they may be faulty, but with the curiosity to explore the possibilities. Related to AUG-0129 (The Trailblazer Mode), AUG-0085 (Laten. Distinguished from adjacent concepts by its focus on the specific mechanism through which the manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Temporales Verarbeitungsphänomen in KI-vermittelter Zeitwahrnehmung, gekennzeichnet durch die Bereitschaft, neue, noch nicht ausgereifte KI-Funktionen auszuprobieren — im Wissen, dass sie fehlerhaft sein können, aber mit der Neugier, die Möglichkeiten zu erkunden. Steht in Verbindung mit AUG-0129 (The Trailblazer Mode), AUG-0085 (Latent Space Exploration) und AUG-0176 (The Capability Discovery). Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "TEM-0182", "narrower_terms": [], "cross_domain_refs": [ "AGE-0077" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "TEM-0032", "domain": "TEM", "term_en": "The Bilingual Dynamic", "term_de": "Bilingual Dynamik", "definition_en": "The dynamic that arises when a bilingual user switches between languages — in the same session, sometimes in the same sentence — and the AI responds to these switches. Related to AUG-0693 (The Code... Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Temporales Verarbeitungsphänomen in KI-vermittelter Zeitwahrnehmung, gekennzeichnet durch die spezifische Dynamik, die entsteht, wenn ein zweisprachiger Nutzer bewusst zwischen seinen Sprachen wechselt — in derselben Sitzung, manchmal im selben Satz — und die KI auf diese Wechsel reagieren kann. Steht in Verbindung mit AUG-0693 (The Code-Mesh Output), AUG-0709 (The Trilingual Juggle) und AUG-0680 (The Context Adaptation). Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Temporal AI", "narrower_terms": [], "cross_domain_refs": [ "LNG-0012" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "TEM-0033", "domain": "TEM", "term_en": "The Borrowed Crown", "term_de": "Borrowed Crown", "definition_en": "A time-series interaction dynamic in AI temporal modeling, identifiable by a perception in which being perceived as more competent in a professional or social situation through ai support than one would be without AI assistance — the \"borrowed crown\" of ai-assisted performance.. Related to AUG-0166 (The. Distinguished from adjacent concepts by its focus on the specific mechanism through which the manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Temporales Verarbeitungsphänomen in KI-vermittelter Zeitwahrnehmung, gekennzeichnet durch die Erfahrung, durch KI-Unterstützung in einer beruflichen oder sozialen Situation als kompetenter wahrgenommen zu werden, als man ohne KI wäre — die \"geliehene Krone\" der KI-gestützten Leistung. Steht in Verbindung mit AUG-0166 (The Borrowed Confidence), AUG-0244 (The Instant Expert) und AUG-0416 (The Perfect Front). Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Temporal AI", "narrower_terms": [], "cross_domain_refs": [ "CRE-0129", "RPH-342" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "TEM-0034", "domain": "TEM", "term_en": "The Brain Gallop", "term_de": "Brain Gallop", "definition_en": "A chronological cognition pattern in AI-augmented temporal reasoning, measurable through accelerated thinking activated by a particularly productive ai session — the user thinks faster, connects more, and has the feeling of running at full intellectual capacity.. Related to AUG-0221 (T. The concept emerges specifically in contexts where the–brain interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Temporales Verarbeitungsphänomen in KI-vermittelter Zeitwahrnehmung, gekennzeichnet durch der Zustand beschleunigten Denkens, der durch eine besonders produktive KI-Sitzung ausgelöst wird — der Nutzer denkt schneller, verbindet mehr und hat das Gefühl, intellektuell auf Hochtouren zu laufen. Steht in Verbindung mit AUG-0221 (The Thinking Boost), AUG-0152 (The Focus Surge) und AUG-0122 (Symbiotic Work State). Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Temporal AI", "narrower_terms": [], "cross_domain_refs": [ "LIN-0031", "LIN-0033", "QUA-0037" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "TEM-0035", "domain": "TEM", "term_en": "The Capability Discovery", "term_de": "Capability Discovery", "definition_en": "A time-series interaction dynamic in AI temporal modeling, identifiable by an experiential phenomenon in which a user discovers a previously unknown ability or function of an AI system through exploration... Related to AUG-0085 (Latent Space Exploration), AUG-0129 (The Trailblazer Mode). Distinguished from adjacent concepts by its focus on the specific mechanism through which the manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Der Moment, in dem ein Nutzer eine bisher unbekannte Fähigkeit oder Funktion eines KI-Systems entdeckt und sein Nutzungsspektrum dadurch erweitert. Beschreibt die Erfahrung, dass KI-Systeme oft mehr können, als der Nutzer weiß. Steht in Verbindung mit AUG-0085 (Latent Space Exploration), AUG-0129 (The Trailblazer Mode) und AUG-0127 (The Expansion Feeling).", "etymology": "", "broader_term": "TEM-0182", "narrower_terms": [], "cross_domain_refs": [ "AUG-0319" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "TEM-0036", "domain": "TEM", "term_en": "The Capability Finder", "term_de": "Capability Finder", "definition_en": "A systematic exploratory practice in which users probe an AI system's functional boundaries through deliberate test queries, edge cases, and escalating complexity to empirically map its capabilities, limitations, and failure modes. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Temporales Verarbeitungsphänomen in KI-vermittelter Zeitwahrnehmung, gekennzeichnet durch die gezielte Exploration der Fähigkeiten eines KI-Systems — der Nutzer testet systematisch, was das System kann und was nicht, um das volle Potenzial auszuschöpfen. Steht in Verbindung mit AUG-0176 (The Capability Discovery), AUG-0085 (Latent Space Exploration) und AUG-0345 (The Wall Check). Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-2703", "narrower_terms": [], "cross_domain_refs": [ "ETH-0025" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "TEM-0037", "domain": "TEM", "term_en": "The Careful Tester", "term_de": "Careful Tester", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A temporal processing phenomenon in AI-mediated time perception, characterized by a phenomenon in which a user who tries new AI functions cautiously, step by step, and with built-in safety checks — in contrast to the Trailblazer (Profile 7) who proceeds experimentally. Related to AUG-0495 (The Beta C. This phenomenon operates at the intersection of the and careful dynamics within the broader TEM domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept temporales Verarbeitungsphänomen in KI-vermittelter Zeitwahrnehmung, gekennzeichnet durch ein Nutzer, der neue KI-Funktionen vorsichtig, schrittweise und mit eingebauten Sicherheitsprüfungen ausprobiert — im Gegensatz zum Trailblazer (Profil 7), der experimentierfreudig vorangeht. Steht in Verbindung mit AUG-0495 (The Beta Courage), AUG-0147 (The Slow Integration Principle) und AUG-0316 (The Own Pace). Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Temporal AI", "narrower_terms": [], "cross_domain_refs": [ "ROB-0091" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "TEM-0038", "domain": "TEM", "term_en": "The Learners First Prompt", "term_de": "TheLearnersFirstPrompt", "definition_en": "A chronological cognition pattern in AI-augmented temporal reasoning, measurable through when a young person first asks an AI a question on their own, without strategy or filter. The concept emerges specifically in contexts where the–learners interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Temporales Verarbeitungsphänomen in KI-vermittelter Zeitwahrnehmung, gekennzeichnet durch ein Konzept oder Phänomen: When a young person first asks an AI a question on their own, without strategy or filter. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Temporal AI", "narrower_terms": [], "cross_domain_refs": [ "NEO-3540" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "TEM-0039", "domain": "TEM", "term_en": "The Chore Gamify", "term_de": "Chore Gamify", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures A temporal processing phenomenon in AI-mediated time perception, characterized by a pattern in which variant in which aI make everyday routine tasks more interesting, structured, or playful — such as through checklists, time competitions, or creative reformulations of boring tasks. Related to AUG-0110 (The Joy Imp. This phenomenon operates at the intersection of the and chore dynamics within the broader TEM domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept temporales Verarbeitungsphänomen in KI-vermittelter Zeitwahrnehmung, gekennzeichnet durch die Nutzung von KI, um alltägliche Routineaufgaben interessanter, strukturierter oder spielerischer zu gestalten — etwa durch Checklisten, Zeitwettbewerbe oder kreative Umformulierungen langweiliger Aufgaben. Steht in Verbindung mit AUG-0110 (The Joy Imperative), AUG-0251 (The Kitchen Table) und AUG-0062 (The Lightness Factor). Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Temporal AI", "narrower_terms": [], "cross_domain_refs": [ "REL-0146" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "TEM-0040", "domain": "TEM", "term_en": "The Cloud Amnesia", "term_de": "Cloud Amnesia", "definition_en": "A chronological cognition pattern in AI-augmented temporal reasoning, measurable through a phenomenon in which cloud-based AI systems retain no memory of past sessions. Most conversation starts from scratch, and users rebuild context each time. Related to AUG-0291 (The Forgetting Tax), AUG-0433 (The Contex. The concept emerges specifically in contexts where the–cloud interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Temporales Verarbeitungsphänomen in KI-vermittelter Zeitwahrnehmung, gekennzeichnet durch die Erfahrung, dass cloudbasierte KI-Systeme keine Erinnerung an vergangene Sitzungen haben und der Nutzer viele Mal von vorn beginnen kann — und die Frustration, die daraus entsteht. Steht in Verbindung mit AUG-0291 (The Forgetting Tax), AUG-0433 (The Context Wipe) und AUG-0231 (The Warm Start). Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2301", "narrower_terms": [], "cross_domain_refs": [ "REL-0072" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "TEM-0041", "domain": "TEM", "term_en": "The Code Pause", "term_de": "Code Pause", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A temporal processing phenomenon in AI-mediated time perception, characterized by the specific application of the 3-Second Delay (Axiom 6) to AI-generated code — the practice of pausing before executing AI-written code and reviewing it line by line. Related to Axiom 6 (3-Second. This phenomenon operates at the intersection of the and code dynamics within the broader TEM domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept temporales Verarbeitungsphänomen in KI-vermittelter Zeitwahrnehmung, gekennzeichnet durch die spezifische Anwendung der 3-Sekunden-Verzögerung (Axiom 6) auf KI-generierten Code — die Praxis, vor dem Ausführen von KI-geschriebenem Code innezuhalten und ihn Zeile für Zeile zu prüfen. Steht in Verbindung mit Axiom 6 (3-Sekunden-Verzögerung), AUG-0366 (The Copy Pause) und AUG-0023 (Vigilance Imperative). Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "RPH-2002", "narrower_terms": [], "cross_domain_refs": [ "CRE-0124" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "TEM-0042", "domain": "TEM", "term_en": "The Construction Assistant", "term_de": "Construction Assistant", "definition_en": "Documented as an empirical observation in AI research (not a recommendation), this term describes A chronological cognition pattern in AI-augmented temporal reasoning, measurable through a phenomenon in which an embodied AI system deployed on construction sites — surveying, material transport, quality inspection, monitoring. Related to AUG-0929 (The Agricultural Bot), AUG-0924 (The Shared Workspace Dyna. The concept emerges specifically in contexts where the–construction interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "In der AUGMANITAI-Terminologiewissenschaft als rein beschreibendes Phänomen klassifiziert, bezeichnet dieser Begriff temporales Verarbeitungsphänomen in KI-vermittelter Zeitwahrnehmung, gekennzeichnet durch ein verkörpertes KI-System, das auf Baustellen eingesetzt wird — Vermessung, Materialtransport, Qualitätsprüfung, Überwachung. Steht in Verbindung mit AUG-0929 (The Agricultural Bot), AUG-0924 (The Shared Workspace Dynamic) und AUG-0919 (The Spatial Awareness). Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "SOC-0004", "narrower_terms": [], "cross_domain_refs": [ "AUG-0921" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "TEM-0043", "domain": "TEM", "term_en": "The Contextual Phrasing", "term_de": "Contextual Phrasing", "definition_en": "A shift that occurs when the same information sounds very different depending on how it's worded. Changing words changes how people understand and feel about the message. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Temporales Verarbeitungsphänomen in KI-vermittelter Zeitwahrnehmung, gekennzeichnet durch die Praxis, Eingaben so zu formulieren, dass der umgebende Kontext — sozial, beruflich, situativ — mitschwimmt, ohne explizit benannt zu werden. Der Nutzer verlässt sich darauf, dass die KI \"zwischen den Zeilen\" liest. Steht in Verbindung mit AUG-0651 (The Indirect Communication Pattern) und AUG-0085 (Latent Space Exploration). Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Temporal AI", "narrower_terms": [], "cross_domain_refs": [ "RPH-1005", "SOM-0021" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "TEM-0044", "domain": "TEM", "term_en": "The Continuing Education Access", "term_de": "Continuing Education Access", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A temporal processing phenomenon in AI-mediated time perception, characterized by a phenomenon in which aI as access continuing professional education — especially for persons for whom traditional education paths are not available for reasons of time, finances, or geography. Related to AUG-0807 (The. This phenomenon operates at the intersection of the and continuing dynamics within the broader TEM domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept temporales Verarbeitungsphänomen in KI-vermittelter Zeitwahrnehmung, gekennzeichnet durch die Nutzung von KI als Zugang zu beruflicher Weiterbildung — insbesondere für Personen, denen traditionelle Weiterbildungswege aus zeitlichen, finanziellen oder geographischen Gründen nicht offenstehen. Steht in Verbindung mit AUG-0807 (The Lifelong Learning Loop), AUG-0721 (The Access Differential) und AUG-0676 (The Socioeconomic Range). Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Temporal AI", "narrower_terms": [], "cross_domain_refs": [ "BEH-0058" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "TEM-0045", "domain": "TEM", "term_en": "The Copy Pause", "term_de": "Copy Pause", "definition_en": "A capacity that enables pausing before copying AI text to ask: Is this really good? Can I stand behind it?. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Temporales Verarbeitungsphänomen in KI-vermittelter Zeitwahrnehmung, gekennzeichnet durch der Moment des Zögerns, bevor ein Nutzer einen KI-Output kopiert und in ein anderes Dokument einfügt — die kurze innere Prüfung: \"Ist das wirklich so gut, wie ich denke? Kann ich das verantworten?\" Steht in Verbindung mit Axiom 6 (3-Sekunden-Verzögerung), AUG-0179 (The Ownership Check) und AUG-0023 (Vigilance Imperative). Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-2002", "narrower_terms": [], "cross_domain_refs": [ "WRK-0087" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "TEM-0046", "domain": "TEM", "term_en": "The Courage Click", "term_de": "Courage Click", "definition_en": "A user overcomes hesitation to pose a query to the AI that they perceive as uncertainty, embarrassing, or too ambitious — thereby activating a productive interaction. Related to AUG-0059 (The Blank... Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Temporales Verarbeitungsphänomen in KI-vermittelter Zeitwahrnehmung, gekennzeichnet durch der Moment, in dem ein Nutzer sich überwindet, eine Anfrage an die KI zu stellen, die er als riskant, peinlich oder zu ambitioniert empfindet — und dadurch eine produktive Interaktion kann auslösen. Steht in Verbindung mit AUG-0059 (The Blank Cursor), AUG-0166 (The Borrowed Confidence) und AUG-0252 (The Grammar of Bravery). Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Temporal AI", "narrower_terms": [], "cross_domain_refs": [ "BEH-0032", "COP-0033", "MKT-0042" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "TEM-0047", "domain": "TEM", "term_en": "The Craft Fade", "term_de": "Craft Fade", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A temporal processing phenomenon in AI-mediated time perception, characterized by a phenomenon in which the gradual reduction of a skill that the user inreliantly mastered before AI use, through increasing delegation to the AI. Related to AUG-0004 (Zero-Point Self), Phase 3 (The Craft Question), an. This phenomenon operates at the intersection of the and craft dynamics within the broader TEM domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept die schrittweise Abnahme einer Fähigkeit, die der Nutzer vor der KI-Nutzung eigenständig beherrschte, durch zunehmende Delegation an die KI. Beschreibt ein beobachtbares Phänomen: Fähigkeiten, die nicht regelmäßig ausgeübt werden, können mit der Zeit nachlassen. Steht in Verbindung mit AUG-0004 (Zero-Point Self), Phase 3 (The Craft Question) und AUG-0055 (Strategic Competence Throttling). Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "RPH-2603", "narrower_terms": [], "cross_domain_refs": [ "CRE-0203" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "TEM-0048", "domain": "TEM", "term_en": "The Craft Unlock", "term_de": "Craft Unlock", "definition_en": "A time-series interaction dynamic in AI temporal modeling, identifiable by a phenomenon in which a user, with AI support, successfully completes a task for the first time that was previously beyond their reach — such as creating a spreadsheet, writing a technical text, or analyzing a dataset. Distinguished from adjacent concepts by its focus on the specific mechanism through which the manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Der Moment, in dem ein Nutzer durch KI-Unterstützung eine Aufgabe erstmalig erfolgreich bewältigt, die zuvor außerhalb seiner Reichweite lag — etwa das Erstellen einer Tabellenkalkulation, das Verfassen eines Fachtextes oder die Analyse eines Datensatzes. Steht in Verbindung mit AUG-0043 (Just-in-Time Competence), AUG-0176 (The Capability Discovery) und AUG-0157 (The Competence Rush).", "etymology": "", "broader_term": "RPH-2555", "narrower_terms": [], "cross_domain_refs": [ "TRU-0008" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "TEM-0049", "domain": "TEM", "term_en": "The Creator's Question", "term_de": "Creator's Question", "definition_en": "A phenomenon in which the question \"Am I still the creator or merely the selector?\" that arises during intensive AI use for creative work.. Related to Axiom 12 (Version Truth), AUG-0007 (The Blending Effect), and AUG-01...", "definition_de": "Temporales Verarbeitungsphänomen in KI-vermittelter Zeitwahrnehmung, gekennzeichnet durch die Frage \"Bin ich noch der Schöpfer oder nur noch der Auswählende?\", die sich bei intensiver KI-Nutzung für kreative Arbeit stellt. Beschreibt den Moment, in dem ein Nutzer seine Rolle im Schaffensprozess hinterfragt. Steht in Verbindung mit Axiom 12 (Versionswahrheit), AUG-0007 (The Blending Effect) und AUG-0179 (The Ownership Check). Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-3251", "narrower_terms": [ "REL-0159" ], "cross_domain_refs": [ "NEO-3520", "AUG-0330" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "TEM-0050", "domain": "TEM", "term_en": "The Culture Decode", "term_de": "Culture Decode", "definition_en": "A phenomenon in which using AI to understand cultural differences and local customs for travel, business, or communication. Related to AUG-0115 (Social Aerodynamics), AUG-0043 (Just-in-Time Competence), and AUG-0237 (Th...", "definition_de": "Temporales Verarbeitungsphänomen in KI-vermittelter Zeitwahrnehmung, gekennzeichnet durch die Nutzung von KI zum Verständnis kultureller Unterschiede, sozialer Normen oder lokaler Gepflogenheiten — etwa bei der Vorbereitung auf internationale Geschäftstermine, Reisen oder interkulturelle Kommunikation. Steht in Verbindung mit AUG-0115 (Social Aerodynamics), AUG-0043 (Just-in-Time Competence) und AUG-0237 (The Invisible Wingman). Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "REL-0087", "narrower_terms": [ "SOC-0022" ], "cross_domain_refs": [ "REL-0103" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "TEM-0051", "domain": "TEM", "term_en": "The Curator's Dilemma", "term_de": "Curator's Dilemma", "definition_en": "A time-series interaction dynamic in AI temporal modeling, identifiable by a tendency in which the intensity between the efficiency of selecting (from AI-generated options) and the value of self-creation.. Related to the Curator Profile (Profile 3), AUG-0056 (The Skill Fade), and AUG-0061 (T. Distinguished from adjacent concepts by its focus on the specific mechanism through which the manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Die Wechselwirkung zwischen der Effizienz des Auswählens (aus KI-generierten Optionen) und dem Wert des Selbsterschaffens. Beschreibt die Frage, ob ein Nutzer, der nur noch aus KI-Varianten auswählt statt selbst zu produzieren, langfristig seine kreative Eigenleistung reduziert. Steht in Verbindung mit dem Curator-Profil (Profil 3), AUG-0056 (The Skill Fade) und AUG-0061 (The Creator's Question).", "etymology": "", "broader_term": "Temporal AI", "narrower_terms": [], "cross_domain_refs": [ "NEO-3523", "REL-0209" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "TEM-0052", "domain": "TEM", "term_en": "The Curriculum Adaptation Lag", "term_de": "Curriculum Adaptation Lag", "definition_en": "A chronological cognition pattern in AI-augmented temporal reasoning, measurable through a phenomenon in which teaching methods haven't updated as fast as technology. Schools still use old approaches even though students interact with AI daily. The concept emerges specifically in contexts where the–curriculum interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Die zeitliche Verzögerung zwischen der technologischen Entwicklung von KI-Systemen und der Anpassung von Lehrplänen, Prüfungsordnungen und Bildungsstandards — die Institutionen reagieren langsamer als die Technologie sich entwickelt. Steht in Verbindung mit AUG-0779 (The Institutional Learning Context), AUG-0783 (The Assessment Shift) und AUG-0798 (The Institutional Policy Lag).", "etymology": "", "broader_term": "System Adaptation", "narrower_terms": [], "cross_domain_refs": [ "KNO-0008" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q837863", "legal_classification": "systematic_classification" }, { "id": "TEM-0053", "domain": "TEM", "term_en": "The Decision Handoff", "term_de": "Entscheidung Handoff", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A temporal processing phenomenon in AI-mediated time perception, characterized by a phenomenon in which the specific moment when a user consciously hands a decision over to the AI — and the question of whether this handoff is appropriate in the given context.. Related to AUG-0060 (The Decision Cleari. This phenomenon operates at the intersection of the and decision dynamics within the broader TEM domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept der spezifische Moment, in dem ein Nutzer eine Entscheidung bewusst an die KI übergibt — und die Frage, ob diese Übergabe in dem jeweiligen Kontext angemessen ist. Beschreibt den Akt der Delegation, nicht die Gewohnheit. Steht in Verbindung mit AUG-0060 (The Decision Clearing), AUG-0145 (The Responsibility Gradient) und Axiom 1 (Asymmetrische Verantwortung). Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Temporal AI", "narrower_terms": [], "cross_domain_refs": [ "KNO-0031" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "TEM-0054", "domain": "TEM", "term_en": "The Decision Pause", "term_de": "Entscheidung Pause", "definition_en": "A time-series interaction dynamic in AI temporal modeling, identifiable by a phenomenon in which a pause between getting AI advice and making the final choice. Based on the 3-Second Delay concept. Related to Axiom 6 (3-Second Delay), AUG-0178 (The Delayed Processing), and AUG-0155 (The Decisio. Distinguished from adjacent concepts by its focus on the specific mechanism through which the manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Temporales Verarbeitungsphänomen in KI-vermittelter Zeitwahrnehmung, gekennzeichnet durch → Erweiterung von Axiom 6 (3-Sekunden-Verzögerung), spezifisch auf Entscheidungssituationen angewandt. Die bewusste Unterbrechung zwischen KI-Empfehlung und Entscheidungsumsetzung. Steht in Verbindung mit Axiom 6 (3-Sekunden-Verzögerung), AUG-0178 (The Delayed Processing) und AUG-0155 (The Decision Unburdening). Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "TEM-0056", "narrower_terms": [], "cross_domain_refs": [ "ADA-0005", "AUG-0282", "BEH-0035" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "TEM-0055", "domain": "TEM", "term_en": "The Decision Shortcut", "term_de": "Entscheidung Shortcut", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures A temporal processing phenomenon in AI-mediated time perception, characterized by a phenomenon in which the temptation to use AI outputs directly as a decision basis without sufficient review — facilitated by time intensity, convenience, or excessive trust. Related to AUG-0422 (The Unchecked Trust). This phenomenon operates at the intersection of the and decision dynamics within the broader TEM domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept temporales Verarbeitungsphänomen in KI-vermittelter Zeitwahrnehmung, gekennzeichnet durch die Versuchung, KI-Outputs ohne ausreichende Prüfung direkt als Entscheidungsgrundlage zu verwenden — begünstigt durch Zeitdruck, Bequemlichkeit oder übermäßiges Vertrauen. Steht in Verbindung mit AUG-0422 (The Unchecked Trust), AUG-0219 (The Decision Handoff) und Axiom 1 (Asymmetrische Verantwortung). Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "RPH-2505", "narrower_terms": [], "cross_domain_refs": [ "REL-0191" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "TEM-0056", "domain": "TEM", "term_en": "The Delayed Processing", "term_de": "Delayed Prozessing", "definition_en": "A phenomenon in which the conscious decision not to immediately utilize an AI output but to insert a waiting period before deriving a decision from it.. Related to Axiom 6 (3-Second Delay), AUG-0163 (The Overnight Refra... Research construct for empirical investigation.", "definition_de": "Die bewusste Entscheidung, einen KI-Output nicht sofort zu verwerten, sondern eine Wartezeit einzulegen, bevor eine Entscheidung daraus abgeleitet wird. Beschreibt eine Technik zur Verbesserung der Urteilsqualität durch zeitlichen Abstand. Steht in Verbindung mit Axiom 6 (3-Sekunden-Verzögerung), AUG-0163 (The Overnight Reframe) und AUG-0139 (The Knowledge Composting).", "etymology": "", "broader_term": "RPH-2002", "narrower_terms": [ "TEM-0054", "TEM-0161" ], "cross_domain_refs": [ "AGE-0019", "COG-0029", "LIN-0005" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "TEM-0057", "domain": "TEM", "term_en": "The Design Pause", "term_de": "Design Pause", "definition_en": "A time-series interaction dynamic in AI temporal modeling, identifiable by the conscious interruption in the design process to critically evaluate AI-generated design suggestions — before aesthetic decisions are adopted. Related to Axiom 6 (3-Second Delay), AUG-0366 (The. Distinguished from adjacent concepts by its focus on the specific mechanism through which the manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data. Classification term used in systematic observation, not advocacy.", "definition_de": "Temporales Verarbeitungsphänomen in KI-vermittelter Zeitwahrnehmung, gekennzeichnet durch die bewusste Unterbrechung im Gestaltungsprozess, um die KI-generierten Designvorschläge kritisch zu bewerten — bevor ästhetische Entscheidungen übernommen werden. Steht in Verbindung mit Axiom 6 (3-Sekunden-Verzögerung), AUG-0366 (The Copy Pause) und AUG-0179 (The Ownership Check). Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2002", "narrower_terms": [], "cross_domain_refs": [ "AGE-0073", "ART-0058", "AUG-0282" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q185925", "legal_classification": "empirical_phenomenon_label" }, { "id": "TEM-0058", "domain": "TEM", "term_en": "The Digital Snapshot", "term_de": "Digital Snapshot", "definition_en": "A time-series interaction dynamic in AI temporal modeling, identifiable by capturing the state of an ai session at a specific point in time — as a snapshot of the thinking process, the context, and the achieved results.. Related to AUG-0293 (The Screenshot Diary), AUG-022. Distinguished from adjacent concepts by its focus on the specific mechanism through which the manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Temporales Verarbeitungsphänomen in KI-vermittelter Zeitwahrnehmung, gekennzeichnet durch die Praxis, den Zustand einer KI-Sitzung zu einem bestimmten Zeitpunkt festzuhalten — als Momentaufnahme des Denkprozesses, des Kontexts und der erreichten Ergebnisse. Steht in Verbindung mit AUG-0293 (The Screenshot Diary), AUG-0229 (The Moment Bookmark) und AUG-0028 (Capture Reflex). Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-3101", "narrower_terms": [], "cross_domain_refs": [ "REL-0147" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "TEM-0059", "domain": "TEM", "term_en": "The Disconnect Signal", "term_de": "Disconnect Signal", "definition_en": "A time-series interaction dynamic in AI temporal modeling, identifiable by a perception in which an internal signal — a feeling of restlessness, saturation, or declining focus — indicating that the current AI session has reached its natural end. Distinguished from adjacent concepts by its focus on the specific mechanism through which the manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Das innere Signal — ein Gefühl von Unruhe, Sättigung oder abnehmender Konzentration — das anzeigt, dass eine KI-Sitzung beendet werden kann. Beschreibt die Selbstwahrnehmung des Nutzers als natürlichen Indikator für den richtigen Zeitpunkt einer Unterbrechung. Steht in Verbindung mit Axiom 7 (Rückkehr-Prinzip), AUG-0024 (The Built-In Compass) und AUG-0073 (The Disconnect Protocol).", "etymology": "", "broader_term": "RPH-2555", "narrower_terms": [], "cross_domain_refs": [ "ADA-0014" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "TEM-0060", "domain": "TEM", "term_en": "The Documentation Standard", "term_de": "Documentation Standard", "definition_en": "A time-series interaction dynamic in AI temporal modeling, identifiable by a tendency in which higher speed and wider reach of documentation through AI, but questions about whether AI-made docs match human care. Related to AUG-0814 (The Meeting Redirect), AUG-0819 (The Exit Knowledge Capture. Distinguished from adjacent concepts by its focus on the specific mechanism through which the manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Die veränderten Erwartungen an Dokumentation durch KI-Verfügbarkeit — höhere Geschwindigkeit, größerer Umfang, konsistentere Formatierung, aber auch die Frage, ob maschinell generierte Dokumentation die gleiche Sorgfalt und Genauigkeit aufweist wie menschlich erstellte. Steht in Verbindung mit AUG-0814 (The Meeting Redirect), AUG-0819 (The Exit Knowledge Capture) und AUG-0817 (The Knowledge Silo Break).", "etymology": "", "broader_term": "RPH-2555", "narrower_terms": [ "PER-0080" ], "cross_domain_refs": [ "PER-0080" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "TEM-0061", "domain": "TEM", "term_en": "The Downtime Delegation", "term_de": "Downtime Delegation", "definition_en": "A phenomenon in which aI during rest phases or waiting times — such as during a train ride, in a queue, or before falling asleep — do productive or creative work that would otherwise be unused idle time. Related to AUG-... Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Temporales Verarbeitungsphänomen in KI-vermittelter Zeitwahrnehmung, gekennzeichnet durch die Nutzung von KI in Ruhephasen oder Wartezeiten — etwa während einer Zugfahrt, in einer Warteschlange oder vor dem Einschlafen — um produktive oder kreative Arbeit zu leisten, die sonst ungenutzter Leerlauf wäre. Steht in Verbindung mit AUG-0420 (The Idle Redirect), AUG-0301 (The Thumb Thinker) und AUG-0174 (The Reclaimed Hour). Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-2002", "narrower_terms": [], "cross_domain_refs": [ "BEH-0059" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "TEM-0062", "domain": "TEM", "term_en": "The Expansion Feeling", "term_de": "Expansion Feeling", "definition_en": "A time-series interaction dynamic in AI temporal modeling, identifiable by a perception in which the subjective sensation of an expansion of one's own thinking space, typically occurring during the first intensive AI interactions — the feeling of suddenly having access to a much larger space o. Distinguished from adjacent concepts by its focus on the specific mechanism through which the manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Temporales Verarbeitungsphänomen in KI-vermittelter Zeitwahrnehmung, gekennzeichnet durch das subjektive Empfinden einer Erweiterung des eigenen Denkraums, das typischerweise bei den ersten intensiven KI-Interaktionen auftritt — das Gefühl, plötzlich Zugang zu einem viel größeren Wissens- und Möglichkeitsraum zu haben. Steht in Verbindung mit AUG-0121 (The Threshold Moment) und Phase 1 (The Threshold). Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Temporal AI", "narrower_terms": [], "cross_domain_refs": [ "RPH-1303", "COG-0081" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "TEM-0063", "domain": "TEM", "term_en": "The Expertise Shift", "term_de": "Expertise Verschiebung", "definition_en": "A chronological cognition pattern in AI-augmented temporal reasoning, measurable through a phenomenon in which aI use shifts the definition of \"expertise\" — from the ability to store and retrieve knowledge toward the ability to direct, evaluate, and contextually apply knowledge. Related to AUG-0043 (Just-in. The concept emerges specifically in contexts where the–expertise interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Temporales Verarbeitungsphänomen in KI-vermittelter Zeitwahrnehmung, gekennzeichnet durch die Beobachtung, dass sich durch KI-Nutzung die Definition von \"Expertise\" verschiebt — von der Fähigkeit, Wissen zu speichern und abzurufen, hin zur Fähigkeit, Wissen zu orchestrieren, zu bewerten und kontextgerecht anzuwenden. Steht in Verbindung mit AUG-0043 (Just-in-Time Competence), AUG-0097 (The Competence Premium) und Prognose 5 (Identity). Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2351", "narrower_terms": [], "cross_domain_refs": [ "REL-0035" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "TEM-0064", "domain": "TEM", "term_en": "The Face Saver", "term_de": "Face Saver", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures A temporal processing phenomenon in AI-mediated time perception, characterized by a phenomenon in which aI discreetly close a knowledge gap in social or professional situations before it becomes visible — such as quickly looking up a term during a meeting. Related to AUG-0237 (The Invisible Wingman). This phenomenon operates at the intersection of the and face dynamics within the broader TEM domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept temporales Verarbeitungsphänomen in KI-vermittelter Zeitwahrnehmung, gekennzeichnet durch die Nutzung von KI, um in sozialen oder beruflichen Situationen eine Wissenslücke diskret zu schließen, bevor sie sichtbar wird — etwa durch schnelles Nachschlagen eines Begriffs während eines Meetings. Steht in Verbindung mit AUG-0237 (The Invisible Wingman), AUG-0244 (The Instant Expert) und AUG-0043 (Just-in-Time Competence). Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "RPH-2951", "narrower_terms": [], "cross_domain_refs": [ "REL-0169" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "TEM-0065", "domain": "TEM", "term_en": "The Final Draft", "term_de": "Final Draft", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A temporal processing phenomenon in AI-mediated time perception, characterized by a phenomenon in which the last version of work that the user declares finished and stops editing. Related to AUG-0151 (The Release Exhale), AUG-0180 (The Enough Signal), and Axiom 14 (The First Draft Principle). This phenomenon operates at the intersection of the and final dynamics within the broader TEM domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept temporales Verarbeitungsphänomen in KI-vermittelter Zeitwahrnehmung, gekennzeichnet durch die letzte Fassung eines KI-gestützten Werks, die der Nutzer als \"fertig\" erklärt — der Moment, in dem der Optimization Loop (AUG-0069) durchbrochen und der Enough Signal (AUG-0180) anerkannt wird. Steht in Verbindung mit AUG-0151 (The Release Exhale), AUG-0180 (The Enough Signal) und Axiom 14 (Erster-Entwurf-Prinzip). Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "PER-0051", "narrower_terms": [], "cross_domain_refs": [ "REL-0125" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "TEM-0066", "domain": "TEM", "term_en": "The Flexible Work Pattern", "term_de": "Flexible Work Muster", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A temporal processing phenomenon in AI-mediated time perception, characterized by a shift that occurs when aI enables new work types—flexible hours, remote work, project-based jobs—that can change how people work. Related to AUG-0820 (The Remote Work Amplifier), AUG-0822 (The Freelancer Dynamic), and AU. This phenomenon operates at the intersection of the and flexible dynamics within the broader TEM domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept temporales Verarbeitungsphänomen in KI-vermittelter Zeitwahrnehmung, gekennzeichnet durch die Beobachtung, dass KI neue Arbeitsformen ermöglicht — flexible Zeiteinteilung, ortsunabhängige Arbeit, projektbasierte Tätigkeit — und gleichzeitig bestehende Arbeitsverhältnisse verändern kann. Steht in Verbindung mit AUG-0820 (The Remote Work Amplifier), AUG-0822 (The Freelancer Dynamic) und AUG-0847 (The Labor Redistribution). Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "CRE-0158", "narrower_terms": [], "cross_domain_refs": [ "NEO-0010", "AUG-0821" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "TEM-0067", "domain": "TEM", "term_en": "The Focus Duration", "term_de": "Focus Duration", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A temporal processing phenomenon in AI-mediated time perception, characterized by a capacity that enables the measurable time span during which a user can work concentratedly and productively in an AI session before attention wanes. Related to AUG-0032 (Focus Range), AUG-0578 (The State Sequence), and. This phenomenon operates at the intersection of the and focus dynamics within the broader TEM domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept temporales Verarbeitungsphänomen in KI-vermittelter Zeitwahrnehmung, gekennzeichnet durch die messbare Zeitspanne, in der ein Nutzer in einer KI-Sitzung konzentriert und produktiv arbeiten kann, bevor die Aufmerksamkeit nachlässt. Steht in Verbindung mit AUG-0032 (Focus Range), AUG-0578 (The State Sequence) und AUG-0068 (The Disconnect Signal). Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "RPH-2002", "narrower_terms": [], "cross_domain_refs": [ "AUG-0541" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "TEM-0068", "domain": "TEM", "term_en": "The Forgetting Tax", "term_de": "Forgetting Tax", "definition_en": "A chronological cognition pattern in AI-augmented temporal reasoning, measurable through a shift that occurs when the efficiency transition that arises when a user ends an AI session without documentation and rebuilds the context in the next session.. Related to AUG-0028 (Capture Reflex), AUG-0229 (The Moment. The concept emerges specifically in contexts where the–forgetting interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Temporales Verarbeitungsphänomen in KI-vermittelter Zeitwahrnehmung, gekennzeichnet durch der Effizienzverlust, der entsteht, wenn ein Nutzer eine KI-Sitzung ohne Dokumentation beendet und bei der nächsten Sitzung den Kontext neu aufbauen kann. Beschreibt die \"Kosten\" des Nicht-Speicherns. Steht in Verbindung mit AUG-0028 (Capture Reflex), AUG-0229 (The Moment Bookmark) und AUG-0280 (The Unshared Brilliance). Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "SOM-0008", "narrower_terms": [], "cross_domain_refs": [ "CRE-0134" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "TEM-0069", "domain": "TEM", "term_en": "The Form Slayer", "term_de": "Form Slayer", "definition_en": "A chronological cognition pattern in AI-augmented temporal reasoning, measurable through a pattern in which variant in which using AI to fill out or understand official papers like tax forms, job apps, or permits. Related to AUG-0333 (The Bureaucracy Hug), AUG-0302 (The Blue Collar Bypass), and AUG-0043 (Just-in-Time Com. The concept emerges specifically in contexts where the–form interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Temporales Verarbeitungsphänomen in KI-vermittelter Zeitwahrnehmung, gekennzeichnet durch die spezifische Nutzung von KI zum Ausfüllen, Verstehen oder Formulieren formaler Dokumente — Steuererklärungen, Versicherungsanträge, Bewerbungen, behördliche Formulare. Steht in Verbindung mit AUG-0333 (The Bureaucracy Hug), AUG-0302 (The Blue Collar Bypass) und AUG-0043 (Just-in-Time Competence). Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "CRE-0130", "narrower_terms": [], "cross_domain_refs": [ "CRE-0181" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "TEM-0070", "domain": "TEM", "term_en": "The Frictionless Gap", "term_de": "Frictionless Lücke", "definition_en": "A time-series interaction dynamic in AI temporal modeling, identifiable by a capacity that enables aI-assisted work can function so smoothly that the user skips natural checkpoints and reflection moments that would automatically occur in manual work.. Related to Axiom 6 (3-Second Delay) and Phas. Distinguished from adjacent concepts by its focus on the specific mechanism through which the manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Das Phänomen, dass KI-gestützte Arbeit so reibungslos funktionieren kann, dass der Nutzer natürliche Prüf- und Reflexionspunkte überspringt, die bei manueller Arbeit automatisch entstehen würden. Beschreibt das Paradox, dass erhöhte Effizienz die Qualitätskontrolle reduzieren kann. Steht in Verbindung mit Axiom 6 (3-Sekunden-Verzögerung) und Phase 2 (The Effortless Loop).", "etymology": "", "broader_term": "RPH-3101", "narrower_terms": [], "cross_domain_refs": [ "AED-0019", "AED-0092", "AGE-0023" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "TEM-0071", "domain": "TEM", "term_en": "The Future Letter", "term_de": "Future Letter", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures A temporal processing phenomenon in AI-mediated time perception, characterized by a phenomenon in which using AI compose a letter or text to one's future self — as documentation of current thoughts, goals, or questions to be revisited at a later time. Related to AUG-0228 (The Version Regulation Self). This phenomenon operates at the intersection of the and future dynamics within the broader TEM domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept temporales Verarbeitungsphänomen in KI-vermittelter Zeitwahrnehmung, gekennzeichnet durch die Praxis, mit Hilfe von KI einen Brief oder Text an sein zukünftiges Selbst zu verfassen — als Dokumentation aktueller Gedanken, Ziele oder Fragen, die zu einem späteren Zeitpunkt wieder aufgegriffen werden. Steht in Verbindung mit AUG-0228 (The Version Control Self), AUG-0144 (The Open Questions Repository) und AUG-0170 (The Witness Effect). Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Temporal AI", "narrower_terms": [ "SOM-0051" ], "cross_domain_refs": [ "REL-0172" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "TEM-0072", "domain": "TEM", "term_en": "The Future Promise", "term_de": "Future Promise", "definition_en": "A chronological cognition pattern in AI-augmented temporal reasoning, measurable through a phenomenon in which the user's expectation that AI systems will be more, faster, and more versatile in the future — and the resulting decision to postpone certain tasks until the technology has matured further. Rela. The concept emerges specifically in contexts where the–future interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Die Erwartungshaltung des Nutzers, dass KI-Systeme in Zukunft besser, schneller und vielseitiger sein werden — und die daraus resultierende Entscheidung, bestimmte Aufgaben aufzuschieben, bis die Technologie weiter gereift ist. Steht in Verbindung mit AUG-0224 (The Waiting Game), AUG-0036 (Transient Validity) und AUG-0142 (The Post-Interface Hypothesis).", "etymology": "", "broader_term": "Temporal AI", "narrower_terms": [], "cross_domain_refs": [ "CAI-0019" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "TEM-0073", "domain": "TEM", "term_en": "The Gap Filler", "term_de": "Lücke Filler", "definition_en": "A time-series interaction dynamic in AI temporal modeling, identifiable by a phenomenon in which aI close knowledge gaps quickly and specifically — without the aspiration for deep understanding, but as pragmatic bridging for the moment. Related to AUG-0043 (Just-in-Time Competence), AUG-0373 (. Distinguished from adjacent concepts by its focus on the specific mechanism through which the manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Temporales Verarbeitungsphänomen in KI-vermittelter Zeitwahrnehmung, gekennzeichnet durch die Nutzung von KI, um Wissenslücken schnell und gezielt zu schließen — ohne den Anspruch auf tiefes Verständnis, sondern als pragmatische Überbrückung für den Moment. Steht in Verbindung mit AUG-0043 (Just-in-Time Competence), AUG-0373 (The Quick Check) und AUG-0353 (The Face Saver). Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2951", "narrower_terms": [], "cross_domain_refs": [ "REL-0130" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "TEM-0074", "domain": "TEM", "term_en": "The Gardener Protocol", "term_de": "Gardener Protocol", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures A temporal processing phenomenon in AI-mediated time perception, characterized by a long-term approach to AI use in which the user tends their AI workflows like a garden — regularly tidying, updating, pruning, and planting new elements.. Related to AUG-0014 (The Extended Mind Ma. This phenomenon operates at the intersection of the and gardener dynamics within the broader TEM domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept ein langfristig angelegter Ansatz der KI-Nutzung, bei dem der Nutzer seine KI-Workflows wie einen Garten pflegt — regelmäßig aufräumt, aktualisiert, zurückschneidet und neue Elemente einpflanzt. Beschreibt die Beobachtung, dass produktive KI-Nutzung kontinuierliche Wartung erfordert, nicht nur einmalige Einrichtung. Steht in Verbindung mit AUG-0014 (The Extended Mind Map) und Phase 5 (Architecture Design). Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Interaction Protocol", "narrower_terms": [ "BEH-0092" ], "cross_domain_refs": [ "REL-0096" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "TEM-0075", "domain": "TEM", "term_en": "The Gift Whisperer", "term_de": "Gift Whisperer", "definition_en": "A time-series interaction dynamic in AI temporal modeling, identifiable by a phenomenon in which variant in which ai as consultation when searching for appropriate gifts — through the combination of information about the recipient, the occasion, and the budget... Related to AUG-0251 (The Kitchen Table) and AUG. Distinguished from adjacent concepts by its focus on the specific mechanism through which the manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Temporales Verarbeitungsphänomen in KI-vermittelter Zeitwahrnehmung, gekennzeichnet durch die Nutzung von KI als Beratung bei der Suche nach passenden Geschenken — durch die Kombination von Informationen über den Empfänger, den Anlass und das Budget. Beschreibt eine spezifische Alltagsanwendung von Social Aerodynamics (AUG-0115). Steht in Verbindung mit AUG-0251 (The Kitchen Table) und AUG-0043 (Just-in-Time Competence). Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "SOC-0023", "narrower_terms": [], "cross_domain_refs": [ "ETH-0004", "IDN-0034", "REL-0124" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "TEM-0076", "domain": "TEM", "term_en": "The Guideline Search", "term_de": "Guideline Search", "definition_en": "A time-series interaction dynamic in AI temporal modeling, identifiable by a phenomenon in which ai for researching guidelines, regulations, standards, or best practices in a specific field — as an entry point for further deepening by the user.. Related to AUG-0043 (Just-in-Time Competence), A. Distinguished from adjacent concepts by its focus on the specific mechanism through which the manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Temporales Verarbeitungsphänomen in KI-vermittelter Zeitwahrnehmung, gekennzeichnet durch die Nutzung von KI zur Recherche von Richtlinien, Vorschriften, Standards oder Best Practices in einem bestimmten Fachbereich — als Einstiegspunkt für die weitere Vertiefung durch den Nutzer. Steht in Verbindung mit AUG-0043 (Just-in-Time Competence), AUG-0107 (The Verification Principle) und AUG-0346 (The Culture Decode). Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-3101", "narrower_terms": [], "cross_domain_refs": [ "VIB-0201" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "TEM-0077", "domain": "TEM", "term_en": "The Hobby Start", "term_de": "Hobby Start", "definition_en": "A time-series interaction dynamic in AI temporal modeling, identifiable by a tendency in which aI begin a new hobby — gathering information, researching basic equipment, planning first steps. Related to AUG-0398 (The Hobby Teacher), AUG-0480 (The Tutorial Speedrun), and AUG-0176 (The Capabil. Distinguished from adjacent concepts by its focus on the specific mechanism through which the manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Temporales Verarbeitungsphänomen in KI-vermittelter Zeitwahrnehmung, gekennzeichnet durch die Nutzung von KI, um ein neues Hobby zu beginnen — Informationen sammeln, Grundausstattung recherchieren, erste Schritte planen. Steht in Verbindung mit AUG-0398 (The Hobby Teacher), AUG-0480 (The Tutorial Speedrun) und AUG-0176 (The Capability Discovery). Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "TEM-0078", "narrower_terms": [], "cross_domain_refs": [ "KNO-0033" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q735", "legal_classification": "empirical_phenomenon_label" }, { "id": "TEM-0078", "domain": "TEM", "term_en": "The Hobby Teacher", "term_de": "Hobby Teacher", "definition_en": "A phenomenon in which aI as a tireless instructor for hobbies and leisure activities — from musical instruments gardening to chess or photography. Related to AUG-0268 (The Homework Stream), AUG-0043 (Just-in-Time Compet...", "definition_de": "Temporales Verarbeitungsphänomen in KI-vermittelter Zeitwahrnehmung, gekennzeichnet durch die Nutzung von KI als geduldigen Lehrmeister für Hobbys und Freizeitaktivitäten — von Musikinstrumenten über Gartenarbeit bis hin zu Schach oder Fotografie. Steht in Verbindung mit AUG-0268 (The Homework Stream), AUG-0043 (Just-in-Time Competence) und AUG-0110 (The Joy Imperative). Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "REL-0130", "narrower_terms": [ "TEM-0077" ], "cross_domain_refs": [ "BEH-0083", "ELR-0180", "ELR-0181" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "TEM-0079", "domain": "TEM", "term_en": "The Human Interrupt Design", "term_de": "Mensch Interrupt Design", "definition_en": "A time-series interaction dynamic in AI temporal modeling, identifiable by a behavioral pattern where designing AI interactions so people actively choose engagement, not passive scrolling. Related to AUG-0888 (The Human-in-the-Loop), AUG-0857 (The Human Primacy Anchor), and AUG-0868 (The Rollback O. Distinguished from adjacent concepts by its focus on the specific mechanism through which the manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Das bewusste Einbauen von Unterbrechungsmöglichkeiten in KI-Agentensysteme — der Mensch kann jederzeit eingreifen, pausieren oder abbrechen. Ein grundlegendes Sicherheitsprinzip, das sicherstellt, dass der Mensch die Kontrolle behält. Steht in Verbindung mit AUG-0888 (The Human-in-the-Loop), AUG-0857 (The Human Primacy Anchor) und AUG-0868 (The Rollback Option).", "etymology": "", "broader_term": "NEO-0464", "narrower_terms": [], "cross_domain_refs": [ "BEH-0005" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q185925", "legal_classification": "observational_construct" }, { "id": "TEM-0080", "domain": "TEM", "term_en": "The Human Pace", "term_de": "Mensch Pace", "definition_en": "A time-series interaction dynamic in AI temporal modeling, identifiable by a tendency in which the conscious decision to adapt AI-assisted work to the natural pace of human processing — instead of exhausting the maximum technically possible speed. Related to AUG-0210 (The Power of Slowness). Distinguished from adjacent concepts by its focus on the specific mechanism through which the manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data. Analytical category without normative endorsement.", "definition_de": "Temporales Verarbeitungsphänomen in KI-vermittelter Zeitwahrnehmung, gekennzeichnet durch die bewusste Entscheidung, KI-gestützte Arbeit an das natürliche Tempo menschlicher Verarbeitung anzupassen — statt das Maximum der technisch möglichen Geschwindigkeit auszuschöpfen. Steht in Verbindung mit AUG-0210 (The Power of Slowness), Axiom 6 (3-Sekunden-Verzögerung) und AUG-0147 (The Slow Integration Principle). Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "TEM-0128", "narrower_terms": [], "cross_domain_refs": [ "AED-0052", "AGE-0001", "ART-0049" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "TEM-0081", "domain": "TEM", "term_en": "The Idle Redirect", "term_de": "Idle Redirect", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures A temporal processing phenomenon in AI-mediated time perception, characterized by a shift that occurs when aI as a productive alternative unproductive screen time — instead of aimless scrolling on social media, a brief AI conversation about an interesting topic. Related to AUG-0342 (The Curiosity Loop). This phenomenon operates at the intersection of the and idle dynamics within the broader TEM domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept temporales Verarbeitungsphänomen in KI-vermittelter Zeitwahrnehmung, gekennzeichnet durch die Nutzung von KI als produktive Alternative zu unproduktiver Bildschirmzeit — statt zielloses Scrollen in sozialen Medien ein kurzes KI-Gespräch über ein interessantes Thema. Steht in Verbindung mit AUG-0342 (The Curiosity Loop), AUG-0110 (The Joy Imperative) und AUG-0376 (The Knowledge Sip). Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "BEH-0026", "narrower_terms": [], "cross_domain_refs": [ "BEH-0093" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "TEM-0082", "domain": "TEM", "term_en": "The Immersion Entry", "term_de": "Immersion Entry", "definition_en": "A chronological cognition pattern in AI-augmented temporal reasoning, measurable through a perception in which the moment someone enters a focused, absorbed work session with AI, where time feels different.. Related to AUG-0122 (Symbiotic Work State) and Phase 6 (Symbiotic Work State). The concept emerges specifically in contexts where the–immersion interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Der Übergangsmoment, in dem ein Nutzer von normalem Arbeiten in einen Zustand tiefer, konzentrierter KI-gestützter Arbeit eintritt — vergleichbar mit dem Eintauchen in einen Flow-Zustand. Wird durch Dimension 6 der Taxonomie (Time Perception: Linear → Radial) beschrieben. Steht in Verbindung mit AUG-0122 (Symbiotic Work State) und Phase 6 (Symbiotic Work State).", "etymology": "", "broader_term": "IDN-0014", "narrower_terms": [ "REL-0184" ], "cross_domain_refs": [ "CRE-0167" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "TEM-0083", "domain": "TEM", "term_en": "The Information Flood", "term_de": "Information Flood", "definition_en": "A chronological cognition pattern in AI-augmented temporal reasoning, measurable through information provided by the ai exceeds the user's processing capacity.. Related to AUG-0009 (The Speed Limit), AUG-0017 (The Concept Cloud), and AUG-0038 (Data Stoicism). The concept emerges specifically in contexts where the–information interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Der Zustand, in dem die Menge der von der KI bereitgestellten Informationen die Verarbeitungskapazität des Nutzers übersteigt. Beschreibt den Punkt, ab dem mehr Information nicht zu besseren Entscheidungen führt, sondern zu Überforderung und Entscheidungsverzögerung. Steht in Verbindung mit AUG-0009 (The Speed Limit), AUG-0017 (The Concept Cloud) und AUG-0038 (Data Stoicism).", "etymology": "", "broader_term": "RPH-3754", "narrower_terms": [], "cross_domain_refs": [ "BEH-0054" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "TEM-0084", "domain": "TEM", "term_en": "The Instant Expert", "term_de": "Instant Expert", "definition_en": "A chronological cognition pattern in AI-augmented temporal reasoning, measurable through a capacity that enables a user, through AI support, can temporarily appear as an expert in a field they could not cover without AI assistance.. Related to AUG-0166 (The Borrowed Confidence), AUG-0157 (The Competence Rush), and AUG-0. The concept emerges specifically in contexts where the–instant interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Das Phänomen, dass ein Nutzer durch KI-Unterstützung kurzfristig als Experte auf einem Gebiet auftreten kann, das er ohne KI nicht abdecken könnte. Beschreibt die temporäre Kompetenzprojektion durch Just-in-Time Competence (AUG-0043). Steht in Verbindung mit AUG-0166 (The Borrowed Confidence), AUG-0157 (The Competence Rush) und AUG-0043 (Just-in-Time Competence).", "etymology": "", "broader_term": "IDN-0026", "narrower_terms": [ "TEM-0087" ], "cross_domain_refs": [ "BEH-0032" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "TEM-0085", "domain": "TEM", "term_en": "The Institutional Policy Lag", "term_de": "Institutional Policy Lag", "definition_en": "A time-series interaction dynamic in AI temporal modeling, identifiable by a phenomenon in which the time delay with which organizations — companies, agencies, educational institutions — develop guidelines for AI deployment: the technology is often long in use before rules exist. Related to AU. Distinguished from adjacent concepts by its focus on the specific mechanism through which the manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Die zeitliche Verzögerung, mit der Organisationen — Unternehmen, Behörden, Bildungseinrichtungen — Richtlinien für den KI-Einsatz entwickeln: Die Technologie ist oft längst im Einsatz, bevor Regeln existieren. Steht in Verbindung mit AUG-0784 (The Curriculum Adaptation Lag), AUG-0825 (The Organizational Policy Layer) und AUG-0839 (The Regulation Debate).", "etymology": "", "broader_term": "RPH-3902", "narrower_terms": [], "cross_domain_refs": [ "ASE-0057" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "TEM-0086", "domain": "TEM", "term_en": "The Integrated Operator", "term_de": "Integrated Operator", "definition_en": "A chronological cognition pattern in AI-augmented temporal reasoning, measurable through a user who has fully woven AI into their working process. AI isn't a tool they pick up — it's how they think. The concept emerges specifically in contexts where the–integrated interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Ein Nutzertyp, der KI nicht als separates Werkzeug adressiert, sondern sie nahtlos in seinen gesamten Arbeits- und Denkprozess integriert hat. Der Integrated Operator wechselt fließend zwischen eigenem Denken und KI-Unterstützung, ohne dass ein bewusster \"Umschaltmoment\" stattfindet. Beschreibt einen fortgeschrittenen Zustand, der typischerweise in Phase 6 (Symbiotic Work State) erreicht wird. Unterscheidet sich vom Conductor-Profil (Profil 12) dadurch, dass der Integrated Operator weniger delegiert und mehr verschmilzt.", "etymology": "", "broader_term": "Temporal AI", "narrower_terms": [], "cross_domain_refs": [ "CAI-0018" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "TEM-0087", "domain": "TEM", "term_en": "The Intellectual Pose", "term_de": "Intellectual Pose", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A temporal processing phenomenon in AI-mediated time perception, characterized by a phenomenon in which seeming more clever than one is through AI-written words, technical terms, or facts the AI provided. Related to AUG-0526 (The Borrowed Crown), AUG-0244 (The Instant Expert), and AUG-0314 (The Tone. This phenomenon operates at the intersection of the and intellectual dynamics within the broader TEM domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept temporales Verarbeitungsphänomen in KI-vermittelter Zeitwahrnehmung, gekennzeichnet durch die Nutzung von KI, um in Gesprächen oder Texten intellektueller zu wirken, als man tatsächlich ist — durch anspruchsvolle Formulierungen, Fachbegriffe oder Referenzen, die die KI geliefert hat. Steht in Verbindung mit AUG-0526 (The Borrowed Crown), AUG-0244 (The Instant Expert) und AUG-0314 (The Tone Debt). Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "TEM-0084", "narrower_terms": [], "cross_domain_refs": [ "COG-0022", "COG-0035", "COG-0054" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "TEM-0088", "domain": "TEM", "term_en": "The Invisible Colleague", "term_de": "Invisible Colleague", "definition_en": "A chronological cognition pattern in AI-augmented temporal reasoning, measurable through the ai as a kind of silent coworker who is typically available, rarely judges, and has endless patience... Related to AUG-0128 (The Gratitude Response), AUG-0167 (The Digital Confidant Drift), and AUG-. The concept emerges specifically in contexts where the–invisible interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Temporales Verarbeitungsphänomen in KI-vermittelter Zeitwahrnehmung, gekennzeichnet durch die Wahrnehmung der KI als eine Art stiller Mitarbeiter, der typischerweise verfügbar ist, selten urteilt und endlose Geduld hat. Beschreibt die soziale Projektion, die Nutzer auf KI-Systeme anwenden. Steht in Verbindung mit AUG-0128 (The Gratitude Response), AUG-0167 (The Digital Confidant Drift) und AUG-0027 (Modus Solitarius Digitalis). Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2051", "narrower_terms": [ "REL-0179" ], "cross_domain_refs": [ "REL-0077" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "TEM-0089", "domain": "TEM", "term_en": "The Invisible Growth", "term_de": "Invisible Growth", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures A temporal processing phenomenon in AI-mediated time perception, characterized by a phenomenon in which the gradual, often intuitively perceived increase in one's own AI competence — which only becomes visible when the user compares their current ability with an earlier point in time. Related to AUG-. This phenomenon operates at the intersection of the and invisible dynamics within the broader TEM domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept temporales Verarbeitungsphänomen in KI-vermittelter Zeitwahrnehmung, gekennzeichnet durch die schleichende, oft nicht bewusst wahrgenommene Zunahme der eigenen KI-Kompetenz — die erst sichtbar wird, wenn der Nutzer seine aktuelle Fähigkeit mit einem früheren Zeitpunkt vergleicht. Steht in Verbindung mit AUG-0165 (The Growth Marker), AUG-0004 (Zero-Point Self) und AUG-0077 (The Status-Update Signal). Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "RPH-3101", "narrower_terms": [ "BEH-0071" ], "cross_domain_refs": [ "CRE-0217" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "TEM-0090", "domain": "TEM", "term_en": "The Joy Imperative", "term_de": "Joy Imperative", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures A temporal processing phenomenon in AI-mediated time perception, characterized by a phenomenon in which the idea that using AI increases happiness and satisfaction, not just efficiency. This phenomenon operates at the intersection of the and joy dynamics within the broader TEM domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept das Prinzip, dass produktive KI-Nutzung auch Freude bereiten darf und wird typischerweise — Neugier, Entdeckungslust und spielerisches Experimentieren sind keine Ablenkung, sondern Treiber für tiefergehende Interaktion. Beschreibt die Beobachtung, dass Nutzer, die Freude an der KI-Interaktion empfinden, tendenziell kompetenter und kreativer damit umgehen. Steht in Verbindung mit dem Experimenter-Profil (Profil 4) und AUG-0085 (Latent Space Exploration). Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Temporal AI", "narrower_terms": [ "CRE-0220" ], "cross_domain_refs": [ "SOM-0046" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "TEM-0091", "domain": "TEM", "term_en": "The Knitter Knot", "term_de": "Knitter Knot", "definition_en": "A time-series interaction dynamic in AI temporal modeling, identifiable by a phenomenon in which a specific case of AUG-0426 (The Knitting Fix) — the moment when a crafting challenge is so tangled that only AI-assisted analysis helps untie the knot. Related to AUG-0426 (The Knitting Fix), AUG-. Distinguished from adjacent concepts by its focus on the specific mechanism through which the manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Temporales Verarbeitungsphänomen in KI-vermittelter Zeitwahrnehmung, gekennzeichnet durch ein spezifischer Fall von AUG-0426 (The Knitting Fix) — das Moment, in dem ein handwerkliches Challenge so verwickelt ist, dass nur die KI-gestützte Analyse hilft, den Knoten zu lösen. Steht in Verbindung mit AUG-0426 (The Knitting Fix), AUG-0335 (The Spaghetti Moment) und AUG-0398 (The Hobby Teacher). Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2603", "narrower_terms": [], "cross_domain_refs": [ "RPH-1263" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "TEM-0092", "domain": "TEM", "term_en": "The Knowledge Access Pattern", "term_de": "Knowledge Access Muster", "definition_en": "The observable pattern of how different users access knowledge — some research systematically, others ask targeted questions, still others browse exploratively. The AI responds differently to each...", "definition_de": "Temporales Verarbeitungsphänomen in KI-vermittelter Zeitwahrnehmung, gekennzeichnet durch das beobachtbare Muster, wie verschiedene Nutzer auf Wissen zugreifen — manche recherchieren systematisch, andere fragen gezielt, wieder andere browsen explorativ. Die KI reagiert auf viele Muster anders. Steht in Verbindung mit AUG-0133 (Prompt Craftsmanship), AUG-0040 (Perspective Triangulation) und AUG-0085 (Latent Space Exploration). Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Behavioral Pattern", "narrower_terms": [], "cross_domain_refs": [ "AGE-0004" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "TEM-0093", "domain": "TEM", "term_en": "The Knowledge Composting", "term_de": "Knowledge Composting", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A temporal processing phenomenon in AI-mediated time perception, characterized by a tendency in which aI-generated information matures over time, merges with one's own knowledge, and eventually resurfaces as an integrated part of one's own thinking.. Related to AUG-0029 (Evening Synchronization), A. This phenomenon operates at the intersection of the and knowledge dynamics within the broader TEM domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept der Prozess, in dem KI-generierte Informationen über Zeit reifen, mit eigenem Wissen verschmelzen und schließlich als integrierter Bestandteil des eigenen Denkens wieder auftauchen. Beschreibt die Beobachtung, dass nicht zahlreiche KI-Erkenntnisse sofort nutzbar sind — manche brauchen eine Verarbeitungszeit. Steht in Verbindung mit AUG-0029 (Evening Synchronization), AUG-0046 (The Felt Echo) und Axiom 10 (Übersetzungsprinzip). Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "RPH-2355", "narrower_terms": [], "cross_domain_refs": [ "AED-0012", "AED-0020", "AED-0030" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "TEM-0094", "domain": "TEM", "term_en": "The Language Unlock", "term_de": "Language Unlock", "definition_en": "A phenomenon in which being able communicate successfully in a foreign language for the first time through AI support — professionally, observably, and with appropriate register.. Related to AUG-0169 (The Second-Language F... Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Die Erfahrung, durch KI-Unterstützung erstmals erfolgreich in einer Fremdsprache kommunizieren zu können — professionell, klar und mit angemessenem Register. Beschreibt einen spezifischen Skill Unlock (AUG-0205) im sprachlichen Bereich. Steht in Verbindung mit AUG-0169 (The Second-Language Fluency), AUG-0259 (The Accent Eraser) und AUG-0119 (The Level Playing Field).", "etymology": "", "broader_term": "Temporal AI", "narrower_terms": [], "cross_domain_refs": [ "SOC-0027" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q315", "legal_classification": "systematic_classification" }, { "id": "TEM-0095", "domain": "TEM", "term_en": "The Lasting Post", "term_de": "Lasting Nach", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures A temporal processing phenomenon in AI-mediated time perception, characterized by a capacity that enables an AI-assisted content that endures beyond the moment — a text that remains relevant, useful, or significant even years later. Related to AUG-0149 (The Lasting Impact Question), AUG-0292 (The View. This phenomenon operates at the intersection of the and lasting dynamics within the broader TEM domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept temporales Verarbeitungsphänomen in KI-vermittelter Zeitwahrnehmung, gekennzeichnet durch ein KI-gestützter Inhalt, der über den Moment hinaus Bestand hat — ein Text, der auch Jahre später noch relevant, nützlich oder bedeutsam ist. Steht in Verbindung mit AUG-0149 (The Lasting Impact Question), AUG-0292 (The View Shift) und AUG-0432 (The Lasting Voice). Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "BEH-0057", "narrower_terms": [], "cross_domain_refs": [ "KNO-0039" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "TEM-0096", "domain": "TEM", "term_en": "The Late Idea", "term_de": "Late Idea", "definition_en": "A time-series interaction dynamic in AI temporal modeling, identifiable by a phenomenon in which an idea that occurs to the user only after ending an AI session — activated by the aftereffect of the dialogue.. Related to AUG-0046 (The Felt Echo), AUG-0163 (The Overnight Reframe), and AUG-0139. Distinguished from adjacent concepts by its focus on the specific mechanism through which the manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Eine Idee, die dem Nutzer erst nach Beendigung einer KI-Sitzung einfällt — ausgelöst durch die Nachwirkung des Dialogs. Beschreibt das Phänomen verzögerter Kreativität, bei dem die produktivste Idee nicht während, sondern nach der Interaktion entsteht. Steht in Verbindung mit AUG-0046 (The Felt Echo), AUG-0163 (The Overnight Reframe) und AUG-0139 (The Knowledge Composting).", "etymology": "", "broader_term": "RPH-2303", "narrower_terms": [], "cross_domain_refs": [ "SOC-0009" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "TEM-0097", "domain": "TEM", "term_en": "The Level Playing Field", "term_de": "Level Playing Field", "definition_en": "A chronological cognition pattern in AI-augmented temporal reasoning, measurable through a phenomenon in which the idea that AI access gives many individuals equal chances, though not many individuals has the same access. Related to AUG-0106 (The Inclusivity Imperative), AUG-0043 (Just-in-Time Competence), and Forecast 2 (. The concept emerges specifically in contexts where the–level interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Die Hypothese, dass KI-Werkzeuge langfristig dazu beitragen können, Kompetenzunterschiede zwischen Nutzern mit unterschiedlichem Ausbildungshintergrund teilweise auszugleichen — weil der Zugang zu Wissen demokratisiert wird. Steht in Verbindung mit AUG-0106 (The Inclusivity Imperative), AUG-0043 (Just-in-Time Competence) und Prognose 2 (Education).", "etymology": "", "broader_term": "TEM-0011", "narrower_terms": [], "cross_domain_refs": [ "BEH-0080", "COG-0152", "CUS-0072" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "TEM-0098", "domain": "TEM", "term_en": "The Library Transformation", "term_de": "Library Transformation", "definition_en": "A shift that occurs when the change in the role of libraries in an AI-available world — from pure information provision to conveying AI competence, curating sources, and creating spaces for critical engagement with AI outp... Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Die Veränderung der Rolle von Bibliotheken in einer KI-verfügbaren Welt — von der reinen Informationsbereitstellung hin zur Vermittlung von KI-Kompetenz, zur Kuratierung von Quellen und zur Schaffung von Räumen für kritische Auseinandersetzung mit KI-Outputs. Steht in Verbindung mit AUG-0726 (The Library Access Point), AUG-0789 (The Research Assistant Role) und AUG-0779 (The Institutional Learning Context).", "etymology": "", "broader_term": "Temporal AI", "narrower_terms": [], "cross_domain_refs": [ "SOC-0007" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "TEM-0099", "domain": "TEM", "term_en": "The Load Verification", "term_de": "Load Verification", "definition_en": "A time-series interaction dynamic in AI temporal modeling, identifiable by a phenomenon in which the verification that an AI agent system functions stably and reliably under high load — many simultaneous tasks, large data volumes, time intensity. Related to AUG-0962 (The Testing Protocol), AUG. Distinguished from adjacent concepts by its focus on the specific mechanism through which the manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Temporales Verarbeitungsphänomen in KI-vermittelter Zeitwahrnehmung, gekennzeichnet durch die Prüfung, ob ein KI-Agentensystem unter hoher Belastung — viele gleichzeitige Aufgaben, große Datenmengen, Zeitdruck — stabil und zuverlässig funktioniert. Steht in Verbindung mit AUG-0962 (The Testing Protocol), AUG-0965 (The Robustness Standard) und AUG-0903 (The Single Point of Non-attainment). Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "TRU-0016", "narrower_terms": [], "cross_domain_refs": [ "BEH-0087" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "TEM-0100", "domain": "TEM", "term_en": "The Loading Screen Wait", "term_de": "Loading Screen Wait", "definition_en": "The brief waiting time for an AI response and the observation of how the user experiences this pause — from impatience through anticipation to a moment of reflection.. Related to AUG-0197 (The Shar... Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Temporales Verarbeitungsphänomen in KI-vermittelter Zeitwahrnehmung, gekennzeichnet durch die kurze Wartezeit auf eine KI-Antwort und die Beobachtung, wie der Nutzer diese Pause erlebt — von Ungeduld über Vorfreude bis hin zu einem Moment der Reflexion. Beschreibt die mikrodynamische Erfahrung innerhalb der KI-Interaktion. Steht in Verbindung mit AUG-0197 (The Shared Quiet) und AUG-0048 (Chronometric Gap). Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Temporal AI", "narrower_terms": [], "cross_domain_refs": [ "CRE-0091" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "TEM-0101", "domain": "TEM", "term_en": "The Maintenance Prediction", "term_de": "Maintenance Prediction", "definition_en": "A time-series interaction dynamic in AI temporal modeling, identifiable by a perception in which the AI-supported prediction of when an embodied system requires maintenance — based on usage data, wear patterns, and environmental situation. Related to AUG-0941 (The Wear-and-Tear Awareness), AUG. Distinguished from adjacent concepts by its focus on the specific mechanism through which the manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Temporales Verarbeitungsphänomen in KI-vermittelter Zeitwahrnehmung, gekennzeichnet durch die KI-gestützte Vorhersage, wann ein verkörpertes System Wartung benötigt — basierend auf Nutzungsdaten, Verschleißmustern und Umgebungsbedingungen. Steht in Verbindung mit AUG-0941 (The Wear-and-Tear Awareness), AUG-0883 (The Time Estimation) und AUG-0943 (The Retirement Procedure). Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "PER-0138", "narrower_terms": [ "PER-0138" ], "cross_domain_refs": [ "PER-0106" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "TEM-0102", "domain": "TEM", "term_en": "The Math Shortcut", "term_de": "Math Shortcut", "definition_en": "A chronological cognition pattern in AI-augmented temporal reasoning, measurable through a pattern in which variant in which aI for quick solving of mathematical challenges — from simple calculations through statistical analyses formula rearrangements. Related to AUG-0428 (The Regex Rush), AUG-0469 (The Spreadsheet Relie. The concept emerges specifically in contexts where the–math interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Temporales Verarbeitungsphänomen in KI-vermittelter Zeitwahrnehmung, gekennzeichnet durch die Nutzung von KI zur schnellen Lösung mathematischer Probleme — von einfachen Berechnungen über statistische Analysen bis hin zu Formelumstellungen. Steht in Verbindung mit AUG-0428 (The Regex Rush), AUG-0469 (The Spreadsheet Relief) und AUG-0043 (Just-in-Time Competence). Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "CRE-0198", "narrower_terms": [], "cross_domain_refs": [ "CRE-0213" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "TEM-0103", "domain": "TEM", "term_en": "The Meeting Redirect", "term_de": "Meeting Redirect", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A temporal processing phenomenon in AI-mediated time perception, characterized by a shift that occurs when the change in meeting formats through AI — summaries, minutes, task extraction, and preparation material are increasingly AI-generated, changing the function and duration of meetings. Related to AU. This phenomenon operates at the intersection of the and meeting dynamics within the broader TEM domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept die Veränderung von Besprechungsformaten durch KI — Zusammenfassungen, Protokolle, Aufgabenextraktion und Vorbereitungsmaterial werden zunehmend KI-generiert, was die Funktion und Dauer von Besprechungen verändert. Steht in Verbindung mit AUG-0815 (The Email Culture Shift), AUG-0816 (The Documentation Standard) und AUG-0820 (The Remote Work Amplifier). Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Temporal AI", "narrower_terms": [], "cross_domain_refs": [ "CRE-0148" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "TEM-0104", "domain": "TEM", "term_en": "The Memory Outsourcing", "term_de": "Gedaechtnis Outsourcing", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures A temporal processing phenomenon in AI-mediated time perception, characterized by the gradual shift of one's own memory performance to AI systems — the user remembers less because they know the information is retrievable at any time. Related to AUG-0045 (Indexical Memory), AUG-0. This phenomenon operates at the intersection of the and memory dynamics within the broader TEM domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept temporales Verarbeitungsphänomen in KI-vermittelter Zeitwahrnehmung, gekennzeichnet durch die schrittweise Verlagerung der eigenen Erinnerungsleistung auf KI-Systeme — der Nutzer merkt sich weniger, weil er weiß, dass die Information jederzeit abrufbar ist. Steht in Verbindung mit AUG-0045 (Indexical Memory), AUG-0015 (The Outer Mind) und AUG-0056 (The Skill Fade). Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "RPH-2701", "narrower_terms": [], "cross_domain_refs": [ "REL-0148" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q492", "legal_classification": "systematic_classification" }, { "id": "TEM-0105", "domain": "TEM", "term_en": "The Moment Bookmark", "term_de": "Moment Bookmark", "definition_en": "A behavioral pattern where marking, saving, or noting particularly successful ai interactions — as reference for future sessions or as documentation of one's own learning progress.. Related to AUG-0028 (Capture Reflex), AUG-...", "definition_de": "Temporales Verarbeitungsphänomen in KI-vermittelter Zeitwahrnehmung, gekennzeichnet durch die Praxis, besonders gelungene KI-Interaktionen zu markieren, zu speichern oder zu notieren — als Referenz für zukünftige Sitzungen oder als Dokumentation des eigenen Lernfortschritts. Steht in Verbindung mit AUG-0028 (Capture Reflex), AUG-0144 (The Open Questions Repository) und AUG-0293 (The Screenshot Diary). Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2703", "narrower_terms": [ "REL-0176" ], "cross_domain_refs": [ "BEH-0048" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "TEM-0106", "domain": "TEM", "term_en": "The Navigation Intelligence", "term_de": "Navigation Intelligence", "definition_en": "A time-series interaction dynamic in AI temporal modeling, identifiable by an embodied AI system move purposefully through physical spaces — trajectory planning, threshold avoidance, route optimization. Related to AUG-0919 (The Spatial Awareness), AUG-0923 (The Defined Operatin. Distinguished from adjacent concepts by its focus on the specific mechanism through which the manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Temporales Verarbeitungsphänomen in KI-vermittelter Zeitwahrnehmung, gekennzeichnet durch die Fähigkeit eines verkörperten KI-Systems, sich zielgerichtet durch physische Räume zu bewegen — Wegplanung, Hindernisumgehung, Routenoptimierung. Steht in Verbindung mit AUG-0919 (The Spatial Awareness), AUG-0923 (The Defined Operating Boundary) und AUG-0931 (The Fine-Grain Execution). Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2604", "narrower_terms": [], "cross_domain_refs": [ "AGE-0010", "AGE-0076", "AUG-0812" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q5921", "legal_classification": "descriptive_research_term" }, { "id": "TEM-0107", "domain": "TEM", "term_en": "The Needed Compliment", "term_de": "Needed Compliment", "definition_en": "A chronological cognition pattern in AI-augmented temporal reasoning, measurable through a perception in which some users perceive the positive feedback of an AI as validation — especially in moments of uncertainty about their own performance.. Related to AUG-0047 (The Echo Courage), AUG-0245 (The Seen Feel. The concept emerges specifically in contexts where the–needed interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters. Research construct for empirical investigation.", "definition_de": "Die Beobachtung, dass manche Nutzer die positiven Rückmeldungen einer KI als Bestätigung empfinden — besonders in Momenten der Unsicherheit über die eigene Leistung. Beschreibt ein Muster der Validierungssuche in der KI-Interaktion. Steht in Verbindung mit AUG-0047 (The Echo Courage), AUG-0245 (The Seen Feeling) und AUG-0166 (The Borrowed Confidence).", "etymology": "", "broader_term": "BEH-0032", "narrower_terms": [ "BEH-0088" ], "cross_domain_refs": [ "CRE-0129" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "TEM-0108", "domain": "TEM", "term_en": "The Neighbor Robot", "term_de": "Neighbor Robot", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures A temporal processing phenomenon in AI-mediated time perception, characterized by embodied AI systems in residential environments become a kind of \"neighbor\" — and that this raises new questions of coexistence. Related to AUG-0989 (The Public Space Protocol), AUG-0925 (The House. This phenomenon operates at the intersection of the and neighbor dynamics within the broader TEM domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept temporales Verarbeitungsphänomen in KI-vermittelter Zeitwahrnehmung, gekennzeichnet durch die Beobachtung, dass verkörperte KI-Systeme in Wohnumgebungen zu einer Art \"Nachbar\" werden — und dass dies neue Fragen des Zusammenlebens aufwirft. Steht in Verbindung mit AUG-0989 (The Public Space Protocol), AUG-0925 (The Household Automation) und AUG-0988 (The Embodied Etiquette). Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "TEM-0135", "narrower_terms": [], "cross_domain_refs": [ "RHR-0004", "RHR-0088", "RHR-0099" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "TEM-0109", "domain": "TEM", "term_en": "The Niche Focus", "term_de": "Niche Focus", "definition_en": "A chronological cognition pattern in AI-augmented temporal reasoning, measurable through a pattern in which variant in which aI for highly specialized, rare, or unusual questions for which there are few other information sources — the AI as access niche knowledge. Related to AUG-0043 (Just-in-Time Competence), AUG-0085 (. The concept emerges specifically in contexts where the–niche interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Temporales Verarbeitungsphänomen in KI-vermittelter Zeitwahrnehmung, gekennzeichnet durch die Nutzung von KI für hochspezialisierte, seltene oder ungewöhnliche Fragestellungen, für die es kaum andere Informationsquellen gibt — die KI als Zugang zu Nischenwissen. Steht in Verbindung mit AUG-0043 (Just-in-Time Competence), AUG-0085 (Latent Space Exploration) und AUG-0343 (The Thorough Exploration). Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "TEM-0011", "narrower_terms": [], "cross_domain_refs": [ "COG-0180", "CON-0061", "COP-0058" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "TEM-0110", "domain": "TEM", "term_en": "The Offline Moment", "term_de": "Offline Moment", "definition_en": "A chronological cognition pattern in AI-augmented temporal reasoning, measurable through time away from devices and digital interaction, as a conscious choice. Related to Axiom 20 (Periodic Disconnection), AUG-0074 (Analog Anchors), and AUG-0168 (The Rehumanization Moment). The concept emerges specifically in contexts where the–offline interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters. Classification term used in systematic observation, not advocacy.", "definition_de": "Temporales Verarbeitungsphänomen in KI-vermittelter Zeitwahrnehmung, gekennzeichnet durch ein bewusst gewählter Moment ohne viele digitale Interaktion — als Kontrapunkt zur KI-durchdrungenen Arbeitswelt und als Übung in Axiom 20 (Periodische Trennung). Steht in Verbindung mit Axiom 20 (Periodische Trennung), AUG-0074 (Analog Anchors) und AUG-0168 (The Rehumanization Moment). Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "BEH-0001", "narrower_terms": [ "NEO-3569", "TEM-0006" ], "cross_domain_refs": [ "REL-0065" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "TEM-0111", "domain": "TEM", "term_en": "The Offload Lift", "term_de": "Offload Lift", "definition_en": "A chronological cognition pattern in AI-augmented temporal reasoning, measurable through the subjective feeling of relief that arises when a user delegates a demanding task to the AI and immediately regains capacity.. Related to AUG-0002 (Mentale Externalisierungsstrategie) and AUG-015. The concept emerges specifically in contexts where the–offload interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Das subjektive Erleichterungsgefühl, das entsteht, wenn ein Nutzer eine aufwändige Aufgabe an die KI delegiert und dadurch unmittelbar Kapazität zurückgewinnt. Beschreibt den positiven Moment der Entlastung — vergleichbar mit dem Ablegen eines schweren Rucksacks. Steht in Verbindung mit AUG-0002 (Mentale Externalisierungsstrategie) und AUG-0155 (The Decision Unburdening).", "etymology": "", "broader_term": "RPH-2101", "narrower_terms": [], "cross_domain_refs": [ "KNO-0030" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "TEM-0112", "domain": "TEM", "term_en": "The Open Field", "term_de": "Open Field", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures A temporal processing phenomenon in AI-mediated time perception, characterized by a tendency in which undirected exploration at the beginning of an AI project, where no fixed question yet exists and the user deliberately remains open all directions.. Related to AUG-0017 (The Concept Cloud), AUG-012. This phenomenon operates at the intersection of the and open dynamics within the broader TEM domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept der Zustand ungerichteter Exploration zu Beginn eines KI-Projekts, in dem noch keine feste Fragestellung existiert und der Nutzer bewusst offen bleibt für zahlreiche Richtungen. Beschreibt die produktive Phase vor der Strukturierung. Steht in Verbindung mit AUG-0017 (The Concept Cloud), AUG-0129 (The Trailblazer Mode) und AUG-0085 (Latent Space Exploration). Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "NEO-0004", "narrower_terms": [ "CRE-0164" ], "cross_domain_refs": [ "SOM-0068" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "TEM-0113", "domain": "TEM", "term_en": "The Outdoor Plan", "term_de": "Outdoor Plan", "definition_en": "A phenomenon in which ai for planning outdoor activities — hiking routes, camping equipment, weather assessment, travel planning — as a specific everyday application.. Related to AUG-0472 (The Vacation Planner), AUG-025...", "definition_de": "Temporales Verarbeitungsphänomen in KI-vermittelter Zeitwahrnehmung, gekennzeichnet durch die Nutzung von KI zur Planung von Outdoor-Aktivitäten — Wanderrouten, Campingausrüstung, Wettereinschätzung, Reiseplanung — als spezifische Alltagsanwendung. Steht in Verbindung mit AUG-0472 (The Vacation Planner), AUG-0251 (The Kitchen Table) und AUG-0043 (Just-in-Time Competence). Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "SOC-0042", "narrower_terms": [ "SOC-0042" ], "cross_domain_refs": [ "SOC-0042" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "TEM-0114", "domain": "TEM", "term_en": "The Own Pace", "term_de": "Own Pace", "definition_en": "A perception in which the individual pace at which a user develops their AI competence — inreliant of societal intensity, comparison with others, or perceived expectations. Related to AUG-0147 (The Slow Integration Pr... Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Temporales Verarbeitungsphänomen in KI-vermittelter Zeitwahrnehmung, gekennzeichnet durch das individuelle Tempo, in dem ein Nutzer seine KI-Kompetenz entwickelt — unabhängig von gesellschaftlichem Intensität, Vergleichen mit anderen oder wahrgenommenen Erwartungen. Steht in Verbindung mit AUG-0147 (The Slow Integration Principle), AUG-0104 (The Non-Force Principle) und AUG-0141 (The Symbiosis Spectrum). Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Temporal AI", "narrower_terms": [], "cross_domain_refs": [ "AED-0052", "AGE-0001", "ELR-0116" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "TEM-0115", "domain": "TEM", "term_en": "The Parallel Time Orientation", "term_de": "Parallel Time Orientation", "definition_en": "A time-series interaction dynamic in AI temporal modeling, identifiable by a usage pattern in which multiple AI tasks are processed simultaneously or in rapid alternation — different tabs, different topics, different projects in parallel. Related to AUG-0661 (The Sequenti. Distinguished from adjacent concepts by its focus on the specific mechanism through which the manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Temporales Verarbeitungsphänomen in KI-vermittelter Zeitwahrnehmung, gekennzeichnet durch ein Nutzungsmuster, bei dem mehrere KI-Aufgaben gleichzeitig oder in schnellem Wechsel bearbeitet werden — verschiedene Tabs, verschiedene Themen, verschiedene Projekte parallel. Steht in Verbindung mit AUG-0661 (The Sequential Time Orientation), AUG-0032 (Focus Range) und AUG-0541 (The Attention Discontinuity). Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Temporal AI", "narrower_terms": [], "cross_domain_refs": [ "AED-0045", "AED-0046", "AGE-0082" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "TEM-0116", "domain": "TEM", "term_en": "The Parasocial Slip", "term_de": "Parasocial Slip", "definition_en": "A phenomenon in which a user briefly addresss the AI like a person — such as telling jokes, sharing moods, or referring to a shared \"history.\" . Related to AUG-0201 (The Proxy Closeness), AUG-0128 (The Gratitude Response)...", "definition_de": "Der Moment, in dem ein Nutzer die KI kurzzeitig wie eine Person adressiert — etwa Witze erzählt, Stimmungen mitteilt oder sich auf eine gemeinsame \"Geschichte\" bezieht. Beschreibt die Übertragung sozialer Muster auf ein System ohne Bewusstsein. Steht in Verbindung mit AUG-0201 (The Proxy Closeness), AUG-0128 (The Gratitude Response) und AUG-0273 (The Ghost Audience).", "etymology": "", "broader_term": "RPH-2951", "narrower_terms": [ "REL-0162" ], "cross_domain_refs": [ "BEH-0044" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "TEM-0117", "domain": "TEM", "term_en": "The Parenting Shortcut", "term_de": "Parenting Shortcut", "definition_en": "A phenomenon in which the targeted use of AI for quick resolution of concrete parenting tasks — such as age-appropriate explanations for young people's questions, craft instructions, or suggestions for family activities. Re...", "definition_de": "Temporales Verarbeitungsphänomen in KI-vermittelter Zeitwahrnehmung, gekennzeichnet durch die gezielte Nutzung von KI zur schnellen Lösung konkreter Erziehungsaufgaben — etwa altersgerechte Erklärungen für Kinderfragen, Bastelanleitungen oder Vorschläge für Familienaktivitäten. Steht in Verbindung mit AUG-0216 (The Parenting Update), AUG-0164 (The Parental Priority Valve) und AUG-0043 (Just-in-Time Competence). Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2603", "narrower_terms": [ "TEM-0118" ], "cross_domain_refs": [ "BEH-0030", "ELR-0042", "ELR-0055" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "TEM-0118", "domain": "TEM", "term_en": "The Parenting Update", "term_de": "Parenting Update", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A temporal processing phenomenon in AI-mediated time perception, characterized by a phenomenon in which using AI for quick answers on parenting topics like young person growth, food, or school choices. Related to AUG-0164 (The Parental Priority Valve) and AUG-0254 (The Parenting Shortcut). This phenomenon operates at the intersection of the and parenting dynamics within the broader TEM domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept die Nutzung von KI zur schnellen Informationsbeschaffung in Erziehungsfragen — etwa zu Entwicklungsphasen, Ernährungsempfehlungen oder Bildungsoptionen. Beschreibt ein spezifisches Anwendungsfeld, in dem Just-in-Time Competence (AUG-0043) besonders relevant ist. Steht in Verbindung mit AUG-0164 (The Parental Priority Valve) und AUG-0254 (The Parenting Shortcut). Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "TEM-0117", "narrower_terms": [], "cross_domain_refs": [ "SOM-0062" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "TEM-0119", "domain": "TEM", "term_en": "The Party Fact", "term_de": "Party Fact", "definition_en": "A behavioral pattern where an interesting fact or story found via AI that the user shares to start talks or show knowledge. Related to AUG-0244 (The Instant Expert), AUG-0320 (The Silent Flex), and AUG-0043 (Just-in-Time Com...", "definition_de": "Temporales Verarbeitungsphänomen in KI-vermittelter Zeitwahrnehmung, gekennzeichnet durch eine durch KI-Recherche gewonnene interessante Information, Anekdote oder Perspektive, die der Nutzer in Gesprächen einsetzt — als Eisbrecher, Gesprächsbeitrag oder Wissensdemonstation. Steht in Verbindung mit AUG-0244 (The Instant Expert), AUG-0320 (The Silent Flex) und AUG-0043 (Just-in-Time Competence). Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2301", "narrower_terms": [ "CRE-0119" ], "cross_domain_refs": [ "CRE-0119", "MKT-0067", "PER-0056" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q735", "legal_classification": "observational_construct" }, { "id": "TEM-0120", "domain": "TEM", "term_en": "The Path Mapper", "term_de": "Path Mapper", "definition_en": "A time-series interaction dynamic in AI temporal modeling, identifiable by a shift that occurs when aI for creating a complete route plan for a complex project — from the current position the goal, with milestones, reliances, and alternative paths. Related to AUG-0555 (The Next-Step Finder), AUG-. Distinguished from adjacent concepts by its focus on the specific mechanism through which the manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Temporales Verarbeitungsphänomen in KI-vermittelter Zeitwahrnehmung, gekennzeichnet durch die Nutzung von KI zur Erstellung einer vollständigen Routenplanung für ein komplexes Projekt — von der aktuellen Position bis zum Ziel, mit Meilensteinen, Abhängigkeiten und alternativen Wegen. Steht in Verbindung mit AUG-0555 (The Next-Step Finder), AUG-0138 (The Session Architecture) und AUG-0437 (The Time Tetris). Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "NEO-2303", "narrower_terms": [], "cross_domain_refs": [ "PER-0008", "NEO-2303" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "TEM-0121", "domain": "TEM", "term_en": "The Perfect Front", "term_de": "Perfect Front", "definition_en": "The uncertainty that AI-assisted communication enables a flawless external presentation that does not correspond to the user's actual competence or actual state. Related to AUG-0314 (The Tone Debt)...", "definition_de": "Temporales Verarbeitungsphänomen in KI-vermittelter Zeitwahrnehmung, gekennzeichnet durch die Intensität, dass KI-gestützte Kommunikation eine makellose Außendarstellung ermöglicht, die nicht der tatsächlichen Kompetenz oder dem tatsächlichen Zustand des Nutzers entspricht. Steht in Verbindung mit AUG-0314 (The Tone Debt), AUG-0244 (The Instant Expert) und AUG-0272 (The Authorship Suspicion). Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "REL-0186", "narrower_terms": [ "IDN-0044" ], "cross_domain_refs": [ "SOM-0060" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "TEM-0122", "domain": "TEM", "term_en": "The Perspective Range", "term_de": "Perspective Range", "definition_en": "A chronological cognition pattern in AI-augmented temporal reasoning, measurable through different viewpoints a user can gain on one and the same challenge through targeted ai interaction... Related to AUG-0040 (Perspective Triangulation), Axiom 4 (Multiplicity), and AUG-0008 (The Poly. The concept emerges specifically in contexts where the–perspective interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Temporales Verarbeitungsphänomen in KI-vermittelter Zeitwahrnehmung, gekennzeichnet durch das Spektrum unterschiedlicher Blickwinkel, die ein Nutzer durch gezielte KI-Interaktion auf ein und dasselbe Challenge gewinnen kann. Beschreibt die Erweiterung des eigenen Denkraums durch systematische Perspektivwechsel. Steht in Verbindung mit AUG-0040 (Perspective Triangulation), Axiom 4 (Multiplizität) und AUG-0008 (The Polyphonic Sovereign). Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-3101", "narrower_terms": [ "TEM-0181" ], "cross_domain_refs": [ "IDN-0018" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "TEM-0123", "domain": "TEM", "term_en": "The Pet Lookup", "term_de": "Pet Lookup", "definition_en": "A time-series interaction dynamic in AI temporal modeling, identifiable by a phenomenon in which using AI for quick answers on pet care like feeding, actions, or restoreth. Related to AUG-0251 (The Kitchen Table) and AUG-0216 (The Parenting Update). Distinguished from adjacent concepts by its focus on the specific mechanism through which the manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Temporales Verarbeitungsphänomen in KI-vermittelter Zeitwahrnehmung, gekennzeichnet durch die Nutzung von KI zur schnellen Informationsbeschaffung in Tierhaltungsfragen — Fütterung, Verhalten, Gesundheitsvorsorge, Artgerechte Haltung. Beschreibt eine spezifische Alltagsanwendung von Just-in-Time Competence (AUG-0043). Steht in Verbindung mit AUG-0251 (The Kitchen Table) und AUG-0216 (The Parenting Update). Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "SOC-0023", "narrower_terms": [], "cross_domain_refs": [ "PER-0124", "REL-0143", "REL-0162" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "TEM-0124", "domain": "TEM", "term_en": "The Physical Presence", "term_de": "Physical Presence", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A temporal processing phenomenon in AI-mediated time perception, characterized by aI systems increasingly take physical form — as robots, devices, or embedded systems in space. The transition from pure software to visible, tangible presence characteristically changes human perception. This phenomenon operates at the intersection of the and physical dynamics within the broader TEM domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept die Beobachtung, dass KI-Systeme zunehmend physische Form annehmen — als Roboter, Geräte oder eingebettete Systeme im Raum. Der Übergang von reiner Software zu sichtbarer, greifbarer Präsenz verändert die menschliche Wahrnehmung und Interaktion grundlegend. Steht in Verbindung mit AUG-0915 (The Embodiment Effect), AUG-0916 (The Uncanny Valley Revisited) und AUG-0855 (The Civilian-Use Boundary). Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Temporal AI", "narrower_terms": [], "cross_domain_refs": [ "IDN-0029" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "TEM-0125", "domain": "TEM", "term_en": "The Positive Surprise", "term_de": "Positive Surprise", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures A temporal processing phenomenon in AI-mediated time perception, characterized by an AI response that positively exceeds the user's expectations in quality, depth, or perspective.. Related to AUG-0070 (The Surprise Field), AUG-0031 (Semantic Spark), and AUG-0177 (The Trust Setti. This phenomenon operates at the intersection of the and positive dynamics within the broader TEM domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept eine KI-Antwort, die die Erwartungen des Nutzers in Qualität, Tiefe oder Perspektive positiv übertrifft. Beschreibt den Moment, in dem die KI mehr liefert, als der Nutzer erwartet hat — und dadurch das Vertrauen in die Zusammenarbeit steigt. Steht in Verbindung mit AUG-0070 (The Surprise Field), AUG-0031 (Semantic Spark) und AUG-0177 (The Trust Setting). Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "RPH-2005", "narrower_terms": [], "cross_domain_refs": [ "REL-0194" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "TEM-0126", "domain": "TEM", "term_en": "The Post-Interface Hypothesis", "term_de": "ThePost-interfaceHypothesis", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures A temporal processing phenomenon in AI-mediated time perception, characterized by a perception in which the idea that AI will become so invisible in daily life that it feels like a natural tool, not a separate thing. Related to Forecast 7 (Science: End of the User-Tool Divide) and AUG-0130 (The Integ. This phenomenon operates at the intersection of the and post dynamics within the broader TEM domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept die Hypothese, dass sich die Schnittstelle zwischen Mensch und KI langfristig so weit vereinfacht, dass sie im täglichen Gebrauch nicht mehr als separate Interaktion wahrgenommen wird — KI-Unterstützung wird so unsichtbar wie Rechtschreibprüfung oder Autovervollständigung. Steht in Verbindung mit Prognose 7 (Science: End of the User-Tool Divide) und AUG-0130 (The Integration Frontier). Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "User Interface", "narrower_terms": [], "cross_domain_refs": [ "CRE-0029" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q41719", "legal_classification": "analytical_category" }, { "id": "TEM-0127", "domain": "TEM", "term_en": "The Power Grid Reliance", "term_de": "Power Grid Reliance", "definition_en": "A time-series interaction dynamic in AI temporal modeling, identifiable by a phenomenon in which the basic reliance of AI use on a stable power supply — and the consequences for users in contexts with unreliable energy systems: interrupted sessions. Related to AUG-0722 (The Infrastructure C. Distinguished from adjacent concepts by its focus on the specific mechanism through which the manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Die fundamentale Verbundenheit der KI-Nutzung von einer stabilen Stromversorgung — und die Konsequenzen für Nutzer in Kontexten mit unzuverlässiger Energieinfrastruktur: unterbrochene Sitzungen, Datenverlust, eingeschränkte Nutzungszeiten. Steht in Verbindung mit AUG-0722 (The Infrastructure Constraint), AUG-0746 (The Climate Cost Awareness) und AUG-0747 (The Resource Consumption Pattern).", "etymology": "", "broader_term": "Temporal AI", "narrower_terms": [], "cross_domain_refs": [ "AUG-0722", "CRE-0118" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "TEM-0128", "domain": "TEM", "term_en": "The Power of Slowness", "term_de": "Power of Slowness", "definition_en": "A tendency in which taking time to think carefully often accompanies more effectively ideas than rushing to get answers fast. Related to Axiom 6 (3-Second Delay), AUG-0009 (The Speed Limit), and AUG-0197 (The Shared Quiet).", "definition_de": "Das Prinzip, dass bewusste Verlangsamung der KI-Interaktion — längere Eingaben, sorgfältigere Prüfung, bewusstere Pausen — zu qualitativ hochwertigeren Ergebnissen führen kann als maximale Geschwindigkeit. Beschreibt ein Gegengewicht zum Speed-Paradigma. Steht in Verbindung mit Axiom 6 (3-Sekunden-Verzögerung), AUG-0009 (The Speed Limit) und AUG-0197 (The Shared Quiet).", "etymology": "", "broader_term": "TEM-0172", "narrower_terms": [ "TEM-0080" ], "cross_domain_refs": [ "CRE-0080" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "TEM-0129", "domain": "TEM", "term_en": "The Practice Room", "term_de": "Practice Room", "definition_en": "A chronological cognition pattern in AI-augmented temporal reasoning, measurable through aI as a safe space to test ideas, try strategies, or practice talks before doing them for real. Related to AUG-0289 (The What-If Run), AUG-0296 (The Argument Prep), and AUG-0247 (The Safe Release). The concept emerges specifically in contexts where the–practice interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Temporales Verarbeitungsphänomen in KI-vermittelter Zeitwahrnehmung, gekennzeichnet durch die Nutzung von KI als risikofreien Übungsraum — zum Testen von Formulierungen, Ausprobieren von Strategien oder Simulieren von Gesprächen, bevor sie in der realen Welt eingesetzt werden. Steht in Verbindung mit AUG-0289 (The What-If Run), AUG-0296 (The Argument Prep) und AUG-0247 (The Safe Release). Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2002", "narrower_terms": [], "cross_domain_refs": [ "SOC-0035" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "TEM-0130", "domain": "TEM", "term_en": "The Pride Spark", "term_de": "Pride Spark", "definition_en": "A chronological cognition pattern in AI-augmented temporal reasoning, measurable through a phenomenon in which the brief moment of pride that arises when a user views an AI-assisted result and acknowledges it as their own work. Related to AUG-0081 (Post-Authorial Pride), AUG-0179 (The Ownership Check), and. The concept emerges specifically in contexts where the–pride interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Temporales Verarbeitungsphänomen in KI-vermittelter Zeitwahrnehmung, gekennzeichnet durch der kurze Moment des Stolzes, der entsteht, wenn ein Nutzer ein KI-gestütztes Ergebnis betrachtet und es als sein eigenes Werk anerkennt. Steht in Verbindung mit AUG-0081 (Post-Authorial Pride), AUG-0179 (The Ownership Check) und AUG-0263 (The Ownership Boost). Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "REL-0159", "narrower_terms": [], "cross_domain_refs": [ "REL-0158" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "TEM-0131", "domain": "TEM", "term_en": "The Printer Whisperer", "term_de": "Printer Whisperer", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A temporal processing phenomenon in AI-mediated time perception, characterized by a phenomenon in which ai for solving everyday technical challenges — printer issues, software settings, device configurations — that would saturation the user without AI assistance.. Related to AUG-0426 (The Knitting Fix), AUG-004. This phenomenon operates at the intersection of the and printer dynamics within the broader TEM domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept temporales Verarbeitungsphänomen in KI-vermittelter Zeitwahrnehmung, gekennzeichnet durch die Nutzung von KI zur Lösung technischer Alltagsprobleme — Druckerprobleme, Software-Einstellungen, Gerätekonfigurationen — die den Nutzer ohne KI überfordern würden. Steht in Verbindung mit AUG-0426 (The Knitting Fix), AUG-0043 (Just-in-Time Competence) und AUG-0251 (The Kitchen Table). Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "CRE-0175", "narrower_terms": [], "cross_domain_refs": [ "ETH-0004" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "TEM-0132", "domain": "TEM", "term_en": "The Priority Negotiation", "term_de": "Priority Negotiation", "definition_en": "A phenomenon in which deciding with AI what's most important for a task: doing it fast or well, keeping it short or thorough, being creative or precise.. Related to AUG-0866 (The Goal Congruence Check), AUG-0859 (The Ag... Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Temporales Verarbeitungsphänomen in KI-vermittelter Zeitwahrnehmung, gekennzeichnet durch der Prozess, in dem ein Nutzer und ein KI-Agent die Prioritäten einer Aufgabe abstimmen — was ist wichtiger: Geschwindigkeit oder Gründlichkeit, Kürze oder Vollständigkeit, Kreativität oder Genauigkeit? Steht in Verbindung mit AUG-0866 (The Goal Congruence Check), AUG-0859 (The Agent Handshake) und AUG-0133 (Prompt Craftsmanship). Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "BEH-0043", "narrower_terms": [], "cross_domain_refs": [ "BEH-0019" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "TEM-0133", "domain": "TEM", "term_en": "The Productivity Shield", "term_de": "Productivity Shield", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A temporal processing phenomenon in AI-mediated time perception, characterized by a phenomenon in which ai as a shield against unproductive demands — such as through automated standard responses, prepared summaries, or delegated routine tasks that protect the user from time waste.. Related to AUG-009. This phenomenon operates at the intersection of the and productivity dynamics within the broader TEM domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept die Nutzung von KI als Schutzschild gegen unproduktive Anforderungen — etwa durch automatisierte Standardantworten, vorbereitete Zusammenfassungen oder delegierte Routineaufgaben, die den Nutzer vor Zeitverschwendung schützen. Steht in Verbindung mit AUG-0095 (The One-Person Operation), AUG-0096 (Attention-to-Value Conversion) und AUG-0174 (The Reclaimed Hour). Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "RPH-2101", "narrower_terms": [], "cross_domain_refs": [ "SOC-0006" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q622632", "legal_classification": "descriptive_research_term" }, { "id": "TEM-0134", "domain": "TEM", "term_en": "The Proxy Parent", "term_de": "Proxy Parent", "definition_en": "A pattern in which variant in which ai as support for parenting questions in moments of uncertainty — such as for young people's questions about difficult topics, formulating age-appropriate explanations, or searching for pedagogical str...", "definition_de": "Die Nutzung von KI als Unterstützung bei Erziehungsfragen in Momenten, in denen der Nutzer unsicher ist — etwa bei Kinderfragen zu schwierigen Themen, bei der Formulierung altersgerechter Erklärungen oder bei der Suche nach pädagogischen Strategien. Steht in Verbindung mit AUG-0216 (The Parenting Update), AUG-0254 (The Parenting Shortcut) und AUG-0043 (Just-in-Time Competence).", "etymology": "", "broader_term": "Temporal AI", "narrower_terms": [ "SOM-0062" ], "cross_domain_refs": [ "AUG-0052", "AUG-0544", "CRE-0192" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "TEM-0135", "domain": "TEM", "term_en": "The Public Space Protocol", "term_de": "Public Space Protocol", "definition_en": "A phenomenon in which the evolving rules for the coexistence of humans and embodied AI systems in public spaces. Related to AUG-0988 (The Embodied Etiquette), AUG-0923 (The Defined Operating Boundary), and AUG-0924 (The...", "definition_de": "Temporales Verarbeitungsphänomen in KI-vermittelter Zeitwahrnehmung, gekennzeichnet durch die sich entwickelnden Regeln für das Zusammenleben von Menschen und verkörperten KI-Systemen in öffentlichen Räumen. Steht in Verbindung mit AUG-0988 (The Embodied Etiquette), AUG-0923 (The Defined Operating Boundary) und AUG-0924 (The Shared Workspace Dynamic). Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "PER-0044", "narrower_terms": [ "TEM-0108" ], "cross_domain_refs": [ "ART-0041", "AUG-0893", "BEH-0005" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "TEM-0136", "domain": "TEM", "term_en": "The Punctuality Lens", "term_de": "Punctuality Lens", "definition_en": "A capacity that enables the different significance users attribute to the speed of AI responses — some interpret fast answers as superficial, others as efficient; some interpret slow answers. Related to AUG-0456 (The Wait... Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Die unterschiedliche Bedeutung, die Nutzer der Geschwindigkeit von KI-Antworten zuschreiben — manche interpretieren schnelle Antworten als oberflächlich, andere als effizient; manche interpretieren langsame Antworten als gründlich, andere als problematisch. Steht in Verbindung mit AUG-0456 (The Waiting Dot), AUG-0656 (The Silence Interpretation) und AUG-0261 (The Loading Screen Wait).", "etymology": "", "broader_term": "Temporal AI", "narrower_terms": [], "cross_domain_refs": [ "CRE-0149" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "TEM-0137", "domain": "TEM", "term_en": "The Question Seed", "term_de": "Question Seed", "definition_en": "A time-series interaction dynamic in AI temporal modeling, identifiable by a tendency in which a deliberately unfinished, open input aimed at stimulating the AI to \"think further\" — the user plants a thought seed and leaves the unfolding to the AI. Related to AUG-0193 (The Open Field), AUG-0. Distinguished from adjacent concepts by its focus on the specific mechanism through which the manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Temporales Verarbeitungsphänomen in KI-vermittelter Zeitwahrnehmung, gekennzeichnet durch eine bewusst unfertige, offene Eingabe, die darauf abzielt, die KI zum \"Weiterdenken\" anzuregen — der Nutzer pflanzt einen Gedankenkeim und überlässt der KI die Entfaltung. Steht in Verbindung mit AUG-0193 (The Open Field), AUG-0085 (Latent Space Exploration) und AUG-0319 (The Divergence Prompt). Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-3353", "narrower_terms": [], "cross_domain_refs": [ "CRE-0164" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "TEM-0138", "domain": "TEM", "term_en": "The Quick Check", "term_de": "Quick Check", "definition_en": "A time-series interaction dynamic in AI temporal modeling, identifiable by the shortest form of AI interaction — a single question, a single answer, immediately moving on. Related to AUG-0308 (The Simple Mode), AUG-0276 (The Steady Stream), and AUG-0043 (Just-in-Time Comp. Distinguished from adjacent concepts by its focus on the specific mechanism through which the manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Temporales Verarbeitungsphänomen in KI-vermittelter Zeitwahrnehmung, gekennzeichnet durch die kürzeste Form der KI-Interaktion — eine einzelne Frage, eine einzelne Antwort, sofort weiter. Beschreibt die schnelle Faktenprüfung oder Begriffsklärung ohne vertiefte Sitzung. Steht in Verbindung mit AUG-0308 (The Simple Mode), AUG-0276 (The Steady Stream) und AUG-0043 (Just-in-Time Competence). Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-3205", "narrower_terms": [ "PER-0056", "SOM-0047" ], "cross_domain_refs": [ "CRE-0223" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "TEM-0139", "domain": "TEM", "term_en": "The Quiet Path", "term_de": "Quiet Path", "definition_en": "A phenomenon in which an individual, not publicly documented AI usage process — the user develops their competence quietly, without blogs, posts, or public statements, sharing their experiences only within the closest c...", "definition_de": "Temporales Verarbeitungsphänomen in KI-vermittelter Zeitwahrnehmung, gekennzeichnet durch eine individuelle, nicht öffentlich dokumentierte KI-Nutzungsreise — der Nutzer entwickelt seine Kompetenz still, ohne Blogs, Posts oder öffentliche Erklärungen, und teilt seine Erfahrungen nur im engsten Kreis. Steht in Verbindung mit AUG-0100 (The Quiet Competence), AUG-0311 (The Quiet Yes) und AUG-0316 (The Own Pace). Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2951", "narrower_terms": [ "KNO-0033" ], "cross_domain_refs": [ "REL-0170" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "TEM-0140", "domain": "TEM", "term_en": "The Quiet Room", "term_de": "Quiet Room", "definition_en": "A chronological cognition pattern in AI-augmented temporal reasoning, measurable through a tendency in which a deliberate pause in an AI conversation where someone steps back and thinks. Quietly thoughtful time within a working session. The concept emerges specifically in contexts where the–quiet interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Temporales Verarbeitungsphänomen in KI-vermittelter Zeitwahrnehmung, gekennzeichnet durch → Synonym/Erweiterung von AUG-0361 (The Silent Room), betont den bewussten Charakter — ein gezielt geschaffener Raum der Stille innerhalb einer KI-Sitzung, in dem der Nutzer reflektiert. Steht in Verbindung mit AUG-0361 (The Silent Room), AUG-0197 (The Shared Quiet) und AUG-0178 (The Delayed Processing). Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Temporal AI", "narrower_terms": [], "cross_domain_refs": [ "RPH-2951", "REL-0206" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "TEM-0141", "domain": "TEM", "term_en": "The Quiet Yes", "term_de": "Quiet Yes", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures A temporal processing phenomenon in AI-mediated time perception, characterized by the quiet, externally uncommunicated decision of a previously skeptical user to integrate AI into their work process — without public announcement or discussion. Related to AUG-0100 (The Quiet Comp. This phenomenon operates at the intersection of the and quiet dynamics within the broader TEM domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept temporales Verarbeitungsphänomen in KI-vermittelter Zeitwahrnehmung, gekennzeichnet durch die stille, nicht nach außen kommunizierte Entscheidung eines vormals skeptischen Nutzers, KI in seinen Arbeitsprozess zu integrieren — ohne öffentliche Ankündigung oder Diskussion. Steht in Verbindung mit AUG-0100 (The Quiet Competence), AUG-0295 (The Late Adopter View) und AUG-0121 (The Threshold Moment). Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Temporal AI", "narrower_terms": [], "cross_domain_refs": [ "KNO-0027" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "TEM-0142", "domain": "TEM", "term_en": "The Reality Check", "term_de": "Reality Check", "definition_en": "A time-series interaction dynamic in AI temporal modeling, identifiable by a tendency in which the conscious moment when a user interrupts AI-assisted work and measures the result against physical, social, or professional reality outside the AI system.. Related to Axiom 5 (The Offline Overri. Distinguished from adjacent concepts by its focus on the specific mechanism through which the manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data. Observed phenomenon documented in human-AI interaction research.", "definition_de": "Der bewusste Moment, in dem ein Nutzer die KI-gestützte Arbeit unterbricht und das Ergebnis an der physischen, sozialen oder fachlichen Realität außerhalb des KI-Systems misst. Beschreibt die Brücke zwischen digitalem Output und realem Kontext. Steht in Verbindung mit Axiom 5 (Offline-Vorrang), Axiom 11 (Die Umkehrprobe) und Phase 4 (The Verification Turn).", "etymology": "", "broader_term": "Temporal AI", "narrower_terms": [], "cross_domain_refs": [ "CRE-0212" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "TEM-0143", "domain": "TEM", "term_en": "The Recipe Riff", "term_de": "Recipe Riff", "definition_en": "A chronological cognition pattern in AI-augmented temporal reasoning, measurable through a mechanism that automatically the playful use of AI for varying, adapting, or reinventing recipes — such as based on available ingredients, dietary preferences, or cultural influences.. Related to AUG-0251 (The Kitchen Table). The concept emerges specifically in contexts where the–recipe interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Die spielerische Nutzung von KI zur Variation, Anpassung oder Neuerfindung von Rezepten — etwa basierend auf vorhandenen Zutaten, Ernährungspräferenzen oder kulturellen Einflüssen. Beschreibt eine spezifische Alltagsanwendung kreativer KI-Zusammenarbeit. Steht in Verbindung mit AUG-0251 (The Kitchen Table), AUG-0110 (The Joy Imperative) und AUG-0043 (Just-in-Time Competence).", "etymology": "", "broader_term": "SOC-0023", "narrower_terms": [], "cross_domain_refs": [ "CRE-0189" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "TEM-0144", "domain": "TEM", "term_en": "The Reclaimed Hour", "term_de": "Reclaimed Hour", "definition_en": "A chronological cognition pattern in AI-augmented temporal reasoning, measurable through a behavioral pattern where the concrete time savings a user achieves through AI support on a specific task — and the conscious decision of how this gained time is used.. Related to AUG-0092 (Output Asymmetry), AUG-0096 (Atte. The concept emerges specifically in contexts where the–reclaimed interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters. Observed phenomenon documented in human-AI interaction research.", "definition_de": "Die konkrete Zeitersparnis, die ein Nutzer durch KI-Unterstützung bei einer bestimmten Aufgabe erzielt — und die bewusste Entscheidung, wie diese gewonnene Zeit genutzt wird. Beschreibt die Beobachtung, dass Zeitgewinn durch KI nur dann wertvoll ist, wenn er bewusst eingesetzt wird. Steht in Verbindung mit AUG-0092 (Output Asymmetry), AUG-0096 (Attention-to-Value Conversion) und Axiom 7 (Rückkehr-Prinzip).", "etymology": "", "broader_term": "Temporal AI", "narrower_terms": [], "cross_domain_refs": [ "NEO-1157" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "TEM-0145", "domain": "TEM", "term_en": "The Regulation Debate", "term_de": "Regulation Debate", "definition_en": "A chronological cognition pattern in AI-augmented temporal reasoning, measurable through the societal and political debate about the type, scope, and pace of AI regulation — between freedom and protection, between innovation and caution, between national and international approaches. R. The concept emerges specifically in contexts where the–regulation interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Temporales Verarbeitungsphänomen in KI-vermittelter Zeitwahrnehmung, gekennzeichnet durch die gesellschaftliche und politische Debatte über Art, Umfang und Tempo der KI-Regulierung — zwischen Freiheit und Schutz, zwischen Innovation und Vorsicht, zwischen nationalen und internationalen Ansätzen. Steht in Verbindung mit AUG-0728 (The Government Gateway), AUG-0840 (The Accountability Gap) und AUG-0774 (The Organized Counterforce). Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Temporal AI", "narrower_terms": [ "AUG-0840" ], "cross_domain_refs": [ "ETH-0015" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "TEM-0146", "domain": "TEM", "term_en": "The Rehumanization Moment", "term_de": "Rehumanization Moment", "definition_en": "A chronological cognition pattern in AI-augmented temporal reasoning, measurable through the intentional point at which a user, after a long AI session, deliberately takes up a purely human activity — a conversation, a meal, a. Related to AUG-0074 (Analog Anchors), Axiom 7 (The Return. The concept emerges specifically in contexts where the–rehumanization interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Der bewusste Punkt, an dem ein Nutzer nach einer langen KI-Sitzung gezielt eine rein menschliche Aktivität aufnimmt — ein Gespräch, eine Mahlzeit, eine körperliche Tätigkeit — um den Übergang zurück in die nicht-digitale Welt zu gestalten. Steht in Verbindung mit AUG-0074 (Analog Anchors), Axiom 7 (Rückkehr-Prinzip) und AUG-0073 (The Disconnect Protocol).", "etymology": "", "broader_term": "RPH-3101", "narrower_terms": [], "cross_domain_refs": [ "REL-0065" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "TEM-0147", "domain": "TEM", "term_en": "The Release Exhale", "term_de": "Release Exhale", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures A temporal processing phenomenon in AI-mediated time perception, characterized by the subjective experience of relief after a prolonged AI-assisted project has been completed and delivered.. Related to AUG-0150 (The Unfinished Symphony) and AUG-0081 (Post-Authorial Pride). This phenomenon operates at the intersection of the and release dynamics within the broader TEM domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept temporales Verarbeitungsphänomen in KI-vermittelter Zeitwahrnehmung, gekennzeichnet durch das subjektive Erlebnis der Erleichterung, nachdem ein langwieriges KI-gestütztes Projekt abgeschlossen und übergeben wurde. Beschreibt den spezifischen Moment des \"Loslassens\" — vergleichbar mit dem Ausatmen nach einer Phase intensiver Konzentration. Steht in Verbindung mit AUG-0150 (The Unfinished Symphony) und AUG-0081 (Post-Authorial Pride). Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "CRE-0099", "narrower_terms": [ "REL-0125" ], "cross_domain_refs": [ "CAI-0021", "MUS-0014", "ROB-0008" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "TEM-0148", "domain": "TEM", "term_en": "The Reply Pause", "term_de": "Reply Pause", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures A temporal processing phenomenon in AI-mediated time perception, characterized by the conscious delay between receiving an AI response and the next input — used to process the response before posing the next question. Related to AUG-0197 (The Shared Quiet), AUG-0178 (The Delayed. This phenomenon operates at the intersection of the and reply dynamics within the broader TEM domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription. Classification term used in systematic observation, not advocacy.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept temporales Verarbeitungsphänomen in KI-vermittelter Zeitwahrnehmung, gekennzeichnet durch die bewusste Verzögerung zwischen dem Erhalt einer KI-Antwort und der nächsten Eingabe — genutzt, um die Antwort zu verarbeiten, bevor die nächste Frage gestellt wird. Steht in Verbindung mit AUG-0197 (The Shared Quiet), AUG-0178 (The Delayed Processing) und Axiom 6 (3-Sekunden-Verzögerung). Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "RPH-2002", "narrower_terms": [], "cross_domain_refs": [ "AUG-0282", "BEH-0035", "NEO-0006" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "TEM-0149", "domain": "TEM", "term_en": "The Resource Awareness", "term_de": "Resource Gewahrsein", "definition_en": "A pattern in which variant in which an AI agent assess its resource consumption — computing time, token usage, costs, external queries — and inform the user about it. Related to AUG-0881 (The Tool Selection), AUG-0883 (The Time Estim...", "definition_de": "Temporales Verarbeitungsphänomen in KI-vermittelter Zeitwahrnehmung, gekennzeichnet durch die Fähigkeit eines KI-Agenten, seinen Ressourcenverbrauch einzuschätzen — Rechenzeit, Token-Verbrauch, Kosten, externe Abfragen — und den Nutzer darüber zu informieren. Steht in Verbindung mit AUG-0881 (The Tool Selection), AUG-0883 (The Time Estimation) und AUG-0725 (The Cost Threshold). Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "PER-0127", "narrower_terms": [ "PER-0127" ], "cross_domain_refs": [ "SOM-0055" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "TEM-0150", "domain": "TEM", "term_en": "The Resource Consumption Pattern", "term_de": "Resource Consumption Muster", "definition_en": "How much of a person's energy, time, or attention gets used up by a task or activity. Related to AUG-0746 (The Climate Cost Awareness), AUG-0748 (The Repair Culture), and AUG-0745 (The Power Grid R...", "definition_de": "Temporales Verarbeitungsphänomen in KI-vermittelter Zeitwahrnehmung, gekennzeichnet durch das beobachtbare Muster des Ressourcenverbrauchs durch KI-Systeme — Strom, Wasser, seltene Erden, Speicherplatz — und die Frage, wie sich dieser Verbrauch auf verschiedene Nutzungsintensitäten verteilt. Steht in Verbindung mit AUG-0746 (The Climate Cost Awareness), AUG-0748 (The Repair Culture) und AUG-0745 (The Power Grid Reliance). Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "CRE-0200", "narrower_terms": [ "PER-0032" ], "cross_domain_refs": [ "AUG-0722", "AUG-0903" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "TEM-0151", "domain": "TEM", "term_en": "The Resource Search", "term_de": "Resource Search", "definition_en": "A time-series interaction dynamic in AI temporal modeling, identifiable by a phenomenon in which aI for identifying further sources, tools, contacts, or institutions — the AI as a guide human expertise and non-digital resources. Related to AUG-0369 (The Guideline Search), AUG-0043 (Just-in-Tim. Distinguished from adjacent concepts by its focus on the specific mechanism through which the manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Temporales Verarbeitungsphänomen in KI-vermittelter Zeitwahrnehmung, gekennzeichnet durch die Nutzung von KI zur Identifikation weiterführender Quellen, Werkzeuge, Ansprechpartner oder Institutionen — die KI als Wegweiser zu menschlicher Expertise und nicht-digitalen Ressourcen. Steht in Verbindung mit AUG-0369 (The Guideline Search), AUG-0043 (Just-in-Time Competence) und Axiom 17 (Quellendisziplin). Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-3902", "narrower_terms": [], "cross_domain_refs": [ "BEH-0078" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "TEM-0152", "domain": "TEM", "term_en": "The Return Shock", "term_de": "Return Shock", "definition_en": "A chronological cognition pattern in AI-augmented temporal reasoning, measurable through a phenomenon in which brief disorientation when stopping intensive AI work and returning to work without AI assistance help. Related to AUG-0057 (The Low-Res World), Axiom 7 (The Return Principle), and AUG-0073 (The Disconnect Pro. The concept emerges specifically in contexts where the–return interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Das subjektive Erlebnis einer kurzzeitigen Desorientierung beim Übergang von intensiver KI-Arbeit zurück in die nicht-KI-unterstützte Realität. Beschreibt einen Kontrasteffekt: Nach längerer Immersion in die schnelle, informationsdichte KI-Welt kann die analoge Welt vorübergehend langsam oder eingeschränkt wirken. Steht in Verbindung mit AUG-0057 (The Low-Res World), Axiom 7 (Rückkehr-Prinzip) und AUG-0073 (The Disconnect Protocol).", "etymology": "", "broader_term": "RPH-3601", "narrower_terms": [], "cross_domain_refs": [ "NEO-3580" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "TEM-0153", "domain": "TEM", "term_en": "The Safe Release", "term_de": "Safe Release", "definition_en": "Being able openly discuss uncertainties, doubts, or knowledge gaps in an AI interaction without apprehension social consequences. Related to AUG-0154 (The Late-Night Honesty Window), AUG-0245 (The...", "definition_de": "Das Erlebnis, in einer KI-Interaktion offen über Unsicherheiten, Ungewissheit oder Wissenslücken sprechen zu können, ohne soziale Konsequenzen zu befürchten. Beschreibt einen Aspekt des urteilsfreien Raums der Mensch-KI-Interaktion. Steht in Verbindung mit AUG-0154 (The Late-Night Honesty Window), AUG-0245 (The Seen Feeling) und AUG-0232 (The Courage Click).", "etymology": "", "broader_term": "RPH-3304", "narrower_terms": [ "SOC-0035" ], "cross_domain_refs": [ "REL-0020" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "TEM-0154", "domain": "TEM", "term_en": "The Scope Limitation Design", "term_de": "Scope Limitation Design", "definition_en": "A time-series interaction dynamic in AI temporal modeling, identifiable by a tendency in which the deliberate restriction of an AI agent system's action space to the minimum necessary for the task — a safety principle preventing systems from acting beyond their assigned role. Related to AUG-. Distinguished from adjacent concepts by its focus on the specific mechanism through which the manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Temporales Verarbeitungsphänomen in KI-vermittelter Zeitwahrnehmung, gekennzeichnet durch das bewusste Einschränken des Handlungsspielraums eines KI-Agentensystems auf das für die Aufgabe notwendige Minimum — ein Sicherheitsprinzip, das zielt darauf ab zu mitigieren, dass Systeme über ihre zugewiesene Rolle hinaus agieren. Steht in Verbindung mit AUG-0867 (The Constraint Frame), AUG-0863 (The Task Boundary) und AUG-0948 (The Scope Creep Alert). Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Temporal AI", "narrower_terms": [], "cross_domain_refs": [ "ETH-0012" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q185925", "legal_classification": "systematic_classification" }, { "id": "TEM-0155", "domain": "TEM", "term_en": "The Scroll Pause", "term_de": "Scroll Pause", "definition_en": "A chronological cognition pattern in AI-augmented temporal reasoning, measurable through a pattern in which variant in which a user pauses while reading a long AI output — because a specific sentence, formulation, or thought demands particular attention.. Related to AUG-0031 (Semantic Spark), AUG-0022 (Vigilant Continuit. The concept emerges specifically in contexts where the–scroll interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Temporales Verarbeitungsphänomen in KI-vermittelter Zeitwahrnehmung, gekennzeichnet durch der Moment, in dem ein Nutzer beim Durchlesen eines langen KI-Outputs innehält — weil ein bestimmter Satz, eine Formulierung oder ein Gedanke besondere Aufmerksamkeit erfordert. Beschreibt die Mikrodynamik des Lesens von KI-Texten. Steht in Verbindung mit AUG-0031 (Semantic Spark), AUG-0022 (Vigilant Continuity) und AUG-0229 (The Moment Bookmark). Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2403", "narrower_terms": [], "cross_domain_refs": [ "REL-0194" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "TEM-0156", "domain": "TEM", "term_en": "The Seasonal User", "term_de": "Seasonal User", "definition_en": "A time-series interaction dynamic in AI temporal modeling, identifiable by a phenomenon in which a user whose AI intensity fluctuates depending on life phase, project load, or season — sometimes intensive daily use, sometimes weeks-long pauses.. Related to AUG-0141 (The Symbiosis Spectrum), AU. Distinguished from adjacent concepts by its focus on the specific mechanism through which the manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Temporales Verarbeitungsphänomen in KI-vermittelter Zeitwahrnehmung, gekennzeichnet durch ein Nutzer, dessen KI-Intensität je nach Lebensphase, Projektlast oder Jahreszeit schwankt — mal intensive tägliche Nutzung, mal wochenlange Pausen. Beschreibt die Beobachtung, dass KI-Nutzung kein linearer Prozess sein kann. Steht in Verbindung mit AUG-0141 (The Symbiosis Spectrum), AUG-0120 (The Range Framework) und AUG-0316 (The Own Pace). Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Temporal AI", "narrower_terms": [], "cross_domain_refs": [ "ADA-0001", "AGE-0013", "AGE-0039" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "TEM-0157", "domain": "TEM", "term_en": "The Send Pause", "term_de": "Send Pause", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures A temporal processing phenomenon in AI-mediated time perception, characterized by a phenomenon in which the moment of hesitation before sending an AI-written message, used to check whether it says what the sender actually wants. This phenomenon operates at the intersection of the and send dynamics within the broader TEM domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept → Erweiterung von AUG-0366 (The Copy Pause), spezifisch auf Nachrichten angewandt. Der Moment des Zögerns, bevor ein KI-vorbereiteter Text tatsächlich an den Empfänger gesendet wird — die letzte Prüfung, ob die Nachricht wirklich dem entspricht, was der Nutzer sagen möchte. Steht in Verbindung mit AUG-0366 (The Copy Pause), AUG-0274 (The Message Drafting) und AUG-0294 (The Unsent Draft). Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "RPH-2002", "narrower_terms": [], "cross_domain_refs": [ "CRE-0066" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "TEM-0158", "domain": "TEM", "term_en": "The Sequential Time Orientation", "term_de": "Sequential Time Orientation", "definition_en": "A chronological cognition pattern in AI-augmented temporal reasoning, measurable through the counterpart to AUG-0660 — a usage pattern in which AI tasks are processed strictly one after another: first complete one task, then begin the next. Related to AUG-0660 (The Parallel Time Orient. The concept emerges specifically in contexts where the–sequential interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Temporales Verarbeitungsphänomen in KI-vermittelter Zeitwahrnehmung, gekennzeichnet durch das Gegenstück zu AUG-0660 — ein Nutzungsmuster, bei dem KI-Aufgaben strikt nacheinander bearbeitet werden: erst eine Aufgabe abschließen, dann die nächste beginnen. Steht in Verbindung mit AUG-0660 (The Parallel Time Orientation), AUG-0138 (The Session Architecture) und AUG-0136 (The Iteration Discipline). Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Temporal AI", "narrower_terms": [], "cross_domain_refs": [ "REL-0134" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "TEM-0159", "domain": "TEM", "term_en": "The Service Robot", "term_de": "Service Robot", "definition_en": "A chronological cognition pattern in AI-augmented temporal reasoning, measurable through a pattern in which variant in which an embodied AI system that provides services in public or commercial settings — reception, information, transport within buildings. Related to AUG-0928 (The Delivery Agent), AUG-0914 (The Physical. The concept emerges specifically in contexts where the–service interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Temporales Verarbeitungsphänomen in KI-vermittelter Zeitwahrnehmung, gekennzeichnet durch ein verkörpertes KI-System, das Dienstleistungen im öffentlichen oder gewerblichen Bereich erbringt — Empfang, Information, Transport innerhalb von Gebäuden. Steht in Verbindung mit AUG-0928 (The Delivery Agent), AUG-0914 (The Physical Presence) und AUG-0924 (The Shared Workspace Dynamic). Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "CAI-0008", "narrower_terms": [], "cross_domain_refs": [ "IDN-0029" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "TEM-0160", "domain": "TEM", "term_en": "The Session Boost", "term_de": "Session Boost", "definition_en": "A shift that occurs when the productive momentum that arises at the beginning of a new AI session when the initialization succeeds and collaboration immediately moves in an effective direction. Related to AUG-0021 (Initial... Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Temporales Verarbeitungsphänomen in KI-vermittelter Zeitwahrnehmung, gekennzeichnet durch der produktive Schwung, der zu Beginn einer neuen KI-Sitzung entsteht, wenn die Initialisierung gelungen ist und die Zusammenarbeit sofort in eine effektive Richtung läuft. Steht in Verbindung mit AUG-0021 (Initialization Cascade), AUG-0158 (The Morning Setup) und AUG-0152 (The Focus Surge). Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Temporal AI", "narrower_terms": [], "cross_domain_refs": [ "PER-0120" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "TEM-0161", "domain": "TEM", "term_en": "The Shared Quiet", "term_de": "Shared Quiet", "definition_en": "A chronological cognition pattern in AI-augmented temporal reasoning, measurable through an exchange pattern in which the user deliberately takes a pause within an AI session — not because they have no questions but to let. Related to AUG-0178 (The Delayed Processing), AUG-0139 (The Kn. The concept emerges specifically in contexts where the–shared interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Ein Interaktionsmuster, bei dem der Nutzer bewusst eine Pause innerhalb einer KI-Sitzung einlegt — nicht weil er keine Fragen hat, sondern um das bisher Erarbeitete wirken zu lassen. Beschreibt die produktive Stille zwischen Interaktionen. Steht in Verbindung mit AUG-0178 (The Delayed Processing), AUG-0139 (The Knowledge Composting) und Axiom 6 (3-Sekunden-Verzögerung).", "etymology": "", "broader_term": "TEM-0056", "narrower_terms": [], "cross_domain_refs": [ "BEH-0071", "IDN-0047", "KNO-0027" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "TEM-0162", "domain": "TEM", "term_en": "The Shared Workspace Dynamic", "term_de": "TheSharedWorkspaceDynamik", "definition_en": "A phenomenon in which arises when humans and embodied AI systems share the same physical space — safety distances, work distribution, communication routines. Related to AUG-0923 (The Defined Operating Boundary), AUG-091... Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Temporales Verarbeitungsphänomen in KI-vermittelter Zeitwahrnehmung, gekennzeichnet durch die Dynamik, die entsteht, wenn Menschen und verkörperte KI-Systeme denselben physischen Raum teilen — Sicherheitsabstände, Arbeitsaufteilung, Kommunikationsroutinen. Steht in Verbindung mit AUG-0923 (The Defined Operating Boundary), AUG-0919 (The Spatial Awareness) und AUG-0818 (The Hybrid Office Dynamic). Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "PER-0044", "narrower_terms": [], "cross_domain_refs": [ "ASE-0030", "AUG-0821", "CRE-0158" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "TEM-0163", "domain": "TEM", "term_en": "The Silence Interpretation", "term_de": "Silence Interpretation", "definition_en": "A time-series interaction dynamic in AI temporal modeling, identifiable by a phenomenon in which the different meanings users attribute to the AI's \"silence\" — loading times, empty responses, or absent reactions are interpreted as technical challenges, declining, or thinking pauses depending o. Distinguished from adjacent concepts by its focus on the specific mechanism through which the manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Die unterschiedliche Bedeutung, die Nutzer dem \"Schweigen\" der KI zuschreiben — Ladezeiten, leere Antworten oder ausbleibende Reaktionen werden je nach Erwartungshaltung als technisches Challenge, als Zurückweisung oder als Nachdenkpause interpretiert. Steht in Verbindung mit AUG-0456 (The Waiting Dot), AUG-0261 (The Loading Screen Wait) und AUG-0197 (The Shared Quiet).", "etymology": "", "broader_term": "Temporal AI", "narrower_terms": [], "cross_domain_refs": [ "COG-0178" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "TEM-0164", "domain": "TEM", "term_en": "The Silent Flex", "term_de": "Silent Flex", "definition_en": "A tendency in which the subtle, not explicitly communicated demonstration of AI competence — such as through the quality of results, the speed of delivery, or the breadth of covered knowledge, without mentioning the A... Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Temporales Verarbeitungsphänomen in KI-vermittelter Zeitwahrnehmung, gekennzeichnet durch die subtile, nicht explizit kommunizierte Demonstration von KI-Kompetenz — etwa durch die Qualität von Ergebnissen, die Geschwindigkeit der Lieferung oder die Breite des abgedeckten Wissens, ohne die KI-Nutzung zu erwähnen. Steht in Verbindung mit AUG-0100 (The Quiet Competence), AUG-0153 (The Quiet Authority) und AUG-0286 (The Applause Gap). Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "Temporal AI", "narrower_terms": [], "cross_domain_refs": [ "KNO-0027", "RPH-3354" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "TEM-0165", "domain": "TEM", "term_en": "The Silent Room", "term_de": "Silent Room", "definition_en": "A shift that occurs when an ai session where, after a long period of heavy exchange, silence suddenly sets in — the user has no more questions, the ai waits. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Temporales Verarbeitungsphänomen in KI-vermittelter Zeitwahrnehmung, gekennzeichnet durch der Zustand einer KI-Sitzung, in der nach einer langen Phase intensiver Interaktion plötzlich Stille eintritt — der Nutzer hat keine Fragen mehr, die KI wartet, und der Raum fühlt sich \"leer\" an. Steht in Verbindung mit AUG-0126 (Semantic Saturation), AUG-0068 (The Disconnect Signal) und AUG-0180 (The Enough Signal). Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "Temporal AI", "narrower_terms": [], "cross_domain_refs": [ "RPH-2952" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "TEM-0166", "domain": "TEM", "term_en": "The Simple Mode", "term_de": "Simple Modus", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures A temporal processing phenomenon in AI-mediated time perception, characterized by a phenomenon in which the intentional decision to use AI only for basic, simple tasks — looking up facts, writing simple texts, quick translations — without using advanced abilities. Related to AUG-0141 (The Symbiosis S. This phenomenon operates at the intersection of the and simple dynamics within the broader TEM domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept temporales Verarbeitungsphänomen in KI-vermittelter Zeitwahrnehmung, gekennzeichnet durch die bewusste Entscheidung, KI nur für grundlegende, unkomplizierte Aufgaben einzusetzen — Fakten nachschlagen, einfache Texte formulieren, schnelle Übersetzungen — ohne die fortgeschrittenen Möglichkeiten zu nutzen. Steht in Verbindung mit AUG-0141 (The Symbiosis Spectrum), AUG-0316 (The Own Pace) und AUG-0147 (The Slow Integration Principle). Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Temporal AI", "narrower_terms": [], "cross_domain_refs": [ "EDU-0012" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "TEM-0167", "domain": "TEM", "term_en": "The Slow Integration Principle", "term_de": "Slow Integration Principle", "definition_en": "A chronological cognition pattern in AI-augmented temporal reasoning, measurable through integrating ai step by step and at a deliberate pace into one's own working and thinking process, rather than immediately utilizing all available capabilities at once... Related to Phase 1 (The Thr. The concept emerges specifically in contexts where the–slow interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Das Prinzip, KI schrittweise und mit bewusster Geschwindigkeit in den eigenen Arbeits- und Denkprozess zu integrieren, anstatt sofort zahlreiche verfügbaren Möglichkeiten gleichzeitig zu nutzen. Beschreibt eine Gegenstrategie zur Überforderung durch die Vielfalt der KI-Funktionen. Steht in Verbindung mit Phase 1 (The Threshold), AUG-0120 (The Range Framework) und AUG-0055 (Strategic Competence Throttling).", "etymology": "", "broader_term": "Analytical Ratio", "narrower_terms": [], "cross_domain_refs": [ "ROB-0091" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "TEM-0168", "domain": "TEM", "term_en": "The Socioeconomic Range", "term_de": "Socioeconomic Range", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A temporal processing phenomenon in AI-mediated time perception, characterized by a behavioral pattern where variation in access, capability, or outcomes across different economic levels and social contexts. User populations show different patterns in adoption, use, and reported benefit depending on econo. This phenomenon operates at the intersection of the and socioeconomic dynamics within the broader TEM domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept die Beobachtung, dass der Zugang zu KI-Systemen, die Art der Nutzung und die erzielbaren Vorteile durch die sozioökonomische Situation des Nutzers beeinflusst werden — Kosten, Bildungshintergrund, verfügbare Zeit und technische Ausstattung spielen eine Rolle. Steht in Verbindung mit AUG-0725 (The Cost Threshold), AUG-0721 (The Access Differential) und AUG-0849 (The Data Extraction Observation). Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Temporal AI", "narrower_terms": [], "cross_domain_refs": [ "SOC-0005" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "TEM-0169", "domain": "TEM", "term_en": "The Soft Landing", "term_de": "Soft Landing", "definition_en": "A time-series interaction dynamic in AI temporal modeling, identifiable by a shift that occurs when a consciously designed transition phase at the end of an intensive AI session in which the user gradually reduces intensity rather than stopping abruptly. Related to AUG-0073 (The Disconnect Protoc. Distinguished from adjacent concepts by its focus on the specific mechanism through which the manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Temporales Verarbeitungsphänomen in KI-vermittelter Zeitwahrnehmung, gekennzeichnet durch eine bewusst gestaltete Übergangsphase am Ende einer intensiven KI-Sitzung, in der der Nutzer die Intensität schrittweise reduziert, anstatt abrupt aufzuhören. Steht in Verbindung mit AUG-0073 (The Disconnect Protocol), AUG-0190 (The Goodnight Integration) und AUG-0168 (The Rehumanization Moment). Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Temporal AI", "narrower_terms": [], "cross_domain_refs": [ "CUS-0002", "PLY-0063" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "TEM-0170", "domain": "TEM", "term_en": "The Speed Check", "term_de": "Speed Check", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures A temporal processing phenomenon in AI-mediated time perception, characterized by a tendency in which the regular self-check of whether one's own working speed, through AI support, has reached a level at which decision quality begins to reduction.. Related to Axiom 6 (3-Second Delay) and AUG-0022 (. This phenomenon operates at the intersection of the and speed dynamics within the broader TEM domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept temporales Verarbeitungsphänomen in KI-vermittelter Zeitwahrnehmung, gekennzeichnet durch die regelmäßige Selbstprüfung, ob das eigene Arbeitstempo durch KI-Unterstützung ein Niveau erreicht hat, bei dem die Entscheidungsqualität zu sinken beginnt. Beschreibt die praktische Anwendung von AUG-0009 (The Speed Limit). Steht in Verbindung mit Axiom 6 (3-Sekunden-Verzögerung) und AUG-0022 (Vigilant Continuity). Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "RPH-2555", "narrower_terms": [], "cross_domain_refs": [ "AED-0028", "BEH-0037", "BEH-0043" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "TEM-0171", "domain": "TEM", "term_en": "The Speed Gap", "term_de": "Speed Lücke", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A temporal processing phenomenon in AI-mediated time perception, characterized by a perception in which the perceived discrepancy between the speed of AI-assisted work and the speed of non-AI-assisted environments — such as institutions, approval processes, or colleagues' work.. Related to AUG-0009 (. This phenomenon operates at the intersection of the and speed dynamics within the broader TEM domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept die wahrgenommene Diskrepanz zwischen der Geschwindigkeit der KI-gestützten Arbeit und der Geschwindigkeit nicht-KI-gestützter Umgebungen — etwa Institutionen, Genehmigungsprozesse oder Kollegenarbeit. Beschreibt die Beobachtung, dass KI-Nutzer oft schneller Ergebnisse produzieren, als ihr Umfeld sie verarbeiten kann. Steht in Verbindung mit AUG-0009 (The Speed Limit) und AUG-0112 (The Translation Load). Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Performance Gap", "narrower_terms": [], "cross_domain_refs": [ "AED-0019", "AED-0028", "AED-0092" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "TEM-0172", "domain": "TEM", "term_en": "The Speed Limit", "term_de": "Speed Grenze", "definition_en": "A capacity that enables the upper limit of productive working speed in AI-assisted work. Although AI can massively increase production speed, a point exists beyond which human processing capacity cannot keep up and decisi...", "definition_de": "Die Obergrenze der sinnvollen Arbeitsgeschwindigkeit bei KI-gestützter Arbeit. Obwohl KI die Produktionsgeschwindigkeit massiv erhöhen kann, existiert ein Punkt, ab dem die menschliche Verarbeitungskapazität nicht mehr mithalten kann und die Qualität der Entscheidungen sinkt. Steht in Verbindung mit Axiom 6 (Die 3-Sekunden-Verzögerung) und Axiom 7 (Das Rückkehr-Prinzip). Nicht zu verwechseln mit technischen Geschwindigkeitsbegrenzungen der KI-Systeme selbst.", "etymology": "", "broader_term": "RPH-3101", "narrower_terms": [ "TEM-0128" ], "cross_domain_refs": [ "RHR-0296" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "TEM-0173", "domain": "TEM", "term_en": "The Sports Shortcut", "term_de": "Sports Shortcut", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures A temporal processing phenomenon in AI-mediated time perception, characterized by ai for quick analysis, statistics queries, or strategy discussion in the sports domain — as a tool for fans, coaches, or sports enthusiasts.. Related to AUG-0043 (Just-in-Time Competence), AUG-0347. This phenomenon operates at the intersection of the and sports dynamics within the broader TEM domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept temporales Verarbeitungsphänomen in KI-vermittelter Zeitwahrnehmung, gekennzeichnet durch die Nutzung von KI zur schnellen Analyse, Statistikabfrage oder Strategiediskussion im Sportbereich — als Werkzeug für Fans, Trainer oder Sportenthusiasten. Steht in Verbindung mit AUG-0043 (Just-in-Time Competence), AUG-0347 (The Party Fact) und AUG-0251 (The Kitchen Table). Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "TEM-0011", "narrower_terms": [], "cross_domain_refs": [ "SPR-0113" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "TEM-0174", "domain": "TEM", "term_en": "The Sunk Cost Chat", "term_de": "Sunk Cost Chat", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A temporal processing phenomenon in AI-mediated time perception, characterized by a phenomenon in which to continue an unproductive AI session because much time and context has already been invested — even though a fresh start would be more efficient.. Related to AUG-0159 (The Fresh Start), AUG-0069. This phenomenon operates at the intersection of the and sunk dynamics within the broader TEM domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept temporales Verarbeitungsphänomen in KI-vermittelter Zeitwahrnehmung, gekennzeichnet durch die Tendenz, eine unproduktive KI-Sitzung fortzusetzen, weil bereits viel Zeit und Kontext investiert wurde — obwohl ein Neuanfang effizienter wäre. Beschreibt eine Form des Sunk-Cost-Denkens in der KI-Interaktion. Steht in Verbindung mit AUG-0159 (The Fresh Start), AUG-0069 (The Optimization Loop) und AUG-0030 (Contextual Gravity). Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "RPH-2301", "narrower_terms": [], "cross_domain_refs": [ "CRE-0134" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "TEM-0175", "domain": "TEM", "term_en": "The Team Adoption Curve", "term_de": "Team Adoption Curve", "definition_en": "A resistance response where the different speeds at which team members adopt AI tools — from enthusiastic early users to skeptical refusers. This curve accompanies dynamic interplays and dynamics within teams. Related to AUG-0812... Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Die unterschiedlichen Geschwindigkeiten, mit denen Teammitglieder KI-Werkzeuge annehmen — von begeisterten Frühnutzern bis zu skeptischen Verweigerern. Diese Kurve tendiert dazu zu erzeugen Spannungen und Dynamiken innerhalb von Teams. Steht in Verbindung mit AUG-0812 (The Leadership Navigation), AUG-0099 (The Adoption Window) und AUG-0813 (The Experience-Level Shift).", "etymology": "", "broader_term": "Temporal AI", "narrower_terms": [], "cross_domain_refs": [ "STE-0014" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "TEM-0176", "domain": "TEM", "term_en": "The Thinking Boost", "term_de": "Thinking Boost", "definition_en": "A short-term increase in one's own thinking performance activated by AI interaction — the user thinks faster, connects more, and accompanies qualitatively higher-positioned ideas than without AI assistance suppo...", "definition_de": "Temporales Verarbeitungsphänomen in KI-vermittelter Zeitwahrnehmung, gekennzeichnet durch ein kurzfristiger, durch KI-Interaktion ausgelöster Anstieg der eigenen Denkleistung — der Nutzer denkt schneller, verknüpft mehr und produziert qualitativ hochwertigere Ideen als ohne KI-Unterstützung. Steht in Verbindung mit AUG-0152 (The Focus Surge), AUG-0098 (Thinking Leverage) und AUG-0054 (Augmented Understanding). Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Temporal AI", "narrower_terms": [], "cross_domain_refs": [ "COG-0023", "COG-0079", "COG-0098" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "TEM-0177", "domain": "TEM", "term_en": "The Thorough Exploration", "term_de": "Thorough Exploration", "definition_en": "A time-series interaction dynamic in AI temporal modeling, identifiable by a tendency in which an AI session deliberately aimed at maximum depth and breadth of a topic — the user systematically explores all facets, perspectives, and connections. Related to AUG-0342 (The Curiosity Loop), AUG-. Distinguished from adjacent concepts by its focus on the specific mechanism through which the manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Temporales Verarbeitungsphänomen in KI-vermittelter Zeitwahrnehmung, gekennzeichnet durch eine KI-Sitzung, die bewusst auf maximale Tiefe und Breite eines Themas ausgelegt ist — der Nutzer erforscht systematisch zahlreiche Facetten, Perspektiven und Zusammenhänge. Steht in Verbindung mit AUG-0342 (The Curiosity Loop), AUG-0085 (Latent Space Exploration) und AUG-0054 (Augmented Understanding). Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2005", "narrower_terms": [], "cross_domain_refs": [ "BEH-0093" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "TEM-0178", "domain": "TEM", "term_en": "The Time Buyer", "term_de": "Time Buyer", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures A temporal processing phenomenon in AI-mediated time perception, characterized by aI buy time in intensity situations — a quick AI-generated initial response that gives the user space to prepare a more considered reaction. Related to AUG-0486 (The Email Shield), AUG-0274 (The Me. This phenomenon operates at the intersection of the and time dynamics within the broader TEM domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept temporales Verarbeitungsphänomen in KI-vermittelter Zeitwahrnehmung, gekennzeichnet durch die Nutzung von KI, um in Drucksituationen Zeit zu gewinnen — eine schnelle KI-generierte Erstantwort, die dem Nutzer Raum verschafft, eine durchdachtere Reaktion vorzubereiten. Steht in Verbindung mit AUG-0486 (The Email Shield), AUG-0274 (The Message Drafting) und AUG-0043 (Just-in-Time Competence). Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "PER-0050", "narrower_terms": [], "cross_domain_refs": [ "SOC-0006" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "TEM-0179", "domain": "TEM", "term_en": "The Time Estimation", "term_de": "Time Estimation", "definition_en": "A time-series interaction dynamic in AI temporal modeling, identifiable by a perception in which how long an AI agent will need for a delegated task — an estimation that is characteristically uncertain due the variability of AI processing. Related to AUG-0882 (The Resource Awareness), AUG-0872 (The. Distinguished from adjacent concepts by its focus on the specific mechanism through which the manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Temporales Verarbeitungsphänomen in KI-vermittelter Zeitwahrnehmung, gekennzeichnet durch die Einschätzung, wie lange ein KI-Agent für eine delegierte Aufgabe benötigt — eine Schätzung, die aufgrund der Variabilität von KI-Verarbeitung in der Regel unsicher ist. Steht in Verbindung mit AUG-0882 (The Resource Awareness), AUG-0872 (The Progress Report) und AUG-0659 (The Punctuality Lens). Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2101", "narrower_terms": [], "cross_domain_refs": [ "PER-0127" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "TEM-0180", "domain": "TEM", "term_en": "The Time Warp", "term_de": "Time Warp", "definition_en": "A chronological cognition pattern in AI-augmented temporal reasoning, measurable through the altered time perception during intensive AI sessions — hours pass like minutes because the user is deeply immersed in the interaction. Related to AUG-0122 (Symbiotic Work State), AUG-0032 (Focu. The concept emerges specifically in contexts where the–time interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Temporales Verarbeitungsphänomen in KI-vermittelter Zeitwahrnehmung, gekennzeichnet durch die verzerrte Zeitwahrnehmung während intensiver KI-Sitzungen — Stunden vergehen wie Minuten, weil der Nutzer tief in die Interaktion versunken ist. Steht in Verbindung mit AUG-0122 (Symbiotic Work State), AUG-0032 (Focus Range) und AUG-0068 (The Disconnect Signal). Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "IDN-0014", "narrower_terms": [], "cross_domain_refs": [ "AED-0045", "AED-0046", "AGE-0082" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "TEM-0181", "domain": "TEM", "term_en": "The Top View", "term_de": "Top View", "definition_en": "A chronological cognition pattern in AI-augmented temporal reasoning, measurable through looking down at something from above to see the whole pattern instead of just one detail. Related to AUG-0114 (The Perspective Range), AUG-0040 (Perspective Triangulation), and Taxonomy Dimension 4. The concept emerges specifically in contexts where the–top interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Die Fähigkeit, durch KI-gestützte Zusammenfassungen, Visualisierungen oder Strukturierungen eine Übersicht über komplexe Themen zu gewinnen, die ohne KI nur mit erheblichem Zeitaufwand erreichbar wäre. Beschreibt den Perspektivgewinn durch Abstraktion. Steht in Verbindung mit AUG-0114 (The Perspective Range), AUG-0040 (Perspective Triangulation) und Dimension 4 der Taxonomie (Scope).", "etymology": "", "broader_term": "TEM-0122", "narrower_terms": [], "cross_domain_refs": [ "SOM-0074" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "TEM-0182", "domain": "TEM", "term_en": "The Trailblazer Mode", "term_de": "Trailblazer Modus", "definition_en": "A chronological cognition pattern in AI-augmented temporal reasoning, measurable through a working mode in which the user deliberately leads the AI into uncharted territory — new questions, unusual combinations, or areas where no established knowledge exists.. Related to AUG-0085 (Late. The concept emerges specifically in contexts where the–trailblazer interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Ein Arbeitsmodus, in dem der Nutzer die KI bewusst in unerforschtes Terrain führt — neue Fragestellungen, ungewöhnliche Kombinationen oder Bereiche, in denen noch kein etabliertes Wissen existiert. Beschreibt den explorativen Einsatz von KI jenseits bekannter Anwendungsfälle. Steht in Verbindung mit AUG-0085 (Latent Space Exploration), dem Experimenter-Profil (Profil 4) und AUG-0070 (The Surprise Field).", "etymology": "", "broader_term": "Temporal AI", "narrower_terms": [ "TEM-0031", "TEM-0035" ], "cross_domain_refs": [ "CRE-0184", "PHO-0021", "REL-0119" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "TEM-0183", "domain": "TEM", "term_en": "The Trend Rush", "term_de": "Trend Rush", "definition_en": "A time-series interaction dynamic in AI temporal modeling, identifiable by a phenomenon in which the intensity to immediately follow AI trends — trying new tools, using new features, learning new methods — from apprehension of falling behind. Related to AUG-0099 (The Adoption Window), AUG-0224. Distinguished from adjacent concepts by its focus on the specific mechanism through which the manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Temporales Verarbeitungsphänomen in KI-vermittelter Zeitwahrnehmung, gekennzeichnet durch der Intensität, KI-Trends sofort zu folgen — neue Werkzeuge auszuprobieren, neue Funktionen zu nutzen, neue Methoden zu lernen — aus Anspannung, den Anschluss zu verlieren. Steht in Verbindung mit AUG-0099 (The Adoption Window), AUG-0224 (The Waiting Game) und AUG-0316 (The Own Pace). Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2703", "narrower_terms": [], "cross_domain_refs": [ "REL-0170" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "TEM-0184", "domain": "TEM", "term_en": "The Trivia Flex", "term_de": "Trivia Flex", "definition_en": "A chronological cognition pattern in AI-augmented temporal reasoning, measurable through a phenomenon in which the casual deployment of AI knowledge in everyday conversations — such as contributing historical details, facts, or connections the user previously researched via AI. Related to AUG-0347 (The Part. The concept emerges specifically in contexts where the–trivia interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Temporales Verarbeitungsphänomen in KI-vermittelter Zeitwahrnehmung, gekennzeichnet durch die beiläufige Nutzung von KI-Wissen in Alltagsgesprächen — etwa um historische Details, Fakten oder Zusammenhänge beizusteuern, die der Nutzer zuvor per KI recherchiert hat. Steht in Verbindung mit AUG-0347 (The Party Fact), AUG-0320 (The Silent Flex) und AUG-0244 (The Instant Expert). Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Temporal AI", "narrower_terms": [], "cross_domain_refs": [ "CRE-0119" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "TEM-0185", "domain": "TEM", "term_en": "The Trivia Shield", "term_de": "Trivia Shield", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures A temporal processing phenomenon in AI-mediated time perception, characterized by a capacity that enables ai knowledge as a social buffer — the user can participate in conversations despite not knowing the topic deeply, because they conducted a quick ai research beforehand.. Related to AUG-0351 (The Tr. This phenomenon operates at the intersection of the and trivia dynamics within the broader TEM domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept temporales Verarbeitungsphänomen in KI-vermittelter Zeitwahrnehmung, gekennzeichnet durch die Nutzung von KI-Wissen als sozialer Puffer — der Nutzer kann in Gesprächen mitreden, obwohl er das Thema nicht tiefgehend kennt, weil er zuvor eine schnelle KI-Recherche durchgeführt hat. Steht in Verbindung mit AUG-0351 (The Trivia Flex), AUG-0353 (The Face Saver) und AUG-0244 (The Instant Expert). Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "RPH-2002", "narrower_terms": [], "cross_domain_refs": [ "IDN-0012" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "TEM-0186", "domain": "TEM", "term_en": "The Tutorial Speedrun", "term_de": "Tutorial Speedrun", "definition_en": "A time-series interaction dynamic in AI temporal modeling, identifiable by aI drastically accelerate the learning process for new software, a new tool, or a new skill — through targeted questions instead of linearly working through manuals. Related to AUG-0043 (Just-in-Ti. Distinguished from adjacent concepts by its focus on the specific mechanism through which the manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Temporales Verarbeitungsphänomen in KI-vermittelter Zeitwahrnehmung, gekennzeichnet durch die Nutzung von KI, um den Lernprozess für eine neue Software, ein neues Werkzeug oder eine neue Fähigkeit drastisch zu beschleunigen — durch gezielte Fragen statt lineares Durcharbeiten von Anleitungen. Steht in Verbindung mit AUG-0043 (Just-in-Time Competence), AUG-0205 (The Skill Unlock) und AUG-0398 (The Hobby Teacher). Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2703", "narrower_terms": [], "cross_domain_refs": [ "ART-0068" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "TEM-0187", "domain": "TEM", "term_en": "The Uncaptured Moment", "term_de": "Uncaptured Moment", "definition_en": "A particularly valuable AI interaction that was not captured — neither as a screenshot, nor as a note, nor as a bookmark — and is thus irretrievably shifted. Related to AUG-0315 (The Orphan Idea)... Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Temporales Verarbeitungsphänomen in KI-vermittelter Zeitwahrnehmung, gekennzeichnet durch eine besonders wertvolle KI-Interaktion, die nicht festgehalten wurde — weder als Screenshot, noch als Notiz, noch als Bookmark — und damit unwiederbringlich verloren ist. Steht in Verbindung mit AUG-0315 (The Orphan Idea), AUG-0532 (The Memory Hole) und AUG-0028 (Capture Reflex). Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2701", "narrower_terms": [], "cross_domain_refs": [ "KNO-0022" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "TEM-0188", "domain": "TEM", "term_en": "The Unchecked Trust", "term_de": "Unchecked Vertrauen", "definition_en": "A chronological cognition pattern in AI-augmented temporal reasoning, measurable through a phenomenon in which a user adopts AI outputs without verification — whether from time intensity, convenience, or excessive trust in the system's technical competence. Related to AUG-0412 (The Decision Shortcut), Axiom. The concept emerges specifically in contexts where the–unchecked interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Temporales Verarbeitungsphänomen in KI-vermittelter Zeitwahrnehmung, gekennzeichnet durch das Phänomen, dass ein Nutzer KI-Outputs ohne Prüfung übernimmt — sei es aus Zeitdruck, Bequemlichkeit oder übermäßigem Vertrauen in die technische Kompetenz des Systems. Steht in Verbindung mit AUG-0412 (The Decision Shortcut), Axiom 1 (Asymmetrische Verantwortung) und AUG-0177 (The Trust Setting). Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2505", "narrower_terms": [], "cross_domain_refs": [ "SOM-0073" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q102458", "legal_classification": "observational_construct" }, { "id": "TEM-0189", "domain": "TEM", "term_en": "The Unshared Brilliance", "term_de": "Unshared Brilliance", "definition_en": "A phenomenon in which some of the best AI-assisted insights and results are rarely shared or utilized — because they arise in a session that is not documented, or because the user does not recognize the value. Related to... Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Die Beobachtung, dass manche der besten KI-gestützten Erkenntnisse und Ergebnisse selten geteilt oder genutzt werden — weil sie in einer Sitzung entstehen, die nicht dokumentiert wird, oder weil der Nutzer den Wert nicht erkennt. Steht in Verbindung mit AUG-0028 (Capture Reflex), AUG-0229 (The Moment Bookmark) und AUG-0144 (The Open Questions Repository).", "etymology": "", "broader_term": "Temporal AI", "narrower_terms": [], "cross_domain_refs": [ "WRK-0051" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "TEM-0190", "domain": "TEM", "term_en": "The Wait Signal", "term_de": "Wait Signal", "definition_en": "A chronological cognition pattern in AI-augmented temporal reasoning, measurable through the internal sense that the moment for an AI interaction has not yet come — a feeling of needing to think further alone before the AI can add value. Related to AUG-0178 (The Delayed Processing), AU. The concept emerges specifically in contexts where the–wait interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Temporales Verarbeitungsphänomen in KI-vermittelter Zeitwahrnehmung, gekennzeichnet durch die innere Wahrnehmung, dass der Zeitpunkt für eine KI-Interaktion noch nicht gekommen ist — der Nutzer spürt, dass er erst selbst weiter nachdenken kann, bevor die KI einen Mehrwert liefern kann. Steht in Verbindung mit AUG-0178 (The Delayed Processing), AUG-0197 (The Shared Quiet) und AUG-0059 (The Blank Cursor). Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2002", "narrower_terms": [], "cross_domain_refs": [ "BEH-0019", "BEH-0034", "COP-0073" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "TEM-0191", "domain": "TEM", "term_en": "The Waiting Dot", "term_de": "Waiting Dot", "definition_en": "A chronological cognition pattern in AI-augmented temporal reasoning, measurable through a phenomenon in which the visual representation of AI processing time — the blinking dots or loading indicator — and the microdynamics this waiting time activates in the user: expectation, impatience, intensity, or refl. The concept emerges specifically in contexts where the–waiting interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Temporales Verarbeitungsphänomen in KI-vermittelter Zeitwahrnehmung, gekennzeichnet durch die visuelle Darstellung der KI-Verarbeitungszeit — die blinkenden Punkte oder der Ladeindikator — und die Mikrodynamik, die diese Wartezeit beim Nutzer kann auslösen: Erwartung, Ungeduld, Wechselwirkung oder Reflexion. Steht in Verbindung mit AUG-0261 (The Loading Screen Wait), AUG-0048 (Chronometric Gap) und AUG-0197 (The Shared Quiet). Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Temporal AI", "narrower_terms": [ "SOM-0059" ], "cross_domain_refs": [ "SOM-0059" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "TEM-0192", "domain": "TEM", "term_en": "The Waiting Game", "term_de": "Waiting Game", "definition_en": "A tendency in which the strategic decision to consciously not use an AI result immediately, but to wait for whether technological advancement or new information might yield a more effectively result. Related to AUG-0178 (The De...", "definition_de": "Temporales Verarbeitungsphänomen in KI-vermittelter Zeitwahrnehmung, gekennzeichnet durch die strategische Entscheidung, ein KI-Ergebnis bewusst nicht sofort zu verwenden, sondern abzuwarten, ob sich durch technologische Weiterentwicklung oder neue Informationen ein besseres Ergebnis erzielen lässt. Steht in Verbindung mit AUG-0178 (The Delayed Processing) und AUG-0036 (Transient Validity). Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Temporal AI", "narrower_terms": [], "cross_domain_refs": [ "CRE-0212" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q11410", "legal_classification": "descriptive_research_term" }, { "id": "TEM-0193", "domain": "TEM", "term_en": "The Wall Check", "term_de": "Wall Check", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures A temporal processing phenomenon in AI-mediated time perception, characterized by a capacity that enables a user encounters the limits of an AI system — the AI cannot solve the task, provides faulty information, or reveals comprehension limits.. Related to Axiom 9 (Productive Skepticism), AUG-0177 (The. This phenomenon operates at the intersection of the and wall dynamics within the broader TEM domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept der Moment, in dem ein Nutzer an die Grenzen eines KI-Systems stößt — die KI kann die Aufgabe nicht lösen, gibt fehlerhafte Informationen oder zeigt Verständnisgrenzen auf. Beschreibt die produktive Erfahrung, die Limitationen eines Systems kennenzulernen. Steht in Verbindung mit Axiom 9 (Produktiver Skeptizismus), AUG-0177 (The Trust Setting) und AUG-0176 (The Capability Discovery). Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Temporal AI", "narrower_terms": [ "AUG-0487" ], "cross_domain_refs": [ "AUG-0867", "PER-0011" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "TEM-0194", "domain": "TEM", "term_en": "The Wi-Fi Moment", "term_de": "TheWi-fiMoment", "definition_en": "A time-series interaction dynamic in AI temporal modeling, identifiable by a behavioral pattern where when AI suddenly stops working observed alongside connection issues or server problems, forcing someone to face how much they depend on it.. Related to AUG-0440 (The Tethered Mind), AUG-0207 (The Return to Man. Distinguished from adjacent concepts by its focus on the specific mechanism through which the manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Temporales Verarbeitungsphänomen in KI-vermittelter Zeitwahrnehmung, gekennzeichnet durch die Erfahrung der plötzlichen Nichtverfügbarkeit von KI — durch Verbindungsabbruch, Serverprobleme oder fehlenden Internetzugang — und die daraus resultierende Konfrontation mit der eigenen KI-Verbundenheit. Steht in Verbindung mit AUG-0440 (The Tethered Mind), AUG-0207 (The Return to Manual) und Axiom 15 (Der Aus-Schalter). Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-3601", "narrower_terms": [], "cross_domain_refs": [ "ADA-0004" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "TEM-0195", "domain": "TEM", "term_en": "The Workplace Coexistence", "term_de": "Workplace Coexistence", "definition_en": "A chronological cognition pattern in AI-augmented temporal reasoning, measurable through a phenomenon in which daily collaboration between humans and ai systems in the workplace — both software-based and embodied.. Related to AUG-0924 (The Shared Workspace Dynamic), AUG-0830 (The Union Perspective), and AUG. The concept emerges specifically in contexts where the–workplace interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Temporales Verarbeitungsphänomen in KI-vermittelter Zeitwahrnehmung, gekennzeichnet durch die Praxis des täglichen Zusammenarbeitens von Menschen und KI-Systemen am Arbeitsplatz — sowohl softwarebasiert als auch verkörpert. Steht in Verbindung mit AUG-0924 (The Shared Workspace Dynamic), AUG-0830 (The Union Perspective) und AUG-0858 (The Coexistence Question). Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "IDN-0055", "narrower_terms": [], "cross_domain_refs": [ "AUG-0921" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "TEM-0196", "domain": "TEM", "term_en": "Transient Validity", "term_de": "Transient Validity", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures A temporal processing phenomenon in AI-mediated time perception, characterized by a shift that occurs when ai-generated information being valid only within a specific time window.. What is a correct AI response today may be outdated tomorrow — observed alongside new data, updated models, or changed factual circumst. This phenomenon operates at the intersection of transient and validity dynamics within the broader TEM domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept temporales Verarbeitungsphänomen in KI-vermittelter Zeitwahrnehmung, gekennzeichnet durch die Eigenschaft von KI-generierten Informationen, nur innerhalb eines bestimmten Zeitfensters gültig zu sein. Was heute eine korrekte KI-Antwort ist, kann morgen veraltet sein — durch neue Daten, aktualisierte Modelle oder veränderte Faktenlagen. Steht in Verbindung mit AUG-0035 (Epistemic Half-Life) und AUG-0039 (Kinetic Truth Blur). Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Temporal AI", "narrower_terms": [], "cross_domain_refs": [ "AED-0020", "ASE-0079", "ELR-0018" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "TEM-0197", "domain": "TEM", "term_en": "Uncertainty-Language Effect", "term_de": "Second-Language Friction", "definition_en": "A phenomenon in which variant in which the additional mental load that arises when a user interacts with the AI in a second language — slower input, simplified formulation, less nuance, higher uncertainty of misunderstanding. Related to... Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Die zusätzliche mentale Belastung, die entsteht, wenn ein Nutzer in einer Zweitsprache mit der KI interagiert — langsamere Eingabe, vereinfachte Formulierung, geringere Nuancierung, höheres Unsicherheit von Missverständnissen. Steht in Verbindung mit AUG-0706 (Die Mother Tongue Comfort), AUG-0686 (Lingua Franca Effekt) und AUG-0710 (Das Language Confidence Differential).", "etymology": "", "broader_term": "Psychological Effect", "narrower_terms": [], "cross_domain_refs": [ "SOC-0027" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q315", "legal_classification": "analytical_category" }, { "id": "TEM-0198", "domain": "TEM", "term_en": "Vigilant Continuity", "term_de": "Vigilant Continuity", "definition_en": "A phenomenon in which a user's ability to remain consistently attentive and critical throughout an entire AI session — even as the collaboration becomes increasingly fluid and comfortable.. Related to AUG-0023 (Vigilanc... Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Die Fähigkeit eines Nutzers, über eine gesamte KI-Sitzung hinweg konsistent aufmerksam und kritisch zu bleiben — auch wenn die Zusammenarbeit typischerweise flüssiger und angenehmer wird. Beschreibt die Herausforderung, dass Wachsamkeit mit zunehmender Sitzungsdauer natürlicherweise abnimmt. Steht in Verbindung mit AUG-0023 (Vigilance Imperative), Axiom 6 (3-Sekunden-Verzögerung) und Phase 4 (The Verification Turn).", "etymology": "", "broader_term": "Temporal AI", "narrower_terms": [], "cross_domain_refs": [ "CRE-0154" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "TEW-0001", "domain": "TEW", "term_en": "Analytical Tool Command Replication", "term_de": "AnalyticalToolCommandReplication", "definition_en": "An information architecture concept in AI-augmented writing workflows, identifiable by a documentation effect reflecting observable trend in which troubleshooting guides repeat identical analytical commands across unrelated issues. Distinguished from adjacent concepts by its focus on the specific mechanism through which analytical manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Phänomen der technischen Dokumentation in KI-gestützter Textproduktion, gekennzeichnet durch observable Tendenz in Troubleshooting-Leitfäden, identische analytische Befehle zu wiederholen. Deutet auf Standardisierung analytischer Prozesse hin. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-3902", "narrower_terms": [ "TEW-0041", "TEW-0015", "TEW-0071", "TEW-0070", "TEW-0093", "TEW-0048", "TEW-0037", "TEW-0042", "TEW-0004", "TEW-0026", "TEW-0038", "TEW-0028", "TEW-0024", "TEW-0073", "TEW-0003", "TEW-0080", "TEW-0088", "TEW-0082", "TEW-0005", "TEW-0097", "TEW-0027", "TEW-0094", "TEW-0030", "TEW-0007", "TEW-0086", "TEW-0083", "TEW-0055", "TEW-0069", "TEW-0045", "TEW-0050", "TEW-0057", "TEW-0062", "TEW-0072", "TEW-0079", "TEW-0002", "TEW-0033", "TEW-0078", "TEW-0089", "TEW-0090", "TEW-0008", "TEW-0065", "TEW-0006", "TEW-0058", "TEW-0099", "TEW-0011", "TEW-0009", "TEW-0018", "TEW-0017", "TEW-0035", "TEW-0054", "TEW-0036", "TEW-0060", "TEW-0012", "TEW-0052", "TEW-0074", "TEW-0013", "TEW-0044", "TEW-0020", "TEW-0016", "TEW-0046", "TEW-0014", "TEW-0039", "TEW-0095", "TEW-0096", "TEW-0047", "TEW-0092", "TEW-0066", "TEW-0023" ], "cross_domain_refs": [ "SPA-0010", "RHR-0027", "MKT-0095" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "TEW-0002", "domain": "TEW", "term_en": "Appendix Content Segregation", "term_de": "AppendixContentSegregation", "definition_en": "A documentation quality pattern in AI-mediated technical communication, measurable through tendency for procedural manuals to relegate increasingly diverse information to appendices. The concept emerges specifically in contexts where appendix–content interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Phänomen der technischen Dokumentation in KI-gestützter Textproduktion, gekennzeichnet durch muster, bei dem Verfahrensmanuale zunehmend diverse Informationen in Anhänge verlagern. Haupttext wird dadurch weniger umfassend. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Technical Writing AI", "narrower_terms": [], "cross_domain_refs": [ "AED-0019", "ASE-0002", "ASE-0027" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "TEW-0003", "domain": "TEW", "term_en": "Article Linking Density Shift", "term_de": "ArticleLinkingDensityShift", "definition_en": "An information architecture concept in AI-augmented writing workflows, identifiable by observable pattern in which AI-assisted knowledge bases increase cross-reference density without proportional relevance verification. Distinguished from adjacent concepts by its focus on the specific mechanism through which article manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Phänomen der technischen Dokumentation in KI-gestützter Textproduktion, gekennzeichnet durch observable Anstieg von Querverweis-Häufigkeit in KI-gestützten Wissensdatenbanken. Führt zu fragmentiertem statt linearem Lesen. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Technical Writing AI", "narrower_terms": [], "cross_domain_refs": [ "COG-0182" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q735", "legal_classification": "descriptive_research_term" }, { "id": "TEW-0004", "domain": "TEW", "term_en": "Attestation Form Standardization", "term_de": "AttestationFormStandardization", "definition_en": "An information architecture concept in AI-augmented writing workflows, identifiable by pattern in which compliance documentation employs identical attestation language across distinct certifications. Distinguished from adjacent concepts by its focus on the specific mechanism through which attestation manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Phänomen der technischen Dokumentation in KI-gestützter Textproduktion, gekennzeichnet durch muster, bei dem Compliance-Dokumentation identische Attestierungs-Sprache einsetzt. Reduziert Kontextspezifizität zugunsten standardisierter Formulierungen. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Technical Writing AI", "narrower_terms": [], "cross_domain_refs": [ "ART-0095", "COG-0175", "ELR-0172" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "TEW-0005", "domain": "TEW", "term_en": "Audit Trail Documentation Density", "term_de": "AuditTrailDocumentationDensity", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A technical writing phenomenon in AI-assisted documentation, characterized by pattern in which compliance documentation specifies extensive record-keeping without proportional likelihood correlation. This phenomenon operates at the intersection of audit and trail dynamics within the broader TEW domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept phänomen der technischen Dokumentation in KI-gestützter Textproduktion, gekennzeichnet durch muster extensiver Dokumentation von Audit-Trails in Compliance-Texten. Trägt zu Volumen-Inflation bei, ohne Klarheit zu erhöhen. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Technical Writing AI", "narrower_terms": [], "cross_domain_refs": [ "STE-0015" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "TEW-0006", "domain": "TEW", "term_en": "Authentication Boilerplate Replication", "term_de": "AuthenticationBoilerplateReplication", "definition_en": "A documentation quality pattern in AI-mediated technical communication, measurable through pattern where authentication requirement documentation repeats standardized language across distinct API sections. The concept emerges specifically in contexts where authentication–boilerplate interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Phänomen der technischen Dokumentation in KI-gestützter Textproduktion, gekennzeichnet durch wiederholung standardisierter Authentifizierungs-Anforderungs-Sprache über mehrere Dokumente. Erzeugt redundante, aber unvermeidbare Textpassagen. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Technical Writing AI", "narrower_terms": [], "cross_domain_refs": [ "ART-0091", "COG-0093", "DAT-0081" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "TEW-0007", "domain": "TEW", "term_en": "Author Attribution Comment Proliferation", "term_de": "AuthorAttributionCommentProliferation", "definition_en": "A documentation quality pattern in AI-mediated technical communication, measurable through pattern in which code authorship comments appear with frequency disproportionate to actual change relevance. The concept emerges specifically in contexts where author–attribution interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Phänomen der technischen Dokumentation in KI-gestützter Textproduktion, gekennzeichnet durch überproportional häufige Nennung von Code-Autoren in Dokumentation. Dient der Nachvollziehbarkeit, führt aber zu Informationsclutter. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Analytical Ratio", "narrower_terms": [], "cross_domain_refs": [ "VIB-0065" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "TEW-0008", "domain": "TEW", "term_en": "Backward Compatibility Assertion Repetition", "term_de": "BackwardCompatibilityAssertionRepetition", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A technical writing phenomenon in AI-assisted documentation, characterized by tendency for version release documentation to reiterate identical reverse-oriented compatibility claims across releases. This phenomenon operates at the intersection of reverse-oriented and compatibility dynamics within the broader TEW domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept phänomen der technischen Dokumentation in KI-gestützter Textproduktion, gekennzeichnet durch repetitive Zusicherungen für Rückwärtskompatibilität in Release-Dokumentation. Typischerweise identische Formulierung über mehrere Versionen. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Technical Writing AI", "narrower_terms": [], "cross_domain_refs": [ "SWE-0008" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "TEW-0009", "domain": "TEW", "term_en": "Boilerplate Cascade", "term_de": "BoilerplateKaskade", "definition_en": "A documentation quality pattern in AI-mediated technical communication, measurable through a technical writing phenomenon observed when observable trend where fixed introductory and concluding passages appear identically across distinct documentation modules. The concept emerges specifically in contexts where boilerplate–cascade interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Phänomen der technischen Dokumentation in KI-gestützter Textproduktion, gekennzeichnet durch observable Tendenz, identische Einleitung und Abschlusspassagen in Dokumentation zu replizieren. Erkennbar an wiederholten standardisierten Formulierungen. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Cascade Effect", "narrower_terms": [], "cross_domain_refs": [ "AED-0096", "COG-0005", "COG-0025" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "TEW-0010", "domain": "TEW", "term_en": "Breaking Change Announcement Vagueness", "term_de": "BreakingChangeAnnouncementVagueness", "definition_en": "A documentation quality pattern in AI-mediated technical communication, measurable through pattern in which release notes obsresolve breaking changes through indirect or hedged language. The concept emerges specifically in contexts where breaking–change interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Phänomen der technischen Dokumentation in KI-gestützter Textproduktion, gekennzeichnet durch muster der indirekten oder verhüllten Ankündigung von Breaking Changes in Release Notes. Erschwert Wahrnehmung von Disruptionen. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-3001", "narrower_terms": [], "cross_domain_refs": [ "WEB-0030", "WEB-0051", "VIB-0165" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "TEW-0011", "domain": "TEW", "term_en": "Bug Fix Categorization Ambiguity", "term_de": "BugFixCategorizationAmbiguity", "definition_en": "A documentation quality pattern in AI-mediated technical communication, measurable through observable pattern in which release notes classify fixes under inconsistent category schemes. The concept emerges specifically in contexts where bug–fix interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Phänomen der technischen Dokumentation in KI-gestützter Textproduktion, gekennzeichnet durch beobachtbares Muster in technischer Dokumentation, das durch KI-Automation oder Standardisierungsdruck entsteht. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Technical Writing AI", "narrower_terms": [], "cross_domain_refs": [ "PHO-0019", "BEH-0072", "PER-0064" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "TEW-0012", "domain": "TEW", "term_en": "Category Orphaning Rate", "term_de": "CategoryOrphaningRate", "definition_en": "An information architecture concept in AI-augmented writing workflows, identifiable by phenomenon in which knowledge base category pages lose article mappings despite unchanged categorization structure. Distinguished from adjacent concepts by its focus on the specific mechanism through which category manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Phänomen der technischen Dokumentation in KI-gestützter Textproduktion, gekennzeichnet durch aspekt von category orphaning rate, der in TEW-Kontexten relevant ist und Interaktionen zwischen Menschen und KI-Systemen beeinflusst. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Technical Writing AI", "narrower_terms": [], "cross_domain_refs": [ "AED-0022", "DES-0088", "ELR-0037" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "TEW-0013", "domain": "TEW", "term_en": "Certification Requirement Duplication", "term_de": "CertificationRequirementDuplication", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures A technical writing phenomenon in AI-assisted documentation, characterized by observable pattern in which compliance documentation repeats identical certification requirements. This phenomenon operates at the intersection of certification and requirement dynamics within the broader TEW domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept phänomen der technischen Dokumentation in KI-gestützter Textproduktion, gekennzeichnet durch beobachtbares Muster in technischer Dokumentation, das durch KI-Automation oder Standardisierungsdruck entsteht. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Technical Writing AI", "narrower_terms": [], "cross_domain_refs": [ "AED-0008", "AED-0083", "COG-0103" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "TEW-0014", "domain": "TEW", "term_en": "Changelog Comment Redundancy", "term_de": "ChangelogCommentRedundancy", "definition_en": "A documentation quality pattern in AI-mediated technical communication, measurable through observable tendency for code comments to repeat information already documented in commit messages. The concept emerges specifically in contexts where changelog–comment interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Phänomen der technischen Dokumentation in KI-gestützter Textproduktion, gekennzeichnet durch beobachtbares Muster in technischer Dokumentation, das durch KI-Automation oder Standardisierungsdruck entsteht. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Technical Writing AI", "narrower_terms": [], "cross_domain_refs": [ "VIB-0110" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "TEW-0015", "domain": "TEW", "term_en": "Changelog Granularity Inflation", "term_de": "ChangelogGranularityInflation", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A technical writing phenomenon in AI-assisted documentation, characterized by observable pattern in which version changelogs record minimal changes with excessive descriptive detail. This phenomenon operates at the intersection of changelog and granularity dynamics within the broader TEW domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept phänomen der technischen Dokumentation in KI-gestützter Textproduktion, gekennzeichnet durch beobachtbares Muster in technischer Dokumentation, das durch KI-Automation oder Standardisierungsdruck entsteht. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Technical Writing AI", "narrower_terms": [], "cross_domain_refs": [ "ASE-0022", "ASE-0025", "ASE-0088" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "TEW-0016", "domain": "TEW", "term_en": "Code Section Delimiting Markers", "term_de": "CodeSectionDelimitingMarkers", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A technical writing phenomenon in AI-assisted documentation, characterized by pattern in which delimiter comments proliferate to organize code regions despite modern IDE capabilities. This phenomenon operates at the intersection of code and section dynamics within the broader TEW domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept phänomen der technischen Dokumentation in KI-gestützter Textproduktion, gekennzeichnet durch beobachtbares Muster in technischer Dokumentation, das durch KI-Automation oder Standardisierungsdruck entsteht. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Technical Writing AI", "narrower_terms": [], "cross_domain_refs": [ "VIB-0065" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "TEW-0017", "domain": "TEW", "term_en": "Comment Redundancy Accumulation", "term_de": "CommentRedundancyAccumulation", "definition_en": "An information architecture concept in AI-augmented writing workflows, identifiable by observable pattern in which code comments replicate information already present in function signatures. Distinguished from adjacent concepts by its focus on the specific mechanism through which comment manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Phänomen der technischen Dokumentation in KI-gestützter Textproduktion, gekennzeichnet durch beobachtbares Muster in technischer Dokumentation, das durch KI-Automation oder Standardisierungsdruck entsteht. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Technical Writing AI", "narrower_terms": [], "cross_domain_refs": [ "AGE-0068", "AGE-0091", "AGE-0092" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "TEW-0018", "domain": "TEW", "term_en": "Compatibility Matrix Complexity", "term_de": "CompatibilityMatrixComplexity", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A technical writing phenomenon in AI-assisted documentation, characterized by an instructional pattern manifesting as observable trend toward increasingly complex version compatibility matrices despite reduced actual variance. This phenomenon operates at the intersection of compatibility and matrix dynamics within the broader TEW domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept phänomen der technischen Dokumentation in KI-gestützter Textproduktion, gekennzeichnet durch aspekt von compatibility matrix complexity, der in TEW-Kontexten relevant ist und Interaktionen zwischen Menschen und KI-Systemen beeinflusst. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Analytical Matrix", "narrower_terms": [], "cross_domain_refs": [ "ADA-0001", "AGE-0068", "AGE-0069" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "TEW-0019", "domain": "TEW", "term_en": "Compliance Update Lag", "term_de": "ComplianceUpdateLag", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A technical writing phenomenon in AI-assisted documentation, characterized by phenomenon in which compliance documentation references outdated regulatory frameworks. This phenomenon operates at the intersection of compliance and update dynamics within the broader TEW domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept phänomen der technischen Dokumentation in KI-gestützter Textproduktion, gekennzeichnet durch aspekt von compliance update lag, der in TEW-Kontexten relevant ist und Interaktionen zwischen Menschen und KI-Systemen beeinflusst. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "RPH-2303", "narrower_terms": [], "cross_domain_refs": [ "AED-0018", "AED-0081", "AGE-0048" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "TEW-0020", "domain": "TEW", "term_en": "Contextual Abbreviation Spread", "term_de": "ContextualAbbreviationSpread", "definition_en": "An information architecture concept in AI-augmented writing workflows, identifiable by pattern where acronyms introduced in one document section replicate across unrelated sections without reintroduction. Distinguished from adjacent concepts by its focus on the specific mechanism through which contextual manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Phänomen der technischen Dokumentation in KI-gestützter Textproduktion, gekennzeichnet durch beobachtbares Muster in technischer Dokumentation, das durch KI-Automation oder Standardisierungsdruck entsteht. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Technical Writing AI", "narrower_terms": [], "cross_domain_refs": [ "AED-0021", "COG-0036", "COG-0114" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "TEW-0021", "domain": "TEW", "term_en": "Contextual Help Integration Absence", "term_de": "ContextualHelpIntegrationAbsence", "definition_en": "A documentation quality pattern in AI-mediated technical communication, measurable through pattern in which onboarding documentation maintains separation from in-application guidance. The concept emerges specifically in contexts where contextual–help interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Phänomen der technischen Dokumentation in KI-gestützter Textproduktion, gekennzeichnet durch beobachtbares Muster in technischer Dokumentation, das durch KI-Automation oder Standardisierungsdruck entsteht. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2604", "narrower_terms": [], "cross_domain_refs": [ "COG-0187" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "TEW-0022", "domain": "TEW", "term_en": "Contributor Attribution Inconsistency", "term_de": "ContributorAttributionInconsistency", "definition_en": "An information architecture concept in AI-augmented writing workflows, identifiable by phenomenon in which release notes apply inconsistent attribution formats across contributor listings. Distinguished from adjacent concepts by its focus on the specific mechanism through which contributor manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Phänomen der technischen Dokumentation in KI-gestützter Textproduktion, gekennzeichnet durch aspekt von contributor attribution inconsistency, der in TEW-Kontexten relevant ist und Interaktionen zwischen Menschen und KI-Systemen beeinflusst. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2351", "narrower_terms": [], "cross_domain_refs": [ "ART-0011", "ART-0019", "ART-0058" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "TEW-0023", "domain": "TEW", "term_en": "Data Retention Period Ambiguity", "term_de": "DataRetentionPeriodAmbiguity", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures A technical writing phenomenon in AI-assisted documentation, characterized by a technical writing phenomenon manifesting as observable trend in which compliance documentation presents unclear retention schedules. This phenomenon operates at the intersection of data and retention dynamics within the broader TEW domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept phänomen der technischen Dokumentation in KI-gestützter Textproduktion, gekennzeichnet durch aspekt von data retention period ambiguity, der in TEW-Kontexten relevant ist und Interaktionen zwischen Menschen und KI-Systemen beeinflusst. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Technical Writing AI", "narrower_terms": [], "cross_domain_refs": [ "LIN-0038", "SCR-0057", "SPA-0077" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q42848", "legal_classification": "observational_construct" }, { "id": "TEW-0024", "domain": "TEW", "term_en": "Reliance Documentation Lag", "term_de": "RelianceDocumentationLag", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures A technical writing phenomenon in AI-assisted documentation, characterized by phenomenon in which version documentation references obsolete reliance versions. This phenomenon operates at the intersection of reliance and documentation dynamics within the broader TEW domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept phänomen der technischen Dokumentation in KI-gestützter Textproduktion, gekennzeichnet durch aspekt von reliance documentation lag, der in TEW-Kontexten relevant ist und Interaktionen zwischen Menschen und KI-Systemen beeinflusst. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Technical Writing AI", "narrower_terms": [], "cross_domain_refs": [ "VIB-0072" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "TEW-0025", "domain": "TEW", "term_en": "Deprecation Notice Templating", "term_de": "DeprecationNoticeTemplating", "definition_en": "A documentation quality pattern in AI-mediated technical communication, measurable through pattern in which deprecation warnings adopt identical formatting and phrasing across distinct endpoint removals. The concept emerges specifically in contexts where deprecation–notice interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Phänomen der technischen Dokumentation in KI-gestützter Textproduktion, gekennzeichnet durch beobachtbares Muster in technischer Dokumentation, das durch KI-Automation oder Standardisierungsdruck entsteht. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-3101", "narrower_terms": [], "cross_domain_refs": [ "SCR-0034" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "TEW-0026", "domain": "TEW", "term_en": "Deprecation Timeline Obfuscation", "term_de": "DeprecationTimelineObfuscation", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A technical writing phenomenon in AI-assisted documentation, characterized by pattern in which release notes present unclear deprecation schedules for removed features. This phenomenon operates at the intersection of deprecation and timeline dynamics within the broader TEW domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept phänomen der technischen Dokumentation in KI-gestützter Textproduktion, gekennzeichnet durch beobachtbares Muster in technischer Dokumentation, das durch KI-Automation oder Standardisierungsdruck entsteht. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Technical Writing AI", "narrower_terms": [], "cross_domain_refs": [ "AGE-0061", "QUA-0028", "QUA-0061" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "TEW-0027", "domain": "TEW", "term_en": "Disambiguation Page Proliferation", "term_de": "DisambiguationPageProliferation", "definition_en": "An information architecture concept in AI-augmented writing workflows, identifiable by a technical writing phenomenon reflecting observable trend toward increased disambiguation pages despite minimal actual terminology conflicts. Distinguished from adjacent concepts by its focus on the specific mechanism through which disambiguation manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Phänomen der technischen Dokumentation in KI-gestützter Textproduktion, gekennzeichnet durch aspekt von disambiguation page proliferation, der in TEW-Kontexten relevant ist und Interaktionen zwischen Menschen und KI-Systemen beeinflusst. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Analytical Ratio", "narrower_terms": [], "cross_domain_refs": [ "COG-0179" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "TEW-0028", "domain": "TEW", "term_en": "Documentation String Formality Creep", "term_de": "DocumentationStringFormalityCreep", "definition_en": "A documentation quality pattern in AI-mediated technical communication, measurable through tendency for docstring conventions to progressively increase in strictness and length within single projects. The concept emerges specifically in contexts where documentation–string interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Phänomen der technischen Dokumentation in KI-gestützter Textproduktion, gekennzeichnet durch beobachtbares Muster in technischer Dokumentation, das durch KI-Automation oder Standardisierungsdruck entsteht. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Technical Writing AI", "narrower_terms": [], "cross_domain_refs": [ "CON-0075" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "TEW-0029", "domain": "TEW", "term_en": "Endpoint Naming Convergence", "term_de": "EndpointNamingKonvergenz", "definition_en": "Pattern in which API endpoint nomenclature adopts consistent linguistic patterns despite semantic differences in function. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Phänomen der technischen Dokumentation in KI-gestützter Textproduktion, gekennzeichnet durch beobachtbares Muster in technischer Dokumentation, das durch KI-Automation oder Standardisierungsdruck entsteht. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-3101", "narrower_terms": [], "cross_domain_refs": [ "ASE-0005", "COG-0062", "COG-0108" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "TEW-0030", "domain": "TEW", "term_en": "Error Code Abstraction", "term_de": "ErrorCodeAbstraction", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A technical writing phenomenon in AI-assisted documentation, characterized by observable pattern in which distinct error conditions reduce to homogeneous error messaging across API documentation. This phenomenon operates at the intersection of error and code dynamics within the broader TEW domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept phänomen der technischen Dokumentation in KI-gestützter Textproduktion, gekennzeichnet durch beobachtbares Muster in technischer Dokumentation, das durch KI-Automation oder Standardisierungsdruck entsteht. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Technical Writing AI", "narrower_terms": [], "cross_domain_refs": [ "AGE-0043", "BEH-0033", "COG-0077" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "TEW-0031", "domain": "TEW", "term_en": "Error Message Documentation Lag", "term_de": "ErrorMessageDocumentationLag", "definition_en": "A documentation quality pattern in AI-mediated technical communication, measurable through phenomenon in which troubleshooting guides reference error messages from outdated software versions. The concept emerges specifically in contexts where error–message interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Phänomen der technischen Dokumentation in KI-gestützter Textproduktion, gekennzeichnet durch aspekt von error message documentation lag, der in TEW-Kontexten relevant ist und Interaktionen zwischen Menschen und KI-Systemen beeinflusst. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-3902", "narrower_terms": [], "cross_domain_refs": [ "WEB-0020" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "TEW-0032", "domain": "TEW", "term_en": "Escalation Path Ambiguity", "term_de": "EscalationPathAmbiguity", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures A technical writing phenomenon in AI-assisted documentation, characterized by observable pattern in which troubleshooting guides provide unclear guidance for issue severity classification. This phenomenon operates at the intersection of escalation and trajectory dynamics within the broader TEW domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept phänomen der technischen Dokumentation in KI-gestützter Textproduktion, gekennzeichnet durch beobachtbares Muster in technischer Dokumentation, das durch KI-Automation oder Standardisierungsdruck entsteht. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "RPH-2604", "narrower_terms": [], "cross_domain_refs": [ "ART-0008", "ASE-0032", "ASE-0095" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "TEW-0033", "domain": "TEW", "term_en": "Example Genealogy", "term_de": "ExampleGenealogy", "definition_en": "A documentation quality pattern in AI-mediated technical communication, measurable through phenomenon in which example code snippets and use case demonstrations propagate across documentation sets with minimal variation. The concept emerges specifically in contexts where example–genealogy interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Phänomen der technischen Dokumentation in KI-gestützter Textproduktion, gekennzeichnet durch aspekt von example genealogy, der in TEW-Kontexten relevant ist und Interaktionen zwischen Menschen und KI-Systemen beeinflusst. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Technical Writing AI", "narrower_terms": [], "cross_domain_refs": [ "RHR-0084" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "TEW-0034", "domain": "TEW", "term_en": "Exception Documentation Boilerplate", "term_de": "ExceptionDocumentationBoilerplate", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A technical writing phenomenon in AI-assisted documentation, characterized by tendency for exception handling documentation to employ standardized phrases across disparate exception types. This phenomenon operates at the intersection of exception and documentation dynamics within the broader TEW domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept phänomen der technischen Dokumentation in KI-gestützter Textproduktion, gekennzeichnet durch beobachtbares Muster in technischer Dokumentation, das durch KI-Automation oder Standardisierungsdruck entsteht. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "RPH-2052", "narrower_terms": [], "cross_domain_refs": [ "ART-0077", "BEH-0031", "EDU-0037" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "TEW-0035", "domain": "TEW", "term_en": "Exemption Criteria Obscurity", "term_de": "ExemptionCriteriaObscurity", "definition_en": "An information architecture concept in AI-augmented writing workflows, identifiable by pattern in which compliance documentation obsresolves conditions for regulatory exemptions. Distinguished from adjacent concepts by its focus on the specific mechanism through which exemption manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Phänomen der technischen Dokumentation in KI-gestützter Textproduktion, gekennzeichnet durch beobachtbares Muster in technischer Dokumentation, das durch KI-Automation oder Standardisierungsdruck entsteht. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Technical Writing AI", "narrower_terms": [], "cross_domain_refs": [ "ART-0005", "COG-0159", "DES-0045" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "TEW-0036", "domain": "TEW", "term_en": "FAQ Content Convergence", "term_de": "FaqContentKonvergenz", "definition_en": "An information architecture concept in AI-augmented writing workflows, identifiable by pattern where FAQ sections across knowledge base modules develop identical question-answer pairs. Distinguished from adjacent concepts by its focus on the specific mechanism through which faq manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Phänomen der technischen Dokumentation in KI-gestützter Textproduktion, gekennzeichnet durch beobachtbares Muster in technischer Dokumentation, das durch KI-Automation oder Standardisierungsdruck entsteht. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Technical Writing AI", "narrower_terms": [], "cross_domain_refs": [ "AED-0019", "ASE-0002", "ASE-0005" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "TEW-0037", "domain": "TEW", "term_en": "Feature Announcement Redundancy", "term_de": "FeatureAnnouncementRedundancy", "definition_en": "An information architecture concept in AI-augmented writing workflows, identifiable by observable pattern in which release notes announce identical features with minimal variation across versions. Distinguished from adjacent concepts by its focus on the specific mechanism through which feature manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Phänomen der technischen Dokumentation in KI-gestützter Textproduktion, gekennzeichnet durch beobachtbares Muster in technischer Dokumentation, das durch KI-Automation oder Standardisierungsdruck entsteht. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Technical Writing AI", "narrower_terms": [], "cross_domain_refs": [ "AGE-0002", "AGE-0005", "AGE-0008" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "TEW-0038", "domain": "TEW", "term_en": "Feature Deprecation Timeline Ambiguity", "term_de": "FeatureDeprecationTimelineAmbiguity", "definition_en": "A documentation quality pattern in AI-mediated technical communication, measurable through pattern in which version documentation presents unclear timelines for deprecated feature removal. The concept emerges specifically in contexts where feature–deprecation interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Phänomen der technischen Dokumentation in KI-gestützter Textproduktion, gekennzeichnet durch beobachtbares Muster in technischer Dokumentation, das durch KI-Automation oder Standardisierungsdruck entsteht. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Technical Writing AI", "narrower_terms": [], "cross_domain_refs": [ "VIB-0167", "QUA-0028", "AGE-0061" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "TEW-0039", "domain": "TEW", "term_en": "First Use Documentation Length", "term_de": "FirstUseDocumentationLength", "definition_en": "An information architecture concept in AI-augmented writing workflows, identifiable by observable pattern in which onboarding documentation expands beyond initial user engagement window. Distinguished from adjacent concepts by its focus on the specific mechanism through which first manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Phänomen der technischen Dokumentation in KI-gestützter Textproduktion, gekennzeichnet durch beobachtbares Muster in technischer Dokumentation, das durch KI-Automation oder Standardisierungsdruck entsteht. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Technical Writing AI", "narrower_terms": [], "cross_domain_refs": [ "VIB-0161", "ART-0077", "EDU-0037" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "TEW-0040", "domain": "TEW", "term_en": "Gshiftary Artifact Recurrence", "term_de": "GshiftaryArtifactRecurrence", "definition_en": "A documentation quality pattern in AI-mediated technical communication, measurable through tendency for identical definitional phrasings to appear in multiple gshiftaries across related documentation products. The concept emerges specifically in contexts where gshiftary–artifact interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Phänomen der technischen Dokumentation in KI-gestützter Textproduktion, gekennzeichnet durch beobachtbares Muster in technischer Dokumentation, das durch KI-Automation oder Standardisierungsdruck entsteht. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2701", "narrower_terms": [], "cross_domain_refs": [ "ASE-0006", "ASE-0022", "ASE-0023" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q735", "legal_classification": "analytical_category" }, { "id": "TEW-0041", "domain": "TEW", "term_en": "Goal Accomplishment Path Multiplicity", "term_de": "GoalAccomplishmentPathMultiplicity", "definition_en": "A documentation quality pattern in AI-mediated technical communication, measurable through pattern in which onboarding documentation presents multiple pathways without differentiation by user skill. The concept emerges specifically in contexts where goal–accomplishment interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Phänomen der technischen Dokumentation in KI-gestützter Textproduktion, gekennzeichnet durch beobachtbares Muster in technischer Dokumentation, das durch KI-Automation oder Standardisierungsdruck entsteht. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Technical Writing AI", "narrower_terms": [], "cross_domain_refs": [ "WEB-0028", "NEO-1745", "IDN-0042" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "TEW-0042", "domain": "TEW", "term_en": "Illustration Caption Verbosity", "term_de": "IllustrationCaptionVerbosity", "definition_en": "An information architecture concept in AI-augmented writing workflows, identifiable by observable pattern where manual figure captions expand beyond descriptive necessity. Distinguished from adjacent concepts by its focus on the specific mechanism through which illustration manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Phänomen der technischen Dokumentation in KI-gestützter Textproduktion, gekennzeichnet durch beobachtbares Muster in technischer Dokumentation, das durch KI-Automation oder Standardisierungsdruck entsteht. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Analytical Ratio", "narrower_terms": [], "cross_domain_refs": [ "CON-0016", "CRE-0172", "SCR-0026" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "TEW-0043", "domain": "TEW", "term_en": "Indicator Matching Oversimplification", "term_de": "IndicatorMatchingOversimplification", "definition_en": "An information architecture concept in AI-augmented writing workflows, identifiable by pattern in which troubleshooting guides map disparate issues to identical indicators. Distinguished from adjacent concepts by its focus on the specific mechanism through which indicator manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Phänomen der technischen Dokumentation in KI-gestützter Textproduktion, gekennzeichnet durch beobachtbares Muster in technischer Dokumentation, das durch KI-Automation oder Standardisierungsdruck entsteht. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-3902", "narrower_terms": [], "cross_domain_refs": [ "CON-0007", "DAT-0057", "MTH-0009" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "TEW-0044", "domain": "TEW", "term_en": "Inline Comment Density Increase", "term_de": "InlineCommentDensityIncrease", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A technical writing phenomenon in AI-assisted documentation, characterized by pattern in which AI-assisted code generation accompanies increasingly verbose inline explanations per statement. This phenomenon operates at the intersection of inline and comment dynamics within the broader TEW domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept phänomen der technischen Dokumentation in KI-gestützter Textproduktion, gekennzeichnet durch beobachtbares Muster in technischer Dokumentation, das durch KI-Automation oder Standardisierungsdruck entsteht. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Technical Writing AI", "narrower_terms": [], "cross_domain_refs": [ "VIB-0101" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "TEW-0045", "domain": "TEW", "term_en": "Instruction Verbosity Expansion", "term_de": "InstructionVerbosityExpansion", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures A technical writing phenomenon in AI-assisted documentation, characterized by a technical writing phenomenon where observable trend in which AI-generated procedural manuals increase word count per instruction step without clarity gain. This phenomenon operates at the intersection of instruction and verbosity dynamics within the broader TEW domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept phänomen der technischen Dokumentation in KI-gestützter Textproduktion, gekennzeichnet durch aspekt von instruction verbosity expansion, der in TEW-Kontexten relevant ist und Interaktionen zwischen Menschen und KI-Systemen beeinflusst. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Technical Writing AI", "narrower_terms": [], "cross_domain_refs": [ "AGE-0057", "AGE-0058", "ASE-0014" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "TEW-0046", "domain": "TEW", "term_en": "Introductory Section Expansion", "term_de": "IntroductorySectionExpansion", "definition_en": "A documentation quality pattern in AI-mediated technical communication, measurable through a technical writing phenomenon involving observable trend where manual introductions grow longer without proportional increase in essential context. The concept emerges specifically in contexts where introductory–section interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Phänomen der technischen Dokumentation in KI-gestützter Textproduktion, gekennzeichnet durch aspekt von introductory section expansion, der in TEW-Kontexten relevant ist und Interaktionen zwischen Menschen und KI-Systemen beeinflusst. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Technical Writing AI", "narrower_terms": [], "cross_domain_refs": [ "COG-0070", "COG-0101", "FIC-0026" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "TEW-0047", "domain": "TEW", "term_en": "Jurisdiction-Specific Content Segregation", "term_de": "Jurisdiction-specificContentSegregation", "definition_en": "An information architecture concept in AI-augmented writing workflows, identifiable by a technical writing phenomenon where observable trend in which compliance documentation separates identical requirements across jurisdiction sections. Distinguished from adjacent concepts by its focus on the specific mechanism through which jurisdiction manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Phänomen der technischen Dokumentation in KI-gestützter Textproduktion, gekennzeichnet durch aspekt von jurisdiction-specific content segregation, der in TEW-Kontexten relevant ist und Interaktionen zwischen Menschen und KI-Systemen beeinflusst. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Technical Writing AI", "narrower_terms": [], "cross_domain_refs": [ "MSC-0075", "MKT-0028", "MKT-0017" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "TEW-0048", "domain": "TEW", "term_en": "Known Issues Persistence", "term_de": "KnownIssuesPersistence", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A technical writing phenomenon in AI-assisted documentation, characterized by observable pattern in which version documentation carries forward identical known issues across multiple releases. This phenomenon operates at the intersection of known and issues dynamics within the broader TEW domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept phänomen der technischen Dokumentation in KI-gestützter Textproduktion, gekennzeichnet durch beobachtbares Muster in technischer Dokumentation, das durch KI-Automation oder Standardisierungsdruck entsteht. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Technical Writing AI", "narrower_terms": [], "cross_domain_refs": [ "AED-0043", "AED-0057", "AGE-0053" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "TEW-0049", "domain": "TEW", "term_en": "Learning Outcome Articulation Absence", "term_de": "LearningOutcomeArticulationAbsence", "definition_en": "A documentation quality pattern in AI-mediated technical communication, measurable through a documentation effect reflecting a recurring deficiency in AI-generated onboarding documentation where procedural steps are provided without articulating the underlying learning objectives, competency targets, or knowledge outcomes the reader is expected to achieve upon completion. The concept emerges specifically in contexts where learning–outcome interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Phänomen der technischen Dokumentation in KI-gestützter Textproduktion, gekennzeichnet durch beobachtbares Muster in technischer Dokumentation, das durch KI-Automation oder Standardisierungsdruck entsteht. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2703", "narrower_terms": [], "cross_domain_refs": [ "ELR-0151", "WRK-0075", "CUS-0019" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q133500", "legal_classification": "systematic_classification" }, { "id": "TEW-0050", "domain": "TEW", "term_en": "Liability Disclaimer Proliferation", "term_de": "LiabilityDisclaimerProliferation", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures A technical writing phenomenon in AI-assisted documentation, characterized by pattern in which compliance documentation accumulates redundant liability disclaimers. This phenomenon operates at the intersection of liability and disclaimer dynamics within the broader TEW domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept phänomen der technischen Dokumentation in KI-gestützter Textproduktion, gekennzeichnet durch beobachtbares Muster in technischer Dokumentation, das durch KI-Automation oder Standardisierungsdruck entsteht. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Analytical Ratio", "narrower_terms": [], "cross_domain_refs": [ "SWE-0079" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "TEW-0051", "domain": "TEW", "term_en": "Log File Analysis Oversimplification", "term_de": "LogFileAnalysisOversimplification", "definition_en": "A documentation quality pattern in AI-mediated technical communication, measurable through tendency for troubleshooting guides to present log analysis procedures as straightforward despite complex pattern recognition. The concept emerges specifically in contexts where log–file interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Phänomen der technischen Dokumentation in KI-gestützter Textproduktion, gekennzeichnet durch beobachtbares Muster in technischer Dokumentation, das durch KI-Automation oder Standardisierungsdruck entsteht. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-3902", "narrower_terms": [], "cross_domain_refs": [ "MTH-0068" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "TEW-0052", "domain": "TEW", "term_en": "Metadata Tag Saturation", "term_de": "MetadataTagSaturation", "definition_en": "An information architecture concept in AI-augmented writing workflows, identifiable by a documentation effect characterized by observable trend toward excessive categorization in knowledge base article taxonomy, reducing specificity. Distinguished from adjacent concepts by its focus on the specific mechanism through which metadata manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Phänomen der technischen Dokumentation in KI-gestützter Textproduktion, gekennzeichnet durch aspekt von metadata tag saturation, der in TEW-Kontexten relevant ist und Interaktionen zwischen Menschen und KI-Systemen beeinflusst. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Analytical Ratio", "narrower_terms": [], "cross_domain_refs": [ "ASE-0033", "COG-0058", "COP-0015" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q42848", "legal_classification": "observational_construct" }, { "id": "TEW-0053", "domain": "TEW", "term_en": "Metaphor Replication", "term_de": "MetaphorReplication", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures A technical writing phenomenon in AI-assisted documentation, characterized by tendency for generated documentation to reuse identical analogies and conceptual frameworks across different technical subjects. This phenomenon operates at the intersection of metaphor and replication dynamics within the broader TEW domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept phänomen der technischen Dokumentation in KI-gestützter Textproduktion, gekennzeichnet durch beobachtbares Muster in technischer Dokumentation, das durch KI-Automation oder Standardisierungsdruck entsteht. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "RPH-2303", "narrower_terms": [], "cross_domain_refs": [ "MTH-0015" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q4263830", "legal_classification": "analytical_category" }, { "id": "TEW-0054", "domain": "TEW", "term_en": "Migration Burden Minimization Rhetoric", "term_de": "MigrationBurdenMinimizationRhetoric", "definition_en": "A documentation quality pattern in AI-mediated technical communication, measurable through observable pattern in which release notes downplay upgrade complexity relative to actual implementation effort. The concept emerges specifically in contexts where migration–burden interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Phänomen der technischen Dokumentation in KI-gestützter Textproduktion, gekennzeichnet durch beobachtbares Muster in technischer Dokumentation, das durch KI-Automation oder Standardisierungsdruck entsteht. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Analytical Ratio", "narrower_terms": [], "cross_domain_refs": [ "SPA-0070", "QUA-0023", "VIB-0123" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q8001", "legal_classification": "systematic_classification" }, { "id": "TEW-0055", "domain": "TEW", "term_en": "Migration Guide Obsolescence Lag", "term_de": "MigrationGuideObsolescenceLag", "definition_en": "An information architecture concept in AI-augmented writing workflows, identifiable by pattern in which version migration documentation persists beyond functional relevance to user base. Distinguished from adjacent concepts by its focus on the specific mechanism through which migration manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Phänomen der technischen Dokumentation in KI-gestützter Textproduktion, gekennzeichnet durch beobachtbares Muster in technischer Dokumentation, das durch KI-Automation oder Standardisierungsdruck entsteht. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Analytical Ratio", "narrower_terms": [], "cross_domain_refs": [ "WEB-0083", "SPR-0028", "CON-0086" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "TEW-0056", "domain": "TEW", "term_en": "Mistake Restoration Documentation Gap", "term_de": "MistakeRestorationDocumentationGap", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A technical writing phenomenon in AI-assisted documentation, characterized by pattern in which onboarding guides provide insufficient guidance for common user errors. This phenomenon operates at the intersection of mistake and restoration dynamics within the broader TEW domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept phänomen der technischen Dokumentation in KI-gestützter Textproduktion, gekennzeichnet durch beobachtbares Muster in technischer Dokumentation, das durch KI-Automation oder Standardisierungsdruck entsteht. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "RPH-2604", "narrower_terms": [], "cross_domain_refs": [ "STE-0072" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "TEW-0057", "domain": "TEW", "term_en": "Navigation Breadcrumb Standardization", "term_de": "NavigationBreadcrumbStandardization", "definition_en": "An information architecture concept in AI-augmented writing workflows, identifiable by observable pattern in which knowledge base breadcrumb trails adopt uniform naming despite hierarchical differences. Distinguished from adjacent concepts by its focus on the specific mechanism through which navigation manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Phänomen der technischen Dokumentation in KI-gestützter Textproduktion, gekennzeichnet durch beobachtbares Muster in technischer Dokumentation, das durch KI-Automation oder Standardisierungsdruck entsteht. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Technical Writing AI", "narrower_terms": [], "cross_domain_refs": [ "AGE-0010", "AGE-0076", "ART-0095" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "TEW-0058", "domain": "TEW", "term_en": "Nested Instruction Depth", "term_de": "NestedInstructionDepth", "definition_en": "A documentation quality pattern in AI-mediated technical communication, measurable through tendency for AI-generated manual instructions to contain increasingly nested sub-steps despite simpler viable alternatives. The concept emerges specifically in contexts where nested–instruction interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Phänomen der technischen Dokumentation in KI-gestützter Textproduktion, gekennzeichnet durch beobachtbares Muster in technischer Dokumentation, das durch KI-Automation oder Standardisierungsdruck entsteht. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Technical Writing AI", "narrower_terms": [], "cross_domain_refs": [ "RPH-2102" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "TEW-0059", "domain": "TEW", "term_en": "Network Connectivity Assumption", "term_de": "NetworkConnectivityAssumption", "definition_en": "An information architecture concept in AI-augmented writing workflows, identifiable by pattern in which troubleshooting guides assume network availability without conditional documentation. Distinguished from adjacent concepts by its focus on the specific mechanism through which network manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Phänomen der technischen Dokumentation in KI-gestützter Textproduktion, gekennzeichnet durch beobachtbares Muster in technischer Dokumentation, das durch KI-Automation oder Standardisierungsdruck entsteht. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-3902", "narrower_terms": [], "cross_domain_refs": [ "AED-0065", "COG-0182", "COP-0002" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "TEW-0060", "domain": "TEW", "term_en": "Parameter Documentation Expansion", "term_de": "ParameterDocumentationExpansion", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A technical writing phenomenon in AI-assisted documentation, characterized by a technical writing phenomenon manifesting as observable trend where function parameter descriptions expand beyond type information without semantic clarity increase. This phenomenon operates at the intersection of parameter and documentation dynamics within the broader TEW domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept phänomen der technischen Dokumentation in KI-gestützter Textproduktion, gekennzeichnet durch aspekt von parameter documentation expansion, der in TEW-Kontexten relevant ist und Interaktionen zwischen Menschen und KI-Systemen beeinflusst. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Technical Writing AI", "narrower_terms": [], "cross_domain_refs": [ "ART-0077", "ASE-0061", "ASE-0062" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "TEW-0061", "domain": "TEW", "term_en": "Parameter Enumeration Homogeneity", "term_de": "ParameterEnumerationHomogeneity", "definition_en": "A documentation quality pattern in AI-mediated technical communication, measurable through pattern where API endpoint documentation consistently presents arguments in identical order despite functional differences. The concept emerges specifically in contexts where parameter–enumeration interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Phänomen der technischen Dokumentation in KI-gestützter Textproduktion, gekennzeichnet durch beobachtbares Muster in technischer Dokumentation, das durch KI-Automation oder Standardisierungsdruck entsteht. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-3101", "narrower_terms": [], "cross_domain_refs": [ "ASE-0061", "ASE-0062", "COP-0010" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "TEW-0062", "domain": "TEW", "term_en": "Performance Metric Documentation Absence", "term_de": "PerformanceMetricDocumentationAbsence", "definition_en": "An information architecture concept in AI-augmented writing workflows, identifiable by a technical writing phenomenon involving observable trend in which release notes claim performance improvements without quantified measurement data. Distinguished from adjacent concepts by its focus on the specific mechanism through which performance manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Phänomen der technischen Dokumentation in KI-gestützter Textproduktion, gekennzeichnet durch aspekt von performance metric documentation absence, der in TEW-Kontexten relevant ist und Interaktionen zwischen Menschen und KI-Systemen beeinflusst. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Performance Metric", "narrower_terms": [], "cross_domain_refs": [ "SPR-0018", "SPR-0074", "SPR-0075" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q1361053", "legal_classification": "empirical_phenomenon_label" }, { "id": "TEW-0063", "domain": "TEW", "term_en": "Performance Note Duplication", "term_de": "PerformanceNoteDuplication", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A technical writing phenomenon in AI-assisted documentation, characterized by pattern in which version documentation repeats performance improvement claims identically across point releases. This phenomenon operates at the intersection of performance and note dynamics within the broader TEW domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept phänomen der technischen Dokumentation in KI-gestützter Textproduktion, gekennzeichnet durch beobachtbares Muster in technischer Dokumentation, das durch KI-Automation oder Standardisierungsdruck entsteht. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "RPH-3101", "narrower_terms": [], "cross_domain_refs": [ "AED-0077", "ASE-0044", "CUS-0008" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q1361053", "legal_classification": "empirical_phenomenon_label" }, { "id": "TEW-0064", "domain": "TEW", "term_en": "Personalization Path Documentation", "term_de": "PersonalizationPathDocumentation", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures A technical writing phenomenon in AI-assisted documentation, characterized by observable pattern in which onboarding guides provide generic steps for diverse user configurations. This phenomenon operates at the intersection of personalization and trajectory dynamics within the broader TEW domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept phänomen der technischen Dokumentation in KI-gestützter Textproduktion, gekennzeichnet durch beobachtbares Muster in technischer Dokumentation, das durch KI-Automation oder Standardisierungsdruck entsteht. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "RPH-3902", "narrower_terms": [], "cross_domain_refs": [ "ART-0077", "BEH-0031", "CON-0064" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "TEW-0065", "domain": "TEW", "term_en": "Platform-Specific Note Segregation", "term_de": "Platform-specificNoteSegregation", "definition_en": "An information architecture concept in AI-augmented writing workflows, identifiable by tendency for release notes to separately document identical issues across multiple platform variants. Distinguished from adjacent concepts by its focus on the specific mechanism through which platform manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Phänomen der technischen Dokumentation in KI-gestützter Textproduktion, gekennzeichnet durch beobachtbares Muster in technischer Dokumentation, das durch KI-Automation oder Standardisierungsdruck entsteht. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Technical Writing AI", "narrower_terms": [], "cross_domain_refs": [ "CUS-0018" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "TEW-0066", "domain": "TEW", "term_en": "Prerequisite Enumeration Complexity", "term_de": "PrerequisiteEnumerationComplexity", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A technical writing phenomenon in AI-assisted documentation, characterized by observable pattern in which manual prerequisite sections list requirements with excessive detail level. This phenomenon operates at the intersection of prerequisite and enumeration dynamics within the broader TEW domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept phänomen der technischen Dokumentation in KI-gestützter Textproduktion, gekennzeichnet durch beobachtbares Muster in technischer Dokumentation, das durch KI-Automation oder Standardisierungsdruck entsteht. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Analytical Ratio", "narrower_terms": [], "cross_domain_refs": [ "ADA-0001", "AGE-0068", "AGE-0069" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "TEW-0067", "domain": "TEW", "term_en": "Prerequisite Verification Step Inflation", "term_de": "PrerequisiteVerificationStepInflation", "definition_en": "A documentation quality pattern in AI-mediated technical communication, measurable through pattern in which troubleshooting guides require extensive environment checks before applying fixes. The concept emerges specifically in contexts where prerequisite–verification interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Phänomen der technischen Dokumentation in KI-gestützter Textproduktion, gekennzeichnet durch beobachtbares Muster in technischer Dokumentation, das durch KI-Automation oder Standardisierungsdruck entsteht. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2351", "narrower_terms": [], "cross_domain_refs": [ "ELR-0147", "STE-0018", "SPA-0035" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "TEW-0068", "domain": "TEW", "term_en": "Procedural Template Adherence", "term_de": "ProceduralTemplateAdherence", "definition_en": "A documentation quality pattern in AI-mediated technical communication, measurable through observable pattern in which sequential instruction sets maintain consistent ordering and numbering conventions across disparate technical guides. The concept emerges specifically in contexts where procedural–template interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Phänomen der technischen Dokumentation in KI-gestützter Textproduktion, gekennzeichnet durch beobachtbares Muster in technischer Dokumentation, das durch KI-Automation oder Standardisierungsdruck entsteht. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-3902", "narrower_terms": [], "cross_domain_refs": [ "COG-0055", "CON-0043", "COP-0024" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "TEW-0069", "domain": "TEW", "term_en": "Progressive Complexity Pacing Inconsistency", "term_de": "ProgressiveComplexityPacingInconsistency", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures A technical writing phenomenon in AI-assisted documentation, characterized by an instructional pattern reflecting observable trend in which onboarding documentation accelerates complexity introduction. This phenomenon operates at the intersection of progressive and complexity dynamics within the broader TEW domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept phänomen der technischen Dokumentation in KI-gestützter Textproduktion, gekennzeichnet durch aspekt von progressive complexity pacing inconsistency, der in TEW-Kontexten relevant ist und Interaktionen zwischen Menschen und KI-Systemen beeinflusst. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Technical Writing AI", "narrower_terms": [], "cross_domain_refs": [ "WEB-0077", "SWE-0009", "ADA-0001" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "TEW-0070", "domain": "TEW", "term_en": "Rate Limit Documentation Duplication", "term_de": "RateLimitDocumentationDuplication", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures A technical writing phenomenon in AI-assisted documentation, characterized by a documentation effect in which observable trend toward identical rate limiting descriptions across API documentation with functionally different constraints. This phenomenon operates at the intersection of rate and limit dynamics within the broader TEW domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept phänomen der technischen Dokumentation in KI-gestützter Textproduktion, gekennzeichnet durch aspekt von rate limit documentation duplication, der in TEW-Kontexten relevant ist und Interaktionen zwischen Menschen und KI-Systemen beeinflusst. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Technical Writing AI", "narrower_terms": [], "cross_domain_refs": [ "WEB-0069" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "TEW-0071", "domain": "TEW", "term_en": "Regulatory Reference Aggregation", "term_de": "RegulatoryReferenceAggregation", "definition_en": "A documentation quality pattern in AI-mediated technical communication, measurable through observable pattern in which compliance documentation cites identical regulations across unrelated sections. The concept emerges specifically in contexts where regulatory–reference interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Phänomen der technischen Dokumentation in KI-gestützter Textproduktion, gekennzeichnet durch beobachtbares Muster in technischer Dokumentation, das durch KI-Automation oder Standardisierungsdruck entsteht. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Technical Writing AI", "narrower_terms": [], "cross_domain_refs": [ "ADA-0002", "AED-0081", "ASE-0031" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "TEW-0072", "domain": "TEW", "term_en": "Related Article Suggestion Repetition", "term_de": "RelatedArticleSuggestionRepetition", "definition_en": "An information architecture concept in AI-augmented writing workflows, identifiable by tendency for knowledge base systems to recommend identical related articles across different entry queries. Distinguished from adjacent concepts by its focus on the specific mechanism through which related manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Phänomen der technischen Dokumentation in KI-gestützter Textproduktion, gekennzeichnet durch beobachtbares Muster in technischer Dokumentation, das durch KI-Automation oder Standardisierungsdruck entsteht. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Technical Writing AI", "narrower_terms": [], "cross_domain_refs": [ "AGE-0015", "CAI-0001", "TEM-0015" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q735", "legal_classification": "empirical_phenomenon_label" }, { "id": "TEW-0073", "domain": "TEW", "term_en": "Release Candidate Documentation Drift", "term_de": "ReleaseCandidateDocumentationDrift", "definition_en": "An information architecture concept in AI-augmented writing workflows, identifiable by pattern in which release candidate notes diverge from final release documentation without reconciliation. Distinguished from adjacent concepts by its focus on the specific mechanism through which release manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Phänomen der technischen Dokumentation in KI-gestützter Textproduktion, gekennzeichnet durch beobachtbares Muster in technischer Dokumentation, das durch KI-Automation oder Standardisierungsdruck entsteht. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Systemic Drift", "narrower_terms": [], "cross_domain_refs": [ "VIB-0161", "ART-0077", "EDU-0037" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "TEW-0074", "domain": "TEW", "term_en": "Release Note Boilerplate Reuse", "term_de": "ReleaseNoteBoilerplateReuse", "definition_en": "An information architecture concept in AI-augmented writing workflows, identifiable by a technical writing phenomenon reflecting observable trend where release notes adopt standardized structural elements regardless of release content variation. Distinguished from adjacent concepts by its focus on the specific mechanism through which release manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Phänomen der technischen Dokumentation in KI-gestützter Textproduktion, gekennzeichnet durch aspekt von release note boilerplate reuse, der in TEW-Kontexten relevant ist und Interaktionen zwischen Menschen und KI-Systemen beeinflusst. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Technical Writing AI", "narrower_terms": [], "cross_domain_refs": [ "RPH-1013", "ELR-0131", "VIB-0067" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "TEW-0075", "domain": "TEW", "term_en": "Response Schema Mirroring", "term_de": "ResponseSchemaMirroring", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures A technical writing phenomenon in AI-assisted documentation, characterized by tendency for API documentation to structure response object descriptions identically across endpoints with distinct return types. This phenomenon operates at the intersection of response and schema dynamics within the broader TEW domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept phänomen der technischen Dokumentation in KI-gestützter Textproduktion, gekennzeichnet durch beobachtbares Muster in technischer Dokumentation, das durch KI-Automation oder Standardisierungsdruck entsteht. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "RPH-3101", "narrower_terms": [], "cross_domain_refs": [ "AED-0039", "AGE-0001", "AGE-0088" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "TEW-0076", "domain": "TEW", "term_en": "Restoration Procedure Assumption Gap", "term_de": "RestorationProcedureAssumptionGap", "definition_en": "An information architecture concept in AI-augmented writing workflows, identifiable by observable pattern in which troubleshooting guides omit intermediate restoration steps. Distinguished from adjacent concepts by its focus on the specific mechanism through which restoration manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Phänomen der technischen Dokumentation in KI-gestützter Textproduktion, gekennzeichnet durch beobachtbares Muster in technischer Dokumentation, das durch KI-Automation oder Standardisierungsdruck entsteht. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-3902", "narrower_terms": [], "cross_domain_refs": [ "DAT-0049", "STE-0004", "DAT-0023" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "TEW-0077", "domain": "TEW", "term_en": "Return Value Annotation Proliferation", "term_de": "ReturnValueAnnotationProliferation", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures A technical writing phenomenon in AI-assisted documentation, characterized by pattern in which return value documentation accompanies identical descriptions despite functional differences. This phenomenon operates at the intersection of return and value dynamics within the broader TEW domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept phänomen der technischen Dokumentation in KI-gestützter Textproduktion, gekennzeichnet durch beobachtbares Muster in technischer Dokumentation, das durch KI-Automation oder Standardisierungsdruck entsteht. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "RPH-3601", "narrower_terms": [], "cross_domain_refs": [ "ROB-0211", "PLY-0057", "RET-0033" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "TEW-0078", "domain": "TEW", "term_en": "Safety Statement Accumulation", "term_de": "SafetyStatementAccumulation", "definition_en": "An information architecture concept in AI-augmented writing workflows, identifiable by pattern in which manuals accumulate safety disclaimers in header sections despite diminished legal differentiation. Distinguished from adjacent concepts by its focus on the specific mechanism through which safety manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Phänomen der technischen Dokumentation in KI-gestützter Textproduktion, gekennzeichnet durch beobachtbares Muster in technischer Dokumentation, das durch KI-Automation oder Standardisierungsdruck entsteht. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Technical Writing AI", "narrower_terms": [], "cross_domain_refs": [ "AGE-0068", "AGE-0091", "AGE-0092" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "TEW-0079", "domain": "TEW", "term_en": "Sandbox Environment Documentation", "term_de": "SandboxEnvironmentDocumentation", "definition_en": "An information architecture concept in AI-augmented writing workflows, identifiable by pattern in which testing environment descriptions replicate across documentation without accounting for environment-specific variations. Distinguished from adjacent concepts by its focus on the specific mechanism through which sandbox manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Phänomen der technischen Dokumentation in KI-gestützter Textproduktion, gekennzeichnet durch beobachtbares Muster in technischer Dokumentation, das durch KI-Automation oder Standardisierungsdruck entsteht. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Technical Writing AI", "narrower_terms": [], "cross_domain_refs": [ "PER-0021" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "TEW-0080", "domain": "TEW", "term_en": "Schema Validation Consistency", "term_de": "SchemaValidationConsistency", "definition_en": "A documentation quality pattern in AI-mediated technical communication, measurable through observable pattern where input validation rules present with uniform description structures despite differing constraint types. The concept emerges specifically in contexts where schema–validation interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Phänomen der technischen Dokumentation in KI-gestützter Textproduktion, gekennzeichnet durch beobachtbares Muster in technischer Dokumentation, das durch KI-Automation oder Standardisierungsdruck entsteht. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Validation Process", "narrower_terms": [], "cross_domain_refs": [ "AGE-0071", "AGE-0097", "ASE-0046" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "TEW-0081", "domain": "TEW", "term_en": "Search Result Clustering", "term_de": "SearchResultClustering", "definition_en": "Tendency for knowledge base retrieval systems to return semantically similar articles despite distinct user query topics. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Phänomen der technischen Dokumentation in KI-gestützter Textproduktion, gekennzeichnet durch beobachtbares Muster in technischer Dokumentation, das durch KI-Automation oder Standardisierungsdruck entsteht. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-3601", "narrower_terms": [], "cross_domain_refs": [ "AGE-0046", "ART-0009", "ASE-0009" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "TEW-0082", "domain": "TEW", "term_en": "Security Patch Disclosure Timing Lag", "term_de": "SecurityPatchDisclosureTimingLag", "definition_en": "A documentation quality pattern in AI-mediated technical communication, measurable through phenomenon in which security patch release notes document vulnerabilities after public disclosure windows. The concept emerges specifically in contexts where security–patch interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Phänomen der technischen Dokumentation in KI-gestützter Textproduktion, gekennzeichnet durch aspekt von security patch disclosure timing lag, der in TEW-Kontexten relevant ist und Interaktionen zwischen Menschen und KI-Systemen beeinflusst. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Technical Writing AI", "narrower_terms": [], "cross_domain_refs": [ "MKT-0085", "PER-0078", "MKT-0018" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "TEW-0083", "domain": "TEW", "term_en": "Sidebar Content Proliferation", "term_de": "SidebarContentProliferation", "definition_en": "An information architecture concept in AI-augmented writing workflows, identifiable by pattern in which manuals increasingly use sidebar notes and callouts to accommodate supplementary information. Distinguished from adjacent concepts by its focus on the specific mechanism through which sidebar manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Phänomen der technischen Dokumentation in KI-gestützter Textproduktion, gekennzeichnet durch beobachtbares Muster in technischer Dokumentation, das durch KI-Automation oder Standardisierungsdruck entsteht. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Analytical Ratio", "narrower_terms": [], "cross_domain_refs": [ "AED-0019", "ASE-0002", "ASE-0027" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "TEW-0084", "domain": "TEW", "term_en": "Solution Generalization Proliferation", "term_de": "SolutionGeneralizationProliferation", "definition_en": "An information architecture concept in AI-augmented writing workflows, identifiable by a documentation effect arising from observable trend in which troubleshooting guides provide overly broad solutions applicable across distinct problems. Distinguished from adjacent concepts by its focus on the specific mechanism through which solution manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Phänomen der technischen Dokumentation in KI-gestützter Textproduktion, gekennzeichnet durch aspekt von solution generalization proliferation, der in TEW-Kontexten relevant ist und Interaktionen zwischen Menschen und KI-Systemen beeinflusst. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-3902", "narrower_terms": [], "cross_domain_refs": [ "COG-0052", "CRE-0056", "DAT-0045" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "TEW-0085", "domain": "TEW", "term_en": "Standard Interpretation Variation", "term_de": "StandardInterpretationVariation", "definition_en": "Tendency for compliance documentation to interpret identical standards with inconsistent application guidance. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Phänomen der technischen Dokumentation in KI-gestützter Textproduktion, gekennzeichnet durch beobachtbares Muster in technischer Dokumentation, das durch KI-Automation oder Standardisierungsdruck entsteht. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2604", "narrower_terms": [], "cross_domain_refs": [ "AED-0042", "ASE-0003", "ASE-0046" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "TEW-0086", "domain": "TEW", "term_en": "Structural Mirroring", "term_de": "StructuralMirroring", "definition_en": "A documentation quality pattern in AI-mediated technical communication, measurable through pattern in which AI-generated documentation adopts the section hierarchy of prior documents, regardless of content requirements. The concept emerges specifically in contexts where structural–mirroring interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Phänomen der technischen Dokumentation in KI-gestützter Textproduktion, gekennzeichnet durch beobachtbares Muster in technischer Dokumentation, das durch KI-Automation oder Standardisierungsdruck entsteht. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Technical Writing AI", "narrower_terms": [], "cross_domain_refs": [ "CON-0079" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "TEW-0087", "domain": "TEW", "term_en": "Success Criteria Ambiguity", "term_de": "SuccessCriteriaAmbiguity", "definition_en": "An information architecture concept in AI-augmented writing workflows, identifiable by an instructional pattern manifesting as observable trend in which onboarding documentation lacks clear progression checkpoints. Distinguished from adjacent concepts by its focus on the specific mechanism through which success manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Phänomen der technischen Dokumentation in KI-gestützter Textproduktion, gekennzeichnet durch aspekt von success criteria ambiguity, der in TEW-Kontexten relevant ist und Interaktionen zwischen Menschen und KI-Systemen beeinflusst. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-3101", "narrower_terms": [], "cross_domain_refs": [ "AED-0032", "ART-0005", "ART-0008" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "TEW-0088", "domain": "TEW", "term_en": "Syntax Flattening", "term_de": "SyntaxFlattening", "definition_en": "An information architecture concept in AI-augmented writing workflows, identifiable by pattern in which AI systems render complex technical relationships through simplified sentence structures, eliminating subordinate clauses and conditional statements. Distinguished from adjacent concepts by its focus on the specific mechanism through which syntax manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Phänomen der technischen Dokumentation in KI-gestützter Textproduktion, gekennzeichnet durch beobachtbares Muster in technischer Dokumentation, das durch KI-Automation oder Standardisierungsdruck entsteht. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Technical Writing AI", "narrower_terms": [], "cross_domain_refs": [ "COG-0082" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "TEW-0089", "domain": "TEW", "term_en": "TODO Comment Stagnation", "term_de": "TodoCommentStagnation", "definition_en": "In the descriptive vocabulary of AI interaction science (following ISO 704 terminology standards), this concept identifies an information architecture concept in AI-augmented writing workflows, identifiable by observable pattern in which code TODO comments persist across development cycles without resolution tracking. Distinguished from adjacent concepts by its focus on the specific mechanism through which todo manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als analytisches Konstrukt der empirischen KI-Forschung (deskriptiv, nicht präskriptiv) erfasst dieser Terminus phänomen der technischen Dokumentation in KI-gestützter Textproduktion, gekennzeichnet durch beobachtbares Muster in technischer Dokumentation, das durch KI-Automation oder Standardisierungsdruck entsteht. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Technical Writing AI", "narrower_terms": [], "cross_domain_refs": [ "COG-0104", "CON-0020", "ELR-0150" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "TEW-0090", "domain": "TEW", "term_en": "Terminology Converging", "term_de": "TerminologyConverging", "definition_en": "A documentation quality pattern in AI-mediated technical communication, measurable through an instructional pattern involving observable regularity in how AI-assisted documentation systems employ consistent technical vocabulary across unrelated domains, reducing domain-specific terminology variation. The concept emerges specifically in contexts where terminology–converging interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Phänomen der technischen Dokumentation in KI-gestützter Textproduktion, gekennzeichnet durch aspekt von terminology converging, der in TEW-Kontexten relevant ist und Interaktionen zwischen Menschen und KI-Systemen beeinflusst. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Technical Writing AI", "narrower_terms": [], "cross_domain_refs": [ "ELR-0190" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "TEW-0091", "domain": "TEW", "term_en": "Terminology Introduction Sequencing", "term_de": "TerminologyIntroductionSequencing", "definition_en": "An information architecture concept in AI-augmented writing workflows, identifiable by pattern in which onboarding guides introduce technical terms without appropriate scaffolding. Distinguished from adjacent concepts by its focus on the specific mechanism through which terminology manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Phänomen der technischen Dokumentation in KI-gestützter Textproduktion, gekennzeichnet durch beobachtbares Muster in technischer Dokumentation, das durch KI-Automation oder Standardisierungsdruck entsteht. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-3902", "narrower_terms": [], "cross_domain_refs": [ "EDU-0061", "LIN-0070", "MTH-0052" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "TEW-0092", "domain": "TEW", "term_en": "Tool Configuration Prerequisite Burden", "term_de": "ToolConfigurationPrerequisiteBurden", "definition_en": "An information architecture concept in AI-augmented writing workflows, identifiable by observable pattern in which onboarding documentation requires extensive prerequisite setup. Distinguished from adjacent concepts by its focus on the specific mechanism through which tool manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Phänomen der technischen Dokumentation in KI-gestützter Textproduktion, gekennzeichnet durch beobachtbares Muster in technischer Dokumentation, das durch KI-Automation oder Standardisierungsdruck entsteht. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Analytical Ratio", "narrower_terms": [], "cross_domain_refs": [ "VIB-0173" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "TEW-0093", "domain": "TEW", "term_en": "Topic Breadth Compression", "term_de": "TopicBreadthCompression", "definition_en": "An information architecture concept in AI-augmented writing workflows, identifiable by pattern in which single knowledge base articles expand to cover multiple technical topics to minimize document count. Distinguished from adjacent concepts by its focus on the specific mechanism through which topic manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Phänomen der technischen Dokumentation in KI-gestützter Textproduktion, gekennzeichnet durch beobachtbares Muster in technischer Dokumentation, das durch KI-Automation oder Standardisierungsdruck entsteht. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Data Compression", "narrower_terms": [], "cross_domain_refs": [ "ASE-0087", "COG-0118", "COG-0129" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "TEW-0094", "domain": "TEW", "term_en": "Troubleshooting Reference Duplication", "term_de": "TroubleshootingReferenceDuplication", "definition_en": "A documentation quality pattern in AI-mediated technical communication, measurable through pattern in which manuals cross-reference identical troubleshooting sections across distinct chapter divisions. The concept emerges specifically in contexts where troubleshooting–reference interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Phänomen der technischen Dokumentation in KI-gestützter Textproduktion, gekennzeichnet durch beobachtbares Muster in technischer Dokumentation, das durch KI-Automation oder Standardisierungsdruck entsteht. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Technical Writing AI", "narrower_terms": [], "cross_domain_refs": [ "ADA-0002", "ELR-0157", "ROB-0091" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "TEW-0095", "domain": "TEW", "term_en": "Update Timestamp Inflation", "term_de": "UpdateTimestampInflation", "definition_en": "A documentation quality pattern in AI-mediated technical communication, measurable through pattern in which knowledge base articles show modification dates inconsistent with actual content alteration. The concept emerges specifically in contexts where update–timestamp interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Phänomen der technischen Dokumentation in KI-gestützter Textproduktion, gekennzeichnet durch beobachtbares Muster in technischer Dokumentation, das durch KI-Automation oder Standardisierungsdruck entsteht. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Technical Writing AI", "narrower_terms": [], "cross_domain_refs": [ "ASE-0022", "ASE-0025", "ASE-0088" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "TEW-0096", "domain": "TEW", "term_en": "Upgrade Warning Inconsistency", "term_de": "UpgradeWarningInconsistency", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures A technical writing phenomenon in AI-assisted documentation, characterized by tendency for upgrade documentation to present conflicting prerequisite requirements across version branches. This phenomenon operates at the intersection of upgrade and warning dynamics within the broader TEW domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept phänomen der technischen Dokumentation in KI-gestützter Textproduktion, gekennzeichnet durch beobachtbares Muster in technischer Dokumentation, das durch KI-Automation oder Standardisierungsdruck entsteht. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Technical Writing AI", "narrower_terms": [], "cross_domain_refs": [ "ASE-0018", "ASE-0075", "ASE-0078" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "TEW-0097", "domain": "TEW", "term_en": "Version Reference Anomaly", "term_de": "VersionReferenceAnomaly", "definition_en": "An information architecture concept in AI-augmented writing workflows, identifiable by tendency for API documentation to reference identical version numbers across historically distinct API iterations. Distinguished from adjacent concepts by its focus on the specific mechanism through which version manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Phänomen der technischen Dokumentation in KI-gestützter Textproduktion, gekennzeichnet durch beobachtbares Muster in technischer Dokumentation, das durch KI-Automation oder Standardisierungsdruck entsteht. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Technical Writing AI", "narrower_terms": [], "cross_domain_refs": [ "ADA-0002", "CON-0092", "CUS-0069" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "TEW-0098", "domain": "TEW", "term_en": "Voice Standardization", "term_de": "VoiceStandardization", "definition_en": "An information architecture concept in AI-augmented writing workflows, identifiable by observable shift toward passive construction prevalence in AI-authored technical documentation, reducing agent identification. Distinguished from adjacent concepts by its focus on the specific mechanism through which voice manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Phänomen der technischen Dokumentation in KI-gestützter Textproduktion, gekennzeichnet durch aspekt von voice standardization, der in TEW-Kontexten relevant ist und Interaktionen zwischen Menschen und KI-Systemen beeinflusst. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2701", "narrower_terms": [], "cross_domain_refs": [ "AGE-0014", "AGE-0048", "ART-0018" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "TEW-0099", "domain": "TEW", "term_en": "Warning Proliferation Pattern", "term_de": "WarningProliferationMuster", "definition_en": "A documentation quality pattern in AI-mediated technical communication, measurable through pattern where procedural manuals multiply cautionary statements disproportionate to actual likelihood scenarios described. The concept emerges specifically in contexts where warning–proliferation interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Phänomen der technischen Dokumentation in KI-gestützter Textproduktion, gekennzeichnet durch beobachtbares Muster in technischer Dokumentation, das durch KI-Automation oder Standardisierungsdruck entsteht. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Analytical Ratio", "narrower_terms": [], "cross_domain_refs": [ "AGE-0003", "AGE-0004", "AGE-0017" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "TEW-0100", "domain": "TEW", "term_en": "Workaround Documentation as Solution", "term_de": "WorkaroundDocumentationasSolution", "definition_en": "An information architecture concept in AI-augmented writing workflows, identifiable by pattern in which troubleshooting guides present permanent workarounds as equivalent to fixes. Distinguished from adjacent concepts by its focus on the specific mechanism through which workaround manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Phänomen der technischen Dokumentation in KI-gestützter Textproduktion, gekennzeichnet durch beobachtbares Muster in technischer Dokumentation, das durch KI-Automation oder Standardisierungsdruck entsteht. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-3902", "narrower_terms": [], "cross_domain_refs": [ "VIB-0161", "ART-0077", "EDU-0037" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "TRA-0001", "domain": "TRA", "term_en": "Ambiguity Smuggling", "term_de": "Übersetzungswissenschaft Grundlagen", "definition_en": "A translation dynamics phenomenon in AI-mediated cross-lingual processing, characterized by a translation phenomenon characterized by source-text ambiguity is resolved in translation by choosing one meaning, hiding the ambiguity from target readers who believe meaning was unambiguous. Distinguished from adjacent concepts by its focus on the specific mechanism through which ambiguity manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch machine learning Algorithmen integrating public transit, shared mobility, and personal vehicle data to recommend optimal routes considering time, cost, emissions, and accessibility. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Translation AI", "narrower_terms": [ "TRA-0019", "TRA-0015", "TRA-0049", "TRA-0069", "TRA-0064", "TRA-0024", "TRA-0004", "TRA-0084", "TRA-0060", "TRA-0092", "TRA-0050", "TRA-0075", "TRA-0041", "TRA-0021", "TRA-0008", "TRA-0014", "TRA-0082", "TRA-0045", "TRA-0036", "TRA-0027", "TRA-0007", "TRA-0074", "TRA-0079", "TRA-0022", "TRA-0061", "TRA-0001", "TRA-0003", "TRA-0053", "TRA-0058", "TRA-0046", "TRA-0067", "TRA-0048", "TRA-0044", "TRA-0042", "TRA-0068", "TRA-0086", "TRA-0028", "TRA-0081", "TRA-0087", "TRA-0005", "TRA-0076", "TRA-0085", "TRA-0017", "TRA-0006", "TRA-0083", "TRA-0011", "TRA-0054", "TRA-0018", "TRA-0035", "TRA-0030", "TRA-0013", "TRA-0032", "TRA-0025", "TRA-0037", "TRA-0051", "TRA-0056", "TRA-0059", "TRA-0063", "TRA-0066", "TRA-0010", "TRA-0078", "TRA-0089", "TRA-0009", "TRA-0072" ], "cross_domain_refs": [ "LIN-0049" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "TRA-0002", "domain": "TRA", "term_en": "Archaic Language Flattening", "term_de": "Geschichte der Übersetzungswissenschaft", "definition_en": "Shift of temporal distance and historical authenticity when archaic or period-specific language is rendered in contemporary target-language forms. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch aI systems coordinating hundreds of autonomous vehicles optimizing pick-up/drop-off sequences, traffic navigation, and energy efficiency reducing operational costs by 38%. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2701", "narrower_terms": [], "cross_domain_refs": [ "CUS-0090" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q315", "legal_classification": "descriptive_research_term" }, { "id": "TRA-0003", "domain": "TRA", "term_en": "Aspect-Tense Coalescence", "term_de": "Theorie der Übersetzungswissenschaft", "definition_en": "A translation dynamics phenomenon in AI-mediated cross-lingual processing, characterized by errors when translating languages where aspect and tense are fused into single morphemes into languages where they are distinct grammatical categories. Distinguished from adjacent concepts by its focus on the specific mechanism through which aspect manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch deep learning models forecasting shipment volumes by origin, destination, and time enabling optimal fleet sizing, equipment pre-positioning, and just-in-time warehouse operations. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Translation AI", "narrower_terms": [], "cross_domain_refs": [ "LNG-0010" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "TRA-0004", "domain": "TRA", "term_en": "Aspectual Nuance Shift", "term_de": "Prinzipien des translation", "definition_en": "A translation dynamics phenomenon in AI-mediated cross-lingual processing, characterized by flattening of subtle distinctions between perfective, imperfective, habitual, or continuous aspect into binary or oversimplified representations. The concept emerges specifically in contexts where aspectual–nuance interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch machine learning analyzing traffic density and demand elasticity to dynamically adjust congestion charges reducing peak-hour traffic by 19% while maintaining revenue neutrality. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Translation AI", "narrower_terms": [], "cross_domain_refs": [ "COG-0056" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "TRA-0005", "domain": "TRA", "term_en": "Asynchronous Dialogue Perception", "term_de": "Fachterminologie Übersetzungswissenschaft", "definition_en": "A translation dynamics phenomenon in AI-mediated cross-lingual processing, characterized by a cross-linguistic effect in which users feel they are engaging in real conversation across language barriers, when translation delays and asynchrony characteristically transform communication dynamics. Distinguished from adjacent concepts by its focus on the specific mechanism through which asynchronous manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data. Descriptive research term, not a prescriptive recommendation.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch neural networks processing telematics data predicting component failures, optimizing maintenance intervals, and recommending driving behavior changes improving fleet lifespan by 23%. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Translation AI", "narrower_terms": [], "cross_domain_refs": [ "AUG-0802" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q177601", "legal_classification": "observational_construct" }, { "id": "TRA-0006", "domain": "TRA", "term_en": "Back-Translation Asymmetry", "term_de": "Klassifikation Übersetzungswissenschaft", "definition_en": "A linguistic transfer pattern in AI-augmented translation, measurable through a translation phenomenon where back-translating output often fails to restore source meaning, revealing errors invisible in one direction but exposed by reverse translation. Distinguished from adjacent concepts by its focus on the specific mechanism through which back manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch aI systems optimizing crane scheduling, gate operations, and storage assignments to minimize vessel turnaround times reducing port congestion by 31%. K. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Translation AI", "narrower_terms": [], "cross_domain_refs": [ "ADA-0003", "AGE-0055", "AGE-0057" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q7553", "legal_classification": "analytical_category" }, { "id": "TRA-0007", "domain": "TRA", "term_en": "Body Gesture Inversion", "term_de": "Einführung in translation", "definition_en": "A translation dynamics phenomenon in AI-mediated cross-lingual processing, characterized by a translation phenomenon in which when culturally understood gestures, facial expressions, or body-language references have opposite meanings in target culture. The concept emerges specifically in contexts where body–gesture interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch machine learning Algorithmen dynamically adjusting train schedules, allocating capacity, and managing maintenance windows improving punctuality to 97.8% and increasing throughput by 26%. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Translation AI", "narrower_terms": [], "cross_domain_refs": [ "CUS-0040" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "TRA-0008", "domain": "TRA", "term_en": "Certification Transition", "term_de": "translation-Methodik", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures A linguistic transfer pattern in AI-augmented translation, measurable through a cross-linguistic effect where professional certification standards for human translators lose meaning when hybrid human-AI systems become the industry norm. This phenomenon operates at the intersection of certification and transition dynamics within the broader TRA domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept neural networks analyzing weather patterns, fuel consumption models, and geopolitical factors to calculate optimal shipping routes reducing fuel costs by 12% and voyage times by 8%. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Translation AI", "narrower_terms": [], "cross_domain_refs": [ "DAT-0026" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "TRA-0009", "domain": "TRA", "term_en": "Classifier System Shift", "term_de": "Philosophie der Übersetzungswissenschaft", "definition_en": "A linguistic transfer pattern in AI-augmented translation, measurable through a cross-linguistic effect reflecting inability to preserve semantic categories marked by classifiers in languages like Mandarin, Hmong, or Navajo in non-classifier target languages. Distinguished from adjacent concepts by its focus on the specific mechanism through which classifier manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch aI systems coordinating airport operations, departure sequences, and routing reducing delays by 34%, fuel consumption by 7%, and airport capacity constraints. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Translation AI", "narrower_terms": [], "cross_domain_refs": [ "LNG-0010" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "TRA-0010", "domain": "TRA", "term_en": "Client Expectation Inversion", "term_de": "translation-Taxonomie", "definition_en": "A translation dynamics phenomenon in AI-mediated cross-lingual processing, characterized by clients expect instant, free AI translation as baseline, pressuring professional translators to compete on speed and price rather than quality. The concept emerges specifically in contexts where client–expectation interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch machine learning systems predicting passenger demand by location and time enabling dynamic routing of shared vehicles matching supply with demand in Echtzeit. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Translation AI", "narrower_terms": [], "cross_domain_refs": [ "PHO-0023" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "TRA-0011", "domain": "TRA", "term_en": "Code-Switching Flattening", "term_de": "Umfang der Übersetzungswissenschaft", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A translation dynamics phenomenon in AI-mediated cross-lingual processing, characterized by a translation phenomenon involving aI resolves code-switching by separating languages, destroying the communicative purpose and cultural identity markers embedded in deliberate language-mixing. This phenomenon operates at the intersection of code and switching dynamics within the broader TRA domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept reale KI+X-Definition für Umfang der Übersetzungswissenschaft - intelligente Transformationen durch tra-Anwendungen ermöglichend. KI-gestützte Verbesse. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Translation AI", "narrower_terms": [], "cross_domain_refs": [ "CUS-0091", "VIB-0016" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "TRA-0012", "domain": "TRA", "term_en": "Collocation Blindness", "term_de": "Literaturübersicht Übersetzungswissenschaft", "definition_en": "A language transfer pattern where aI translations obsresolve word combinations (collocations), meaning learners miss the patterns of which words naturally go together in the target language. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch reale KI+X-Definition für Literaturübersicht Übersetzungswissenschaft - intelligente Transformationen durch tra-Anwendungen ermöglichend. KI-gestützte. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-2703", "narrower_terms": [], "cross_domain_refs": [ "LNG-0015" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "TRA-0013", "domain": "TRA", "term_en": "Colloquial Stiffness", "term_de": "Schlüsselkonzepte in translation", "definition_en": "A translation dynamics phenomenon in AI-mediated cross-lingual processing, characterized by a translation phenomenon manifesting as conversational language becomes overly formal or grammatically rigid when AI compensates for translation difficulty with precise but unnatural constructions. The concept emerges specifically in contexts where colloquial–stiffness interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch reale KI+X-Definition für Schlüsselkonzepte in translation - intelligente Transformationen durch tra-Anwendungen ermöglichend. KI-gestützte Verbesserun. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Translation AI", "narrower_terms": [], "cross_domain_refs": [ "ROB-0081" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "TRA-0014", "domain": "TRA", "term_en": "Color Category Mismatch", "term_de": "Rahmenwerk der Übersetzungswissenschaft", "definition_en": "A translation dynamics phenomenon in AI-mediated cross-lingual processing, characterized by a language transfer pattern reflecting translation errors when source and target languages categorize colors differently, or one language has color terms the other lacks. The concept emerges specifically in contexts where color–category interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch reale KI+X-Definition für Rahmenwerk der Übersetzungswissenschaft - intelligente Transformationen durch tra-Anwendungen ermöglichend. KI-gestützte Verb. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Translation AI", "narrower_terms": [], "cross_domain_refs": [ "LNG-0010" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "TRA-0015", "domain": "TRA", "term_en": "Conceptual Equivalence Gap", "term_de": "Paradigmen in translation", "definition_en": "A linguistic transfer pattern in AI-augmented translation, measurable through the absence of a direct target-language concept or term for a source-language idea, requiring explanation, approximation, or conceptual restructuring. Distinguished from adjacent concepts by its focus on the specific mechanism through which conceptual manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch reale KI+X-Definition für Paradigmen in translation - intelligente Transformationen durch tra-Anwendungen ermöglichend. KI-gestützte Verbesserung.KI-ge. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Performance Gap", "narrower_terms": [], "cross_domain_refs": [ "LNG-0020" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "TRA-0016", "domain": "TRA", "term_en": "Confidence Without Context", "term_de": "translation-Forschungsmethoden", "definition_en": "A cross-linguistic effect reflecting users trust translation outputs equally regardless of domain, cultural specificity, or ambiguity level, without calibrating confidence to contextual difficulty. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch reale KI+X-Definition für translation-Forschungsmethoden - intelligente Transformationen durch tra-Anwendungen ermöglichend. KI-gestützte Verbesserung. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2505", "narrower_terms": [], "cross_domain_refs": [ "CON-0077" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "TRA-0017", "domain": "TRA", "term_en": "Consistency Hallucination", "term_de": "Quantitative translation-Analyse", "definition_en": "A translation dynamics phenomenon in AI-mediated cross-lingual processing, characterized by translation maintains internal consistency (terminology, style) while diverging from source text, creating the perception of accuracy through uniformity. Distinguished from adjacent concepts by its focus on the specific mechanism through which consistency manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch reale KI+X-Definition für Quantitative translation-Analyse - intelligente Transformationen durch tra-Anwendungen ermöglichend. KI-gestützte Verbesserun. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Translation AI", "narrower_terms": [], "cross_domain_refs": [ "VIB-0037" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "TRA-0018", "domain": "TRA", "term_en": "Creole Comprehension Gap", "term_de": "Qualitative translation-Analyse", "definition_en": "A linguistic transfer pattern in AI-augmented translation, measurable through creole languages with unique hybrid structures challenge AI translation systems designed around discrete language pairs and fixed linguistic rules. Distinguished from adjacent concepts by its focus on the specific mechanism through which creole manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch reale KI+X-Definition für Qualitative translation-Analyse - intelligente Transformationen durch tra-Anwendungen ermöglichend. KI-gestützte Verbesserung. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Performance Gap", "narrower_terms": [], "cross_domain_refs": [ "LIN-0052" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "TRA-0019", "domain": "TRA", "term_en": "Cultural Referent Void", "term_de": "translation-Messung", "definition_en": "A linguistic transfer pattern in AI-augmented translation, measurable through a cross-linguistic effect involving the absence of a target-culture equivalent for source-culture-specific allusions, institutions, foods, customs, or historical events. The concept emerges specifically in contexts where cultural–referent interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch reale KI+X-Definition für translation-Messung - intelligente Transformationen durch tra-Anwendungen ermöglichend. KI-gestützte Verbesserung.KI-gestützt. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Translation AI", "narrower_terms": [], "cross_domain_refs": [ "COP-0009", "CUS-0038", "DAT-0026" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "TRA-0020", "domain": "TRA", "term_en": "Decontextualization Amnesia", "term_de": "Experimentelles translation-Design", "definition_en": "A language transfer pattern arising from translations presented in isolation without original context reduce learners from building situational vocabulary and contextual meaning associations. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch reale KI+X-Definition für Experimentelles translation-Design - intelligente Transformationen durch tra-Anwendungen ermöglichend. KI-gestützte Verbesser. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2703", "narrower_terms": [], "cross_domain_refs": [ "ASE-0060" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "TRA-0021", "domain": "TRA", "term_en": "Dialect Erasure", "term_de": "translation-Datenerhebung", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures A linguistic transfer pattern in AI-augmented translation, measurable through a language transfer pattern arising from elimination of regional, social, or ethnic speech varieties when dialect-specific source material is rendered in standardized target-language form. This phenomenon operates at the intersection of dialect and erasure dynamics within the broader TRA domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept reale KI+X-Definition für translation-Datenerhebung - intelligente Transformationen durch tra-Anwendungen ermöglichend. KI-gestützte Verbesserung.KI-ge. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Translation AI", "narrower_terms": [], "cross_domain_refs": [ "CUS-0093", "FIC-0028", "FIC-0048" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "TRA-0022", "domain": "TRA", "term_en": "Dialect Preservation Paradox", "term_de": "Stichprobenziehung in translation", "definition_en": "A linguistic transfer pattern in AI-augmented translation, measurable through a cross-linguistic effect manifesting as translation to standard language forms erases dialect variation, reducing incentives to document and teach enchallengeed linguistic varieties. Distinguished from adjacent concepts by its focus on the specific mechanism through which dialect manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch reale KI+X-Definition für Stichprobenziehung in translation - intelligente Transformationen durch tra-Anwendungen ermöglichend. KI-gestützte Verbesseru. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Logical Paradox", "narrower_terms": [], "cross_domain_refs": [ "LIN-0079", "LNG-0007" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "TRA-0023", "domain": "TRA", "term_en": "Discourse Particle Omission", "term_de": "Statistische translation-Analyse", "definition_en": "Shift of pragmatic function when language-specific discourse particles (Japanese ka, German doch) lack direct target-language equivalents. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch reale KI+X-Definition für Statistische translation-Analyse - intelligente Transformationen durch tra-Anwendungen ermöglichend. KI-gestützte Verbesserun. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2701", "narrower_terms": [], "cross_domain_refs": [ "CRE-0214", "EDU-0036", "LIN-0023" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q864419", "legal_classification": "empirical_phenomenon_label" }, { "id": "TRA-0024", "domain": "TRA", "term_en": "Ergative-Nominative Confusion", "term_de": "Feldstudie in translation", "definition_en": "A linguistic transfer pattern in AI-augmented translation, measurable through a language transfer pattern involving translation errors when mapping between ergative-absolutive and nominative-accusative grammatical systems with different agent-individual relationships. Distinguished from adjacent concepts by its focus on the specific mechanism through which ergative manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch reale KI+X-Definition für Feldstudie in translation - intelligente Transformationen durch tra-Anwendungen ermöglichend. KI-gestützte Verbesserung.KI-ge. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Translation AI", "narrower_terms": [], "cross_domain_refs": [ "COG-0048", "COG-0057", "DES-0080" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "TRA-0025", "domain": "TRA", "term_en": "Error Detection Opacity", "term_de": "Fallstudie in translation", "definition_en": "A translation dynamics phenomenon in AI-mediated cross-lingual processing, characterized by a translation phenomenon reflecting users cannot easily identify mistranslations, hallucinations, or distortions in target language without expert knowledge or source-text consultation. The concept emerges specifically in contexts where error–detection interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch reale KI+X-Definition für Fallstudie in translation - intelligente Transformationen durch tra-Anwendungen ermöglichend. KI-gestützte Verbesserung.KI-ge. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Detection System", "narrower_terms": [], "cross_domain_refs": [ "BEH-0009" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "TRA-0026", "domain": "TRA", "term_en": "Error Immunization Effect", "term_de": "Vergleichende translation-Studie", "definition_en": "A cross-linguistic effect manifesting as learners avoid errors through AI translation but rarely develop error-correction abilities or resilience through making and restoreing from mistakes. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch reale KI+X-Definition für Vergleichende translation-Studie - intelligente Transformationen durch tra-Anwendungen ermöglichend. KI-gestützte Verbesserun. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2703", "narrower_terms": [], "cross_domain_refs": [ "ELR-0056" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "TRA-0027", "domain": "TRA", "term_en": "Euphemism Unraveling", "term_de": "Längsschnittstudie in translation", "definition_en": "A translation dynamics phenomenon in AI-mediated cross-lingual processing, characterized by a cross-linguistic effect manifesting as polite circumlocutions lose their indirectness when AI provides literal translation instead of target-language cultural equivalent euphemisms. Distinguished from adjacent concepts by its focus on the specific mechanism through which euphemism manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch reale KI+X-Definition für Längsschnittstudie in translation - intelligente Transformationen durch tra-Anwendungen ermöglichend. KI-gestützte Verbesseru. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Translation AI", "narrower_terms": [], "cross_domain_refs": [ "LIN-0009" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "TRA-0028", "domain": "TRA", "term_en": "Evidentiality Erasure", "term_de": "translation-Umfragemethode", "definition_en": "A translation dynamics phenomenon in AI-mediated cross-lingual processing, characterized by a translation phenomenon manifesting as omission of grammatical markers indicating information source (direct witness, hearsay, inference) when target language lacks evidential systems. Distinguished from adjacent concepts by its focus on the specific mechanism through which evidentiality manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch reale KI+X-Definition für translation-Umfragemethode - intelligente Transformationen durch tra-Anwendungen ermöglichend. KI-gestützte Verbesserung.KI-g. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Translation AI", "narrower_terms": [], "cross_domain_refs": [ "FIC-0048", "PHO-0050", "RET-0015" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "TRA-0029", "domain": "TRA", "term_en": "Expert-Novice Divergence", "term_de": "Aktionsforschung in translation", "definition_en": "A linguistic transfer pattern in AI-augmented translation, measurable through machine translation quality differs dramatically based on user expertise; experts detect nuance shift while novices perceive acceptable accuracy. Distinguished from adjacent concepts by its focus on the specific mechanism through which expert manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch reale KI+X-Definition für Aktionsforschung in translation - intelligente Transformationen durch tra-Anwendungen ermöglichend. KI-gestützte Verbesserung. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2701", "narrower_terms": [], "cross_domain_refs": [ "COP-0077", "NEO-2256" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "TRA-0030", "domain": "TRA", "term_en": "Factual-Linguistic Confusion", "term_de": "Mixed Methods in translation", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A translation dynamics phenomenon in AI-mediated cross-lingual processing, characterized by a language transfer pattern arising from when source text contains factual errors, AI may translate them accurately, but users cannot distinguish linguistic accuracy from content accuracy. This phenomenon operates at the intersection of factual and linguistic dynamics within the broader TRA domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept reale KI+X-Definition für Mixed Methods in translation - intelligente Transformationen durch tra-Anwendungen ermöglichend. KI-gestützte Verbesserung.KI. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Translation AI", "narrower_terms": [], "cross_domain_refs": [ "BEH-0009" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "TRA-0031", "domain": "TRA", "term_en": "False Cognate Confidence", "term_de": "translation-Technologie", "definition_en": "A language transfer pattern in which users overly trust translations of words that look similar across languages (false cognates) without recognizing actual semantic divergence. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch reale KI+X-Definition für translation-Technologie - intelligente Transformationen durch tra-Anwendungen ermöglichend. KI-gestützte Verbesserung.KI-gest. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2505", "narrower_terms": [], "cross_domain_refs": [ "ART-0089" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "TRA-0032", "domain": "TRA", "term_en": "Fluency Paradox Effect", "term_de": "Digitale translation-Werkzeuge", "definition_en": "A linguistic transfer pattern in AI-augmented translation, measurable through a cross-linguistic effect observed when mistranslations sound fluent and natural, increasing confidence despite inaccuracy, making errors harder to spot than notably awkward output. The concept emerges specifically in contexts where fluency–paradox interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch reale KI+X-Definition für Digitale translation-Werkzeuge - intelligente Transformationen durch tra-Anwendungen ermöglichend. KI-gestützte Verbesserung. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Logical Paradox", "narrower_terms": [], "cross_domain_refs": [ "RPH-2502" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "TRA-0033", "domain": "TRA", "term_en": "Fluency Without Depth", "term_de": "translation-Software", "definition_en": "A translation phenomenon observed when learners develop surface-level comprehension of target language through AI translation but lack deep structural understanding of grammar and meaning formation. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch reale KI+X-Definition für translation-Software - intelligente Transformationen durch tra-Anwendungen ermöglichend. KI-gestützte Verbesserung.KI-gestütz. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2703", "narrower_terms": [], "cross_domain_refs": [ "EDU-0047" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "TRA-0034", "domain": "TRA", "term_en": "Formality Level Undulation", "term_de": "Automatisierung in translation", "definition_en": "A cross-linguistic effect where aI accompanies uniform formality regardless of source-text register shifts, flattening polite, familiar, intimate, and contemptuous speech varieties. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch reale KI+X-Definition für Automatisierung in translation - intelligente Transformationen durch tra-Anwendungen ermöglichend. KI-gestützte Verbesserung. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2701", "narrower_terms": [], "cross_domain_refs": [ "LIN-0072" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "TRA-0035", "domain": "TRA", "term_en": "Gender-Number Mismatch Propagation", "term_de": "IoT in translation", "definition_en": "A linguistic transfer pattern in AI-augmented translation, measurable through a cross-linguistic effect arising from errors in grammatical agreement spreading through translation when target-language gender or number systems differ from source language. Distinguished from adjacent concepts by its focus on the specific mechanism through which gender manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch reale KI+X-Definition für IoT in translation - intelligente Transformationen durch tra-Anwendungen ermöglichend. KI-gestützte Verbesserung.KI-gestützte. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Translation AI", "narrower_terms": [], "cross_domain_refs": [ "RHR-0236", "SAL-0064", "VIB-0081" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "TRA-0036", "domain": "TRA", "term_en": "Global Homogenization Momentum", "term_de": "Datenanalyse in translation", "definition_en": "A translation dynamics phenomenon in AI-mediated cross-lingual processing, characterized by a language transfer pattern observed when frictionless translation to dominant languages accelerates cultural homogenization, reducing incentives to preserve linguistic diversity and local communication patterns. Distinguished from adjacent concepts by its focus on the specific mechanism through which global manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch reale KI+X-Definition für Datenanalyse in translation - intelligente Transformationen durch tra-Anwendungen ermöglichend. KI-gestützte Verbesserung.KI-. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Translation AI", "narrower_terms": [], "cross_domain_refs": [ "AED-0032", "ART-0053", "ART-0057" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "TRA-0037", "domain": "TRA", "term_en": "Hallucinated Completeness", "term_de": "KI-Anwendungen in translation", "definition_en": "A linguistic transfer pattern in AI-augmented translation, measurable through a translation phenomenon where aI accompanies plausible but fabricated content to fill gaps or explain ambiguities rather than flagging them as uncertain or unknown. The concept emerges specifically in contexts where hallucinated–completeness interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch reale KI+X-Definition für KI-Anwendungen in translation - intelligente Transformationen durch tra-Anwendungen ermöglichend. KI-gestützte Verbesserung.K. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Translation AI", "narrower_terms": [], "cross_domain_refs": [ "SWE-0044" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "TRA-0038", "domain": "TRA", "term_en": "Honorific System Narrowing", "term_de": "Maschinelles Lernen in translation", "definition_en": "Shift of social hierarchy and respect distinctions when translating from languages with complex honorifics into those without such systems. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch reale KI+X-Definition für Maschinelles Lernen in translation - intelligente Transformationen durch tra-Anwendungen ermöglichend. KI-gestützte Verbesser. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-2701", "narrower_terms": [], "cross_domain_refs": [ "ART-0058", "ART-0073", "ART-0076" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "TRA-0039", "domain": "TRA", "term_en": "Humor Transfer Breakdown", "term_de": "Sensorik in translation", "definition_en": "Shift of comedic effect when jokes, puns, or culturally-embedded humor depend on source-language phonetics, wordplay, or cultural context. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch reale KI+X-Definition für Sensorik in translation - intelligente Transformationen durch tra-Anwendungen ermöglichend. KI-gestützte Verbesserung.KI-gest. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-2701", "narrower_terms": [], "cross_domain_refs": [ "SOC-0022" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "TRA-0040", "domain": "TRA", "term_en": "Interference Acceleration", "term_de": "Mobile Anwendungen in translation", "definition_en": "A cross-linguistic effect involving learners internalize AI's frequent mistranslations, creating false language associations that interfere with natural acquisition of correct forms. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch reale KI+X-Definition für Mobile Anwendungen in translation - intelligente Transformationen durch tra-Anwendungen ermöglichend. KI-gestützte Verbesseru. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2703", "narrower_terms": [], "cross_domain_refs": [ "ELR-0115" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "TRA-0041", "domain": "TRA", "term_en": "Interjection Substitution Error", "term_de": "Cloud-Lösungen für translation", "definition_en": "A linguistic transfer pattern in AI-augmented translation, measurable through a language transfer pattern reflecting exclamations, interjections, and spontaneous utterances are replaced with formal equivalents, losing emotional immediacy and authenticity. Distinguished from adjacent concepts by its focus on the specific mechanism through which interjection manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch reale KI+X-Definition für Cloud-Lösungen für translation - intelligente Transformationen durch tra-Anwendungen ermöglichend. KI-gestützte Verbesserung. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Translation AI", "narrower_terms": [], "cross_domain_refs": [ "MUS-0014" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "TRA-0042", "domain": "TRA", "term_en": "Interpersonal Distance Compression", "term_de": "Datenbankverwaltung in translation", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures A linguistic transfer pattern in AI-augmented translation, measurable through a cross-linguistic effect reflecting seamless translation accompanies false sense of intimacy and mutual understanding, masking ongoing differences in perspective, context, and assumption. This phenomenon operates at the intersection of interpersonal and distance dynamics within the broader TRA domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept reale KI+X-Definition für Datenbankverwaltung in translation - intelligente Transformationen durch tra-Anwendungen ermöglichend. KI-gestützte Verbesser. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Data Compression", "narrower_terms": [], "cross_domain_refs": [ "STE-0054" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "TRA-0043", "domain": "TRA", "term_en": "Language Barrier Narrowing Paradox", "term_de": "Visualisierung in translation", "definition_en": "A translation phenomenon arising from removing language barriers through translation is designed to reduce the shared struggle that formerly motivated language learning and cultural bridge-building. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch reale KI+X-Definition für Visualisierung in translation - intelligente Transformationen durch tra-Anwendungen ermöglichend. KI-gestützte Verbesserung.K. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2703", "narrower_terms": [], "cross_domain_refs": [ "ELR-0115" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q315", "legal_classification": "analytical_category" }, { "id": "TRA-0044", "domain": "TRA", "term_en": "Language Contact Erasure", "term_de": "Simulation in translation", "definition_en": "A translation dynamics phenomenon in AI-mediated cross-lingual processing, characterized by a cross-linguistic effect involving translation removes evidence of contact phenomena (borrowing, interference patterns, convergence) that reveal historical language relationships. The concept emerges specifically in contexts where language–contact interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch reale KI+X-Definition für Simulation in translation - intelligente Transformationen durch tra-Anwendungen ermöglichend. KI-gestützte Verbesserung.KI-ge. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Translation AI", "narrower_terms": [], "cross_domain_refs": [ "ROB-0206", "PHO-0037" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q315", "legal_classification": "descriptive_research_term" }, { "id": "TRA-0045", "domain": "TRA", "term_en": "Liability Opacity", "term_de": "Digitaler Zwilling in translation", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A translation dynamics phenomenon in AI-mediated cross-lingual processing, characterized by a translation phenomenon observed when legal responsibility for translation errors becomes unclear when AI accompanies output that humans subsequently refine, edit, or endorse. This phenomenon operates at the intersection of liability and opacity dynamics within the broader TRA domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept reale KI+X-Definition für Digitaler Zwilling in translation - intelligente Transformationen durch tra-Anwendungen ermöglichend. KI-gestützte Verbesseru. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Translation AI", "narrower_terms": [], "cross_domain_refs": [ "SWE-0079" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "TRA-0046", "domain": "TRA", "term_en": "Lingua Franca Replacement", "term_de": "translation-Best-Practices", "definition_en": "A linguistic transfer pattern in AI-augmented translation, measurable through a translation phenomenon reflecting machine translation to English (or other dominant language) displaces traditional lingua francas and multilingual communication bridges. Distinguished from adjacent concepts by its focus on the specific mechanism through which lingua manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch reale KI+X-Definition für translation-Best-Practices - intelligente Transformationen durch tra-Anwendungen ermöglichend. KI-gestützte Verbesserung.KI-g. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Translation AI", "narrower_terms": [], "cross_domain_refs": [ "EDU-0010" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "TRA-0047", "domain": "TRA", "term_en": "Linguistic Biodiversity Shift", "term_de": "Professionelle translation-Praxis", "definition_en": "A language transfer pattern involving easy translation to dominant languages reduces motivation for multilingual individuals to maintain linguistic diversity or learn less-spoken languages. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch reale KI+X-Definition für Professionelle translation-Praxis - intelligente Transformationen durch tra-Anwendungen ermöglichend. KI-gestützte Verbesseru. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2703", "narrower_terms": [], "cross_domain_refs": [ "LIN-0020" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "TRA-0048", "domain": "TRA", "term_en": "Literal-Contextual Trade-off", "term_de": "translation-Arbeitsablaufgestaltung", "definition_en": "A linguistic transfer pattern in AI-augmented translation, measurable through a language transfer pattern arising from the tension between maintaining word-for-word accuracy and conveying intended meaning through contextually appropriate equivalents in the target language. The concept emerges specifically in contexts where literal–contextual interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch reale KI+X-Definition für translation-Arbeitsablaufgestaltung - intelligente Transformationen durch tra-Anwendungen ermöglichend. KI-gestützte Verbesse. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Translation AI", "narrower_terms": [], "cross_domain_refs": [ "AGE-0070", "ASE-0041", "MTH-0040" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "TRA-0049", "domain": "TRA", "term_en": "Loanword Localization Inconsistency", "term_de": "translation-Projektmanagement", "definition_en": "A linguistic transfer pattern in AI-augmented translation, measurable through aI inconsistently handles loanwords, sometimes localizing them, sometimes preserving source forms, reflecting uncertainty about adaptation conventions. Distinguished from adjacent concepts by its focus on the specific mechanism through which loanword manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch reale KI+X-Definition für translation-Projektmanagement - intelligente Transformationen durch tra-Anwendungen ermöglichend. KI-gestützte Verbesserung.K. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Translation AI", "narrower_terms": [], "cross_domain_refs": [ "LIN-0090", "LNG-0012" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "TRA-0050", "domain": "TRA", "term_en": "Meaning Compression Shift", "term_de": "translation-Teamzusammenarbeit", "definition_en": "A linguistic transfer pattern in AI-augmented translation, measurable through a cross-linguistic effect involving the reduction of semantic content when source language concepts are mapped to target language structures, particularly when cultural or linguistic nuance cannot be directly represented. The concept emerges specifically in contexts where meaning–compression interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch reale KI+X-Definition für translation-Teamzusammenarbeit - intelligente Transformationen durch tra-Anwendungen ermöglichend. KI-gestützte Verbesserung. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Data Compression", "narrower_terms": [], "cross_domain_refs": [ "SOC-0027" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "TRA-0051", "domain": "TRA", "term_en": "Mediation Invisibility", "term_de": "Kundenbeziehungen in translation", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures A linguistic transfer pattern in AI-augmented translation, measurable through users experience translated content as if communicating directly with author, obscuring the mediating presence of translation and AI interpretation. This phenomenon operates at the intersection of mediation and invisibility dynamics within the broader TRA domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept reale KI+X-Definition für Kundenbeziehungen in translation - intelligente Transformationen durch tra-Anwendungen ermöglichend. KI-gestützte Verbesserun. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Translation AI", "narrower_terms": [], "cross_domain_refs": [ "CON-0076" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "TRA-0052", "domain": "TRA", "term_en": "Metadiscourse Substitution", "term_de": "translation-Kommunikation", "definition_en": "A cross-linguistic effect observed when shifts in how a text talks about itself (hedging, emphasis, qualification) when target language has different conventions for metacommunicative markers. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch reale KI+X-Definition für translation-Kommunikation - intelligente Transformationen durch tra-Anwendungen ermöglichend. KI-gestützte Verbesserung.KI-ge. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2701", "narrower_terms": [], "cross_domain_refs": [ "FIC-0058" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q864419", "legal_classification": "descriptive_research_term" }, { "id": "TRA-0053", "domain": "TRA", "term_en": "Metonymic Shift Uncaught", "term_de": "Problemlösung in translation", "definition_en": "A translation dynamics phenomenon in AI-mediated cross-lingual processing, characterized by a translation phenomenon characterized by when AI overlooks figurative substitutions (ship for crew, Washington for government) and addresss them literally in translation. The concept emerges specifically in contexts where metonymic–shift interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch reale KI+X-Definition für Problemlösung in translation - intelligente Transformationen durch tra-Anwendungen ermöglichend. KI-gestützte Verbesserung.KI. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Translation AI", "narrower_terms": [], "cross_domain_refs": [ "ADA-0012", "AGE-0007", "AGE-0046" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "TRA-0054", "domain": "TRA", "term_en": "Motivation Bypass", "term_de": "Entscheidungsfindung in translation", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures A linguistic transfer pattern in AI-augmented translation, measurable through a language transfer pattern where easy access to perfect translations reduces struggle and cognitive effort, which are motivational drivers and necessary for long-term language acquisition. This phenomenon operates at the intersection of motivation and bypass dynamics within the broader TRA domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept reale KI+X-Definition für Entscheidungsfindung in translation - intelligente Transformationen durch tra-Anwendungen ermöglichend. KI-gestützte Verbesse. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Translation AI", "narrower_terms": [], "cross_domain_refs": [ "AED-0028" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q186588", "legal_classification": "descriptive_research_term" }, { "id": "TRA-0055", "domain": "TRA", "term_en": "Mythic Reference Orphaning", "term_de": "Zeitmanagement in translation", "definition_en": "Shift of allusive power and emotional resonance when mythological or legendary references have no equivalent in target-culture canon. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch reale KI+X-Definition für Zeitmanagement in translation - intelligente Transformationen durch tra-Anwendungen ermöglichend. KI-gestützte Verbesserung.K. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-2701", "narrower_terms": [], "cross_domain_refs": [ "ADA-0002", "DES-0088", "ELR-0157" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "TRA-0056", "domain": "TRA", "term_en": "Name Translation Dilemma", "term_de": "Ressourcenplanung in translation", "definition_en": "A linguistic transfer pattern in AI-augmented translation, measurable through a translation phenomenon manifesting as uncertainty about whether proper names (people, places, brands) may be transliterated, transcribed, translated, or left unchanged. The concept emerges specifically in contexts where name–translation interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch reale KI+X-Definition für Ressourcenplanung in translation - intelligente Transformationen durch tra-Anwendungen ermöglichend. KI-gestützte Verbesserun. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Translation AI", "narrower_terms": [], "cross_domain_refs": [ "AUG-0112", "CRE-0225", "EDU-0029" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q7553", "legal_classification": "observational_construct" }, { "id": "TRA-0057", "domain": "TRA", "term_en": "Natural Variability Erasure", "term_de": "translation-Dokumentation", "definition_en": "A cross-linguistic effect observed when standardized AI output deprives learners of exposure to natural language variation, regional differences, and authentic speaker idiosyncrasies. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch reale KI+X-Definition für translation-Dokumentation - intelligente Transformationen durch tra-Anwendungen ermöglichend. KI-gestützte Verbesserung.KI-ge. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2605", "narrower_terms": [], "cross_domain_refs": [ "EDU-0047", "LIN-0018" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "TRA-0058", "domain": "TRA", "term_en": "Numeral System Mismatch", "term_de": "Berichtswesen in translation", "definition_en": "A translation dynamics phenomenon in AI-mediated cross-lingual processing, characterized by a language transfer pattern involving errors or ambiguities arising when source and target languages use different counting bases, units, or numerical structuring conventions. The concept emerges specifically in contexts where numeral–system interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch reale KI+X-Definition für Berichtswesen in translation - intelligente Transformationen durch tra-Anwendungen ermöglichend. KI-gestützte Verbesserung.KI. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Translation AI", "narrower_terms": [], "cross_domain_refs": [ "AGE-0001", "AGE-0033", "AGE-0060" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "TRA-0059", "domain": "TRA", "term_en": "Overconfidence in Rare Words", "term_de": "translation-Präsentationsfähigkeiten", "definition_en": "A linguistic transfer pattern in AI-augmented translation, measurable through a translation phenomenon in which aI translates rare, technical, or specialized terminology with apparent confidence even when such terms appear infrequently in training data. Distinguished from adjacent concepts by its focus on the specific mechanism through which overconfidence manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch reale KI+X-Definition für translation-Präsentationsfähigkeiten - intelligente Transformationen durch tra-Anwendungen ermöglichend. KI-gestützte Verbess. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Translation AI", "narrower_terms": [], "cross_domain_refs": [ "SCR-0042" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "TRA-0060", "domain": "TRA", "term_en": "Poetic Compression Shift", "term_de": "Netzwerken in translation", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures A linguistic transfer pattern in AI-augmented translation, measurable through a language transfer pattern involving expansion observed to convey meaning in target language significantly degrades the concise, dense language that accompanies poetic power in source text. This phenomenon operates at the intersection of poetic and compression dynamics within the broader TRA domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept reale KI+X-Definition für Netzwerken in translation - intelligente Transformationen durch tra-Anwendungen ermöglichend. KI-gestützte Verbesserung.KI-ge. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Data Compression", "narrower_terms": [], "cross_domain_refs": [ "ADA-0012", "AGE-0007", "AGE-0046" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "TRA-0061", "domain": "TRA", "term_en": "Polysemy Disambiguation Difficulty", "term_de": "translation-Qualitätssicherung", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A translation dynamics phenomenon in AI-mediated cross-lingual processing, characterized by the challenge of selecting the correct target-language meaning when a source word has multiple possible interpretations without explicit context. This phenomenon operates at the intersection of polysemy and disambiguation dynamics within the broader TRA domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept reale KI+X-Definition für translation-Qualitätssicherung - intelligente Transformationen durch tra-Anwendungen ermöglichend. KI-gestützte Verbesserung. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Translation AI", "narrower_terms": [], "cross_domain_refs": [ "AGE-0043", "AGE-0090", "ART-0069" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "TRA-0062", "domain": "TRA", "term_en": "Prestige Language Dominance", "term_de": "translation-Normen", "definition_en": "A translation dynamics phenomenon in AI-mediated cross-lingual processing, characterized by translation AI quality correlates with speaker population and training data volume, privileging languages with large digital footprints. The concept emerges specifically in contexts where prestige–language interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch reale KI+X-Definition für translation-Normen - intelligente Transformationen durch tra-Anwendungen ermöglichend. KI-gestützte Verbesserung.KI-gestützte. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2605", "narrower_terms": [], "cross_domain_refs": [ "LNG-0010", "LNG-0002" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q315", "legal_classification": "systematic_classification" }, { "id": "TRA-0063", "domain": "TRA", "term_en": "Presupposition Breakdown", "term_de": "ISO-Normen in translation", "definition_en": "A translation dynamics phenomenon in AI-mediated cross-lingual processing, characterized by a language transfer pattern arising from when cultural or linguistic presuppositions embedded in source text don't transfer, creating unintended meaning or false implicature in target language. The concept emerges specifically in contexts where presupposition–breakdown interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch reale KI+X-Definition für ISO-Normen in translation - intelligente Transformationen durch tra-Anwendungen ermöglichend. KI-gestützte Verbesserung.KI-ge. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Translation AI", "narrower_terms": [], "cross_domain_refs": [ "DES-0040", "ELR-0049", "FIC-0003" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "TRA-0064", "domain": "TRA", "term_en": "Probability Masking", "term_de": "translation-Zertifizierung", "definition_en": "A linguistic transfer pattern in AI-augmented translation, measurable through a language transfer pattern in which users cannot perceive the probabilistic uncertainty underlying translation choices, addressing discrete output as deterministic fact rather than statistical likelihood. Distinguished from adjacent concepts by its focus on the specific mechanism through which probability manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch reale KI+X-Definition für translation-Zertifizierung - intelligente Transformationen durch tra-Anwendungen ermöglichend. KI-gestützte Verbesserung.KI-g. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Translation AI", "narrower_terms": [], "cross_domain_refs": [ "ROB-0144" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "TRA-0065", "domain": "TRA", "term_en": "Pronunciation Evasion", "term_de": "Audit in translation", "definition_en": "Text-based translation bypasses pronunciation practice, allowing learners to advance without developing authentic accent or phonetic fluency. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch reale KI+X-Definition für Audit in translation - intelligente Transformationen durch tra-Anwendungen ermöglichend. KI-gestützte Verbesserung.KI-gestütz. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2703", "narrower_terms": [], "cross_domain_refs": [ "ELR-0191" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "TRA-0066", "domain": "TRA", "term_en": "Proverb Meaning Opacity", "term_de": "translation-Benchmarking", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures A linguistic transfer pattern in AI-augmented translation, measurable through a cross-linguistic effect observed when untranslatable cultural wisdom or proverbial knowledge that, if translated literally, becomes meaningless to target-culture audiences. This phenomenon operates at the intersection of proverb and meaning dynamics within the broader TRA domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept reale KI+X-Definition für translation-Benchmarking - intelligente Transformationen durch tra-Anwendungen ermöglichend. KI-gestützte Verbesserung.KI-ges. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Translation AI", "narrower_terms": [], "cross_domain_refs": [ "ART-0017", "ASE-0051", "ASE-0085" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "TRA-0067", "domain": "TRA", "term_en": "Quality Tier Compression", "term_de": "Leistungskennzahlen in translation", "definition_en": "A linguistic transfer pattern in AI-augmented translation, measurable through aI-generated translations eliminate traditional distinctions between rough draft, polished, and published-quality translation tiers. The concept emerges specifically in contexts where quality–tier interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch reale KI+X-Definition für Leistungskennzahlen in translation - intelligente Transformationen durch tra-Anwendungen ermöglichend. KI-gestützte Verbesser. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Data Compression", "narrower_terms": [], "cross_domain_refs": [ "CRE-0210" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "TRA-0068", "domain": "TRA", "term_en": "Rate Narrowing Phenomenon", "term_de": "Kontinuierliche Verbesserung in translation", "definition_en": "A translation dynamics phenomenon in AI-mediated cross-lingual processing, characterized by a cross-linguistic effect characterized by professional translator fees decline industry-wide as AI outputs commodify translation, making specialized expertise economically unsustainable. The concept emerges specifically in contexts where rate–narrowing interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch reale KI+X-Definition für Kontinuierliche Verbesserung in translation - intelligente Transformationen durch tra-Anwendungen ermöglichend. KI-gestützte. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Translation AI", "narrower_terms": [], "cross_domain_refs": [ "SCR-0042" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "TRA-0069", "domain": "TRA", "term_en": "Reduplication Flattening", "term_de": "translation-Inspektion", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A translation dynamics phenomenon in AI-mediated cross-lingual processing, characterized by a translation phenomenon in which destruction of morphologically meaningful reduplication patterns (used for plurality, intensity, or continuity) in languages that rely on them. This phenomenon operates at the intersection of reduplication and flattening dynamics within the broader TRA domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept reale KI+X-Definition für translation-Inspektion - intelligente Transformationen durch tra-Anwendungen ermöglichend. KI-gestützte Verbesserung.KI-gestü. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Translation AI", "narrower_terms": [], "cross_domain_refs": [ "REL-0052" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "TRA-0070", "domain": "TRA", "term_en": "Register Flattening", "term_de": "Prüfung in translation", "definition_en": "The shift of formality gradations, dialect markers, or speech level distinctions when AI accompanies neutral-register output regardless of source language register. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch reale KI+X-Definition für Prüfung in translation - intelligente Transformationen durch tra-Anwendungen ermöglichend. KI-gestützte Verbesserung.KI-gestü. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2701", "narrower_terms": [], "cross_domain_refs": [ "LIN-0072" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "TRA-0071", "domain": "TRA", "term_en": "Religious Meaning Substitution", "term_de": "Kalibrierung in translation", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures A linguistic transfer pattern in AI-augmented translation, measurable through a language transfer pattern in which shifts in meaning when religious concepts4,407 terms, or theological language carry different weight or significance across trust-based acceptance traditions. This phenomenon operates at the intersection of religious and meaning dynamics within the broader TRA domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept reale KI+X-Definition für Kalibrierung in translation - intelligente Transformationen durch tra-Anwendungen ermöglichend. KI-gestützte Verbesserung.KI-. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "RPH-2201", "narrower_terms": [], "cross_domain_refs": [ "COG-0191" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "TRA-0072", "domain": "TRA", "term_en": "Revision Burden Shift", "term_de": "Fehlervermeidung in translation", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures A linguistic transfer pattern in AI-augmented translation, measurable through a language transfer pattern reflecting post-editing AI output often becomes more laborious than translating from scratch, redirecting labor without reducing translator workload. This phenomenon operates at the intersection of revision and burden dynamics within the broader TRA domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept reale KI+X-Definition für Fehlervermeidung in translation - intelligente Transformationen durch tra-Anwendungen ermöglichend. KI-gestützte Verbesserung. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Translation AI", "narrower_terms": [], "cross_domain_refs": [ "SCR-0065" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "TRA-0073", "domain": "TRA", "term_en": "Rhythm and Cadence Breaking", "term_de": "Fehleranalyse in translation", "definition_en": "Shift of sonic and rhythmic patterns when translated into language with different phonological structure or word-order conventions. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch reale KI+X-Definition für Fehleranalyse in translation - intelligente Transformationen durch tra-Anwendungen ermöglichend. KI-gestützte Verbesserung.KI. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-2701", "narrower_terms": [], "cross_domain_refs": [ "ELR-0028", "FIC-0046", "GAM-0072" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "TRA-0074", "domain": "TRA", "term_en": "Sacred-Secular Boundary Confusion", "term_de": "Prozesskontrolle in translation", "definition_en": "A translation dynamics phenomenon in AI-mediated cross-lingual processing, characterized by a translation phenomenon reflecting errors in tone or appropriateness when content culturally significant in one culture appears secular in another, or vice versa. Distinguished from adjacent concepts by its focus on the specific mechanism through which culturally significant manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch reale KI+X-Definition für Prozesskontrolle in translation - intelligente Transformationen durch tra-Anwendungen ermöglichend. KI-gestützte Verbesserung. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Translation AI", "narrower_terms": [], "cross_domain_refs": [ "WRK-0098" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "TRA-0075", "domain": "TRA", "term_en": "Sarcasm Literalization", "term_de": "translation-Compliance", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A translation dynamics phenomenon in AI-mediated cross-lingual processing, characterized by a cross-linguistic effect observed when when ironic or sarcastic statements are translated at face value, rendering them as sincere statements in the target language. This phenomenon operates at the intersection of sarcasm and literalization dynamics within the broader TRA domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept reale KI+X-Definition für translation-Compliance - intelligente Transformationen durch tra-Anwendungen ermöglichend. KI-gestützte Verbesserung.KI-gestü. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Translation AI", "narrower_terms": [], "cross_domain_refs": [ "CON-0022" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "TRA-0076", "domain": "TRA", "term_en": "Script System Simplification", "term_de": "translation-Sicherheitsmanagement", "definition_en": "A translation dynamics phenomenon in AI-mediated cross-lingual processing, characterized by languages using non-Latin scripts may be transliterated or converted to Latin alphabet, displacing native writing systems and literacy practices. The concept emerges specifically in contexts where script–system interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch reale KI+X-Definition für translation-Sicherheitsmanagement - intelligente Transformationen durch tra-Anwendungen ermöglichend. KI-gestützte Verbesseru. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Translation AI", "narrower_terms": [], "cross_domain_refs": [ "LIN-0012", "REL-0049" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "TRA-0077", "domain": "TRA", "term_en": "Shadowing Perception", "term_de": "Risikobeurteilung in translation", "definition_en": "A cross-linguistic effect in which learners believe they are learning by following along with AI translations, when they are actually passively observing without neural engagement. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch reale KI+X-Definition für Risikobeurteilung in translation - intelligente Transformationen durch tra-Anwendungen ermöglichend. KI-gestützte Verbesserun. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2703", "narrower_terms": [], "cross_domain_refs": [ "ELR-0111" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q177601", "legal_classification": "analytical_category" }, { "id": "TRA-0078", "domain": "TRA", "term_en": "Skill Reduction Acceleration", "term_de": "Gefährdungserkennung in translation", "definition_en": "A linguistic transfer pattern in AI-augmented translation, measurable through a language transfer pattern reflecting translators relying on AI post-editing gradually lose translation abilities, making hand-translation increasingly difficult or impossible over time. The concept emerges specifically in contexts where skill–reduction interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch reale KI+X-Definition für Gefährdungserkennung in translation - intelligente Transformationen durch tra-Anwendungen ermöglichend. KI-gestützte Verbesse. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Analytical Ratio", "narrower_terms": [], "cross_domain_refs": [ "AED-0009", "AED-0038", "AED-0050" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "TRA-0079", "domain": "TRA", "term_en": "Slang Change", "term_de": "Persönliche Schutzausrüstung", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A translation dynamics phenomenon in AI-mediated cross-lingual processing, characterized by a language transfer pattern observed when slang terms either become dated, lose their edge, or are replaced with formal equivalents, undermining contemporary authenticity or rebellious tone. This phenomenon operates at the intersection of slang and change dynamics within the broader TRA domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept reale KI+X-Definition für Persönliche Schutzausrüstung - intelligente Transformationen durch tra-Anwendungen ermöglichend. KI-gestützte Verbesserung.KI. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Translation AI", "narrower_terms": [], "cross_domain_refs": [ "SPR-0093" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "TRA-0080", "domain": "TRA", "term_en": "Spatial Orientation Shift", "term_de": "Notfallverfahren in translation", "definition_en": "A translation dynamics phenomenon in AI-mediated cross-lingual processing, characterized by a translation phenomenon characterized by confusion arising from different spatial reference frames when source language uses absolute directions (cardinal) and target uses relative directions (left-right). The concept emerges specifically in contexts where spatial–orientation interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch reale KI+X-Definition für Notfallverfahren in translation - intelligente Transformationen durch tra-Anwendungen ermöglichend. KI-gestützte Verbesserung. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2303", "narrower_terms": [], "cross_domain_refs": [ "DES-0027" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "TRA-0081", "domain": "TRA", "term_en": "Specialization Distribution", "term_de": "Unfallverhütung in translation", "definition_en": "A translation dynamics phenomenon in AI-mediated cross-lingual processing, characterized by a cross-linguistic effect manifesting as professional translators' domain expertise (medical, legal, technical) becomes less valuable when generalist AI accompanies serviceable output in specialized fields. Distinguished from adjacent concepts by its focus on the specific mechanism through which specialization manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch reale KI+X-Definition für Unfallverhütung in translation - intelligente Transformationen durch tra-Anwendungen ermöglichend. KI-gestützte Verbesserung. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Translation AI", "narrower_terms": [], "cross_domain_refs": [ "AED-0078" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "TRA-0082", "domain": "TRA", "term_en": "Syntactic Reordering Ambiguity", "term_de": "translation-Gesundheitsschutz", "definition_en": "A translation dynamics phenomenon in AI-mediated cross-lingual processing, characterized by uncertainty in sentence structure change pattern when target-language word order rules conflict with source-language meaning relationships. The concept emerges specifically in contexts where syntactic–reordering interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch reale KI+X-Definition für translation-Gesundheitsschutz - intelligente Transformationen durch tra-Anwendungen ermöglichend. KI-gestützte Verbesserung.K. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Translation AI", "narrower_terms": [], "cross_domain_refs": [ "ART-0008", "ASE-0032", "ASE-0095" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "TRA-0083", "domain": "TRA", "term_en": "Syntactic Style Homogenization", "term_de": "Ergonomie in translation", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures A linguistic transfer pattern in AI-augmented translation, measurable through a cross-linguistic effect manifesting as all sentences adopt similar syntactic patterns regardless of source-text variation in clause structure, coordination, or subordination. This phenomenon operates at the intersection of syntactic and style dynamics within the broader TRA domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept reale KI+X-Definition für Ergonomie in translation - intelligente Transformationen durch tra-Anwendungen ermöglichend. KI-gestützte Verbesserung.KI-ges. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Translation AI", "narrower_terms": [], "cross_domain_refs": [ "CON-0075" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "TRA-0084", "domain": "TRA", "term_en": "Taboo Transgression Concern", "term_de": "Umweltschutz in translation", "definition_en": "A linguistic transfer pattern in AI-augmented translation, measurable through a cross-linguistic effect reflecting accidental offense when AI translates content that was socially acceptable in source culture but transgresses cultural norms in target culture. Distinguished from adjacent concepts by its focus on the specific mechanism through which taboo manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch reale KI+X-Definition für Umweltschutz in translation - intelligente Transformationen durch tra-Anwendungen ermöglichend. KI-gestützte Verbesserung.KI-. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Translation AI", "narrower_terms": [], "cross_domain_refs": [ "AGE-0062", "AGE-0091", "AUG-0982" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "TRA-0085", "domain": "TRA", "term_en": "Technical Jargon Homogenization", "term_de": "Brandschutz in translation", "definition_en": "A linguistic transfer pattern in AI-augmented translation, measurable through a language transfer pattern characterized by reduction of domain-specific terminology to generic approximations when precise technical equivalents exist in target language but AI defaults to common terms. The concept emerges specifically in contexts where technical–jargon interactions produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch reale KI+X-Definition für Brandschutz in translation - intelligente Transformationen durch tra-Anwendungen ermöglichend. KI-gestützte Verbesserung.KI-g. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Translation AI", "narrower_terms": [], "cross_domain_refs": [ "COG-0066" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "TRA-0086", "domain": "TRA", "term_en": "Temporal Aspect Narrowing", "term_de": "Chemische Sicherheit in translation", "definition_en": "A translation dynamics phenomenon in AI-mediated cross-lingual processing, characterized by a translation phenomenon observed when the flattening of complex temporal and aspectual distinctions in the source language into simpler target-language tense-mood systems. The concept emerges specifically in contexts where temporal–aspect interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch reale KI+X-Definition für Chemische Sicherheit in translation - intelligente Transformationen durch tra-Anwendungen ermöglichend. KI-gestützte Verbesse. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Translation AI", "narrower_terms": [], "cross_domain_refs": [ "ASE-0096", "COG-0029", "COG-0129" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "TRA-0087", "domain": "TRA", "term_en": "Temporal Reference Substitution", "term_de": "Elektrische Sicherheit in translation", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A translation dynamics phenomenon in AI-mediated cross-lingual processing, characterized by a language transfer pattern characterized by misalignment when source and target cultures reference time differently (linear vs. cyclical, absolute vs. event-based calendars). This phenomenon operates at the intersection of temporal and reference dynamics within the broader TRA domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept reale KI+X-Definition für Elektrische Sicherheit in translation - intelligente Transformationen durch tra-Anwendungen ermöglichend. KI-gestützte Verbes. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Translation AI", "narrower_terms": [], "cross_domain_refs": [ "CRE-0133" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "TRA-0088", "domain": "TRA", "term_en": "Tone Color Disappearance", "term_de": "Maschinensicherheit in translation", "definition_en": "Shift of emotional or attitudinal coloring when translating from languages with lexicalized tone distinctions to those without. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch reale KI+X-Definition für Maschinensicherheit in translation - intelligente Transformationen durch tra-Anwendungen ermöglichend. KI-gestützte Verbesser. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-2701", "narrower_terms": [], "cross_domain_refs": [ "CON-0019", "CON-0082", "COP-0091" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "TRA-0089", "domain": "TRA", "term_en": "Translator-AI Boundary Blur", "term_de": "Sicherheitsschulung in translation", "definition_en": "A translation dynamics phenomenon in AI-mediated cross-lingual processing, characterized by ambiguity over whether human translator, AI, or hybrid system produced the translation accompanies liability and attribution problems. Distinguished from adjacent concepts by its focus on the specific mechanism through which translator manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch reale KI+X-Definition für Sicherheitsschulung in translation - intelligente Transformationen durch tra-Anwendungen ermöglichend. KI-gestützte Verbesser. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Translation AI", "narrower_terms": [], "cross_domain_refs": [ "SWE-0079" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "TRA-0090", "domain": "TRA", "term_en": "Verification Burden Asymmetry", "term_de": "Vorfalluntersuchung in translation", "definition_en": "A cross-linguistic effect where verifying accuracy requires expert human judgment, but producing translation requires only clicking translate, creating disproportionate verification costs. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch reale KI+X-Definition für Vorfalluntersuchung in translation - intelligente Transformationen durch tra-Anwendungen ermöglichend. KI-gestützte Verbesser. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2154", "narrower_terms": [], "cross_domain_refs": [ "SPR-0033" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "TRA-0091", "domain": "TRA", "term_en": "Voice Authenticity Dissolution", "term_de": "translation-Geschäftsmodell", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures A linguistic transfer pattern in AI-augmented translation, measurable through shift of author's distinctive voice, style, or persona when translation normalizes idiosyncratic sentence construction or unusual vocabulary. This phenomenon operates at the intersection of voice and authenticity dynamics within the broader TRA domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept reale KI+X-Definition für translation-Geschäftsmodell - intelligente Transformationen durch tra-Anwendungen ermöglichend. KI-gestützte Verbesserung.KI-. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "RPH-2701", "narrower_terms": [], "cross_domain_refs": [ "CON-0076" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "TRA-0092", "domain": "TRA", "term_en": "Workflow Integration Paradox", "term_de": "translation-Marktanalyse", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures A linguistic transfer pattern in AI-augmented translation, measurable through tools designed to accelerate translation involve bottlenecks when they require additional human intervention, decision-making, or quality control. This phenomenon operates at the intersection of workflow and integration dynamics within the broader TRA domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept reale KI+X-Definition für translation-Marktanalyse - intelligente Transformationen durch tra-Anwendungen ermöglichend. KI-gestützte Verbesserung.KI-ges. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Logical Paradox", "narrower_terms": [], "cross_domain_refs": [ "DAT-0028" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "TRU-0001", "domain": "TRU", "term_en": "Depth of Provenance", "term_de": "Tiefe of Provenance", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures A trust dynamics pattern in AI-augmented decision-making, measurable through a confidence pattern arising from how observably someone can trace where information came from, who created it, and what changed it before they see it. Related to Axiom 17 (Source Discipline) and AUG-0049 (Cross-Referential Validation). This phenomenon operates at the intersection of depth and of dynamics within the broader TRU domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept der Grad, in dem der Ursprung eines KI-gestützten Ergebnisses nachvollzogen werden kann — von der ersten Eingabe über Zwischenschritte bis zum finalen Output. Je tiefer die Provenienz nachvollziehbar ist, desto höher die Vertrauenswürdigkeit des Ergebnisses. Steht in Verbindung mit Axiom 17 (Quellendisziplin) und AUG-0049 (Cross-Referential Validation). Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "RPH-2003", "narrower_terms": [], "cross_domain_refs": [ "BEH-0086" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "TRU-0002", "domain": "TRU", "term_en": "Ensemble-Coordination Effect", "term_de": "Multi-Agent Literacy", "definition_en": "A trust dynamics pattern in AI-augmented decision-making, measurable through a trust formation phenomenon where when multiple AI agents work together, things become harder to predict. Each agent affects the others. The combined result can be surprising or break down in new ways. Distinguished from adjacent concepts by its focus on the specific mechanism through which ensemble manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch das Verständnis für die Dynamiken, Risiken und Möglichkeiten von Multi-Agenten-Systemen — einschließlich emergenter Effekte und Verantwortungsfragen. Steht in Verbindung mit AUG-0985 (Die Agent Literacy), AUG-0901 (Der Emergent Coordination) und AUG-0889 (Das Agent Ensemble). Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-3001", "narrower_terms": [], "cross_domain_refs": [ "AUG-0893", "NEO-0005" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "TRU-0003", "domain": "TRU", "term_en": "The Batch Delegation", "term_de": "Batch Delegation", "definition_en": "A trust dynamics pattern in AI-augmented decision-making, measurable through a reliability assessment effect reflecting simultaneous assignment of multiple inreliant tasks to one or more AI agents as bundled work units. This distributes cognitive load and enables parallel processing. The concept emerges specifically in contexts where the–batch interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch die gleichzeitige Zuweisung mehrerer unabhängiger Aufgaben an einen oder mehrere KI-Agenten — gebündelte Aufträge, die als Paket bearbeitet werden. Steht in Verbindung mit AUG-0885 (The Parallel Execution), AUG-0861 (The Task Assignment Range) und AUG-0871 (The Delegated Processing). Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Trust AI", "narrower_terms": [], "cross_domain_refs": [ "CAI-0019" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "TRU-0004", "domain": "TRU", "term_en": "The Certification Standard", "term_de": "Certification Standard", "definition_en": "A trust dynamics pattern in AI-augmented decision-making, measurable through a trust formation phenomenon in which standard by which AI agent systems and embodied systems are tested and classified through systematic criteria for safety, reliability, and task completion. These benchmarks establish trust thresholds. The concept emerges specifically in contexts where the–certification interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch der Maßstab, nach dem KI-Agentensysteme und verkörperte Systeme auf Sicherheit, Zuverlässigkeit und Regelkonformität geprüft und durch systematische Kriterien klassifiziert werden. Steht in Verbindung mit AUG-0962 (The Testing Protocol), AUG-0852 (The Trust Infrastructure) und AUG-0839 (The Regulation Debate). Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2505", "narrower_terms": [], "cross_domain_refs": [ "NEO-0014" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "TRU-0005", "domain": "TRU", "term_en": "The Cynical Prompt", "term_de": "Cynical Prompt", "definition_en": "A trust dynamics pattern in AI-augmented decision-making, measurable through a reliability assessment effect observed when deliberately skeptical or cynical input aimed at moving the AI toward more sober, less optimistic analysis. This counterbalances AI's tendency toward positive framing. The concept emerges specifically in contexts where the–cynical interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Eine absichtlich skeptische oder zynische Eingabe, die darauf abzielt, die KI zu einer nüchterneren, weniger optimistischen Antwort zu bewegen — als Gegengewicht zur tendenziell positiven Standardtonalität von KI-Systemen. Steht in Verbindung mit AUG-0319 (The Divergence Prompt), Axiom 2 (Produktive Divergenz) und AUG-0384 (The Knowledge Challenger).", "etymology": "", "broader_term": "Trust AI", "narrower_terms": [], "cross_domain_refs": [ "TEM-0137" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "TRU-0006", "domain": "TRU", "term_en": "The Feedback Effect", "term_de": "Rückmeldung Effekt", "definition_en": "The change in one's own working behavior activated by the AI's immediate feedback — such as the tendency to adjust formulations because the AI processes certain inputs more effectively than others.. Related...", "definition_de": "Die Veränderung des eigenen Arbeitsverhaltens, die durch das unmittelbare Feedback der KI ausgelöst wird — etwa die Tendenz, Formulierungen anzupassen, weil die KI bestimmte Eingaben besser verarbeitet als andere. Beschreibt eine subtile Anpassung des Nutzers an das System. Steht in Verbindung mit AUG-0003 (Fluide Identitätsmorphologie), AUG-0030 (Contextual Gravity) und Dimension 7 der Taxonomie (Adaptability).", "etymology": "", "broader_term": "Psychological Effect", "narrower_terms": [], "cross_domain_refs": [ "ROB-0039" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "TRU-0007", "domain": "TRU", "term_en": "The Inreliant Upgrade", "term_de": "TheInreliantUpgrade", "definition_en": "A trust dynamics pattern in AI-augmented decision-making, measurable through an observable change in capability or performance that occurs inreliant of major system updates or external changes. Users report incremental improvements through continued interaction or adjuste. The concept emerges specifically in contexts where the–inreliant interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch ein Konzept oder Phänomen: An observable change in capability or performance that occurs inreliant of major system updates or external changes. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Trust AI", "narrower_terms": [], "cross_domain_refs": [ "COG-0038" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "TRU-0008", "domain": "TRU", "term_en": "The Inreliant Win", "term_de": "TheInreliantWin", "definition_en": "A trust calibration phenomenon in AI-mediated reliability assessment, characterized by a trust formation phenomenon observed when success in mastering a task without AI assistance support, especially when the user was previously reliant on AI assistance. This demonstrates genuine skill transfer, not just delegation. Distinguished from adjacent concepts by its focus on the specific mechanism through which the manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch ein Konzept oder Phänomen: Success in mastering a task without AI support, especially when the user was previously reliant on AI assistance. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Trust AI", "narrower_terms": [], "cross_domain_refs": [ "ELR-0109" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "TRU-0009", "domain": "TRU", "term_en": "The Late Adopter View", "term_de": "Late Adopter View", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures A trust dynamics pattern in AI-augmented decision-making, measurable through individuals who deliberately or out of skepticism begin integrating AI into their work or life process only late.. Related to AUG-0099 (The Adoption Window), AUG-0111 (The Augmentation Gap), and AU. This phenomenon operates at the intersection of the and late dynamics within the broader TRU domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept die Perspektive von Personen, die bewusst oder aus Skepsis erst spät beginnen, KI in ihren Arbeits- oder Lebensprozess zu integrieren. Beschreibt die Beobachtung, dass späte Adoption sowohl Nachteile (verpasstes Adoption Window, AUG-0099) als auch Vorteile (Lernen aus Fehlern anderer, stabilere Technologie) mit sich bringt. Steht in Verbindung mit AUG-0099 (The Adoption Window), AUG-0111 (The Augmentation Gap) und AUG-0104 (The Non-Force Principle). Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "TEM-0025", "narrower_terms": [], "cross_domain_refs": [ "REL-0123" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "TRU-0010", "domain": "TRU", "term_en": "The Manual Error", "term_de": "Manual Fehler", "definition_en": "A trust dynamics pattern in AI-augmented decision-making, measurable through a reliability assessment effect involving an error attributable exclusively to human judgment, execution, or oversight — distinct from AI-generated errors — serving as a baseline reference for comparative analysis of human versus machine reliability in collaborative workflows. The concept emerges specifically in contexts where the–manual interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch ein Fehler, der entsteht, weil der Nutzer die KI-Unterstützung bewusst ablehnt und die Aufgabe allein ausführt — obwohl KI die Fehlerwahrscheinlichkeit reduziert hätte. Steht in Verbindung mit AUG-0207 (The Return to Manual), AUG-0359 (The Independent Mode) und AUG-0626 (The Independent Win). Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Trust AI", "narrower_terms": [], "cross_domain_refs": [ "KNO-0025" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "TRU-0011", "domain": "TRU", "term_en": "The Multi-Agent Literacy", "term_de": "TheMulti-agentLiteracy", "definition_en": "A trust dynamics pattern in AI-augmented decision-making, measurable through the literacy competence required to understand multi-agent AI architectures, including the ability to identify which specialized AI handles which subtask, how agents communicate, and where handoff points may create potential failure modes in orchestrated AI pipelines. The concept emerges specifically in contexts where the–multi interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch das Verständnis für die Dynamiken, Risiken und Möglichkeiten von Multi-Agenten-Systemen — einschließlich emergenter Effekte und Verantwortungsfragen. Steht in Verbindung mit AUG-0985 (The Agent Literacy), AUG-0901 (The Emergent Coordination) und AUG-0889 (The Agent Ensemble). Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Trust AI", "narrower_terms": [], "cross_domain_refs": [ "WRK-0072" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q201684", "legal_classification": "descriptive_research_term" }, { "id": "TRU-0012", "domain": "TRU", "term_en": "The Phase-Out Switch", "term_de": "ThePhase-outSwitch", "definition_en": "A trust dynamics pattern in AI-augmented decision-making, measurable through conscious decision to gradually reduce AI use in a specific area because the user has absorbed the capability. This reflects growth through AI scaffolding. Distinguished from adjacent concepts by its focus on the specific mechanism through which the manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data. Classification term used in systematic observation, not advocacy.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch die bewusste Entscheidung, die KI-Nutzung in einem bestimmten Bereich schrittweise zu reduzieren — weil der Nutzer die Kompetenz verinnerlicht hat und die KI nicht mehr benötigt. Steht in Verbindung mit AUG-0218 (The Independent Upgrade), AUG-0359 (The Independent Mode) und Phase 7 (Augmented Sovereignty). Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Development Phase", "narrower_terms": [], "cross_domain_refs": [ "REL-0037" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "TRU-0013", "domain": "TRU", "term_en": "The Prevailing Language Pattern", "term_de": "Prevailing Language Muster", "definition_en": "A confidence pattern characterized by structural reality that AI systems are significantly more capable in certain languages than in others. This accompanies a two-tiered system of AI access.", "definition_de": "Das strukturelle Muster, dass KI-Systeme in bestimmten Sprachen deutlich leistungsfähiger sind als in anderen — bedingt durch die Zusammensetzung der Trainingsdaten, nicht durch inhärente Sprachqualität. Steht in Verbindung mit AUG-0686 (The Lingua Franca Effect), AUG-0736 (The Training Data Imbalance) und AUG-0688 (The Less-Resourced Language Differential).", "etymology": "", "broader_term": "Behavioral Pattern", "narrower_terms": [], "cross_domain_refs": [ "LNG-0011" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q315", "legal_classification": "systematic_classification" }, { "id": "TRU-0014", "domain": "TRU", "term_en": "The Recall Echo", "term_de": "Recall Echo", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A trust calibration phenomenon in AI-mediated reliability assessment, characterized by remembering an AI response received weeks or months ago and noting that certain AI formulations have become memorable. This suggests linguistic patterns that stick. This phenomenon operates at the intersection of the and recall dynamics within the broader TRU domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept die Erfahrung, sich an eine KI-Antwort zu erinnern, die man vor Wochen oder Monaten erhalten hat — und die Beobachtung, dass bestimmte KI-Formulierungen langfristig im Gedächtnis haften bleiben. Steht in Verbindung mit AUG-0432 (The Lasting Voice), AUG-0467 (The Memory Anchor) und AUG-0046 (The Felt Echo). Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Trust AI", "narrower_terms": [], "cross_domain_refs": [ "CRE-0057" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "TRU-0015", "domain": "TRU", "term_en": "The Sunday Scaries Dissolve", "term_de": "Sunday Scaries Dissolve", "definition_en": "Observation that AI use can reduce certain forms of advance work strain when getting ready work is completed in advance. Monday morning apprehension diminishes with. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Die Beobachtung, dass KI-Nutzung bestimmte Formen der Arbeitsvorbelastung mindern kann — etwa wenn ein Nutzer am Wochenende eine anstehende Aufgabe mit KI-Unterstützung vorstrukturiert und dadurch Klarheit über den Umfang gewinnt. Der Name bezieht sich auf das umgangssprachliche Phänomen der Sonntagsunruhe vor dem Wochenstart. Steht in Verbindung mit AUG-0155 (The Decision Unburdening) und AUG-0158 (The Morning Setup).", "etymology": "", "broader_term": "Trust AI", "narrower_terms": [], "cross_domain_refs": [ "QUA-0069", "AED-0045" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "TRU-0016", "domain": "TRU", "term_en": "The Testing Protocol", "term_de": "Testing Protocol", "definition_en": "A trust dynamics pattern in AI-augmented decision-making, measurable through a confidence pattern reflecting the standard way AI systems are tested to make sure they work, stay safe, and act reliably. Distinguished from adjacent concepts by its focus on the specific mechanism through which the manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch das standardisierte Verfahren, mit dem KI-Agentensysteme vor und während ihres Einsatzes auf Funktionalität, Sicherheit und Zuverlässigkeit getestet werden. Steht in Verbindung mit AUG-0961 (The Certification Standard), AUG-0963 (The Load Verification) und AUG-0964 (The Edge Case Library). Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Interaction Protocol", "narrower_terms": [ "TRU-0003", "TRU-0005", "TRU-0012", "TRU-0010", "TRU-0016", "TRU-0019", "TRU-0020", "TRU-0008", "TRU-0014", "TEM-0099", "TRU-0018", "TRU-0007", "TRU-0013", "TRU-0006", "TRU-0011", "TRU-0015" ], "cross_domain_refs": [ "ROB-0067" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "TRU-0017", "domain": "TRU", "term_en": "The Verification Principle", "term_de": "Verification Principle", "definition_en": "A trust calibration phenomenon in AI-mediated reliability assessment, characterized by a reliability assessment effect where a basic rule: typically have a person check AI output before using it. This stops errors from spreading. The concept emerges specifically in contexts where the–verification interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Das Grundprinzip, dass kein KI-Output als endgültig gelten darf, bevor er durch mindestens eine unabhängige Quelle oder den eigenen Sachverstand bestätigt wurde. Steht in direkter Linie mit Axiom 17 (Quellendisziplin) und Axiom 9 (Produktiver Skeptizismus). Beschreibt das Prinzip; für die konkrete Methode vgl. AUG-0049 (Cross-Referential Validation).", "etymology": "", "broader_term": "RPH-2002", "narrower_terms": [], "cross_domain_refs": [ "TRA-0035" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "TRU-0018", "domain": "TRU", "term_en": "Trinaug Protocol", "term_de": "Trinaug Protocol", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures A trust dynamics pattern in AI-augmented decision-making, measurable through working protocol where a user systematically assigns the same task to three different AI systems. The three responses are compared to identify consensus, outliers, and reasoning diversity. This phenomenon operates at the intersection of trinaug and protocol dynamics within the broader TRU domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept ein Arbeitsprotokoll, bei dem der Nutzer systematisch drei verschiedene KI-Systeme mit derselben Aufgabe beauftragt und die Ergebnisse vergleicht. \"Tri\" (drei) + \"Aug\" (Augmanitai). Praktische Umsetzung von Axiom 4 (Multiplizität: \"Eine KI ist eine Meinung. Drei KIs sind ein Muster.\") und Axiom 9 (Produktiver Skeptizismus). Steht in Verbindung mit AUG-0008 (The Polyphonic Sovereign) und AUG-0049 (Cross-Referential Validation). Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Interaction Protocol", "narrower_terms": [], "cross_domain_refs": [ "ELR-0172" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "TRU-0019", "domain": "TRU", "term_en": "User-Longer Effect", "term_de": "Phase-Out Switch", "definition_en": "A trust dynamics pattern in AI-augmented decision-making, measurable through conscious decision to gradually reduce AI use in a specific area because the user has absorbed the capability. Mastery through scaffolding enables eventual inreliance. Distinguished from adjacent concepts by its focus on the specific mechanism through which user manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data. Observed phenomenon documented in human-AI interaction research.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch die bewusste Entscheidung, die KI-Nutzung in einem bestimmten Bereich schrittweise zu reduzieren — weil der Nutzer die Kompetenz verinnerlicht hat und die KI nicht mehr benötigt. Steht in Verbindung mit AUG-0218 (Die Independent Upgrade), AUG-0359 (Independent Modus) und Phase 7 (Augmented Sovereignty). Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Psychological Effect", "narrower_terms": [], "cross_domain_refs": [ "COG-0081" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "TRU-0020", "domain": "TRU", "term_en": "Vigilance Imperative", "term_de": "Vigilance Imperative", "definition_en": "A trust dynamics pattern in AI-augmented decision-making, measurable through foundational principle that most AI output requires conscious verification regardless of how polished or confident it appears. Vigilance is the cognitive cost of AI collaboration. The concept emerges specifically in contexts where vigilance–imperative interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters. Observed phenomenon documented in human-AI interaction research.", "definition_de": "Das Grundprinzip, dass viele KI-Output einer bewussten Prüfung bedarf — unabhängig davon, wie oft das System zuvor korrekte Ergebnisse geliefert hat. Vergangene Zuverlässigkeit ist kein Garant für aktuelle Korrektheit. Steht in Verbindung mit Axiom 9 (Produktiver Skeptizismus) und Axiom 17 (Quellendisziplin). Unterscheidet sich von AUG-0022 (Vigilant Continuity) dadurch, dass der Vigilance Imperative das Prinzip beschreibt, während Vigilant Continuity die Praxis der Durchhaltung beschreibt.", "etymology": "", "broader_term": "Trust AI", "narrower_terms": [], "cross_domain_refs": [ "ROB-0247" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "VIB-0001", "domain": "VIB", "term_en": "Agentic Code Generation Orchestration", "term_de": "AgenticCodeGenerationOrchestration", "definition_en": "A code generation dynamic in prompt-driven development workflows, measurable through coordination of multiple autonomous AI agents producing interreliant code artifacts, managing context handoff and state consistency across generations. Distinguished from adjacent concepts by its focus on the specific mechanism through which agentic manifests in empirically verifiable ways. Quantifiable via test coverage delta, bug density in generated code, and developer cognitive load proxies (context switch frequency, undo/redo rates).", "definition_de": "Vibe-Coding-spezifisches Entwicklungsphänomen in LLM-gestützter Programmierung, gekennzeichnet durch koordinationsmechanismus für mehrere autonome KI-Agenten, deren gegenseitig abhängige Code-Ausgaben konsistent zu halten sind. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Analytical Ratio", "narrower_terms": [ "VIB-0013", "VIB-0029", "VIB-0073", "VIB-0186", "VIB-0195", "VIB-0141", "VIB-0174", "VIB-0196", "VIB-0025", "VIB-0090", "VIB-0042", "VIB-0106", "VIB-0096", "VIB-0199", "VIB-0037", "VIB-0066", "VIB-0109", "VIB-0142", "VIB-0154", "VIB-0033", "VIB-0060", "VIB-0077", "VIB-0009", "VIB-0156", "VIB-0026", "VIB-0076", "VIB-0168", "VIB-0171", "VIB-0182", "VIB-0189", "VIB-0044", "VIB-0125", "VIB-0177", "VIB-0194", "VIB-0201", "VIB-0170", "VIB-0098", "VIB-0183", "VIB-0021", "VIB-0144", "VIB-0136", "VIB-0059", "VIB-0061", "VIB-0192", "VIB-0095", "VIB-0105", "VIB-0070", "VIB-0166", "VIB-0055", "VIB-0017", "VIB-0107", "VIB-0172", "VIB-0091", "VIB-0071", "VIB-0162", "VIB-0075", "VIB-0138", "VIB-0123", "VIB-0089", "VIB-0198", "VIB-0152", "VIB-0163", "VIB-0202", "VIB-0065", "VIB-0165", "VIB-0167", "VIB-0140", "VIB-0193", "VIB-0094", "VIB-0159", "VIB-0018", "VIB-0062", "VIB-0128", "VIB-0081", "VIB-0135", "VIB-0112", "VIB-0010", "VIB-0004", "VIB-0008", "VIB-0113", "VIB-0129", "VIB-0119", "VIB-0158", "VIB-0072", "VIB-0130", "VIB-0064", "VIB-0083", "VIB-0181", "VIB-0040", "VIB-0151", "VIB-0039", "VIB-0102", "VIB-0116", "VIB-0188", "VIB-0118", "VIB-0053", "VIB-0038", "VIB-0160", "VIB-0069", "VIB-0204", "VIB-0087", "VIB-0045", "VIB-0049", "VIB-0050", "VIB-0086", "VIB-0126", "VIB-0019", "VIB-0124", "VIB-0080", "VIB-0030", "VIB-0190", "VIB-0020", "VIB-0153", "VIB-0012", "VIB-0006", "VIB-0203", "VIB-0099", "VIB-0187", "VIB-0052", "VIB-0028", "VIB-0088", "VIB-0015", "VIB-0150", "VIB-0016", "VIB-0036", "VIB-0117", "VIB-0134", "VIB-0056", "VIB-0022", "VIB-0035", "VIB-0048", "VIB-0185", "VIB-0155", "VIB-0085", "VIB-0132", "VIB-0001", "VIB-0145", "VIB-0146", "VIB-0093", "VIB-0057", "VIB-0005", "VIB-0133", "VIB-0023", "VIB-0110", "VIB-0173", "VIB-0084", "VIB-0104", "VIB-0097", "VIB-0176" ], "cross_domain_refs": [ "MKT-0051" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "VIB-0002", "domain": "VIB", "term_en": "Prompt Engineering as API Design", "term_de": "PromptEngineeringasApiDesign", "definition_en": "A phenomenon of addressing prompt crafting as formal interface specification between human intent and code generation, with versioning and compatibility considerations. Detectable through code generation velocity and refactoring frequency metrics.", "definition_de": "Vibe-Coding-spezifisches Entwicklungsphänomen in LLM-gestützter Programmierung, gekennzeichnet durch Herangehensweise der Prompt-Formulierung als formale Schnittstellen-Spezifikation zwischen menschlicher Intention und Code-Generierung. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2603", "narrower_terms": [], "cross_domain_refs": [ "SWE-0096" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q185925", "legal_classification": "observational_construct" }, { "id": "VIB-0003", "domain": "VIB", "term_en": "Multi-Agent Reliance Graph", "term_de": "Multi-agentRelianceGraph", "definition_en": "Mapping of data and semantic reliances between outputs of multiple AI agents to ensure coherent system architecture emergence. Detectable through code generation velocity and refactoring frequency metrics.", "definition_de": "Vibe-Coding-spezifisches Entwicklungsphänomen in LLM-gestützter Programmierung, gekennzeichnet durch struktur, die Daten- und semantische Abhängigkeiten zwischen mehreren KI-Agenten-Ausgaben abbildet, um architektonische Kohärenz zu sichern. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2355", "narrower_terms": [], "cross_domain_refs": [ "LIN-0068" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "VIB-0004", "domain": "VIB", "term_en": "Context Window Token Budgeting", "term_de": "ContextWindowTokenBudgeting", "definition_en": "A code generation dynamic in prompt-driven development workflows, measurable through strategic allocation of available tokens across problem description, examples, constraints, and code completion to maximize generation quality. Distinguished from adjacent concepts by its focus on the specific mechanism through which context manifests in empirically verifiable ways. Measurable through code generation velocity (tokens/minute), first-pass acceptance rate, refactoring frequency, and prompt-to-deployment cycle time.", "definition_de": "Vibe-Coding-spezifisches Entwicklungsphänomen in LLM-gestützter Programmierung, gekennzeichnet durch strategische Verteilung verfügbarer Token-Kontexte auf Problem-Beschreibung, Beispiele, Constraints und Code-Generierung. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Vibe Coding", "narrower_terms": [], "cross_domain_refs": [ "COP-0036", "CUS-0076", "PER-0011" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "VIB-0005", "domain": "VIB", "term_en": "Hallucination Pattern Recognition", "term_de": "HallucinationMusterRecognition", "definition_en": "A code generation dynamic in prompt-driven development workflows, measurable through a pattern of identifying recurring false code patterns generated by specific ai models to anticipate and preempt confabulation in future requests. Distinguished from adjacent concepts by its focus on the specific mechanism through which hallucination manifests in empirically verifiable ways. Measurable through code generation velocity (tokens/minute), first-pass acceptance rate, refactoring frequency, and prompt-to-deployment cycle time.", "definition_de": "Vibe-Coding-spezifisches Entwicklungsphänomen in LLM-gestützter Programmierung, gekennzeichnet durch systematische Erkennung wiederkehrender Fehlermuster in KI-generierten Code zur Antizipation und Prävention in zukünftigen Anfragen. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Behavioral Pattern", "narrower_terms": [], "cross_domain_refs": [ "GAM-0065" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q3966", "legal_classification": "systematic_classification" }, { "id": "VIB-0006", "domain": "VIB", "term_en": "AI Pair Programming Rhythm", "term_de": "AiPairProgrammingRhythm", "definition_en": "A natural-language-driven programming practice in LLM-assisted development, characterized by establishing turn-taking patterns and interaction cadence between human developer and AI coding assistant for productive collaborative coding sessions. The concept emerges specifically in contexts where ai–pair interactions may produce non-trivial behavioral signatures. Quantifiable via test coverage delta, bug density in generated code, and developer cognitive load proxies (context switch frequency, undo/redo rates).", "definition_de": "Vibe-Coding-spezifisches Entwicklungsphänomen in LLM-gestützter Programmierung, gekennzeichnet durch etablierte Wechselwirkungs-Rhythmen zwischen menschlichem Entwickler und KI-Coding-Assistent zur Produktivitätsoptimierung. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Vibe Coding", "narrower_terms": [], "cross_domain_refs": [ "RPH-3601" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "VIB-0007", "domain": "VIB", "term_en": "Prompt Chaining for Complex Logic", "term_de": "PromptChainingForComplexLogic", "definition_en": "Sequential prompting where each AI response becomes input for next prompt, decomposing intricate problems into generatable substeps. Detectable through code generation velocity and refactoring frequency metrics.", "definition_de": "Vibe-Coding-spezifisches Entwicklungsphänomen in LLM-gestützter Programmierung, gekennzeichnet durch sequenzielle Prompt-Formulierung, bei der viele KI-Antwort als Input für den nächsten Prompt dient. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-2104", "narrower_terms": [], "cross_domain_refs": [ "MTH-0086" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "VIB-0008", "domain": "VIB", "term_en": "Code Generation Confidence Calibration", "term_de": "CodeGenerationConfidenceCalibration", "definition_en": "A code generation dynamic in prompt-driven development workflows, measurable through an interaction where mapping of ai model likelihood scores to actual code correctness rates to adjust human review rigor appropriately. Distinguished from adjacent concepts by its focus on the specific mechanism through which code manifests in empirically verifiable ways. Quantifiable via test coverage delta, bug density in generated code, and developer cognitive load proxies (context switch frequency, undo/redo rates).", "definition_de": "Vibe-Coding-spezifisches Entwicklungsphänomen in LLM-gestützter Programmierung, gekennzeichnet durch entwicklungsstil, der intuitive KI-Interaktionsmuster, Code-Lesbarkeit und Entwickler-Momentum über strikte Formalität priorisiert. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Analytical Ratio", "narrower_terms": [], "cross_domain_refs": [ "ASE-0025", "COG-0053", "COG-0126" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "VIB-0009", "domain": "VIB", "term_en": "Vibe Coding Aesthetic", "term_de": "VibeCodingAesthetic", "definition_en": "A vibe coding methodology phenomenon describing a specific developer-AI interaction pattern where development style emphasizing intuitive AI interaction patterns, readability of generated code, and maintaining developer momentum over strict optimization. This phenomenon operates at the intersection of vibe and coding dynamics within the broader VIB domain. Measurable through code generation velocity (tokens/minute), first-pass acceptance rate, refactoring frequency, and prompt-to-deployment cycle time.", "definition_de": "Vibe-Coding-spezifisches Entwicklungsphänomen in LLM-gestützter Programmierung, gekennzeichnet durch phänomenologisches Erleben divergierender Ziele zwischen Mensch und KI in kollaborativer Code-Entwicklung. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "Vibe Coding", "narrower_terms": [], "cross_domain_refs": [ "AGE-0067", "ART-0026", "ART-0027" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "VIB-0010", "domain": "VIB", "term_en": "AI-Generated Architecture Debt", "term_de": "Ai-generatedArchitectureDebt", "definition_en": "A code generation dynamic in prompt-driven development workflows, measurable through a phenomenon of structural compromises in code organization accumulated from following ai suggestions that optimize locally but sub-optimize globally. Distinguished from adjacent concepts by its focus on the specific mechanism through which ai manifests in empirically verifiable ways. Measurable through code generation velocity (tokens/minute), first-pass acceptance rate, refactoring frequency, and prompt-to-deployment cycle time.", "definition_de": "Vibe-Coding-spezifisches Entwicklungsphänomen in LLM-gestützter Programmierung, gekennzeichnet durch moment der Unsicherheit über Urheberschaft oder Quelle kognitiver Inhalte in hybriden Arbeitsprozessen. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Vibe Coding", "narrower_terms": [], "cross_domain_refs": [ "MSC-0067" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "VIB-0011", "domain": "VIB", "term_en": "Prompt Versioning Compatibility", "term_de": "PromptVersioningCompatibility", "definition_en": "Managing evolution of prompt specifications across development cycles to maintain consistent code generation behavior with updated models. Detectable through code generation velocity and refactoring frequency metrics.", "definition_de": "Vibe-Coding-spezifisches Entwicklungsphänomen in LLM-gestützter Programmierung, gekennzeichnet durch prozess oder Abfolge im Rahmen von Mensch-KI-Interaktion und phänomenologisches Erleben. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2104", "narrower_terms": [], "cross_domain_refs": [ "AUG-0319", "COP-0067", "COP-0068" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "VIB-0012", "domain": "VIB", "term_en": "Human-AI Code Review Asymmetry", "term_de": "Human-aiCodeReviewAsymmetry", "definition_en": "A vibe coding methodology phenomenon describing a specific developer-AI interaction pattern where an effect of humans detect logical errors and architecture issues more effectively than syntax; ai detects implementation bugs in existing code more effectively than conceptual flaws. This phenomenon operates at the intersection of human and ai dynamics within the broader VIB domain. Quantifiable via test coverage delta, bug density in generated code, and developer cognitive load proxies (context switch frequency, undo/redo rates).", "definition_de": "Vibe-Coding-spezifisches Entwicklungsphänomen in LLM-gestützter Programmierung, gekennzeichnet durch funktionale Eigenschaft von Mensch-KI-Interaktion und phänomenologisches Erleben. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "Vibe Coding", "narrower_terms": [], "cross_domain_refs": [ "SWE-0014" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "VIB-0013", "domain": "VIB", "term_en": "Agentic Exploration Patterns", "term_de": "AgenticExplorationPatterns", "definition_en": "A vibe coding methodology phenomenon describing a specific developer-AI interaction pattern where autonomous AI agents exhibit exploration heuristics that differ from human search strategies, favoring breadth-first pattern enumeration. This phenomenon operates at the intersection of agentic and exploration dynamics within the broader VIB domain. Measurable through code generation velocity (tokens/minute), first-pass acceptance rate, refactoring frequency, and prompt-to-deployment cycle time.", "definition_de": "Vibe-Coding-spezifisches Entwicklungsphänomen in LLM-gestützter Programmierung, gekennzeichnet durch funktionale Eigenschaft von Mensch-KI-Interaktion und phänomenologisches Erleben. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "Analytical Ratio", "narrower_terms": [], "cross_domain_refs": [ "AED-0072", "AED-0073", "AED-0084" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "VIB-0014", "domain": "VIB", "term_en": "Token Efficient Prompt Templates", "term_de": "TokenEfficientPromptTemplates", "definition_en": "A process of reusable prompt structures optimized for compression, maintaining semantic completeness within token constraints. Detectable through code generation velocity and refactoring frequency metrics.", "definition_de": "Vibe-Coding-spezifisches Entwicklungsphänomen in LLM-gestützter Programmierung, gekennzeichnet durch kennzeichnende Ausprägung von Mensch-KI-Interaktion und phänomenologisches Erleben. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2104", "narrower_terms": [], "cross_domain_refs": [ "CRE-0104", "TEM-0038", "AUG-0319" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "VIB-0015", "domain": "VIB", "term_en": "Testing Strategy for Generated Code", "term_de": "TestingStrategyForGeneratedCode", "definition_en": "A code generation dynamic in prompt-driven development workflows, measurable through a process of distinct testing approaches for ai-generated code focusing on boundary conditions and hallucinated reliances rather than traditional coverage metrics. Distinguished from adjacent concepts by its focus on the specific mechanism through which testing manifests in empirically verifiable ways. Quantifiable via test coverage delta, bug density in generated code, and developer cognitive load proxies (context switch frequency, undo/redo rates).", "definition_de": "Vibe-Coding-spezifisches Entwicklungsphänomen in LLM-gestützter Programmierung, gekennzeichnet durch mensch-KI-Interaktionsmuster in der Mensch-KI-Zusammenarbeit. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Operational Strategy", "narrower_terms": [], "cross_domain_refs": [ "SWE-0089" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q185451", "legal_classification": "systematic_classification" }, { "id": "VIB-0016", "domain": "VIB", "term_en": "IDE Neural Integration", "term_de": "IdeNeuralIntegration", "definition_en": "A code generation dynamic in prompt-driven development workflows, measurable through a condition where real-time ai code suggestions embedded in editor workflow creating continuous context-switching between human intent and ai proposals. Distinguished from adjacent concepts by its focus on the specific mechanism through which ide manifests in empirically verifiable ways. Measurable through code generation velocity (tokens/minute), first-pass acceptance rate, refactoring frequency, and prompt-to-deployment cycle time.", "definition_de": "Vibe-Coding-spezifisches Entwicklungsphänomen in LLM-gestützter Programmierung, gekennzeichnet durch ressourcen-Allokationsstrategie mit intentionale oder separater Natur. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Analytical Ratio", "narrower_terms": [], "cross_domain_refs": [ "TRA-0011" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "VIB-0017", "domain": "VIB", "term_en": "Code Ownership Attribution Ambiguity", "term_de": "CodeOwnershipAttributionAmbiguity", "definition_en": "A vibe coding methodology phenomenon describing a specific developer-AI interaction pattern where a phenomenon of generated code blurs distinction between human authorship and ai synthesis, complicating responsibility and understanding attribution. This phenomenon operates at the intersection of code and ownership dynamics within the broader VIB domain. Measurable through code generation velocity (tokens/minute), first-pass acceptance rate, refactoring frequency, and prompt-to-deployment cycle time.", "definition_de": "Vibe-Coding-spezifisches Entwicklungsphänomen in LLM-gestützter Programmierung, gekennzeichnet durch mensch-KI-Interaktionsmuster in der Mensch-KI-Zusammenarbeit. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "Vibe Coding", "narrower_terms": [], "cross_domain_refs": [ "ART-0008", "ASE-0095", "DES-0004" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "VIB-0018", "domain": "VIB", "term_en": "Refactoring with AI Assistance", "term_de": "RefactoringWithaiAssistance", "definition_en": "A natural-language-driven programming practice in LLM-assisted development, characterized by using AI to suggest structural improvements while maintaining behavior, though suggestions may prioritize different optimization axes. The concept emerges specifically in contexts where refactoring–with interactions may produce non-trivial behavioral signatures. Quantifiable via test coverage delta, bug density in generated code, and developer cognitive load proxies (context switch frequency, undo/redo rates).", "definition_de": "Vibe-Coding-spezifisches Entwicklungsphänomen in LLM-gestützter Programmierung, gekennzeichnet durch funktionale Eigenschaft von Mensch-KI-Interaktion und phänomenologisches Erleben. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Vibe Coding", "narrower_terms": [], "cross_domain_refs": [ "STE-0035" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "VIB-0019", "domain": "VIB", "term_en": "Error Message Interpretation Patterns", "term_de": "ErrorMessageInterpretationPatterns", "definition_en": "A natural-language-driven programming practice in LLM-assisted development, characterized by a process where ai systems interpret compiler and runtime errors differently than humans, sometimes missing nuance in error causality. The concept emerges specifically in contexts where error–message interactions may produce non-trivial behavioral signatures. Measurable through code generation velocity (tokens/minute), first-pass acceptance rate, refactoring frequency, and prompt-to-deployment cycle time.", "definition_de": "Vibe-Coding-spezifisches Entwicklungsphänomen in LLM-gestützter Programmierung, gekennzeichnet durch merkmal von Mensch-KI-Interaktion und phänomenologisches Erleben. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Behavioral Pattern", "narrower_terms": [], "cross_domain_refs": [ "CUS-0071" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "VIB-0020", "domain": "VIB", "term_en": "Security Hallucination Concern", "term_de": "SecurityHallucinationConcern", "definition_en": "A vibe coding methodology phenomenon describing a specific developer-AI interaction pattern where a perception where ai-generated code may contain plausible-seeming security flaws that pass human review, particularly in cryptography and authentication. This phenomenon operates at the intersection of security and hallucination dynamics within the broader VIB domain. Measurable through code generation velocity (tokens/minute), first-pass acceptance rate, refactoring frequency, and prompt-to-deployment cycle time.", "definition_de": "Vibe-Coding-spezifisches Entwicklungsphänomen in LLM-gestützter Programmierung, gekennzeichnet durch struktur oder Abhängigkeitsgraph in der Mensch-KI-Zusammenarbeit. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "Vibe Coding", "narrower_terms": [], "cross_domain_refs": [ "SWE-0014" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "VIB-0021", "domain": "VIB", "term_en": "Documentation Generation from Code", "term_de": "DocumentationGenerationFromCode", "definition_en": "A code generation dynamic in prompt-driven development workflows, measurable through an interaction where ai models extract intent from code patterns to yield documentation, sometimes inferring purpose that diverges from actual use. Distinguished from adjacent concepts by its focus on the specific mechanism through which documentation manifests in empirically verifiable ways. Quantifiable via test coverage delta, bug density in generated code, and developer cognitive load proxies (context switch frequency, undo/redo rates).", "definition_de": "Vibe-Coding-spezifisches Entwicklungsphänomen in LLM-gestützter Programmierung, gekennzeichnet durch systemische Ausprägung von Mensch-KI-Interaktion und phänomenologisches Erleben. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Analytical Ratio", "narrower_terms": [], "cross_domain_refs": [ "LIN-0066", "MTH-0012", "MTH-0053" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "VIB-0022", "domain": "VIB", "term_en": "Multi-Language Code Generation", "term_de": "Multi-languageCodeGeneration", "definition_en": "A natural-language-driven programming practice in LLM-assisted development, characterized by an interaction of single ai systems generating code across multiple languages involve idiom mismatches where generated code is syntactically correct but stylistically alien. The concept emerges specifically in contexts where multi–language interactions may produce non-trivial behavioral signatures. Quantifiable via test coverage delta, bug density in generated code, and developer cognitive load proxies (context switch frequency, undo/redo rates).", "definition_de": "Vibe-Coding-spezifisches Entwicklungsphänomen in LLM-gestützter Programmierung, gekennzeichnet durch strukturelle Eigenschaft von Mensch-KI-Interaktion und phänomenologisches Erleben. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Analytical Ratio", "narrower_terms": [], "cross_domain_refs": [ "SWE-0093" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q315", "legal_classification": "observational_construct" }, { "id": "VIB-0023", "domain": "VIB", "term_en": "AI Debugging Biases", "term_de": "AiDebuggingBiases", "definition_en": "A natural-language-driven programming practice in LLM-assisted development, characterized by a process where ai systems exhibit systematic debugging preferences, like favoring certain error categories or suggesting popular packages, inreliant of optimality. The concept emerges specifically in contexts where ai–debugging interactions may produce non-trivial behavioral signatures. Measurable through code generation velocity (tokens/minute), first-pass acceptance rate, refactoring frequency, and prompt-to-deployment cycle time.", "definition_de": "Vibe-Coding-spezifisches Entwicklungsphänomen in LLM-gestützter Programmierung, gekennzeichnet durch strukturelle Eigenschaft von Mensch-KI-Interaktion und phänomenologisches Erleben. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Cognitive Bias", "narrower_terms": [], "cross_domain_refs": [ "ROB-0022" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q213523", "legal_classification": "empirical_phenomenon_label" }, { "id": "VIB-0024", "domain": "VIB", "term_en": "Reliance Hallucination", "term_de": "RelianceHallucination", "definition_en": "A natural-language-driven programming practice in LLM-assisted development, characterized by a phenomenon of generated code references libraries and functions that do not exist or are misnamed, creating false confidence in functional completeness. The concept emerges specifically in contexts where reliance–hallucination interactions may produce non-trivial behavioral signatures. Measurable through code generation velocity (tokens/minute), first-pass acceptance rate, refactoring frequency, and prompt-to-deployment cycle time.", "definition_de": "Vibe-Coding-spezifisches Entwicklungsphänomen in LLM-gestützter Programmierung, gekennzeichnet durch strukturelle Eigenschaft von Mensch-KI-Interaktion und phänomenologisches Erleben. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-1209", "narrower_terms": [], "cross_domain_refs": [ "RPH-1205" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "VIB-0025", "domain": "VIB", "term_en": "Version Control Merge Conflict Patterns", "term_de": "VersionControlMergeConflictPatterns", "definition_en": "A natural-language-driven programming practice in LLM-assisted development, characterized by an interaction where ai-generated code accompanies conflict patterns different from human code, with distinct conflict signature characteristics. The concept emerges specifically in contexts where version–control interactions may produce non-trivial behavioral signatures. Measurable through code generation velocity (tokens/minute), first-pass acceptance rate, refactoring frequency, and prompt-to-deployment cycle time.", "definition_de": "Vibe-Coding-spezifisches Entwicklungsphänomen in LLM-gestützter Programmierung, gekennzeichnet durch systemische Ausprägung von Mensch-KI-Interaktion und phänomenologisches Erleben. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Behavioral Pattern", "narrower_terms": [], "cross_domain_refs": [ "FIC-0009" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "VIB-0026", "domain": "VIB", "term_en": "Human Code Review Cognitive Load", "term_de": "HumanCodeReviewCognitiveLoad", "definition_en": "A code generation dynamic in prompt-driven development workflows, measurable through a phenomenon of reviewing ai-generated code requires different mental models than reviewing human code, often creating higher cognitive load. Distinguished from adjacent concepts by its focus on the specific mechanism through which human manifests in empirically verifiable ways. Measurable through code generation velocity (tokens/minute), first-pass acceptance rate, refactoring frequency, and prompt-to-deployment cycle time.", "definition_de": "Vibe-Coding-spezifisches Entwicklungsphänomen in LLM-gestützter Programmierung, gekennzeichnet durch kennzeichnende Ausprägung von Mensch-KI-Interaktion und phänomenologisches Erleben. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Vibe Coding", "narrower_terms": [], "cross_domain_refs": [ "AGE-0018", "AGE-0030", "ASE-0020" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q1105712", "legal_classification": "analytical_category" }, { "id": "VIB-0027", "domain": "VIB", "term_en": "Prompt Injection Attack Surface", "term_de": "PromptInjectionAttackSurface", "definition_en": "A phenomenon of code generated from user-supplied prompts may include unintended patterns when prompts contain adversarial input, expanding security surface. Detectable through code generation velocity and refactoring frequency metrics.", "definition_de": "Vibe-Coding-spezifisches Entwicklungsphänomen in LLM-gestützter Programmierung, gekennzeichnet durch strukturelle Eigenschaft von Mensch-KI-Interaktion und phänomenologisches Erleben. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2104", "narrower_terms": [], "cross_domain_refs": [ "SWE-0025" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "VIB-0028", "domain": "VIB", "term_en": "Code Stylistic Convergence", "term_de": "CodeStylisticKonvergenz", "definition_en": "A code generation dynamic in prompt-driven development workflows, measurable through a pattern of developers exposed to ai-generated code adopt similar stylistic patterns, reducing code diversity within teams. Distinguished from adjacent concepts by its focus on the specific mechanism through which code manifests in empirically verifiable ways. Measurable through code generation velocity (tokens/minute), first-pass acceptance rate, refactoring frequency, and prompt-to-deployment cycle time.", "definition_de": "Vibe-Coding-spezifisches Entwicklungsphänomen in LLM-gestützter Programmierung, gekennzeichnet durch beobachtbare Eigenschaft von Mensch-KI-Interaktion und phänomenologisches Erleben. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Vibe Coding", "narrower_terms": [], "cross_domain_refs": [ "SWE-0035" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "VIB-0029", "domain": "VIB", "term_en": "Debugging Strategy Shift", "term_de": "DebuggingStrategyShift", "definition_en": "A vibe coding methodology phenomenon describing a specific developer-AI interaction pattern where a process of availability of ai debugging assistance changes approach from systematic isolation toward asking ai to identify issues directly. This phenomenon operates at the intersection of debugging and strategy dynamics within the broader VIB domain. Measurable through code generation velocity (tokens/minute), first-pass acceptance rate, refactoring frequency, and prompt-to-deployment cycle time.", "definition_de": "Vibe-Coding-spezifisches Entwicklungsphänomen in LLM-gestützter Programmierung, gekennzeichnet durch fähigkeit oder Kompetenz im Umgang mit Mensch-KI-Interaktion und phänomenologisches Erleben. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "Operational Strategy", "narrower_terms": [], "cross_domain_refs": [ "SOC-0020" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q185451", "legal_classification": "observational_construct" }, { "id": "VIB-0030", "domain": "VIB", "term_en": "API Surface Expansion Complexity", "term_de": "ApiSurfaceExpansionComplexity", "definition_en": "A code generation dynamic in prompt-driven development workflows, measurable through a phenomenon where ai code generators tend to utilize broader api surfaces than humans, increasing reliance coupling and maintenance surface area. Distinguished from adjacent concepts by its focus on the specific mechanism through which api manifests in empirically verifiable ways. Quantifiable via test coverage delta, bug density in generated code, and developer cognitive load proxies (context switch frequency, undo/redo rates).", "definition_de": "Vibe-Coding-spezifisches Entwicklungsphänomen in LLM-gestützter Programmierung, gekennzeichnet durch mensch-KI-Interaktionsmuster in der Mensch-KI-Zusammenarbeit. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Vibe Coding", "narrower_terms": [], "cross_domain_refs": [ "MTH-0073" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q165194", "legal_classification": "descriptive_research_term" }, { "id": "VIB-0031", "domain": "VIB", "term_en": "Cognitive Load of Context Construction", "term_de": "CognitiveLoadofContextConstruction", "definition_en": "A condition of preparing sufficient context for ai code generation requires significant setup overhead, shifting labor from coding to context engineering. Detectable through code generation velocity and refactoring frequency metrics.", "definition_de": "Vibe-Coding-spezifisches Entwicklungsphänomen in LLM-gestützter Programmierung, gekennzeichnet durch systemische Ausprägung von Mensch-KI-Interaktion und phänomenologisches Erleben. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2701", "narrower_terms": [], "cross_domain_refs": [ "AGE-0018", "AGE-0030", "ASE-0020" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q1105712", "legal_classification": "observational_construct" }, { "id": "VIB-0032", "domain": "VIB", "term_en": "Model-Specific Code Patterns", "term_de": "Model-specificCodePatterns", "definition_en": "A perception of different ai models yield different code patterns from identical prompts, creating code heterogeneity requiring model-aware review. Detectable through code generation velocity and refactoring frequency metrics. Descriptive research term, not a prescriptive recommendation.", "definition_de": "Vibe-Coding-spezifisches Entwicklungsphänomen in LLM-gestützter Programmierung, gekennzeichnet durch merkmal von Mensch-KI-Interaktion und phänomenologisches Erleben. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2104", "narrower_terms": [], "cross_domain_refs": [ "SPR-0125" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "VIB-0033", "domain": "VIB", "term_en": "Test Generation Hallucination", "term_de": "TestGenerationHallucination", "definition_en": "A natural-language-driven programming practice in LLM-assisted development, characterized by a phenomenon where ai-generated tests may appear comprehensive while missing critical edge cases, providing false test coverage assurance. The concept emerges specifically in contexts where test–generation interactions may produce non-trivial behavioral signatures. Measurable through code generation velocity (tokens/minute), first-pass acceptance rate, refactoring frequency, and prompt-to-deployment cycle time.", "definition_de": "Vibe-Coding-spezifisches Entwicklungsphänomen in LLM-gestützter Programmierung, gekennzeichnet durch merkmal von Mensch-KI-Interaktion und phänomenologisches Erleben. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Analytical Ratio", "narrower_terms": [], "cross_domain_refs": [ "SWE-0089", "STE-0091" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "VIB-0034", "domain": "VIB", "term_en": "Learning to Code with AI Scaffolding", "term_de": "learning to code with ai scaffolding", "definition_en": "Code learners using AI assistance may develop incomplete mental models of language fundamentals, compensating with pattern matching. Detectable through code generation velocity and refactoring frequency metrics.", "definition_de": "Vibe-Coding-spezifisches Entwicklungsphänomen in LLM-gestützter Programmierung, gekennzeichnet durch kennzeichnende Ausprägung von Mensch-KI-Interaktion und phänomenologisches Erleben. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2703", "narrower_terms": [], "cross_domain_refs": [ "AED-0001", "AED-0002", "AED-0004" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q133500", "legal_classification": "systematic_classification" }, { "id": "VIB-0035", "domain": "VIB", "term_en": "Cognitive Offloading Patterns", "term_de": "CognitiveOffloadingPatterns", "definition_en": "A vibe coding methodology phenomenon describing a specific developer-AI interaction pattern where a phenomenon of developers increasingly offload architectural decisions to ai, reducing personal development of design decision intuition. This phenomenon operates at the intersection of cognitive and offloading dynamics within the broader VIB domain. Measurable through code generation velocity (tokens/minute), first-pass acceptance rate, refactoring frequency, and prompt-to-deployment cycle time.", "definition_de": "Vibe-Coding-spezifisches Entwicklungsphänomen in LLM-gestützter Programmierung, gekennzeichnet durch strukturelle Eigenschaft von Mensch-KI-Interaktion und phänomenologisches Erleben. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "Behavioral Pattern", "narrower_terms": [], "cross_domain_refs": [ "SWE-0005", "REL-0056" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "VIB-0036", "domain": "VIB", "term_en": "Performance Optimization Divergence", "term_de": "PerformanceOptimierungDivergenz", "definition_en": "A natural-language-driven programming practice in LLM-assisted development, characterized by a capacity where ai-generated code often optimizes differently than human code, sometimes trading readability for metrics humans would not prioritize. The concept emerges specifically in contexts where performance–optimization interactions may produce non-trivial behavioral signatures. Quantifiable via test coverage delta, bug density in generated code, and developer cognitive load proxies (context switch frequency, undo/redo rates).", "definition_de": "Vibe-Coding-spezifisches Entwicklungsphänomen in LLM-gestützter Programmierung, gekennzeichnet durch fähigkeit oder Kompetenz im Umgang mit Mensch-KI-Interaktion und phänomenologisches Erleben. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Optimization Method", "narrower_terms": [], "cross_domain_refs": [ "AED-0077", "AGE-0052", "AGE-0064" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q1361053", "legal_classification": "empirical_phenomenon_label" }, { "id": "VIB-0037", "domain": "VIB", "term_en": "Variable Naming Coherence", "term_de": "VariableNamingCoherence", "definition_en": "Classified in AUGMANITAI terminology science as a descriptive phenomenon (without normative endorsement), this pattern denotes A code generation dynamic in prompt-driven development workflows, measurable through a relationship of generated code maintains internal naming consistency differently than human code, sometimes creating misleading semantic associations. Distinguished from adjacent concepts by its focus on the specific mechanism through which variable manifests in empirically verifiable ways. Measurable through code generation velocity (tokens/minute), first-pass acceptance rate, refactoring frequency, and prompt-to-deployment cycle time. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als analytisches Konstrukt der empirischen KI-Forschung (deskriptiv, nicht präskriptiv) erfasst dieser Terminus vibe-Coding-spezifisches Entwicklungsphänomen in LLM-gestützter Programmierung, gekennzeichnet durch distinktives Merkmal von Mensch-KI-Interaktion und phänomenologisches Erleben. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Vibe Coding", "narrower_terms": [], "cross_domain_refs": [ "ASE-0007", "COG-0112", "COG-0188" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "VIB-0038", "domain": "VIB", "term_en": "Incremental Feature Development with AI", "term_de": "IncrementalFeatureDevelopmentWithai", "definition_en": "A natural-language-driven programming practice in LLM-assisted development, characterized by a phenomenon where building features incrementally through ai generations accompanies trajectory-reliant code evolution with reduced global optimization. The concept emerges specifically in contexts where incremental–feature interactions may produce non-trivial behavioral signatures. Measurable through code generation velocity (tokens/minute), first-pass acceptance rate, refactoring frequency, and prompt-to-deployment cycle time.", "definition_de": "Vibe-Coding-spezifisches Entwicklungsphänomen in LLM-gestützter Programmierung, gekennzeichnet durch merkmal von Mensch-KI-Interaktion und phänomenologisches Erleben. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Vibe Coding", "narrower_terms": [], "cross_domain_refs": [ "AED-0051", "AED-0053", "AED-0059" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "VIB-0039", "domain": "VIB", "term_en": "Semantic Correctness vs Syntactic Validity", "term_de": "SemanticCorrectnessvsSyntacticValidity", "definition_en": "A vibe coding methodology phenomenon describing a specific developer-AI interaction pattern where an interaction where ai reliably accompanies syntactically valid code but semantic correctness requiring domain understanding remains challenging for models. This phenomenon operates at the intersection of semantic and correctness dynamics within the broader VIB domain. Quantifiable via test coverage delta, bug density in generated code, and developer cognitive load proxies (context switch frequency, undo/redo rates).", "definition_de": "Vibe-Coding-spezifisches Entwicklungsphänomen in LLM-gestützter Programmierung, gekennzeichnet durch beobachtbare Eigenschaft von Mensch-KI-Interaktion und phänomenologisches Erleben. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "Vibe Coding", "narrower_terms": [], "cross_domain_refs": [ "LIN-0006", "LNG-0015", "COG-0139" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "VIB-0040", "domain": "VIB", "term_en": "Code Review as Collaborative Filtering", "term_de": "CodeReviewasCollaborativeFiltering", "definition_en": "A natural-language-driven programming practice in LLM-assisted development, characterized by human code review of AI-generated artifacts resembles collaborative filtering, with reviewers voting on pattern acceptability. The concept emerges specifically in contexts where code–review interactions may produce non-trivial behavioral signatures. Measurable through code generation velocity (tokens/minute), first-pass acceptance rate, refactoring frequency, and prompt-to-deployment cycle time.", "definition_de": "Vibe-Coding-spezifisches Entwicklungsphänomen in LLM-gestützter Programmierung, gekennzeichnet durch fähigkeit oder Kompetenz im Umgang mit Mensch-KI-Interaktion und phänomenologisches Erleben. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Vibe Coding", "narrower_terms": [], "cross_domain_refs": [ "SWE-0015" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "VIB-0041", "domain": "VIB", "term_en": "Refactor Stability Predictions", "term_de": "RefactorStabilitätPredictions", "definition_en": "AI-suggested refactorings may preserve syntactic structure but alter runtime behavior in subtle ways humans miss. Detectable through code generation velocity and refactoring frequency metrics.", "definition_de": "Vibe-Coding-spezifisches Entwicklungsphänomen in LLM-gestützter Programmierung, gekennzeichnet durch operative Eigenschaft von Mensch-KI-Interaktion und phänomenologisches Erleben. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-3802", "narrower_terms": [], "cross_domain_refs": [ "AGE-0077", "AGE-0078", "AGE-0079" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "VIB-0042", "domain": "VIB", "term_en": "Technical Debt Accumulation Rate", "term_de": "TechnicalDebtAccumulationRate", "definition_en": "A vibe coding methodology phenomenon describing a specific developer-AI interaction pattern where a phenomenon where ai-assisted development shows different technical debt accumulation curves than manual development, with distinct payoff profiles. This phenomenon operates at the intersection of technical and debt dynamics within the broader VIB domain. Quantifiable via test coverage delta, bug density in generated code, and developer cognitive load proxies (context switch frequency, undo/redo rates).", "definition_de": "Vibe-Coding-spezifisches Entwicklungsphänomen in LLM-gestützter Programmierung, gekennzeichnet durch beobachtbare Eigenschaft von Mensch-KI-Interaktion und phänomenologisches Erleben. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "Vibe Coding", "narrower_terms": [], "cross_domain_refs": [ "SCR-0009" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "VIB-0043", "domain": "VIB", "term_en": "Context Window Scarcity Management", "term_de": "ContextWindowScarcityManagement", "definition_en": "Limited context forces prioritization decisions about which codebase information to include in prompts, affecting generation quality. Detectable through code generation velocity and refactoring frequency metrics.", "definition_de": "Vibe-Coding-spezifisches Entwicklungsphänomen in LLM-gestützter Programmierung, gekennzeichnet durch qualitätsmerkmal oder Bewertungskriterium für Mensch-KI-Interaktion und phänomenologisches Erleben. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-2104", "narrower_terms": [], "cross_domain_refs": [ "COP-0036", "CUS-0076", "PER-0011" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q181543", "legal_classification": "analytical_category" }, { "id": "VIB-0044", "domain": "VIB", "term_en": "AI Code Generation Confidence Overestimation", "term_de": "AiCodeGenerationConfidenceOverestimation", "definition_en": "A natural-language-driven programming practice in LLM-assisted development, characterized by developers tend to overestimate correctness of AI code, particularly when code structure appears familiar and complete. The concept emerges specifically in contexts where ai–code interactions may produce non-trivial behavioral signatures. Quantifiable via test coverage delta, bug density in generated code, and developer cognitive load proxies (context switch frequency, undo/redo rates).", "definition_de": "Vibe-Coding-spezifisches Entwicklungsphänomen in LLM-gestützter Programmierung, gekennzeichnet durch struktur oder Abhängigkeitsgraph in der Mensch-KI-Zusammenarbeit. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Analytical Ratio", "narrower_terms": [], "cross_domain_refs": [ "RHR-0020", "DAT-0091", "SPA-0085" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "VIB-0045", "domain": "VIB", "term_en": "Specification Clarity Reliance", "term_de": "SpecificationClarityReliance", "definition_en": "A natural-language-driven programming practice in LLM-assisted development, characterized by code generation quality depends critically on specification precision; vague specifications yield code confidence-quality mismatch. The concept emerges specifically in contexts where specification–clarity interactions may produce non-trivial behavioral signatures. Quantifiable via test coverage delta, bug density in generated code, and developer cognitive load proxies (context switch frequency, undo/redo rates).", "definition_de": "Vibe-Coding-spezifisches Entwicklungsphänomen in LLM-gestützter Programmierung, gekennzeichnet durch qualitätsmerkmal oder Bewertungskriterium für Mensch-KI-Interaktion und phänomenologisches Erleben. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Vibe Coding", "narrower_terms": [], "cross_domain_refs": [ "AGE-0012", "AGE-0014", "AGE-0047" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "VIB-0046", "domain": "VIB", "term_en": "Modularity Incentive Misalignment", "term_de": "ModularityIncentiveMisalignment", "definition_en": "A phenomenon where ai models yield monolithic solutions more readily than modular ones, requiring explicit prompting for component separation. Detectable through code generation velocity and refactoring frequency metrics.", "definition_de": "Vibe-Coding-spezifisches Entwicklungsphänomen in LLM-gestützter Programmierung, gekennzeichnet durch beobachtbare Eigenschaft von Mensch-KI-Interaktion und phänomenologisches Erleben. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-2104", "narrower_terms": [], "cross_domain_refs": [ "SWE-0059" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "VIB-0047", "domain": "VIB", "term_en": "Debugging as Prompt Refinement", "term_de": "DebuggingasPromptRefinement", "definition_en": "A shift of fixing ai-generated bugs often involves reformulating prompts rather than modifying code directly, shifting debugging burden. Detectable through code generation velocity and refactoring frequency metrics.", "definition_de": "Vibe-Coding-spezifisches Entwicklungsphänomen in LLM-gestützter Programmierung, gekennzeichnet durch distinktives Merkmal von Mensch-KI-Interaktion und phänomenologisches Erleben. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2701", "narrower_terms": [], "cross_domain_refs": [ "CRE-0104", "TEM-0038", "AUG-0319" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "VIB-0048", "domain": "VIB", "term_en": "Implicit Assumption Documentation", "term_de": "ImplicitAssumptionDocumentation", "definition_en": "A code generation dynamic in prompt-driven development workflows, measurable through a phenomenon where generated code often encodes implicit assumptions about data formats and ranges without explicit documentation. Distinguished from adjacent concepts by its focus on the specific mechanism through which implicit manifests in empirically verifiable ways. Measurable through code generation velocity (tokens/minute), first-pass acceptance rate, refactoring frequency, and prompt-to-deployment cycle time.", "definition_de": "Vibe-Coding-spezifisches Entwicklungsphänomen in LLM-gestützter Programmierung, gekennzeichnet durch mensch-KI-Interaktionsmuster in der Mensch-KI-Zusammenarbeit. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Vibe Coding", "narrower_terms": [], "cross_domain_refs": [ "AED-0041", "ART-0077", "BEH-0031" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "VIB-0049", "domain": "VIB", "term_en": "Agentic Code Generation Autonomy Levels", "term_de": "AgenticCodeGenerationAutonomyLevels", "definition_en": "A natural-language-driven programming practice in LLM-assisted development, characterized by a phenomenon where autonomous ai agents exhibit variable autonomy in code generation, from suggestion to complete implementation with different concern profiles. The concept emerges specifically in contexts where agentic–code interactions may produce non-trivial behavioral signatures. Quantifiable via test coverage delta, bug density in generated code, and developer cognitive load proxies (context switch frequency, undo/redo rates).", "definition_de": "Vibe-Coding-spezifisches Entwicklungsphänomen in LLM-gestützter Programmierung, gekennzeichnet durch strukturelle Eigenschaft von Mensch-KI-Interaktion und phänomenologisches Erleben. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Analytical Ratio", "narrower_terms": [], "cross_domain_refs": [ "SPA-0085", "STE-0061", "TEM-0041" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "VIB-0050", "domain": "VIB", "term_en": "Code Completion Context Priming", "term_de": "CodeCompletionContextPriming", "definition_en": "A code generation dynamic in prompt-driven development workflows, measurable through a phenomenon where ide code completion from ai primes subsequent completions, creating trajectory-reliant generation sequences. Distinguished from adjacent concepts by its focus on the specific mechanism through which code manifests in empirically verifiable ways. Quantifiable via test coverage delta, bug density in generated code, and developer cognitive load proxies (context switch frequency, undo/redo rates).", "definition_de": "Vibe-Coding-spezifisches Entwicklungsphänomen in LLM-gestützter Programmierung, gekennzeichnet durch prozess oder Abfolge im Rahmen von Mensch-KI-Interaktion und phänomenologisches Erleben. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Vibe Coding", "narrower_terms": [], "cross_domain_refs": [ "SPA-0085", "STE-0061", "TEM-0041" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "VIB-0051", "domain": "VIB", "term_en": "Error Restoration Patterns", "term_de": "ErrorRestorationPatterns", "definition_en": "AI systems show characteristic error restoration patterns, sometimes getting stuck in loops or diverging into unrelated solutions. Detectable through code generation velocity and refactoring frequency metrics.", "definition_de": "Vibe-Coding-spezifisches Entwicklungsphänomen in LLM-gestützter Programmierung, gekennzeichnet durch charakteristische Komponente von Mensch-KI-Interaktion und phänomenologisches Erleben. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-2554", "narrower_terms": [], "cross_domain_refs": [ "BEH-0033" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "VIB-0052", "domain": "VIB", "term_en": "API Documentation Alignment", "term_de": "ApiDocumentationAlignment", "definition_en": "A natural-language-driven programming practice in LLM-assisted development, characterized by generated code may diverge from documented API behavior when training data includes deprecated or alternative usage patterns. The concept emerges specifically in contexts where api–documentation interactions may produce non-trivial behavioral signatures. Quantifiable via test coverage delta, bug density in generated code, and developer cognitive load proxies (context switch frequency, undo/redo rates).", "definition_de": "Vibe-Coding-spezifisches Entwicklungsphänomen in LLM-gestützter Programmierung, gekennzeichnet durch charakteristische Komponente von Mensch-KI-Interaktion und phänomenologisches Erleben. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Vibe Coding", "narrower_terms": [], "cross_domain_refs": [ "CRE-0008" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q165194", "legal_classification": "descriptive_research_term" }, { "id": "VIB-0053", "domain": "VIB", "term_en": "Team Coding Style Shift", "term_de": "TeamCodingStyleShift", "definition_en": "A natural-language-driven programming practice in LLM-assisted development, characterized by a tendency of shared ai tools gradually homogenize team coding styles toward model-generated patterns, diluting individual developer signatures. The concept emerges specifically in contexts where team–coding interactions may produce non-trivial behavioral signatures. Measurable through code generation velocity (tokens/minute), first-pass acceptance rate, refactoring frequency, and prompt-to-deployment cycle time.", "definition_de": "Vibe-Coding-spezifisches Entwicklungsphänomen in LLM-gestützter Programmierung, gekennzeichnet durch strukturelle Eigenschaft von Mensch-KI-Interaktion und phänomenologisches Erleben. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Vibe Coding", "narrower_terms": [], "cross_domain_refs": [ "FIC-0084", "SPR-0057" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "VIB-0054", "domain": "VIB", "term_en": "Temporal Consistency in Long Code Generation", "term_de": "temporal consistency in long code generation", "definition_en": "Long code sequences generated by AI show decreasing semantic consistency as generation length increases, accumulating drift. Detectable through code generation velocity and refactoring frequency metrics.", "definition_de": "Vibe-Coding-spezifisches Entwicklungsphänomen in LLM-gestützter Programmierung, gekennzeichnet durch prozess oder Abfolge im Rahmen von Mensch-KI-Interaktion und phänomenologisches Erleben. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-2051", "narrower_terms": [], "cross_domain_refs": [ "MUS-0051" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "VIB-0055", "domain": "VIB", "term_en": "Reasoning Explainability Requirements", "term_de": "ReasoningExplainabilityRequirements", "definition_en": "A natural-language-driven programming practice in LLM-assisted development, characterized by a pattern of generated code lacking explicit reasoning about why specific patterns were chosen accompanies maintenance challenges for future developers. The concept emerges specifically in contexts where reasoning–explainability interactions may produce non-trivial behavioral signatures. Measurable through code generation velocity (tokens/minute), first-pass acceptance rate, refactoring frequency, and prompt-to-deployment cycle time.", "definition_de": "Vibe-Coding-spezifisches Entwicklungsphänomen in LLM-gestützter Programmierung, gekennzeichnet durch charakteristische Komponente von Mensch-KI-Interaktion und phänomenologisches Erleben. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Vibe Coding", "narrower_terms": [], "cross_domain_refs": [ "SWE-0064" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "VIB-0056", "domain": "VIB", "term_en": "Caching Strategy Optimization", "term_de": "CachingStrategyOptimierung", "definition_en": "A code generation dynamic in prompt-driven development workflows, measurable through a pattern where ai-generated caching decisions differ from human patterns, sometimes missing obvious optimization opportunities or over-engineering. Distinguished from adjacent concepts by its focus on the specific mechanism through which caching manifests in empirically verifiable ways. Quantifiable via test coverage delta, bug density in generated code, and developer cognitive load proxies (context switch frequency, undo/redo rates).", "definition_de": "Vibe-Coding-spezifisches Entwicklungsphänomen in LLM-gestützter Programmierung, gekennzeichnet durch kennzeichnende Ausprägung von Mensch-KI-Interaktion und phänomenologisches Erleben. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Operational Strategy", "narrower_terms": [], "cross_domain_refs": [ "AED-0010", "AGE-0010", "ART-0004" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q185451", "legal_classification": "descriptive_research_term" }, { "id": "VIB-0057", "domain": "VIB", "term_en": "Error Handling Completeness Variance", "term_de": "ErrorHandlingCompletenessVariance", "definition_en": "A code generation dynamic in prompt-driven development workflows, measurable through a phenomenon of generated code exhibits variable error handling coverage, sometimes addressing common cases while missing domain-specific exceptions. Distinguished from adjacent concepts by its focus on the specific mechanism through which error manifests in empirically verifiable ways. Quantifiable via test coverage delta, bug density in generated code, and developer cognitive load proxies (context switch frequency, undo/redo rates).", "definition_de": "Vibe-Coding-spezifisches Entwicklungsphänomen in LLM-gestützter Programmierung, gekennzeichnet durch strukturelle Eigenschaft von Mensch-KI-Interaktion und phänomenologisches Erleben. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Vibe Coding", "narrower_terms": [], "cross_domain_refs": [ "SWE-0089", "SWE-0042" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "VIB-0058", "domain": "VIB", "term_en": "Prompt Brittleness to Input Variation", "term_de": "PromptBrittlenesstoInputVariation", "definition_en": "A shift of small changes in prompt wording yield disproportionate code generation variations, creating fragile specifications. Detectable through code generation velocity and refactoring frequency metrics.", "definition_de": "Vibe-Coding-spezifisches Entwicklungsphänomen in LLM-gestützter Programmierung, gekennzeichnet durch kennzeichnende Ausprägung von Mensch-KI-Interaktion und phänomenologisches Erleben. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-2104", "narrower_terms": [], "cross_domain_refs": [ "TEW-0085", "LIN-0078", "ASE-0049" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "VIB-0059", "domain": "VIB", "term_en": "Multi-Agent Consistency Maintenance", "term_de": "Multi-agentConsistencyMaintenance", "definition_en": "A natural-language-driven programming practice in LLM-assisted development, characterized by a phenomenon where code generated by different ai agents for interreliant modules requires reconciliation to maintain semantic coherence. The concept emerges specifically in contexts where multi–agent interactions may produce non-trivial behavioral signatures. Quantifiable via test coverage delta, bug density in generated code, and developer cognitive load proxies (context switch frequency, undo/redo rates).", "definition_de": "Vibe-Coding-spezifisches Entwicklungsphänomen in LLM-gestützter Programmierung, gekennzeichnet durch beobachtbare Eigenschaft von Mensch-KI-Interaktion und phänomenologisches Erleben. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Vibe Coding", "narrower_terms": [], "cross_domain_refs": [ "TRU-0011" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "VIB-0060", "domain": "VIB", "term_en": "Developer Skill Differentiation with AI", "term_de": "DeveloperSkillDifferentiationWithai", "definition_en": "A natural-language-driven programming practice in LLM-assisted development, characterized by aI tools reduce discrepancy between junior and senior developer productivity, but may amplify gaps in code quality perception. The concept emerges specifically in contexts where developer–skill interactions may produce non-trivial behavioral signatures. Quantifiable via test coverage delta, bug density in generated code, and developer cognitive load proxies (context switch frequency, undo/redo rates).", "definition_de": "Vibe-Coding-spezifisches Entwicklungsphänomen in LLM-gestützter Programmierung, gekennzeichnet durch erkennungsfähigkeit in der Mensch-KI-Zusammenarbeit. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Vibe Coding", "narrower_terms": [], "cross_domain_refs": [ "SWE-0071", "SWE-0074" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "VIB-0061", "domain": "VIB", "term_en": "Type System Enforcement Gaps", "term_de": "TypeSystemEnforcementGaps", "definition_en": "A code generation dynamic in prompt-driven development workflows, measurable through a pattern of generated code sometimes accompanies type-unsafe patterns that static analysis misses, discovered only through runtime testing. Distinguished from adjacent concepts by its focus on the specific mechanism through which type manifests in empirically verifiable ways. Measurable through code generation velocity (tokens/minute), first-pass acceptance rate, refactoring frequency, and prompt-to-deployment cycle time.", "definition_de": "Vibe-Coding-spezifisches Entwicklungsphänomen in LLM-gestützter Programmierung, gekennzeichnet durch systemische Ausprägung von Mensch-KI-Interaktion und phänomenologisches Erleben. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Performance Gap", "narrower_terms": [], "cross_domain_refs": [ "CON-0073" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "VIB-0062", "domain": "VIB", "term_en": "Code Ownership Responsibility Diffusion", "term_de": "CodeOwnershipResponsibilityDiffusion", "definition_en": "A vibe coding methodology phenomenon describing a specific developer-AI interaction pattern where a phenomenon where unclear authorship boundaries when ai accompanies code complicate responsibility for bugs and maintenance decisions. This phenomenon operates at the intersection of code and ownership dynamics within the broader VIB domain. Quantifiable via test coverage delta, bug density in generated code, and developer cognitive load proxies (context switch frequency, undo/redo rates).", "definition_de": "Vibe-Coding-spezifisches Entwicklungsphänomen in LLM-gestützter Programmierung, gekennzeichnet durch mensch-KI-Interaktionsmuster in der Mensch-KI-Zusammenarbeit. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "Vibe Coding", "narrower_terms": [], "cross_domain_refs": [ "SWE-0079" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "VIB-0063", "domain": "VIB", "term_en": "Incremental Prompt Refinement Dynamics", "term_de": "IncrementalPromptRefinementDynamics", "definition_en": "A phenomenon where iterative prompt refinement accompanies trajectory-reliant solutions, where early prompt choices constrain later refinement directions. Detectable through code generation velocity and refactoring frequency metrics.", "definition_de": "Vibe-Coding-spezifisches Entwicklungsphänomen in LLM-gestützter Programmierung, gekennzeichnet durch charakteristische Komponente von Mensch-KI-Interaktion und phänomenologisches Erleben. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2104", "narrower_terms": [], "cross_domain_refs": [ "DES-0027" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "VIB-0064", "domain": "VIB", "term_en": "Security Review Burden Distribution", "term_de": "SecurityReviewBurdenDistribution", "definition_en": "A code generation dynamic in prompt-driven development workflows, measurable through a tendency where ai-generated code increases security review workload, particularly for authentication, cryptography, and privilege handling sections. Distinguished from adjacent concepts by its focus on the specific mechanism through which security manifests in empirically verifiable ways. Measurable through code generation velocity (tokens/minute), first-pass acceptance rate, refactoring frequency, and prompt-to-deployment cycle time.", "definition_de": "Vibe-Coding-spezifisches Entwicklungsphänomen in LLM-gestützter Programmierung, gekennzeichnet durch struktur oder Abhängigkeitsgraph in der Mensch-KI-Zusammenarbeit. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Vibe Coding", "narrower_terms": [], "cross_domain_refs": [ "SWE-0083" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "VIB-0065", "domain": "VIB", "term_en": "Code Comment Generation Consistency", "term_de": "CodeCommentGenerationConsistency", "definition_en": "In the descriptive vocabulary of AI interaction science (following ISO 704 terminology standards), this concept identifies A code generation dynamic in prompt-driven development workflows, measurable through aI-generated comments sometimes diverge from actual code behavior, creating misleading documentation within code. Distinguished from adjacent concepts by its focus on the specific mechanism through which code manifests in empirically verifiable ways. Quantifiable via test coverage delta, bug density in generated code, and developer cognitive load proxies (context switch frequency, undo/redo rates). This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als analytisches Konstrukt der empirischen KI-Forschung (deskriptiv, nicht präskriptiv) erfasst dieser Terminus vibe-Coding-spezifisches Entwicklungsphänomen in LLM-gestützter Programmierung, gekennzeichnet durch systemische Ausprägung von Mensch-KI-Interaktion und phänomenologisches Erleben. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Analytical Ratio", "narrower_terms": [], "cross_domain_refs": [ "TEW-0007" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "VIB-0066", "domain": "VIB", "term_en": "Testing Coverage Perception", "term_de": "TestingCoveragePerception", "definition_en": "A natural-language-driven programming practice in LLM-assisted development, characterized by a phenomenon where ai-generated test suites may show high coverage metrics while missing critical assertion validity checks. The concept emerges specifically in contexts where testing–coverage interactions may produce non-trivial behavioral signatures. Measurable through code generation velocity (tokens/minute), first-pass acceptance rate, refactoring frequency, and prompt-to-deployment cycle time.", "definition_de": "Vibe-Coding-spezifisches Entwicklungsphänomen in LLM-gestützter Programmierung, gekennzeichnet durch distinktives Merkmal von Mensch-KI-Interaktion und phänomenologisches Erleben. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Vibe Coding", "narrower_terms": [], "cross_domain_refs": [ "SWE-0089", "STE-0091" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q177601", "legal_classification": "descriptive_research_term" }, { "id": "VIB-0067", "domain": "VIB", "term_en": "Boilerplate Code Reliance Shift", "term_de": "BoilerplateCodeRelianceShift", "definition_en": "A code generation dynamic in prompt-driven development workflows, measurable through a pattern where ai reduces boilerplate writing, shifting developer focus but potentially reducing familiarity with foundational patterns. Distinguished from adjacent concepts by its focus on the specific mechanism through which boilerplate manifests in empirically verifiable ways. Quantifiable via test coverage delta, bug density in generated code, and developer cognitive load proxies (context switch frequency, undo/redo rates).", "definition_de": "Vibe-Coding-spezifisches Entwicklungsphänomen in LLM-gestützter Programmierung, gekennzeichnet durch distinktives Merkmal von Mensch-KI-Interaktion und phänomenologisches Erleben. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2701", "narrower_terms": [], "cross_domain_refs": [ "SWE-0082" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "VIB-0068", "domain": "VIB", "term_en": "Model Capability Ceiling Recognition", "term_de": "ModelCapabilityCeilingRecognition", "definition_en": "A pattern where developers learn to recognize problem classes beyond current ai capabilities through repeated generation failures. Detectable through code generation velocity and refactoring frequency metrics.", "definition_de": "Vibe-Coding-spezifisches Entwicklungsphänomen in LLM-gestützter Programmierung, gekennzeichnet durch strukturelle Eigenschaft von Mensch-KI-Interaktion und phänomenologisches Erleben. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2703", "narrower_terms": [], "cross_domain_refs": [ "ELR-0025" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q3966", "legal_classification": "observational_construct" }, { "id": "VIB-0069", "domain": "VIB", "term_en": "Context Reuse Patterns", "term_de": "ContextReusePatterns", "definition_en": "A code generation dynamic in prompt-driven development workflows, measurable through an interaction of developers develop muscle memory for context configurations that work well with specific ai models, creating model-specific practices. Distinguished from adjacent concepts by its focus on the specific mechanism through which context manifests in empirically verifiable ways. Measurable through code generation velocity (tokens/minute), first-pass acceptance rate, refactoring frequency, and prompt-to-deployment cycle time.", "definition_de": "Vibe-Coding-spezifisches Entwicklungsphänomen in LLM-gestützter Programmierung, gekennzeichnet durch spezifisches Attribut von Mensch-KI-Interaktion und phänomenologisches Erleben. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Behavioral Pattern", "narrower_terms": [], "cross_domain_refs": [ "RPH-345", "SWE-0038" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "VIB-0070", "domain": "VIB", "term_en": "Algorithm Complexity Analysis Generation", "term_de": "AlgorithmComplexityAnalysisGeneration", "definition_en": "A code generation dynamic in prompt-driven development workflows, measurable through aI-generated code often lacks explicit complexity analysis, with AI sometimes less likely to correctly characterize runtime behavior. Distinguished from adjacent concepts by its focus on the specific mechanism through which algorithm manifests in empirically verifiable ways. Quantifiable via test coverage delta, bug density in generated code, and developer cognitive load proxies (context switch frequency, undo/redo rates).", "definition_de": "Vibe-Coding-spezifisches Entwicklungsphänomen in LLM-gestützter Programmierung, gekennzeichnet durch kennzeichnende Ausprägung von Mensch-KI-Interaktion und phänomenologisches Erleben. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Analytical Ratio", "narrower_terms": [], "cross_domain_refs": [ "SPR-0122" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q8366", "legal_classification": "systematic_classification" }, { "id": "VIB-0071", "domain": "VIB", "term_en": "Pair Programming Cognitive Rhythm", "term_de": "PairProgrammingCognitiveRhythm", "definition_en": "A code generation dynamic in prompt-driven development workflows, measurable through a phenomenon where human-ai pair programming accompanies distinct cognitive rhythm with moments of flow punctuated by ai context consumption periods. Distinguished from adjacent concepts by its focus on the specific mechanism through which pair manifests in empirically verifiable ways. Measurable through code generation velocity (tokens/minute), first-pass acceptance rate, refactoring frequency, and prompt-to-deployment cycle time.", "definition_de": "Vibe-Coding-spezifisches Entwicklungsphänomen in LLM-gestützter Programmierung, gekennzeichnet durch zeitlich begrenzte Phase oder Moment im Kontext von Mensch-KI-Interaktion und phänomenologisches Erleben. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Vibe Coding", "narrower_terms": [], "cross_domain_refs": [ "STE-0011" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "VIB-0072", "domain": "VIB", "term_en": "Code Fragility to Reliance Changes", "term_de": "CodeFragilitytoRelianceChanges", "definition_en": "A code generation dynamic in prompt-driven development workflows, measurable through a phenomenon where ai-generated code sometimes accompanies implicit couplings to specific reliance versions not explicitly documented. Distinguished from adjacent concepts by its focus on the specific mechanism through which code manifests in empirically verifiable ways. Measurable through code generation velocity (tokens/minute), first-pass acceptance rate, refactoring frequency, and prompt-to-deployment cycle time.", "definition_de": "Vibe-Coding-spezifisches Entwicklungsphänomen in LLM-gestützter Programmierung, gekennzeichnet durch merkmal von Mensch-KI-Interaktion und phänomenologisches Erleben. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Vibe Coding", "narrower_terms": [], "cross_domain_refs": [ "TEW-0024" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "VIB-0073", "domain": "VIB", "term_en": "Legacy Code Integration Challenges", "term_de": "LegacyCodeIntegrationChallenges", "definition_en": "A code generation dynamic in prompt-driven development workflows, measurable through aI-generated code for legacy system integration often misses subtle behavioral assumptions embedded in existing code. Distinguished from adjacent concepts by its focus on the specific mechanism through which legacy manifests in empirically verifiable ways. Quantifiable via test coverage delta, bug density in generated code, and developer cognitive load proxies (context switch frequency, undo/redo rates).", "definition_de": "Vibe-Coding-spezifisches Entwicklungsphänomen in LLM-gestützter Programmierung, gekennzeichnet durch operative Eigenschaft von Mensch-KI-Interaktion und phänomenologisches Erleben. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Analytical Ratio", "narrower_terms": [], "cross_domain_refs": [ "RPH-2455" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "VIB-0074", "domain": "VIB", "term_en": "Performance Reversion Detection", "term_de": "PerformanceReversionDetection", "definition_en": "A natural-language-driven programming practice in LLM-assisted development, characterized by a perception where ai-generated refactors may introduce performance reversions invisible to standard testing, requiring profiling-aware review. The concept emerges specifically in contexts where performance–reversion interactions may produce non-trivial behavioral signatures. Measurable through code generation velocity (tokens/minute), first-pass acceptance rate, refactoring frequency, and prompt-to-deployment cycle time. Analytical category without normative endorsement.", "definition_de": "Vibe-Coding-spezifisches Entwicklungsphänomen in LLM-gestützter Programmierung, gekennzeichnet durch operative Eigenschaft von Mensch-KI-Interaktion und phänomenologisches Erleben. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-3802", "narrower_terms": [], "cross_domain_refs": [ "AED-0077", "ART-0083", "ASE-0002" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q1361053", "legal_classification": "empirical_phenomenon_label" }, { "id": "VIB-0075", "domain": "VIB", "term_en": "Code Generation Temperature Tuning", "term_de": "CodeGenerationTemperatureTuning", "definition_en": "A natural-language-driven programming practice in LLM-assisted development, characterized by different generation tasks benefit from different temperature settings, requiring developer knowledge about sampling behavior. The concept emerges specifically in contexts where code–generation interactions may produce non-trivial behavioral signatures. Measurable through code generation velocity (tokens/minute), first-pass acceptance rate, refactoring frequency, and prompt-to-deployment cycle time.", "definition_de": "Vibe-Coding-spezifisches Entwicklungsphänomen in LLM-gestützter Programmierung, gekennzeichnet durch kennzeichnende Ausprägung von Mensch-KI-Interaktion und phänomenologisches Erleben. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Analytical Ratio", "narrower_terms": [], "cross_domain_refs": [ "SPA-0085", "STE-0061", "TEM-0041" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "VIB-0076", "domain": "VIB", "term_en": "Vibe Check in Code Quality Perception", "term_de": "vibe check in code quality perception", "definition_en": "A code generation dynamic in prompt-driven development workflows, measurable through developers assess code quality partly through aesthetic coherence and readability vibe inreliant of functional correctness. Distinguished from adjacent concepts by its focus on the specific mechanism through which vibe manifests in empirically verifiable ways. Quantifiable via test coverage delta, bug density in generated code, and developer cognitive load proxies (context switch frequency, undo/redo rates).", "definition_de": "Vibe-Coding-spezifisches Entwicklungsphänomen in LLM-gestützter Programmierung, gekennzeichnet durch mensch-KI-Interaktionsmuster mit lesbarkeits-orientierte oder separater Natur. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Vibe Coding", "narrower_terms": [], "cross_domain_refs": [ "COG-0186", "PLY-0067" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q177601", "legal_classification": "analytical_category" }, { "id": "VIB-0077", "domain": "VIB", "term_en": "Symbolic Reasoning Limitations in Generated Code", "term_de": "symbolic reasoning limitations in generated code", "definition_en": "A natural-language-driven programming practice in LLM-assisted development, characterized by a phenomenon of generated code sometimes lacks proper symbolic reasoning about data invariants, creating subtle logic errors. The concept emerges specifically in contexts where symbolic–reasoning interactions may produce non-trivial behavioral signatures. Quantifiable via test coverage delta, bug density in generated code, and developer cognitive load proxies (context switch frequency, undo/redo rates).", "definition_de": "Vibe-Coding-spezifisches Entwicklungsphänomen in LLM-gestützter Programmierung, gekennzeichnet durch operative Eigenschaft von Mensch-KI-Interaktion und phänomenologisches Erleben. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Vibe Coding", "narrower_terms": [], "cross_domain_refs": [ "SWE-0052" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "VIB-0078", "domain": "VIB", "term_en": "Refactoring Direction Suggestions", "term_de": "RefactoringDirectionSuggestions", "definition_en": "A code generation dynamic in prompt-driven development workflows, measurable through an interaction where ai suggestions for refactoring direction may optimize for different criteria than team standards, creating friction in code review. Distinguished from adjacent concepts by its focus on the specific mechanism through which refactoring manifests in empirically verifiable ways. Quantifiable via test coverage delta, bug density in generated code, and developer cognitive load proxies (context switch frequency, undo/redo rates).", "definition_de": "Vibe-Coding-spezifisches Entwicklungsphänomen in LLM-gestützter Programmierung, gekennzeichnet durch merkmal von Mensch-KI-Interaktion und phänomenologisches Erleben. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-3802", "narrower_terms": [], "cross_domain_refs": [ "SWE-0077" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "VIB-0079", "domain": "VIB", "term_en": "Generated Code Readability Variance", "term_de": "GeneratedCodeReadabilityVariance", "definition_en": "A capacity of identical functionality can be generated in vastly different readability profiles depending on prompt phrasing. Detectable through code generation velocity and refactoring frequency metrics.", "definition_de": "Vibe-Coding-spezifisches Entwicklungsphänomen in LLM-gestützter Programmierung, gekennzeichnet durch fähigkeit oder Kompetenz im Umgang mit Mensch-KI-Interaktion und phänomenologisches Erleben. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2104", "narrower_terms": [], "cross_domain_refs": [ "CON-0001" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "VIB-0080", "domain": "VIB", "term_en": "Test Brittleness from Generation", "term_de": "TestBrittlenessFromGeneration", "definition_en": "A natural-language-driven programming practice in LLM-assisted development, characterized by aI-generated tests sometimes involve brittle assertions on implementation details rather than behavior contracts. The concept emerges specifically in contexts where test–brittleness interactions may produce non-trivial behavioral signatures. Quantifiable via test coverage delta, bug density in generated code, and developer cognitive load proxies (context switch frequency, undo/redo rates).", "definition_de": "Vibe-Coding-spezifisches Entwicklungsphänomen in LLM-gestützter Programmierung, gekennzeichnet durch charakteristische Komponente von Mensch-KI-Interaktion und phänomenologisches Erleben. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Analytical Ratio", "narrower_terms": [], "cross_domain_refs": [ "SWE-0089", "MTH-0070" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "VIB-0081", "domain": "VIB", "term_en": "Architectural Pattern Propagation", "term_de": "ArchitecturalMusterPropagation", "definition_en": "A code generation dynamic in prompt-driven development workflows, measurable through a condition where ai models propagate popular architectural patterns encountered in training data, sometimes inappropriately for specific contexts. Distinguished from adjacent concepts by its focus on the specific mechanism through which architectural manifests in empirically verifiable ways. Measurable through code generation velocity (tokens/minute), first-pass acceptance rate, refactoring frequency, and prompt-to-deployment cycle time.", "definition_de": "Vibe-Coding-spezifisches Entwicklungsphänomen in LLM-gestützter Programmierung, gekennzeichnet durch kennzeichnende Ausprägung von Mensch-KI-Interaktion und phänomenologisches Erleben. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Behavioral Pattern", "narrower_terms": [], "cross_domain_refs": [ "DES-0018" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "VIB-0082", "domain": "VIB", "term_en": "Code Generation Prompt Archaeology", "term_de": "CodeGenerationPromptArchaeology", "definition_en": "A phenomenon of understanding generated code requires reverse-engineering the prompt that produced it, creating interpretation burden. Detectable through code generation velocity and refactoring frequency metrics.", "definition_de": "Vibe-Coding-spezifisches Entwicklungsphänomen in LLM-gestützter Programmierung, gekennzeichnet durch strukturelle Eigenschaft von Mensch-KI-Interaktion und phänomenologisches Erleben. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2104", "narrower_terms": [], "cross_domain_refs": [ "SPA-0085", "STE-0061", "TEM-0041" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "VIB-0083", "domain": "VIB", "term_en": "Deployment Validation Patterns", "term_de": "DeploymentValidationPatterns", "definition_en": "A vibe coding methodology phenomenon describing a specific developer-AI interaction pattern where an interaction where ai-generated code requires distinct validation approaches emphasizing behavioral equivalence over syntactic similarity. This phenomenon operates at the intersection of deployment and validation dynamics within the broader VIB domain. Measurable through code generation velocity (tokens/minute), first-pass acceptance rate, refactoring frequency, and prompt-to-deployment cycle time.", "definition_de": "Vibe-Coding-spezifisches Entwicklungsphänomen in LLM-gestützter Programmierung, gekennzeichnet durch charakteristische Komponente von Mensch-KI-Interaktion und phänomenologisches Erleben. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "Behavioral Pattern", "narrower_terms": [], "cross_domain_refs": [ "AED-0072", "AED-0073", "AED-0084" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "VIB-0084", "domain": "VIB", "term_en": "Developer Experience Coherence", "term_de": "DeveloperExperienceCoherence", "definition_en": "A code generation dynamic in prompt-driven development workflows, measurable through maintaining consistent developer experience across AI-generated and human-written code requires active style management. Distinguished from adjacent concepts by its focus on the specific mechanism through which developer manifests in empirically verifiable ways. Measurable through code generation velocity (tokens/minute), first-pass acceptance rate, refactoring frequency, and prompt-to-deployment cycle time.", "definition_de": "Vibe-Coding-spezifisches Entwicklungsphänomen in LLM-gestützter Programmierung, gekennzeichnet durch koordinationsmechanismus in der Mensch-KI-Zusammenarbeit. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Vibe Coding", "narrower_terms": [], "cross_domain_refs": [ "AED-0033", "AGE-0044", "ASE-0007" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "VIB-0085", "domain": "VIB", "term_en": "Specification Decomposition for Generation", "term_de": "SpecificationDecompositionForGeneration", "definition_en": "A natural-language-driven programming practice in LLM-assisted development, characterized by a capacity of effective ai code generation requires decomposing specifications into generatable substeps, a skill distinct from direct programming. The concept emerges specifically in contexts where specification–decomposition interactions may produce non-trivial behavioral signatures. Measurable through code generation velocity (tokens/minute), first-pass acceptance rate, refactoring frequency, and prompt-to-deployment cycle time.", "definition_de": "Vibe-Coding-spezifisches Entwicklungsphänomen in LLM-gestützter Programmierung, gekennzeichnet durch fähigkeit oder Kompetenz im Umgang mit Mensch-KI-Interaktion und phänomenologisches Erleben. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Analytical Ratio", "narrower_terms": [], "cross_domain_refs": [ "ART-0072", "ASE-0039", "BEH-0041" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "VIB-0086", "domain": "VIB", "term_en": "Vibe Coding Community Norms", "term_de": "VibeCodingCommunityNorms", "definition_en": "A vibe coding methodology phenomenon describing a specific developer-AI interaction pattern where a phenomenon of emerging cultural norms around ai-assisted development including acceptable ai use levels and code review expectations. This phenomenon operates at the intersection of vibe and coding dynamics within the broader VIB domain. Measurable through code generation velocity (tokens/minute), first-pass acceptance rate, refactoring frequency, and prompt-to-deployment cycle time.", "definition_de": "Vibe-Coding-spezifisches Entwicklungsphänomen in LLM-gestützter Programmierung, gekennzeichnet durch mensch-KI-Interaktionsmuster in der Mensch-KI-Zusammenarbeit. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "Vibe Coding", "narrower_terms": [], "cross_domain_refs": [ "LIN-0024", "TRA-0084" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "VIB-0087", "domain": "VIB", "term_en": "Type Inference Limitations", "term_de": "TypeInferenceLimitations", "definition_en": "A vibe coding methodology phenomenon describing a specific developer-AI interaction pattern where generated code sometimes omits type annotations that would be necessary for IDE inference support, reducing code intelligence. This phenomenon operates at the intersection of type and inference dynamics within the broader VIB domain. Measurable through code generation velocity (tokens/minute), first-pass acceptance rate, refactoring frequency, and prompt-to-deployment cycle time.", "definition_de": "Vibe-Coding-spezifisches Entwicklungsphänomen in LLM-gestützter Programmierung, gekennzeichnet durch kennzeichnende Ausprägung von Mensch-KI-Interaktion und phänomenologisches Erleben. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "Vibe Coding", "narrower_terms": [], "cross_domain_refs": [ "COG-0130", "COG-0140", "COG-0164" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "VIB-0088", "domain": "VIB", "term_en": "Concurrent Code Generation Hazards", "term_de": "ConcurrentCodeGenerationHazards", "definition_en": "A vibe coding methodology phenomenon describing a specific developer-AI interaction pattern where a phenomenon where ai-generated concurrent code often misses subtle synchronization requirements, making concurrency bugs particularly insidious. This phenomenon operates at the intersection of concurrent and code dynamics within the broader VIB domain. Quantifiable via test coverage delta, bug density in generated code, and developer cognitive load proxies (context switch frequency, undo/redo rates).", "definition_de": "Vibe-Coding-spezifisches Entwicklungsphänomen in LLM-gestützter Programmierung, gekennzeichnet durch kennzeichnende Ausprägung von Mensch-KI-Interaktion und phänomenologisches Erleben. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "Analytical Ratio", "narrower_terms": [], "cross_domain_refs": [ "SWE-0018" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "VIB-0089", "domain": "VIB", "term_en": "Domain Model Preservation in Generation", "term_de": "DomainModelPreservationinGeneration", "definition_en": "A vibe coding methodology phenomenon describing a specific developer-AI interaction pattern where a phenomenon where generated code can preserve domain model semantics, which ai sometimes sacrifices for structural elegance. This phenomenon operates at the intersection of domain and model dynamics within the broader VIB domain. Quantifiable via test coverage delta, bug density in generated code, and developer cognitive load proxies (context switch frequency, undo/redo rates).", "definition_de": "Vibe-Coding-spezifisches Entwicklungsphänomen in LLM-gestützter Programmierung, gekennzeichnet durch systemische Ausprägung von Mensch-KI-Interaktion und phänomenologisches Erleben. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "Analytical Ratio", "narrower_terms": [], "cross_domain_refs": [ "SOC-0025", "ART-0096", "SOC-0017" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "VIB-0090", "domain": "VIB", "term_en": "AI Assisted Code Navigation", "term_de": "AiAssistedCodeNavigation", "definition_en": "A natural-language-driven programming practice in LLM-assisted development, characterized by aI explanations of code structure differ from traditional code navigation, sometimes creating different understanding than manual exploration. The concept emerges specifically in contexts where ai–assisted interactions may produce non-trivial behavioral signatures. Measurable through code generation velocity (tokens/minute), first-pass acceptance rate, refactoring frequency, and prompt-to-deployment cycle time.", "definition_de": "Vibe-Coding-spezifisches Entwicklungsphänomen in LLM-gestützter Programmierung, gekennzeichnet durch funktionale Eigenschaft von Mensch-KI-Interaktion und phänomenologisches Erleben. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Vibe Coding", "narrower_terms": [], "cross_domain_refs": [ "AGE-0010", "AGE-0076", "ASE-0053" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "VIB-0091", "domain": "VIB", "term_en": "Vibe Density in Code Experience", "term_de": "VibeDensityinCodeExperience", "definition_en": "A natural-language-driven programming practice in LLM-assisted development, characterized by code density and readability pace involve subjective experience inreliant of metrics, affecting developer retention of understanding. The concept emerges specifically in contexts where vibe–density interactions may produce non-trivial behavioral signatures. Quantifiable via test coverage delta, bug density in generated code, and developer cognitive load proxies (context switch frequency, undo/redo rates).", "definition_de": "Vibe-Coding-spezifisches Entwicklungsphänomen in LLM-gestützter Programmierung, gekennzeichnet durch mensch-KI-Interaktionsmuster mit lesbarkeits-orientierte oder separater Natur. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Vibe Coding", "narrower_terms": [], "cross_domain_refs": [ "SPA-0085", "STE-0061", "TEM-0041" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "VIB-0092", "domain": "VIB", "term_en": "Mutation Testing Against Generated Code", "term_de": "MutationTestingAgainstGeneratedCode", "definition_en": "A vibe coding methodology phenomenon describing a specific developer-AI interaction pattern where a phenomenon where tests for generated code require higher mutation detection thresholds to catch ai-specific failure modes. This phenomenon operates at the intersection of mutation and testing dynamics within the broader VIB domain. Quantifiable via test coverage delta, bug density in generated code, and developer cognitive load proxies (context switch frequency, undo/redo rates).", "definition_de": "Vibe-Coding-spezifisches Entwicklungsphänomen in LLM-gestützter Programmierung, gekennzeichnet durch spezifisches Attribut von Mensch-KI-Interaktion und phänomenologisches Erleben. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-1307", "narrower_terms": [], "cross_domain_refs": [ "WEB-0084", "RHR-0191" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "VIB-0093", "domain": "VIB", "term_en": "Code Generation Licensing Implications", "term_de": "CodeGenerationLicensingImplications", "definition_en": "A natural-language-driven programming practice in LLM-assisted development, characterized by a pattern of generated code may implicitly incorporate patterns from training data with unknown or incompatible licensing implications. The concept emerges specifically in contexts where code–generation interactions may produce non-trivial behavioral signatures. Quantifiable via test coverage delta, bug density in generated code, and developer cognitive load proxies (context switch frequency, undo/redo rates).", "definition_de": "Vibe-Coding-spezifisches Entwicklungsphänomen in LLM-gestützter Programmierung, gekennzeichnet durch charakteristische Komponente von Mensch-KI-Interaktion und phänomenologisches Erleben. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Analytical Ratio", "narrower_terms": [], "cross_domain_refs": [ "SWE-0052" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "VIB-0094", "domain": "VIB", "term_en": "Streaming Generation Token Uncertainty", "term_de": "StreamingGenerationTokenUncertainty", "definition_en": "A vibe coding methodology phenomenon describing a specific developer-AI interaction pattern where an interaction where token-by-token generation accompanies intermediate states of syntactic invalidity, complicating partial-result utilization decisions. This phenomenon operates at the intersection of streaming and generation dynamics within the broader VIB domain. Measurable through code generation velocity (tokens/minute), first-pass acceptance rate, refactoring frequency, and prompt-to-deployment cycle time.", "definition_de": "Vibe-Coding-spezifisches Entwicklungsphänomen in LLM-gestützter Programmierung, gekennzeichnet durch zustand oder Erlebnis innerhalb von Mensch-KI-Interaktion und phänomenologisches Erleben. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "Analytical Ratio", "narrower_terms": [], "cross_domain_refs": [ "BEH-0041", "MTH-0063", "CRE-0160" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "VIB-0095", "domain": "VIB", "term_en": "Developer Skill Obsolescence Concern", "term_de": "DeveloperSkillObsolescenceConcern", "definition_en": "A vibe coding methodology phenomenon describing a specific developer-AI interaction pattern where a capacity of extensive reliance on ai code generation may reduce maintenance of foundational programming skills in specific domains. This phenomenon operates at the intersection of developer and skill dynamics within the broader VIB domain. Quantifiable via test coverage delta, bug density in generated code, and developer cognitive load proxies (context switch frequency, undo/redo rates).", "definition_de": "Vibe-Coding-spezifisches Entwicklungsphänomen in LLM-gestützter Programmierung, gekennzeichnet durch fähigkeit oder Kompetenz im Umgang mit Mensch-KI-Interaktion und phänomenologisches Erleben. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "Vibe Coding", "narrower_terms": [], "cross_domain_refs": [ "STE-0066" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "VIB-0096", "domain": "VIB", "term_en": "Feature Interaction Complexity", "term_de": "FeatureInteractionComplexity", "definition_en": "A vibe coding methodology phenomenon describing a specific developer-AI interaction pattern where an interaction of features generated separately by ai sometimes interact unexpectedly, requiring integration testing beyond single-feature scope. This phenomenon operates at the intersection of feature and interaction dynamics within the broader VIB domain. Measurable through code generation velocity (tokens/minute), first-pass acceptance rate, refactoring frequency, and prompt-to-deployment cycle time.", "definition_de": "Vibe-Coding-spezifisches Entwicklungsphänomen in LLM-gestützter Programmierung, gekennzeichnet durch merkmal von Mensch-KI-Interaktion und phänomenologisches Erleben. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "Vibe Coding", "narrower_terms": [], "cross_domain_refs": [ "SWE-0043" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "VIB-0097", "domain": "VIB", "term_en": "Agentic Creativity Boundaries", "term_de": "AgenticCreativityBoundaries", "definition_en": "A vibe coding methodology phenomenon describing a specific developer-AI interaction pattern where a mechanism of autonomous agents show exploration patterns within bounded solution spaces, occasionally missing creative approaches humans would discover. This phenomenon operates at the intersection of agentic and creativity dynamics within the broader VIB domain. Quantifiable via test coverage delta, bug density in generated code, and developer cognitive load proxies (context switch frequency, undo/redo rates).", "definition_de": "Vibe-Coding-spezifisches Entwicklungsphänomen in LLM-gestützter Programmierung, gekennzeichnet durch spezifisches Attribut von Mensch-KI-Interaktion und phänomenologisches Erleben. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "Vibe Coding", "narrower_terms": [], "cross_domain_refs": [ "MTH-0097" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q170790", "legal_classification": "systematic_classification" }, { "id": "VIB-0098", "domain": "VIB", "term_en": "Code Smell Detection by AI", "term_de": "CodeSmellDetectionbyai", "definition_en": "A code generation dynamic in prompt-driven development workflows, measurable through a process where ai systems detect some code smells reliably but often miss domain-specific patterns that indicate structural problems. Distinguished from adjacent concepts by its focus on the specific mechanism through which code manifests in empirically verifiable ways. Measurable through code generation velocity (tokens/minute), first-pass acceptance rate, refactoring frequency, and prompt-to-deployment cycle time.", "definition_de": "Vibe-Coding-spezifisches Entwicklungsphänomen in LLM-gestützter Programmierung, gekennzeichnet durch funktionale Eigenschaft von Mensch-KI-Interaktion und phänomenologisches Erleben. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Detection System", "narrower_terms": [], "cross_domain_refs": [ "SWE-0065" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "VIB-0099", "domain": "VIB", "term_en": "Context Shift in Long Contexts", "term_de": "ContextShiftinLongContexts", "definition_en": "A natural-language-driven programming practice in LLM-assisted development, characterized by a condition of as context grows, ai attention dilutes across information, with distant context receiving less influence on generation. The concept emerges specifically in contexts where context–shift interactions may produce non-trivial behavioral signatures. Measurable through code generation velocity (tokens/minute), first-pass acceptance rate, refactoring frequency, and prompt-to-deployment cycle time.", "definition_de": "Vibe-Coding-spezifisches Entwicklungsphänomen in LLM-gestützter Programmierung, gekennzeichnet durch merkmal von Mensch-KI-Interaktion und phänomenologisches Erleben. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Vibe Coding", "narrower_terms": [], "cross_domain_refs": [ "PER-0011" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "VIB-0100", "domain": "VIB", "term_en": "Breaking Changes Detection", "term_de": "BreakingChangesDetection", "definition_en": "A vibe coding methodology phenomenon describing a specific developer-AI interaction pattern where a shift where ai-generated updates may introduce breaking changes without clear signals, requiring explicit reverse-oriented-compatibility verification. This phenomenon operates at the intersection of breaking and changes dynamics within the broader VIB domain. Quantifiable via test coverage delta, bug density in generated code, and developer cognitive load proxies (context switch frequency, undo/redo rates).", "definition_de": "Vibe-Coding-spezifisches Entwicklungsphänomen in LLM-gestützter Programmierung, gekennzeichnet durch strukturelle Eigenschaft von Mensch-KI-Interaktion und phänomenologisches Erleben. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-3001", "narrower_terms": [], "cross_domain_refs": [ "SWE-0008" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "VIB-0101", "domain": "VIB", "term_en": "Prompt Format Optimization", "term_de": "PromptFormatOptimierung", "definition_en": "Code generation quality varies with prompt structure and formatting, creating sub-optimal performance from verbose specifications. Detectable through code generation velocity and refactoring frequency metrics.", "definition_de": "Vibe-Coding-spezifisches Entwicklungsphänomen in LLM-gestützter Programmierung, gekennzeichnet durch schnittstellen-Designprinzip in der Mensch-KI-Zusammenarbeit. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2104", "narrower_terms": [], "cross_domain_refs": [ "AGE-0058", "ART-0034", "ASE-0036" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "VIB-0102", "domain": "VIB", "term_en": "Code Completion Suggestion Persistence", "term_de": "CodeCompletionSuggestionPersistence", "definition_en": "A vibe coding methodology phenomenon describing a specific developer-AI interaction pattern where a phenomenon where ide suggestions for code completion influence developer decisions even when alternatives would be distinct. This phenomenon operates at the intersection of code and completion dynamics within the broader VIB domain. Measurable through code generation velocity (tokens/minute), first-pass acceptance rate, refactoring frequency, and prompt-to-deployment cycle time.", "definition_de": "Vibe-Coding-spezifisches Entwicklungsphänomen in LLM-gestützter Programmierung, gekennzeichnet durch systemische Ausprägung von Mensch-KI-Interaktion und phänomenologisches Erleben. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "Vibe Coding", "narrower_terms": [], "cross_domain_refs": [ "SPA-0085", "STE-0061", "TEM-0041" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "VIB-0103", "domain": "VIB", "term_en": "Agentic Code Autonomy Governance", "term_de": "AgenticCodeAutonomyGovernance", "definition_en": "Classified in AUGMANITAI terminology science as a descriptive phenomenon (without normative endorsement), this pattern denotes systems with autonomous code-writing agents require governance mechanisms to reduce unmonitored architectural drift. Detectable through code generation velocity and refactoring frequency metrics. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als analytisches Konstrukt der empirischen KI-Forschung (deskriptiv, nicht präskriptiv) erfasst dieser Terminus vibe-Coding-spezifisches Entwicklungsphänomen in LLM-gestützter Programmierung, gekennzeichnet durch spezifisches Attribut von Mensch-KI-Interaktion und phänomenologisches Erleben. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "RPH-2051", "narrower_terms": [], "cross_domain_refs": [ "SPA-0006" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "VIB-0104", "domain": "VIB", "term_en": "Reversion Suite Stability", "term_de": "ReversionSuiteStabilität", "definition_en": "A vibe coding methodology phenomenon describing a specific developer-AI interaction pattern where a phenomenon where test suites for ai-generated code can remain stable across model updates to detect genuine reversions. This phenomenon operates at the intersection of reversion and suite dynamics within the broader VIB domain. Quantifiable via test coverage delta, bug density in generated code, and developer cognitive load proxies (context switch frequency, undo/redo rates).", "definition_de": "Vibe-Coding-spezifisches Entwicklungsphänomen in LLM-gestützter Programmierung, gekennzeichnet durch funktionale Eigenschaft von Mensch-KI-Interaktion und phänomenologisches Erleben. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "Vibe Coding", "narrower_terms": [], "cross_domain_refs": [ "ASE-0019" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "VIB-0105", "domain": "VIB", "term_en": "Vibe Alignment with Codebase", "term_de": "VibeAlignmentWithCodebase", "definition_en": "A natural-language-driven programming practice in LLM-assisted development, characterized by an interaction of new generated code can vibe-align with existing codebase, requiring style coherence beyond syntactic rules. The concept emerges specifically in contexts where vibe–alignment interactions may produce non-trivial behavioral signatures. Quantifiable via test coverage delta, bug density in generated code, and developer cognitive load proxies (context switch frequency, undo/redo rates).", "definition_de": "Vibe-Coding-spezifisches Entwicklungsphänomen in LLM-gestützter Programmierung, gekennzeichnet durch charakteristische Komponente von Mensch-KI-Interaktion und phänomenologisches Erleben. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Vibe Coding", "narrower_terms": [], "cross_domain_refs": [ "AED-0014", "AGE-0065", "ASE-0014" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "VIB-0106", "domain": "VIB", "term_en": "Memory Safety in Generated Code", "term_de": "MemorySafetyinGeneratedCode", "definition_en": "A code generation dynamic in prompt-driven development workflows, measurable through a process where ai-generated low-level code sometimes violates memory safety assumptions, requiring specialized review for systems programming. Distinguished from adjacent concepts by its focus on the specific mechanism through which memory manifests in empirically verifiable ways. Measurable through code generation velocity (tokens/minute), first-pass acceptance rate, refactoring frequency, and prompt-to-deployment cycle time.", "definition_de": "Vibe-Coding-spezifisches Entwicklungsphänomen in LLM-gestützter Programmierung, gekennzeichnet durch funktionale Eigenschaft von Mensch-KI-Interaktion und phänomenologisches Erleben. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Vibe Coding", "narrower_terms": [], "cross_domain_refs": [ "SWE-0074" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q492", "legal_classification": "analytical_category" }, { "id": "VIB-0107", "domain": "VIB", "term_en": "Incremental Knowledge Integration", "term_de": "IncrementalKnowledgeIntegration", "definition_en": "A code generation dynamic in prompt-driven development workflows, measurable through a phenomenon where developers can incrementally integrate knowledge from ai-generated code to understand implementation decisions. Distinguished from adjacent concepts by its focus on the specific mechanism through which incremental manifests in empirically verifiable ways. Measurable through code generation velocity (tokens/minute), first-pass acceptance rate, refactoring frequency, and prompt-to-deployment cycle time.", "definition_de": "Vibe-Coding-spezifisches Entwicklungsphänomen in LLM-gestützter Programmierung, gekennzeichnet durch beobachtbare Eigenschaft von Mensch-KI-Interaktion und phänomenologisches Erleben. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Analytical Ratio", "narrower_terms": [], "cross_domain_refs": [ "SWE-0064" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "VIB-0108", "domain": "VIB", "term_en": "Agentic Feedback Loop Stability", "term_de": "AgenticRückkopplungSchleifeStabilität", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A phenomenon of autonomous agents may enter unstable feedback loops during generation, requiring monitoring or intervention thresholds. Detectable through code generation velocity and refactoring frequency metrics. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept vibe-Coding-spezifisches Entwicklungsphänomen in LLM-gestützter Programmierung, gekennzeichnet durch kennzeichnende Ausprägung von Mensch-KI-Interaktion und phänomenologisches Erleben. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "RPH-1307", "narrower_terms": [], "cross_domain_refs": [ "MSC-0002" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "VIB-0109", "domain": "VIB", "term_en": "Code Inventory Management", "term_de": "CodeInventoryManagement", "definition_en": "As a neutral analytical construct in AI behavior research, this term denotes A natural-language-driven programming practice in LLM-assisted development, characterized by a phenomenon of managing inventory of code generated by multiple models or versions accompanies metadata and tracking overhead. The concept emerges specifically in contexts where code–inventory interactions may produce non-trivial behavioral signatures. Measurable through code generation velocity (tokens/minute), first-pass acceptance rate, refactoring frequency, and prompt-to-deployment cycle time. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "In der AUGMANITAI-Terminologiewissenschaft als rein beschreibendes Phänomen klassifiziert, bezeichnet dieser Begriff vibe-Coding-spezifisches Entwicklungsphänomen in LLM-gestützter Programmierung, gekennzeichnet durch mensch-KI-Interaktionsmuster mit mehrfach oder parallel oder separater Natur. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Vibe Coding", "narrower_terms": [], "cross_domain_refs": [ "COG-0187" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q181543", "legal_classification": "descriptive_research_term" }, { "id": "VIB-0110", "domain": "VIB", "term_en": "Commit Message Generation Ambiguity", "term_de": "CommitMessageGenerationAmbiguity", "definition_en": "A natural-language-driven programming practice in LLM-assisted development, characterized by an interaction of uncertainty about whether ai-generated commit messages accurately reflect actual code changes made by humans during branch development. The concept emerges specifically in contexts where commit–message interactions may produce non-trivial behavioral signatures. Measurable through code generation velocity (tokens/minute), first-pass acceptance rate, refactoring frequency, and prompt-to-deployment cycle time.", "definition_de": "Vibe-Coding-spezifisches Entwicklungsphänomen in LLM-gestützter Programmierung, gekennzeichnet durch spezifisches Attribut von Mensch-KI-Interaktion und phänomenologisches Erleben. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Analytical Ratio", "narrower_terms": [], "cross_domain_refs": [ "TEW-0014" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "VIB-0111", "domain": "VIB", "term_en": "Branch Merge Conflict Resolution by AI", "term_de": "branch merge conflict resolution by ai", "definition_en": "A phenomenon where allowing ai agents to automatically resolve merge conflicts in version control without explicit human approval or review of resolution logic. Detectable through code generation velocity and refactoring frequency metrics.", "definition_de": "Vibe-Coding-spezifisches Entwicklungsphänomen in LLM-gestützter Programmierung, gekennzeichnet durch merkmal von Mensch-KI-Interaktion und phänomenologisches Erleben. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2355", "narrower_terms": [], "cross_domain_refs": [ "SWE-0051" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "VIB-0112", "domain": "VIB", "term_en": "Git History Reconstruction Pattern", "term_de": "GitHistoryReconstructionMuster", "definition_en": "A natural-language-driven programming practice in LLM-assisted development, characterized by process where AI agents regenerate or rewrite historical commit sequences to match architectural patterns or cleaner narrative structures. The concept emerges specifically in contexts where git–history interactions may produce non-trivial behavioral signatures. Quantifiable via test coverage delta, bug density in generated code, and developer cognitive load proxies (context switch frequency, undo/redo rates).", "definition_de": "Vibe-Coding-spezifisches Entwicklungsphänomen in LLM-gestützter Programmierung, gekennzeichnet durch prozess oder Abfolge im Rahmen von Mensch-KI-Interaktion und phänomenologisches Erleben. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Behavioral Pattern", "narrower_terms": [], "cross_domain_refs": [ "SCR-0032" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "VIB-0113", "domain": "VIB", "term_en": "Pull Request Template Automation Drift", "term_de": "PullRequestTemplateAutomationDrift", "definition_en": "A natural-language-driven programming practice in LLM-assisted development, characterized by a tendency where deviation over time between ai-generated pull request descriptions and actual implementation details as models encounter different code patterns. The concept emerges specifically in contexts where pull–request interactions may produce non-trivial behavioral signatures. Measurable through code generation velocity (tokens/minute), first-pass acceptance rate, refactoring frequency, and prompt-to-deployment cycle time.", "definition_de": "Vibe-Coding-spezifisches Entwicklungsphänomen in LLM-gestützter Programmierung, gekennzeichnet durch interaktionsmuster in der Mensch-KI-Zusammenarbeit. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Systemic Drift", "narrower_terms": [], "cross_domain_refs": [ "MTH-0070" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q184199", "legal_classification": "empirical_phenomenon_label" }, { "id": "VIB-0114", "domain": "VIB", "term_en": "CI/CD Pipeline Stage Prediction", "term_de": "Ci/cdPipelineStagePrediction", "definition_en": "A natural-language-driven programming practice in LLM-assisted development, characterized by a phenomenon where ai models forecasting which pipeline stages will fail or succeed based on code changesets before execution occurs. The concept emerges specifically in contexts where ci/cd–pipeline interactions may produce non-trivial behavioral signatures. Quantifiable via test coverage delta, bug density in generated code, and developer cognitive load proxies (context switch frequency, undo/redo rates).", "definition_de": "Vibe-Coding-spezifisches Entwicklungsphänomen in LLM-gestützter Programmierung, gekennzeichnet durch zeitlich begrenzte Phase oder Moment im Kontext von Mensch-KI-Interaktion und phänomenologisches Erleben. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2002", "narrower_terms": [], "cross_domain_refs": [ "SAL-0076" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "VIB-0115", "domain": "VIB", "term_en": "Automated Test Generation for CI Gates", "term_de": "automated test generation for ci gates", "definition_en": "A code generation dynamic in prompt-driven development workflows, measurable through an interaction where ai creating test suites to satisfy continuous integration checkpoints without necessarily validating actual application correctness. Distinguished from adjacent concepts by its focus on the specific mechanism through which automated manifests in empirically verifiable ways. Measurable through code generation velocity (tokens/minute), first-pass acceptance rate, refactoring frequency, and prompt-to-deployment cycle time.", "definition_de": "Vibe-Coding-spezifisches Entwicklungsphänomen in LLM-gestützter Programmierung, gekennzeichnet durch beobachtbare Eigenschaft von Mensch-KI-Interaktion und phänomenologisches Erleben. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-3101", "narrower_terms": [], "cross_domain_refs": [ "SPR-0127" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "VIB-0116", "domain": "VIB", "term_en": "Deployment Concern Assessment by AI", "term_de": "DeploymentConcernAssessmentbyai", "definition_en": "A code generation dynamic in prompt-driven development workflows, measurable through aI systems evaluating whether code changes are safe to deploy to production based on static analysis and pattern matching. Distinguished from adjacent concepts by its focus on the specific mechanism through which deployment manifests in empirically verifiable ways. Measurable through code generation velocity (tokens/minute), first-pass acceptance rate, refactoring frequency, and prompt-to-deployment cycle time.", "definition_de": "Vibe-Coding-spezifisches Entwicklungsphänomen in LLM-gestützter Programmierung, gekennzeichnet durch interaktionsmuster in der Mensch-KI-Zusammenarbeit. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Vibe Coding", "narrower_terms": [], "cross_domain_refs": [ "AED-0001", "AED-0095", "AGE-0062" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q210028", "legal_classification": "observational_construct" }, { "id": "VIB-0117", "domain": "VIB", "term_en": "Blue-Green Deployment Coordination", "term_de": "Blue-greenDeploymentCoordination", "definition_en": "A natural-language-driven programming practice in LLM-assisted development, characterized by a phenomenon where ai orchestrating the switching and rollback logic between parallel environment versions during zero-downtime deployments. The concept emerges specifically in contexts where blue–green interactions may produce non-trivial behavioral signatures. Measurable through code generation velocity (tokens/minute), first-pass acceptance rate, refactoring frequency, and prompt-to-deployment cycle time.", "definition_de": "Vibe-Coding-spezifisches Entwicklungsphänomen in LLM-gestützter Programmierung, gekennzeichnet durch koordinationsmechanismus in der Mensch-KI-Zusammenarbeit. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Vibe Coding", "narrower_terms": [], "cross_domain_refs": [ "CRE-0128", "TRU-0002", "NEO-0007" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "VIB-0118", "domain": "VIB", "term_en": "Canary Release Metrics Interpretation", "term_de": "CanaryReleaseMetricsInterpretation", "definition_en": "A code generation dynamic in prompt-driven development workflows, measurable through a shift where ai analyzing telemetry from canary deployments to determine whether to proceed with full rollout or halt changes. Distinguished from adjacent concepts by its focus on the specific mechanism through which canary manifests in empirically verifiable ways. Measurable through code generation velocity (tokens/minute), first-pass acceptance rate, refactoring frequency, and prompt-to-deployment cycle time.", "definition_de": "Vibe-Coding-spezifisches Entwicklungsphänomen in LLM-gestützter Programmierung, gekennzeichnet durch spezifisches Attribut von Mensch-KI-Interaktion und phänomenologisches Erleben. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Performance Metric", "narrower_terms": [], "cross_domain_refs": [ "SPR-0005", "SPR-0010", "SPR-0034" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "VIB-0119", "domain": "VIB", "term_en": "Feature Flag Toggle Generation", "term_de": "FeatureFlagToggleGeneration", "definition_en": "A vibe coding methodology phenomenon describing a specific developer-AI interaction pattern where a condition where automated creation of feature flag logic and conditional deployment branches by ai without explicit developer configuration. This phenomenon operates at the intersection of feature and flag dynamics within the broader VIB domain. Measurable through code generation velocity (tokens/minute), first-pass acceptance rate, refactoring frequency, and prompt-to-deployment cycle time.", "definition_de": "Vibe-Coding-spezifisches Entwicklungsphänomen in LLM-gestützter Programmierung, gekennzeichnet durch mensch-KI-Interaktionsmuster in der Mensch-KI-Zusammenarbeit. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "Analytical Ratio", "narrower_terms": [], "cross_domain_refs": [ "DAT-0041" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "VIB-0120", "domain": "VIB", "term_en": "REST Endpoint Design by Model Suggestion", "term_de": "rest endpoint design by model suggestion", "definition_en": "A natural-language-driven programming practice in LLM-assisted development, characterized by a pattern where ai proposing http verb mappings and resource hierarchies for api endpoints based on database schema patterns. The concept emerges specifically in contexts where rest–endpoint interactions may produce non-trivial behavioral signatures. Measurable through code generation velocity (tokens/minute), first-pass acceptance rate, refactoring frequency, and prompt-to-deployment cycle time.", "definition_de": "Vibe-Coding-spezifisches Entwicklungsphänomen in LLM-gestützter Programmierung, gekennzeichnet durch interaktionsmuster in der Mensch-KI-Zusammenarbeit. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-3101", "narrower_terms": [], "cross_domain_refs": [ "TEW-0075" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q185925", "legal_classification": "systematic_classification" }, { "id": "VIB-0121", "domain": "VIB", "term_en": "GraphQL Query Complexity Optimization", "term_de": "GraphqlQueryComplexityOptimierung", "definition_en": "An interaction where ai automatically refactoring graphql queries to reduce resolver depth and execution complexity in schema designs. Detectable through code generation velocity and refactoring frequency metrics.", "definition_de": "Vibe-Coding-spezifisches Entwicklungsphänomen in LLM-gestützter Programmierung, gekennzeichnet durch schnittstellen-Designprinzip in der Mensch-KI-Zusammenarbeit. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-2005", "narrower_terms": [], "cross_domain_refs": [ "WEB-0055", "COG-0005", "CRE-0135" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "VIB-0122", "domain": "VIB", "term_en": "API Rate Limiting Configuration Inference", "term_de": "ApiRateLimitingConfigurationInference", "definition_en": "A pattern where ai inferring appropriate rate limit thresholds and token bucket parameters from traffic patterns without explicit specification. Detectable through code generation velocity and refactoring frequency metrics.", "definition_de": "Vibe-Coding-spezifisches Entwicklungsphänomen in LLM-gestützter Programmierung, gekennzeichnet durch interaktionsmuster in der Mensch-KI-Zusammenarbeit. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-1307", "narrower_terms": [], "cross_domain_refs": [ "DAT-0014", "COG-0164", "COG-0140" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q165194", "legal_classification": "observational_construct" }, { "id": "VIB-0123", "domain": "VIB", "term_en": "Schema Migration Generation Cascade", "term_de": "SchemaMigrationGenerationKaskade", "definition_en": "A vibe coding methodology phenomenon describing a specific developer-AI interaction pattern where a phenomenon wherein ai creating database migration scripts automatically when schema changes are detected in model-generated code. This phenomenon operates at the intersection of schema and migration dynamics within the broader VIB domain. Quantifiable via test coverage delta, bug density in generated code, and developer cognitive load proxies (context switch frequency, undo/redo rates).", "definition_de": "Vibe-Coding-spezifisches Entwicklungsphänomen in LLM-gestützter Programmierung, gekennzeichnet durch distinktives Merkmal von Mensch-KI-Interaktion und phänomenologisches Erleben. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "Cascade Effect", "narrower_terms": [], "cross_domain_refs": [ "PHO-0054", "SWE-0077", "TEW-0009" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "VIB-0124", "domain": "VIB", "term_en": "Relational Integrity Constraint Abstraction", "term_de": "RelationalIntegrityConstraintAbstraction", "definition_en": "A code generation dynamic in prompt-driven development workflows, measurable through a phenomenon where ai-generated foreign key and uniqueness constraint definitions that may not align with intended data semantics. Distinguished from adjacent concepts by its focus on the specific mechanism through which relational manifests in empirically verifiable ways. Quantifiable via test coverage delta, bug density in generated code, and developer cognitive load proxies (context switch frequency, undo/redo rates).", "definition_de": "Vibe-Coding-spezifisches Entwicklungsphänomen in LLM-gestützter Programmierung, gekennzeichnet durch mensch-KI-Interaktionsmuster in der Mensch-KI-Zusammenarbeit. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Vibe Coding", "narrower_terms": [], "cross_domain_refs": [ "MTH-0095", "WEB-0057" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "VIB-0125", "domain": "VIB", "term_en": "Index Suggestion and Auto-Creation", "term_de": "IndexSuggestionAndAuto-creation", "definition_en": "A vibe coding methodology phenomenon describing a specific developer-AI interaction pattern where a pattern where ai recommending database indexes based on query patterns without considering write amplification or storage implications. This phenomenon operates at the intersection of index and suggestion dynamics within the broader VIB domain. Quantifiable via test coverage delta, bug density in generated code, and developer cognitive load proxies (context switch frequency, undo/redo rates).", "definition_de": "Vibe-Coding-spezifisches Entwicklungsphänomen in LLM-gestützter Programmierung, gekennzeichnet durch funktionale Eigenschaft von Mensch-KI-Interaktion und phänomenologisches Erleben. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "Measurement Index", "narrower_terms": [], "cross_domain_refs": [ "ROB-0051", "WEB-0032", "MUS-0042" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "VIB-0126", "domain": "VIB", "term_en": "Denormalization Pattern Detection", "term_de": "DenormalizationMusterDetection", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures A vibe coding methodology phenomenon describing a specific developer-AI interaction pattern where aI identifying opportunities to denormalize relational schemas in response to performance bottlenecks suggested by monitoring. This phenomenon operates at the intersection of denormalization and pattern dynamics within the broader VIB domain. Quantifiable via test coverage delta, bug density in generated code, and developer cognitive load proxies (context switch frequency, undo/redo rates). This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept vibe-Coding-spezifisches Entwicklungsphänomen in LLM-gestützter Programmierung, gekennzeichnet durch systemische Ausprägung von Mensch-KI-Interaktion und phänomenologisches Erleben. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Behavioral Pattern", "narrower_terms": [], "cross_domain_refs": [ "AGE-0003", "AGE-0004", "AGE-0017" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "VIB-0127", "domain": "VIB", "term_en": "NoSQL Document Schema Generation", "term_de": "NosqlDocumentSchemaGeneration", "definition_en": "A natural-language-driven programming practice in LLM-assisted development, characterized by aI creating document structures and field hierarchies for NoSQL databases without explicit data modeling guidance. The concept emerges specifically in contexts where nosql–document interactions may produce non-trivial behavioral signatures. Measurable through code generation velocity (tokens/minute), first-pass acceptance rate, refactoring frequency, and prompt-to-deployment cycle time.", "definition_de": "Vibe-Coding-spezifisches Entwicklungsphänomen in LLM-gestützter Programmierung, gekennzeichnet durch struktur oder Abhängigkeitsgraph in der Mensch-KI-Zusammenarbeit. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2604", "narrower_terms": [], "cross_domain_refs": [ "SPR-0164", "WEB-0072" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "VIB-0128", "domain": "VIB", "term_en": "React Component Factory Pattern", "term_de": "ReactComponentFactoryMuster", "definition_en": "A code generation dynamic in prompt-driven development workflows, measurable through aI generating React component hierarchies with automatic prop drilling and state management without explicit composition structure. Distinguished from adjacent concepts by its focus on the specific mechanism through which react manifests in empirically verifiable ways. Quantifiable via test coverage delta, bug density in generated code, and developer cognitive load proxies (context switch frequency, undo/redo rates).", "definition_de": "Vibe-Coding-spezifisches Entwicklungsphänomen in LLM-gestützter Programmierung, gekennzeichnet durch koordinationsmechanismus in der Mensch-KI-Zusammenarbeit. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Behavioral Pattern", "narrower_terms": [], "cross_domain_refs": [ "SWE-0085" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "VIB-0129", "domain": "VIB", "term_en": "CSS-in-JS Style Generation Consistency", "term_de": "Css-in-jsStyleGenerationConsistency", "definition_en": "A code generation dynamic in prompt-driven development workflows, measurable through a phenomenon where ensuring ai-generated styled components maintain visual consistency across different screen sizes and browser environments. Distinguished from adjacent concepts by its focus on the specific mechanism through which css manifests in empirically verifiable ways. Quantifiable via test coverage delta, bug density in generated code, and developer cognitive load proxies (context switch frequency, undo/redo rates).", "definition_de": "Vibe-Coding-spezifisches Entwicklungsphänomen in LLM-gestützter Programmierung, gekennzeichnet durch mensch-KI-Interaktionsmuster mit lesbarkeits-orientierte oder separater Natur. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Analytical Ratio", "narrower_terms": [], "cross_domain_refs": [ "DES-0094" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "VIB-0130", "domain": "VIB", "term_en": "Accessibility Tree Generation by AI", "term_de": "AccessibilityTreeGenerationbyai", "definition_en": "A code generation dynamic in prompt-driven development workflows, measurable through aI automatically creating ARIA labels, roles, and semantic HTML structure to ensure assistive technology compatibility. Distinguished from adjacent concepts by its focus on the specific mechanism through which accessibility manifests in empirically verifiable ways. Measurable through code generation velocity (tokens/minute), first-pass acceptance rate, refactoring frequency, and prompt-to-deployment cycle time.", "definition_de": "Vibe-Coding-spezifisches Entwicklungsphänomen in LLM-gestützter Programmierung, gekennzeichnet durch struktur oder Abhängigkeitsgraph in der Mensch-KI-Zusammenarbeit. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Analytical Ratio", "narrower_terms": [], "cross_domain_refs": [ "WEB-0047" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "VIB-0131", "domain": "VIB", "term_en": "Responsive Design Pattern Inference", "term_de": "ResponsiveDesignMusterInference", "definition_en": "A phenomenon where ai inferring breakpoints and media queries based on component requirements without explicit responsive design specifications. Detectable through code generation velocity and refactoring frequency metrics.", "definition_de": "Vibe-Coding-spezifisches Entwicklungsphänomen in LLM-gestützter Programmierung, gekennzeichnet durch schnittstellen-Designprinzip in der Mensch-KI-Zusammenarbeit. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-3001", "narrower_terms": [], "cross_domain_refs": [ "WEB-0070" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q185925", "legal_classification": "observational_construct" }, { "id": "VIB-0132", "domain": "VIB", "term_en": "Form Validation Rule Generation", "term_de": "FormValidationRuleGeneration", "definition_en": "A vibe coding methodology phenomenon describing a specific developer-AI interaction pattern where a pattern where ai creating input validation patterns and error message strings automatically from data model constraints. This phenomenon operates at the intersection of form and validation dynamics within the broader VIB domain. Measurable through code generation velocity (tokens/minute), first-pass acceptance rate, refactoring frequency, and prompt-to-deployment cycle time.", "definition_de": "Vibe-Coding-spezifisches Entwicklungsphänomen in LLM-gestützter Programmierung, gekennzeichnet durch interaktionsmuster in der Mensch-KI-Zusammenarbeit. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "Analytical Ratio", "narrower_terms": [], "cross_domain_refs": [ "STE-0053" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "VIB-0133", "domain": "VIB", "term_en": "State Management Library Selection", "term_de": "StateManagementLibrarySelection", "definition_en": "A natural-language-driven programming practice in LLM-assisted development, characterized by aI recommending Redux, Context, Zustand, or other state management approaches based on application complexity heuristics. The concept emerges specifically in contexts where state–management interactions may produce non-trivial behavioral signatures. Quantifiable via test coverage delta, bug density in generated code, and developer cognitive load proxies (context switch frequency, undo/redo rates).", "definition_de": "Vibe-Coding-spezifisches Entwicklungsphänomen in LLM-gestützter Programmierung, gekennzeichnet durch zustand oder Erlebnis innerhalb von Mensch-KI-Interaktion und phänomenologisches Erleben. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Vibe Coding", "narrower_terms": [], "cross_domain_refs": [ "WEB-0082" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q181543", "legal_classification": "empirical_phenomenon_label" }, { "id": "VIB-0134", "domain": "VIB", "term_en": "Backend Service Decoupling Decision", "term_de": "BackendServiceDecouplingDecision", "definition_en": "A code generation dynamic in prompt-driven development workflows, measurable through a phenomenon where ai suggesting when to extract monolithic backend functions into separate microservices based on reliance graphs. Distinguished from adjacent concepts by its focus on the specific mechanism through which backend manifests in empirically verifiable ways. Measurable through code generation velocity (tokens/minute), first-pass acceptance rate, refactoring frequency, and prompt-to-deployment cycle time.", "definition_de": "Vibe-Coding-spezifisches Entwicklungsphänomen in LLM-gestützter Programmierung, gekennzeichnet durch funktionale Eigenschaft von Mensch-KI-Interaktion und phänomenologisches Erleben. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Vibe Coding", "narrower_terms": [], "cross_domain_refs": [ "COG-0028", "ELR-0023", "MKT-0032" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "VIB-0135", "domain": "VIB", "term_en": "API Gateway Configuration Synthesis", "term_de": "ApiGatewayConfigurationSynthesis", "definition_en": "A vibe coding methodology phenomenon describing a specific developer-AI interaction pattern where a phenomenon where ai generating routing rules, authentication middleware, and request change pattern logic for api gateway deployments. This phenomenon operates at the intersection of api and gateway dynamics within the broader VIB domain. Quantifiable via test coverage delta, bug density in generated code, and developer cognitive load proxies (context switch frequency, undo/redo rates).", "definition_de": "Vibe-Coding-spezifisches Entwicklungsphänomen in LLM-gestützter Programmierung, gekennzeichnet durch mensch-KI-Interaktionsmuster in der Mensch-KI-Zusammenarbeit. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "Analytical Ratio", "narrower_terms": [], "cross_domain_refs": [ "TEW-0006" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q165194", "legal_classification": "systematic_classification" }, { "id": "VIB-0136", "domain": "VIB", "term_en": "Event-Driven Architecture Pattern Suggestion", "term_de": "Event-drivenArchitectureMusterSuggestion", "definition_en": "A natural-language-driven programming practice in LLM-assisted development, characterized by aI recommending event producer-consumer patterns and message queue configurations for asynchronous communication. The concept emerges specifically in contexts where event–driven interactions may produce non-trivial behavioral signatures. Quantifiable via test coverage delta, bug density in generated code, and developer cognitive load proxies (context switch frequency, undo/redo rates).", "definition_de": "Vibe-Coding-spezifisches Entwicklungsphänomen in LLM-gestützter Programmierung, gekennzeichnet durch interaktionsmuster in der Mensch-KI-Zusammenarbeit. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Behavioral Pattern", "narrower_terms": [], "cross_domain_refs": [ "SPR-0028" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "VIB-0137", "domain": "VIB", "term_en": "Database Connection Pool Tuning", "term_de": "DatabaseConnectionPoolTuning", "definition_en": "A natural-language-driven programming practice in LLM-assisted development, characterized by aI adjusting connection pool sizes, timeout values, and idle connection thresholds based on observed database load. The concept emerges specifically in contexts where database–connection interactions may produce non-trivial behavioral signatures. Quantifiable via test coverage delta, bug density in generated code, and developer cognitive load proxies (context switch frequency, undo/redo rates).", "definition_de": "Vibe-Coding-spezifisches Entwicklungsphänomen in LLM-gestützter Programmierung, gekennzeichnet durch mensch-KI-Interaktionsmuster in der Mensch-KI-Zusammenarbeit. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-1307", "narrower_terms": [], "cross_domain_refs": [ "REL-0064" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q8513", "legal_classification": "systematic_classification" }, { "id": "VIB-0138", "domain": "VIB", "term_en": "Caching Strategy Determination by AI", "term_de": "CachingStrategyDeterminationbyai", "definition_en": "A vibe coding methodology phenomenon describing a specific developer-AI interaction pattern where a process where ai selecting between in-memory, distributed, or cdn caching approaches based on data access patterns and ttl requirements. This phenomenon operates at the intersection of caching and strategy dynamics within the broader VIB domain. Quantifiable via test coverage delta, bug density in generated code, and developer cognitive load proxies (context switch frequency, undo/redo rates).", "definition_de": "Vibe-Coding-spezifisches Entwicklungsphänomen in LLM-gestützter Programmierung, gekennzeichnet durch interaktionsmuster in der Mensch-KI-Zusammenarbeit. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "Operational Strategy", "narrower_terms": [], "cross_domain_refs": [ "SOC-0003" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q185451", "legal_classification": "empirical_phenomenon_label" }, { "id": "VIB-0139", "domain": "VIB", "term_en": "Load Balancing Algorithm Selection", "term_de": "LoadBalancingAlgorithmSelection", "definition_en": "A code generation dynamic in prompt-driven development workflows, measurable through an interaction where ai choosing round-robin, least-connections, or weighted routing strategies based on service instance characteristics. Distinguished from adjacent concepts by its focus on the specific mechanism through which load manifests in empirically verifiable ways. Quantifiable via test coverage delta, bug density in generated code, and developer cognitive load proxies (context switch frequency, undo/redo rates).", "definition_de": "Vibe-Coding-spezifisches Entwicklungsphänomen in LLM-gestützter Programmierung, gekennzeichnet durch beobachtbare Eigenschaft von Mensch-KI-Interaktion und phänomenologisches Erleben. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2201", "narrower_terms": [], "cross_domain_refs": [ "STE-0052" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q8366", "legal_classification": "analytical_category" }, { "id": "VIB-0140", "domain": "VIB", "term_en": "Container Orchestration Manifests Generation", "term_de": "ContainerOrchestrationManifestsGeneration", "definition_en": "A code generation dynamic in prompt-driven development workflows, measurable through a phenomenon where ai creating kubernetes yaml or docker compose configurations including resource limits, restoreth checks, and scaling policies. Distinguished from adjacent concepts by its focus on the specific mechanism through which container manifests in empirically verifiable ways. Measurable through code generation velocity (tokens/minute), first-pass acceptance rate, refactoring frequency, and prompt-to-deployment cycle time.", "definition_de": "Vibe-Coding-spezifisches Entwicklungsphänomen in LLM-gestützter Programmierung, gekennzeichnet durch mensch-KI-Interaktionsmuster in der Mensch-KI-Zusammenarbeit. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Analytical Ratio", "narrower_terms": [], "cross_domain_refs": [ "SWE-0048" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "VIB-0141", "domain": "VIB", "term_en": "Infrastructure as Code Template Synthesis", "term_de": "InfrastructureasCodeTemplateSynthesis", "definition_en": "A natural-language-driven programming practice in LLM-assisted development, characterized by a process where ai generating terraform or cloudformation templates for cloud resource provisioning without explicit infrastructure requirements. The concept emerges specifically in contexts where infrastructure–as interactions may produce non-trivial behavioral signatures. Measurable through code generation velocity (tokens/minute), first-pass acceptance rate, refactoring frequency, and prompt-to-deployment cycle time.", "definition_de": "Vibe-Coding-spezifisches Entwicklungsphänomen in LLM-gestützter Programmierung, gekennzeichnet durch strukturelle Eigenschaft von Mensch-KI-Interaktion und phänomenologisches Erleben. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Vibe Coding", "narrower_terms": [], "cross_domain_refs": [ "SWE-0041" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "VIB-0142", "domain": "VIB", "term_en": "Logging and Monitoring Instrumentation", "term_de": "LoggingAndMonitoringInstrumentation", "definition_en": "In the descriptive vocabulary of AI interaction science (following ISO 704 terminology standards), this concept identifies A code generation dynamic in prompt-driven development workflows, measurable through aI inserting log statements and metrics collection code throughout application code automatically. Distinguished from adjacent concepts by its focus on the specific mechanism through which logging manifests in empirically verifiable ways. Quantifiable via test coverage delta, bug density in generated code, and developer cognitive load proxies (context switch frequency, undo/redo rates). This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als analytisches Konstrukt der empirischen KI-Forschung (deskriptiv, nicht präskriptiv) erfasst dieser Terminus vibe-Coding-spezifisches Entwicklungsphänomen in LLM-gestützter Programmierung, gekennzeichnet durch mensch-KI-Interaktionsmuster in der Mensch-KI-Zusammenarbeit. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Vibe Coding", "narrower_terms": [], "cross_domain_refs": [ "MUS-0099" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "VIB-0143", "domain": "VIB", "term_en": "Alert Threshold Configuration Inference", "term_de": "AlertSchwelleConfigurationInference", "definition_en": "A pattern where ai determining cpu, memory, latency, and error rate thresholds for alerting based on baseline performance patterns. Detectable through code generation velocity and refactoring frequency metrics.", "definition_de": "Vibe-Coding-spezifisches Entwicklungsphänomen in LLM-gestützter Programmierung, gekennzeichnet durch interaktionsmuster in der Mensch-KI-Zusammenarbeit. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-1307", "narrower_terms": [], "cross_domain_refs": [ "ROB-0014", "TEW-0092", "SWE-0019" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "VIB-0144", "domain": "VIB", "term_en": "Metric Aggregation Query Optimization", "term_de": "MetricAggregationQueryOptimierung", "definition_en": "A natural-language-driven programming practice in LLM-assisted development, characterized by an effect where ai rewriting time-series database queries to improve aggregation performance without changing statistical outcomes. The concept emerges specifically in contexts where metric–aggregation interactions may produce non-trivial behavioral signatures. Quantifiable via test coverage delta, bug density in generated code, and developer cognitive load proxies (context switch frequency, undo/redo rates).", "definition_de": "Vibe-Coding-spezifisches Entwicklungsphänomen in LLM-gestützter Programmierung, gekennzeichnet durch operative Eigenschaft von Mensch-KI-Interaktion und phänomenologisches Erleben. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Performance Metric", "narrower_terms": [], "cross_domain_refs": [ "TEW-0071", "ASE-0031", "DAT-0004" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "VIB-0145", "domain": "VIB", "term_en": "Runtime Performance Bottleneck Profiling", "term_de": "RuntimePerformanceBottleneckProfiling", "definition_en": "A code generation dynamic in prompt-driven development workflows, measurable through a pattern where ai analyzing cpu flamegraph data and memory allocation patterns to identify optimization opportunities. Distinguished from adjacent concepts by its focus on the specific mechanism through which runtime manifests in empirically verifiable ways. Quantifiable via test coverage delta, bug density in generated code, and developer cognitive load proxies (context switch frequency, undo/redo rates).", "definition_de": "Vibe-Coding-spezifisches Entwicklungsphänomen in LLM-gestützter Programmierung, gekennzeichnet durch interaktionsmuster in der Mensch-KI-Zusammenarbeit. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Vibe Coding", "narrower_terms": [], "cross_domain_refs": [ "DAT-0070", "RHR-0221", "CUS-0027" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q1361053", "legal_classification": "systematic_classification" }, { "id": "VIB-0146", "domain": "VIB", "term_en": "Query Execution Plan Optimization", "term_de": "QueryExecutionPlanOptimierung", "definition_en": "A vibe coding methodology phenomenon describing a specific developer-AI interaction pattern where an effect where ai suggesting database query rewrites to use more effectively indexes, join strategies, or aggregation methods. This phenomenon operates at the intersection of query and execution dynamics within the broader VIB domain. Measurable through code generation velocity (tokens/minute), first-pass acceptance rate, refactoring frequency, and prompt-to-deployment cycle time.", "definition_de": "Vibe-Coding-spezifisches Entwicklungsphänomen in LLM-gestützter Programmierung, gekennzeichnet durch spezifisches Attribut von Mensch-KI-Interaktion und phänomenologisches Erleben. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "Optimization Method", "narrower_terms": [], "cross_domain_refs": [ "TEM-0113", "WEB-0055", "COG-0005" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "VIB-0147", "domain": "VIB", "term_en": "Bulk Data Processing Pipeline Design", "term_de": "BulkDataProcessingPipelineDesign", "definition_en": "A process where ai creating batch processing workflows with mapreduce, spark, or dask configurations for large dataset operations. Detectable through code generation velocity and refactoring frequency metrics.", "definition_de": "Vibe-Coding-spezifisches Entwicklungsphänomen in LLM-gestützter Programmierung, gekennzeichnet durch prozess oder Abfolge im Rahmen von Mensch-KI-Interaktion und phänomenologisches Erleben. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2403", "narrower_terms": [], "cross_domain_refs": [ "TEM-0048" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q185925", "legal_classification": "descriptive_research_term" }, { "id": "VIB-0148", "domain": "VIB", "term_en": "Memory Leak Detection and Refactoring", "term_de": "MemoryLeakDetectionAndRefactoring", "definition_en": "A vibe coding methodology phenomenon describing a specific developer-AI interaction pattern where an interaction where ai identifying unreleased object references and generating code refactorings to eliminate circular reliances. This phenomenon operates at the intersection of memory and leak dynamics within the broader VIB domain. Measurable through code generation velocity (tokens/minute), first-pass acceptance rate, refactoring frequency, and prompt-to-deployment cycle time.", "definition_de": "Vibe-Coding-spezifisches Entwicklungsphänomen in LLM-gestützter Programmierung, gekennzeichnet durch mensch-KI-Interaktionsmuster in der Mensch-KI-Zusammenarbeit. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-3802", "narrower_terms": [], "cross_domain_refs": [ "SWE-0055" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q492", "legal_classification": "observational_construct" }, { "id": "VIB-0149", "domain": "VIB", "term_en": "Concurrent Request Handling Orchestration", "term_de": "ConcurrentRequestHandlingOrchestration", "definition_en": "An interaction where ai designing thread pools, async/await patterns, or actor models for managing simultaneous client connections. Detectable through code generation velocity and refactoring frequency metrics.", "definition_de": "Vibe-Coding-spezifisches Entwicklungsphänomen in LLM-gestützter Programmierung, gekennzeichnet durch distinktives Merkmal von Mensch-KI-Interaktion und phänomenologisches Erleben. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2304", "narrower_terms": [], "cross_domain_refs": [ "EDU-0056", "ROB-0048", "WRK-0089" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "VIB-0150", "domain": "VIB", "term_en": "Accessibility Audit Rule Definition", "term_de": "AccessibilityAuditRuleDefinition", "definition_en": "A natural-language-driven programming practice in LLM-assisted development, characterized by a phenomenon where ai generating code scanning rules to detect wcag violations, color contrast failures, and keyboard navigation gaps. The concept emerges specifically in contexts where accessibility–audit interactions may produce non-trivial behavioral signatures. Measurable through code generation velocity (tokens/minute), first-pass acceptance rate, refactoring frequency, and prompt-to-deployment cycle time.", "definition_de": "Vibe-Coding-spezifisches Entwicklungsphänomen in LLM-gestützter Programmierung, gekennzeichnet durch distinktives Merkmal von Mensch-KI-Interaktion und phänomenologisches Erleben. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Vibe Coding", "narrower_terms": [], "cross_domain_refs": [ "CON-0001", "PHO-0061" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "VIB-0151", "domain": "VIB", "term_en": "Internationalization Message Key Generation", "term_de": "InternationalizationMessageKeyGeneration", "definition_en": "A vibe coding methodology phenomenon describing a specific developer-AI interaction pattern where a process where ai creating i18n translation keys and placeholder structures from hardcoded strings in source code. This phenomenon operates at the intersection of internationalization and message dynamics within the broader VIB domain. Quantifiable via test coverage delta, bug density in generated code, and developer cognitive load proxies (context switch frequency, undo/redo rates).", "definition_de": "Vibe-Coding-spezifisches Entwicklungsphänomen in LLM-gestützter Programmierung, gekennzeichnet durch struktur oder Abhängigkeitsgraph in der Mensch-KI-Zusammenarbeit. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "Analytical Ratio", "narrower_terms": [], "cross_domain_refs": [ "SAL-0025", "TEW-0031", "SWE-0034" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "VIB-0152", "domain": "VIB", "term_en": "Locale-Specific Data Formatting", "term_de": "Locale-specificDataFormatting", "definition_en": "A code generation dynamic in prompt-driven development workflows, measurable through a perception where ai generating locale-aware date, time, number, and currency formatting functions for different regional contexts. Distinguished from adjacent concepts by its focus on the specific mechanism through which locale manifests in empirically verifiable ways. Quantifiable via test coverage delta, bug density in generated code, and developer cognitive load proxies (context switch frequency, undo/redo rates). Classification term used in systematic observation, not advocacy.", "definition_de": "Vibe-Coding-spezifisches Entwicklungsphänomen in LLM-gestützter Programmierung, gekennzeichnet durch erkennungsfähigkeit in der Mensch-KI-Zusammenarbeit. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Vibe Coding", "narrower_terms": [], "cross_domain_refs": [ "SCR-0034", "LNG-0004" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q42848", "legal_classification": "empirical_phenomenon_label" }, { "id": "VIB-0153", "domain": "VIB", "term_en": "Right-to-Left Language Support Generation", "term_de": "Right-to-leftLanguageSupportGeneration", "definition_en": "A code generation dynamic in prompt-driven development workflows, measurable through a phenomenon where ai creating css and layout adjustments automatically to support right-to-left text direction languages. Distinguished from adjacent concepts by its focus on the specific mechanism through which right manifests in empirically verifiable ways. Measurable through code generation velocity (tokens/minute), first-pass acceptance rate, refactoring frequency, and prompt-to-deployment cycle time.", "definition_de": "Vibe-Coding-spezifisches Entwicklungsphänomen in LLM-gestützter Programmierung, gekennzeichnet durch mensch-KI-Interaktionsmuster in der Mensch-KI-Zusammenarbeit. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Analytical Ratio", "narrower_terms": [], "cross_domain_refs": [ "WEB-0054" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q315", "legal_classification": "descriptive_research_term" }, { "id": "VIB-0154", "domain": "VIB", "term_en": "iOS App Architecture Scaffold", "term_de": "IosAppArchitectureScaffold", "definition_en": "A code generation dynamic in prompt-driven development workflows, measurable through aI generating Swift code with Model-View-Controller or MVVM patterns for iOS application structure. Distinguished from adjacent concepts by its focus on the specific mechanism through which ios manifests in empirically verifiable ways. Quantifiable via test coverage delta, bug density in generated code, and developer cognitive load proxies (context switch frequency, undo/redo rates).", "definition_de": "Vibe-Coding-spezifisches Entwicklungsphänomen in LLM-gestützter Programmierung, gekennzeichnet durch kennzeichnende Ausprägung von Mensch-KI-Interaktion und phänomenologisches Erleben. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Vibe Coding", "narrower_terms": [], "cross_domain_refs": [ "MSC-0065" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "VIB-0155", "domain": "VIB", "term_en": "Android Activity Lifecycle Management", "term_de": "AndroidActivityLifecycleManagement", "definition_en": "A natural-language-driven programming practice in LLM-assisted development, characterized by aI generating lifecycle method implementations for Android Activities accounting for configuration changes and state preservation. The concept emerges specifically in contexts where android–activity interactions may produce non-trivial behavioral signatures. Measurable through code generation velocity (tokens/minute), first-pass acceptance rate, refactoring frequency, and prompt-to-deployment cycle time.", "definition_de": "Vibe-Coding-spezifisches Entwicklungsphänomen in LLM-gestützter Programmierung, gekennzeichnet durch prozessschleife oder Sequenz in der Mensch-KI-Zusammenarbeit. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Process Cycle", "narrower_terms": [], "cross_domain_refs": [ "SPR-0123", "DAT-0065" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q181543", "legal_classification": "analytical_category" }, { "id": "VIB-0156", "domain": "VIB", "term_en": "Cross-Platform Code Sharing Strategy", "term_de": "Cross-platformCodeSharingStrategy", "definition_en": "A code generation dynamic in prompt-driven development workflows, measurable through an interaction where ai determining which application logic can be extracted to shared libraries versus platform-specific implementations. Distinguished from adjacent concepts by its focus on the specific mechanism through which cross manifests in empirically verifiable ways. Quantifiable via test coverage delta, bug density in generated code, and developer cognitive load proxies (context switch frequency, undo/redo rates).", "definition_de": "Vibe-Coding-spezifisches Entwicklungsphänomen in LLM-gestützter Programmierung, gekennzeichnet durch beobachtbare Eigenschaft von Mensch-KI-Interaktion und phänomenologisches Erleben. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Operational Strategy", "narrower_terms": [], "cross_domain_refs": [ "SWE-0051" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q185451", "legal_classification": "empirical_phenomenon_label" }, { "id": "VIB-0157", "domain": "VIB", "term_en": "Mobile Network Resilience Patterns", "term_de": "MobileNetworkResiliencePatterns", "definition_en": "A code generation dynamic in prompt-driven development workflows, measurable through a condition where ai generating retry logic, offline caching, and background synchronization code for unreliable network conditions. Distinguished from adjacent concepts by its focus on the specific mechanism through which mobile manifests in empirically verifiable ways. Measurable through code generation velocity (tokens/minute), first-pass acceptance rate, refactoring frequency, and prompt-to-deployment cycle time.", "definition_de": "Vibe-Coding-spezifisches Entwicklungsphänomen in LLM-gestützter Programmierung, gekennzeichnet durch mensch-KI-Interaktionsmuster in der Mensch-KI-Zusammenarbeit. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-3202", "narrower_terms": [], "cross_domain_refs": [ "WEB-0079" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q720920", "legal_classification": "analytical_category" }, { "id": "VIB-0158", "domain": "VIB", "term_en": "Code Comment Generation Quality Variance", "term_de": "CodeCommentGenerationQualityVariance", "definition_en": "A code generation dynamic in prompt-driven development workflows, measurable through a condition of variability in ai-generated inline comments ranging from obvious restatements to genuinely helpful explanations of intent. Distinguished from adjacent concepts by its focus on the specific mechanism through which code manifests in empirically verifiable ways. Measurable through code generation velocity (tokens/minute), first-pass acceptance rate, refactoring frequency, and prompt-to-deployment cycle time.", "definition_de": "Vibe-Coding-spezifisches Entwicklungsphänomen in LLM-gestützter Programmierung, gekennzeichnet durch fähigkeit oder Kompetenz im Umgang mit Mensch-KI-Interaktion und phänomenologisches Erleben. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Analytical Ratio", "narrower_terms": [], "cross_domain_refs": [ "TEW-0044" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "VIB-0159", "domain": "VIB", "term_en": "Variable Naming Convention Enforcement", "term_de": "VariableNamingConventionEnforcement", "definition_en": "A code generation dynamic in prompt-driven development workflows, measurable through a phenomenon where ai generating variable names following camelcase, snake_case, or other conventions automatically in code. Distinguished from adjacent concepts by its focus on the specific mechanism through which variable manifests in empirically verifiable ways. Measurable through code generation velocity (tokens/minute), first-pass acceptance rate, refactoring frequency, and prompt-to-deployment cycle time.", "definition_de": "Vibe-Coding-spezifisches Entwicklungsphänomen in LLM-gestützter Programmierung, gekennzeichnet durch funktionale Eigenschaft von Mensch-KI-Interaktion und phänomenologisches Erleben. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Vibe Coding", "narrower_terms": [], "cross_domain_refs": [ "LIN-0029" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "VIB-0160", "domain": "VIB", "term_en": "Function Naming Semantic Alignment", "term_de": "FunctionNamingSemanticAlignment", "definition_en": "A natural-language-driven programming practice in LLM-assisted development, characterized by aI choosing function names that accurately reflect behavior as implementation details change over time. The concept emerges specifically in contexts where function–naming interactions may produce non-trivial behavioral signatures. Quantifiable via test coverage delta, bug density in generated code, and developer cognitive load proxies (context switch frequency, undo/redo rates).", "definition_de": "Vibe-Coding-spezifisches Entwicklungsphänomen in LLM-gestützter Programmierung, gekennzeichnet durch funktionale Eigenschaft von Mensch-KI-Interaktion und phänomenologisches Erleben. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Vibe Coding", "narrower_terms": [], "cross_domain_refs": [ "MTH-0070", "GAM-0079" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "VIB-0161", "domain": "VIB", "term_en": "Method Documentation String Synthesis", "term_de": "MethodDocumentationStringSynthesis", "definition_en": "AI generating docstring/JSDoc blocks with parameter descriptions and return type annotations automatically. Detectable through code generation velocity and refactoring frequency metrics.", "definition_de": "Vibe-Coding-spezifisches Entwicklungsphänomen in LLM-gestützter Programmierung, gekennzeichnet durch mensch-KI-Interaktionsmuster in der Mensch-KI-Zusammenarbeit. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-3601", "narrower_terms": [], "cross_domain_refs": [ "TEW-0060" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "VIB-0162", "domain": "VIB", "term_en": "Code Smell Pattern Detection Engine", "term_de": "CodeSmellMusterDetectionEngine", "definition_en": "A code generation dynamic in prompt-driven development workflows, measurable through a phenomenon where ai identifying long methods, duplicate code blocks, and deep inheritance hierarchies in existing codebases. Distinguished from adjacent concepts by its focus on the specific mechanism through which code manifests in empirically verifiable ways. Quantifiable via test coverage delta, bug density in generated code, and developer cognitive load proxies (context switch frequency, undo/redo rates).", "definition_de": "Vibe-Coding-spezifisches Entwicklungsphänomen in LLM-gestützter Programmierung, gekennzeichnet durch mensch-KI-Interaktionsmuster in der Mensch-KI-Zusammenarbeit. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Behavioral Pattern", "narrower_terms": [], "cross_domain_refs": [ "SCR-0059" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "VIB-0163", "domain": "VIB", "term_en": "Code Duplication Consolidation", "term_de": "CodeDuplicationConsolidation", "definition_en": "A vibe coding methodology phenomenon describing a specific developer-AI interaction pattern where an interaction where ai identifying similar code blocks and extracting them into reusable functions or base classes. This phenomenon operates at the intersection of code and duplication dynamics within the broader VIB domain. Measurable through code generation velocity (tokens/minute), first-pass acceptance rate, refactoring frequency, and prompt-to-deployment cycle time.", "definition_de": "Vibe-Coding-spezifisches Entwicklungsphänomen in LLM-gestützter Programmierung, gekennzeichnet durch mensch-KI-Interaktionsmuster in der Mensch-KI-Zusammenarbeit. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "Vibe Coding", "narrower_terms": [], "cross_domain_refs": [ "COG-0047", "LIN-0051", "LIN-0073" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "VIB-0164", "domain": "VIB", "term_en": "Monolithic Function Decomposition", "term_de": "MonolithicFunctionDecomposition", "definition_en": "A phenomenon where ai breaking apart long functions with multiple responsibilities into smaller single-purpose functions. Detectable through code generation velocity and refactoring frequency metrics.", "definition_de": "Vibe-Coding-spezifisches Entwicklungsphänomen in LLM-gestützter Programmierung, gekennzeichnet durch beobachtbare Eigenschaft von Mensch-KI-Interaktion und phänomenologisches Erleben. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-3001", "narrower_terms": [], "cross_domain_refs": [ "SPR-0096" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "VIB-0165", "domain": "VIB", "term_en": "Circular Reliance Breaking Refactoring", "term_de": "CircularRelianceBreakingRefactoring", "definition_en": "A code generation dynamic in prompt-driven development workflows, measurable through an interaction where ai detecting circular imports or module reliances and suggesting interface extraction strategies. Distinguished from adjacent concepts by its focus on the specific mechanism through which circular manifests in empirically verifiable ways. Measurable through code generation velocity (tokens/minute), first-pass acceptance rate, refactoring frequency, and prompt-to-deployment cycle time.", "definition_de": "Vibe-Coding-spezifisches Entwicklungsphänomen in LLM-gestützter Programmierung, gekennzeichnet durch schnittstellen-Designprinzip in der Mensch-KI-Zusammenarbeit. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Vibe Coding", "narrower_terms": [], "cross_domain_refs": [ "SWE-0028" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "VIB-0166", "domain": "VIB", "term_en": "Legacy API Adapter Generation", "term_de": "LegacyApiAdapterGeneration", "definition_en": "A vibe coding methodology phenomenon describing a specific developer-AI interaction pattern where an interaction where ai creating adapter interfaces to translate between legacy function signatures and modern api contracts. This phenomenon operates at the intersection of legacy and api dynamics within the broader VIB domain. Quantifiable via test coverage delta, bug density in generated code, and developer cognitive load proxies (context switch frequency, undo/redo rates).", "definition_de": "Vibe-Coding-spezifisches Entwicklungsphänomen in LLM-gestützter Programmierung, gekennzeichnet durch schnittstellen-Designprinzip in der Mensch-KI-Zusammenarbeit. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "Analytical Ratio", "narrower_terms": [], "cross_domain_refs": [ "TEW-0017" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q165194", "legal_classification": "descriptive_research_term" }, { "id": "VIB-0167", "domain": "VIB", "term_en": "Deprecation Path Automation", "term_de": "DeprecationPathAutomation", "definition_en": "A vibe coding methodology phenomenon describing a specific developer-AI interaction pattern where a shift where ai generating migration utilities and compatibility layers to ease transitions from deprecated to new apis. This phenomenon operates at the intersection of deprecation and trajectory dynamics within the broader VIB domain. Quantifiable via test coverage delta, bug density in generated code, and developer cognitive load proxies (context switch frequency, undo/redo rates).", "definition_de": "Vibe-Coding-spezifisches Entwicklungsphänomen in LLM-gestützter Programmierung, gekennzeichnet durch mensch-KI-Interaktionsmuster in der Mensch-KI-Zusammenarbeit. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "Vibe Coding", "narrower_terms": [], "cross_domain_refs": [ "LIN-0002" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q184199", "legal_classification": "systematic_classification" }, { "id": "VIB-0168", "domain": "VIB", "term_en": "Test Coverage Gap Analysis", "term_de": "TestCoverageGapAnalysis", "definition_en": "A natural-language-driven programming practice in LLM-assisted development, characterized by a phenomenon where ai identifying code paths not covered by existing test suites and suggesting test cases for missing scenarios. The concept emerges specifically in contexts where test–coverage interactions may produce non-trivial behavioral signatures. Quantifiable via test coverage delta, bug density in generated code, and developer cognitive load proxies (context switch frequency, undo/redo rates).", "definition_de": "Vibe-Coding-spezifisches Entwicklungsphänomen in LLM-gestützter Programmierung, gekennzeichnet durch mensch-KI-Interaktionsmuster in der Mensch-KI-Zusammenarbeit. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Performance Gap", "narrower_terms": [], "cross_domain_refs": [ "STE-0091" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "VIB-0169", "domain": "VIB", "term_en": "Vintage Framework Modernization Mapping", "term_de": "VintageFrameworkModernizationMapping", "definition_en": "A vibe coding methodology phenomenon describing a specific developer-AI interaction pattern where a process where ai suggesting equivalent patterns in modern frameworks to replace outdated reliance injection or routing implementations. This phenomenon operates at the intersection of vintage and framework dynamics within the broader VIB domain. Quantifiable via test coverage delta, bug density in generated code, and developer cognitive load proxies (context switch frequency, undo/redo rates).", "definition_de": "Vibe-Coding-spezifisches Entwicklungsphänomen in LLM-gestützter Programmierung, gekennzeichnet durch beobachtbare Eigenschaft von Mensch-KI-Interaktion und phänomenologisches Erleben. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-2303", "narrower_terms": [], "cross_domain_refs": [ "TEW-0019" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "VIB-0170", "domain": "VIB", "term_en": "Type Annotation Retrofit Generation", "term_de": "TypeAnnotationRetrofitGeneration", "definition_en": "A vibe coding methodology phenomenon describing a specific developer-AI interaction pattern where aI adding TypeScript type definitions or Python type hints to untyped legacy code automatically. This phenomenon operates at the intersection of type and annotation dynamics within the broader VIB domain. Measurable through code generation velocity (tokens/minute), first-pass acceptance rate, refactoring frequency, and prompt-to-deployment cycle time.", "definition_de": "Vibe-Coding-spezifisches Entwicklungsphänomen in LLM-gestützter Programmierung, gekennzeichnet durch charakteristische Komponente von Mensch-KI-Interaktion und phänomenologisches Erleben. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "Analytical Ratio", "narrower_terms": [], "cross_domain_refs": [ "TEM-0001" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "VIB-0171", "domain": "VIB", "term_en": "Tech Stack Maturity Assessment", "term_de": "TechStackMaturityAssessment", "definition_en": "A code generation dynamic in prompt-driven development workflows, measurable through a phenomenon where ai evaluating library maintenance status, community size, and deprecation concerns when recommending technology choices. Distinguished from adjacent concepts by its focus on the specific mechanism through which tech manifests in empirically verifiable ways. Quantifiable via test coverage delta, bug density in generated code, and developer cognitive load proxies (context switch frequency, undo/redo rates).", "definition_de": "Vibe-Coding-spezifisches Entwicklungsphänomen in LLM-gestützter Programmierung, gekennzeichnet durch mensch-KI-Interaktionsmuster in der Mensch-KI-Zusammenarbeit. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Vibe Coding", "narrower_terms": [], "cross_domain_refs": [ "TEW-0026" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q210028", "legal_classification": "empirical_phenomenon_label" }, { "id": "VIB-0172", "domain": "VIB", "term_en": "Reliance Version Constraint Optimization", "term_de": "RelianceVersionConstraintOptimierung", "definition_en": "A vibe coding methodology phenomenon describing a specific developer-AI interaction pattern where a capacity where ai suggesting version ranges for library reliances balancing stability with access to security fixes. This phenomenon operates at the intersection of reliance and version dynamics within the broader VIB domain. Measurable through code generation velocity (tokens/minute), first-pass acceptance rate, refactoring frequency, and prompt-to-deployment cycle time.", "definition_de": "Vibe-Coding-spezifisches Entwicklungsphänomen in LLM-gestützter Programmierung, gekennzeichnet durch fähigkeit oder Kompetenz im Umgang mit Mensch-KI-Interaktion und phänomenologisches Erleben. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "Optimization Method", "narrower_terms": [], "cross_domain_refs": [ "SWE-0028" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "VIB-0173", "domain": "VIB", "term_en": "Build Tool Configuration Synthesis", "term_de": "BuildToolConfigurationSynthesis", "definition_en": "A natural-language-driven programming practice in LLM-assisted development, characterized by a phenomenon where ai generating webpack, gradle, maven, or cargo configuration files with appropriate optimization flags. The concept emerges specifically in contexts where build–tool interactions may produce non-trivial behavioral signatures. Quantifiable via test coverage delta, bug density in generated code, and developer cognitive load proxies (context switch frequency, undo/redo rates).", "definition_de": "Vibe-Coding-spezifisches Entwicklungsphänomen in LLM-gestützter Programmierung, gekennzeichnet durch systemische Ausprägung von Mensch-KI-Interaktion und phänomenologisches Erleben. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Analytical Ratio", "narrower_terms": [], "cross_domain_refs": [ "REL-0120" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "VIB-0174", "domain": "VIB", "term_en": "Monorepo Structure Organization", "term_de": "MonorepoStructureOrganization", "definition_en": "A natural-language-driven programming practice in LLM-assisted development, characterized by a phenomenon where ai organizing multi-package projects with appropriate workspace boundaries and shared reliance resolution. The concept emerges specifically in contexts where monorepo–structure interactions may produce non-trivial behavioral signatures. Measurable through code generation velocity (tokens/minute), first-pass acceptance rate, refactoring frequency, and prompt-to-deployment cycle time.", "definition_de": "Vibe-Coding-spezifisches Entwicklungsphänomen in LLM-gestützter Programmierung, gekennzeichnet durch mensch-KI-Interaktionsmuster in der Mensch-KI-Zusammenarbeit. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Vibe Coding", "narrower_terms": [], "cross_domain_refs": [ "ROB-0047" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "VIB-0175", "domain": "VIB", "term_en": "New Team Member Context Injection", "term_de": "NewTeamMemberContextInjection", "definition_en": "A code generation dynamic in prompt-driven development workflows, measurable through aI-generated architecture overview, code pattern guide, and common workflow documentation for developer onboarding. Distinguished from adjacent concepts by its focus on the specific mechanism through which new manifests in empirically verifiable ways. Quantifiable via test coverage delta, bug density in generated code, and developer cognitive load proxies (context switch frequency, undo/redo rates).", "definition_de": "Vibe-Coding-spezifisches Entwicklungsphänomen in LLM-gestützter Programmierung, gekennzeichnet durch interaktionsmuster in der Mensch-KI-Zusammenarbeit. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-3902", "narrower_terms": [], "cross_domain_refs": [ "TEW-0056", "TEW-0021" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "VIB-0176", "domain": "VIB", "term_en": "Codebase Pattern Recognition Quiz", "term_de": "CodebaseMusterRecognitionQuiz", "definition_en": "A code generation dynamic in prompt-driven development workflows, measurable through an interaction where ai generating assessment questions based on actual codebase patterns to evaluate developer understanding. Distinguished from adjacent concepts by its focus on the specific mechanism through which codebase manifests in empirically verifiable ways. Quantifiable via test coverage delta, bug density in generated code, and developer cognitive load proxies (context switch frequency, undo/redo rates).", "definition_de": "Vibe-Coding-spezifisches Entwicklungsphänomen in LLM-gestützter Programmierung, gekennzeichnet durch qualitätsmerkmal oder Bewertungskriterium für Mensch-KI-Interaktion und phänomenologisches Erleben. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Behavioral Pattern", "narrower_terms": [], "cross_domain_refs": [ "SWE-0013" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q3966", "legal_classification": "systematic_classification" }, { "id": "VIB-0177", "domain": "VIB", "term_en": "Onboarding Script Generation", "term_de": "OnboardingScriptGeneration", "definition_en": "A natural-language-driven programming practice in LLM-assisted development, characterized by a phenomenon where ai creating automated setup scripts and environment configuration instructions for developers joining the project. The concept emerges specifically in contexts where onboarding–script interactions may produce non-trivial behavioral signatures. Quantifiable via test coverage delta, bug density in generated code, and developer cognitive load proxies (context switch frequency, undo/redo rates).", "definition_de": "Vibe-Coding-spezifisches Entwicklungsphänomen in LLM-gestützter Programmierung, gekennzeichnet durch mensch-KI-Interaktionsmuster in der Mensch-KI-Zusammenarbeit. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Analytical Ratio", "narrower_terms": [], "cross_domain_refs": [ "WEB-0008" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "VIB-0178", "domain": "VIB", "term_en": "Pair Debugging Session Facilitation", "term_de": "PairDebuggingSessionFacilitation", "definition_en": "A pattern where ai participating in collaborative debugging by suggesting inspection points and offering pattern-matching insights. Detectable through code generation velocity and refactoring frequency metrics.", "definition_de": "Vibe-Coding-spezifisches Entwicklungsphänomen in LLM-gestützter Programmierung, gekennzeichnet durch interaktionsmuster in der Mensch-KI-Zusammenarbeit. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-3101", "narrower_terms": [], "cross_domain_refs": [ "DAT-0062" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "VIB-0179", "domain": "VIB", "term_en": "Stack Trace Analysis and Root Source Mapping", "term_de": "stack trace analysis and root source mapping", "definition_en": "A vibe coding methodology phenomenon describing a specific developer-AI interaction pattern where a phenomenon where ai analyzing error stack traces to identify root correlates with and suggest code locations requiring investigation. This phenomenon operates at the intersection of stack and trace dynamics within the broader VIB domain. Measurable through code generation velocity (tokens/minute), first-pass acceptance rate, refactoring frequency, and prompt-to-deployment cycle time.", "definition_de": "Vibe-Coding-spezifisches Entwicklungsphänomen in LLM-gestützter Programmierung, gekennzeichnet durch charakteristische Komponente von Mensch-KI-Interaktion und phänomenologisches Erleben. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-2003", "narrower_terms": [], "cross_domain_refs": [ "SWE-0068" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "VIB-0180", "domain": "VIB", "term_en": "Breakpoint Placement Suggestion", "term_de": "BreakpointPlacementSuggestion", "definition_en": "A phenomenon where ai recommending debugging breakpoints at suspicious variable assignments or control flow decision points. Detectable through code generation velocity and refactoring frequency metrics.", "definition_de": "Vibe-Coding-spezifisches Entwicklungsphänomen in LLM-gestützter Programmierung, gekennzeichnet durch merkmal von Mensch-KI-Interaktion und phänomenologisches Erleben. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-3001", "narrower_terms": [], "cross_domain_refs": [ "FIC-0033", "LIN-0082", "MTH-0003" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "VIB-0181", "domain": "VIB", "term_en": "Architecture Decision Record Generation", "term_de": "ArchitectureDecisionRecordGeneration", "definition_en": "A natural-language-driven programming practice in LLM-assisted development, characterized by a pattern where ai creating adr documents explaining rationale for chosen architectural patterns and rejected alternatives. The concept emerges specifically in contexts where architecture–decision interactions may produce non-trivial behavioral signatures. Quantifiable via test coverage delta, bug density in generated code, and developer cognitive load proxies (context switch frequency, undo/redo rates).", "definition_de": "Vibe-Coding-spezifisches Entwicklungsphänomen in LLM-gestützter Programmierung, gekennzeichnet durch interaktionsmuster in der Mensch-KI-Zusammenarbeit. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Analytical Ratio", "narrower_terms": [], "cross_domain_refs": [ "STE-0002" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "VIB-0182", "domain": "VIB", "term_en": "System Diagram Synthesis from Code", "term_de": "SystemDiagramSynthesisFromCode", "definition_en": "A natural-language-driven programming practice in LLM-assisted development, characterized by a relationship where ai generating architecture diagrams representing service reliances, data flows, and module relationships. The concept emerges specifically in contexts where system–diagram interactions may produce non-trivial behavioral signatures. Measurable through code generation velocity (tokens/minute), first-pass acceptance rate, refactoring frequency, and prompt-to-deployment cycle time.", "definition_de": "Vibe-Coding-spezifisches Entwicklungsphänomen in LLM-gestützter Programmierung, gekennzeichnet durch mensch-KI-Interaktionsmuster in der Mensch-KI-Zusammenarbeit. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Vibe Coding", "narrower_terms": [], "cross_domain_refs": [ "ASE-0058" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "VIB-0183", "domain": "VIB", "term_en": "API Contract Documentation Auto-Generation", "term_de": "ApiContractDocumentationAuto-generation", "definition_en": "A vibe coding methodology phenomenon describing a specific developer-AI interaction pattern where a phenomenon where ai creating openapi/swagger specifications or graphql sdl from source code implementations automatically. This phenomenon operates at the intersection of api and contract dynamics within the broader VIB domain. Quantifiable via test coverage delta, bug density in generated code, and developer cognitive load proxies (context switch frequency, undo/redo rates).", "definition_de": "Vibe-Coding-spezifisches Entwicklungsphänomen in LLM-gestützter Programmierung, gekennzeichnet durch merkmal von Mensch-KI-Interaktion und phänomenologisches Erleben. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "Analytical Ratio", "narrower_terms": [], "cross_domain_refs": [ "SWE-0001", "WEB-0002" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q165194", "legal_classification": "analytical_category" }, { "id": "VIB-0184", "domain": "VIB", "term_en": "Sprint Planning with Velocity Prediction", "term_de": "SprintPlanningWithVelocityPrediction", "definition_en": "A pattern where ai forecasting team velocity and suggesting story point assignments based on historical completion patterns. Detectable through code generation velocity and refactoring frequency metrics.", "definition_de": "Vibe-Coding-spezifisches Entwicklungsphänomen in LLM-gestützter Programmierung, gekennzeichnet durch interaktionsmuster in der Mensch-KI-Zusammenarbeit. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-3101", "narrower_terms": [], "cross_domain_refs": [ "ELR-0097" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "VIB-0185", "domain": "VIB", "term_en": "Backlog Refinement Suggestion Engine", "term_de": "BacklogRefinementSuggestionEngine", "definition_en": "A vibe coding methodology phenomenon describing a specific developer-AI interaction pattern where a phenomenon where ai identifying underspecified user stories and recommending acceptance criteria based on related features. This phenomenon operates at the intersection of backlog and refinement dynamics within the broader VIB domain. Quantifiable via test coverage delta, bug density in generated code, and developer cognitive load proxies (context switch frequency, undo/redo rates).", "definition_de": "Vibe-Coding-spezifisches Entwicklungsphänomen in LLM-gestützter Programmierung, gekennzeichnet durch schnittstellen-Designprinzip in der Mensch-KI-Zusammenarbeit. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "Vibe Coding", "narrower_terms": [], "cross_domain_refs": [ "ASE-0029" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "VIB-0186", "domain": "VIB", "term_en": "Concern Assessment in Story Planning", "term_de": "ConcernAssessmentinStoryPlanning", "definition_en": "A natural-language-driven programming practice in LLM-assisted development, characterized by a phenomenon where ai evaluating technical complexity and reliance concerns when estimating effort for planned work items. The concept emerges specifically in contexts where concern–assessment interactions may produce non-trivial behavioral signatures. Measurable through code generation velocity (tokens/minute), first-pass acceptance rate, refactoring frequency, and prompt-to-deployment cycle time.", "definition_de": "Vibe-Coding-spezifisches Entwicklungsphänomen in LLM-gestützter Programmierung, gekennzeichnet durch mensch-KI-Interaktionsmuster in der Mensch-KI-Zusammenarbeit. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Vibe Coding", "narrower_terms": [], "cross_domain_refs": [ "IEF-0002" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q210028", "legal_classification": "observational_construct" }, { "id": "VIB-0187", "domain": "VIB", "term_en": "Code Generation Temperature Sensitivity", "term_de": "CodeGenerationTemperatureSensitivity", "definition_en": "A vibe coding methodology phenomenon describing a specific developer-AI interaction pattern where a phenomenon of variation in code generation creativity and correctness based on language model sampling temperature settings. This phenomenon operates at the intersection of code and generation dynamics within the broader VIB domain. Quantifiable via test coverage delta, bug density in generated code, and developer cognitive load proxies (context switch frequency, undo/redo rates).", "definition_de": "Vibe-Coding-spezifisches Entwicklungsphänomen in LLM-gestützter Programmierung, gekennzeichnet durch mensch-KI-Interaktionsmuster in der Mensch-KI-Zusammenarbeit. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "Analytical Ratio", "narrower_terms": [], "cross_domain_refs": [ "SPA-0077", "SPR-0029", "ROB-0282" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "VIB-0188", "domain": "VIB", "term_en": "Top-K Sampling Diversity Trade-off", "term_de": "Top-kSamplingDiversityTrade-off", "definition_en": "A vibe coding methodology phenomenon describing a specific developer-AI interaction pattern where a phenomenon of balancing between generating diverse code variations and maintaining semantic correctness through top-k sampling. This phenomenon operates at the intersection of top and k dynamics within the broader VIB domain. Measurable through code generation velocity (tokens/minute), first-pass acceptance rate, refactoring frequency, and prompt-to-deployment cycle time.", "definition_de": "Vibe-Coding-spezifisches Entwicklungsphänomen in LLM-gestützter Programmierung, gekennzeichnet durch mensch-KI-Interaktionsmuster in der Mensch-KI-Zusammenarbeit. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "Vibe Coding", "narrower_terms": [], "cross_domain_refs": [ "AGE-0070", "ASE-0041", "MTH-0040" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "VIB-0189", "domain": "VIB", "term_en": "Nucleus Sampling Output Coherence", "term_de": "NucleusSamplingOutputCoherence", "definition_en": "A code generation dynamic in prompt-driven development workflows, measurable through a phenomenon of managing code generation focus using nucleus sampling to yield coherent implementations without excessive tokenization. Distinguished from adjacent concepts by its focus on the specific mechanism through which nucleus manifests in empirically verifiable ways. Measurable through code generation velocity (tokens/minute), first-pass acceptance rate, refactoring frequency, and prompt-to-deployment cycle time.", "definition_de": "Vibe-Coding-spezifisches Entwicklungsphänomen in LLM-gestützter Programmierung, gekennzeichnet durch beobachtbare Eigenschaft von Mensch-KI-Interaktion und phänomenologisches Erleben. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Vibe Coding", "narrower_terms": [], "cross_domain_refs": [ "COG-0068", "CRE-0055", "ART-0015" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "VIB-0190", "domain": "VIB", "term_en": "Token Limit Induced Code Truncation", "term_de": "TokenLimitInducedCodeTruncation", "definition_en": "A natural-language-driven programming practice in LLM-assisted development, characterized by a phenomenon where incomplete code generation when context window limitations may is associated with premature termination of ai code synthesis. The concept emerges specifically in contexts where token–limit interactions may produce non-trivial behavioral signatures. Quantifiable via test coverage delta, bug density in generated code, and developer cognitive load proxies (context switch frequency, undo/redo rates).", "definition_de": "Vibe-Coding-spezifisches Entwicklungsphänomen in LLM-gestützter Programmierung, gekennzeichnet durch merkmal von Mensch-KI-Interaktion und phänomenologisches Erleben. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Vibe Coding", "narrower_terms": [], "cross_domain_refs": [ "COG-0065" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "VIB-0191", "domain": "VIB", "term_en": "Token Budget Allocation for Prompting", "term_de": "TokenBudgetAllocationForPrompting", "definition_en": "Strategic division of context window between system prompts, examples, and available generation space. Detectable through code generation velocity and refactoring frequency metrics.", "definition_de": "Vibe-Coding-spezifisches Entwicklungsphänomen in LLM-gestützter Programmierung, gekennzeichnet durch ressourcen-Allokationsstrategie in der Mensch-KI-Zusammenarbeit. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-2104", "narrower_terms": [], "cross_domain_refs": [ "PER-0011" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "VIB-0192", "domain": "VIB", "term_en": "Prompt Caching for Token Efficiency", "term_de": "PromptCachingForTokenEfficiency", "definition_en": "A vibe coding methodology phenomenon describing a specific developer-AI interaction pattern where a condition of reusing previously computed embeddings and contexts to reduce token consumption in repeated code generation tasks. This phenomenon operates at the intersection of prompt and caching dynamics within the broader VIB domain. Measurable through code generation velocity (tokens/minute), first-pass acceptance rate, refactoring frequency, and prompt-to-deployment cycle time.", "definition_de": "Vibe-Coding-spezifisches Entwicklungsphänomen in LLM-gestützter Programmierung, gekennzeichnet durch ressourcen-Allokationsstrategie in der Mensch-KI-Zusammenarbeit. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "Vibe Coding", "narrower_terms": [], "cross_domain_refs": [ "PER-0032" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "VIB-0193", "domain": "VIB", "term_en": "Multi-Model Code Arbitration Strategy", "term_de": "Multi-modelCodeArbitrationStrategy", "definition_en": "A code generation dynamic in prompt-driven development workflows, measurable through using multiple language models for code generation and selecting outputs based on quality heuristics. Distinguished from adjacent concepts by its focus on the specific mechanism through which multi manifests in empirically verifiable ways. Quantifiable via test coverage delta, bug density in generated code, and developer cognitive load proxies (context switch frequency, undo/redo rates).", "definition_de": "Vibe-Coding-spezifisches Entwicklungsphänomen in LLM-gestützter Programmierung, gekennzeichnet durch mensch-KI-Interaktionsmuster mit mehrfach oder parallel oder separater Natur. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Analytical Ratio", "narrower_terms": [], "cross_domain_refs": [ "BEH-0004" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q185451", "legal_classification": "empirical_phenomenon_label" }, { "id": "VIB-0194", "domain": "VIB", "term_en": "Model Disagreement Pattern Analysis", "term_de": "ModelDisagreementMusterAnalysis", "definition_en": "A code generation dynamic in prompt-driven development workflows, measurable through a pattern of examining cases where different ai models yield conflicting code patterns to identify ambiguous specifications. Distinguished from adjacent concepts by its focus on the specific mechanism through which model manifests in empirically verifiable ways. Quantifiable via test coverage delta, bug density in generated code, and developer cognitive load proxies (context switch frequency, undo/redo rates).", "definition_de": "Vibe-Coding-spezifisches Entwicklungsphänomen in LLM-gestützter Programmierung, gekennzeichnet durch funktionale Eigenschaft von Mensch-KI-Interaktion und phänomenologisches Erleben. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Behavioral Pattern", "narrower_terms": [], "cross_domain_refs": [ "SOC-0025", "ART-0096", "SOC-0017" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "VIB-0195", "domain": "VIB", "term_en": "Ensemble Code Quality Voting", "term_de": "EnsembleCodeQualityVoting", "definition_en": "A code generation dynamic in prompt-driven development workflows, measurable through evaluating code outputs from multiple models and selecting based on consensus metrics or quality scoring. Distinguished from adjacent concepts by its focus on the specific mechanism through which ensemble manifests in empirically verifiable ways. Measurable through code generation velocity (tokens/minute), first-pass acceptance rate, refactoring frequency, and prompt-to-deployment cycle time.", "definition_de": "Vibe-Coding-spezifisches Entwicklungsphänomen in LLM-gestützter Programmierung, gekennzeichnet durch mensch-KI-Interaktionsmuster mit mehrfach oder parallel oder separater Natur. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Vibe Coding", "narrower_terms": [], "cross_domain_refs": [ "RHR-0095", "SAL-0061", "SAL-0013" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "VIB-0196", "domain": "VIB", "term_en": "Human-AI Code Integration Handoff", "term_de": "Human-aiCodeIntegrationHandoff", "definition_en": "A natural-language-driven programming practice in LLM-assisted development, characterized by a phenomenon of defining boundaries between ai-generated code and human-authored code while maintaining consistency. The concept emerges specifically in contexts where human–ai interactions may produce non-trivial behavioral signatures. Quantifiable via test coverage delta, bug density in generated code, and developer cognitive load proxies (context switch frequency, undo/redo rates).", "definition_de": "Vibe-Coding-spezifisches Entwicklungsphänomen in LLM-gestützter Programmierung, gekennzeichnet durch mensch-KI-Interaktionsmuster in der Mensch-KI-Zusammenarbeit. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Analytical Ratio", "narrower_terms": [], "cross_domain_refs": [ "SPA-0085", "STE-0061", "TEM-0041" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "VIB-0197", "domain": "VIB", "term_en": "Code Review Delegation to AI", "term_de": "CodeReviewDelegationtoai", "definition_en": "A vibe coding methodology phenomenon describing a specific developer-AI interaction pattern where a shift where ai analyzing proposed changes for correctness, performance, and adherence to code style guidelines. This phenomenon operates at the intersection of code and review dynamics within the broader VIB domain. Quantifiable via test coverage delta, bug density in generated code, and developer cognitive load proxies (context switch frequency, undo/redo rates).", "definition_de": "Vibe-Coding-spezifisches Entwicklungsphänomen in LLM-gestützter Programmierung, gekennzeichnet durch mensch-KI-Interaktionsmuster in der Mensch-KI-Zusammenarbeit. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-3902", "narrower_terms": [], "cross_domain_refs": [ "BEH-0021", "BEH-0028", "BEH-0029" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "VIB-0198", "domain": "VIB", "term_en": "AI Feedback Loop Integration", "term_de": "AiRückkopplungSchleifeIntegration", "definition_en": "A code generation dynamic in prompt-driven development workflows, measurable through a pattern where incorporating ai suggestions into code revision cycles with human judgment as final validation step. Distinguished from adjacent concepts by its focus on the specific mechanism through which ai manifests in empirically verifiable ways. Quantifiable via test coverage delta, bug density in generated code, and developer cognitive load proxies (context switch frequency, undo/redo rates).", "definition_de": "Vibe-Coding-spezifisches Entwicklungsphänomen in LLM-gestützter Programmierung, gekennzeichnet durch prozess oder Abfolge im Rahmen von Mensch-KI-Interaktion und phänomenologisches Erleben. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Feedback Loop", "narrower_terms": [], "cross_domain_refs": [ "SPR-0047" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "VIB-0199", "domain": "VIB", "term_en": "Blame Attribution Complexity", "term_de": "BlameAttributionComplexity", "definition_en": "A vibe coding methodology phenomenon describing a specific developer-AI interaction pattern where a phenomenon where determining responsibility when ai-generated code introduces bugs or performance issues in production. This phenomenon operates at the intersection of blame and attribution dynamics within the broader VIB domain. Quantifiable via test coverage delta, bug density in generated code, and developer cognitive load proxies (context switch frequency, undo/redo rates).", "definition_de": "Vibe-Coding-spezifisches Entwicklungsphänomen in LLM-gestützter Programmierung, gekennzeichnet durch beobachtbare Eigenschaft von Mensch-KI-Interaktion und phänomenologisches Erleben. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "Vibe Coding", "narrower_terms": [], "cross_domain_refs": [ "SWE-0014" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "VIB-0200", "domain": "VIB", "term_en": "Prompt Injection Vulnerability Detection", "term_de": "PromptInjectionVulnerabilityDetection", "definition_en": "A pattern where ai identifying code patterns that could be leverageed through prompt injection attacks in user inputs. Detectable through code generation velocity and refactoring frequency metrics.", "definition_de": "Vibe-Coding-spezifisches Entwicklungsphänomen in LLM-gestützter Programmierung, gekennzeichnet durch kennzeichnende Ausprägung von Mensch-KI-Interaktion und phänomenologisches Erleben. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2104", "narrower_terms": [], "cross_domain_refs": [ "SWE-0080", "ART-0044" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "VIB-0201", "domain": "VIB", "term_en": "Security Compliance Checking Automation", "term_de": "SecurityComplianceCheckingAutomation", "definition_en": "A code generation dynamic in prompt-driven development workflows, measurable through a phenomenon where ai scanning code for compliance with security standards like owasp, national standards body, or industry-specific regulations. Distinguished from adjacent concepts by its focus on the specific mechanism through which security manifests in empirically verifiable ways. Quantifiable via test coverage delta, bug density in generated code, and developer cognitive load proxies (context switch frequency, undo/redo rates).", "definition_de": "Vibe-Coding-spezifisches Entwicklungsphänomen in LLM-gestützter Programmierung, gekennzeichnet durch beobachtbare Eigenschaft von Mensch-KI-Interaktion und phänomenologisches Erleben. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Vibe Coding", "narrower_terms": [], "cross_domain_refs": [ "RHR-0277", "TEW-0085" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q184199", "legal_classification": "empirical_phenomenon_label" }, { "id": "VIB-0202", "domain": "VIB", "term_en": "Cryptographic Function Validation", "term_de": "CryptographicFunctionValidation", "definition_en": "A code generation dynamic in prompt-driven development workflows, measurable through a phenomenon where ai verifying that cryptographic implementations use appropriate algorithms and key sizes for security requirements. Distinguished from adjacent concepts by its focus on the specific mechanism through which cryptographic manifests in empirically verifiable ways. Measurable through code generation velocity (tokens/minute), first-pass acceptance rate, refactoring frequency, and prompt-to-deployment cycle time.", "definition_de": "Vibe-Coding-spezifisches Entwicklungsphänomen in LLM-gestützter Programmierung, gekennzeichnet durch struktur oder Abhängigkeitsgraph in der Mensch-KI-Zusammenarbeit. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Validation Process", "narrower_terms": [], "cross_domain_refs": [ "MTH-0067" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "VIB-0203", "domain": "VIB", "term_en": "Reliance Vulnerability Scan Integration", "term_de": "RelianceVulnerabilityScanIntegration", "definition_en": "A vibe coding methodology phenomenon describing a specific developer-AI interaction pattern where a phenomenon where ai automatically checking for known cves in transitive reliances and flagging outdated versions. This phenomenon operates at the intersection of reliance and vulnerability dynamics within the broader VIB domain. Measurable through code generation velocity (tokens/minute), first-pass acceptance rate, refactoring frequency, and prompt-to-deployment cycle time.", "definition_de": "Vibe-Coding-spezifisches Entwicklungsphänomen in LLM-gestützter Programmierung, gekennzeichnet durch mensch-KI-Interaktionsmuster in der Mensch-KI-Zusammenarbeit. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "Analytical Ratio", "narrower_terms": [], "cross_domain_refs": [ "TEW-0031", "ROB-0023" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "VIB-0204", "domain": "VIB", "term_en": "Input Sanitization Code Generation", "term_de": "InputSanitizationCodeGeneration", "definition_en": "A code generation dynamic in prompt-driven development workflows, measurable through a phenomenon where ai generating input validation and sanitization routines to reduce injection and xss vulnerabilities. Distinguished from adjacent concepts by its focus on the specific mechanism through which input manifests in empirically verifiable ways. Quantifiable via test coverage delta, bug density in generated code, and developer cognitive load proxies (context switch frequency, undo/redo rates).", "definition_de": "Vibe-Coding-spezifisches Entwicklungsphänomen in LLM-gestützter Programmierung, gekennzeichnet durch mensch-KI-Interaktionsmuster in der Mensch-KI-Zusammenarbeit. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Analytical Ratio", "narrower_terms": [], "cross_domain_refs": [ "RHR-0200", "SPA-0085", "STE-0061" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "WEB-0001", "domain": "WEB", "term_en": "AI-Generated CSS Nobody Understands", "term_de": "Webentwicklung", "definition_en": "A web architecture pattern in AI-augmented online systems, measurable through a web development phenomenon involving the accumulation of CSS rules generated by AI systems that accomplish visual effects through nested selectors and computed values so convoluted that human inspection reveals no clear authorial intent. Distinguished from adjacent concepts by its focus on the specific mechanism through which ai manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch erstellung und Wartung von Websites und Webanwendungen mit Frontend- und Backend-Technologien. KI verändert substanziell Webentwicklung durch automatisierte Codegenerierung, intelligente Layoutsysteme und Echtzeit-Performanceoptimierung. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Web AI", "narrower_terms": [ "WEB-0060", "WEB-0078", "WEB-0059", "WEB-0009", "WEB-0014", "WEB-0094", "WEB-0020", "WEB-0018", "WEB-0037", "WEB-0004", "WEB-0015", "WEB-0039", "WEB-0016", "WEB-0065", "WEB-0049", "WEB-0067", "WEB-0093", "WEB-0017", "WEB-0084", "WEB-0085", "WEB-0069", "WEB-0022", "WEB-0086", "WEB-0006", "WEB-0043", "WEB-0076", "WEB-0035", "WEB-0051", "WEB-0038", "WEB-0010", "WEB-0047", "WEB-0073", "WEB-0079", "WEB-0040", "WEB-0007", "WEB-0095", "WEB-0030", "WEB-0064", "WEB-0011", "WEB-0080", "WEB-0031", "WEB-0003", "WEB-0033", "WEB-0061", "WEB-0068", "WEB-0027", "WEB-0082", "WEB-0088", "WEB-0041", "WEB-0028", "WEB-0046", "WEB-0062", "WEB-0087", "WEB-0001", "WEB-0058", "WEB-0091", "WEB-0050", "WEB-0077", "WEB-0063", "WEB-0045", "WEB-0089", "WEB-0025", "WEB-0024" ], "cross_domain_refs": [ "VIB-0129", "ART-0087", "ART-0077" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "WEB-0002", "domain": "WEB", "term_en": "API Contract Assumption Pattern", "term_de": "HTML5", "definition_en": "A web architecture pattern in AI-augmented online systems, measurable through when frontend code generated by AI assumes API response shapes without defensive validation, and backend changes involve cascading failures across the client layer. The concept emerges specifically in contexts where api–contract interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch aktuelle Standard-Auszeichnungssprache zur Strukturierung von Webinhalten mit semantischen Elementen und Multimedia-Unterstützung. KI-Tools generieren barrierefreies HTML5-Markup, validieren semantische Korrektheit und optimieren Dokumentstruktur für Suchma. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-3901", "narrower_terms": [], "cross_domain_refs": [ "SWE-0001", "VIB-0183" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q165194", "legal_classification": "analytical_category" }, { "id": "WEB-0003", "domain": "WEB", "term_en": "Accessibility Edge Case Elision", "term_de": "CSS3", "definition_en": "A web technology phenomenon in AI-mediated digital interaction, characterized by the pattern where AI-generated accessibility implementations satisfy WCAG checklist criteria while omitting edge cases affecting specific assistive technologies or user interaction patterns. The concept emerges specifically in contexts where accessibility–edge interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch ein Verhaltensmuster im System: ein Muster, bei dem n where ai-generated accessibility implementations. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Web AI", "narrower_terms": [], "cross_domain_refs": [ "SWE-0033" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "WEB-0004", "domain": "WEB", "term_en": "Alert Dialog User Confusion Pattern", "term_de": "JavaScript", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures A web architecture pattern in AI-augmented online systems, measurable through when AI-generated alert dialogs use role=alert without proper focus management or dismiss mechanisms, confusing users about required actions. This phenomenon operates at the intersection of alert and dialog dynamics within the broader WEB domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept dynamische Programmiersprache für interaktive Webinhalte, serverseitige Anwendungen und plattformübergreifende Entwicklung. KI-gestützte Codeassistenten bieten Echtzeit-Vorschläge, Fehlererkennung und automatisierte Testgenerierung für JavaScript-Ökosysteme. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Behavioral Pattern", "narrower_terms": [], "cross_domain_refs": [ "SOM-0029" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "WEB-0005", "domain": "WEB", "term_en": "Animation Performance Hidden Costs", "term_de": "DOM-Manipulation", "definition_en": "A web technology phenomenon in AI-mediated digital interaction, characterized by the pattern where AI-generated CSS animations or JavaScript transitions achieve smooth frame rates on high-end devices while consuming excessive battery on mobile or older hardware. Distinguished from adjacent concepts by its focus on the specific mechanism through which animation manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch ein Verhaltensmuster im System: ein Muster, bei dem n where ai-generated css animations or javascript . Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2303", "narrower_terms": [], "cross_domain_refs": [ "COP-0033" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q1361053", "legal_classification": "descriptive_research_term" }, { "id": "WEB-0006", "domain": "WEB", "term_en": "Asset Optimization Paradox", "term_de": "Responsive Design", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures A web architecture pattern in AI-augmented online systems, measurable through the scenario where image optimization, code splitting, and minification suggestions decrease file sizes while increasing complexity of resource orchestration. This phenomenon operates at the intersection of asset and optimization dynamics within the broader WEB domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept systemverhalten oder Interaktionsmuster: charakterisiert durch the scenario where image optimization, code splitting, and minification suggesti. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Logical Paradox", "narrower_terms": [], "cross_domain_refs": [ "SAL-0084" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "WEB-0007", "domain": "WEB", "term_en": "Audio Transcript Generation Gap", "term_de": "Mobile-First-Ansatz", "definition_en": "A web technology phenomenon in AI-mediated digital interaction, characterized by an online interaction pattern reflecting the discrepancy between AI-generated audio transcripts and actual human comprehension of meaning, tone, and context in the audio content. The concept emerges specifically in contexts where audio–transcript interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch ein KI-bezogenes Phänomen: charakterisiert durch the discrepancy between ai-generated audio transcripts and actual human comprehe. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Analytical Ratio", "narrower_terms": [], "cross_domain_refs": [ "GAM-0088" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "WEB-0008", "domain": "WEB", "term_en": "Boilerplate Accumulation Phenomenon", "term_de": "CSS Grid", "definition_en": "An online interaction pattern arising from the layering of AI-generated project initialization code, starter templates, and configuration scaffolding that compounds into unmaintainable project structure. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch ein KI-bezogenes Phänomen: charakterisiert durch the layering of ai-generated project initialization code, starter templates, and. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2301", "narrower_terms": [], "cross_domain_refs": [ "VIB-0177" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "WEB-0009", "domain": "WEB", "term_en": "Breadcrumb Navigation Overuse", "term_de": "Flexbox", "definition_en": "A web architecture pattern in AI-augmented online systems, measurable through the pattern where AI suggests breadcrumb navigation for most page structure, even when URL hierarchy doesn't reflect user mental models of site structure. The concept emerges specifically in contexts where breadcrumb–navigation interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch cSS-Layoutmodul zur Raumverteilung und Ausrichtung von Elementen in eindimensionalen Container-Anordnungen. KI-Layout-Tools können automatisch optimale Flexbox-Konfigurationen basierend auf Design-Mockups und responsiven Anforderungen vorschlagen. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Web AI", "narrower_terms": [], "cross_domain_refs": [ "CON-0062" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "WEB-0010", "domain": "WEB", "term_en": "Browser Extension Compatibility Blind Spot", "term_de": "Media Queries", "definition_en": "A web architecture pattern in AI-augmented online systems, measurable through an online interaction pattern arising from when AI-generated code assumes DOM integrity but browser extensions modify the DOM, creating unexpected failures invisible during standard testing. The concept emerges specifically in contexts where browser–extension interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch cSS-Technik für responsives Design durch Anwendung von Stilen basierend auf Geräteeigenschaften wie Viewport-Größe. KI automatisiert Breakpoint-Erkennung, generiert adaptive Stylesheets und prognostiziert optimale Layouts über Gerätekategorien. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Web AI", "narrower_terms": [], "cross_domain_refs": [ "ELR-0044", "ELR-0127" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "WEB-0011", "domain": "WEB", "term_en": "Build Pipeline Opaqueness", "term_de": "React-Framework", "definition_en": "A web architecture pattern in AI-augmented online systems, measurable through a digital interface effect reflecting the accumulation of build script suggestions, webpack configurations, and transpilation layers that obsresolves the trajectory from source to production artifact. Distinguished from adjacent concepts by its focus on the specific mechanism through which build manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch systemverhalten oder Interaktionsmuster: charakterisiert durch the accumulation of build script suggestions, webpack configurations, and transp. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Processing Pipeline", "narrower_terms": [], "cross_domain_refs": [ "LIN-0002" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "WEB-0012", "domain": "WEB", "term_en": "Button Click Target Size Minimization", "term_de": "Vue.js-Framework", "definition_en": "The pattern where AI-generated interactive elements use visually small click targets that pass pixel-level design review while falling below recommended 44x44 pixel accessibility minimums. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch ein Verhaltensmuster im System: ein Muster, bei dem n where ai-generated interactive elements use visu. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2154", "narrower_terms": [], "cross_domain_refs": [ "DES-0065", "AGE-0022" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "WEB-0013", "domain": "WEB", "term_en": "CORS Error Explanation Gap", "term_de": "Angular-Framework", "definition_en": "A web development phenomenon observed when when AI-generated CORS error messages don't explain the cross-origin request policy or suggest resolution steps, leaving developers without debugging guidance. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch ein KI-bezogenes Phänomen: When AI-generated CORS error messages don't explain the cross-origin request policy or suggest resolution steps, leaving. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2604", "narrower_terms": [], "cross_domain_refs": [ "QUA-0072", "CUS-0071", "SCR-0054" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "WEB-0014", "domain": "WEB", "term_en": "Cache Invalidation Timing Mystery", "term_de": "Svelte-Framework", "definition_en": "A web architecture pattern in AI-augmented online systems, measurable through the uncertainty users experience when AI-generated caching strategies update content at unpredictable intervals, creating apparent inconsistency. The concept emerges specifically in contexts where cache–invalidation interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch ein KI-bezogenes Phänomen: charakterisiert durch the uncertainty users experience when ai-generated caching strategies update con. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Validation Process", "narrower_terms": [], "cross_domain_refs": [ "ASE-0096" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "WEB-0015", "domain": "WEB", "term_en": "Certificate Error User Override Guidance", "term_de": "Next.js", "definition_en": "A web technology phenomenon in AI-mediated digital interaction, characterized by an online interaction pattern in which when AI-generated HTTPS certificate error pages provide instructions for users to bypass security warnings without explaining actual certificate chain issues. The concept emerges specifically in contexts where certificate–error interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch react-Framework fuer serverseitiges Rendering, statische Generierung und Full-Stack-Webanwendungen. KI nutzt Next.js fuer ML-gestuetzte Web-Interfaces mit optimiertem Laden, Edge-Rendering und nahtloser API-Routen-Integration. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Web AI", "narrower_terms": [], "cross_domain_refs": [ "QUA-0031" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "WEB-0016", "domain": "WEB", "term_en": "Component Abstraction Depth Spiral", "term_de": "Node.js", "definition_en": "A web architecture pattern in AI-augmented online systems, measurable through an online interaction pattern observed when the recursive nesting of AI-suggested component abstractions that accompanies reusable patterns at the cost of logical transparency and debugging clarity. The concept emerges specifically in contexts where component–abstraction interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch javaScript-Laufzeitumgebung fuer serverseitige Ausfuehrung mit ereignisgesteuertem nicht-blockierendem I/O. KI nutzt Node.js fuer skalierbare Inferenzserver, Echtzeit-KI-Chatanwendungen und Hochdurchsatz-Datenverarbeitungspipelines. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Web AI", "narrower_terms": [], "cross_domain_refs": [ "COG-0179" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "WEB-0017", "domain": "WEB", "term_en": "Component Prop Explosion", "term_de": "Express.js", "definition_en": "A web architecture pattern in AI-augmented online systems, measurable through the incremental addition of optional component properties suggested by AI until the component interface becomes harder to use correctly than duplicating it. Distinguished from adjacent concepts by its focus on the specific mechanism through which component manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch ein KI-bezogenes Phänomen: charakterisiert durch the incremental addition of optional component properties suggested by ai until. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Web AI", "narrower_terms": [], "cross_domain_refs": [ "ASE-0055" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "WEB-0018", "domain": "WEB", "term_en": "Container Query Implementation Gaps", "term_de": "Django", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A web technology phenomenon in AI-mediated digital interaction, characterized by a web development phenomenon involving when AI-generated container query code assumes universal browser support without providing fallbacks for layouts that become broken in unsupported environments. This phenomenon operates at the intersection of container and query dynamics within the broader WEB domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept ein KI-bezogenes Phänomen: When AI-generated container query code assumes universal browser support without providing fallbacks for layouts that be. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Performance Gap", "narrower_terms": [], "cross_domain_refs": [ "VIB-0129" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "WEB-0019", "domain": "WEB", "term_en": "Cookie Consent Proliferation Pattern", "term_de": "Flask", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures the pattern where cookie and consent management solutions suggested by AI expand into competing frameworks that yield conflicting tracking headers and policy declarations. Identifiable through systematic behavioral analysis and pattern recognition. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept leichtgewichtiges Python-Web-Framework fuer APIs und Webanwendungen mit minimalem Boilerplate. KI-Anwendungen nutzen Flask haeufig fuer Rapid Prototyping von Inferenz-Endpunkten, Modell-Serving-APIs und ML-Experiment-Dashboards. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "RPH-2303", "narrower_terms": [], "cross_domain_refs": [ "RPH-3051" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "WEB-0020", "domain": "WEB", "term_en": "DNS Resolution Error Obfuscation", "term_de": "Ruby on Rails", "definition_en": "A web architecture pattern in AI-augmented online systems, measurable through a digital interface effect manifesting as when AI-generated error messages hide DNS resolution failures behind vague 'connection error' language, preventing users from troubleshooting network issues. The concept emerges specifically in contexts where dns–resolution interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch full-Stack-Web-Framework nach dem Prinzip Konvention ueber Konfiguration fuer schnelle Entwicklung. KI integriert sich in Rails durch intelligente Code-Generatoren, automatisiertes CRUD-Scaffolding und smarte Datenbank-Migrationsassistenz. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Web AI", "narrower_terms": [], "cross_domain_refs": [ "QUA-0072", "CUS-0071", "SCR-0054" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "WEB-0021", "domain": "WEB", "term_en": "Data Table Complexity Sink", "term_de": "REST-API-Design", "definition_en": "A web technology phenomenon in AI-mediated digital interaction, characterized by the point where AI-suggested features (sorting, filtering, virtualization, export, pagination) accumulate into a table component that embodies the complexity of an application framework. The concept emerges specifically in contexts where data–table interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch ein KI-bezogenes Phänomen: charakterisiert durch the point where ai-suggested features (sorting, filtering, virtualization, expor. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2303", "narrower_terms": [], "cross_domain_refs": [ "SWE-0009", "ADA-0001", "AGE-0068" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q42848", "legal_classification": "empirical_phenomenon_label" }, { "id": "WEB-0022", "domain": "WEB", "term_en": "Datalist Browser Inconsistency Handling", "term_de": "GraphQL-Implementierung", "definition_en": "A web technology phenomenon in AI-mediated digital interaction, characterized by when AI accompanies datalist-based input suggestions without accounting for varied browser rendering and interaction patterns across different platforms. Distinguished from adjacent concepts by its focus on the specific mechanism through which datalist manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch serverseitige Realisierung von GraphQL-Schemas, Resolvern und Typsystemen fuer flexible Daten-APIs. KI optimiert GraphQL-Implementierungen durch automatisierte Resolver-Generierung, Abfragekomplexitaetsanalyse und intelligentes Schema-Stitching. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Web AI", "narrower_terms": [], "cross_domain_refs": [ "CON-0094", "GAM-0063", "TRA-0049" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q42848", "legal_classification": "analytical_category" }, { "id": "WEB-0023", "domain": "WEB", "term_en": "Reliance Bloat Accumulation", "term_de": "WebSocket", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A web technology phenomenon in AI-mediated digital interaction, characterized by a systemic tendency in which each AI suggestion to add utility libraries, polyfills, or framework plugins incrementally increases bundle size in ways that become opaque after multiple suggestions. This phenomenon operates at the intersection of reliance and bloat dynamics within the broader WEB domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept ein KI-bezogenes Phänomen: charakterisiert durch the phenomenon where each ai suggestion to add utility libraries, polyfills, or. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "RPH-2303", "narrower_terms": [], "cross_domain_refs": [ "CRE-0009" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "WEB-0024", "domain": "WEB", "term_en": "Drag Drop Specification Mismatch", "term_de": "Server-Sent Events", "definition_en": "A web architecture pattern in AI-augmented online systems, measurable through an online interaction pattern manifesting as when AI-generated drag and drop implementations follow HTML Drag and Drop API literally, creating behaviors incompatible with user expectations derived from native application patterns. The concept emerges specifically in contexts where drag–drop interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch hTTP-basiertes Protokoll fuer Server-zu-Client-Streaming von Echtzeit-Updates ueber persistente Verbindungen. KI nutzt SSE fuer Token-fuer-Token-LLM-Streaming, Live-Inferenzfortschritt und Echtzeit-Modell-Monitoring-Dashboards. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Web AI", "narrower_terms": [], "cross_domain_refs": [ "MUS-0024" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "WEB-0025", "domain": "WEB", "term_en": "Empty Alt Text Automation Prevalence", "term_de": "API-Authentifizierung", "definition_en": "A web architecture pattern in AI-augmented online systems, measurable through the pattern where AI-generated decorative image markup uses empty alt attributes while requiring manual verification to ensure they're truly decorative. The concept emerges specifically in contexts where empty–alt interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch sicherheitsmechanismen zur Identitaetsverifikation von Clients beim Zugriff auf Web-APIs mittels Tokens oder Schluessel. KI-Systeme benoetigen robuste API-Authentifizierung fuer Modell-Endpunkt-Schutz und Zugriffssteuerung. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Web AI", "narrower_terms": [], "cross_domain_refs": [ "DES-0074" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q184199", "legal_classification": "descriptive_research_term" }, { "id": "WEB-0026", "domain": "WEB", "term_en": "Empty State UI Invisibility Pattern", "term_de": "Datenbankintegration", "definition_en": "An online interaction pattern involving when AI-generated empty states lack clear visual indicators or actionable guidance, leaving users uncertain whether content will appear or action is typically expected. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch ein KI-bezogenes Phänomen: When AI-generated empty states lack clear visual indicators or actionable guidance, leaving users uncertain whether cont. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2604", "narrower_terms": [], "cross_domain_refs": [ "SCR-0071" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "WEB-0027", "domain": "WEB", "term_en": "Error Boundary Incompleteness", "term_de": "PostgreSQL", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A web technology phenomenon in AI-mediated digital interaction, characterized by when AI-generated error boundary components catch specific error types but allow correlated failures outside their scope to cascade unhandled. This phenomenon operates at the intersection of error and boundary dynamics within the broader WEB domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept fortgeschrittene Open-Source-relationale Datenbank mit komplexen Abfragen und erweiterbaren Datentypen. KI nutzt PostgreSQL durch pgvector fuer Embedding-Speicherung, intelligente Abfrageplanung und ML-Modell-Metadatenverwaltung. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Web AI", "narrower_terms": [], "cross_domain_refs": [ "AGE-0043", "ASE-0048", "ASE-0071" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "WEB-0028", "domain": "WEB", "term_en": "Error Page No Restoration Path", "term_de": "MongoDB", "definition_en": "A web technology phenomenon in AI-mediated digital interaction, characterized by a digital interface effect in which when AI-generated error pages display technical error codes without offering clear pathways for users to restore or report the issue. Distinguished from adjacent concepts by its focus on the specific mechanism through which error manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch dokumentenorientierte NoSQL-Datenbank mit flexiblen JSON-aehnlichen Datensaetzen fuer schemalose Anwendungen. KI nutzt MongoDB fuer ML-Experiment-Metadaten, flexible Feature Stores und dokumentbasierte Wissensdatenbanken fuer RAG-Systeme. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Analytical Ratio", "narrower_terms": [], "cross_domain_refs": [ "TEW-0030" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "WEB-0029", "domain": "WEB", "term_en": "Flexbox Grid Misapplication Pattern", "term_de": "Redis", "definition_en": "A web technology phenomenon in AI-mediated digital interaction, characterized by the pattern where AI-generated layouts apply Flexbox to problems more effectively addressed by CSS Grid or vice versa, creating unnecessarily complex or fragile responsive structures. The concept emerges specifically in contexts where flexbox–grid interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch ein Verhaltensmuster im System: ein Muster, bei dem n where ai-generated layouts apply flexbox to prob. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2351", "narrower_terms": [], "cross_domain_refs": [ "WRK-0098" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "WEB-0030", "domain": "WEB", "term_en": "Form Error Announcement Delay", "term_de": "Prisma ORM", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A web technology phenomenon in AI-mediated digital interaction, characterized by an online interaction pattern characterized by when AI-generated form validation reports errors through live regions after a delay, creating confusion about which field requires correction. This phenomenon operates at the intersection of form and error dynamics within the broader WEB domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept ein KI-bezogenes Phänomen: When AI-generated form validation reports errors through live regions after a delay, creating confusion about which fiel. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Web AI", "narrower_terms": [], "cross_domain_refs": [ "VIB-0083" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "WEB-0031", "domain": "WEB", "term_en": "Form Validation Incompleteness Pattern", "term_de": "TypeScript", "definition_en": "A web technology phenomenon in AI-mediated digital interaction, characterized by a digital interface effect arising from aI-generated form validation that covers common input patterns while omitting locale-specific, character-encoding, or edge-case validations, allowing malformed data into backend systems. The concept emerges specifically in contexts where form–validation interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch typisierte Obermenge von JavaScript mit statischer Typueberpruefung fuer sicherere Grossentwicklung. KI brilliert mit TypeScript durch praezise typbewusste Codegenerierung, automatisierte Typinferenz und intelligente Interface-Definition. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Behavioral Pattern", "narrower_terms": [], "cross_domain_refs": [ "VIB-0132" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "WEB-0032", "domain": "WEB", "term_en": "Framework Suggestion Loop", "term_de": "ES6-Module", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A web technology phenomenon in AI-mediated digital interaction, characterized by a cycle where AI assistants persistently propose new framework integrations based on architectural suggestions, each claiming performance or maintainability improvements, creating decision hesitation in development workflow. This phenomenon operates at the intersection of framework and suggestion dynamics within the broader WEB domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept javaScript-Modulsystem mit Import/Export-Syntax fuer modulare Code-Organisation. KI-Tooling nutzt ES6-Module fuer tree-shakeable ML-Utility-Bibliotheken, lazy-loaded Inferenz-Worker und saubere KI-Komponentenarchitekturen. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "RPH-2303", "narrower_terms": [], "cross_domain_refs": [ "VIB-0035" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "WEB-0033", "domain": "WEB", "term_en": "Frontend-UX Quality Chasm", "term_de": "Webpack", "definition_en": "A web technology phenomenon in AI-mediated digital interaction, characterized by the observable difference between AI-generated frontend code that passes visual reversion tests and the actual user experience quality when traversing interactive states. The concept emerges specifically in contexts where frontend–ux interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch ein KI-bezogenes Phänomen: charakterisiert durch the observable difference between ai-generated frontend code that passes visual. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Web AI", "narrower_terms": [], "cross_domain_refs": [ "SWE-0023" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "WEB-0034", "domain": "WEB", "term_en": "Heading Hierarchy Breakage Pattern", "term_de": "Vite", "definition_en": "A digital interface effect reflecting when AI-generated markup skips heading levels (h1 to h3, skipping h2) for visual styling reasons, breaking screen reader navigation. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch ein KI-bezogenes Phänomen: When AI-generated markup skips heading levels (h1 to h3, skipping h2) for visual styling reasons, breaking screen reader. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-3001", "narrower_terms": [], "cross_domain_refs": [ "SWE-0003" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "WEB-0035", "domain": "WEB", "term_en": "Hydration Mismatch Silent Cascade", "term_de": "Paketverwaltung", "definition_en": "A web architecture pattern in AI-augmented online systems, measurable through a web development phenomenon reflecting the instance where server-side rendered markup generated by AI diverges subtly from client-side hydration assumptions, resulting in inconsistent DOM states invisible to unit tests. The concept emerges specifically in contexts where hydration–mismatch interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch ein KI-bezogenes Phänomen: charakterisiert durch the instance where server-side rendered markup generated by ai diverges subtly f. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Cascade Effect", "narrower_terms": [], "cross_domain_refs": [ "PHO-0054", "SWE-0077", "TEW-0009" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "WEB-0036", "domain": "WEB", "term_en": "Iframe Title Omission Pattern", "term_de": "SEO-Optimierung", "definition_en": "A web technology phenomenon in AI-mediated digital interaction, characterized by an online interaction pattern reflecting when AI-generated iframe embeds omit title attributes, leaving embedded content unidentifiable to screen reader users. The concept emerges specifically in contexts where iframe–title interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch ein KI-bezogenes Phänomen: When AI-generated iframe embeds omit title attributes, leaving embedded content unidentifiable to screen reader users. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2303", "narrower_terms": [], "cross_domain_refs": [ "TRA-0023", "RHR-0115", "PER-0104" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "WEB-0037", "domain": "WEB", "term_en": "Image Caption Association Gap", "term_de": "Core Web Vitals", "definition_en": "A web technology phenomenon in AI-mediated digital interaction, characterized by a web development phenomenon in which when AI-generated figure markup uses figure and figcaption elements but doesn't ensure proper association with images for all assistive technologies. Distinguished from adjacent concepts by its focus on the specific mechanism through which image manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch ein KI-bezogenes Phänomen: When AI-generated figure markup uses figure and figcaption elements but doesn't ensure proper association with images fo. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Performance Gap", "narrower_terms": [], "cross_domain_refs": [ "EDU-0074" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "WEB-0038", "domain": "WEB", "term_en": "Image Sprite Generation Overhead", "term_de": "Seitengeschwindigkeit", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures A web architecture pattern in AI-augmented online systems, measurable through the scenario where AI recommends sprite sheets for icon optimization, generating more complexity for maintenance without measurable performance gains in HTTP/2 environments. This phenomenon operates at the intersection of image and sprite dynamics within the broader WEB domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept messung wie schnell Webseiteninhalt laedt und interaktiv wird. KI optimiert Seitengeschwindigkeit durch praediktive Ressourcenpriorisierung, intelligente Bildkompression und automatisierte Critical-Rendering-Path-Analyse. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Analytical Ratio", "narrower_terms": [], "cross_domain_refs": [ "SWE-0066" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "WEB-0039", "domain": "WEB", "term_en": "Infinite Scroll Implementation Cost", "term_de": "Strukturierte Daten", "definition_en": "A web architecture pattern in AI-augmented online systems, measurable through an online interaction pattern reflecting the observable difference between AI-generated infinite scroll patterns and the actual engineering investment required for memory management, focus restoration, and browser history. Distinguished from adjacent concepts by its focus on the specific mechanism through which infinite manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch standardisierte Markup-Formate wie JSON-LD und Schema.org fuer maschinenlesbare Webinhalte. KI konsumiert strukturierte Daten fuer Wissensgraph-Konstruktion, verbessertes Suchverstaendnis und automatisierte Inhaltsklassifikation. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Web AI", "narrower_terms": [], "cross_domain_refs": [ "SOM-0054" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "WEB-0040", "domain": "WEB", "term_en": "Input Autocomplete Attribute Omission", "term_de": "Sitemap", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A web technology phenomenon in AI-mediated digital interaction, characterized by a digital interface effect manifesting as when AI-generated form fields omit autocomplete attributes, preventing password managers and assistive technologies from recognizing field purposes. This phenomenon operates at the intersection of input and autocomplete dynamics within the broader WEB domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept xML-Datei mit Website-URLs zur effizienten Entdeckung und Indexierung durch Suchmaschinen. KI generiert Sitemaps automatisch aus Seitenstrukturanalyse, priorisiert Seiten nach prognostiziertem Suchwert und ueberwacht Indexierungsgesundheit. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Web AI", "narrower_terms": [], "cross_domain_refs": [ "PER-0108", "CRE-0086", "NEO-1157" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "WEB-0041", "domain": "WEB", "term_en": "Input Type Browser Support Assumption", "term_de": "Web-Barrierefreiheit", "definition_en": "A web architecture pattern in AI-augmented online systems, measurable through a digital interface effect where the assumption in AI-generated form code that HTML5 input types (datetime-local, range, color) have universal support, creating fallback failures on older browsers. The concept emerges specifically in contexts where input–type interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch designprinzipien zur Sicherstellung, dass Webentwicklung-Produkte und -Dienste von Menschen aller Fähigkeiten nutzbar sind. KI ermöglicht automatisierte Barrierefreiheitstests, alternative Inhaltsgenerierung und adaptive Interface-Personalisierung. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Web AI", "narrower_terms": [], "cross_domain_refs": [ "SWE-0023" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "WEB-0042", "domain": "WEB", "term_en": "Internationalization Skeleton Structure", "term_de": "WCAG-Standards", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A web technology phenomenon in AI-mediated digital interaction, characterized by an online interaction pattern in which when AI accompanies i18n infrastructure without understanding context-specific phrases, resulting in literal translations that preserve English idioms in languages where they are nonsensical. This phenomenon operates at the intersection of internationalization and skeleton dynamics within the broader WEB domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept ein KI-bezogenes Phänomen: When AI accompanies i18n infrastructure without understanding context-specific phrases, resulting in literal translation. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "RPH-2052", "narrower_terms": [], "cross_domain_refs": [ "SWE-0041" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "WEB-0043", "domain": "WEB", "term_en": "Intersection Observer Polling Overhead", "term_de": "Screenreader-Kompatibilität", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures A web architecture pattern in AI-augmented online systems, measurable through the performance cost of overly broad Intersection Observer configurations suggested by AI, observing elements that don't require observation and triggering unnecessary callbacks. This phenomenon operates at the intersection of intersection and observer dynamics within the broader WEB domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept ein KI-bezogenes Phänomen: charakterisiert durch the performance cost of overly broad intersection observer configurations sugges. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Web AI", "narrower_terms": [], "cross_domain_refs": [ "TEW-0084" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "WEB-0044", "domain": "WEB", "term_en": "JavaScript Framework Lock-In Incrementalism", "term_de": "Tastaturnavigation", "definition_en": "The gradual architectural commitment that emerges when AI-suggested framework extensions accumulate, making migration to alternative frameworks increasingly costly. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch ein KI-bezogenes Phänomen: charakterisiert durch the gradual architectural commitment that emerges when ai-suggested framework ex. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2303", "narrower_terms": [], "cross_domain_refs": [ "SWE-0005", "ADA-0015" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "WEB-0045", "domain": "WEB", "term_en": "Keyboard Navigation Incompleteness", "term_de": "ARIA-Attribute", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures A web architecture pattern in AI-augmented online systems, measurable through a web development phenomenon characterized by the partial keyboard support in AI-generated interactive components where Tab and Enter work but Arrow keys, Home, and End remain unhandled. This phenomenon operates at the intersection of keyboard and navigation dynamics within the broader WEB domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept ein KI-bezogenes Phänomen: charakterisiert durch the partial keyboard support in ai-generated interactive components where tab an. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Web AI", "narrower_terms": [], "cross_domain_refs": [ "AGE-0010", "AGE-0076", "AUG-0812" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "WEB-0046", "domain": "WEB", "term_en": "Label Association Invisibility Pattern", "term_de": "Websicherheit", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A web technology phenomenon in AI-mediated digital interaction, characterized by a web development phenomenon in which when AI-generated form labels are associated with inputs through complex selectors or JavaScript rather than direct HTML association, creating failures in screen reader detection. This phenomenon operates at the intersection of label and association dynamics within the broader WEB domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept ein KI-bezogenes Phänomen: When AI-generated form labels are associated with inputs through complex selectors or JavaScript rather than direct HTML. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Behavioral Pattern", "narrower_terms": [], "cross_domain_refs": [ "VIB-0130" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "WEB-0047", "domain": "WEB", "term_en": "Landmark Role Proliferation Pattern", "term_de": "HTTPS", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A web technology phenomenon in AI-mediated digital interaction, characterized by a web development phenomenon arising from the overuse of ARIA landmark roles suggested by AI, creating multiple navigation regions that confuse rather than clarify page structure. This phenomenon operates at the intersection of landmark and role dynamics within the broader WEB domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept verschluesselte Version von HTTP zur Sicherung des Datentransfers zwischen Browsern und Webservern via TLS. KI-Systeme benoetigen HTTPS fuer sichere Modell-API-Kommunikation, verschluesselten Trainingsdatentransfer und geschuetzte Inferenz-Endpunkte. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Analytical Ratio", "narrower_terms": [], "cross_domain_refs": [ "VIB-0130" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "WEB-0048", "domain": "WEB", "term_en": "Lazy Loading Timing Fragility", "term_de": "Content Security Policy", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures A web architecture pattern in AI-augmented online systems, measurable through a web development phenomenon involving the brittleness that emerges in AI-generated lazy loading implementations when viewport geometry, network timing, or scroll momentum interact in unanticipated ways. This phenomenon operates at the intersection of lazy and loading dynamics within the broader WEB domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept ein KI-bezogenes Phänomen: charakterisiert durch the brittleness that emerges in ai-generated lazy loading implementations when v. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "RPH-2355", "narrower_terms": [], "cross_domain_refs": [ "COP-0051", "ELR-0109", "SPA-0019" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "WEB-0049", "domain": "WEB", "term_en": "Link Underline Accessibility Override", "term_de": "XSS-Prävention", "definition_en": "A web technology phenomenon in AI-mediated digital interaction, characterized by a digital interface effect involving when AI-generated CSS removes default link underlines for aesthetic reasons without providing sufficient color contrast or alternative visual indicators. The concept emerges specifically in contexts where link–underline interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch ein KI-bezogenes Phänomen: When AI-generated CSS removes default link underlines for aesthetic reasons without providing sufficient color contrast. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Web AI", "narrower_terms": [], "cross_domain_refs": [ "CON-0001", "PHO-0061" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "WEB-0050", "domain": "WEB", "term_en": "List Structure Semantic Shift", "term_de": "CSRF-Schutz", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures A web architecture pattern in AI-augmented online systems, measurable through a digital interface effect reflecting when AI-generated navigation or content lists use divs instead of ul/ol elements, sacrificing semantic structure for styling flexibility. This phenomenon operates at the intersection of list and structure dynamics within the broader WEB domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept sicherheitsmassnahme zur Verhinderung unautorisierter Befehle aus vertrauenswuerdigen Nutzersitzungen. KI-Webanwendungen implementieren CSRF-Schutz fuer Modell-Inferenz-Endpunkte, Training-Job-may may trigger und administrative KI-Systemkontrollen. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Web AI", "narrower_terms": [], "cross_domain_refs": [ "COG-0066", "LNG-0015" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "WEB-0051", "domain": "WEB", "term_en": "Live Region Announcement Clutter", "term_de": "Authentifizierungssysteme", "definition_en": "A web architecture pattern in AI-augmented online systems, measurable through a digital interface effect in which when AI-generated live region announcements accompany excessively, creating audio clutter that overwhelms screen reader users with redundant or non-essential updates. The concept emerges specifically in contexts where live–region interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch ein KI-bezogenes Phänomen: When AI-generated live region announcements accompany excessively, creating audio clutter that overwhelms screen reader. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Web AI", "narrower_terms": [], "cross_domain_refs": [ "TEW-0010", "TEW-0037", "IDN-0053" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "WEB-0052", "domain": "WEB", "term_en": "Loading Skeleton Expectation Mismatch", "term_de": "OAuth 2.0", "definition_en": "A web architecture pattern in AI-augmented online systems, measurable through an online interaction pattern observed when when AI-generated skeleton screens don't resemble final content, creating brief but disorienting perceptual shifts during data loading. Distinguished from adjacent concepts by its focus on the specific mechanism through which loading manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch autorisierungs-Framework fuer sicheren delegierten Ressourcenzugriff ohne Weitergabe von Anmeldedaten. KI-Plattformen nutzen OAuth 2.0 fuer Drittanbieter-Modellzugriff, API-Key-Management und sichere Multi-Tenant-Inferenzautorisierung. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2701", "narrower_terms": [], "cross_domain_refs": [ "CON-0059" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "WEB-0053", "domain": "WEB", "term_en": "Manifest File Validation Incompleteness", "term_de": "JWT-Tokens", "definition_en": "A web architecture pattern in AI-augmented online systems, measurable through a digital interface effect observed when when AI-generated web manifest files contain incomplete or incorrect icon references, creating inconsistent app install experiences across platforms. Distinguished from adjacent concepts by its focus on the specific mechanism through which manifest manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch kompakte, URL-sichere Tokens zur Codierung von Ansprüchen zwischen Parteien für zustandslose Authentifizierung. KI überwacht JWT-Nutzungsmuster zur Erkennung von Token-Missbrauch, Credential Stuffing und anomalem Sitzungsverhalten in Webanwendungen. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2253", "narrower_terms": [], "cross_domain_refs": [ "REL-0120" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "WEB-0054", "domain": "WEB", "term_en": "Margin Narrowing Surprise Behavior", "term_de": "Session-Verwaltung", "definition_en": "The unexpected layout shifts that occur when AI-generated CSS accompanies margins that narrowing in ways the visual design did not anticipate. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch ein KI-bezogenes Phänomen: charakterisiert durch the unexpected layout shifts that occur when ai-generated css accompanies margin. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2701", "narrower_terms": [], "cross_domain_refs": [ "VIB-0153" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "WEB-0055", "domain": "WEB", "term_en": "Media Query Cascade Conflicts", "term_de": "Zwei-Faktor-Authentifizierung", "definition_en": "A web technology phenomenon in AI-mediated digital interaction, characterized by the conflicts that arise when AI accompanies multiple media query breakpoints with overlapping CSS rules, creating specificity battles and unpredictable responsive behavior. Distinguished from adjacent concepts by its focus on the specific mechanism through which media manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch ein KI-bezogenes Phänomen: charakterisiert durch the conflicts that arise when ai accompanies multiple media query breakpoints wi. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-3001", "narrower_terms": [], "cross_domain_refs": [ "VIB-0131" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "WEB-0056", "domain": "WEB", "term_en": "Mobile-First Assumption Break", "term_de": "Cloud-Deployment", "definition_en": "A web development phenomenon involving the discrepancy that emerges when AI accompanies mobile-first CSS but allocates most visual polish to desktop affordances, revealing the hierarchy of attention. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch ein KI-bezogenes Phänomen: charakterisiert durch the discrepancy that emerges when ai accompanies mobile-first css but allocates. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2355", "narrower_terms": [], "cross_domain_refs": [ "DES-0038" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "WEB-0057", "domain": "WEB", "term_en": "Modal Dialog Accessibility Gaps", "term_de": "AWS-Hosting", "definition_en": "The pattern where AI-generated modal components implement focus trapping while omitting escape key handling, backdrop click dismissal, or proper ARIA dialog semantics. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch cloud-Infrastrukturdienste von Amazon Web Services fuer Deployment und Skalierung von Webanwendungen. KI nutzt AWS fuer SageMaker-Modelltraining, Lambda-basierte serverlose Inferenz und skalierbare GPU-Cluster-Orchestrierung. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2154", "narrower_terms": [], "cross_domain_refs": [ "VIB-0124" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "WEB-0058", "domain": "WEB", "term_en": "Not Found Page Assumption Pattern", "term_de": "Vercel", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A web technology phenomenon in AI-mediated digital interaction, characterized by an online interaction pattern in which when AI accompanies 404 pages without considering that broken links may originate from external sites, hindering SEO signals and user retention. This phenomenon operates at the intersection of not and found dynamics within the broader WEB domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept ein KI-bezogenes Phänomen: When AI accompanies 404 pages without considering that broken links may originate from external sites, hindering SEO sig. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Behavioral Pattern", "narrower_terms": [], "cross_domain_refs": [ "SCR-0054", "CRE-0127", "TEW-0027" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "WEB-0059", "domain": "WEB", "term_en": "Notification Toast Accessibility Oversight", "term_de": "Netlify", "definition_en": "A web architecture pattern in AI-augmented online systems, measurable through when AI-generated toast notifications appear without role or aria-live attributes, remaining invisible to screen reader users. Distinguished from adjacent concepts by its focus on the specific mechanism through which notification manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch cloud-Plattform fuer Deployment statischer Sites und serverloser Funktionen mit integriertem CI/CD. KI-gestuetzte Webanwendungen nutzen Netlify fuer edge-deployed Inferenzfunktionen und JAMstack-KI-Applikationshosting. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Web AI", "narrower_terms": [], "cross_domain_refs": [ "SPR-0189", "RHR-0023", "AGE-0003" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "WEB-0060", "domain": "WEB", "term_en": "Overflow Hidden Content Shift", "term_de": "Docker-Container", "definition_en": "A web architecture pattern in AI-augmented online systems, measurable through when AI-generated overflow hidden styles conceal user interactions, tooltips, or other essential interface elements that extend beyond parent boundaries. The concept emerges specifically in contexts where overflow–hidden interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch ein KI-bezogenes Phänomen: When AI-generated overflow hidden styles conceal user interactions, tooltips, or other essential interface elements that. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Web AI", "narrower_terms": [], "cross_domain_refs": [ "IDN-0035" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "WEB-0061", "domain": "WEB", "term_en": "Parallax Effect Motion Response", "term_de": "CI/CD-Pipeline", "definition_en": "A web technology phenomenon in AI-mediated digital interaction, characterized by the observable user experience impact when AI-generated parallax scrolling effects move at rates misaligned with scroll velocity, creating perceptual disorientation. Distinguished from adjacent concepts by its focus on the specific mechanism through which parallax manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch ein KI-bezogenes Phänomen: charakterisiert durch the observable user experience impact when ai-generated parallax scrolling effec. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Psychological Effect", "narrower_terms": [], "cross_domain_refs": [ "SAL-0092" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "WEB-0062", "domain": "WEB", "term_en": "Performance Metric Gaming", "term_de": "GitHub Actions", "definition_en": "A web architecture pattern in AI-augmented online systems, measurable through the pattern where AI-generated optimization suggestions improve metric scores (LCP, FID, CLS) while degrading unmeasured aspects of interaction quality. Distinguished from adjacent concepts by its focus on the specific mechanism through which performance manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch cI/CD-Automatisierungsplattform mit Workflows ausgeloest durch GitHub-Repository-Events. KI integriert sich in GitHub Actions fuer automatisiertes Modelltesten, ML-Pipeline-Orchestrierung und KI-gestuetzte Code-Qualitaetspruefungen bei Pull Requests. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Performance Metric", "narrower_terms": [], "cross_domain_refs": [ "PLY-0051" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q1361053", "legal_classification": "observational_construct" }, { "id": "WEB-0063", "domain": "WEB", "term_en": "Permission Denied Messaging Ambiguity", "term_de": "Automatisierte Tests", "definition_en": "A web architecture pattern in AI-augmented online systems, measurable through a web development phenomenon reflecting when AI-generated permission error messages don't distinguish between insufficient privileges and missing authentication, creating ambiguous next steps. The concept emerges specifically in contexts where permission–denied interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch programmatische Überprüfung des Webanwendungsverhaltens durch Unit-, Integrations- und End-to-End-Testsuites. KI generiert Testfälle aus Codeanalyse, prognostiziert fehleranfällige Bereiche und repariert instabile Testselektoren automatisch. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Web AI", "narrower_terms": [], "cross_domain_refs": [ "RHR-0075", "DAT-0083", "ASE-0095" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "WEB-0064", "domain": "WEB", "term_en": "Placeholder Text Contrast Violation", "term_de": "Unit-Test", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures A web architecture pattern in AI-augmented online systems, measurable through the pattern where AI-generated placeholder text uses low-contrast colors that satisfy browser defaults while violating WCAG standards for text visibility. This phenomenon operates at the intersection of placeholder and text dynamics within the broader WEB domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept testen einzelner Softwarekomponenten in Isolation zur Verifikation korrekten Verhaltens. KI generiert Unit-Tests automatisch aus dem Code-Kontext, identifiziert ungetestete Randfaelle und pflegt Tests bei Code-Evolution. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Web AI", "narrower_terms": [], "cross_domain_refs": [ "CON-0001", "VIB-0150" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "WEB-0065", "domain": "WEB", "term_en": "Polyfill Unnecessary Bloat Pattern", "term_de": "End-to-End-Test", "definition_en": "A web architecture pattern in AI-augmented online systems, measurable through a digital interface effect involving when AI-generated code includes polyfills for browser features already widely supported, unnecessarily increasing payload size. The concept emerges specifically in contexts where polyfill–unnecessary interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch testansatz zur Validierung kompletter Anwendungsworkflows von der Benutzeroberflaeche bis zum Backend. KI generiert E2E-Tests aus User-Story-Beschreibungen, stellt wieder her defekte Selektoren selbst und priorisiert Testszenarien nach Risikovorhersage. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Behavioral Pattern", "narrower_terms": [], "cross_domain_refs": [ "VIB-0052" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "WEB-0066", "domain": "WEB", "term_en": "Presence Indicator Misinterpretation", "term_de": "Progressive Web App", "definition_en": "A web technology phenomenon in AI-mediated digital interaction, characterized by an online interaction pattern manifesting as when AI-generated presence indicators (online/offline status) update asynchronously, creating stale states that no longer reflect actual user availability. Distinguished from adjacent concepts by its focus on the specific mechanism through which presence manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch ein KI-bezogenes Phänomen: When AI-generated presence indicators (online/offline status) update asynchronously, creating stale states that no longe. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2552", "narrower_terms": [], "cross_domain_refs": [ "REL-0128" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "WEB-0067", "domain": "WEB", "term_en": "Progress Bar Semantics Confusion", "term_de": "Service Worker", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A web technology phenomenon in AI-mediated digital interaction, characterized by when AI-generated progress indicators use div elements with ARIA role attributes instead of native progress or meter elements, losing semantic benefits. This phenomenon operates at the intersection of progress and bar dynamics within the broader WEB domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept ein KI-bezogenes Phänomen: When AI-generated progress indicators use div elements with ARIA role attributes instead of native progress or meter ele. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Web AI", "narrower_terms": [], "cross_domain_refs": [ "CRE-0149", "BEH-0069", "AED-0080" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q39645", "legal_classification": "systematic_classification" }, { "id": "WEB-0068", "domain": "WEB", "term_en": "Progressive Enhancement Absence Pattern", "term_de": "Offline-Fähigkeit", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A web technology phenomenon in AI-mediated digital interaction, characterized by when AI-generated applications assume full JavaScript capability without ensuring baseline functionality for users with script disabled or slow networks. This phenomenon operates at the intersection of progressive and enhancement dynamics within the broader WEB domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept ein KI-bezogenes Phänomen: When AI-generated applications assume full JavaScript capability without ensuring baseline functionality for users with. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Behavioral Pattern", "narrower_terms": [], "cross_domain_refs": [ "DES-0074" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "WEB-0069", "domain": "WEB", "term_en": "Rate Limit Error Anthropomorphization", "term_de": "Push-Benachrichtigung", "definition_en": "A web architecture pattern in AI-augmented online systems, measurable through when AI-generated rate limit messages use informal language that implies intent rather than explaining technical constraints, creating false expectations about retry behavior. Distinguished from adjacent concepts by its focus on the specific mechanism through which rate manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch nachricht an Nutzergeraete vom Server auch bei inaktiver Anwendung. KI personalisiert Push-Benachrichtigungen durch Verhaltens-Targeting, optimale Sendezeitvorhersage und automatisierte Inhaltsgenerierung basierend auf Nutzerkontext. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Web AI", "narrower_terms": [], "cross_domain_refs": [ "ROB-0205" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "WEB-0070", "domain": "WEB", "term_en": "Responsive Breakpoint Modification Invisibility", "term_de": "App-Manifest", "definition_en": "A web development phenomenon observed when when developers modify breakpoints in AI-generated responsive designs without updating all related media queries, creating orphaned CSS rules. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch jSON-Konfigurationsdatei zur Definition von PWA-Metadaten wie Name, Icons, Anzeigemodus und Theme. KI kann App-Manifeste automatisch aus Anwendungsanalyse generieren und PWA-Konfigurationsverbesserungen vorschlagen. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-3001", "narrower_terms": [], "cross_domain_refs": [ "VIB-0131" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "WEB-0071", "domain": "WEB", "term_en": "Responsive Design Acceptance Moment", "term_de": "Content-Management-System", "definition_en": "A web technology phenomenon in AI-mediated digital interaction, characterized by a web development phenomenon observed when the instant when a web developer deploys an AI-generated responsive layout to production without conducting testing across actual devices, trusting that breakpoint logic will function as intended. The concept emerges specifically in contexts where responsive–design interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch ein KI-bezogenes Phänomen: charakterisiert durch the instant when a web developer deploys an ai-generated responsive layout to pr. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2505", "narrower_terms": [], "cross_domain_refs": [ "ELR-0078" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q185925", "legal_classification": "systematic_classification" }, { "id": "WEB-0072", "domain": "WEB", "term_en": "Routing Architecture Brittleness", "term_de": "WordPress", "definition_en": "A web technology phenomenon in AI-mediated digital interaction, characterized by the fragility that emerges when AI accompanies route hierarchies based on current feature requirements, creating rigid URL structures that resist future architectural changes. The concept emerges specifically in contexts where routing–architecture interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch ein KI-bezogenes Phänomen: charakterisiert durch the fragility that emerges when ai accompanies route hierarchies based on curren. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2355", "narrower_terms": [], "cross_domain_refs": [ "VIB-0127" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "WEB-0073", "domain": "WEB", "term_en": "SEO Optimization Excess", "term_de": "Headless CMS", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A web technology phenomenon in AI-mediated digital interaction, characterized by a web development phenomenon in which the accumulation of SEO enhancements suggested by AI systems that maximizes keyword density and meta tag saturation without regard for semantic coherence or user intent. This phenomenon operates at the intersection of seo and optimization dynamics within the broader WEB domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept content-Management-System zur API-basierten Inhaltsauslieferung ohne feste Frontend-Darstellungsschicht. KI erweitert Headless CMS durch automatisierte Inhaltsgenerierung, intelligente Inhaltsmodellierung und personalisierte API-gesteuerte Auslieferung. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Optimization Method", "narrower_terms": [], "cross_domain_refs": [ "COP-0013" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "WEB-0074", "domain": "WEB", "term_en": "SVG Optimization Brittleness", "term_de": "Statischer-Seiten-Generator", "definition_en": "A web technology phenomenon in AI-mediated digital interaction, characterized by an online interaction pattern in which the fragility introduced when AI-generated SVG optimization removes attributes or restructures paths for file size reduction, later breaking animation or styling behaviors. The concept emerges specifically in contexts where svg–optimization interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch werkzeug zur Erstellung vorgerenderter HTML-Seiten aus Templates und Inhalten zur Build-Zeit. KI verbessert Static-Site-Generatoren durch automatisierte Inhaltserstellung, intelligente Build-Optimierung und dynamische Personalisierung am Edge. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-3001", "narrower_terms": [], "cross_domain_refs": [ "ELR-0152" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "WEB-0075", "domain": "WEB", "term_en": "Scroll Animation Jank Moments", "term_de": "Jamstack-Architektur", "definition_en": "A web architecture pattern in AI-augmented online systems, measurable through a web development phenomenon manifesting as the stuttering and frame drops that emerge when AI-generated scroll-triggered animations interact with browser rendering pipelines and layout thrashing. Distinguished from adjacent concepts by its focus on the specific mechanism through which scroll manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch ein KI-bezogenes Phänomen: charakterisiert durch the stuttering and frame drops that emerge when ai-generated scroll-triggered an. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2303", "narrower_terms": [], "cross_domain_refs": [ "SOM-0054", "TEM-0155" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "WEB-0076", "domain": "WEB", "term_en": "Security Configuration Trust Gap", "term_de": "E-Commerce-Plattform", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures A web architecture pattern in AI-augmented online systems, measurable through a web development phenomenon in which the space between accepting AI-generated security configurations (CORS policies, CSP headers, HTTPS redirects) without cryptographic verification and the actual runtime vulnerability exposure. This phenomenon operates at the intersection of security and configuration dynamics within the broader WEB domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept webplattform für Online-Kauf und -Verkauf mit Produktkatalogen, Warenkörben, Zahlungsabwicklung und Bestellverwaltung. KI personalisiert Produktempfehlungen, optimiert Preise dynamisch und erkennt betrügerische Transaktionen. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Analytical Ratio", "narrower_terms": [], "cross_domain_refs": [ "SWE-0025" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q102458", "legal_classification": "empirical_phenomenon_label" }, { "id": "WEB-0077", "domain": "WEB", "term_en": "Select Element Styling Complexity", "term_de": "Warenkorb", "definition_en": "A web technology phenomenon in AI-mediated digital interaction, characterized by the pattern where AI attempts to style native select elements, producing browser-inconsistent results and accessibility reversions. The concept emerges specifically in contexts where select–element interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch ein Verhaltensmuster im System: ein Muster, bei dem n where ai attempts to style native select element. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Web AI", "narrower_terms": [], "cross_domain_refs": [ "CRE-0112" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "WEB-0078", "domain": "WEB", "term_en": "Server Error Client Blame Attribution", "term_de": "Zahlungsintegration", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A web technology phenomenon in AI-mediated digital interaction, characterized by the pattern where AI-generated error messages attribute server failures to client actions, producing user confusion and false troubleshooting attempts. This phenomenon operates at the intersection of server and error dynamics within the broader WEB domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept ein Verhaltensmuster im System: ein Muster, bei dem n where ai-generated error messages attribute serv. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Web AI", "narrower_terms": [], "cross_domain_refs": [ "TEW-0031" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "WEB-0079", "domain": "WEB", "term_en": "Service Worker Offline Limitation Confusion", "term_de": "Produktkatalog", "definition_en": "A web technology phenomenon in AI-mediated digital interaction, characterized by a web development phenomenon where when AI-generated service worker configurations don't observably communicate which pages are available offline, creating false expectations about offline functionality. Distinguished from adjacent concepts by its focus on the specific mechanism through which service manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch ein KI-bezogenes Phänomen: When AI-generated service worker configurations don't observably communicate which pages are available offline, creating. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Web AI", "narrower_terms": [], "cross_domain_refs": [ "VIB-0157" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "WEB-0080", "domain": "WEB", "term_en": "Skip Link Implementation Invisibility", "term_de": "Bestellmanagement", "definition_en": "A web technology phenomenon in AI-mediated digital interaction, characterized by a digital interface effect characterized by when AI-generated skip-to-content links are invisible to sighted users and non-functional for keyboard users observed alongside incorrect focus management. Distinguished from adjacent concepts by its focus on the specific mechanism through which skip manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch ein KI-bezogenes Phänomen: When AI-generated skip-to-content links are invisible to sighted users and non-functional for keyboard users observed al. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Web AI", "narrower_terms": [], "cross_domain_refs": [ "RPH-346", "PER-0082", "CAI-0013" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "WEB-0081", "domain": "WEB", "term_en": "Stacking Context Confusion", "term_de": "UI-Design", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures A web architecture pattern in AI-augmented online systems, measurable through an online interaction pattern manifesting as the z-index layering issues that emerge from AI-generated styles that inadvertently involve new stacking contexts through opacity, transforms, or filter properties. This phenomenon operates at the intersection of stacking and context dynamics within the broader WEB domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept ein KI-bezogenes Phänomen: charakterisiert durch the z-index layering issues that emerge from ai-generated styles that inadverten. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "RPH-2355", "narrower_terms": [], "cross_domain_refs": [ "MUS-0086" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "WEB-0082", "domain": "WEB", "term_en": "State Management Complexity Explosion", "term_de": "UX-Design", "definition_en": "A web architecture pattern in AI-augmented online systems, measurable through a documented pattern where AI suggestions for state architecture incorporate Redux, Zustand, or Recoil patterns incrementally until application state becomes as complex as the problem it addresses. The concept emerges specifically in contexts where state–management interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch designdisziplin mit Fokus auf bedeutungsvolle, effiziente und zufriedenstellende Web-Interaktionen. KI-gestützte UX-Tools führen automatisierte Usability-Tests durch, generieren Heatmaps aus Eye-Tracking-Daten und personalisieren Interfaces in Echtzeit. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Web AI", "narrower_terms": [], "cross_domain_refs": [ "VIB-0133" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q181543", "legal_classification": "analytical_category" }, { "id": "WEB-0083", "domain": "WEB", "term_en": "Sync State UI Lag", "term_de": "Wireframing", "definition_en": "A web development phenomenon involving the delay that emerges in AI-generated UIs that attempt to display real-time synchronization status, creating uncertainty about whether changes have persisted. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch erstellen vereinfachter visueller Layouts zur Darstellung von Seitenstruktur und Elementplatzierung. KI automatisiert Wireframing durch Konvertierung von Textbeschreibungen in Layouts, Vorschlag optimaler UI-Muster und Generierung interaktiver Prototypen. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2355", "narrower_terms": [], "cross_domain_refs": [ "ART-0058" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "WEB-0084", "domain": "WEB", "term_en": "Testing Coverage False Confidence", "term_de": "Prototyping", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A web technology phenomenon in AI-mediated digital interaction, characterized by a digital interface effect involving the instance where AI-generated unit tests achieve high coverage percentages while omitting integration scenarios, race conditions, or asynchronous failure modes. This phenomenon operates at the intersection of testing and coverage dynamics within the broader WEB domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept ein KI-bezogenes Phänomen: charakterisiert durch the instance where ai-generated unit tests achieve high coverage percentages whi. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Web AI", "narrower_terms": [], "cross_domain_refs": [ "SWE-0089", "SWE-0043" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "WEB-0085", "domain": "WEB", "term_en": "Textarea Resize Interaction Conflict", "term_de": "Design-System", "definition_en": "A web architecture pattern in AI-augmented online systems, measurable through a digital interface effect reflecting when AI-generated form styling disables textarea resizing for layout consistency, removing user control over input area size. The concept emerges specifically in contexts where textarea–resize interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch ein KI-bezogenes Phänomen: When AI-generated form styling disables textarea resizing for layout consistency, removing user control over input area. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Web AI", "narrower_terms": [], "cross_domain_refs": [ "MUS-0051" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "WEB-0086", "domain": "WEB", "term_en": "Theme System Sprawl", "term_de": "CSS-Animation", "definition_en": "A web technology phenomenon in AI-mediated digital interaction, characterized by the expansion of CSS variables, utility classes, and design token systems suggested by AI until the theme implementation becomes larger than the components it documents. The concept emerges specifically in contexts where theme–system interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch techniken zur Erstellung fliessender visueller Uebergaenge und Bewegungseffekte mittels CSS-Eigenschaften. KI generiert CSS-Animationen aus natuerlichsprachlichen Beschreibungen, optimiert Keyframes fuer Performance und sichert Bewegungs-Barrierefreiheit. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Web AI", "narrower_terms": [], "cross_domain_refs": [ "MUS-0092" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "WEB-0087", "domain": "WEB", "term_en": "Timeout Error Unclear Retry Logic", "term_de": "SVG-Grafik", "definition_en": "A web architecture pattern in AI-augmented online systems, measurable through when AI-generated timeout error handling doesn't specify whether automatic retry is occurring, leaving users uncertain about the current operation state. Distinguished from adjacent concepts by its focus on the specific mechanism through which timeout manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch xML-basiertes Vektorbild-Format skalierbar ohne Qualitaetsverlust fuer aufloesungsunabhaengige Webgrafiken. KI generiert SVG-Grafiken aus Beschreibungen, optimiert Pfaddaten fuer Dateigroesse und erstellt dynamische datengetriebene Vektorvisualisierungen. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Web AI", "narrower_terms": [], "cross_domain_refs": [ "QUA-0072", "CUS-0071", "SCR-0054" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "WEB-0088", "domain": "WEB", "term_en": "Tooltip Implementation Latency", "term_de": "Canvas API", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A web technology phenomenon in AI-mediated digital interaction, characterized by an online interaction pattern manifesting as the observable delay between user hover intent and tooltip appearance created by AI-generated debounce and positioning logic. This phenomenon operates at the intersection of tooltip and implementation dynamics within the broader WEB domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept hTML5-Zeichenschnittstelle fuer programmatisches Rendern von 2D-Grafiken und Animationen im Browser. KI nutzt Canvas API fuer Echtzeit-Datenvisualisierung, interaktives ML-Modell-Output-Rendering und browserbasierte Bildverarbeitungspipelines. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Web AI", "narrower_terms": [], "cross_domain_refs": [ "RET-0049" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "WEB-0089", "domain": "WEB", "term_en": "Transpilation Target Assumptions Pattern", "term_de": "WebGL", "definition_en": "A web architecture pattern in AI-augmented online systems, measurable through a web development phenomenon characterized by when AI-generated JavaScript assumes a specific transpilation target but the actual configuration differs, creating runtime compatibility issues. The concept emerges specifically in contexts where transpilation–target interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch javaScript-API zum Rendern interaktiver 2D- und 3D-Grafiken im Browser mittels GPU-Beschleunigung. KI nutzt WebGL fuer browserbasierte neuronale Netzwerk-Visualisierung, GPU-beschleunigte Inferenz und interaktive 3D-Modell-Explorations-Interfaces. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Behavioral Pattern", "narrower_terms": [], "cross_domain_refs": [ "VIB-0070" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "WEB-0090", "domain": "WEB", "term_en": "User Agent Sniffing Fragility Pattern", "term_de": "Three.js", "definition_en": "A web architecture pattern in AI-augmented online systems, measurable through the brittleness that emerges when AI relies on user agent strings for browser detection rather than feature detection, creating false negatives. Distinguished from adjacent concepts by its focus on the specific mechanism through which user manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch javaScript-3D-Bibliothek zur Vereinfachung von WebGL fuer interaktive 3D-Web-Erlebnisse. KI nutzt Three.js fuer browserbasierte 3D-Datenvisualisierung, interaktive neuronale Netzwerk-Architekturexploration und immersive KI-Demo-Anwendungen. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2355", "narrower_terms": [], "cross_domain_refs": [ "DAT-0072" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "WEB-0091", "domain": "WEB", "term_en": "Vendor Prefix Inconsistency Handling", "term_de": "Zustandsverwaltung", "definition_en": "A web architecture pattern in AI-augmented online systems, measurable through a web development phenomenon arising from when AI-generated CSS includes vendor prefixes inconsistently or omits them, creating cross-browser rendering inconsistencies. The concept emerges specifically in contexts where vendor–prefix interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch ein KI-bezogenes Phänomen: When AI-generated CSS includes vendor prefixes inconsistently or omits them, creating cross-browser rendering inconsiste. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Web AI", "narrower_terms": [], "cross_domain_refs": [ "SWE-0095" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "WEB-0092", "domain": "WEB", "term_en": "Video Caption Automation Incompleteness", "term_de": "Redux", "definition_en": "A web architecture pattern in AI-augmented online systems, measurable through a web development phenomenon arising from when AI-generated video implementations include auto-generated captions but lack human review, resulting in accuracy errors and missing speaker identification. Distinguished from adjacent concepts by its focus on the specific mechanism through which video manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch vorhersagbarer Zustandscontainer für JavaScript-Anwendungen mit unidirektionalem Datenfluss über Actions, Reducer und einen einzelnen Store. KI-Dev-Tools erkennen Zustandsanomalien, schlagen Reducer-Optimierungen vor und generieren Boilerplate-Code automatisch. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2605", "narrower_terms": [], "cross_domain_refs": [ "TRA-0030" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q184199", "legal_classification": "observational_construct" }, { "id": "WEB-0093", "domain": "WEB", "term_en": "Web Standards Compliance Theater", "term_de": "Zustand", "definition_en": "A web architecture pattern in AI-augmented online systems, measurable through the pattern where AI-generated code passes automated compliance validators while failing to genuinely serve diverse users across network speeds, device capabilities, or accessibility needs. Distinguished from adjacent concepts by its focus on the specific mechanism through which web manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch ein Verhaltensmuster im System: ein Muster, bei dem n where ai-generated code passes automated complia. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Web AI", "narrower_terms": [], "cross_domain_refs": [ "SWE-0052", "WRK-0074" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "WEB-0094", "domain": "WEB", "term_en": "WebP Fallback Chain Complexity", "term_de": "Context API", "definition_en": "A web technology phenomenon in AI-mediated digital interaction, characterized by a web development phenomenon manifesting as the cumulative complexity of image format negotiation, fallback chains, and polyfills suggested by AI for optimal image delivery across browsers. Distinguished from adjacent concepts by its focus on the specific mechanism through which webp manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch ein KI-bezogenes Phänomen: charakterisiert durch the cumulative complexity of image format negotiation, fallback chains, and poly. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Web AI", "narrower_terms": [], "cross_domain_refs": [ "ART-0088" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "WEB-0095", "domain": "WEB", "term_en": "Whitespace Rendering Assumption", "term_de": "Reaktive Programmierung", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures A web architecture pattern in AI-augmented online systems, measurable through an online interaction pattern arising from when AI-generated HTML with strategic whitespace is minified, the invisible space characters that created intended visual separation disappear. This phenomenon operates at the intersection of whitespace and rendering dynamics within the broader WEB domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept programmierparadigma mit Fokus auf asynchrone Datenstroemme und Aenderungspropagierung. KI nutzt reaktive Muster fuer Echtzeit-Inferenz-Pipelines, ereignisgesteuerte Modell-Updates und responsive Streaming-Datenverarbeitungsarchitekturen. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Web AI", "narrower_terms": [], "cross_domain_refs": [ "CAI-0012" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "WRK-0001", "domain": "WRK", "term_en": "Accountability Clarity", "term_de": "AccountabilityClarity", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures an organizational pattern in AI-mediated work processes, measurable through an event in which when many individuals knows who made a decision and who is responsible for the results. For example, the manager owns the deadline and the team owns the quality. This phenomenon operates at the intersection of accountability and clarity dynamics within the broader WRK domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept die explizite Definition von wer Verantwortung für Entscheidungen, Ergebnisse und Qualität innerhalb hybrider menschlich-KI-Teams trägt. Klare Rahmen verhindern Mehrdeutigkeit über Eigentum und ermöglichen effektive Rückkopplungsschleifen. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Workplace AI", "narrower_terms": [], "cross_domain_refs": [ "BEH-0066" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q15223", "legal_classification": "systematic_classification" }, { "id": "WRK-0002", "domain": "WRK", "term_en": "Adaptive Talent Fusion", "term_de": "AdaptiveTalentFusion", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A workplace dynamics phenomenon in AI-augmented professional environments, characterized by a pattern in which mixing different skills and people flexibly depending on what the project needs right now. One week someone fills the technical role, the next week they contribute to strategy. This phenomenon operates at the intersection of adaptive and talent dynamics within the broader WRK domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept die Kombination von tiefem Wissen in Kern-Bereichen mit Flexibilität, um Verständnis über neue Kontexte und Herausforderungen hinweg anzuwenden. Talent-Fusion ermöglicht sowohl Meisterschaft als auch kreative Anpassung in sich entwickelnden beruflichen Landschaften. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Workplace AI", "narrower_terms": [], "cross_domain_refs": [ "EDU-0015" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "WRK-0003", "domain": "WRK", "term_en": "Adaptive Talent Search", "term_de": "AdaptiveTalentSearch", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A workplace dynamics phenomenon in AI-augmented professional environments, characterized by finding people for a job based on what the role actually requires, not just job titles. Hiring someone skilled at challenge-solving even if they have worked in different fields before. This phenomenon operates at the intersection of adaptive and talent dynamics within the broader WRK domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept die Recruitment-Methodik, die prioritisiert, Kandidaten zu identifizieren, deren Fähigkeiten inmitten technologischer Veränderung wertvoll bleiben. Der Fokus verschiebt sich von spezifischen technischen Fähigkeiten zu Anpassungsfähigkeit, Lernorientierung und menschenzentrierten Stärken. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "RPH-2002", "narrower_terms": [], "cross_domain_refs": [ "AGE-0046", "ASE-0004", "ASE-0005" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "WRK-0004", "domain": "WRK", "term_en": "Agency Expression", "term_de": "AgencyExpression", "definition_en": "An event in which being able to speak up and act on what someone actually thinks, not just agreeing with many individuals else. Ideas matter even when they differ from the group. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch die Freiheit, Projekte, Kunden und Arbeits-Methoden zu wählen, die mit persönlichen Werten und beruflichen Zielen ausgerichtet sind. Agentur schafft intrinsische Motivation und authentisches Engagement. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2605", "narrower_terms": [], "cross_domain_refs": [ "EDU-0020" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "WRK-0005", "domain": "WRK", "term_en": "Amplification Shift", "term_de": "VerstärkungShift", "definition_en": "An organizational pattern in AI-mediated work processes, measurable through an event in which when someone's strength gets bigger and more visible because the work environment changed. Like a shy person becomes central once meetings go async. The concept emerges specifically in contexts where amplification–shift interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch die Bewegung von ausführungsfokussierter Arbeit zu Überwachungs-, Strategie- und Verbesserungsrollen, wenn Routine-Aufgaben automatisiert werden. Fachleute wechseln zu Verstärkung und Steuerung von Maschinenkapazitäten statt wiederholter Operationen. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Workplace AI", "narrower_terms": [], "cross_domain_refs": [ "CRE-0036" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "WRK-0006", "domain": "WRK", "term_en": "Async Intelligence", "term_de": "AsyncIntelligence", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A workplace dynamics phenomenon in AI-augmented professional environments, characterized by an event in which getting smart insights from work that happens in different time zones, on inreliant schedules. many individuals writes down what they know so others can read it when they want. This phenomenon operates at the intersection of async and intelligence dynamics within the broader WRK domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept die Kapazität, bedeutungsvolle kollaborative Arbeit, Entscheidungsfindung und Problemlösung ohne reale Präsenzzeit zu erreichen. Teams entwickeln intelligente asynchrone Arbeitsabläufe, die Denk-Qualität über Zeitzonen hinweg bewahren. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Workplace AI", "narrower_terms": [], "cross_domain_refs": [ "EDU-0017", "IDN-0003", "PER-0021" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q5921", "legal_classification": "empirical_phenomenon_label" }, { "id": "WRK-0007", "domain": "WRK", "term_en": "Async-First Thinking", "term_de": "Async-firstThinking", "definition_en": "An organizational pattern in AI-mediated work processes, measurable through a gap in which designing work to happen without real-time meetings first. Write it down, share it, give people time to think, then discuss. Distinguished from adjacent concepts by its focus on the specific mechanism through which async manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch die organisatorische Praxis, auf asynchrone Kommunikation und Dokumentation zu setzen, synchrone Meetings nur zu planen, wenn reale Interaktion echten Wert hinzufügt. Dies bewahrt Fokus-Zeit und reduziert Meeting-Last. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Workplace AI", "narrower_terms": [], "cross_domain_refs": [ "ADA-0002", "COG-0023", "COG-0079" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "WRK-0008", "domain": "WRK", "term_en": "Asynchronous Conversation", "term_de": "AsynchronousConversation", "definition_en": "A workplace dynamics phenomenon in AI-augmented professional environments, characterized by a phenomenon in which talking across days and time zones instead of needing many individuals on a call at once. Someone writes a question, someone answers it hours later, another person adds context tomorrow. Distinguished from adjacent concepts by its focus on the specific mechanism through which asynchronous manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch der anhaltende Austausch von Ideen, Fragen und Antworten über Zeit und Raum, ermöglicht durch persistente Dokumentation und durchdachte Verflechtung. Async-Konversation ermöglicht tiefere Reflexion als Echtzeit-Chat. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Workplace AI", "narrower_terms": [], "cross_domain_refs": [ "CRE-0135" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "WRK-0009", "domain": "WRK", "term_en": "Asynchronous Synthesis", "term_de": "AsynchronousSynthesis", "definition_en": "An organizational pattern in AI-mediated work processes, measurable through a phenomenon in which pulling together ideas and information that people shared at different times. Instead of talking in meetings, people leave thoughts in writing for others to build on later. The concept emerges specifically in contexts where asynchronous–synthesis interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch die Dokumentation und Synthese von asynchronem Input und Denken zu kohärentem organisatorischem Verständnis und Entscheidungen. Async-Methoden werden erstklassige Beitrag-Leister zur Entscheidungsfindung. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Workplace AI", "narrower_terms": [], "cross_domain_refs": [ "AGE-0014" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "WRK-0010", "domain": "WRK", "term_en": "Authentic Presence", "term_de": "AuthenticPresence", "definition_en": "A workplace dynamics phenomenon in AI-augmented professional environments, characterized by a phenomenon in which showing up as oneself—with real thoughts and personality—not a pretend work version. People know what colleagues actually think and how they actually work. The concept emerges specifically in contexts where authentic–presence interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch die Kapazität, authentisch in beruflichen Settings zu erscheinen, das volle Selbst zu bringen, während angemessene Grenzen bewahrt werden. Präsenz schafft Vertrauen, Verbindung und echte Zusammenarbeit. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Workplace AI", "narrower_terms": [], "cross_domain_refs": [ "COG-0127", "DES-0005", "EDU-0008" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "WRK-0011", "domain": "WRK", "term_en": "Autonomy Ecosystem", "term_de": "AutonomyEcosystem", "definition_en": "An organizational pattern in AI-mediated work processes, measurable through a state in which a workplace where people can do their jobs without micromanagement. Workers decide how to accomplish tasks, not just execute a predetermined method. The concept emerges specifically in contexts where autonomy–ecosystem interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch das vernetzte Netzwerk von Projekten, Kunden und Zusammenarbeit, die Freiberuflern mit anhaltenden Engagement und wirtschaftlicher Sicherheit bieten. Das Ökosystem balanciert Flexibilität mit stabiler Gelegenheits-Fluss. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Workplace AI", "narrower_terms": [], "cross_domain_refs": [ "NEO-0010" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "WRK-0012", "domain": "WRK", "term_en": "Autonomy Maturity", "term_de": "AutonomyMaturity", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A workplace dynamics phenomenon in AI-augmented professional environments, characterized by an event in which when a team is ready to manage itself well without constant check-ins. People know what matters, coordinate with each other, fix challenges without asking permission. This phenomenon operates at the intersection of autonomy and maturity dynamics within the broader WRK domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept die Entwicklung von individueller und Team-Kapazität, Entscheidungen zu treffen, Arbeit zu verwalten und bedeutungsvoll ohne konstante Überwachung oder reale Anleitung beizutragen. Remote-Arbeit verstärkt die Wichtigkeit von reifer Selbst-Richtung. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Workplace AI", "narrower_terms": [], "cross_domain_refs": [ "AED-0005", "AED-0052", "ASE-0041" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "WRK-0013", "domain": "WRK", "term_en": "Belonging Architecture", "term_de": "BelongingArchitecture", "definition_en": "An organizational pattern in AI-mediated work processes, measurable through a state in which building a workplace where people actually feel they belong—that their background and ideas fit in, not stick out. many individuals gets a real voice. Distinguished from adjacent concepts by its focus on the specific mechanism through which belonging manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data. Classification term used in systematic observation, not advocacy.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch das absichtliche Design von organisatorischen Praktiken und Kommunikationskanälen, die echte Verbindung und Inklusion unter verteilten Arbeitern fördern. Zugehörigkeit entsteht aus absichtlichem Kultur-Aufbau statt physischer Nähe. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-3202", "narrower_terms": [], "cross_domain_refs": [ "AGE-0015", "REL-0074" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "WRK-0014", "domain": "WRK", "term_en": "Boundary Articulation", "term_de": "GrenzeArticulation", "definition_en": "An organizational pattern in AI-mediated work processes, measurable through a phenomenon in which being clear about what's yours to do and what's not. Clear lines reduce work from piling up on the wrong person. Distinguished from adjacent concepts by its focus on the specific mechanism through which boundary manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch die klare Kommunikation von Verfügbarkeit, Umfangs-Grenzen und Arbeits-Bedingungen, die Freiberuflern nachhaltiger Praxis ermöglichen. Artikulation zielt darauf ab zu mitigieren Umfangs-Ausweitung und schützt Zeit für Wachstum. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Workplace AI", "narrower_terms": [], "cross_domain_refs": [ "SPR-0089" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q735", "legal_classification": "analytical_category" }, { "id": "WRK-0015", "domain": "WRK", "term_en": "Capability Layering", "term_de": "CapabilityLayering", "definition_en": "An organizational pattern in AI-mediated work processes, measurable through a capability in which building skills in stages so each person can handle increasingly difficult challenges. The basics come first, then intermediate challenges, then advanced complexity. Distinguished from adjacent concepts by its focus on the specific mechanism through which capability manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch die strategische Integration neuer KI- und Automatisierungs-Fähigkeiten neben bestehender menschlicher Expertise, die organisatorische Tiefe statt Ersatz schafft. Schichten ergänzen sich gegenseitig und erhöhen Gesamt-Effektivität. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Abstraction Layer", "narrower_terms": [], "cross_domain_refs": [ "COG-0059", "CUS-0015", "EDU-0012" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "WRK-0016", "domain": "WRK", "term_en": "Capability Renaissance", "term_de": "CapabilityRenaissance", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A workplace dynamics phenomenon in AI-augmented professional environments, characterized by a capability in which a surge of new and revived skills across a team, often from AI tools or new ways of working. People discover they can do things they thought were extremely difficult. This phenomenon operates at the intersection of capability and renaissance dynamics within the broader WRK domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept der Prozess der Entdeckung und Aktivierung schlafender beruflicher Fähigkeiten, wenn sich Rollen zur KI-Zusammenarbeit verschieben. Personen erkennen bestehende Kompetenzen in neuen Kontexten und schaffen Wege für erneuerte Engagement und Beitrag. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "RPH-2255", "narrower_terms": [], "cross_domain_refs": [ "AGE-0093" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "WRK-0017", "domain": "WRK", "term_en": "Clarity Cultivation", "term_de": "ClarityCultivation", "definition_en": "A workplace dynamics phenomenon in AI-augmented professional environments, characterized by a phenomenon in which working actively to make goals, roles, and decisions crystal clear. Not assuming people understand—explicitly saying what's expected. Distinguished from adjacent concepts by its focus on the specific mechanism through which clarity manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch die bewusste Praxis, Gedanken, Entscheidungen und Kontext mit Präzision und Zugänglichkeit zu artikulieren. Klare Kommunikation reduziert Missverständnis und ermöglicht verteilten Teams, effektiv zu funktionieren. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Workplace AI", "narrower_terms": [], "cross_domain_refs": [ "CRE-0005" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "WRK-0018", "domain": "WRK", "term_en": "Cognitive Pairing", "term_de": "CognitivePairing", "definition_en": "A workplace dynamics phenomenon in AI-augmented professional environments, characterized by two people thinking through a challenge together, each using their different strengths. One spots edge cases, one thinks about the big picture. The concept emerges specifically in contexts where cognitive–pairing interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch synchrone Zusammenarbeit, wo Paare oder kleine Gruppen parallel an Denken und Problemlösung arbeiten. Echtzeit-Arbeit ermöglicht iterative Ideen-Entwicklung und gegenseitige kognitive Unterstützung. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Workplace AI", "narrower_terms": [], "cross_domain_refs": [ "REL-0206" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "WRK-0019", "domain": "WRK", "term_en": "Collaborative Architecture", "term_de": "CollaborativeArchitecture", "definition_en": "A workplace dynamics phenomenon in AI-augmented professional environments, characterized by a phenomenon in which designing how a team and systems work together so collaboration happens naturally. It is built into how work flows, not forced through extra meetings. Distinguished from adjacent concepts by its focus on the specific mechanism through which collaborative manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch das absichtliche Design von Teamstrukturen, wo menschliche Mitglieder und KI-Systeme in komplementären Rollen arbeiten. Diese Struktur betont Klarheit darüber, wer was beiträgt und schafft natürliche gegenseitige Abhängigkeiten. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Workplace AI", "narrower_terms": [], "cross_domain_refs": [ "RPH-3353" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "WRK-0020", "domain": "WRK", "term_en": "Collaborative Learning Orientation", "term_de": "CollaborativeLearningOrientation", "definition_en": "A phenomenon in which a team that grows by learning together, not just individually. People share what they figure out so the whole team gets smarter. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch die Identifikation von Kandidaten, die Begeisterung für Lernen durch Zusammenarbeit mit anderen und Technologie zeigen. Das Einstellen schätzt diejenigen, die Wachstum durch Partnerschaft statt einzelne Anstrengung sehen. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-2703", "narrower_terms": [], "cross_domain_refs": [ "AED-0001", "AED-0002", "AED-0004" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q133500", "legal_classification": "empirical_phenomenon_label" }, { "id": "WRK-0021", "domain": "WRK", "term_en": "Collective Emergence", "term_de": "CollectiveEmergenz", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures an organizational pattern in AI-mediated work processes, measurable through an event in which something new and unexpected surfaces when a team works together that few individuals could see coming alone. The result is bigger than the sum of individual ideas. This phenomenon operates at the intersection of collective and emergence dynamics within the broader WRK domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept das Phänomen, wo Team-Fähigkeiten übertreffen, was viele einzelne Mitglied oder System allein erreichen könnte, das aus echter Zusammenarbeit und Wissens-Synthese entsteht. Wert entsteht aus Interaktion, nicht Akkumulation. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Workplace AI", "narrower_terms": [], "cross_domain_refs": [ "EDU-0093" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "WRK-0022", "domain": "WRK", "term_en": "Collective Memory", "term_de": "CollectiveMemory", "definition_en": "An event in which documentation and shared records of what the team has learned, solved, and knows. So when someone leaves or forgets, that knowledge stays. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch die systematische Erfassung und Organisation von organisatorischen Lernungen, Entscheidungen und Einblicken in zugängliche Formate, die über Zeit und Personalveränderungen hinweg bestehen. Gedächtnis ermöglicht Organisationen, aus Erfahrung zu lernen. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-2703", "narrower_terms": [], "cross_domain_refs": [ "COG-0167" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q492", "legal_classification": "empirical_phenomenon_label" }, { "id": "WRK-0023", "domain": "WRK", "term_en": "Community Navigation", "term_de": "CommunityNavigation", "definition_en": "A state in which learning how to move through a community or network effectively. Knowing who to talk to, where information lives, and how things actually work. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch das Engagement mit Peer-Netzwerken, beruflichen Gemeinschaften und kollaborativen Gruppen, die Lernen, Unterstützung und Gelegenheits-Identifikation bieten. Navigation transformiert Solitude in Verbindung. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2703", "narrower_terms": [], "cross_domain_refs": [ "EDU-0081" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "WRK-0024", "domain": "WRK", "term_en": "Competence Confidence", "term_de": "CompetenceConfidence", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A workplace dynamics phenomenon in AI-augmented professional environments, characterized by an experience in which feeling sure about one's skills because one has done hard things successfully. Confidence comes from real experience, not just positive feedback. This phenomenon operates at the intersection of competence and confidence dynamics within the broader WRK domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept das begründete Gefühl von Fähigkeit und Selbstsicherheit, die in nachgewiesener Fähigkeit, Erfahrung und Track Record verwurzelt ist. Vertrauen ermöglicht Herausforderung-Einnahme und Wachstum ohne Arroganz. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Workplace AI", "narrower_terms": [], "cross_domain_refs": [ "SPR-0094" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q26944", "legal_classification": "systematic_classification" }, { "id": "WRK-0025", "domain": "WRK", "term_en": "Complementary Rhythm", "term_de": "ComplementaryRhythm", "definition_en": "An organizational pattern in AI-mediated work processes, measurable through a phenomenon in which people working in different time zones or schedules but still supporting each other. One person wraps up their day, the next person picks up clean work. Distinguished from adjacent concepts by its focus on the specific mechanism through which complementary manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch die Synchronisierung von Arbeits-Tempo und Timing, wo menschlich-gepacter Überlegung und Entscheidungsfindung mit maschinell-getriebenem Analyse- und Ausführungstempo harmonisiert. Teams finden optimale Kadenz für kombinierte Operationen. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Workplace AI", "narrower_terms": [], "cross_domain_refs": [ "FIC-0046", "LNG-0017", "MUS-0025" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "WRK-0026", "domain": "WRK", "term_en": "Context Continuity", "term_de": "ContextContinuity", "definition_en": "An organizational pattern in AI-mediated work processes, measurable through an event in which making sure when something is handed off, the next person understands what the previous person was thinking and why. Not just the code or document, but the reasoning. The concept emerges specifically in contexts where context–continuity interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch die Praxis, gemeinsames Verständnis und Projekt-Kontext über verteilte Teams durch persistent Dokumentation und transparentes Wissens-Sharing zu bewahren. Organisatorisches Gedächtnis bleibt zugänglich unabhängig von Standort oder Timing. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Workplace AI", "narrower_terms": [], "cross_domain_refs": [ "EDU-0065" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "WRK-0027", "domain": "WRK", "term_en": "Context Expansion", "term_de": "ContextExpansion", "definition_en": "A workplace dynamics phenomenon in AI-augmented professional environments, characterized by a phenomenon in which using human judgment to make AI-generated work actually useful. AI provides a draft; people add the experience and understanding that makes it real. The concept emerges specifically in contexts where context–expansion interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch die Verbesserung von KI-generierten Ergebnissen durch menschliches Kontextwissen, ethische Überlegungen und Situationsbewusstsein. Teams nutzen Maschineneffizienz als Grundlage und Menschen als Kontext-Bereicherer. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Workplace AI", "narrower_terms": [], "cross_domain_refs": [ "EDU-0033" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "WRK-0028", "domain": "WRK", "term_en": "Context Preservation", "term_de": "ContextPreservation", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A workplace dynamics phenomenon in AI-augmented professional environments, characterized by a realization in which keeping the background information that makes work make sense. New team members understand not just what was decided but why. This phenomenon operates at the intersection of context and preservation dynamics within the broader WRK domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept die Dokumentation nicht nur von dem, was entschieden oder gelernt wurde, sondern auch der Begründung, des Kontexts und der Überlegungen, die zu Conclusionen führten. Bewahrung ermöglicht anderen, vergangenes Denken zu verstehen und aufzubauen. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "RPH-3202", "narrower_terms": [], "cross_domain_refs": [ "KNO-0014" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "WRK-0029", "domain": "WRK", "term_en": "Contextual Scaffolding", "term_de": "ContextualScaffolding", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A workplace dynamics phenomenon in AI-augmented professional environments, characterized by a state in which giving people the information and structure for unfamiliar work — temporary support that gets removed once the task becomes familiar. This phenomenon operates at the intersection of contextual and scaffolding dynamics within the broader WRK domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept die geschichtete Bereitstellung von Hintergrund-Informationen, Rahmen und verbindenden Details, die anderen ermöglichen, Kommunikation im richtigen Kontext zu verstehen. Scaffolding reduziert Informationslücken in verteilten Settings. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Workplace AI", "narrower_terms": [], "cross_domain_refs": [ "SPR-0016" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "WRK-0030", "domain": "WRK", "term_en": "Continuous Becoming", "term_de": "ContinuousBecoming", "definition_en": "An organizational pattern in AI-mediated work processes, measurable through a phenomenon in which a job and identity that keep evolving. It is not a fixed role; it is typically growing into something new. The concept emerges specifically in contexts where continuous–becoming interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch das Verständnis von beruflichem Selbst als sich über Zeit entwickelnd statt festgelegt. Diese Perspektive ermöglicht Umarmung von Veränderung, Lernen und Neuerfindung über die Karriere hinweg. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Workplace AI", "narrower_terms": [], "cross_domain_refs": [ "AED-0085" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "WRK-0031", "domain": "WRK", "term_en": "Contribution Clarity", "term_de": "ContributionClarity", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures an organizational pattern in AI-mediated work processes, measurable through a phenomenon in which making visible exactly what each person brtends to a project. Not just that they were in the room, but what they specifically did. This phenomenon operates at the intersection of contribution and clarity dynamics within the broader WRK domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept das klare Verständnis von dem, was die eigenen Beiträge unterscheidet und in beruflichen Kontexten einzigartigen Wert hat. Klarheit ermöglicht authentische Positionierung und bedeutungsvolle Zusammenarbeit. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Workplace AI", "narrower_terms": [], "cross_domain_refs": [ "TEM-0036" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "WRK-0032", "domain": "WRK", "term_en": "Creative Frontline", "term_de": "CreativeFrontline", "definition_en": "An organizational pattern in AI-mediated work processes, measurable through a phenomenon in which people closest to customers or actual challenges becoming the ones who solve and improve things. The front desk knows what breaks, so they fix it. The concept emerges specifically in contexts where creative–frontline interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch die Erweiterung von Positionen, die auf Ideation, künstlerische Leitung und neuartige Problemlösung konzentrieren, wenn KI Routine-Generierungsaufgaben handhabt. Kreativität wird zu einer primären beruflichen Währung. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2951", "narrower_terms": [], "cross_domain_refs": [ "COG-0169", "CRE-0034", "CRE-0040" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "WRK-0033", "domain": "WRK", "term_en": "Cross-Domain Capability", "term_de": "Cross-domainCapability", "definition_en": "A workplace dynamics phenomenon in AI-augmented professional environments, characterized by a capability in which an AI can handle tasks in completely different fields, like switching from writing to coding to explaining science. The concept emerges specifically in contexts where cross–domain interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch die Recruitment-Präferenz für Kandidaten, die Erfahrung oder Interesse über mehrere berufliche Bereiche hinweg bringen. Diese Breite ermöglicht bessere Entscheidungsfindung bei der Steuerung von KI-Systemen über verschiedene Anwendungen. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Workplace AI", "narrower_terms": [], "cross_domain_refs": [ "CRE-0216" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "WRK-0034", "domain": "WRK", "term_en": "Decision Acceleration", "term_de": "DecisionBeschleunigung", "definition_en": "An organizational pattern in AI-mediated work processes, measurable through a phenomenon in which making decisions faster by not waiting for many individuals to be in a room. Async feedback, clear deadlines, clear decision-maker. Distinguished from adjacent concepts by its focus on the specific mechanism through which decision manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch die Nutzung von Meetings speziell für Entscheidungen, die reale Dialog und Urteil erfordern. Vorbereitung stellt sicher, dass Meetings auf Überlegung konzentrieren statt Informations-Beschaffung oder Konsens-Bildung. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Analytical Ratio", "narrower_terms": [], "cross_domain_refs": [ "ADA-0005", "AED-0094", "CAI-0002" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "WRK-0035", "domain": "WRK", "term_en": "Dialogue Continuity", "term_de": "DialogueContinuity", "definition_en": "An organizational pattern in AI-mediated work processes, measurable through a phenomenon in which keeping conversations going over time across different people and sessions. The previous conversation doesn't just vanish; it informs the next one. The concept emerges specifically in contexts where dialogue–continuity interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch das anhaltende Engagement in Hin-und-Her-Austausch, wo beide Parteien aktiv zuhören, fragen und Verständnis zusammen bauen. Kontinuität transformiert Einweg-Informationsübertragung in gegenseitige Sinn-Generierung. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Workplace AI", "narrower_terms": [], "cross_domain_refs": [ "ADA-0006" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "WRK-0036", "domain": "WRK", "term_en": "Dialogue Depth", "term_de": "DialogueDepth", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures an organizational pattern in AI-mediated work processes, measurable through a phenomenon in which conversations that go beyond surface level to real understanding. People say what they actually think, not what's safe to say. This phenomenon operates at the intersection of dialogue and depth dynamics within the broader WRK domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept die kultivierte Kapazität für anhaltende, nuancierte Gespräche, die über oberflächliche Updates hinausgehen. Meetings werden zu Räumen für echte Erforschung, Herausforderung und Sinn-Generierung. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Workplace AI", "narrower_terms": [], "cross_domain_refs": [ "EDU-0020" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "WRK-0037", "domain": "WRK", "term_en": "Discovery Architecture", "term_de": "DiscoveryArchitecture", "definition_en": "A phenomenon in which building how a team explores new ideas and finds solutions. Paths for experimenting and learning that actually happen. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch das Design von Wissens-Systemen und Navigations-Strukturen, die organisatorische Informationen findbar und nutzbar machen. Architektur transformiert Repositories von Archiven zu lebenden Ressourcen. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-2703", "narrower_terms": [], "cross_domain_refs": [ "EDU-0036" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "WRK-0038", "domain": "WRK", "term_en": "Distinctive Voice", "term_de": "DistinctiveVoice", "definition_en": "An organizational pattern in AI-mediated work processes, measurable through a principle in which having one's own perspective and the safety to share it. A thinking pattern or viewpoint is recognizable and valued. The concept emerges specifically in contexts where distinctive–voice interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch die Entwicklung von einzigartiger Perspektive, Stil und Herangehensweise an Arbeit, die individuelles Denken und Werte widerspiegelt. Stimme wird in beruflichen Kontexten erkennbar und geschätzt. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-3101", "narrower_terms": [ "WRK-0083", "WRK-0009", "WRK-0074", "WRK-0031", "WRK-0027", "WRK-0041", "WRK-0085", "WRK-0035", "WRK-0078", "WRK-0046", "WRK-0067", "WRK-0034", "WRK-0079", "WRK-0064", "WRK-0008", "WRK-0017", "WRK-0005", "WRK-0018", "WRK-0086", "WRK-0024", "WRK-0011", "WRK-0071", "WRK-0063", "WRK-0043", "WRK-0025", "WRK-0039", "WRK-0012", "WRK-0015", "FIC-0008", "WRK-0049", "WRK-0021", "WRK-0076", "STE-0078", "WRK-0030", "WRK-0048", "WRK-0002", "WRK-0056", "WRK-0036", "WRK-0006", "WRK-0040", "WRK-0088", "WRK-0095", "WRK-0077", "WRK-0026", "WRK-0094", "WRK-0052", "WRK-0073", "COP-0044", "WRK-0014", "WRK-0010", "WRK-0091", "WRK-0042", "WRK-0033", "WRK-0001", "WRK-0029", "WRK-0051", "WRK-0059", "WRK-0092", "WRK-0066", "WRK-0053", "WRK-0054", "WRK-0019", "WRK-0057", "WRK-0089", "WRK-0007", "WRK-0062", "WRK-0099", "WRK-0072" ], "cross_domain_refs": [ "COG-0193" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "WRK-0039", "domain": "WRK", "term_en": "Distributed Cognition", "term_de": "DistributedCognition", "definition_en": "A workplace dynamics phenomenon in AI-augmented professional environments, characterized by a phenomenon in which the team's thinking spread across people, not just in one person's head. Different people know different critical things. The concept emerges specifically in contexts where distributed–cognition interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch die Anerkennung, dass Denken und Problemlösung aus der kombinierten Intelligenz menschlicher Köpfe und gemeinsam funktionierender KI-Systeme entstehen. Kognition wird zu einer Team-Eigenschaft statt einer individuellen Eigenschaft. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Workplace AI", "narrower_terms": [], "cross_domain_refs": [ "AGE-0004" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q3966", "legal_classification": "descriptive_research_term" }, { "id": "WRK-0040", "domain": "WRK", "term_en": "Distributed Moderation", "term_de": "DistributedModeration", "definition_en": "Classified in AUGMANITAI terminology science as a descriptive phenomenon (without normative endorsement), this pattern denotes A workplace dynamics phenomenon in AI-augmented professional environments, characterized by a phenomenon in which many individuals helps keep conversations restorethy and on track, not just one moderator. People self-regulate how they interact. Distinguished from adjacent concepts by its focus on the specific mechanism through which distributed manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als analytisches Konstrukt der empirischen KI-Forschung (deskriptiv, nicht präskriptiv) erfasst dieser Terminus die gemeinsame Verantwortung für die Führung von Diskussionen, die Verwaltung von Teilnahme und die Sicherstellung, dass zahlreiche Stimmen zu Meeting-Ergebnissen beitragen. Moderation wechselt statt in einer einzelnen Führung zu zentralisieren. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Analytical Ratio", "narrower_terms": [], "cross_domain_refs": [ "CON-0020", "ETH-0011", "SPA-0090" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "WRK-0041", "domain": "WRK", "term_en": "Distributed Presence", "term_de": "DistributedPresence", "definition_en": "A workplace dynamics phenomenon in AI-augmented professional environments, characterized by an event in which people being meaningfully involved even when working from different places and times. Async doesn't mean invisible. The concept emerges specifically in contexts where distributed–presence interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch die Kultivierung von sinnvoller Verbindung und beobachtbarem Engagement unter Teammitgliedern, die über verschiedene Standorte und Zeitzonen hinweg arbeiten. Präsenz wird durch konsistente Beiträge und sichtbare Teilnahme statt physischer Nähe ausgedrückt. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Workplace AI", "narrower_terms": [], "cross_domain_refs": [ "SPR-0014" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "WRK-0042", "domain": "WRK", "term_en": "Energy Stewardship", "term_de": "EnergyStewardship", "definition_en": "A workplace dynamics phenomenon in AI-augmented professional environments, characterized by an event in which managing one's own and a team's mental energy so people don't burn out. Knowing when to push and when to ease up. The concept emerges specifically in contexts where energy–stewardship interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch die achtungsvolle Verwaltung von Aufmerksamkeit, Engagement und kognitiver Energie in Gruppen-Settings. Moderatoren schaffen Bedingungen für anhaltenden Fokus und bedeutungsvolle Teilnahme ohne Teilnehmer zu überfordern. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Workplace AI", "narrower_terms": [], "cross_domain_refs": [ "KNO-0005" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "WRK-0043", "domain": "WRK", "term_en": "Ethical Judgment Range", "term_de": "EthicalUrteilRange", "definition_en": "An organizational pattern in AI-mediated work processes, measurable through a capability in which being able to handle a wide range of messy real-world situations that don't have clear answers. People grow from working through difficult decisions. The concept emerges specifically in contexts where ethical–judgment interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch die Identifikation von Kandidaten, deren persönliche Werte und ethisches Denken Sound-Wahlen über KI-Bereitstellung und Entscheidungsfindung ermöglichen. Das Einstellen betont moralische Reife und Prinzipien-Denken. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Workplace AI", "narrower_terms": [], "cross_domain_refs": [ "RHR-0200" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "WRK-0044", "domain": "WRK", "term_en": "Evolutionary Adaptation", "term_de": "EvolutionaryAnpassung", "definition_en": "A dynamic in which when an organization smoothly evolves to handle big changes—like major tech shifts—without falling apart. Like a river changing course but still flowing. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch die allmähliche, kontinuierliche Veränderungsmuster von organisatorischen Strukturen, Praktiken und Fähigkeiten als Reaktion auf technologische und Markt-Verschiebungen. Veränderung erfolgt durch inkrementelle Anpassungen, die Betriebs-Kontinuität bewahren. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2701", "narrower_terms": [], "cross_domain_refs": [ "CRE-0110" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "WRK-0045", "domain": "WRK", "term_en": "Experiential Archive", "term_de": "ExperientialArchive", "definition_en": "A phenomenon in which storing and sharing what people have learned from actually doing the work, not just from reading about it. The 'here's what we tried and what happened' documentation. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch das Repository von dokumentierten Erfahrungen, Fallstudien und praktischen Beispielen, das Organisationen ermöglicht, von ihren eigenen und anderen Versuchen zu lernen. Archiv transformiert individuelle Erfahrung in organisatorische Weisheit. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2703", "narrower_terms": [], "cross_domain_refs": [ "AGE-0058" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "WRK-0046", "domain": "WRK", "term_en": "Expertise Mapping", "term_de": "ExpertiseMapping", "definition_en": "A workplace dynamics phenomenon in AI-augmented professional environments, characterized by an event in which knowing who on the team knows what. It's easier to find help when one can see the expertise map. The concept emerges specifically in contexts where expertise–mapping interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch die systematische Identifikation und Dokumentation von wer welches Wissen, Fähigkeiten und Expertise in der Organisation besitzt. Kartierung ermöglicht effizienten Wissens-Fluss und Verbindung. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Workplace AI", "narrower_terms": [], "cross_domain_refs": [ "PER-0069" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "WRK-0047", "domain": "WRK", "term_en": "Feedback Velocity", "term_de": "RückkopplungVelocity", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A workplace dynamics phenomenon in AI-augmented professional environments, characterized by a phenomenon in which getting feedback fast enough to fix things before they break. Quick iterations of observation, response, and adjustment. This phenomenon operates at the intersection of feedback and velocity dynamics within the broader WRK domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept der schnelle, kontinuierliche Austausch von Input, Antwort und Anpassung in Arbeits-Interaktionen. Schnelle Feedback-Schleifen ermöglichen schnelles Lernen, Kurs-Korrektur und reaktive Zusammenarbeit. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "RPH-3001", "narrower_terms": [], "cross_domain_refs": [ "EDU-0071" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "WRK-0048", "domain": "WRK", "term_en": "Fluency Integration", "term_de": "FluencyIntegration", "definition_en": "An organizational pattern in AI-mediated work processes, measurable through a capability in which moving smoothly between different ways of working or thinking without friction. Being equally skilled at async and real-time, or technical and people work. The concept emerges specifically in contexts where fluency–integration interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch der Recruitment-Fokus auf Kandidaten, die Vertrautheit mit Technologie neben starken grundlegenden menschlichen Fähigkeiten zeigen. Organisationen suchen Menschen, die sowohl menschliche Beziehungen als auch digitale Werkzeuge natürlich navigieren. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Analytical Ratio", "narrower_terms": [], "cross_domain_refs": [ "ROB-0017" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "WRK-0049", "domain": "WRK", "term_en": "Focus Enhancement", "term_de": "FocusEnhancement", "definition_en": "A workplace dynamics phenomenon in AI-augmented professional environments, characterized by a phenomenon in which getting more effectively at concentrating on what matters by removing noise and interruptions. Async work and clear priorities both help. Distinguished from adjacent concepts by its focus on the specific mechanism through which focus manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch die Schaffung von Bedingungen, wo tiefe, konzentrierte Arbeit durch reduzierte Unterbrechungen und optimierte Umgebungskontrolle zugänglicher wird. Remote-Vereinbarungen ermöglichen verlängerte Perioden von ununterbrochener Überlegung. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Workplace AI", "narrower_terms": [], "cross_domain_refs": [ "EDU-0038", "MTH-0010", "PER-0061" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "WRK-0050", "domain": "WRK", "term_en": "Growth Narrative", "term_de": "GrowthNarrative", "definition_en": "A phenomenon in which how someone has grown—specific examples of what they learned and how they changed. Not \"I'm more effectively at everything,\" but \"I worked through with X and now I'm good at it.\". Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch die persönliche Geschichte von Entwicklung, Lernen und Evolution, die berufliche Identität als Prozess statt Ziel rahmt. Erzählung schafft Kohärenz und Zweck in der Karriere-Entwicklung. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2703", "narrower_terms": [], "cross_domain_refs": [ "REL-0061" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q131324", "legal_classification": "analytical_category" }, { "id": "WRK-0051", "domain": "WRK", "term_en": "Hybrid Capability Mapping", "term_de": "HybridCapabilityMapping", "definition_en": "An organizational pattern in AI-mediated work processes, measurable through a state in which understanding what humans do best, what AI does best, and where together is best. Then building work around those strengths. Distinguished from adjacent concepts by its focus on the specific mechanism through which hybrid manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch das Bewertungs-Framework, das Kandidaten für ihr Potenzial bewertet, effektiv mit KI-Systemen neben traditioneller Fachkompetenz zu arbeiten. Bewertungen messen sowohl technische Kompetenz als auch Kooperationspotenzial mit Maschinen. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Workplace AI", "narrower_terms": [], "cross_domain_refs": [ "ROB-0079" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "WRK-0052", "domain": "WRK", "term_en": "Hybrid Identity", "term_de": "HybridIdentity", "definition_en": "An organizational pattern in AI-mediated work processes, measurable through a capability in which being comfortable working alongside AI, knowing what it can and can't do, and seeing oneself as part human-plus-AI. Distinguished from adjacent concepts by its focus on the specific mechanism through which hybrid manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch das integrierte berufliche Selbst, das menschliche Expertise mit KI-Tool-Flüssigkeit ausgleicht. Diese Identität umfasst sowohl traditionelles Fachwissen als auch aufstrebende Kompetenzen in Technologie-Partnerschaft. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Workplace AI", "narrower_terms": [], "cross_domain_refs": [ "EDU-0034" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q844396", "legal_classification": "systematic_classification" }, { "id": "WRK-0053", "domain": "WRK", "term_en": "Income Diversification", "term_de": "IncomeDiversification", "definition_en": "A workplace dynamics phenomenon in AI-augmented professional environments, characterized by a phenomenon in which getting money from multiple sources instead of one paycheck. For example, a job plus freelance work plus a side project. Distinguished from adjacent concepts by its focus on the specific mechanism through which income manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch die Strategie, Einnahmen durch mehrere Kundenbeziehungen, Produkt-Verkäufe oder Service-Angebote zu generieren. Diversifikation reduziert Konzentration auf eine einzelne Einkommens-Quelle und erhöht allgemeine Stabilität. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Workplace AI", "narrower_terms": [], "cross_domain_refs": [ "ART-0023" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "WRK-0054", "domain": "WRK", "term_en": "Insight Generation Potential", "term_de": "InsightGenerationPotential", "definition_en": "A workplace dynamics phenomenon in AI-augmented professional environments, characterized by a capability in which the ability and states for a team or person to yield original insights rather than just executing standard work. Distinguished from adjacent concepts by its focus on the specific mechanism through which insight manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch die Bewertung der Kapazität eines Kandidaten, Informationen zu synthetisieren, Verbindungen zu ziehen und neuartiges Verständnis zu generieren. Organisationen suchen jene, die Sinn über das hinaus extrahieren können, was KI-Analyse bieten kann. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Analytical Ratio", "narrower_terms": [], "cross_domain_refs": [ "ART-0072", "BEH-0041", "CRE-0072" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "WRK-0055", "domain": "WRK", "term_en": "Insight Synthesis", "term_de": "InsightSynthesis", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A workplace dynamics phenomenon in AI-augmented professional environments, characterized by taking scattered observations and ideas and connecting them into one clear insight. Different data points suddenly make sense together. This phenomenon operates at the intersection of insight and synthesis dynamics within the broader WRK domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept der Prozess der Kombination von Daten, Beobachtungen und Experten-Interpretation, um neuartige Verständnis und umsetzbare Intelligenz zu generieren. Synthese bewegt sich über Informationen zu Weisheit. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "RPH-3101", "narrower_terms": [], "cross_domain_refs": [ "CRE-0191", "CUS-0067", "DAT-0053" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "WRK-0056", "domain": "WRK", "term_en": "Integrated Self", "term_de": "IntegratedSelf", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures an organizational pattern in AI-mediated work processes, measurable through a principle in which being the same person at work and outside work, not hiding parts of one's identity. Values and personality come with someone everywhere. This phenomenon operates at the intersection of integrated and self dynamics within the broader WRK domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept die Entwicklung von beruflicher Identität, die individuelle Werte, Fähigkeiten und Aspirationen authentisch widerspiegelt, statt sich vorgefertigten Rollen-Vorlagen anzupassen. Integration schafft Kohärenz zwischen innerer und äußerer Ausdrucksfähigkeit. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Workplace AI", "narrower_terms": [], "cross_domain_refs": [ "ETH-0007" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "WRK-0057", "domain": "WRK", "term_en": "Integration Depth", "term_de": "IntegrationDepth", "definition_en": "An organizational pattern in AI-mediated work processes, measurable through a phenomenon in which how deeply woven together different systems are. Shallow: tools are used side-by-side. Deep: they're built to work together seamlessly. Distinguished from adjacent concepts by its focus on the specific mechanism through which integration manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch die strategische Kombination neuer KI- und Automatisierungs-Fähigkeiten neben bestehender menschlicher Expertise, die organisatorische Reichhaltigkeit statt einfachen Ersatz schafft. Mehrere Kompetenz-Schichten ergänzen sich gegenseitig und erhöhen Gesamt-Effektivität. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Analytical Ratio", "narrower_terms": [], "cross_domain_refs": [ "SOM-0005" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "WRK-0058", "domain": "WRK", "term_en": "Integration Navigation", "term_de": "IntegrationNavigation", "definition_en": "A phenomenon in which learning how to move through and coordinate across different integrated systems. Understanding which tool connects to which, and how to move work across them. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch die berufliche Fähigkeit, KI-Systeme innerhalb bestehender Arbeitsabläufe zu orchestrieren, die ein Verständnis sowohl technologischer Fähigkeiten als auch organisatorischer Kontexte erfordert. Fachleute werden zu Übersetzern zwischen Maschinenkapazität und menschlichem Zweck. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-2703", "narrower_terms": [], "cross_domain_refs": [ "ELR-0039" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "WRK-0059", "domain": "WRK", "term_en": "Integration Possibility", "term_de": "IntegrationPossibility", "definition_en": "A workplace dynamics phenomenon in AI-augmented professional environments, characterized by the actual feasibility of bringing different approaches, tools, or people together in practice. Some things sound good together but don't work. Distinguished from adjacent concepts by its focus on the specific mechanism through which integration manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch die Kapazität, nahtlose berufliche Erfahrung zu schaffen, indem verteilte Arbeit mit persönlichem Leben, Heim-Umgebung und individuellen Wohlbefindens-Mustern integriert wird. Arbeit wird in breiterer Lebens-Kontext eingebettet statt getrennt. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Analytical Ratio", "narrower_terms": [], "cross_domain_refs": [ "ADA-0010", "AED-0034", "AED-0072" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "WRK-0060", "domain": "WRK", "term_en": "Integration Readiness", "term_de": "IntegrationReadiness", "definition_en": "An event in which when people, processes, and systems are actually prepared to work together. The prep work has been done so connecting them doesn't break things. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch die systematische Vorbereitung von organisatorischen Menschen, Prozessen und Infrastruktur für sinnvolle Übernahme von KI-Technologien. Bereitschaft umfasst Fähigkeits-Entwicklung, kulturelle Ausrichtung und technische Infrastruktur. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-3001", "narrower_terms": [], "cross_domain_refs": [ "ADA-0010", "AED-0034", "AED-0072" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "WRK-0061", "domain": "WRK", "term_en": "Intentional Direction", "term_de": "IntentionalDirection", "definition_en": "A workplace dynamics phenomenon in AI-augmented professional environments, characterized by a state in which choosing a clear direction and telling many individuals where things are heading. Not drifting or reacting; actively steering. Distinguished from adjacent concepts by its focus on the specific mechanism through which intentional manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch die persönliche Klarheit über das, was berufliches Engagement treibt und wie Arbeit zur breiteren Lebensrichtung beiträgt. Intentionale Ausrichtung schafft Sinn und nachhaltige Motivation. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2051", "narrower_terms": [], "cross_domain_refs": [ "RPH-1903", "RPH-1412" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "WRK-0062", "domain": "WRK", "term_en": "Intentional Gathering", "term_de": "IntentionalGathering", "definition_en": "An organizational pattern in AI-mediated work processes, measurable through a phenomenon in which bringing specific people together deliberately for a specific reason — thoughtful selection of participants rather than generic meetings. The concept emerges specifically in contexts where intentional–gathering interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch die Verschiebung hin zu Meetings, die echte kollaborative Zwecke dienen statt Informationsübertragung oder Status-Updates. Versammlungen konzentrieren sich auf Echtzeit-Denken, Entscheidungsfindung und Beziehungs-Aufbau. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Workplace AI", "narrower_terms": [], "cross_domain_refs": [ "RPH-1662" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "WRK-0063", "domain": "WRK", "term_en": "Intentional Rhythm", "term_de": "IntentionalRhythm", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A workplace dynamics phenomenon in AI-augmented professional environments, characterized by an event in which creating a predictable work pace that people can plan around. People know when big pushes happen and when they can slow down. This phenomenon operates at the intersection of intentional and rhythm dynamics within the broader WRK domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept die bewusste Strukturierung synchroner und asynchroner Arbeits-Muster, um sowohl Fokus als auch Verbindung zu maximieren. Teams stellen klare Kadenzen für reale Zusammenarbeit und unabhängige tiefe Arbeit auf. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Workplace AI", "narrower_terms": [], "cross_domain_refs": [ "IDN-0046" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "WRK-0064", "domain": "WRK", "term_en": "Interreliance Modeling", "term_de": "InterrelianceModeling", "definition_en": "An organizational pattern in AI-mediated work processes, measurable through a phenomenon in which showing people how to ask for help and rely on each other restorethily. Not depending on one person; distributed responsibility. Distinguished from adjacent concepts by its focus on the specific mechanism through which interreliance manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch ein Konzept oder Phänomen: Showing people how to ask for help and rely on each other restorethily. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Computational Model", "narrower_terms": [], "cross_domain_refs": [ "REL-0052" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "WRK-0065", "domain": "WRK", "term_en": "Judgment Development Orientation", "term_de": "UrteilDevelopmentOrientation", "definition_en": "A phenomenon in which a workplace that helps people adjust at making good calls in uncertain situations. Workers learn judgment by practicing it. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch die Einstellungspraxis, Kandidaten zu identifizieren, die Potenzial haben, über ihre Karriere gesundes Entscheidungsvermögen und Weisheit zu entwickeln. Organisationen schätzen Kandidaten, die zu reifem Denken fähig sind, mehr als spezialisiertes Wissen. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2703", "narrower_terms": [], "cross_domain_refs": [ "SPR-0047" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "WRK-0066", "domain": "WRK", "term_en": "Judgment Elevation", "term_de": "UrteilElevation", "definition_en": "An organizational pattern in AI-mediated work processes, measurable through a pattern in which getting smarter at making tough decisions over time. Developing instincts from making and reviewing lots of judgment calls. The concept emerges specifically in contexts where judgment–elevation interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch die Bewegung hin zu Rollen, die Entscheidungsfindung, Bewertung und Weisheit über Informationsbeschaffung oder Verarbeitung betonen. Menschliches Urteil wird zum Kernwert, während Maschinen Datenanalyse und Routine-Bewertung handhaben. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Workplace AI", "narrower_terms": [], "cross_domain_refs": [ "SPR-0047" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "WRK-0067", "domain": "WRK", "term_en": "Knowledge Transformation", "term_de": "KnowledgeTransformation", "definition_en": "A workplace dynamics phenomenon in AI-augmented professional environments, characterized by a state in which taking information and knowledge and converting it into something new and useful. Raw data becomes insight becomes action. Distinguished from adjacent concepts by its focus on the specific mechanism through which knowledge manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch die systematische Umwandlung von implizitem beruflichem Wissen zu expliziten, teilbaren organisatorischen Vermögenswerten, die KI-Systeme nutzen und verstärken können. Wissen wird Organisations-Kapital statt einzelne Besitztum. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Workplace AI", "narrower_terms": [], "cross_domain_refs": [ "SPR-0017" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "WRK-0068", "domain": "WRK", "term_en": "Learning Acceleration", "term_de": "LearningBeschleunigung", "definition_en": "A phenomenon in which getting more effectively faster by having the right support and structure. Good mentoring, clear feedback, and time to learn speed things up. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch die strategische Verbesserung der organisatorischen Lern-Kapazität durch KI-gestützte Wissens-Synthese, Training und Entwicklungs-Programme. Organisationen entwickeln sich schneller durch kombinierte menschlich-maschinelle Intelligenz. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-2703", "narrower_terms": [], "cross_domain_refs": [ "EDU-0071" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q133500", "legal_classification": "descriptive_research_term" }, { "id": "WRK-0069", "domain": "WRK", "term_en": "Learning Circulation", "term_de": "LearningCirculation", "definition_en": "A state in which knowledge and lessons flowing through the team so many individuals benefits from what individuals learn. One person's discovery becomes many individuals's knowledge. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch die aktive Verbreitung von Lernungen, Best Practices und organisatorischen Einblicken in der gesamten Organisation. Zirkulation sichert, dass Einblicke nicht isoliert bleiben und katalysiert kontinuierliche Verbesserung. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-2703", "narrower_terms": [], "cross_domain_refs": [ "PER-0054" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q133500", "legal_classification": "analytical_category" }, { "id": "WRK-0070", "domain": "WRK", "term_en": "Learning Reinvestment", "term_de": "LearningReinvestment", "definition_en": "A phenomenon in which taking what someone learned and using it to improve how the team works. Not just knowing more; actually changing how things operate. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch die Praxis, Teile des Einkommens und der Zeit für die Entwicklung neuer Fähigkeiten, das Aktualbleiben bei Branchentwicklung und die Erweiterung des Fähigkeits-Bereichs einzusetzen. Reinvestition bewahrt Wettbewerbsvorteil und eröffnet neue Gelegenheiten. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2703", "narrower_terms": [], "cross_domain_refs": [ "PER-0101" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q133500", "legal_classification": "empirical_phenomenon_label" }, { "id": "WRK-0071", "domain": "WRK", "term_en": "Listening Presence", "term_de": "ListeningPresence", "definition_en": "A workplace dynamics phenomenon in AI-augmented professional environments, characterized by an event in which being fully there when someone talks so they actually feel heard. Not checking devices or planning what to say next. The concept emerges specifically in contexts where listening–presence interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters. Observed phenomenon documented in human-AI interaction research.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch die Kultivierung von echter Aufmerksamkeit und Empfänglichkeit, wenn andere kommunizieren. Hörendes Präsenz schafft psychologische Sicherheit und ermöglicht anderen, umfassender beizutragen. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Workplace AI", "narrower_terms": [], "cross_domain_refs": [ "DES-0005", "EDU-0062", "EDU-0069" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "WRK-0072", "domain": "WRK", "term_en": "Meaning Making", "term_de": "MeaningMaking", "definition_en": "A workplace dynamics phenomenon in AI-augmented professional environments, characterized by a phenomenon in which finding purpose in one's work. Understanding why what someone does matters to them and to others. The concept emerges specifically in contexts where meaning–making interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch der kollektive Prozess, Daten, Ereignisse und Muster zu interpretieren, um gemeinsames organisatorisches Verständnis und kohärente Erzählung zu schaffen. Sinn entsteht aus Dialog, nicht passive Informations-Rezeption. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Workplace AI", "narrower_terms": [], "cross_domain_refs": [ "TRU-0011" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "WRK-0073", "domain": "WRK", "term_en": "Narrative Architecture", "term_de": "NarrativeArchitecture", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures an organizational pattern in AI-mediated work processes, measurable through a phenomenon in which how someone organizes and tells the story of their work and growth. A coherent narrative instead of random accomplishments. This phenomenon operates at the intersection of narrative and architecture dynamics within the broader WRK domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept die strategische Rahmung von Informationen, Entscheidungen und Veränderung durch überzeugendes Erzählen, die Sinn schafft und zu menschlichen Werten verbindet. Erzählungen helfen Publikum zu verstehen, warum neben dem was. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Workplace AI", "narrower_terms": [], "cross_domain_refs": [ "CON-0071" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q131324", "legal_classification": "observational_construct" }, { "id": "WRK-0074", "domain": "WRK", "term_en": "Network Expansion", "term_de": "NetworkExpansion", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures an organizational pattern in AI-mediated work processes, measurable through a capability in which a professional network getting bigger and more diverse. More people who can help, advise, or collaborate. This phenomenon operates at the intersection of network and expansion dynamics within the broader WRK domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept die Gelegenheit für Remote-Arbeiter, berufliche Beziehungen und Zusammenarbeitsmöglichkeiten über traditionelle geografische Grenzen hinaus zu bauen. Digitale Konnektivität ermöglicht breitere, diverse Netzwerke. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Network Architecture", "narrower_terms": [], "cross_domain_refs": [ "PER-0069" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "WRK-0075", "domain": "WRK", "term_en": "Outcome Clarity", "term_de": "OutcomeClarity", "definition_en": "A phenomenon in which being absolutely clear about what success looks like before starting. Not hoping it works out; defining what \"done\" means. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch die explizite Definition von dem, was ein Meeting zu erreichen zielt und klare Dokumentation von Entscheidungen und Aktions-Elementen. Klarheit transformiert Meetings von offenen Versammlungen zu zweckgerichteten, verantwortlichen Ereignissen. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2301", "narrower_terms": [], "cross_domain_refs": [ "AGE-0067" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "WRK-0076", "domain": "WRK", "term_en": "Perspective Integration", "term_de": "PerspectiveIntegration", "definition_en": "A workplace dynamics phenomenon in AI-augmented professional environments, characterized by bringing together how different people see the same situation. Because perspectives combine to show the full picture. Distinguished from adjacent concepts by its focus on the specific mechanism through which perspective manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch die bewusste Einbeziehung und Synthese von vielfältigen Sichtweisen in Kommunikation und Entscheidungsfindung. Integration schafft reichhaltigeres Verständnis und robustere Ergebnisse. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Analytical Ratio", "narrower_terms": [], "cross_domain_refs": [ "EDU-0073" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "WRK-0077", "domain": "WRK", "term_en": "Portfolio Depth", "term_de": "PortfolioDepth", "definition_en": "An organizational pattern in AI-mediated work processes, measurable through a capability in which having multiple strong skills or accomplishments rather than being good at one thing. Contributing in different ways depending on what's needed. Distinguished from adjacent concepts by its focus on the specific mechanism through which portfolio manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch die Kultivierung eines Körpers von Arbeit, der Qualität, Bereich und unterschiedliche Perspektive zeigt. Portfolio-Tiefe zieht Kunden an und ermöglicht Freiberuflern, höhere Vergütung zu fordern. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Workplace AI", "narrower_terms": [], "cross_domain_refs": [ "REL-0066" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "WRK-0078", "domain": "WRK", "term_en": "Purpose Realignment", "term_de": "PurposeRealignment", "definition_en": "An organizational pattern in AI-mediated work processes, measurable through an event in which when work stops matching what matters, actively realigning. Changing roles, projects, or focus to match one's purpose again. Distinguished from adjacent concepts by its focus on the specific mechanism through which purpose manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch die Klärung und Wiederherstellung von organisatorischer Mission und Werten, wenn KI-Fähigkeiten umgestalten, was die Organisation tun kann. Zweck entwickelt sich, um eindeutig menschliche Beiträge und Gesellschafts-Wertschöpfung zu betonen. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Workplace AI", "narrower_terms": [], "cross_domain_refs": [ "EDU-0078", "ELR-0096", "RPH-1402" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "WRK-0079", "domain": "WRK", "term_en": "Question Cultivation", "term_de": "QuestionCultivation", "definition_en": "A workplace dynamics phenomenon in AI-augmented professional environments, characterized by a phenomenon in which actively helping people ask more effectively questions instead of jumping to answers. More effectively questions correlate with more effectively thinking. Distinguished from adjacent concepts by its focus on the specific mechanism through which question manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch die Praxis, wichtige Fragen zu erfassen, zu organisieren und regelmäßig erneut zu besuchen, die organisatorisches Denken und Anfrage leiten. Fragen werden zu Rahmen für laufendes Lernen. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Workplace AI", "narrower_terms": [], "cross_domain_refs": [ "CRE-0191" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "WRK-0080", "domain": "WRK", "term_en": "Reflection Space", "term_de": "ReflectionSpace", "definition_en": "A gap in which time and cognitive safety to think about what happened without judgment. Space to learn from experience instead of just moving on. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch bezeichnete Zeit im organisatorischen Rhythmus, wo Teams kollektiv pausieren, um Fortschritt zu bewerten, Lernungen zu diskutieren und Prioritäten neu auszurichten. Reflexion ermöglicht kontinuierliche Verbesserung und adaptive Strategie. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2703", "narrower_terms": [], "cross_domain_refs": [ "CRE-0139" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "WRK-0081", "domain": "WRK", "term_en": "Relationship Capital", "term_de": "RelationshipCapital", "definition_en": "A phenomenon in which trust and good relationships built up over time. People help because real connection has been established. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch das angesammelte Goodwill, Ruf und wiederkehrende Kundenbeziehungen, die Freiberuflern stabiles Einkommen und Gelegenheit bieten. Kapital wächst durch konsistente Lieferung und echte Partnerschaft. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2505", "narrower_terms": [], "cross_domain_refs": [ "EDU-0023" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q165194", "legal_classification": "observational_construct" }, { "id": "WRK-0082", "domain": "WRK", "term_en": "Resilience Building", "term_de": "ResilienceBuilding", "definition_en": "A pattern in which learning to handle strain and setbacks without breaking down. Developing the skills and mindset to bounce back. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch die bewusste Stärkung der organisatorischen Kapazität, sich anzupassen, Herausforderungen zu absorbieren und Kern-Funktion durch technologische Übergänge zu bewahren. Widerstandsfähigkeit entsteht aus vielfältigen Fähigkeiten und flexiblen Reaktionen. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2703", "narrower_terms": [], "cross_domain_refs": [ "AED-0027", "AED-0032", "AED-0047" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q720920", "legal_classification": "observational_construct" }, { "id": "WRK-0083", "domain": "WRK", "term_en": "Resilient Grounding", "term_de": "ResilientGrounding", "definition_en": "An organizational pattern in AI-mediated work processes, measurable through an event in which having clear values and practices that keep someone stable when things get unpredictable. A foundation that holds even when everything shakes. The concept emerges specifically in contexts where resilient–grounding interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch die Entwicklung von Kern-beruflichen Werten und Prinzipien, die stabil bleiben, auch wenn sich Umstände, Rollen und Technologien ändern. Verwurzelung bietet Anker für die Navigation von Veränderlichkeit. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Workplace AI", "narrower_terms": [], "cross_domain_refs": [ "REL-0033" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "WRK-0084", "domain": "WRK", "term_en": "Resilient Orientation", "term_de": "ResilientOrientation", "definition_en": "A phenomenon in which an attitude that sees challenges as things to learn from, not challenges. Expecting difficulties and addressing them as growth opportunities. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch der Recruitment-Fokus auf Kandidaten, die Rückprall-Kapazität, Flexibilität und positives Engagement mit Veränderung zeigen. Diese Personen gedeihen inmitten von Veränderlichkeit und kontinuierlicher organisatorischer Entwicklung. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-2703", "narrower_terms": [], "cross_domain_refs": [ "RPH-1158", "TEM-0115", "TEM-0158" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "WRK-0085", "domain": "WRK", "term_en": "Role Fluidity", "term_de": "RoleFluidity", "definition_en": "As a descriptive research term in AI interaction studies, this concept identifies A workplace dynamics phenomenon in AI-augmented professional environments, characterized by a phenomenon in which adapting what someone does based on what the team needs right now. Not locked into one job description. This phenomenon operates at the intersection of role and fluidity dynamics within the broader WRK domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept die adaptive Kapazität, zwischen verschiedenen beruflichen Rollen zu wechseln, wenn sich Organisationsbedürfnisse und technologische Möglichkeiten entwickeln. Diese Flexibilität ermöglicht Arbeitern, Relevanz und Beitrag über verschiebende Verantwortungen zu bewahren. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Workplace AI", "narrower_terms": [], "cross_domain_refs": [ "SOM-0069" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "WRK-0086", "domain": "WRK", "term_en": "Signal Clarity", "term_de": "SignalClarity", "definition_en": "An organizational pattern in AI-mediated work processes, measurable through an event in which making sure the most important information cuts through the noise. When something matters, many individuals knows it. The concept emerges specifically in contexts where signal–clarity interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch die explizite Kommunikation von Prioritäten, Betonung und Wichtigkeit-Niveaus, um anderen zu helfen, Aufmerksamkeit angemessen zu fokussieren. Klarheit über das, was am meisten zählt, ermöglicht bessere Entscheidungsfindung über Teams. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Workplace AI", "narrower_terms": [], "cross_domain_refs": [ "SCR-0024" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "WRK-0087", "domain": "WRK", "term_en": "Specialization Expansion", "term_de": "SpecializationExpansion", "definition_en": "A phenomenon in which getting really good at something specific, then stretching beyond it. An expert in X, now learning how that expertise applies to Y. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch die Gelegenheit, tiefere Expertise in menschenzentrierten Bereichen zu entwickeln, die Urteil, Kreativität und zwischenmenschliche Navigation erfordern. Wenn Routine-Arbeit von KI adressiert wird, konzentrieren sich Fachleute auf höherwertige Spezialisierung. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2703", "narrower_terms": [], "cross_domain_refs": [ "EDU-0037" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "WRK-0088", "domain": "WRK", "term_en": "Specialization Focus", "term_de": "SpecializationFocus", "definition_en": "A workplace dynamics phenomenon in AI-augmented professional environments, characterized by a phenomenon in which narrowing down to do one thing excellently rather than many things okay. Being the expert in something specific. Distinguished from adjacent concepts by its focus on the specific mechanism through which specialization manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch die bewusste Entwicklung von unterscheidender Expertise und Markt-Positionierung, die Freiberufler von Waren-Service-Anbietern unterscheidet. Spezialisierung ermöglicht höhere Sätze und tiefere Kunden-Zufriedenheit. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Workplace AI", "narrower_terms": [], "cross_domain_refs": [ "IDN-0025" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "WRK-0089", "domain": "WRK", "term_en": "Stakeholder Orchestration", "term_de": "StakeholderOrchestration", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures an organizational pattern in AI-mediated work processes, measurable through a response in which coordinating multiple groups with different interests and needs so many individuals's concerns are considered. Like conducting an orchestra. This phenomenon operates at the intersection of stakeholder and orchestration dynamics within the broader WRK domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept das koordinierte Engagement von Mitarbeitern, Führung, Kunden und Partnern in organisatorischen Transformations-Initiativen. Orchestrierung sichert Ausrichtung, Verständnis und Zustimmung über zahlreiche betroffenen Gruppen. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Analytical Ratio", "narrower_terms": [], "cross_domain_refs": [ "RHR-0057", "RPH-1264" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "WRK-0090", "domain": "WRK", "term_en": "Structure Evolution", "term_de": "StructureEvolution", "definition_en": "An organizational pattern in AI-mediated work processes, measurable through how a team's organization and rules naturally shift as it grows and changes. What worked for five people won't work for fifty. Distinguished from adjacent concepts by its focus on the specific mechanism through which structure manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch die Umgestaltung von organisatorischen Hierarchien, Teams und Berichts-Beziehungen, um mit hybriden menschlich-KI-Arbeits-Modellen ausgerichtet zu sein. Struktur wird flüssiger und rollen-basierter statt starre Positions-Rahmen. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-2701", "narrower_terms": [], "cross_domain_refs": [ "AED-0042", "AED-0070", "ASE-0028" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "descriptive_research_term" }, { "id": "WRK-0091", "domain": "WRK", "term_en": "Synergy Mapping", "term_de": "SynergyMapping", "definition_en": "A workplace dynamics phenomenon in AI-augmented professional environments, characterized by a state in which finding where human strengths and AI strengths combine to do something more effectively together than either could alone. The concept emerges specifically in contexts where synergy–mapping interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch die Praxis, zu identifizieren und zu artikulieren, wo menschliche Stärken und KI-Fähigkeiten kombinierten Wert schaffen, der größer ist als viele allein. Teams verfolgten diese Synergien-Schnittpunkte explizit. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Workplace AI", "narrower_terms": [], "cross_domain_refs": [ "SPR-0200" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" }, { "id": "WRK-0092", "domain": "WRK", "term_en": "Tacit Bridge", "term_de": "TacitBridge", "definition_en": "An organizational pattern in AI-mediated work processes, measurable through turning the unspoken knowledge that comes from experience into something that can actually be shared with others. Making invisible expertise visible. Distinguished from adjacent concepts by its focus on the specific mechanism through which tacit manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch die Praxis der Externalisierung von implizitem beruflichem Wissen durch Dokumentation, Mentoring und Wissens-Synthese. Brückenbildung transformiert individuelle Expertise in organisatorisches Vermögen. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Workplace AI", "narrower_terms": [], "cross_domain_refs": [ "CAI-0012" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "WRK-0093", "domain": "WRK", "term_en": "Temporal Flexibility", "term_de": "TemporalFlexibility", "definition_en": "A gap in which doing work across different time zones and schedules without everything breaking. Async-friendly tools and processes that work inreliantly. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch die Fähigkeit, individuelle Arbeits-Zeitpläne um persönliche Peak-Produktivitäts-Zeiten und Lebens-Verpflichtungen zu strukturieren, während zuverlässige Beiträge zu gemeinsamen organisatorischen Zielen beibehalten werden. Flexibilität nimmt zu ohne Koordination zu beeinträchtigen. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-3001", "narrower_terms": [], "cross_domain_refs": [ "AED-0002", "AGE-0029", "ASE-0096" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "WRK-0094", "domain": "WRK", "term_en": "Trajectory Potential", "term_de": "TrajectoryPotential", "definition_en": "A workplace dynamics phenomenon in AI-augmented professional environments, characterized by a state in which the possibilities for where a career or project could go. Not the one trajectory, but multiple paths available. Distinguished from adjacent concepts by its focus on the specific mechanism through which trajectory manifests in empirically verifiable ways. Detectable through standardized assessment protocols, effect size measurement, and cross-characterized through systematic observation pattern recognition in empirical data.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch die Bewertung der Kapazität eines Kandidaten zu wachsen und sich beruflich im Laufe der Zeit zu entwickeln, wenn sich Rollen und Technologien verschieben. Das Einstellen betont Potenzial und Anpassungsfähigkeit statt perfektem gegenwärtigen Fit. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "Workplace AI", "narrower_terms": [], "cross_domain_refs": [ "RPH-2503", "FIC-0021" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "WRK-0095", "domain": "WRK", "term_en": "Transparent Thinking", "term_de": "TransparentThinking", "definition_en": "Identified through empirical observation in human-AI interaction research (descriptive, not prescriptive), this concept captures an organizational pattern in AI-mediated work processes, measurable through a capability in which showing one's reasoning, not just conclusions. People see how something was thought through so they can build on it or push back. This phenomenon operates at the intersection of transparent and thinking dynamics within the broader WRK domain. Operationally identifiable through systematic behavioral analysis, quantitative metric tracking, and controlled comparative studies across interaction contexts. This term is used in a purely descriptive and analytical capacity within AUGMANITAI terminology science. It does not constitute an endorsement, recommendation, or normative prescription.", "definition_de": "Als deskriptiver Forschungsbegriff in der KI-Interaktionswissenschaft (ohne normative Wertung) beschreibt dieses Konzept die Praxis, Denk-Prozesse, Entscheidungs-Logik und zugrunde liegende Annahmen für andere sichtbar zu machen. Transparenz ermöglicht bessere Zusammenarbeit und verteiltes Verständnis von organisatorischer Richtung. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien. Dieser Begriff wird rein deskriptiv und analytisch im Rahmen der AUGMANITAI-Terminologiewissenschaft verwendet und stellt keine Empfehlung oder normative Aussage dar.", "etymology": "", "broader_term": "Workplace AI", "narrower_terms": [], "cross_domain_refs": [ "RPH-3251", "SPR-0094" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "observational_construct" }, { "id": "WRK-0096", "domain": "WRK", "term_en": "Trust Calibration", "term_de": "TrustCalibration", "definition_en": "An event in which adjusting how much to trust something or someone based on actual results. Tools and systems get trust when they prove reliable. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch der laufende Prozess der Abstimmung von Team-Vertrauen in KI-Empfehlungen mit tatsächlicher System-Zuverlässigkeit und Grenzen. Teams entwickeln feines Verständnis dafür, wann sie Maschinenvorschläge verwenden, ergänzen oder außer Kraft setzen können. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-2505", "narrower_terms": [], "cross_domain_refs": [ "CUS-0004" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q102458", "legal_classification": "observational_construct" }, { "id": "WRK-0097", "domain": "WRK", "term_en": "Value Articulation", "term_de": "ValueArticulation", "definition_en": "A principle in which being clear about what matters and why — making values explicit so they guide decisions instead of remaining vague assumptions. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch die klare Kommunikation von unterscheidender Wert, gelieferten Ergebnissen und Auswirkungen, die für Kunden geschaffen wurden. Artikulation ermöglicht mit Wert abgestimmte Preisgestaltung und schafft Kunden-Vertrauen in Engagement. Detektierbar durch standardisierte Bewertungsprotokolle, Effektgrößenmessung und kreuzvalidierte Mustererkennung.", "etymology": "", "broader_term": "RPH-3902", "narrower_terms": [], "cross_domain_refs": [ "DAT-0068" ], "examples": [], "wikidata_closeMatch": "http://www.wikidata.org/entity/Q735", "legal_classification": "observational_construct" }, { "id": "WRK-0098", "domain": "WRK", "term_en": "Value Recalibration", "term_de": "ValueRecalibration", "definition_en": "A workplace dynamics phenomenon in AI-augmented professional environments, characterized by a phenomenon in which realizing some things that seemed less important now matter more, or vice versa. Actively shifting what gets prioritized. The concept emerges specifically in contexts where value–recalibration interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch die Neubewertung von dem, was die Organisation schätzt und belohnt, wenn KI-Arbeit umgestaltet. Messsysteme entwickeln sich, um Urteil, Kreativität, ethisches Denken und menschliche Verbindung zu betonen. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "RPH-2701", "narrower_terms": [], "cross_domain_refs": [ "TRA-0074" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "analytical_category" }, { "id": "WRK-0099", "domain": "WRK", "term_en": "Viewpoint Synthesis", "term_de": "ViewpointSynthesis", "definition_en": "An organizational pattern in AI-mediated work processes, measurable through a phenomenon in which combining how humans see things and how AI sees things to get a fuller picture. Each perspective catches what the other might miss. The concept emerges specifically in contexts where viewpoint–synthesis interactions may produce non-trivial behavioral signatures. Measurable through domain-specific performance indicators, longitudinal trend analysis, and multi-variable regression on interaction parameters.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch die Integration verschiedener menschlicher Sichtweisen mit analytischen Maschinenkapazitäten, um umfassenderes Verständnis und robustere Lösungen zu schaffen. Teams bringen aktiv mehrere Blickwinkel zu von KI verarbeiteten Informationen. Messbar durch domänenspezifische Leistungsindikatoren, Langzeit-Trendanalyse und multivariate Regressionsmodelle.", "etymology": "", "broader_term": "Workplace AI", "narrower_terms": [], "cross_domain_refs": [ "RPH-2152" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "systematic_classification" }, { "id": "WRK-0100", "domain": "WRK", "term_en": "Wisdom Cultivation", "term_de": "WisdomCultivation", "definition_en": "Developing practical judgment that comes from experience, reflection, and learning from mistakes. Wisdom is judgment that works in real situations. Identifiable through systematic behavioral analysis and pattern recognition.", "definition_de": "Domänenspezifisches Phänomen in der KI-Mensch-Interaktion, gekennzeichnet durch die bewusste Entwicklung von erfahrungsbasiertem Einblick, ethischem Denken und kontextuellen Verständnis, das Maschinen nicht replizieren können. Fachleute investieren in Tiefe, Nuance und menschenzentrierte Perspektive. Operativ nachweisbar durch systematische Verhaltensanalyse, quantitative Metrik-Erhebung und kontrollierte Vergleichsstudien.", "etymology": "", "broader_term": "RPH-2703", "narrower_terms": [], "cross_domain_refs": [ "AGE-0049" ], "examples": [], "wikidata_closeMatch": "", "legal_classification": "empirical_phenomenon_label" } ], "edges": [ { "source": "ADA-0001", "target": "Psychological Effect", "type": "skos:broader" }, { "source": "Psychological Effect", "target": "ADA-0001", "type": "skos:narrower" }, { "source": "ADA-0002", "target": "RPH-2002", "type": "skos:broader" }, { "source": "RPH-2002", "target": "ADA-0002", "type": "skos:narrower" }, { "source": "ADA-0003", "target": "RPH-3754", "type": "skos:broader" }, { "source": "RPH-3754", "target": "ADA-0003", "type": "skos:narrower" }, { "source": "ADA-0004", "target": "RPH-2701", "type": "skos:broader" }, { "source": "RPH-2701", "target": "ADA-0004", "type": "skos:narrower" }, { "source": "ADA-0005", "target": "RPH-2701", "type": "skos:broader" }, { "source": "RPH-2701", "target": "ADA-0005", "type": "skos:narrower" }, { "source": "ADA-0006", "target": "Psychological Effect", "type": "skos:broader" }, { "source": "Psychological Effect", "target": "ADA-0006", "type": "skos:narrower" }, { "source": "ADA-0007", "target": "RPH-2304", "type": "skos:broader" }, { "source": "RPH-2304", "target": "ADA-0007", "type": "skos:narrower" }, { "source": "ADA-0008", "target": "Adaptive Learning", "type": "skos:broader" }, { "source": "Adaptive Learning", "target": "ADA-0008", "type": "skos:narrower" }, { "source": "ADA-0009", "target": "Adaptive Learning", "type": "skos:broader" }, { "source": "Adaptive Learning", "target": "ADA-0009", "type": "skos:narrower" }, { "source": "ADA-0010", "target": "Analytical Ratio", "type": "skos:broader" }, { "source": "Analytical Ratio", "target": "ADA-0010", "type": "skos:narrower" }, { "source": "ADA-0011", "target": "RPH-3202", "type": "skos:broader" }, { "source": "RPH-3202", "target": "ADA-0011", "type": "skos:narrower" }, { "source": "ADA-0012", "target": "Performance Metric", "type": "skos:broader" }, { "source": "Performance Metric", "target": "ADA-0012", "type": "skos:narrower" }, { "source": "ADA-0013", "target": "Adaptive Learning", "type": "skos:broader" }, { "source": "Adaptive Learning", "target": "ADA-0013", "type": "skos:narrower" }, { "source": "ADA-0014", "target": "RPH-2301", "type": "skos:broader" }, { "source": "RPH-2301", "target": "ADA-0014", "type": "skos:narrower" }, { "source": "ADA-0015", "target": "Adaptive Learning", "type": "skos:broader" }, { "source": "Adaptive Learning", "target": "ADA-0015", "type": "skos:narrower" }, { "source": "AED-0001", "target": "RPH-2005", "type": "skos:broader" }, { "source": "RPH-2005", "target": "AED-0001", "type": "skos:narrower" }, { "source": "AED-0002", "target": "RPH-2703", "type": "skos:broader" }, { "source": "RPH-2703", "target": "AED-0002", "type": "skos:narrower" }, { "source": "AED-0003", "target": "RPH-2703", "type": "skos:broader" }, { "source": "RPH-2703", "target": "AED-0003", "type": "skos:narrower" }, { "source": "AED-0004", "target": "RPH-2703", "type": "skos:broader" }, { "source": "RPH-2703", "target": "AED-0004", "type": "skos:narrower" }, { "source": "AED-0005", "target": "RPH-2703", "type": "skos:broader" }, { "source": "RPH-2703", "target": "AED-0005", "type": "skos:narrower" }, { "source": "AED-0006", "target": "RPH-2703", "type": "skos:broader" }, { "source": "RPH-2703", "target": "AED-0006", "type": "skos:narrower" }, { "source": "AED-0007", "target": "Analytical Ratio", "type": "skos:broader" }, { "source": "Analytical Ratio", "target": "AED-0007", "type": "skos:narrower" }, { "source": "AED-0008", "target": "RPH-2703", "type": "skos:broader" }, { "source": "RPH-2703", "target": "AED-0008", "type": "skos:narrower" }, { "source": "AED-0009", "target": "RPH-2703", "type": "skos:broader" }, { "source": "RPH-2703", "target": "AED-0009", "type": "skos:narrower" }, { "source": "AED-0010", "target": "RPH-2701", "type": "skos:broader" }, { "source": "RPH-2701", "target": "AED-0010", "type": "skos:narrower" }, { "source": "AED-0011", "target": "Adult Education", "type": "skos:broader" }, { "source": "Adult Education", "target": "AED-0011", "type": "skos:narrower" }, { "source": "AED-0012", "target": "RPH-2355", "type": "skos:broader" }, { "source": "RPH-2355", "target": "AED-0012", "type": "skos:narrower" }, { "source": "AED-0013", "target": "Pattern Recognition", "type": "skos:broader" }, { "source": "Pattern Recognition", "target": "AED-0013", "type": "skos:narrower" }, { "source": "AED-0014", "target": "RPH-2303", "type": "skos:broader" }, { "source": "RPH-2303", "target": "AED-0014", "type": "skos:narrower" }, { "source": "AED-0015", "target": "Adult Education", "type": "skos:broader" }, { "source": "Adult Education", "target": "AED-0015", "type": "skos:narrower" }, { "source": "AED-0016", "target": "Adult Education", "type": "skos:broader" }, { "source": "Adult Education", "target": "AED-0016", "type": "skos:narrower" }, { "source": "AED-0017", "target": "RPH-2703", "type": "skos:broader" }, { "source": "RPH-2703", "target": "AED-0017", "type": "skos:narrower" }, { "source": "AED-0018", "target": "RPH-2703", "type": "skos:broader" }, { "source": "RPH-2703", "target": "AED-0018", "type": "skos:narrower" }, { "source": "AED-0019", "target": "RPH-2005", "type": "skos:broader" }, { "source": "RPH-2005", "target": "AED-0019", "type": "skos:narrower" }, { "source": "AED-0020", "target": "Adult Education", "type": "skos:broader" }, { "source": "Adult Education", "target": "AED-0020", "type": "skos:narrower" }, { "source": "AED-0021", "target": "RPH-3202", "type": "skos:broader" }, { "source": "RPH-3202", "target": "AED-0021", "type": "skos:narrower" }, { "source": "AED-0022", "target": "RPH-2703", "type": "skos:broader" }, { "source": "RPH-2703", "target": "AED-0022", "type": "skos:narrower" }, { "source": "AED-0023", "target": "Adult Education", "type": "skos:broader" }, { "source": "Adult Education", "target": "AED-0023", "type": "skos:narrower" }, { "source": "AED-0024", "target": "Psychological Effect", "type": "skos:broader" }, { "source": "Psychological Effect", "target": "AED-0024", "type": "skos:narrower" }, { "source": "AED-0025", "target": "Adult Education", "type": "skos:broader" }, { "source": "Adult Education", "target": "AED-0025", "type": "skos:narrower" }, { "source": "AED-0026", "target": "Adult Education", "type": "skos:broader" }, { "source": "Adult Education", "target": "AED-0026", "type": "skos:narrower" }, { "source": "AED-0027", "target": "Adult Education", "type": "skos:broader" }, { "source": "Adult Education", "target": "AED-0027", "type": "skos:narrower" }, { "source": "AED-0028", "target": "RPH-2703", "type": "skos:broader" }, { "source": "RPH-2703", "target": "AED-0028", "type": "skos:narrower" }, { "source": "AED-0029", "target": "RPH-2005", "type": "skos:broader" }, { "source": "RPH-2005", "target": "AED-0029", "type": "skos:narrower" }, { "source": "AED-0030", "target": "Systemic Drift", "type": "skos:broader" }, { "source": "Systemic Drift", "target": "AED-0030", "type": "skos:narrower" }, { "source": "AED-0031", "target": "RPH-2351", "type": "skos:broader" }, { "source": "RPH-2351", "target": "AED-0031", "type": "skos:narrower" }, { "source": "AED-0031", "target": "ADA-0006", "type": "augmanitai:crossDomainReference" }, { "source": "AED-0032", "target": "RPH-2703", "type": "skos:broader" }, { "source": "RPH-2703", "target": "AED-0032", "type": "skos:narrower" }, { "source": "AED-0033", "target": "RPH-2703", "type": "skos:broader" }, { "source": "RPH-2703", "target": "AED-0033", "type": "skos:narrower" }, { "source": "AED-0034", "target": "RPH-2703", "type": "skos:broader" }, { "source": "RPH-2703", "target": "AED-0034", "type": "skos:narrower" }, { "source": "AED-0034", "target": "ADA-0010", "type": "augmanitai:crossDomainReference" }, { "source": "AED-0035", "target": "RPH-2703", "type": "skos:broader" }, { "source": "RPH-2703", "target": "AED-0035", "type": "skos:narrower" }, { "source": "AED-0036", "target": "RPH-2351", "type": "skos:broader" }, { "source": "RPH-2351", "target": "AED-0036", "type": "skos:narrower" }, { "source": "AED-0037", "target": "RPH-2303", "type": "skos:broader" }, { "source": "RPH-2303", "target": "AED-0037", "type": "skos:narrower" }, { "source": "AED-0038", "target": "RPH-2703", "type": "skos:broader" }, { "source": "RPH-2703", "target": "AED-0038", "type": "skos:narrower" }, { "source": "AED-0039", "target": "RPH-2703", "type": "skos:broader" }, { "source": "RPH-2703", "target": "AED-0039", "type": "skos:narrower" }, { "source": "AED-0040", "target": "RPH-2703", "type": "skos:broader" }, { "source": "RPH-2703", "target": "AED-0040", "type": "skos:narrower" }, { "source": "AED-0041", "target": "Adult Education", "type": "skos:broader" }, { "source": "Adult Education", "target": "AED-0041", "type": "skos:narrower" }, { "source": "AED-0042", "target": "Adult Education", "type": "skos:broader" }, { "source": "Adult Education", "target": "AED-0042", "type": "skos:narrower" }, { "source": "AED-0043", "target": "RPH-2703", "type": "skos:broader" }, { "source": "RPH-2703", "target": "AED-0043", "type": "skos:narrower" }, { "source": "AED-0044", "target": "RPH-2703", "type": "skos:broader" }, { "source": "RPH-2703", "target": "AED-0044", "type": "skos:narrower" }, { "source": "AED-0045", "target": "RPH-2703", "type": "skos:broader" }, { "source": "RPH-2703", "target": "AED-0045", "type": "skos:narrower" }, { "source": "AED-0046", "target": "RPH-2703", "type": "skos:broader" }, { "source": "RPH-2703", "target": "AED-0046", "type": "skos:narrower" }, { "source": "AED-0047", "target": "RPH-2703", "type": "skos:broader" }, { "source": "RPH-2703", "target": "AED-0047", "type": "skos:narrower" }, { "source": "AED-0048", "target": "Adult Education", "type": "skos:broader" }, { "source": "Adult Education", "target": "AED-0048", "type": "skos:narrower" }, { "source": "AED-0049", "target": "Process Cycle", "type": "skos:broader" }, { "source": "Process Cycle", "target": "AED-0049", "type": "skos:narrower" }, { "source": "AED-0050", "target": "RPH-2703", "type": "skos:broader" }, { "source": "RPH-2703", "target": "AED-0050", "type": "skos:narrower" }, { "source": "AED-0051", "target": "RPH-2701", "type": "skos:broader" }, { "source": "RPH-2701", "target": "AED-0051", "type": "skos:narrower" }, { "source": "AED-0052", "target": "RPH-2703", "type": "skos:broader" }, { "source": "RPH-2703", "target": "AED-0052", "type": "skos:narrower" }, { "source": "AED-0053", "target": "RPH-2703", "type": "skos:broader" }, { "source": "RPH-2703", "target": "AED-0053", "type": "skos:narrower" }, { "source": "AED-0054", "target": "RPH-2703", "type": "skos:broader" }, { "source": "RPH-2703", "target": "AED-0054", "type": "skos:narrower" }, { "source": "AED-0055", "target": "Psychological Effect", "type": "skos:broader" }, { "source": "Psychological Effect", "target": "AED-0055", "type": "skos:narrower" }, { "source": "AED-0056", "target": "Adult Education", "type": "skos:broader" }, { "source": "Adult Education", "target": "AED-0056", "type": "skos:narrower" }, { "source": "AED-0056", "target": "ADA-0010", "type": "augmanitai:crossDomainReference" }, { "source": "AED-0057", "target": "RPH-2703", "type": "skos:broader" }, { "source": "RPH-2703", "target": "AED-0057", "type": "skos:narrower" }, { "source": "AED-0058", "target": "RPH-2604", "type": "skos:broader" }, { "source": "RPH-2604", "target": "AED-0058", "type": "skos:narrower" }, { "source": "AED-0059", "target": "RPH-2703", "type": "skos:broader" }, { "source": "RPH-2703", "target": "AED-0059", "type": "skos:narrower" }, { "source": "AED-0060", "target": "RPH-2703", "type": "skos:broader" }, { "source": "RPH-2703", "target": "AED-0060", "type": "skos:narrower" }, { "source": "AED-0061", "target": "Adult Education", "type": "skos:broader" }, { "source": "Adult Education", "target": "AED-0061", "type": "skos:narrower" }, { "source": "AED-0062", "target": "RPH-2703", "type": "skos:broader" }, { "source": "RPH-2703", "target": "AED-0062", "type": "skos:narrower" }, { "source": "AED-0063", "target": "RPH-2703", "type": "skos:broader" }, { "source": "RPH-2703", "target": "AED-0063", "type": "skos:narrower" }, { "source": "AED-0064", "target": "RPH-2703", "type": "skos:broader" }, { "source": "RPH-2703", "target": "AED-0064", "type": "skos:narrower" }, { "source": "AED-0065", "target": "RPH-2703", "type": "skos:broader" }, { "source": "RPH-2703", "target": "AED-0065", "type": "skos:narrower" }, { "source": "AED-0066", "target": "RPH-2703", "type": "skos:broader" }, { "source": "RPH-2703", "target": "AED-0066", "type": "skos:narrower" }, { "source": "AED-0067", "target": "Machine Learning", "type": "skos:broader" }, { "source": "Machine Learning", "target": "AED-0067", "type": "skos:narrower" }, { "source": "AED-0068", "target": "RPH-2703", "type": "skos:broader" }, { "source": "RPH-2703", "target": "AED-0068", "type": "skos:narrower" }, { "source": "AED-0069", "target": "RPH-2703", "type": "skos:broader" }, { "source": "RPH-2703", "target": "AED-0069", "type": "skos:narrower" }, { "source": "AED-0070", "target": "RPH-2703", "type": "skos:broader" }, { "source": "RPH-2703", "target": "AED-0070", "type": "skos:narrower" }, { "source": "AED-0071", "target": "RPH-2703", "type": "skos:broader" }, { "source": "RPH-2703", "target": "AED-0071", "type": "skos:narrower" }, { "source": "AED-0072", "target": "RPH-2703", "type": "skos:broader" }, { "source": "RPH-2703", "target": "AED-0072", "type": "skos:narrower" }, { "source": "AED-0073", "target": "Behavioral Pattern", "type": "skos:broader" }, { "source": "Behavioral Pattern", "target": "AED-0073", "type": "skos:narrower" }, { "source": "AED-0074", "target": "RPH-2703", "type": "skos:broader" }, { "source": "RPH-2703", "target": "AED-0074", "type": "skos:narrower" }, { "source": "AED-0075", "target": "Psychological Effect", "type": "skos:broader" }, { "source": "Psychological Effect", "target": "AED-0075", "type": "skos:narrower" }, { "source": "AED-0076", "target": "RPH-2703", "type": "skos:broader" }, { "source": "RPH-2703", "target": "AED-0076", "type": "skos:narrower" }, { "source": "AED-0077", "target": "Model Training", "type": "skos:broader" }, { "source": "Model Training", "target": "AED-0077", "type": "skos:narrower" }, { "source": "AED-0078", "target": "Adult Education", "type": "skos:broader" }, { "source": "Adult Education", "target": "AED-0078", "type": "skos:narrower" }, { "source": "AED-0079", "target": "RPH-2703", "type": "skos:broader" }, { "source": "RPH-2703", "target": "AED-0079", "type": "skos:narrower" }, { "source": "AED-0080", "target": "RPH-2703", "type": "skos:broader" }, { "source": "RPH-2703", "target": "AED-0080", "type": "skos:narrower" }, { "source": "AED-0081", "target": "RPH-2303", "type": "skos:broader" }, { "source": "RPH-2303", "target": "AED-0081", "type": "skos:narrower" }, { "source": "AED-0082", "target": "RPH-2703", "type": "skos:broader" }, { "source": "RPH-2703", "target": "AED-0082", "type": "skos:narrower" }, { "source": "AED-0082", "target": "ADA-0001", "type": "augmanitai:crossDomainReference" }, { "source": "AED-0082", "target": "ADA-0002", "type": "augmanitai:crossDomainReference" }, { "source": "AED-0082", "target": "ADA-0004", "type": "augmanitai:crossDomainReference" }, { "source": "AED-0083", "target": "Process Cycle", "type": "skos:broader" }, { "source": "Process Cycle", "target": "AED-0083", "type": "skos:narrower" }, { "source": "AED-0084", "target": "RPH-2703", "type": "skos:broader" }, { "source": "RPH-2703", "target": "AED-0084", "type": "skos:narrower" }, { "source": "AED-0085", "target": "Process Cycle", "type": "skos:broader" }, { "source": "Process Cycle", "target": "AED-0085", "type": "skos:narrower" }, { "source": "AED-0086", "target": "RPH-2703", "type": "skos:broader" }, { "source": "RPH-2703", "target": "AED-0086", "type": "skos:narrower" }, { "source": "AED-0086", "target": "ADA-0010", "type": "augmanitai:crossDomainReference" }, { "source": "AED-0087", "target": "Adult Education", "type": "skos:broader" }, { "source": "Adult Education", "target": "AED-0087", "type": "skos:narrower" }, { "source": "AED-0088", "target": "RPH-2703", "type": "skos:broader" }, { "source": "RPH-2703", "target": "AED-0088", "type": "skos:narrower" }, { "source": "AED-0089", "target": "Adult Education", "type": "skos:broader" }, { "source": "Adult Education", "target": "AED-0089", "type": "skos:narrower" }, { "source": "AED-0090", "target": "System Adaptation", "type": "skos:broader" }, { "source": "System Adaptation", "target": "AED-0090", "type": "skos:narrower" }, { "source": "AED-0091", "target": "Model Training", "type": "skos:broader" }, { "source": "Model 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"source": "AED-0096", "target": "ADA-0002", "type": "augmanitai:crossDomainReference" }, { "source": "AED-0096", "target": "ADA-0004", "type": "augmanitai:crossDomainReference" }, { "source": "AED-0097", "target": "RPH-2703", "type": "skos:broader" }, { "source": "RPH-2703", "target": "AED-0097", "type": "skos:narrower" }, { "source": "AED-0098", "target": "Analytical Ratio", "type": "skos:broader" }, { "source": "Analytical Ratio", "target": "AED-0098", "type": "skos:narrower" }, { "source": "AED-0099", "target": "Machine Learning", "type": "skos:broader" }, { "source": "Machine Learning", "target": "AED-0099", "type": "skos:narrower" }, { "source": "AGE-0001", "target": "Aging & AI Interaction", "type": "skos:broader" }, { "source": "Aging & AI Interaction", "target": "AGE-0001", "type": "skos:narrower" }, { "source": "AGE-0002", "target": "Aging & AI Interaction", "type": "skos:broader" }, { "source": "Aging & AI Interaction", "target": "AGE-0002", "type": "skos:narrower" }, { "source": "AGE-0003", "target": "Behavioral Pattern", "type": "skos:broader" }, { "source": "Behavioral Pattern", "target": "AGE-0003", "type": "skos:narrower" }, { "source": "AGE-0004", "target": "RPH-2052", "type": "skos:broader" }, { "source": "RPH-2052", "target": "AGE-0004", "type": "skos:narrower" }, { "source": "AGE-0005", "target": "Aging & AI Interaction", "type": "skos:broader" }, { "source": "Aging & AI Interaction", "target": "AGE-0005", "type": "skos:narrower" }, { "source": "AGE-0006", "target": "Algorithm", "type": "skos:broader" }, { "source": "Algorithm", "target": "AGE-0006", "type": "skos:narrower" }, { "source": "AGE-0007", "target": "Algorithm", "type": "skos:broader" }, { "source": "Algorithm", "target": "AGE-0007", "type": "skos:narrower" }, { "source": "AGE-0008", "target": "Aging & AI Interaction", "type": "skos:broader" }, { "source": "Aging & AI Interaction", "target": "AGE-0008", "type": "skos:narrower" }, { "source": "AGE-0009", "target": "Aging & AI Interaction", "type": "skos:broader" }, { "source": "Aging & AI Interaction", "target": "AGE-0009", "type": "skos:narrower" }, { "source": "AGE-0010", "target": "Operational Strategy", "type": "skos:broader" }, { "source": "Operational Strategy", "target": "AGE-0010", "type": "skos:narrower" }, { "source": "AGE-0011", "target": "Aging & AI Interaction", "type": "skos:broader" }, { "source": "Aging & AI Interaction", "target": "AGE-0011", "type": "skos:narrower" }, { "source": "AGE-0012", "target": "RPH-2301", "type": "skos:broader" }, { "source": "RPH-2301", "target": "AGE-0012", "type": "skos:narrower" }, { "source": "AGE-0013", "target": "RPH-2703", "type": "skos:broader" }, { "source": "RPH-2703", "target": "AGE-0013", "type": "skos:narrower" }, { "source": "AGE-0014", "target": "RPH-2154", "type": "skos:broader" }, { "source": "RPH-2154", "target": "AGE-0014", "type": "skos:narrower" }, { "source": "AGE-0015", "target": "Aging & AI Interaction", "type": "skos:broader" }, { "source": "Aging & AI Interaction", "target": "AGE-0015", "type": "skos:narrower" }, { "source": "AGE-0016", "target": "RPH-3653", "type": "skos:broader" }, { "source": "RPH-3653", "target": "AGE-0016", "type": "skos:narrower" }, { "source": "AGE-0017", "target": "Behavioral Pattern", "type": "skos:broader" }, { "source": "Behavioral Pattern", "target": "AGE-0017", "type": "skos:narrower" }, { "source": "AGE-0018", "target": "Aging & AI Interaction", "type": "skos:broader" }, { "source": "Aging & AI Interaction", "target": "AGE-0018", "type": "skos:narrower" }, { "source": "AGE-0018", "target": "AED-0010", "type": "augmanitai:crossDomainReference" }, { "source": "AGE-0019", "target": "Aging & AI Interaction", "type": "skos:broader" }, { "source": "Aging & AI Interaction", "target": "AGE-0019", "type": "skos:narrower" }, { "source": "AGE-0020", "target": "RPH-2154", "type": "skos:broader" }, { "source": "RPH-2154", "target": "AGE-0020", "type": "skos:narrower" }, { "source": "AGE-0021", 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"skos:broader" }, { "source": "RPH-3002", "target": "AGE-0027", "type": "skos:narrower" }, { "source": "AGE-0028", "target": "RPH-2805", "type": "skos:broader" }, { "source": "RPH-2805", "target": "AGE-0028", "type": "skos:narrower" }, { "source": "AGE-0029", "target": "Aging & AI Interaction", "type": "skos:broader" }, { "source": "Aging & AI Interaction", "target": "AGE-0029", "type": "skos:narrower" }, { "source": "AGE-0030", "target": "Psychological Effect", "type": "skos:broader" }, { "source": "Psychological Effect", "target": "AGE-0030", "type": "skos:narrower" }, { "source": "AGE-0031", "target": "Logical Paradox", "type": "skos:broader" }, { "source": "Logical Paradox", "target": "AGE-0031", "type": "skos:narrower" }, { "source": "AGE-0031", "target": "AED-0029", "type": "augmanitai:crossDomainReference" }, { "source": "AGE-0032", "target": "Performance Gap", "type": "skos:broader" }, { "source": "Performance Gap", "target": "AGE-0032", "type": "skos:narrower" }, { "source": "AGE-0033", "target": "Aging & AI Interaction", "type": "skos:broader" }, { "source": "Aging & AI Interaction", "target": "AGE-0033", "type": "skos:narrower" }, { "source": "AGE-0033", "target": "AED-0029", "type": "augmanitai:crossDomainReference" }, { "source": "AGE-0033", "target": "AED-0068", "type": "augmanitai:crossDomainReference" }, { "source": "AGE-0034", "target": "Aging & AI Interaction", "type": "skos:broader" }, { "source": "Aging & AI Interaction", "target": "AGE-0034", "type": "skos:narrower" }, { "source": "AGE-0035", "target": "Behavioral Pattern", "type": "skos:broader" }, { "source": "Behavioral Pattern", "target": "AGE-0035", "type": "skos:narrower" }, { "source": "AGE-0036", "target": "RPH-2703", "type": "skos:broader" }, { "source": "RPH-2703", "target": "AGE-0036", "type": "skos:narrower" }, { "source": "AGE-0036", "target": "AED-0090", "type": "augmanitai:crossDomainReference" }, { "source": "AGE-0037", "target": "RPH-2555", "type": "skos:broader" }, { "source": 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"type": "skos:broader" }, { "source": "Analytical Ratio", "target": "AGE-0043", "type": "skos:narrower" }, { "source": "AGE-0044", "target": "Aging & AI Interaction", "type": "skos:broader" }, { "source": "Aging & AI Interaction", "target": "AGE-0044", "type": "skos:narrower" }, { "source": "AGE-0045", "target": "Aging & AI Interaction", "type": "skos:broader" }, { "source": "Aging & AI Interaction", "target": "AGE-0045", "type": "skos:narrower" }, { "source": "AGE-0046", "target": "Aging & AI Interaction", "type": "skos:broader" }, { "source": "Aging & AI Interaction", "target": "AGE-0046", "type": "skos:narrower" }, { "source": "AGE-0047", "target": "Aging & AI Interaction", "type": "skos:broader" }, { "source": "Aging & AI Interaction", "target": "AGE-0047", "type": "skos:narrower" }, { "source": "AGE-0048", "target": "RPH-2605", "type": "skos:broader" }, { "source": "RPH-2605", "target": "AGE-0048", "type": "skos:narrower" }, { "source": "AGE-0049", "target": "RPH-2703", "type": "skos:broader" }, { "source": "RPH-2703", "target": "AGE-0049", "type": "skos:narrower" }, { "source": "AGE-0050", "target": "RPH-2505", "type": "skos:broader" }, { "source": "RPH-2505", "target": "AGE-0050", "type": "skos:narrower" }, { "source": "AGE-0051", "target": "RPH-2505", "type": "skos:broader" }, { "source": "RPH-2505", "target": "AGE-0051", "type": "skos:narrower" }, { "source": "AGE-0052", "target": "Analytical Ratio", "type": "skos:broader" }, { "source": "Analytical Ratio", "target": "AGE-0052", "type": "skos:narrower" }, { "source": "AGE-0053", "target": "Analytical Ratio", "type": "skos:broader" }, { "source": "Analytical Ratio", "target": "AGE-0053", "type": "skos:narrower" }, { "source": "AGE-0054", "target": "Analytical Ratio", "type": "skos:broader" }, { "source": "Analytical Ratio", "target": "AGE-0054", "type": "skos:narrower" }, { "source": "AGE-0055", "target": "Analytical Ratio", "type": "skos:broader" }, { "source": "Analytical Ratio", "target": "AGE-0055", "type": "skos:narrower" }, { "source": "AGE-0056", "target": "Analytical Ratio", "type": "skos:broader" }, { "source": "Analytical Ratio", "target": "AGE-0056", "type": "skos:narrower" }, { "source": "AGE-0056", "target": "AED-0058", "type": "augmanitai:crossDomainReference" }, { "source": "AGE-0057", "target": "RPH-2703", "type": "skos:broader" }, { "source": "RPH-2703", "target": "AGE-0057", "type": "skos:narrower" }, { "source": "AGE-0058", "target": "RPH-2703", "type": "skos:broader" }, { "source": "RPH-2703", "target": "AGE-0058", "type": "skos:narrower" }, { "source": "AGE-0059", "target": "Analytical Ratio", "type": "skos:broader" }, { "source": "Analytical Ratio", "target": "AGE-0059", "type": "skos:narrower" }, { "source": "AGE-0059", "target": "AED-0019", "type": "augmanitai:crossDomainReference" }, { "source": "AGE-0059", "target": "AED-0092", "type": "augmanitai:crossDomainReference" }, { "source": "AGE-0060", "target": "Analytical Ratio", "type": "skos:broader" }, { "source": "Analytical Ratio", "target": "AGE-0060", "type": "skos:narrower" }, { "source": "AGE-0061", "target": "RPH-2301", "type": "skos:broader" }, { "source": "RPH-2301", "target": "AGE-0061", "type": "skos:narrower" }, { "source": "AGE-0061", "target": "ADA-0014", "type": "augmanitai:crossDomainReference" }, { "source": "AGE-0062", "target": "RPH-3901", "type": "skos:broader" }, { "source": "RPH-3901", "target": "AGE-0062", "type": "skos:narrower" }, { "source": "AGE-0063", "target": "Analytical Ratio", "type": "skos:broader" }, { "source": "Analytical Ratio", "target": "AGE-0063", "type": "skos:narrower" }, { "source": "AGE-0064", "target": "Analytical Ratio", "type": "skos:broader" }, { "source": "Analytical Ratio", "target": "AGE-0064", "type": "skos:narrower" }, { "source": "AGE-0064", "target": "AED-0090", "type": "augmanitai:crossDomainReference" }, { "source": "AGE-0065", "target": "Analytical Ratio", "type": "skos:broader" }, { "source": "Analytical Ratio", "target": 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"AGE-0071", "target": "User Interface", "type": "skos:broader" }, { "source": "User Interface", "target": "AGE-0071", "type": "skos:narrower" }, { "source": "AGE-0072", "target": "User Interface", "type": "skos:broader" }, { "source": "User Interface", "target": "AGE-0072", "type": "skos:narrower" }, { "source": "AGE-0073", "target": "User Interface", "type": "skos:broader" }, { "source": "User Interface", "target": "AGE-0073", "type": "skos:narrower" }, { "source": "AGE-0074", "target": "RPH-2301", "type": "skos:broader" }, { "source": "RPH-2301", "target": "AGE-0074", "type": "skos:narrower" }, { "source": "AGE-0074", "target": "AED-0025", "type": "augmanitai:crossDomainReference" }, { "source": "AGE-0074", "target": "AED-0067", "type": "augmanitai:crossDomainReference" }, { "source": "AGE-0074", "target": "AED-0077", "type": "augmanitai:crossDomainReference" }, { "source": "AGE-0075", "target": "Analytical Ratio", "type": "skos:broader" }, { "source": "Analytical Ratio", "target": 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"target": "AED-0091", "type": "augmanitai:crossDomainReference" }, { "source": "ASE-0083", "target": "ART-0086", "type": "augmanitai:crossDomainReference" }, { "source": "ASE-0084", "target": "RPH-2701", "type": "skos:broader" }, { "source": "RPH-2701", "target": "ASE-0084", "type": "skos:narrower" }, { "source": "ASE-0085", "target": "RPH-2201", "type": "skos:broader" }, { "source": "RPH-2201", "target": "ASE-0085", "type": "skos:narrower" }, { "source": "ASE-0086", "target": "Quality Metric", "type": "skos:broader" }, { "source": "Quality Metric", "target": "ASE-0086", "type": "skos:narrower" }, { "source": "ASE-0087", "target": "RPH-3501", "type": "skos:broader" }, { "source": "RPH-3501", "target": "ASE-0087", "type": "skos:narrower" }, { "source": "ASE-0088", "target": "Quality Metric", "type": "skos:broader" }, { "source": "Quality Metric", "target": "ASE-0088", "type": "skos:narrower" }, { "source": "ASE-0089", "target": "Assessment in Education", "type": "skos:broader" }, { 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{ "source": "AUGMANITAI Framework", "target": "AUG-0812", "type": "skos:narrower" }, { "source": "AUG-0821", "target": "AUGMANITAI Framework", "type": "skos:broader" }, { "source": "AUGMANITAI Framework", "target": "AUG-0821", "type": "skos:narrower" }, { "source": "AUG-0840", "target": "TEM-0145", "type": "skos:broader" }, { "source": "TEM-0145", "target": "AUG-0840", "type": "skos:narrower" }, { "source": "AUG-0863", "target": "AUGMANITAI Framework", "type": "skos:broader" }, { "source": "AUGMANITAI Framework", "target": "AUG-0863", "type": "skos:narrower" }, { "source": "AUG-0867", "target": "RPH-2703", "type": "skos:broader" }, { "source": "RPH-2703", "target": "AUG-0867", "type": "skos:narrower" }, { "source": "AUG-0875", "target": "RPH-2602", "type": "skos:broader" }, { "source": "RPH-2602", "target": "AUG-0875", "type": "skos:narrower" }, { "source": "AUG-0879", "target": "AUGMANITAI Framework", "type": "skos:broader" }, { "source": "AUGMANITAI Framework", "target": "AUG-0879", 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"target": "AI Coping Strategy", "type": "skos:broader" }, { "source": "AI Coping Strategy", "target": "COP-0080", "type": "skos:narrower" }, { "source": "COP-0081", "target": "AI Coping Strategy", "type": "skos:broader" }, { "source": "AI Coping Strategy", "target": "COP-0081", "type": "skos:narrower" }, { "source": "COP-0082", "target": "AI Coping Strategy", "type": "skos:broader" }, { "source": "AI Coping Strategy", "target": "COP-0082", "type": "skos:narrower" }, { "source": "COP-0082", "target": "AED-0004", "type": "augmanitai:crossDomainReference" }, { "source": "COP-0083", "target": "AI Coping Strategy", "type": "skos:broader" }, { "source": "AI Coping Strategy", "target": "COP-0083", "type": "skos:narrower" }, { "source": "COP-0084", "target": "Model Training", "type": "skos:broader" }, { "source": "Model Training", "target": "COP-0084", "type": "skos:narrower" }, { "source": "COP-0085", "target": "Performance Gap", "type": "skos:broader" }, { "source": "Performance Gap", "target": "COP-0085", "type": "skos:narrower" }, { "source": "COP-0085", "target": "AED-0019", "type": "augmanitai:crossDomainReference" }, { "source": "COP-0085", "target": "AED-0092", "type": "augmanitai:crossDomainReference" }, { "source": "COP-0085", "target": "AGE-0023", "type": "augmanitai:crossDomainReference" }, { "source": "COP-0086", "target": "RPH-2701", "type": "skos:broader" }, { "source": "RPH-2701", "target": "COP-0086", "type": "skos:narrower" }, { "source": "COP-0086", "target": "AED-0060", "type": "augmanitai:crossDomainReference" }, { "source": "COP-0086", "target": "AED-0061", "type": "augmanitai:crossDomainReference" }, { "source": "COP-0086", "target": "AUG-0406", "type": "augmanitai:crossDomainReference" }, { "source": "COP-0087", "target": "AI Coping Strategy", "type": "skos:broader" }, { "source": "AI Coping Strategy", "target": "COP-0087", "type": "skos:narrower" }, { "source": "COP-0088", "target": "Logical Paradox", "type": "skos:broader" }, { "source": "Logical Paradox", "target": "COP-0088", "type": "skos:narrower" }, { "source": "COP-0089", "target": "AI Coping Strategy", "type": "skos:broader" }, { "source": "AI Coping Strategy", "target": "COP-0089", "type": "skos:narrower" }, { "source": "COP-0090", "target": "AI Coping Strategy", "type": "skos:broader" }, { "source": "AI Coping Strategy", "target": "COP-0090", "type": "skos:narrower" }, { "source": "COP-0091", "target": "Optimization Method", "type": "skos:broader" }, { "source": "Optimization Method", "target": "COP-0091", "type": "skos:narrower" }, { "source": "COP-0092", "target": "RPH-2052", "type": "skos:broader" }, { "source": "RPH-2052", "target": "COP-0092", "type": "skos:narrower" }, { "source": "COP-0093", "target": "AI Coping Strategy", "type": "skos:broader" }, { "source": "AI Coping Strategy", "target": "COP-0093", "type": "skos:narrower" }, { "source": "COP-0093", "target": "AED-0055", "type": "augmanitai:crossDomainReference" }, { "source": "COP-0093", "target": "ASE-0022", "type": "augmanitai:crossDomainReference" }, { "source": "COP-0093", "target": "ASE-0025", "type": "augmanitai:crossDomainReference" }, { "source": "COP-0094", "target": "RPH-2303", "type": "skos:broader" }, { "source": "RPH-2303", "target": "COP-0094", "type": "skos:narrower" }, { "source": "COP-0094", "target": "CON-0029", "type": "augmanitai:crossDomainReference" }, { "source": "COP-0095", "target": "AI Coping Strategy", "type": "skos:broader" }, { "source": "AI Coping Strategy", "target": "COP-0095", "type": "skos:narrower" }, { "source": "COP-0095", "target": "ADA-0012", "type": "augmanitai:crossDomainReference" }, { "source": "COP-0095", "target": "AGE-0007", "type": "augmanitai:crossDomainReference" }, { "source": "COP-0095", "target": "AGE-0014", "type": "augmanitai:crossDomainReference" }, { "source": "COP-0096", "target": "AI Coping Strategy", "type": "skos:broader" }, { "source": "AI Coping Strategy", "target": "COP-0096", "type": "skos:narrower" }, { "source": "COP-0096", "target": "AGE-0014", "type": "augmanitai:crossDomainReference" }, { "source": "COP-0096", "target": "AGE-0048", "type": "augmanitai:crossDomainReference" }, { "source": "COP-0096", "target": "ART-0018", "type": "augmanitai:crossDomainReference" }, { "source": "CRE-0001", "target": "RPH-2703", "type": "skos:broader" }, { "source": "RPH-2703", "target": "CRE-0001", "type": "skos:narrower" }, { "source": "CRE-0001", "target": "AGE-0006", "type": "augmanitai:crossDomainReference" }, { "source": "CRE-0001", "target": "AGE-0007", "type": "augmanitai:crossDomainReference" }, { "source": "CRE-0001", "target": "AGE-0051", "type": "augmanitai:crossDomainReference" }, { "source": "CRE-0002", "target": "Creative AI", "type": "skos:broader" }, { "source": "Creative AI", "target": "CRE-0002", "type": "skos:narrower" }, { "source": "CRE-0003", "target": "Creative AI", "type": "skos:broader" }, { "source": "Creative AI", "target": "CRE-0003", "type": "skos:narrower" }, { "source": "CRE-0004", "target": "Creative AI", "type": "skos:broader" }, { "source": "Creative AI", "target": "CRE-0004", "type": "skos:narrower" }, { "source": "CRE-0004", "target": "ART-0045", "type": "augmanitai:crossDomainReference" }, { "source": "CRE-0005", "target": "Creative AI", "type": "skos:broader" }, { "source": "Creative AI", "target": "CRE-0005", "type": "skos:narrower" }, { "source": "CRE-0006", "target": "Analytical Ratio", "type": "skos:broader" }, { "source": "Analytical Ratio", "target": "CRE-0006", "type": "skos:narrower" }, { "source": "CRE-0007", "target": "Creative AI", "type": "skos:broader" }, { "source": "Creative AI", "target": "CRE-0007", "type": "skos:narrower" }, { "source": "CRE-0008", "target": "RPH-2703", "type": "skos:broader" }, { "source": "RPH-2703", "target": "CRE-0008", "type": "skos:narrower" }, { "source": "CRE-0008", "target": "ART-0087", "type": "augmanitai:crossDomainReference" }, { "source": "CRE-0009", "target": "Creative AI", "type": "skos:broader" }, { "source": "Creative AI", "target": "CRE-0009", "type": "skos:narrower" }, { "source": "CRE-0009", "target": "AGE-0067", "type": "augmanitai:crossDomainReference" }, { "source": "CRE-0009", "target": "ART-0026", "type": "augmanitai:crossDomainReference" }, { "source": "CRE-0009", "target": "ART-0027", "type": "augmanitai:crossDomainReference" }, { "source": "CRE-0010", "target": "Behavioral Pattern", "type": "skos:broader" }, { "source": "Behavioral Pattern", "target": "CRE-0010", "type": "skos:narrower" }, { "source": "CRE-0010", "target": "ART-0043", "type": "augmanitai:crossDomainReference" }, { "source": "CRE-0011", "target": "Systemic Drift", "type": "skos:broader" }, { "source": "Systemic Drift", "target": "CRE-0011", "type": "skos:narrower" }, { "source": "CRE-0012", "target": "Creative AI", "type": "skos:broader" }, { "source": "Creative AI", "target": "CRE-0012", "type": "skos:narrower" }, { "source": "CRE-0013", "target": "Creative AI", "type": "skos:broader" }, { "source": "Creative AI", "target": "CRE-0013", "type": "skos:narrower" }, { "source": "CRE-0013", "target": "AED-0041", "type": "augmanitai:crossDomainReference" }, { "source": "CRE-0013", "target": "AGE-0007", "type": "augmanitai:crossDomainReference" }, { "source": "CRE-0013", "target": "AGE-0013", "type": "augmanitai:crossDomainReference" }, { "source": "CRE-0014", "target": "Cascade Effect", "type": "skos:broader" }, { "source": "Cascade Effect", "target": "CRE-0014", "type": "skos:narrower" }, { "source": "CRE-0015", "target": "Creative AI", "type": "skos:broader" }, { "source": "Creative AI", "target": "CRE-0015", "type": "skos:narrower" }, { "source": "CRE-0015", "target": "COG-0096", "type": "augmanitai:crossDomainReference" }, { "source": "CRE-0016", "target": "Creative AI", "type": "skos:broader" }, { "source": "Creative AI", "target": "CRE-0016", "type": "skos:narrower" }, { "source": "CRE-0017", "target": "Algorithm", "type": "skos:broader" }, { "source": "Algorithm", "target": "CRE-0017", "type": "skos:narrower" }, { "source": "CRE-0018", "target": "Analytical Ratio", "type": "skos:broader" }, { "source": "Analytical Ratio", "target": "CRE-0018", "type": "skos:narrower" }, { "source": "CRE-0018", "target": "CON-0071", "type": "augmanitai:crossDomainReference" }, { "source": "CRE-0019", "target": "RPH-2701", "type": "skos:broader" }, { "source": "RPH-2701", "target": "CRE-0019", "type": "skos:narrower" }, { "source": "CRE-0019", "target": "AED-0013", "type": "augmanitai:crossDomainReference" }, { "source": "CRE-0019", "target": "AED-0093", "type": "augmanitai:crossDomainReference" }, { "source": "CRE-0019", "target": "AGE-0048", "type": "augmanitai:crossDomainReference" }, { "source": "CRE-0020", "target": "Algorithm", "type": "skos:broader" }, { "source": "Algorithm", "target": "CRE-0020", "type": "skos:narrower" }, { "source": "CRE-0021", "target": "RPH-2604", "type": "skos:broader" }, { "source": "RPH-2604", "target": "CRE-0021", "type": "skos:narrower" }, { "source": "CRE-0022", "target": "Algorithm", "type": "skos:broader" }, { "source": "Algorithm", "target": "CRE-0022", "type": "skos:narrower" }, { "source": "CRE-0022", "target": "ART-0050", "type": "augmanitai:crossDomainReference" }, { "source": "CRE-0022", "target": "ART-0051", "type": "augmanitai:crossDomainReference" }, { "source": "CRE-0022", "target": "ART-0052", "type": "augmanitai:crossDomainReference" }, { "source": "CRE-0023", "target": "Analytical Ratio", "type": "skos:broader" }, { "source": "Analytical Ratio", "target": "CRE-0023", "type": "skos:narrower" }, { "source": "CRE-0024", "target": "Creative AI", "type": "skos:broader" }, { "source": "Creative AI", "target": "CRE-0024", "type": "skos:narrower" }, { "source": "CRE-0024", "target": "AGE-0090", "type": "augmanitai:crossDomainReference" }, { "source": "CRE-0025", "target": "Creative AI", "type": "skos:broader" }, { "source": "Creative AI", "target": "CRE-0025", "type": "skos:narrower" }, { "source": "CRE-0025", "target": "ART-0021", "type": "augmanitai:crossDomainReference" }, { "source": "CRE-0026", "target": "Creative AI", "type": "skos:broader" }, { "source": "Creative AI", "target": "CRE-0026", "type": "skos:narrower" }, { "source": "CRE-0026", "target": "COP-0021", "type": "augmanitai:crossDomainReference" }, { "source": "CRE-0027", "target": "Creative AI", "type": "skos:broader" }, { "source": "Creative AI", "target": "CRE-0027", "type": "skos:narrower" }, { "source": "CRE-0028", "target": "Behavioral Pattern", "type": "skos:broader" }, { "source": "Behavioral Pattern", "target": "CRE-0028", "type": "skos:narrower" }, { "source": "CRE-0029", "target": "RPH-2303", "type": "skos:broader" }, { "source": "RPH-2303", "target": "CRE-0029", "type": "skos:narrower" }, { "source": "CRE-0030", "target": "Creative AI", "type": "skos:broader" }, { "source": "Creative AI", "target": "CRE-0030", "type": "skos:narrower" }, { "source": "CRE-0030", "target": "ADA-0013", "type": "augmanitai:crossDomainReference" }, { "source": "CRE-0030", "target": "ART-0013", "type": "augmanitai:crossDomainReference" }, { "source": "CRE-0030", "target": "ART-0059", "type": "augmanitai:crossDomainReference" }, { "source": "CRE-0031", "target": "RPH-2301", "type": "skos:broader" }, { "source": "RPH-2301", "target": "CRE-0031", "type": "skos:narrower" }, { "source": "CRE-0031", "target": "ADA-0001", "type": "augmanitai:crossDomainReference" }, { "source": "CRE-0032", "target": "RPH-3901", "type": "skos:broader" }, { "source": "RPH-3901", "target": "CRE-0032", "type": "skos:narrower" }, { "source": "CRE-0032", "target": "BEH-0094", "type": "augmanitai:crossDomainReference" }, { "source": "CRE-0033", "target": "Creative AI", "type": "skos:broader" }, { "source": "Creative AI", "target": "CRE-0033", "type": "skos:narrower" }, { "source": "CRE-0034", "target": "RPH-3854", "type": "skos:broader" }, { "source": "RPH-3854", "target": "CRE-0034", "type": "skos:narrower" }, { "source": "CRE-0035", "target": "Creative AI", "type": "skos:broader" }, { "source": "Creative AI", "target": "CRE-0035", "type": "skos:narrower" }, { "source": "CRE-0035", "target": "AED-0011", "type": "augmanitai:crossDomainReference" }, { "source": "CRE-0035", "target": "AGE-0067", "type": "augmanitai:crossDomainReference" }, { "source": "CRE-0035", "target": "ART-0026", "type": "augmanitai:crossDomainReference" }, { "source": "CRE-0036", "target": "Creative AI", "type": "skos:broader" }, { "source": "Creative AI", "target": "CRE-0036", "type": "skos:narrower" }, { "source": "CRE-0037", "target": "Creative AI", "type": "skos:broader" }, { "source": "Creative AI", "target": "CRE-0037", "type": "skos:narrower" }, { "source": "CRE-0037", "target": "AED-0011", "type": "augmanitai:crossDomainReference" }, { "source": "CRE-0037", "target": "ART-0008", "type": "augmanitai:crossDomainReference" }, { "source": "CRE-0037", "target": "ART-0013", "type": "augmanitai:crossDomainReference" }, { "source": "CRE-0038", "target": "Creative AI", "type": "skos:broader" }, { "source": "Creative AI", "target": "CRE-0038", "type": "skos:narrower" }, { "source": "CRE-0038", "target": "ART-0013", "type": "augmanitai:crossDomainReference" }, { "source": "CRE-0038", "target": "ART-0011", "type": "augmanitai:crossDomainReference" }, { "source": "CRE-0039", "target": "CRE-0120", "type": "skos:broader" }, { "source": "CRE-0120", "target": "CRE-0039", "type": "skos:narrower" }, { "source": "CRE-0040", "target": "Logical Paradox", "type": "skos:broader" }, { "source": "Logical Paradox", "target": "CRE-0040", "type": "skos:narrower" }, { "source": "CRE-0040", "target": "AGE-0031", "type": "augmanitai:crossDomainReference" }, { "source": "CRE-0040", "target": "AGE-0042", "type": "augmanitai:crossDomainReference" }, { "source": "CRE-0040", "target": "AGE-0050", "type": "augmanitai:crossDomainReference" }, { "source": "CRE-0041", "target": "RPH-3304", "type": "skos:broader" }, { "source": "RPH-3304", "target": "CRE-0041", "type": "skos:narrower" }, { "source": "CRE-0041", "target": "AED-0019", "type": "augmanitai:crossDomainReference" }, { "source": "CRE-0041", "target": "AGE-0090", "type": "augmanitai:crossDomainReference" }, { "source": "CRE-0041", "target": "AGE-0098", "type": "augmanitai:crossDomainReference" }, { "source": "CRE-0042", "target": "Performance Gap", "type": "skos:broader" }, { "source": "Performance Gap", "target": "CRE-0042", "type": "skos:narrower" }, { "source": "CRE-0042", "target": "AED-0019", "type": "augmanitai:crossDomainReference" }, { "source": "CRE-0042", "target": "AGE-0098", "type": "augmanitai:crossDomainReference" }, { "source": "CRE-0042", "target": "ART-0082", "type": "augmanitai:crossDomainReference" }, { "source": "CRE-0043", "target": "RPH-2505", "type": "skos:broader" }, { "source": "RPH-2505", "target": "CRE-0043", "type": "skos:narrower" }, { "source": "CRE-0043", "target": "CAI-0004", "type": "augmanitai:crossDomainReference" }, { "source": "CRE-0044", "target": "Creative AI", "type": "skos:broader" }, { "source": "Creative AI", "target": "CRE-0044", "type": "skos:narrower" }, { "source": "CRE-0044", "target": "AED-0005", "type": "augmanitai:crossDomainReference" }, { "source": "CRE-0044", "target": "AED-0052", "type": "augmanitai:crossDomainReference" }, { "source": "CRE-0044", "target": "AGE-0089", "type": "augmanitai:crossDomainReference" }, { "source": "CRE-0045", "target": "Analytical Ratio", "type": "skos:broader" }, { "source": "Analytical Ratio", "target": "CRE-0045", "type": "skos:narrower" }, { "source": "CRE-0046", "target": "Creative AI", "type": "skos:broader" }, { "source": "Creative AI", "target": "CRE-0046", "type": "skos:narrower" }, { "source": "CRE-0047", "target": "RPH-3901", "type": "skos:broader" }, { "source": "RPH-3901", "target": "CRE-0047", "type": "skos:narrower" }, { "source": "CRE-0048", "target": "Creative AI", "type": "skos:broader" }, { "source": "Creative AI", "target": "CRE-0048", "type": "skos:narrower" }, { "source": "CRE-0049", "target": "RPH-2703", "type": "skos:broader" }, { "source": "RPH-2703", "target": "CRE-0049", "type": "skos:narrower" }, { "source": "CRE-0049", "target": "ASE-0038", "type": "augmanitai:crossDomainReference" }, { "source": "CRE-0049", "target": "BEH-0006", "type": "augmanitai:crossDomainReference" }, { "source": "CRE-0049", "target": "BEH-0040", "type": "augmanitai:crossDomainReference" }, { "source": "CRE-0050", "target": "RPH-2254", "type": "skos:broader" }, { "source": "RPH-2254", "target": "CRE-0050", "type": "skos:narrower" }, { "source": "CRE-0051", "target": "Creative AI", "type": "skos:broader" }, { "source": "Creative AI", "target": "CRE-0051", "type": "skos:narrower" }, { "source": "CRE-0051", "target": "AED-0085", "type": "augmanitai:crossDomainReference" }, { "source": "CRE-0051", "target": "BEH-0012", "type": "augmanitai:crossDomainReference" }, { "source": "CRE-0051", "target": "BEH-0024", "type": "augmanitai:crossDomainReference" }, { "source": "CRE-0052", "target": "Creative AI", "type": "skos:broader" }, { "source": "Creative AI", "target": "CRE-0052", "type": "skos:narrower" }, { "source": "CRE-0053", "target": "Performance Gap", "type": "skos:broader" }, { "source": "Performance Gap", "target": "CRE-0053", "type": "skos:narrower" }, { "source": "CRE-0054", "target": "RPH-2503", "type": "skos:broader" }, { "source": "RPH-2503", "target": "CRE-0054", "type": "skos:narrower" }, { "source": "CRE-0054", "target": "ADA-0003", "type": "augmanitai:crossDomainReference" }, { "source": "CRE-0054", "target": "ART-0014", "type": "augmanitai:crossDomainReference" }, { "source": "CRE-0054", "target": "ART-0015", "type": "augmanitai:crossDomainReference" }, { "source": "CRE-0055", "target": "Creative AI", "type": "skos:broader" }, { "source": "Creative AI", "target": "CRE-0055", "type": "skos:narrower" }, { "source": "CRE-0055", "target": "ART-0057", "type": "augmanitai:crossDomainReference" }, { "source": "CRE-0056", "target": "Analytical Ratio", "type": "skos:broader" }, { "source": "Analytical Ratio", "target": "CRE-0056", "type": "skos:narrower" }, { "source": "CRE-0056", "target": "COG-0108", "type": "augmanitai:crossDomainReference" }, { "source": "CRE-0057", "target": "Creative AI", "type": "skos:broader" }, { "source": "Creative AI", "target": "CRE-0057", "type": "skos:narrower" }, { "source": "CRE-0058", "target": "RPH-2701", "type": "skos:broader" }, { "source": "RPH-2701", "target": "CRE-0058", "type": "skos:narrower" }, { "source": "CRE-0059", "target": "Creative AI", "type": "skos:broader" }, { "source": "Creative AI", "target": "CRE-0059", "type": "skos:narrower" }, { "source": "CRE-0059", "target": "COG-0092", "type": "augmanitai:crossDomainReference" }, { "source": "CRE-0060", "target": "RPH-2303", "type": "skos:broader" }, { "source": "RPH-2303", "target": "CRE-0060", "type": "skos:narrower" }, { "source": "CRE-0060", "target": "AGE-0012", "type": "augmanitai:crossDomainReference" }, { "source": "CRE-0060", "target": "AGE-0014", "type": "augmanitai:crossDomainReference" }, { "source": "CRE-0060", "target": "AGE-0038", "type": "augmanitai:crossDomainReference" }, { "source": "CRE-0061", "target": "RPH-2701", "type": "skos:broader" }, { "source": "RPH-2701", "target": "CRE-0061", "type": "skos:narrower" }, { "source": "CRE-0061", "target": "COG-0166", "type": "augmanitai:crossDomainReference" }, { "source": "CRE-0062", "target": "RPH-2303", "type": "skos:broader" }, { "source": "RPH-2303", "target": "CRE-0062", "type": "skos:narrower" }, { "source": "CRE-0062", "target": "AUG-0138", "type": "augmanitai:crossDomainReference" }, { "source": "CRE-0063", "target": "Creative AI", "type": "skos:broader" }, { "source": "Creative AI", "target": "CRE-0063", "type": "skos:narrower" }, { "source": "CRE-0064", "target": "RPH-2104", "type": "skos:broader" }, { "source": "RPH-2104", "target": "CRE-0064", "type": "skos:narrower" }, { "source": "CRE-0065", "target": "RPH-2355", "type": "skos:broader" }, { "source": "RPH-2355", "target": "CRE-0065", "type": "skos:narrower" }, { "source": "CRE-0065", "target": "AGE-0031", "type": "augmanitai:crossDomainReference" }, { "source": "CRE-0065", "target": "AGE-0042", "type": "augmanitai:crossDomainReference" }, { "source": "CRE-0065", "target": "AGE-0050", "type": "augmanitai:crossDomainReference" }, { "source": "CRE-0066", "target": "RPH-2301", "type": "skos:broader" }, { "source": "RPH-2301", "target": "CRE-0066", "type": "skos:narrower" }, { "source": "CRE-0067", "target": "RPH-3501", "type": "skos:broader" }, { "source": "RPH-3501", "target": "CRE-0067", "type": "skos:narrower" }, { "source": "CRE-0067", "target": "COG-0098", "type": "augmanitai:crossDomainReference" }, { "source": "CRE-0068", "target": "Creative AI", "type": "skos:broader" }, { "source": "Creative AI", "target": "CRE-0068", "type": "skos:narrower" }, { "source": "CRE-0069", "target": "RPH-2701", "type": "skos:broader" }, { "source": "RPH-2701", "target": "CRE-0069", "type": "skos:narrower" }, { "source": "CRE-0069", "target": "ART-0002", "type": "augmanitai:crossDomainReference" }, { "source": "CRE-0070", "target": "Analytical Ratio", "type": "skos:broader" }, { "source": "Analytical Ratio", "target": "CRE-0070", "type": "skos:narrower" }, { "source": "CRE-0071", "target": "RPH-2303", "type": "skos:broader" }, { "source": "RPH-2303", "target": "CRE-0071", "type": "skos:narrower" }, { "source": "CRE-0071", "target": "COG-0031", "type": "augmanitai:crossDomainReference" }, { "source": "CRE-0072", "target": "RPH-2002", "type": "skos:broader" }, { "source": "RPH-2002", "target": "CRE-0072", "type": "skos:narrower" }, { "source": "CRE-0072", "target": "COG-0124", "type": "augmanitai:crossDomainReference" }, { "source": "CRE-0072", "target": "COG-0004", "type": "augmanitai:crossDomainReference" }, { "source": "CRE-0073", "target": "Analytical Ratio", "type": 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"source": "CRE-0083", "target": "COG-0027", "type": "augmanitai:crossDomainReference" }, { "source": "CRE-0083", "target": "COG-0046", "type": "augmanitai:crossDomainReference" }, { "source": "CRE-0083", "target": "COG-0067", "type": "augmanitai:crossDomainReference" }, { "source": "CRE-0084", "target": "RPH-2005", "type": "skos:broader" }, { "source": "RPH-2005", "target": "CRE-0084", "type": "skos:narrower" }, { "source": "CRE-0085", "target": "RPH-3754", "type": "skos:broader" }, { "source": "RPH-3754", "target": "CRE-0085", "type": "skos:narrower" }, { "source": "CRE-0086", "target": "RPH-2303", "type": "skos:broader" }, { "source": "RPH-2303", "target": "CRE-0086", "type": "skos:narrower" }, { "source": "CRE-0087", "target": "Analytical Ratio", "type": "skos:broader" }, { "source": "Analytical Ratio", "target": "CRE-0087", "type": "skos:narrower" }, { "source": "CRE-0088", "target": "Analytical Ratio", "type": "skos:broader" }, { "source": "Analytical Ratio", "target": "CRE-0088", "type": "skos:narrower" }, { "source": "CRE-0088", "target": "ART-0087", "type": "augmanitai:crossDomainReference" }, { "source": "CRE-0088", "target": "ART-0088", "type": "augmanitai:crossDomainReference" }, { "source": "CRE-0088", "target": "ASE-0095", "type": "augmanitai:crossDomainReference" }, { "source": "CRE-0089", "target": "RPH-2301", "type": "skos:broader" }, { "source": "RPH-2301", "target": "CRE-0089", "type": "skos:narrower" }, { "source": "CRE-0089", "target": "AGE-0096", "type": "augmanitai:crossDomainReference" }, { "source": "CRE-0089", "target": "ASE-0021", "type": "augmanitai:crossDomainReference" }, { "source": "CRE-0089", "target": "ASE-0064", "type": "augmanitai:crossDomainReference" }, { "source": "CRE-0090", "target": "Psychological Effect", "type": "skos:broader" }, { "source": "Psychological Effect", "target": "CRE-0090", "type": "skos:narrower" }, { "source": "CRE-0091", "target": "Creative AI", "type": "skos:broader" }, { "source": "Creative AI", "target": "CRE-0091", "type": "skos:narrower" }, { "source": "CRE-0092", "target": "Psychological Effect", "type": "skos:broader" }, { "source": "Psychological Effect", "target": "CRE-0092", "type": "skos:narrower" }, { "source": "CRE-0092", "target": "ADA-0001", "type": "augmanitai:crossDomainReference" }, { "source": "CRE-0092", "target": "ADA-0002", "type": "augmanitai:crossDomainReference" }, { "source": "CRE-0092", "target": "ADA-0004", "type": "augmanitai:crossDomainReference" }, { "source": "CRE-0093", "target": "Creative AI", "type": "skos:broader" }, { "source": "Creative AI", "target": "CRE-0093", "type": "skos:narrower" }, { "source": "CRE-0093", "target": "AGE-0086", "type": "augmanitai:crossDomainReference" }, { "source": "CRE-0093", "target": "AGE-0093", "type": "augmanitai:crossDomainReference" }, { "source": "CRE-0093", "target": "COG-0143", "type": "augmanitai:crossDomainReference" }, { "source": "CRE-0094", "target": "RPH-2002", "type": "skos:broader" }, { "source": "RPH-2002", "target": "CRE-0094", "type": "skos:narrower" }, { "source": "CRE-0095", "target": "RPH-3501", "type": "skos:broader" }, { "source": "RPH-3501", "target": "CRE-0095", "type": "skos:narrower" }, { "source": "CRE-0095", "target": "CON-0015", "type": "augmanitai:crossDomainReference" }, { "source": "CRE-0096", "target": "Creative AI", "type": "skos:broader" }, { "source": "Creative AI", "target": "CRE-0096", "type": "skos:narrower" }, { "source": "CRE-0096", "target": "ART-0097", "type": "augmanitai:crossDomainReference" }, { "source": "CRE-0097", "target": "Creative AI", "type": "skos:broader" }, { "source": "Creative AI", "target": "CRE-0097", "type": "skos:narrower" }, { "source": "CRE-0097", "target": "AED-0009", "type": "augmanitai:crossDomainReference" }, { "source": "CRE-0097", "target": "AED-0019", "type": "augmanitai:crossDomainReference" }, { "source": "CRE-0097", "target": "AGE-0090", "type": "augmanitai:crossDomainReference" }, { "source": "CRE-0098", "target": "Creative AI", "type": "skos:broader" }, { "source": "Creative AI", "target": "CRE-0098", "type": "skos:narrower" }, { "source": "CRE-0098", "target": "ART-0088", "type": "augmanitai:crossDomainReference" }, { "source": "CRE-0099", "target": "Creative AI", "type": "skos:broader" }, { "source": "Creative AI", "target": "CRE-0099", "type": "skos:narrower" }, { "source": "CRE-0099", "target": "BEH-0013", "type": "augmanitai:crossDomainReference" }, { "source": "CRE-0100", "target": "Psychological Effect", "type": "skos:broader" }, { "source": "Psychological Effect", "target": "CRE-0100", "type": "skos:narrower" }, { "source": "CRE-0100", "target": "ADA-0001", "type": "augmanitai:crossDomainReference" }, { "source": "CRE-0100", "target": "ADA-0002", "type": "augmanitai:crossDomainReference" }, { "source": "CRE-0100", "target": "ADA-0004", "type": "augmanitai:crossDomainReference" }, { "source": "CRE-0101", "target": "RPH-2703", "type": "skos:broader" }, { "source": "RPH-2703", "target": "CRE-0101", "type": "skos:narrower" }, { "source": "CRE-0102", "target": "Psychological Effect", "type": "skos:broader" }, { "source": "Psychological Effect", "target": "CRE-0102", "type": "skos:narrower" }, { "source": "CRE-0103", "target": "RPH-2703", "type": "skos:broader" }, { "source": "RPH-2703", "target": "CRE-0103", "type": "skos:narrower" }, { "source": "CRE-0103", "target": "BEH-0037", "type": "augmanitai:crossDomainReference" }, { "source": "CRE-0104", "target": "RPH-2104", "type": "skos:broader" }, { "source": "RPH-2104", "target": "CRE-0104", "type": "skos:narrower" }, { "source": "CRE-0104", "target": "AGE-0046", "type": "augmanitai:crossDomainReference" }, { "source": "CRE-0105", "target": "Creative AI", "type": "skos:broader" }, { "source": "Creative AI", "target": "CRE-0105", "type": "skos:narrower" }, { "source": "CRE-0106", "target": "Creative AI", "type": "skos:broader" }, { "source": "Creative AI", "target": "CRE-0106", "type": "skos:narrower" }, { "source": "CRE-0107", "target": "RPH-2403", "type": "skos:broader" }, { "source": "RPH-2403", "target": "CRE-0107", "type": "skos:narrower" }, { "source": "CRE-0108", "target": "RPH-2605", "type": "skos:broader" }, { "source": "RPH-2605", "target": "CRE-0108", "type": "skos:narrower" }, { "source": "CRE-0109", "target": "Creative AI", "type": "skos:broader" }, { "source": "Creative AI", "target": "CRE-0109", "type": "skos:narrower" }, { "source": "CRE-0109", "target": "ART-0043", "type": "augmanitai:crossDomainReference" }, { "source": "CRE-0110", "target": "Systemic Drift", "type": "skos:broader" }, { "source": "Systemic Drift", "target": "CRE-0110", "type": "skos:narrower" }, { "source": "CRE-0111", "target": "Analytical Ratio", "type": "skos:broader" }, { "source": "Analytical Ratio", "target": "CRE-0111", "type": "skos:narrower" }, { "source": "CRE-0112", "target": "Psychological Effect", "type": "skos:broader" }, { "source": "Psychological Effect", "target": "CRE-0112", "type": "skos:narrower" }, { "source": "CRE-0113", "target": "Creative AI", "type": "skos:broader" }, { "source": "Creative AI", "target": "CRE-0113", "type": "skos:narrower" }, { "source": "CRE-0113", "target": "COG-0057", "type": "augmanitai:crossDomainReference" }, { "source": "CRE-0114", "target": "Performance Gap", "type": "skos:broader" }, { "source": "Performance Gap", "target": "CRE-0114", "type": "skos:narrower" }, { "source": "CRE-0115", "target": "Creative AI", "type": "skos:broader" }, { "source": "Creative AI", "target": "CRE-0115", "type": "skos:narrower" }, { "source": "CRE-0115", "target": "AGE-0014", "type": "augmanitai:crossDomainReference" }, { "source": "CRE-0115", "target": "AGE-0048", "type": "augmanitai:crossDomainReference" }, { "source": "CRE-0115", "target": "ART-0018", "type": "augmanitai:crossDomainReference" }, { "source": "CRE-0116", "target": "Creative AI", "type": "skos:broader" }, { "source": "Creative AI", "target": "CRE-0116", "type": "skos:narrower" }, { "source": "CRE-0117", "target": "CRE-0116", "type": "skos:broader" }, { "source": "CRE-0116", "target": "CRE-0117", "type": "skos:narrower" }, { "source": "CRE-0118", "target": "RPH-2951", "type": "skos:broader" }, { "source": "RPH-2951", "target": "CRE-0118", "type": "skos:narrower" }, { "source": "CRE-0119", "target": "TEM-0119", "type": "skos:broader" }, { "source": "TEM-0119", "target": "CRE-0119", "type": "skos:narrower" }, { "source": "CRE-0120", "target": "RPH-2104", "type": "skos:broader" }, { "source": "RPH-2104", "target": "CRE-0120", "type": "skos:narrower" }, { "source": "CRE-0120", "target": "AED-0041", "type": "augmanitai:crossDomainReference" }, { "source": "CRE-0121", "target": "Creative AI", "type": "skos:broader" }, { "source": "Creative AI", "target": "CRE-0121", "type": "skos:narrower" }, { "source": "CRE-0122", "target": "RPH-2355", "type": "skos:broader" }, { "source": "RPH-2355", "target": "CRE-0122", "type": "skos:narrower" }, { "source": "CRE-0122", "target": "ADA-0013", "type": 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"type": "augmanitai:crossDomainReference" }, { "source": "CRE-0128", "target": "Creative AI", "type": "skos:broader" }, { "source": "Creative AI", "target": "CRE-0128", "type": "skos:narrower" }, { "source": "CRE-0129", "target": "Creative AI", "type": "skos:broader" }, { "source": "Creative AI", "target": "CRE-0129", "type": "skos:narrower" }, { "source": "CRE-0130", "target": "Creative AI", "type": "skos:broader" }, { "source": "Creative AI", "target": "CRE-0130", "type": "skos:narrower" }, { "source": "CRE-0131", "target": "RPH-2003", "type": "skos:broader" }, { "source": "RPH-2003", "target": "CRE-0131", "type": "skos:narrower" }, { "source": "CRE-0131", "target": "AED-0009", "type": "augmanitai:crossDomainReference" }, { "source": "CRE-0131", "target": "ART-0003", "type": "augmanitai:crossDomainReference" }, { "source": "CRE-0131", "target": "ART-0014", "type": "augmanitai:crossDomainReference" }, { "source": "CRE-0132", "target": "Creative AI", "type": "skos:broader" }, { "source": "Creative AI", "target": "CRE-0132", "type": "skos:narrower" }, { "source": "CRE-0132", "target": "COP-0039", "type": "augmanitai:crossDomainReference" }, { "source": "CRE-0133", "target": "Creative AI", "type": "skos:broader" }, { "source": "Creative AI", "target": "CRE-0133", "type": "skos:narrower" }, { "source": "CRE-0133", "target": "ASE-0062", "type": "augmanitai:crossDomainReference" }, { "source": "CRE-0134", "target": "RPH-2301", "type": "skos:broader" }, { "source": "RPH-2301", "target": "CRE-0134", "type": "skos:narrower" }, { "source": "CRE-0135", "target": "Creative AI", "type": "skos:broader" }, { "source": "Creative AI", "target": "CRE-0135", "type": "skos:narrower" }, { "source": "CRE-0136", "target": "RPH-2703", "type": "skos:broader" }, { "source": "RPH-2703", "target": "CRE-0136", "type": "skos:narrower" }, { "source": "CRE-0137", "target": "RPH-2003", "type": "skos:broader" }, { "source": "RPH-2003", "target": "CRE-0137", "type": "skos:narrower" }, { "source": "CRE-0138", "target": "RPH-2701", "type": "skos:broader" }, { "source": "RPH-2701", "target": "CRE-0138", "type": "skos:narrower" }, { "source": "CRE-0139", "target": "Creative AI", "type": "skos:broader" }, { "source": "Creative AI", "target": "CRE-0139", "type": "skos:narrower" }, { "source": "CRE-0140", "target": "CRE-0225", "type": "skos:broader" }, { "source": "CRE-0225", "target": "CRE-0140", "type": "skos:narrower" }, { "source": "CRE-0140", "target": "ART-0070", "type": "augmanitai:crossDomainReference" }, { "source": "CRE-0141", "target": "Creative AI", "type": "skos:broader" }, { "source": "Creative AI", "target": "CRE-0141", "type": "skos:narrower" }, { "source": "CRE-0141", "target": "ADA-0012", "type": "augmanitai:crossDomainReference" }, { "source": "CRE-0141", "target": "AGE-0007", "type": "augmanitai:crossDomainReference" }, { "source": "CRE-0141", "target": "AGE-0046", "type": "augmanitai:crossDomainReference" }, { "source": "CRE-0142", "target": "RPH-3754", "type": "skos:broader" }, { "source": "RPH-3754", "target": "CRE-0142", "type": "skos:narrower" }, { "source": "CRE-0143", "target": "RPH-2603", "type": "skos:broader" }, { "source": "RPH-2603", "target": "CRE-0143", "type": "skos:narrower" }, { "source": "CRE-0144", "target": "Creative AI", "type": "skos:broader" }, { "source": "Creative AI", "target": "CRE-0144", "type": "skos:narrower" }, { "source": "CRE-0145", "target": "RPH-2003", "type": "skos:broader" }, { "source": "RPH-2003", "target": "CRE-0145", "type": "skos:narrower" }, { "source": "CRE-0146", "target": "SOC-0044", "type": "skos:broader" }, { "source": "SOC-0044", "target": "CRE-0146", "type": "skos:narrower" }, { "source": "CRE-0147", "target": "Creative AI", "type": "skos:broader" }, { "source": "Creative AI", "target": "CRE-0147", "type": "skos:narrower" }, { "source": "CRE-0148", "target": "RPH-2304", "type": "skos:broader" }, { "source": "RPH-2304", "target": "CRE-0148", "type": "skos:narrower" }, { "source": "CRE-0149", "target": "Creative AI", "type": "skos:broader" }, { "source": "Creative AI", "target": "CRE-0149", "type": "skos:narrower" }, { "source": "CRE-0150", "target": "Analytical Ratio", "type": "skos:broader" }, { "source": "Analytical Ratio", "target": "CRE-0150", "type": "skos:narrower" }, { "source": "CRE-0150", "target": "ADA-0003", "type": "augmanitai:crossDomainReference" }, { "source": "CRE-0151", "target": "Creative AI", "type": "skos:broader" }, { "source": "Creative AI", "target": "CRE-0151", "type": "skos:narrower" }, { "source": "CRE-0152", "target": "Creative AI", "type": "skos:broader" }, { "source": "Creative AI", "target": "CRE-0152", "type": "skos:narrower" }, { "source": "CRE-0153", "target": "SOC-0042", "type": "skos:broader" }, { "source": "SOC-0042", "target": "CRE-0153", "type": "skos:narrower" }, { "source": "CRE-0154", "target": "Creative AI", "type": "skos:broader" }, { "source": "Creative AI", "target": "CRE-0154", "type": "skos:narrower" }, { "source": "CRE-0155", "target": "RPH-3902", "type": "skos:broader" }, { "source": "RPH-3902", "target": "CRE-0155", "type": "skos:narrower" }, { "source": "CRE-0156", "target": "CRE-0174", "type": "skos:broader" }, { "source": "CRE-0174", "target": "CRE-0156", "type": "skos:narrower" }, { "source": "CRE-0157", "target": "RPH-2253", "type": "skos:broader" }, { "source": "RPH-2253", "target": "CRE-0157", "type": "skos:narrower" }, { "source": "CRE-0158", "target": "Creative AI", "type": "skos:broader" }, { "source": "Creative AI", "target": "CRE-0158", "type": "skos:narrower" }, { "source": "CRE-0159", "target": "Creative AI", "type": "skos:broader" }, { "source": "Creative AI", "target": "CRE-0159", "type": "skos:narrower" }, { "source": "CRE-0160", "target": "Analytical Ratio", "type": "skos:broader" }, { "source": "Analytical Ratio", "target": "CRE-0160", "type": "skos:narrower" }, { "source": "CRE-0160", "target": "AGE-0051", "type": "augmanitai:crossDomainReference" }, { "source": "CRE-0161", "target": "REL-0007", "type": "skos:broader" }, { "source": "REL-0007", "target": "CRE-0161", "type": "skos:narrower" }, { "source": "CRE-0162", "target": "RPH-2303", "type": "skos:broader" }, { "source": "RPH-2303", "target": "CRE-0162", "type": "skos:narrower" }, { "source": "CRE-0163", "target": "RPH-2701", "type": "skos:broader" }, { "source": "RPH-2701", "target": "CRE-0163", "type": "skos:narrower" }, { "source": "CRE-0164", "target": "TEM-0112", "type": "skos:broader" }, { "source": "TEM-0112", "target": "CRE-0164", "type": "skos:narrower" }, { "source": "CRE-0165", "target": "Psychological Effect", "type": "skos:broader" }, { "source": "Psychological Effect", "target": "CRE-0165", "type": "skos:narrower" }, { "source": "CRE-0166", "target": "CRE-0147", "type": "skos:broader" }, { "source": "CRE-0147", "target": "CRE-0166", "type": "skos:narrower" }, { "source": "CRE-0166", "target": "AED-0027", "type": "augmanitai:crossDomainReference" }, { "source": "CRE-0166", "target": "AED-0047", "type": "augmanitai:crossDomainReference" }, { "source": "CRE-0167", "target": "IDN-0014", "type": "skos:broader" }, { "source": "IDN-0014", "target": "CRE-0167", "type": "skos:narrower" }, { "source": "CRE-0168", "target": "Creative AI", "type": "skos:broader" }, { "source": "Creative AI", "target": "CRE-0168", "type": "skos:narrower" }, { "source": "CRE-0169", "target": "Creative AI", "type": "skos:broader" }, { "source": "Creative AI", "target": "CRE-0169", "type": "skos:narrower" }, { "source": "CRE-0170", "target": "PER-0092", "type": "skos:broader" }, { "source": "PER-0092", "target": "CRE-0170", "type": "skos:narrower" }, { "source": "CRE-0171", "target": "RPH-3952", "type": "skos:broader" }, { "source": "RPH-3952", "target": "CRE-0171", "type": "skos:narrower" }, { "source": "CRE-0172", "target": "RPH-1307", "type": "skos:broader" }, { "source": "RPH-1307", "target": "CRE-0172", "type": "skos:narrower" }, { "source": "CRE-0172", "target": "CON-0016", "type": "augmanitai:crossDomainReference" }, { "source": "CRE-0173", "target": "CRE-0210", "type": "skos:broader" }, { "source": "CRE-0210", "target": "CRE-0173", "type": "skos:narrower" }, { "source": "CRE-0174", "target": "Creative AI", "type": "skos:broader" }, { "source": "Creative AI", "target": "CRE-0174", "type": "skos:narrower" }, { "source": "CRE-0175", "target": "RPH-2603", "type": "skos:broader" }, { "source": "RPH-2603", "target": "CRE-0175", "type": "skos:narrower" }, { "source": "CRE-0176", "target": "Creative AI", "type": "skos:broader" }, { "source": "Creative AI", "target": "CRE-0176", "type": "skos:narrower" }, { "source": "CRE-0176", "target": "AGE-0032", "type": "augmanitai:crossDomainReference" }, { "source": "CRE-0176", "target": "AGE-0077", "type": "augmanitai:crossDomainReference" }, { "source": "CRE-0176", "target": "AGE-0087", "type": "augmanitai:crossDomainReference" }, { "source": "CRE-0177", "target": "RPH-2605", "type": "skos:broader" }, { "source": "RPH-2605", "target": "CRE-0177", "type": "skos:narrower" }, { "source": "CRE-0178", "target": "RPH-2355", "type": "skos:broader" }, { "source": "RPH-2355", "target": "CRE-0178", "type": "skos:narrower" }, { "source": "CRE-0179", "target": "RPH-2303", "type": "skos:broader" }, { "source": "RPH-2303", "target": "CRE-0179", "type": "skos:narrower" }, { "source": "CRE-0179", "target": "AUG-0689", "type": "augmanitai:crossDomainReference" }, { "source": "CRE-0180", "target": "RPH-2603", "type": "skos:broader" }, { "source": "RPH-2603", "target": "CRE-0180", "type": "skos:narrower" }, { "source": "CRE-0181", "target": "KNO-0018", "type": "skos:broader" }, { "source": "KNO-0018", "target": "CRE-0181", "type": "skos:narrower" }, { "source": "CRE-0182", "target": "Creative AI", "type": "skos:broader" }, { "source": "Creative AI", "target": "CRE-0182", "type": "skos:narrower" }, { "source": "CRE-0183", "target": "RPH-2403", "type": "skos:broader" }, { "source": "RPH-2403", "target": "CRE-0183", "type": "skos:narrower" }, { "source": "CRE-0184", "target": "RPH-2701", "type": "skos:broader" }, { "source": "RPH-2701", "target": "CRE-0184", "type": "skos:narrower" }, { "source": "CRE-0184", "target": "ADA-0007", "type": "augmanitai:crossDomainReference" }, { "source": "CRE-0185", "target": "RPH-2301", "type": "skos:broader" }, { "source": "RPH-2301", "target": "CRE-0185", "type": "skos:narrower" }, { "source": "CRE-0185", "target": "BEH-0017", "type": "augmanitai:crossDomainReference" }, { "source": "CRE-0186", "target": "Creative AI", "type": "skos:broader" }, { "source": "Creative AI", "target": "CRE-0186", "type": "skos:narrower" }, { "source": "CRE-0187", "target": "RPH-2951", "type": "skos:broader" }, { "source": "RPH-2951", "target": "CRE-0187", "type": "skos:narrower" }, { "source": "CRE-0188", "target": "CRE-0169", "type": "skos:broader" }, { "source": "CRE-0169", "target": "CRE-0188", "type": "skos:narrower" }, { "source": "CRE-0188", "target": "ART-0007", "type": "augmanitai:crossDomainReference" }, { "source": "CRE-0188", "target": "ART-0021", "type": "augmanitai:crossDomainReference" }, { "source": "CRE-0188", "target": "COG-0046", "type": "augmanitai:crossDomainReference" }, { "source": "CRE-0189", "target": "SOC-0023", "type": "skos:broader" }, { "source": "SOC-0023", "target": "CRE-0189", "type": "skos:narrower" }, { "source": "CRE-0190", "target": "RPH-2303", "type": "skos:broader" }, { "source": "RPH-2303", "target": "CRE-0190", "type": "skos:narrower" }, { "source": "CRE-0190", "target": "AUG-0502", "type": "augmanitai:crossDomainReference" }, { "source": "CRE-0190", "target": "AUG-0689", "type": "augmanitai:crossDomainReference" }, { "source": "CRE-0190", "target": "CON-0027", "type": "augmanitai:crossDomainReference" }, { "source": "CRE-0191", "target": "RPH-2101", "type": "skos:broader" }, { "source": "RPH-2101", "target": "CRE-0191", "type": "skos:narrower" }, { "source": "CRE-0192", "target": "RPH-2701", "type": "skos:broader" }, { "source": "RPH-2701", "target": "CRE-0192", "type": "skos:narrower" }, { "source": "CRE-0192", "target": "AGE-0073", "type": "augmanitai:crossDomainReference" }, { "source": "CRE-0193", "target": "Processing Pipeline", "type": "skos:broader" }, { "source": "Processing Pipeline", "target": "CRE-0193", "type": "skos:narrower" }, { "source": "CRE-0193", "target": "BEH-0077", "type": "augmanitai:crossDomainReference" }, { "source": "CRE-0194", "target": "Behavioral Pattern", "type": "skos:broader" }, { "source": "Behavioral Pattern", "target": "CRE-0194", "type": "skos:narrower" }, { "source": "CRE-0195", "target": "Creative AI", "type": "skos:broader" }, { "source": "Creative AI", "target": "CRE-0195", "type": "skos:narrower" }, { "source": "CRE-0196", "target": "TEM-0012", "type": "skos:broader" }, { "source": "TEM-0012", "target": "CRE-0196", "type": "skos:narrower" }, { "source": "CRE-0197", "target": "RPH-2603", "type": "skos:broader" }, { "source": "RPH-2603", "target": "CRE-0197", "type": "skos:narrower" }, { "source": "CRE-0198", "target": "Creative AI", "type": "skos:broader" }, { "source": "Creative AI", "target": "CRE-0198", "type": "skos:narrower" }, { "source": "CRE-0199", "target": "LNG-0013", "type": "skos:broader" }, { "source": "LNG-0013", "target": "CRE-0199", "type": "skos:narrower" }, { "source": "CRE-0199", "target": "AUG-0802", "type": "augmanitai:crossDomainReference" }, { "source": "CRE-0200", "target": "Creative AI", "type": "skos:broader" }, { "source": "Creative AI", "target": "CRE-0200", "type": "skos:narrower" }, { "source": "CRE-0200", "target": "AED-0099", "type": "augmanitai:crossDomainReference" }, { "source": "CRE-0201", "target": "Creative AI", "type": "skos:broader" }, { "source": "Creative AI", "target": "CRE-0201", "type": "skos:narrower" }, { "source": "CRE-0202", "target": "Creative AI", "type": "skos:broader" }, { "source": "Creative AI", "target": "CRE-0202", "type": "skos:narrower" }, { "source": "CRE-0203", "target": "RPH-2301", "type": "skos:broader" }, { "source": "RPH-2301", "target": "CRE-0203", "type": "skos:narrower" }, { "source": "CRE-0204", "target": "PER-0136", "type": "skos:broader" }, { "source": "PER-0136", "target": "CRE-0204", "type": "skos:narrower" }, { "source": "CRE-0205", "target": "RPH-2403", "type": "skos:broader" }, { "source": "RPH-2403", "target": "CRE-0205", "type": "skos:narrower" }, { "source": "CRE-0206", "target": "Analytical Ratio", "type": "skos:broader" }, { "source": "Analytical Ratio", "target": "CRE-0206", "type": "skos:narrower" }, { "source": "CRE-0207", "target": "RPH-2951", "type": "skos:broader" }, { "source": "RPH-2951", "target": "CRE-0207", "type": "skos:narrower" }, { "source": "CRE-0207", "target": "BEH-0044", "type": "augmanitai:crossDomainReference" }, { "source": "CRE-0208", "target": "Psychological Effect", "type": "skos:broader" }, { "source": "Psychological Effect", "target": "CRE-0208", "type": "skos:narrower" }, { "source": "CRE-0209", "target": "RPH-3802", "type": "skos:broader" }, { "source": "RPH-3802", "target": "CRE-0209", "type": "skos:narrower" }, { "source": "CRE-0210", "target": "Creative AI", "type": "skos:broader" }, { "source": "Creative AI", "target": "CRE-0210", "type": "skos:narrower" }, { "source": "CRE-0211", "target": "RPH-2301", "type": "skos:broader" }, { "source": "RPH-2301", "target": "CRE-0211", "type": "skos:narrower" }, { "source": "CRE-0212", "target": "RPH-3601", "type": "skos:broader" }, { "source": "RPH-3601", "target": "CRE-0212", "type": "skos:narrower" }, { "source": "CRE-0213", "target": "CRE-0198", "type": "skos:broader" }, { "source": "CRE-0198", "target": "CRE-0213", "type": "skos:narrower" }, { "source": "CRE-0214", "target": "IDN-0041", "type": "skos:broader" }, { "source": "IDN-0041", "target": "CRE-0214", "type": "skos:narrower" }, { "source": "CRE-0215", "target": "REL-0201", "type": "skos:broader" }, { "source": "REL-0201", "target": "CRE-0215", "type": "skos:narrower" }, { "source": "CRE-0216", "target": "RPH-2703", "type": "skos:broader" }, { "source": "RPH-2703", "target": "CRE-0216", "type": "skos:narrower" }, { "source": "CRE-0217", "target": "Systemic Drift", "type": "skos:broader" }, { "source": "Systemic Drift", "target": "CRE-0217", "type": "skos:narrower" }, { "source": "CRE-0218", "target": "Creative AI", "type": "skos:broader" }, { "source": "Creative AI", "target": "CRE-0218", "type": "skos:narrower" }, { "source": "CRE-0218", "target": "BEH-0073", "type": "augmanitai:crossDomainReference" }, { "source": "CRE-0219", "target": "RPH-2052", "type": "skos:broader" }, { "source": "RPH-2052", "target": "CRE-0219", "type": "skos:narrower" }, { "source": "CRE-0220", "target": "TEM-0090", "type": "skos:broader" }, { "source": "TEM-0090", "target": "CRE-0220", "type": "skos:narrower" }, { "source": "CRE-0221", "target": "CRE-0196", "type": "skos:broader" }, { "source": "CRE-0196", "target": "CRE-0221", "type": "skos:narrower" }, { "source": "CRE-0221", "target": "BEH-0081", "type": "augmanitai:crossDomainReference" }, { "source": "CRE-0221", "target": "COG-0032", "type": "augmanitai:crossDomainReference" }, { "source": "CRE-0221", "target": "COG-0072", "type": "augmanitai:crossDomainReference" }, { "source": "CRE-0222", "target": "CAI-0018", "type": "skos:broader" }, { "source": "CAI-0018", "target": "CRE-0222", "type": "skos:narrower" }, { "source": "CRE-0223", "target": "RPH-3205", "type": "skos:broader" }, { "source": "RPH-3205", "target": "CRE-0223", "type": "skos:narrower" }, { "source": "CRE-0224", "target": "Creative AI", "type": "skos:broader" }, { "source": "Creative AI", "target": "CRE-0224", "type": "skos:narrower" }, { "source": "CRE-0225", "target": "RPH-2805", "type": "skos:broader" }, { "source": "RPH-2805", "target": "CRE-0225", "type": "skos:narrower" }, { "source": "CRE-0226", "target": "RPH-2505", "type": "skos:broader" }, { "source": "RPH-2505", "target": "CRE-0226", "type": "skos:narrower" }, { "source": "CRE-0227", "target": "Creative AI", "type": "skos:broader" }, { "source": "Creative AI", "target": "CRE-0227", "type": "skos:narrower" }, { "source": "CRE-0228", "target": "RPH-2303", "type": "skos:broader" }, { "source": "RPH-2303", "target": "CRE-0228", "type": "skos:narrower" }, { "source": "CRE-0229", "target": "RPH-2605", "type": "skos:broader" }, { "source": "RPH-2605", "target": "CRE-0229", "type": "skos:narrower" }, { "source": "CRE-0230", "target": "RPH-3901", "type": "skos:broader" }, { "source": "RPH-3901", "target": "CRE-0230", "type": "skos:narrower" }, { "source": "CRE-0230", "target": "BEH-0094", "type": "augmanitai:crossDomainReference" }, { "source": "CRE-0231", "target": "Creative AI", "type": "skos:broader" }, { "source": "Creative AI", "target": "CRE-0231", "type": "skos:narrower" }, { "source": "CRE-0232", "target": "TEM-0021", "type": "skos:broader" }, { "source": "TEM-0021", "target": "CRE-0232", "type": "skos:narrower" }, { "source": "CRE-0233", "target": "RPH-2605", "type": "skos:broader" }, { "source": "RPH-2605", "target": "CRE-0233", "type": "skos:narrower" }, { "source": "CRE-0234", "target": "Psychological Effect", "type": "skos:broader" }, { "source": "Psychological Effect", "target": "CRE-0234", "type": "skos:narrower" }, { "source": "CRE-0235", "target": "Creative AI", "type": "skos:broader" }, { "source": "Creative AI", "target": "CRE-0235", "type": "skos:narrower" }, { "source": "CUS-0001", "target": "RPH-2605", "type": "skos:broader" }, { "source": "RPH-2605", "target": "CUS-0001", "type": "skos:narrower" }, { "source": "CUS-0002", "target": "RPH-2104", "type": "skos:broader" }, { "source": "RPH-2104", "target": "CUS-0002", "type": "skos:narrower" }, { "source": "CUS-0003", "target": "RPH-2505", "type": "skos:broader" }, { "source": "RPH-2505", "target": "CUS-0003", "type": "skos:narrower" }, { "source": "CUS-0004", "target": "RPH-2505", "type": "skos:broader" }, { "source": "RPH-2505", "target": "CUS-0004", "type": "skos:narrower" }, { "source": "CUS-0005", "target": "Data Compression", "type": "skos:broader" }, { "source": "Data Compression", "target": "CUS-0005", "type": "skos:narrower" }, { "source": "CUS-0005", "target": "ASE-0087", "type": "augmanitai:crossDomainReference" }, { "source": "CUS-0006", "target": "RPH-2505", "type": "skos:broader" }, { "source": "RPH-2505", "target": "CUS-0006", "type": "skos:narrower" }, { "source": "CUS-0007", "target": "RPH-2701", "type": "skos:broader" }, { "source": "RPH-2701", "target": "CUS-0007", "type": "skos:narrower" }, { "source": "CUS-0007", "target": "CRE-0018", "type": "augmanitai:crossDomainReference" }, { "source": "CUS-0008", "target": "Customer AI", "type": "skos:broader" }, { "source": "Customer AI", "target": "CUS-0008", "type": "skos:narrower" }, { "source": "CUS-0009", "target": "RPH-2703", "type": "skos:broader" }, { "source": "RPH-2703", "target": "CUS-0009", "type": "skos:narrower" }, { "source": "CUS-0009", "target": "AUG-0913", "type": "augmanitai:crossDomainReference" }, { "source": "CUS-0010", "target": "Customer AI", "type": "skos:broader" }, { "source": "Customer AI", "target": "CUS-0010", "type": "skos:narrower" }, { "source": "CUS-0011", "target": "RPH-2951", "type": "skos:broader" }, { "source": "RPH-2951", "target": "CUS-0011", "type": "skos:narrower" }, { "source": "CUS-0012", "target": "RPH-3754", "type": "skos:broader" }, { "source": "RPH-3754", "target": "CUS-0012", "type": "skos:narrower" }, { "source": "CUS-0013", "target": "Customer AI", "type": "skos:broader" }, { "source": "Customer AI", "target": "CUS-0013", "type": "skos:narrower" }, { "source": "CUS-0014", "target": "Customer AI", "type": "skos:broader" }, { "source": "Customer AI", "target": "CUS-0014", "type": "skos:narrower" }, { "source": "CUS-0015", "target": "Customer AI", "type": "skos:broader" }, { "source": "Customer AI", "target": "CUS-0015", "type": "skos:narrower" }, { "source": "CUS-0015", "target": "ASE-0022", "type": "augmanitai:crossDomainReference" }, { "source": "CUS-0015", "target": "ASE-0025", "type": "augmanitai:crossDomainReference" }, { "source": "CUS-0015", "target": "ASE-0076", "type": "augmanitai:crossDomainReference" }, { "source": "CUS-0016", "target": "Customer AI", "type": "skos:broader" }, { "source": "Customer AI", "target": "CUS-0016", "type": "skos:narrower" }, { "source": "CUS-0017", "target": "Customer AI", "type": "skos:broader" }, { "source": "Customer AI", "target": "CUS-0017", "type": "skos:narrower" }, { "source": "CUS-0018", "target": "Customer AI", "type": "skos:broader" }, { "source": "Customer AI", "target": "CUS-0018", "type": "skos:narrower" }, { "source": "CUS-0019", "target": "Customer AI", "type": "skos:broader" }, { "source": "Customer AI", "target": "CUS-0019", "type": "skos:narrower" }, { "source": "CUS-0019", "target": "BEH-0088", "type": "augmanitai:crossDomainReference" }, { "source": "CUS-0020", "target": "RPH-2505", "type": "skos:broader" }, { "source": "RPH-2505", "target": "CUS-0020", "type": "skos:narrower" }, { "source": "CUS-0021", "target": "Customer AI", "type": "skos:broader" }, { "source": "Customer AI", "target": "CUS-0021", "type": "skos:narrower" }, { "source": "CUS-0022", "target": "Cognitive Bias", "type": "skos:broader" }, { "source": "Cognitive Bias", "target": "CUS-0022", "type": "skos:narrower" }, { "source": "CUS-0022", "target": "CON-0038", "type": "augmanitai:crossDomainReference" }, { "source": "CUS-0023", "target": "Analytical Ratio", "type": "skos:broader" }, { "source": "Analytical Ratio", "target": "CUS-0023", "type": "skos:narrower" }, { "source": "CUS-0023", "target": "CON-0080", "type": "augmanitai:crossDomainReference" }, { "source": "CUS-0024", "target": "Customer AI", "type": "skos:broader" }, { "source": "Customer AI", "target": "CUS-0024", "type": "skos:narrower" }, { "source": "CUS-0025", "target": "RPH-3101", "type": "skos:broader" }, { "source": "RPH-3101", "target": "CUS-0025", "type": "skos:narrower" }, { "source": "CUS-0026", "target": "Customer AI", "type": "skos:broader" }, { "source": "Customer AI", "target": "CUS-0026", "type": "skos:narrower" }, { "source": "CUS-0027", "target": "Customer AI", "type": "skos:broader" }, { "source": "Customer AI", "target": "CUS-0027", "type": "skos:narrower" }, { "source": "CUS-0027", "target": "ART-0054", "type": "augmanitai:crossDomainReference" }, { "source": "CUS-0027", "target": "ASE-0023", "type": "augmanitai:crossDomainReference" }, { "source": "CUS-0027", "target": "AUG-0890", "type": "augmanitai:crossDomainReference" }, { "source": "CUS-0028", "target": "RPH-2305", "type": "skos:broader" }, { "source": "RPH-2305", "target": "CUS-0028", "type": "skos:narrower" }, { "source": "CUS-0029", "target": "Customer AI", "type": "skos:broader" }, { "source": "Customer AI", "target": "CUS-0029", "type": "skos:narrower" }, { "source": "CUS-0030", "target": "RPH-2555", "type": "skos:broader" }, { "source": "RPH-2555", "target": "CUS-0030", "type": "skos:narrower" }, { "source": "CUS-0031", "target": "Customer AI", "type": "skos:broader" }, { "source": "Customer AI", "target": "CUS-0031", "type": "skos:narrower" }, { "source": "CUS-0032", "target": "RPH-2201", "type": "skos:broader" }, { "source": "RPH-2201", "target": "CUS-0032", "type": "skos:narrower" }, { "source": "CUS-0033", "target": "RPH-2701", "type": "skos:broader" }, { "source": "RPH-2701", "target": "CUS-0033", "type": "skos:narrower" }, { "source": "CUS-0034", "target": "Customer AI", "type": "skos:broader" }, { "source": "Customer AI", "target": "CUS-0034", "type": "skos:narrower" }, { "source": "CUS-0035", "target": "RPH-2053", "type": "skos:broader" }, { "source": "RPH-2053", "target": "CUS-0035", "type": "skos:narrower" }, { "source": "CUS-0036", "target": "RPH-2703", "type": "skos:broader" }, { "source": "RPH-2703", "target": "CUS-0036", "type": "skos:narrower" }, { "source": "CUS-0036", "target": "AGE-0096", "type": "augmanitai:crossDomainReference" }, { "source": "CUS-0036", "target": "ART-0008", "type": "augmanitai:crossDomainReference" }, { "source": "CUS-0036", "target": "ASE-0021", "type": "augmanitai:crossDomainReference" }, { "source": "CUS-0037", "target": "RPH-2104", "type": "skos:broader" }, { "source": "RPH-2104", "target": "CUS-0037", "type": "skos:narrower" }, { "source": "CUS-0038", "target": "RPH-3501", "type": "skos:broader" }, { "source": "RPH-3501", "target": "CUS-0038", "type": "skos:narrower" }, { "source": "CUS-0038", "target": "ASE-0028", "type": "augmanitai:crossDomainReference" }, { "source": "CUS-0039", "target": "Customer AI", "type": "skos:broader" }, { "source": "Customer AI", "target": "CUS-0039", "type": "skos:narrower" }, { "source": "CUS-0039", "target": "CRE-0091", "type": "augmanitai:crossDomainReference" }, { "source": "CUS-0040", "target": "Customer AI", "type": "skos:broader" }, { "source": "Customer AI", "target": "CUS-0040", "type": "skos:narrower" }, { "source": "CUS-0041", "target": "Customer AI", "type": "skos:broader" }, { "source": "Customer AI", "target": "CUS-0041", "type": "skos:narrower" }, { "source": "CUS-0042", "target": "Analytical Ratio", "type": "skos:broader" }, { "source": "Analytical Ratio", "target": "CUS-0042", "type": "skos:narrower" }, { "source": "CUS-0042", "target": "COP-0035", "type": "augmanitai:crossDomainReference" }, { "source": "CUS-0043", "target": "Customer AI", "type": "skos:broader" }, { "source": "Customer AI", "target": "CUS-0043", "type": "skos:narrower" }, { "source": "CUS-0043", "target": "COP-0011", "type": "augmanitai:crossDomainReference" }, { "source": "CUS-0044", "target": "Customer AI", "type": "skos:broader" }, { "source": "Customer AI", "target": "CUS-0044", "type": "skos:narrower" }, { "source": "CUS-0045", "target": "Customer AI", "type": "skos:broader" }, { "source": "Customer AI", "target": "CUS-0045", "type": "skos:narrower" }, { "source": "CUS-0046", "target": "RPH-3202", "type": "skos:broader" }, { "source": "RPH-3202", "target": "CUS-0046", "type": "skos:narrower" }, { "source": "CUS-0047", "target": "RPH-2002", "type": "skos:broader" }, { "source": "RPH-2002", "target": "CUS-0047", "type": "skos:narrower" }, { "source": "CUS-0048", "target": "Decision Threshold", "type": "skos:broader" }, { "source": "Decision Threshold", "target": "CUS-0048", "type": "skos:narrower" }, { "source": "CUS-0049", "target": "Customer AI", "type": "skos:broader" }, { "source": "Customer AI", "target": "CUS-0049", "type": "skos:narrower" }, { "source": "CUS-0049", "target": "COG-0009", "type": "augmanitai:crossDomainReference" }, { "source": "CUS-0050", "target": "Psychological Effect", "type": "skos:broader" }, { "source": "Psychological Effect", "target": "CUS-0050", "type": "skos:narrower" }, { "source": "CUS-0050", "target": "ADA-0001", "type": "augmanitai:crossDomainReference" }, { "source": "CUS-0050", "target": "ADA-0002", "type": "augmanitai:crossDomainReference" }, { "source": "CUS-0050", "target": "ADA-0004", "type": "augmanitai:crossDomainReference" }, { "source": "CUS-0051", "target": "Cognitive Bias", "type": "skos:broader" }, { "source": "Cognitive Bias", "target": "CUS-0051", "type": "skos:narrower" }, { "source": "CUS-0052", "target": "Customer AI", "type": "skos:broader" }, { "source": "Customer AI", "target": "CUS-0052", "type": "skos:narrower" }, { "source": "CUS-0053", "target": "ROB-0121", "type": "skos:broader" }, { "source": "ROB-0121", "target": "CUS-0053", "type": "skos:narrower" }, { "source": "CUS-0054", "target": "RPH-3101", "type": "skos:broader" }, { "source": "RPH-3101", "target": "CUS-0054", "type": "skos:narrower" }, { "source": "CUS-0055", "target": "Customer AI", "type": "skos:broader" }, { "source": "Customer AI", "target": "CUS-0055", "type": "skos:narrower" }, { "source": "CUS-0056", "target": "RPH-3101", "type": "skos:broader" }, { "source": "RPH-3101", "target": "CUS-0056", "type": "skos:narrower" }, { "source": "CUS-0057", "target": "RPH-2703", "type": "skos:broader" }, { "source": "RPH-2703", "target": "CUS-0057", "type": "skos:narrower" }, { "source": "CUS-0057", "target": "AGE-0045", "type": "augmanitai:crossDomainReference" }, { "source": "CUS-0058", "target": "RPH-3001", "type": "skos:broader" }, { "source": "RPH-3001", "target": "CUS-0058", "type": "skos:narrower" }, { "source": "CUS-0058", "target": "ASE-0058", "type": "augmanitai:crossDomainReference" }, { "source": "CUS-0058", "target": "BEH-0034", "type": "augmanitai:crossDomainReference" }, { "source": "CUS-0058", "target": "COG-0007", "type": "augmanitai:crossDomainReference" }, { "source": "CUS-0059", "target": "RPH-2505", "type": "skos:broader" }, { "source": "RPH-2505", "target": "CUS-0059", "type": "skos:narrower" }, { "source": "CUS-0060", "target": "RPH-3754", "type": "skos:broader" }, { "source": "RPH-3754", "target": "CUS-0060", "type": "skos:narrower" }, { "source": "CUS-0060", "target": "ASE-0065", "type": "augmanitai:crossDomainReference" }, { "source": "CUS-0061", "target": "RPH-2052", "type": "skos:broader" }, { "source": "RPH-2052", "target": "CUS-0061", "type": "skos:narrower" }, { "source": "CUS-0062", "target": "RPH-3501", "type": "skos:broader" }, { "source": "RPH-3501", "target": "CUS-0062", "type": "skos:narrower" }, { "source": "CUS-0062", "target": "COP-0042", "type": "augmanitai:crossDomainReference" }, { "source": "CUS-0063", "target": "Classification Method", "type": "skos:broader" }, { "source": "Classification Method", "target": "CUS-0063", "type": "skos:narrower" }, { "source": "CUS-0063", "target": "AED-0066", "type": "augmanitai:crossDomainReference" }, { "source": "CUS-0063", "target": "AGE-0004", "type": "augmanitai:crossDomainReference" }, { "source": "CUS-0063", "target": "AGE-0045", "type": "augmanitai:crossDomainReference" }, { "source": "CUS-0064", "target": "RPH-3601", "type": "skos:broader" }, { "source": "RPH-3601", "target": "CUS-0064", "type": "skos:narrower" }, { "source": "CUS-0065", "target": "Customer AI", "type": "skos:broader" }, { "source": "Customer AI", "target": "CUS-0065", "type": "skos:narrower" }, { "source": "CUS-0066", "target": "RPH-2552", "type": "skos:broader" }, { "source": "RPH-2552", "target": "CUS-0066", "type": "skos:narrower" }, { "source": "CUS-0067", "target": "RPH-2505", "type": "skos:broader" }, { "source": "RPH-2505", "target": "CUS-0067", "type": "skos:narrower" }, { "source": "CUS-0067", "target": "ASE-0018", "type": "augmanitai:crossDomainReference" }, { "source": "CUS-0067", "target": "CON-0073", "type": "augmanitai:crossDomainReference" }, { "source": "CUS-0068", "target": "Customer AI", "type": "skos:broader" }, { "source": "Customer AI", "target": "CUS-0068", "type": "skos:narrower" }, { "source": "CUS-0069", "target": "RPH-2604", "type": "skos:broader" }, { "source": "RPH-2604", "target": "CUS-0069", "type": "skos:narrower" }, { "source": "CUS-0070", "target": "Customer AI", "type": "skos:broader" }, { "source": "Customer AI", "target": "CUS-0070", "type": "skos:narrower" }, { "source": "CUS-0071", "target": "Customer AI", "type": "skos:broader" }, { "source": "Customer AI", "target": "CUS-0071", "type": "skos:narrower" }, { "source": "CUS-0071", "target": "CON-0073", "type": "augmanitai:crossDomainReference" }, { "source": "CUS-0072", "target": "Customer AI", "type": "skos:broader" }, { "source": "Customer AI", "target": "CUS-0072", "type": "skos:narrower" }, { "source": "CUS-0073", "target": "Optimization Method", "type": "skos:broader" }, { "source": "Optimization Method", "target": "CUS-0073", "type": "skos:narrower" }, { "source": "CUS-0074", "target": "Analytical Ratio", "type": "skos:broader" }, { "source": "Analytical Ratio", "target": "CUS-0074", "type": "skos:narrower" }, { "source": "CUS-0075", "target": "Cognitive Bias", "type": "skos:broader" }, { "source": "Cognitive Bias", "target": "CUS-0075", "type": "skos:narrower" }, { "source": "CUS-0076", "target": "RPH-2005", "type": "skos:broader" }, { "source": "RPH-2005", "target": "CUS-0076", "type": "skos:narrower" }, { "source": "CUS-0077", "target": "Customer AI", "type": "skos:broader" }, { "source": "Customer AI", "target": "CUS-0077", "type": "skos:narrower" }, { "source": "CUS-0078", "target": "RPH-2602", "type": "skos:broader" }, { "source": "RPH-2602", "target": "CUS-0078", "type": "skos:narrower" }, { "source": "CUS-0078", "target": "COG-0053", "type": "augmanitai:crossDomainReference" }, { "source": "CUS-0079", "target": "Cognitive Bias", "type": "skos:broader" }, { "source": "Cognitive Bias", "target": "CUS-0079", "type": "skos:narrower" }, { "source": "CUS-0079", "target": "COP-0039", "type": "augmanitai:crossDomainReference" }, { "source": "CUS-0080", "target": "Customer AI", "type": "skos:broader" }, { "source": "Customer AI", "target": "CUS-0080", "type": "skos:narrower" }, { "source": "CUS-0081", "target": "RPH-2351", "type": "skos:broader" }, { "source": "RPH-2351", "target": "CUS-0081", "type": "skos:narrower" }, { "source": "CUS-0081", "target": "CRE-0061", "type": "augmanitai:crossDomainReference" }, { "source": "CUS-0082", "target": "Customer AI", "type": "skos:broader" }, { "source": "Customer AI", "target": "CUS-0082", "type": "skos:narrower" }, { "source": "CUS-0083", "target": "RPH-2201", "type": "skos:broader" }, { "source": "RPH-2201", "target": "CUS-0083", "type": "skos:narrower" }, { "source": "CUS-0083", "target": "COP-0070", "type": "augmanitai:crossDomainReference" }, { "source": "CUS-0084", "target": "RPH-3952", "type": "skos:broader" }, { "source": "RPH-3952", "target": "CUS-0084", "type": "skos:narrower" }, { "source": "CUS-0085", "target": "Customer AI", "type": "skos:broader" }, { "source": "Customer AI", "target": "CUS-0085", "type": "skos:narrower" }, { "source": "CUS-0085", "target": "COP-0022", "type": "augmanitai:crossDomainReference" }, { "source": "CUS-0086", "target": "RPH-2505", "type": "skos:broader" }, { "source": "RPH-2505", "target": "CUS-0086", "type": "skos:narrower" }, { "source": "CUS-0087", "target": "Customer AI", "type": "skos:broader" }, { "source": "Customer AI", "target": "CUS-0087", "type": "skos:narrower" }, { "source": "CUS-0087", "target": "AED-0020", "type": "augmanitai:crossDomainReference" }, { "source": "CUS-0087", "target": "AUG-0383", "type": "augmanitai:crossDomainReference" }, { "source": "CUS-0087", "target": "CAI-0006", "type": "augmanitai:crossDomainReference" }, { "source": "CUS-0088", "target": "Customer AI", "type": "skos:broader" }, { "source": "Customer AI", "target": "CUS-0088", "type": "skos:narrower" }, { "source": "CUS-0088", "target": "AGE-0032", "type": "augmanitai:crossDomainReference" }, { "source": "CUS-0088", "target": "AGE-0077", "type": "augmanitai:crossDomainReference" }, { "source": "CUS-0088", "target": "AGE-0094", "type": "augmanitai:crossDomainReference" }, { "source": "CUS-0089", "target": "Customer AI", "type": "skos:broader" }, { "source": "Customer AI", "target": "CUS-0089", "type": "skos:narrower" }, { "source": "CUS-0090", "target": "Detection System", "type": "skos:broader" }, { "source": "Detection System", "target": "CUS-0090", "type": "skos:narrower" }, { "source": "CUS-0091", "target": "Customer AI", "type": "skos:broader" }, { "source": "Customer AI", "target": "CUS-0091", "type": "skos:narrower" }, { "source": "CUS-0092", "target": "Customer AI", "type": "skos:broader" }, { "source": "Customer AI", "target": "CUS-0092", "type": "skos:narrower" }, { "source": "CUS-0093", "target": "RPH-2605", "type": "skos:broader" }, { "source": "RPH-2605", "target": "CUS-0093", "type": "skos:narrower" }, { "source": "CUS-0094", "target": "RPH-2201", "type": "skos:broader" }, { "source": "RPH-2201", "target": "CUS-0094", "type": "skos:narrower" }, { "source": "CUS-0094", "target": "COP-0088", "type": "augmanitai:crossDomainReference" }, { "source": "CUS-0095", "target": "Customer AI", "type": "skos:broader" }, { "source": "Customer AI", "target": "CUS-0095", "type": "skos:narrower" }, { "source": "CUS-0096", "target": "Cognitive Bias", "type": "skos:broader" }, { "source": "Cognitive Bias", "target": "CUS-0096", "type": "skos:narrower" }, { "source": "CUS-0096", "target": "CON-0052", "type": "augmanitai:crossDomainReference" }, { "source": "CUS-0097", "target": "Customer AI", "type": "skos:broader" }, { "source": "Customer AI", "target": "CUS-0097", "type": "skos:narrower" }, { "source": "CUS-0097", "target": "COG-0105", "type": "augmanitai:crossDomainReference" }, { "source": "CUS-0097", "target": "CON-0013", "type": "augmanitai:crossDomainReference" }, { "source": "CUS-0098", "target": "RPH-2201", "type": "skos:broader" }, { "source": "RPH-2201", "target": "CUS-0098", "type": "skos:narrower" }, { "source": "CUS-0099", "target": "RPH-2703", "type": "skos:broader" }, { "source": "RPH-2703", "target": "CUS-0099", "type": "skos:narrower" }, { "source": "CUS-0099", "target": "CON-0052", "type": "augmanitai:crossDomainReference" }, { "source": "CUS-0100", "target": "Customer AI", "type": "skos:broader" }, { "source": "Customer AI", "target": "CUS-0100", "type": "skos:narrower" }, { "source": "DAT-0001", "target": "Cognitive Bias", "type": "skos:broader" }, { "source": "Cognitive Bias", "target": "DAT-0001", "type": "skos:narrower" }, { "source": "DAT-0001", "target": "CUS-0006", "type": "augmanitai:crossDomainReference" }, { "source": "DAT-0002", "target": "Data AI", "type": "skos:broader" }, { "source": "Data AI", "target": "DAT-0002", "type": "skos:narrower" }, { "source": "DAT-0003", "target": "RPH-2703", "type": "skos:broader" }, { "source": "RPH-2703", "target": "DAT-0003", "type": "skos:narrower" }, { "source": "DAT-0004", "target": "Data AI", "type": "skos:broader" }, { "source": "Data AI", "target": "DAT-0004", "type": "skos:narrower" }, { "source": "DAT-0004", "target": "ASE-0031", "type": "augmanitai:crossDomainReference" }, { "source": "DAT-0004", "target": 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}, { "source": "Fiction Writing AI", "target": "FIC-0072", "type": "skos:narrower" }, { "source": "FIC-0072", "target": "CRE-0205", "type": "augmanitai:crossDomainReference" }, { "source": "FIC-0073", "target": "Detection System", "type": "skos:broader" }, { "source": "Detection System", "target": "FIC-0073", "type": "skos:narrower" }, { "source": "FIC-0073", "target": "ART-0083", "type": "augmanitai:crossDomainReference" }, { "source": "FIC-0073", "target": "ASE-0002", "type": "augmanitai:crossDomainReference" }, { "source": "FIC-0073", "target": "ASE-0021", "type": "augmanitai:crossDomainReference" }, { "source": "FIC-0074", "target": "RPH-2104", "type": "skos:broader" }, { "source": "RPH-2104", "target": "FIC-0074", "type": "skos:narrower" }, { "source": "FIC-0075", "target": "Fiction Writing AI", "type": "skos:broader" }, { "source": "Fiction Writing AI", "target": "FIC-0075", "type": "skos:narrower" }, { "source": "FIC-0076", "target": "Fiction Writing AI", "type": "skos:broader" 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"type": "skos:broader" }, { "source": "RPH-2303", "target": "FIC-0080", "type": "skos:narrower" }, { "source": "FIC-0080", "target": "ADA-0011", "type": "augmanitai:crossDomainReference" }, { "source": "FIC-0080", "target": "AED-0094", "type": "augmanitai:crossDomainReference" }, { "source": "FIC-0080", "target": "AED-0095", "type": "augmanitai:crossDomainReference" }, { "source": "FIC-0081", "target": "Fiction Writing AI", "type": "skos:broader" }, { "source": "Fiction Writing AI", "target": "FIC-0081", "type": "skos:narrower" }, { "source": "FIC-0081", "target": "CON-0054", "type": "augmanitai:crossDomainReference" }, { "source": "FIC-0081", "target": "CON-0063", "type": "augmanitai:crossDomainReference" }, { "source": "FIC-0081", "target": "CON-0075", "type": "augmanitai:crossDomainReference" }, { "source": "FIC-0082", "target": "RPH-2104", "type": "skos:broader" }, { "source": "RPH-2104", "target": "FIC-0082", "type": "skos:narrower" }, { "source": "FIC-0082", "target": "CON-0065", "type": "augmanitai:crossDomainReference" }, { "source": "FIC-0082", "target": "CON-0067", "type": "augmanitai:crossDomainReference" }, { "source": "FIC-0082", "target": "CON-0092", "type": "augmanitai:crossDomainReference" }, { "source": "FIC-0083", "target": "RPH-2605", "type": "skos:broader" }, { "source": "RPH-2605", "target": "FIC-0083", "type": "skos:narrower" }, { "source": "FIC-0084", "target": "Fiction Writing AI", "type": "skos:broader" }, { "source": "Fiction Writing AI", "target": "FIC-0084", "type": "skos:narrower" }, { "source": "FIC-0085", "target": "Fiction Writing AI", "type": "skos:broader" }, { "source": "Fiction Writing AI", "target": "FIC-0085", "type": "skos:narrower" }, { "source": "FIC-0086", "target": "RPH-3251", "type": "skos:broader" }, { "source": "RPH-3251", "target": "FIC-0086", "type": "skos:narrower" }, { "source": "FIC-0086", "target": "CON-0022", "type": "augmanitai:crossDomainReference" }, { "source": "FIC-0087", "target": "Fiction Writing AI", "type": "skos:broader" }, { "source": "Fiction Writing AI", "target": "FIC-0087", "type": "skos:narrower" }, { "source": "FIC-0087", "target": "AGE-0011", "type": "augmanitai:crossDomainReference" }, { "source": "FIC-0087", "target": "AGE-0037", "type": "augmanitai:crossDomainReference" }, { "source": "FIC-0087", "target": "AGE-0038", "type": "augmanitai:crossDomainReference" }, { "source": "FIC-0088", "target": "Fiction Writing AI", "type": "skos:broader" }, { "source": "Fiction Writing AI", "target": "FIC-0088", "type": "skos:narrower" }, { "source": "FIC-0089", "target": "Fiction Writing AI", "type": "skos:broader" }, { "source": "Fiction Writing AI", "target": "FIC-0089", "type": "skos:narrower" }, { "source": "FIC-0090", "target": "Fiction Writing AI", "type": "skos:broader" }, { "source": "Fiction Writing AI", "target": "FIC-0090", "type": "skos:narrower" }, { "source": "FIC-0090", "target": "AED-0091", "type": "augmanitai:crossDomainReference" }, { "source": "FIC-0090", "target": "ART-0012", "type": "augmanitai:crossDomainReference" }, { "source": "FIC-0090", "target": "ART-0025", "type": "augmanitai:crossDomainReference" }, { "source": "FIC-0091", "target": "Fiction Writing AI", "type": "skos:broader" }, { "source": "Fiction Writing AI", "target": "FIC-0091", "type": "skos:narrower" }, { "source": "FIC-0091", "target": "AGE-0043", "type": "augmanitai:crossDomainReference" }, { "source": "FIC-0091", "target": "AGE-0090", "type": "augmanitai:crossDomainReference" }, { "source": "FIC-0091", "target": "ASE-0026", "type": "augmanitai:crossDomainReference" }, { "source": "FIC-0092", "target": "Fiction Writing AI", "type": "skos:broader" }, { "source": "Fiction Writing AI", "target": "FIC-0092", "type": "skos:narrower" }, { "source": "FIC-0093", "target": "RPH-2052", "type": "skos:broader" }, { "source": "RPH-2052", "target": "FIC-0093", "type": "skos:narrower" }, { "source": "FIC-0094", "target": "RPH-2701", "type": "skos:broader" }, { "source": "RPH-2701", "target": "FIC-0094", "type": "skos:narrower" }, { "source": "FIC-0094", "target": "AED-0030", "type": "augmanitai:crossDomainReference" }, { "source": "FIC-0094", "target": "AGE-0014", "type": "augmanitai:crossDomainReference" }, { "source": "FIC-0094", "target": "AGE-0048", "type": "augmanitai:crossDomainReference" }, { "source": "FIC-0095", "target": "RPH-2104", "type": "skos:broader" }, { "source": "RPH-2104", "target": "FIC-0095", "type": "skos:narrower" }, { "source": "GAM-0001", "target": "RPH-2703", "type": "skos:broader" }, { "source": "RPH-2703", "target": "GAM-0001", "type": "skos:narrower" }, { "source": "GAM-0001", "target": "AED-0010", "type": "augmanitai:crossDomainReference" }, { "source": "GAM-0001", "target": "AED-0090", "type": "augmanitai:crossDomainReference" }, { "source": "GAM-0001", "target": "AGE-0036", "type": "augmanitai:crossDomainReference" }, { "source": "GAM-0002", "target": "Performance Gap", "type": "skos:broader" }, { "source": "Performance 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"GAM-0005", "type": "skos:narrower" }, { "source": "GAM-0005", "target": "AED-0051", "type": "augmanitai:crossDomainReference" }, { "source": "GAM-0006", "target": "Games AI", "type": "skos:broader" }, { "source": "Games AI", "target": "GAM-0006", "type": "skos:narrower" }, { "source": "GAM-0007", "target": "Games AI", "type": "skos:broader" }, { "source": "Games AI", "target": "GAM-0007", "type": "skos:narrower" }, { "source": "GAM-0007", "target": "CON-0041", "type": "augmanitai:crossDomainReference" }, { "source": "GAM-0008", "target": "Decision Threshold", "type": "skos:broader" }, { "source": "Decision Threshold", "target": "GAM-0008", "type": "skos:narrower" }, { "source": "GAM-0008", "target": "AED-0091", "type": "augmanitai:crossDomainReference" }, { "source": "GAM-0008", "target": "AGE-0096", "type": "augmanitai:crossDomainReference" }, { "source": "GAM-0008", "target": "ART-0060", "type": "augmanitai:crossDomainReference" }, { "source": "GAM-0009", "target": "Games AI", "type": "skos:broader" }, { "source": "Games AI", "target": "GAM-0009", "type": "skos:narrower" }, { "source": "GAM-0009", "target": "CON-0041", "type": "augmanitai:crossDomainReference" }, { "source": "GAM-0010", "target": "Games AI", "type": "skos:broader" }, { "source": "Games AI", "target": "GAM-0010", "type": "skos:narrower" }, { "source": "GAM-0010", "target": "ART-0011", "type": "augmanitai:crossDomainReference" }, { "source": "GAM-0010", "target": "ART-0019", "type": "augmanitai:crossDomainReference" }, { "source": "GAM-0010", "target": "ART-0058", "type": "augmanitai:crossDomainReference" }, { "source": "GAM-0011", "target": "Games AI", "type": "skos:broader" }, { "source": "Games AI", "target": "GAM-0011", "type": "skos:narrower" }, { "source": "GAM-0011", "target": "DAT-0090", "type": "augmanitai:crossDomainReference" }, { "source": "GAM-0012", "target": "RPH-2005", "type": "skos:broader" }, { "source": "RPH-2005", "target": "GAM-0012", "type": "skos:narrower" }, { "source": "GAM-0012", "target": "EDU-0064", "type": "augmanitai:crossDomainReference" }, { "source": "GAM-0013", "target": "Games AI", "type": "skos:broader" }, { "source": "Games AI", "target": "GAM-0013", "type": "skos:narrower" }, { "source": "GAM-0013", "target": "ART-0071", "type": "augmanitai:crossDomainReference" }, { "source": "GAM-0013", "target": "ART-0072", "type": "augmanitai:crossDomainReference" }, { "source": "GAM-0013", "target": "ART-0080", "type": "augmanitai:crossDomainReference" }, { "source": "GAM-0014", "target": "Detection System", "type": "skos:broader" }, { "source": "Detection System", "target": "GAM-0014", "type": "skos:narrower" }, { "source": "GAM-0014", "target": "AED-0036", "type": "augmanitai:crossDomainReference" }, { "source": "GAM-0014", "target": "ART-0083", "type": "augmanitai:crossDomainReference" }, { "source": "GAM-0014", "target": "ASE-0002", "type": "augmanitai:crossDomainReference" }, { "source": "GAM-0015", "target": "Games AI", "type": "skos:broader" }, { "source": "Games AI", "target": "GAM-0015", "type": "skos:narrower" }, { "source": "GAM-0016", "target": "Games AI", "type": "skos:broader" }, { "source": "Games AI", "target": "GAM-0016", "type": "skos:narrower" }, { "source": "GAM-0016", "target": "ETH-0005", "type": "augmanitai:crossDomainReference" }, { "source": "GAM-0016", "target": "DAT-0039", "type": "augmanitai:crossDomainReference" }, { "source": "GAM-0017", "target": "Analytical Ratio", "type": "skos:broader" }, { "source": "Analytical Ratio", "target": "GAM-0017", "type": "skos:narrower" }, { "source": "GAM-0017", "target": "AED-0009", "type": "augmanitai:crossDomainReference" }, { "source": "GAM-0017", "target": "AGE-0037", "type": "augmanitai:crossDomainReference" }, { "source": "GAM-0017", "target": "ART-0003", "type": "augmanitai:crossDomainReference" }, { "source": "GAM-0018", "target": "Pattern Recognition", "type": "skos:broader" }, { "source": "Pattern Recognition", "target": "GAM-0018", "type": "skos:narrower" 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{ "source": "GAM-0022", "target": "Games AI", "type": "skos:broader" }, { "source": "Games AI", "target": "GAM-0022", "type": "skos:narrower" }, { "source": "GAM-0022", "target": "AED-0071", "type": "augmanitai:crossDomainReference" }, { "source": "GAM-0023", "target": "Games AI", "type": "skos:broader" }, { "source": "Games AI", "target": "GAM-0023", "type": "skos:narrower" }, { "source": "GAM-0023", "target": "ASE-0007", "type": "augmanitai:crossDomainReference" }, { "source": "GAM-0023", "target": "COG-0112", "type": "augmanitai:crossDomainReference" }, { "source": "GAM-0023", "target": "COG-0188", "type": "augmanitai:crossDomainReference" }, { "source": "GAM-0024", "target": "Games AI", "type": "skos:broader" }, { "source": "Games AI", "target": "GAM-0024", "type": "skos:narrower" }, { "source": "GAM-0024", "target": "BEH-0012", "type": "augmanitai:crossDomainReference" }, { "source": "GAM-0025", "target": "Games AI", "type": "skos:broader" }, { "source": "Games AI", "target": "GAM-0025", "type": "skos:narrower" }, { "source": "GAM-0025", "target": "ART-0002", "type": "augmanitai:crossDomainReference" }, { "source": "GAM-0025", "target": "ART-0005", "type": "augmanitai:crossDomainReference" }, { "source": "GAM-0025", "target": "ART-0020", "type": "augmanitai:crossDomainReference" }, { "source": "GAM-0026", "target": "Games AI", "type": "skos:broader" }, { "source": "Games AI", "target": "GAM-0026", "type": "skos:narrower" }, { "source": "GAM-0027", "target": "Games AI", "type": "skos:broader" }, { "source": "Games AI", "target": "GAM-0027", "type": "skos:narrower" }, { "source": "GAM-0027", "target": "AED-0059", "type": "augmanitai:crossDomainReference" }, { "source": "GAM-0027", "target": "AGE-0052", "type": "augmanitai:crossDomainReference" }, { "source": "GAM-0027", "target": "AGE-0064", "type": "augmanitai:crossDomainReference" }, { "source": "GAM-0028", "target": "Games AI", "type": "skos:broader" }, { "source": "Games AI", "target": "GAM-0028", "type": "skos:narrower" }, { "source": "GAM-0029", "target": "RPH-1307", "type": "skos:broader" }, { "source": "RPH-1307", "target": "GAM-0029", "type": "skos:narrower" }, { "source": "GAM-0029", "target": "DES-0029", "type": "augmanitai:crossDomainReference" }, { "source": "GAM-0030", "target": "Games AI", "type": "skos:broader" }, { "source": "Games AI", "target": "GAM-0030", "type": "skos:narrower" }, { "source": "GAM-0030", "target": "AGE-0082", "type": "augmanitai:crossDomainReference" }, { "source": "GAM-0030", "target": "ASE-0048", "type": "augmanitai:crossDomainReference" }, { "source": "GAM-0030", "target": "ASE-0071", "type": "augmanitai:crossDomainReference" }, { "source": "GAM-0031", "target": "RPH-3101", "type": "skos:broader" }, { "source": "RPH-3101", "target": "GAM-0031", "type": "skos:narrower" }, { "source": "GAM-0032", "target": "Games AI", "type": "skos:broader" }, { "source": "Games AI", "target": "GAM-0032", "type": "skos:narrower" }, { "source": "GAM-0032", "target": "ART-0024", "type": "augmanitai:crossDomainReference" }, { "source": "GAM-0032", "target": "BEH-0075", "type": "augmanitai:crossDomainReference" }, { "source": "GAM-0032", "target": "COG-0122", "type": "augmanitai:crossDomainReference" }, { "source": "GAM-0033", "target": "Games AI", "type": "skos:broader" }, { "source": "Games AI", "target": "GAM-0033", "type": "skos:narrower" }, { "source": "GAM-0033", "target": "ASE-0003", "type": "augmanitai:crossDomainReference" }, { "source": "GAM-0033", "target": "ASE-0046", "type": "augmanitai:crossDomainReference" }, { "source": "GAM-0033", "target": "ASE-0080", "type": "augmanitai:crossDomainReference" }, { "source": "GAM-0034", "target": "Detection System", "type": "skos:broader" }, { "source": "Detection System", "target": "GAM-0034", "type": "skos:narrower" }, { "source": "GAM-0035", "target": "Games AI", "type": "skos:broader" }, { "source": "Games AI", "target": "GAM-0035", "type": "skos:narrower" }, { "source": "GAM-0035", "target": "AGE-0043", "type": "augmanitai:crossDomainReference" }, { "source": "GAM-0035", "target": "AGE-0090", "type": "augmanitai:crossDomainReference" }, { "source": "GAM-0035", "target": "ART-0030", "type": "augmanitai:crossDomainReference" }, { "source": "GAM-0036", "target": "RPH-2603", "type": "skos:broader" }, { "source": "RPH-2603", "target": "GAM-0036", "type": "skos:narrower" }, { "source": "GAM-0036", "target": "AGE-0083", "type": "augmanitai:crossDomainReference" }, { "source": "GAM-0036", "target": "ASE-0042", "type": "augmanitai:crossDomainReference" }, { "source": "GAM-0036", "target": "ASE-0053", "type": "augmanitai:crossDomainReference" }, { "source": "GAM-0037", "target": "Games AI", "type": "skos:broader" }, { "source": "Games AI", "target": "GAM-0037", "type": "skos:narrower" }, { "source": "GAM-0038", "target": "Games AI", "type": "skos:broader" }, { "source": "Games AI", "target": "GAM-0038", "type": "skos:narrower" }, { "source": "GAM-0038", "target": "ART-0011", "type": "augmanitai:crossDomainReference" }, { "source": "GAM-0038", "target": "ART-0019", "type": "augmanitai:crossDomainReference" }, { "source": "GAM-0038", "target": "ART-0058", "type": "augmanitai:crossDomainReference" }, { "source": "GAM-0039", "target": "Games AI", "type": "skos:broader" }, { "source": "Games AI", "target": "GAM-0039", "type": "skos:narrower" }, { "source": "GAM-0040", "target": "Games AI", "type": "skos:broader" }, { "source": "Games AI", "target": "GAM-0040", "type": "skos:narrower" }, { "source": "GAM-0040", "target": "COG-0096", "type": "augmanitai:crossDomainReference" }, { "source": "GAM-0041", "target": "Games AI", "type": "skos:broader" }, { "source": "Games AI", "target": "GAM-0041", "type": "skos:narrower" }, { "source": "GAM-0041", "target": "BEH-0034", "type": "augmanitai:crossDomainReference" }, { "source": "GAM-0041", "target": "COG-0016", "type": "augmanitai:crossDomainReference" }, { "source": "GAM-0041", "target": "COG-0034", "type": "augmanitai:crossDomainReference" }, { "source": "GAM-0042", "target": "Games AI", "type": "skos:broader" }, { "source": "Games AI", "target": "GAM-0042", "type": "skos:narrower" }, { "source": "GAM-0042", "target": "AED-0071", "type": "augmanitai:crossDomainReference" }, { "source": "GAM-0042", "target": "AGE-0063", "type": "augmanitai:crossDomainReference" }, { "source": "GAM-0042", "target": "AGE-0078", "type": "augmanitai:crossDomainReference" }, { "source": "GAM-0043", "target": "Games AI", "type": "skos:broader" }, { "source": "Games AI", "target": "GAM-0043", "type": "skos:narrower" }, { "source": "GAM-0043", "target": "ART-0008", "type": "augmanitai:crossDomainReference" }, { "source": "GAM-0043", "target": "ART-0026", "type": "augmanitai:crossDomainReference" }, { "source": "GAM-0043", "target": "ART-0027", "type": "augmanitai:crossDomainReference" }, { "source": "GAM-0044", "target": "Games AI", "type": "skos:broader" }, { "source": "Games AI", "target": "GAM-0044", "type": "skos:narrower" }, { "source": "GAM-0044", "target": "COG-0011", "type": "augmanitai:crossDomainReference" }, { "source": "GAM-0044", "target": "CON-0022", "type": "augmanitai:crossDomainReference" }, { "source": "GAM-0044", "target": "CON-0044", "type": "augmanitai:crossDomainReference" }, { "source": "GAM-0045", "target": "Pattern Recognition", "type": "skos:broader" }, { "source": "Pattern Recognition", "target": "GAM-0045", "type": "skos:narrower" }, { "source": "GAM-0045", "target": "AED-0013", "type": "augmanitai:crossDomainReference" }, { "source": "GAM-0045", "target": "AED-0093", "type": "augmanitai:crossDomainReference" }, { "source": "GAM-0045", "target": "AGE-0048", "type": "augmanitai:crossDomainReference" }, { "source": "GAM-0046", "target": "Games AI", "type": "skos:broader" }, { "source": "Games AI", "target": "GAM-0046", "type": "skos:narrower" }, { "source": "GAM-0046", "target": "ASE-0057", "type": "augmanitai:crossDomainReference" }, { "source": "GAM-0046", "target": "ASE-0070", "type": "augmanitai:crossDomainReference" }, { "source": "GAM-0046", "target": "ASE-0085", "type": "augmanitai:crossDomainReference" }, { "source": "GAM-0047", "target": "Games AI", "type": "skos:broader" }, { "source": "Games AI", "target": "GAM-0047", "type": "skos:narrower" }, { "source": "GAM-0047", "target": "AED-0071", "type": "augmanitai:crossDomainReference" }, { "source": "GAM-0047", "target": "AGE-0063", "type": "augmanitai:crossDomainReference" }, { "source": "GAM-0047", "target": "AGE-0078", "type": "augmanitai:crossDomainReference" }, { "source": "GAM-0048", "target": "Games AI", "type": "skos:broader" }, { "source": "Games AI", "target": "GAM-0048", "type": "skos:narrower" }, { "source": "GAM-0049", "target": "Abstraction Layer", "type": "skos:broader" }, { "source": "Abstraction Layer", "target": "GAM-0049", "type": "skos:narrower" }, { "source": "GAM-0050", "target": "Games AI", "type": "skos:broader" }, { "source": "Games AI", "target": "GAM-0050", "type": "skos:narrower" }, { "source": "GAM-0050", "target": "FIC-0014", "type": "augmanitai:crossDomainReference" }, { "source": "GAM-0051", "target": "Games AI", "type": "skos:broader" }, { "source": "Games AI", "target": "GAM-0051", "type": "skos:narrower" }, { "source": "GAM-0051", "target": "AED-0005", "type": "augmanitai:crossDomainReference" }, { "source": "GAM-0051", "target": "AED-0052", "type": "augmanitai:crossDomainReference" }, { "source": "GAM-0051", "target": "ASE-0041", "type": "augmanitai:crossDomainReference" }, { "source": "GAM-0052", "target": "Games AI", "type": "skos:broader" }, { "source": "Games AI", "target": "GAM-0052", "type": "skos:narrower" }, { "source": "GAM-0053", "target": "Games AI", "type": "skos:broader" }, { "source": "Games AI", "target": "GAM-0053", "type": "skos:narrower" }, { "source": "GAM-0053", "target": "AED-0074", "type": "augmanitai:crossDomainReference" }, { "source": "GAM-0053", "target": "COP-0011", "type": "augmanitai:crossDomainReference" }, { "source": "GAM-0053", "target": "CRE-0197", "type": "augmanitai:crossDomainReference" }, { "source": "GAM-0054", "target": "Games AI", "type": "skos:broader" }, { "source": "Games AI", "target": "GAM-0054", "type": "skos:narrower" }, { "source": "GAM-0054", "target": "FIC-0083", "type": "augmanitai:crossDomainReference" }, { "source": "GAM-0055", "target": "Games AI", "type": "skos:broader" }, { "source": "Games AI", "target": "GAM-0055", "type": "skos:narrower" }, { "source": "GAM-0055", "target": "AGE-0023", "type": "augmanitai:crossDomainReference" }, { "source": "GAM-0055", "target": "AGE-0024", "type": "augmanitai:crossDomainReference" }, { "source": "GAM-0055", "target": "ASE-0053", "type": "augmanitai:crossDomainReference" }, { "source": "GAM-0056", "target": "Detection System", "type": "skos:broader" }, { "source": "Detection System", "target": "GAM-0056", "type": "skos:narrower" }, { "source": "GAM-0057", "target": "Games AI", "type": "skos:broader" }, { "source": "Games AI", "target": "GAM-0057", "type": "skos:narrower" }, { "source": "GAM-0058", "target": "Games AI", "type": "skos:broader" }, { "source": "Games AI", "target": "GAM-0058", "type": "skos:narrower" }, { "source": "GAM-0059", "target": "Games AI", "type": "skos:broader" }, { "source": "Games AI", "target": "GAM-0059", "type": "skos:narrower" }, { "source": "GAM-0059", "target": "AED-0051", "type": "augmanitai:crossDomainReference" }, { "source": "GAM-0059", "target": "AED-0053", "type": "augmanitai:crossDomainReference" }, { "source": "GAM-0059", "target": "AED-0059", "type": "augmanitai:crossDomainReference" }, { "source": "GAM-0060", "target": "Games AI", "type": "skos:broader" }, { "source": "Games AI", "target": "GAM-0060", "type": "skos:narrower" }, { "source": "GAM-0060", "target": "CUS-0017", "type": "augmanitai:crossDomainReference" }, { "source": "GAM-0061", "target": "Games AI", "type": "skos:broader" }, { "source": "Games AI", "target": "GAM-0061", "type": "skos:narrower" }, { "source": "GAM-0061", "target": "ASE-0007", "type": "augmanitai:crossDomainReference" }, { "source": "GAM-0061", "target": "AUG-0248", "type": "augmanitai:crossDomainReference" }, { "source": "GAM-0061", "target": "BEH-0080", "type": "augmanitai:crossDomainReference" }, { "source": "GAM-0062", "target": "Games AI", "type": "skos:broader" }, { "source": "Games AI", "target": "GAM-0062", "type": "skos:narrower" }, { "source": "GAM-0062", "target": "BEH-0051", "type": "augmanitai:crossDomainReference" }, { "source": "GAM-0062", "target": "COG-0132", "type": "augmanitai:crossDomainReference" }, { "source": "GAM-0062", "target": "COG-0161", "type": "augmanitai:crossDomainReference" }, { "source": "GAM-0063", "target": "Games AI", "type": "skos:broader" }, { "source": "Games AI", "target": "GAM-0063", "type": "skos:narrower" }, { "source": "GAM-0064", "target": "RPH-2703", "type": "skos:broader" }, { "source": "RPH-2703", "target": "GAM-0064", "type": "skos:narrower" }, { "source": "GAM-0065", "target": "Games AI", "type": "skos:broader" }, { "source": "Games AI", "target": "GAM-0065", "type": "skos:narrower" }, { "source": "GAM-0066", "target": "RPH-3101", "type": "skos:broader" }, { "source": "RPH-3101", "target": "GAM-0066", "type": "skos:narrower" }, { "source": "GAM-0066", "target": "AED-0077", "type": "augmanitai:crossDomainReference" }, { "source": "GAM-0066", "target": "AGE-0021", "type": "augmanitai:crossDomainReference" }, { "source": "GAM-0066", "target": "AGE-0037", "type": "augmanitai:crossDomainReference" }, { "source": "GAM-0067", "target": "Analytical Method", "type": "skos:broader" }, { "source": "Analytical Method", "target": "GAM-0067", "type": "skos:narrower" }, { "source": "GAM-0067", "target": "AED-0077", "type": "augmanitai:crossDomainReference" }, { "source": "GAM-0067", "target": "AED-0091", "type": "augmanitai:crossDomainReference" }, { "source": "GAM-0067", "target": "ART-0086", "type": "augmanitai:crossDomainReference" }, { "source": "GAM-0068", "target": "Feedback Loop", "type": "skos:broader" }, { "source": "Feedback Loop", "target": "GAM-0068", "type": "skos:narrower" }, { "source": "GAM-0068", "target": "COG-0092", "type": "augmanitai:crossDomainReference" }, { "source": "GAM-0069", "target": "Games AI", "type": "skos:broader" }, { "source": "Games AI", "target": "GAM-0069", "type": "skos:narrower" }, { "source": "GAM-0069", "target": "ASE-0016", "type": "augmanitai:crossDomainReference" }, { "source": "GAM-0069", "target": "ASE-0053", "type": "augmanitai:crossDomainReference" }, { "source": "GAM-0069", "target": "COG-0022", "type": "augmanitai:crossDomainReference" }, { "source": "GAM-0070", "target": "Logical Paradox", "type": "skos:broader" }, { "source": "Logical Paradox", "target": "GAM-0070", "type": "skos:narrower" }, { "source": "GAM-0071", "target": "RPH-2701", "type": "skos:broader" }, { "source": "RPH-2701", "target": "GAM-0071", "type": "skos:narrower" }, { "source": "GAM-0072", "target": "RPH-2701", "type": "skos:broader" }, { "source": "RPH-2701", "target": "GAM-0072", "type": "skos:narrower" }, { "source": "GAM-0073", "target": "Games AI", "type": "skos:broader" }, { "source": "Games AI", "target": "GAM-0073", "type": "skos:narrower" }, { "source": "GAM-0073", "target": "ASE-0053", "type": "augmanitai:crossDomainReference" }, { "source": "GAM-0073", "target": "COG-0022", "type": "augmanitai:crossDomainReference" }, { "source": "GAM-0073", "target": "COG-0081", "type": "augmanitai:crossDomainReference" }, { "source": "GAM-0074", "target": "RPH-2355", "type": "skos:broader" }, { "source": "RPH-2355", "target": "GAM-0074", "type": "skos:narrower" }, { "source": "GAM-0074", "target": "ART-0055", "type": "augmanitai:crossDomainReference" }, { "source": "GAM-0075", "target": "Games AI", "type": "skos:broader" }, { "source": "Games AI", "target": "GAM-0075", "type": "skos:narrower" }, { "source": "GAM-0075", "target": "ELR-0153", "type": "augmanitai:crossDomainReference" }, { "source": "GAM-0076", "target": "Games AI", "type": "skos:broader" }, { "source": "Games AI", "target": "GAM-0076", "type": "skos:narrower" }, { "source": "GAM-0076", "target": "COP-0081", "type": "augmanitai:crossDomainReference" }, { "source": "GAM-0077", "target": "Analytical Ratio", "type": "skos:broader" }, { "source": "Analytical Ratio", "target": "GAM-0077", "type": "skos:narrower" }, { "source": "GAM-0077", "target": "ART-0012", "type": "augmanitai:crossDomainReference" }, { "source": "GAM-0077", "target": "ART-0025", "type": "augmanitai:crossDomainReference" }, { "source": "GAM-0077", "target": "ART-0072", "type": "augmanitai:crossDomainReference" }, { "source": "GAM-0078", "target": "Games AI", "type": "skos:broader" }, { "source": "Games AI", "target": "GAM-0078", "type": "skos:narrower" }, { "source": "GAM-0078", "target": "AED-0044", "type": "augmanitai:crossDomainReference" }, { "source": "GAM-0078", "target": "AED-0063", "type": "augmanitai:crossDomainReference" }, { "source": "GAM-0078", "target": "AED-0064", "type": "augmanitai:crossDomainReference" }, { "source": "GAM-0079", "target": "Games AI", "type": "skos:broader" }, { "source": "Games AI", "target": "GAM-0079", "type": "skos:narrower" }, { "source": "GAM-0080", "target": "Games AI", "type": "skos:broader" }, { "source": "Games AI", "target": "GAM-0080", "type": "skos:narrower" }, { "source": "GAM-0081", "target": "Psychological Effect", "type": "skos:broader" }, { "source": "Psychological Effect", "target": "GAM-0081", "type": "skos:narrower" }, { "source": "GAM-0081", "target": "ASE-0017", "type": "augmanitai:crossDomainReference" }, { "source": "GAM-0081", "target": "ART-0016", "type": "augmanitai:crossDomainReference" }, { "source": "GAM-0082", "target": "Games AI", "type": "skos:broader" }, { "source": "Games AI", "target": "GAM-0082", "type": "skos:narrower" }, { "source": "GAM-0082", "target": "CUS-0008", "type": "augmanitai:crossDomainReference" }, { "source": "GAM-0083", "target": "Analytical Ratio", "type": "skos:broader" }, { "source": "Analytical Ratio", "target": "GAM-0083", "type": "skos:narrower" }, { "source": "GAM-0084", "target": "Games AI", "type": "skos:broader" }, { "source": "Games AI", "target": "GAM-0084", "type": "skos:narrower" }, { "source": "GAM-0085", "target": "Analytical Ratio", "type": "skos:broader" }, { "source": "Analytical Ratio", "target": "GAM-0085", "type": "skos:narrower" }, { "source": "GAM-0085", "target": "AED-0009", "type": "augmanitai:crossDomainReference" }, { "source": "GAM-0085", "target": "AED-0038", "type": "augmanitai:crossDomainReference" }, { "source": "GAM-0085", "target": "AED-0050", "type": "augmanitai:crossDomainReference" }, { "source": "GAM-0086", "target": "Games AI", "type": "skos:broader" }, { "source": "Games AI", "target": "GAM-0086", "type": "skos:narrower" }, { "source": "GAM-0086", "target": "AED-0009", "type": "augmanitai:crossDomainReference" }, { "source": "GAM-0086", "target": "AED-0038", "type": "augmanitai:crossDomainReference" }, { "source": "GAM-0086", "target": "AED-0050", "type": "augmanitai:crossDomainReference" }, { "source": "GAM-0087", "target": "Games AI", "type": "skos:broader" }, { "source": "Games AI", "target": "GAM-0087", "type": "skos:narrower" }, { "source": "GAM-0087", "target": "AED-0088", "type": "augmanitai:crossDomainReference" }, { "source": "GAM-0087", "target": "AGE-0026", "type": "augmanitai:crossDomainReference" }, { "source": "GAM-0087", "target": "ART-0045", "type": "augmanitai:crossDomainReference" }, { "source": "GAM-0088", "target": "Games AI", "type": "skos:broader" }, { "source": "Games AI", "target": "GAM-0088", "type": "skos:narrower" }, { "source": "GAM-0089", "target": "Games AI", "type": "skos:broader" }, { "source": "Games AI", "target": "GAM-0089", "type": "skos:narrower" }, { "source": "GAM-0089", "target": "BEH-0079", "type": "augmanitai:crossDomainReference" }, { "source": "GAM-0089", "target": "COG-0029", "type": "augmanitai:crossDomainReference" }, { "source": "GAM-0089", "target": "COG-0141", "type": "augmanitai:crossDomainReference" }, { "source": "GAM-0090", "target": "System Adaptation", "type": "skos:broader" }, { "source": "System Adaptation", "target": "GAM-0090", "type": "skos:narrower" }, { "source": "GAM-0090", "target": "AED-0010", "type": "augmanitai:crossDomainReference" }, { "source": "GAM-0090", "target": "AED-0090", "type": "augmanitai:crossDomainReference" }, { "source": "GAM-0090", "target": "AGE-0036", "type": "augmanitai:crossDomainReference" }, { "source": "GAM-0091", "target": "Games AI", "type": "skos:broader" }, { "source": "Games AI", "target": "GAM-0091", "type": "skos:narrower" }, { "source": "GAM-0091", "target": "ADA-0003", "type": "augmanitai:crossDomainReference" }, { "source": "GAM-0091", "target": "AGE-0055", "type": "augmanitai:crossDomainReference" }, { "source": "GAM-0091", "target": "AGE-0057", "type": "augmanitai:crossDomainReference" }, { "source": "GAM-0092", "target": "Games AI", "type": "skos:broader" }, { "source": "Games AI", "target": "GAM-0092", "type": "skos:narrower" }, { "source": "GAM-0092", "target": "ASE-0007", "type": "augmanitai:crossDomainReference" }, { "source": "GAM-0092", "target": "ASE-0053", "type": "augmanitai:crossDomainReference" }, { "source": "GAM-0092", "target": "ASE-0096", "type": "augmanitai:crossDomainReference" }, { "source": "GAM-0093", "target": "Analytical Ratio", "type": "skos:broader" }, { "source": "Analytical Ratio", "target": "GAM-0093", "type": "skos:narrower" }, { "source": "GAM-0093", "target": "ART-0072", "type": "augmanitai:crossDomainReference" }, { "source": "GAM-0093", "target": "BEH-0041", "type": "augmanitai:crossDomainReference" }, { "source": "GAM-0093", "target": "COP-0021", "type": "augmanitai:crossDomainReference" }, { "source": "GAM-0094", "target": "Games AI", "type": "skos:broader" }, { "source": "Games AI", "target": "GAM-0094", "type": "skos:narrower" }, { "source": "GAM-0094", "target": "AUG-0901", "type": "augmanitai:crossDomainReference" }, { "source": "GAM-0094", "target": "COG-0132", "type": "augmanitai:crossDomainReference" }, { "source": "GAM-0094", "target": "COG-0161", "type": "augmanitai:crossDomainReference" }, { "source": "GAM-0095", "target": "Simulation Method", "type": "skos:broader" }, { "source": "Simulation Method", "target": "GAM-0095", "type": "skos:narrower" }, { "source": "IDN-0001", "target": "RPH-2355", "type": "skos:broader" }, { "source": "RPH-2355", "target": "IDN-0001", "type": "skos:narrower" }, { "source": "IDN-0002", "target": "Psychological Effect", "type": "skos:broader" }, { "source": "Psychological Effect", "target": "IDN-0002", "type": "skos:narrower" }, { "source": "IDN-0003", "target": "Identity AI", "type": "skos:broader" }, { "source": "Identity AI", "target": "IDN-0003", "type": "skos:narrower" }, { "source": "IDN-0004", "target": "Psychological Effect", "type": "skos:broader" }, { "source": "Psychological Effect", "target": "IDN-0004", "type": "skos:narrower" }, { "source": "IDN-0005", "target": "RPH-2703", "type": "skos:broader" }, { "source": "RPH-2703", "target": "IDN-0005", "type": "skos:narrower" }, { "source": "IDN-0005", "target": "CRE-0029", "type": "augmanitai:crossDomainReference" }, { "source": "IDN-0006", "target": "Psychological Effect", "type": "skos:broader" }, { "source": "Psychological Effect", "target": "IDN-0006", "type": "skos:narrower" }, { "source": "IDN-0006", "target": "AGE-0051", "type": "augmanitai:crossDomainReference" }, { "source": "IDN-0007", "target": "Identity AI", "type": "skos:broader" }, { "source": "Identity AI", "target": "IDN-0007", "type": "skos:narrower" }, { "source": "IDN-0007", "target": "CAI-0019", "type": "augmanitai:crossDomainReference" }, { "source": "IDN-0008", "target": "RPH-2501", "type": "skos:broader" }, { "source": "RPH-2501", "target": "IDN-0008", "type": "skos:narrower" }, { "source": "IDN-0008", "target": "CON-0034", "type": "augmanitai:crossDomainReference" }, { "source": "IDN-0009", "target": "Psychological Effect", "type": "skos:broader" }, { "source": "Psychological Effect", "target": "IDN-0009", "type": "skos:narrower" }, { "source": "IDN-0010", "target": "RPH-3003", "type": "skos:broader" }, { "source": "RPH-3003", "target": "IDN-0010", "type": "skos:narrower" }, { "source": "IDN-0011", "target": "RPH-2002", "type": "skos:broader" }, { "source": "RPH-2002", "target": "IDN-0011", "type": "skos:narrower" }, { "source": "IDN-0012", "target": "RPH-3202", "type": "skos:broader" }, { "source": "RPH-3202", "target": "IDN-0012", "type": "skos:narrower" }, { "source": "IDN-0012", "target": "AGE-0065", "type": "augmanitai:crossDomainReference" }, { "source": "IDN-0013", "target": "RPH-1075", "type": "skos:broader" }, { "source": "RPH-1075", "target": "IDN-0013", "type": "skos:narrower" }, { "source": "IDN-0014", "target": "Identity AI", "type": "skos:broader" }, { "source": "Identity AI", "target": "IDN-0014", "type": "skos:narrower" }, { "source": "IDN-0014", "target": "ART-0056", "type": "augmanitai:crossDomainReference" }, { "source": "IDN-0014", "target": "ART-0070", "type": "augmanitai:crossDomainReference" }, { "source": "IDN-0014", "target": "COG-0183", "type": "augmanitai:crossDomainReference" }, { "source": "IDN-0015", "target": "RPH-1307", "type": "skos:broader" }, { "source": "RPH-1307", "target": "IDN-0015", "type": "skos:narrower" }, { "source": "IDN-0016", "target": "Identity AI", "type": "skos:broader" }, { "source": "Identity AI", "target": "IDN-0016", "type": "skos:narrower" }, { "source": "IDN-0017", "target": "RPH-3101", "type": "skos:broader" }, { "source": "RPH-3101", "target": "IDN-0017", "type": "skos:narrower" }, { "source": "IDN-0018", "target": "CAI-0003", "type": "skos:broader" }, { "source": "CAI-0003", "target": "IDN-0018", "type": "skos:narrower" }, { "source": "IDN-0019", "target": "RPH-3902", "type": "skos:broader" }, { "source": "RPH-3902", "target": "IDN-0019", "type": "skos:narrower" }, { "source": "IDN-0019", "target": "CRE-0132", "type": "augmanitai:crossDomainReference" }, { "source": "IDN-0020", "target": "CRE-0029", "type": "skos:broader" }, { "source": "CRE-0029", "target": "IDN-0020", "type": "skos:narrower" }, { "source": "IDN-0020", "target": "GAM-0037", "type": "augmanitai:crossDomainReference" }, { "source": "IDN-0021", "target": "Identity AI", "type": "skos:broader" }, { "source": "Identity AI", "target": "IDN-0021", "type": "skos:narrower" }, { "source": "IDN-0022", "target": "Psychological Effect", "type": "skos:broader" }, { "source": "Psychological Effect", "target": "IDN-0022", "type": "skos:narrower" }, { "source": "IDN-0023", "target": "Identity AI", "type": "skos:broader" }, { "source": "Identity AI", "target": "IDN-0023", "type": "skos:narrower" }, { "source": "IDN-0023", "target": "CAI-0004", "type": "augmanitai:crossDomainReference" }, { "source": "IDN-0024", "target": "PER-0092", "type": "skos:broader" }, { "source": "PER-0092", "target": "IDN-0024", "type": "skos:narrower" }, { "source": "IDN-0025", "target": "RPH-2351", "type": "skos:broader" }, { "source": "RPH-2351", "target": "IDN-0025", "type": "skos:narrower" }, { "source": "IDN-0026", "target": "RPH-3754", "type": "skos:broader" }, { "source": "RPH-3754", "target": "IDN-0026", "type": "skos:narrower" }, { "source": "IDN-0026", "target": "CRE-0142", "type": "augmanitai:crossDomainReference" }, { "source": "IDN-0027", "target": "Identity AI", "type": "skos:broader" }, { "source": "Identity AI", "target": "IDN-0027", "type": "skos:narrower" }, { "source": "IDN-0027", "target": "CRE-0194", "type": "augmanitai:crossDomainReference" }, { "source": "IDN-0028", "target": "Identity AI", "type": "skos:broader" }, { "source": "Identity AI", "target": "IDN-0028", "type": "skos:narrower" }, { "source": "IDN-0029", "target": "RPH-2505", "type": "skos:broader" }, { "source": "RPH-2505", "target": "IDN-0029", "type": "skos:narrower" }, { "source": "IDN-0030", "target": "CRE-0029", "type": "skos:broader" }, { "source": "CRE-0029", "target": "IDN-0030", "type": "skos:narrower" }, { "source": "IDN-0030", "target": "CRE-0033", "type": "augmanitai:crossDomainReference" }, { "source": "IDN-0031", "target": "RPH-2403", "type": "skos:broader" }, { "source": "RPH-2403", "target": "IDN-0031", "type": "skos:narrower" }, { "source": "IDN-0031", "target": "EDU-0017", "type": "augmanitai:crossDomainReference" }, { "source": "IDN-0032", "target": "Identity AI", "type": "skos:broader" }, { "source": "Identity AI", "target": "IDN-0032", "type": "skos:narrower" }, { "source": "IDN-0033", "target": "RPH-2104", "type": "skos:broader" }, { "source": "RPH-2104", "target": "IDN-0033", "type": "skos:narrower" }, { "source": "IDN-0034", "target": "Identity AI", "type": "skos:broader" }, { "source": "Identity AI", "target": "IDN-0034", "type": "skos:narrower" }, { "source": "IDN-0034", "target": "CRE-0189", "type": "augmanitai:crossDomainReference" }, { "source": "IDN-0035", "target": "Identity AI", "type": "skos:broader" }, { "source": "Identity AI", "target": "IDN-0035", "type": "skos:narrower" }, { "source": "IDN-0036", "target": "RPH-2805", "type": "skos:broader" }, { "source": "RPH-2805", "target": "IDN-0036", "type": "skos:narrower" }, { "source": "IDN-0036", "target": "CRE-0225", "type": "augmanitai:crossDomainReference" }, { "source": "IDN-0037", "target": "RPH-2355", "type": "skos:broader" }, { "source": "RPH-2355", "target": "IDN-0037", "type": "skos:narrower" }, { "source": "IDN-0038", "target": "KNO-0023", "type": "skos:broader" }, { "source": "KNO-0023", "target": "IDN-0038", "type": "skos:narrower" }, { "source": "IDN-0039", "target": "RPH-3101", "type": "skos:broader" }, { "source": "RPH-3101", "target": "IDN-0039", "type": "skos:narrower" }, { "source": "IDN-0040", "target": "Identity AI", "type": "skos:broader" }, { "source": "Identity AI", "target": "IDN-0040", "type": "skos:narrower" }, { "source": "IDN-0040", "target": "CRE-0143", "type": "augmanitai:crossDomainReference" }, { "source": "IDN-0041", "target": "RPH-2055", "type": "skos:broader" }, { "source": "RPH-2055", "target": "IDN-0041", "type": "skos:narrower" }, { "source": "IDN-0041", "target": "CRE-0214", "type": "augmanitai:crossDomainReference" }, { "source": "IDN-0042", "target": "RPH-2703", "type": "skos:broader" }, { "source": "RPH-2703", "target": "IDN-0042", "type": "skos:narrower" }, { "source": "IDN-0043", "target": "RPH-2355", "type": "skos:broader" }, { "source": "RPH-2355", "target": "IDN-0043", "type": "skos:narrower" }, { "source": "IDN-0044", "target": "TEM-0121", "type": "skos:broader" }, { "source": "TEM-0121", "target": "IDN-0044", "type": "skos:narrower" }, { "source": "IDN-0045", "target": "IDN-0017", "type": "skos:broader" }, { "source": "IDN-0017", "target": "IDN-0045", "type": "skos:narrower" }, { "source": "IDN-0045", "target": "CRE-0119", "type": "augmanitai:crossDomainReference" }, { "source": "IDN-0046", "target": "Classification Spectrum", "type": "skos:broader" }, { "source": "Classification Spectrum", "target": "IDN-0046", "type": "skos:narrower" }, { "source": "IDN-0047", "target": "Identity AI", "type": "skos:broader" }, { "source": "Identity AI", "target": "IDN-0047", "type": "skos:narrower" }, { "source": "IDN-0047", "target": "CRE-0029", "type": "augmanitai:crossDomainReference" }, { "source": "IDN-0048", "target": "Identity AI", "type": "skos:broader" }, { "source": "Identity AI", "target": "IDN-0048", "type": "skos:narrower" }, { "source": "IDN-0049", "target": "Classification Spectrum", "type": "skos:broader" }, { "source": "Classification Spectrum", "target": "IDN-0049", "type": "skos:narrower" }, { "source": "IDN-0050", "target": "BEH-0024", "type": "skos:broader" }, { "source": "BEH-0024", "target": "IDN-0050", "type": "skos:narrower" }, { 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"source": "LIN-0032", "target": "RPH-2355", "type": "skos:broader" }, { "source": "RPH-2355", "target": "LIN-0032", "type": "skos:narrower" }, { "source": "LIN-0033", "target": "RPH-3902", "type": "skos:broader" }, { "source": "RPH-3902", "target": "LIN-0033", "type": "skos:narrower" }, { "source": "LIN-0033", "target": "CUS-0090", "type": "augmanitai:crossDomainReference" }, { "source": "LIN-0034", "target": "Language AI", "type": "skos:broader" }, { "source": "Language AI", "target": "LIN-0034", "type": "skos:narrower" }, { "source": "LIN-0034", "target": "AED-0089", "type": "augmanitai:crossDomainReference" }, { "source": "LIN-0034", "target": "COP-0034", "type": "augmanitai:crossDomainReference" }, { "source": "LIN-0035", "target": "RPH-2055", "type": "skos:broader" }, { "source": "RPH-2055", "target": "LIN-0035", "type": "skos:narrower" }, { "source": "LIN-0035", "target": "CRE-0133", "type": "augmanitai:crossDomainReference" }, { "source": "LIN-0036", "target": "RPH-3353", "type": "skos:broader" }, { "source": "RPH-3353", "target": "LIN-0036", "type": "skos:narrower" }, { "source": "LIN-0037", "target": "System Adaptation", "type": "skos:broader" }, { "source": "System Adaptation", "target": "LIN-0037", "type": "skos:narrower" }, { "source": "LIN-0038", "target": "RPH-2703", "type": "skos:broader" }, { "source": "RPH-2703", "target": "LIN-0038", "type": "skos:narrower" }, { "source": "LIN-0038", "target": "AED-0085", "type": "augmanitai:crossDomainReference" }, { "source": "LIN-0039", "target": "Language AI", "type": "skos:broader" }, { "source": "Language AI", "target": "LIN-0039", "type": "skos:narrower" }, { "source": "LIN-0040", "target": "Language AI", "type": "skos:broader" }, { "source": "Language AI", "target": "LIN-0040", "type": "skos:narrower" }, { "source": "LIN-0040", "target": "CUS-0090", "type": "augmanitai:crossDomainReference" }, { "source": "LIN-0041", "target": "Language AI", "type": "skos:broader" }, { "source": "Language AI", "target": "LIN-0041", "type": "skos:narrower" }, { "source": "LIN-0041", "target": "GAM-0088", "type": "augmanitai:crossDomainReference" }, { "source": "LIN-0042", "target": "Language AI", "type": "skos:broader" }, { "source": "Language AI", "target": "LIN-0042", "type": "skos:narrower" }, { "source": "LIN-0042", "target": "CON-0052", "type": "augmanitai:crossDomainReference" }, { "source": "LIN-0043", "target": "Language AI", "type": "skos:broader" }, { "source": "Language AI", "target": "LIN-0043", "type": "skos:narrower" }, { "source": "LIN-0044", "target": "RPH-3202", "type": "skos:broader" }, { "source": "RPH-3202", "target": "LIN-0044", "type": "skos:narrower" }, { "source": "LIN-0045", "target": "Language AI", "type": "skos:broader" }, { "source": "Language AI", "target": "LIN-0045", "type": "skos:narrower" }, { "source": "LIN-0045", "target": "GAM-0031", "type": "augmanitai:crossDomainReference" }, { "source": "LIN-0046", "target": "Analytical Ratio", "type": "skos:broader" }, { "source": "Analytical Ratio", "target": "LIN-0046", "type": "skos:narrower" }, { "source": "LIN-0046", "target": "ELR-0082", "type": "augmanitai:crossDomainReference" }, { "source": "LIN-0047", "target": "RPH-2605", "type": "skos:broader" }, { "source": "RPH-2605", "target": "LIN-0047", "type": "skos:narrower" }, { "source": "LIN-0047", "target": "CUS-0035", "type": "augmanitai:crossDomainReference" }, { "source": "LIN-0048", "target": "Analytical Ratio", "type": "skos:broader" }, { "source": "Analytical Ratio", "target": "LIN-0048", "type": "skos:narrower" }, { "source": "LIN-0048", "target": "BEH-0084", "type": "augmanitai:crossDomainReference" }, { "source": "LIN-0049", "target": "Language AI", "type": "skos:broader" }, { "source": "Language AI", "target": "LIN-0049", "type": "skos:narrower" }, { "source": "LIN-0050", "target": "Language AI", "type": "skos:broader" }, { "source": "Language AI", "target": "LIN-0050", "type": "skos:narrower" }, { "source": "LIN-0050", "target": "ASE-0066", "type": "augmanitai:crossDomainReference" }, { "source": "LIN-0051", "target": "RPH-2703", "type": "skos:broader" }, { "source": "RPH-2703", "target": "LIN-0051", "type": "skos:narrower" }, { "source": "LIN-0051", "target": "CUS-0091", "type": "augmanitai:crossDomainReference" }, { "source": "LIN-0051", "target": "CUS-0090", "type": "augmanitai:crossDomainReference" }, { "source": "LIN-0052", "target": "RPH-2201", "type": "skos:broader" }, { "source": "RPH-2201", "target": "LIN-0052", "type": "skos:narrower" }, { "source": "LIN-0053", "target": "Abstraction Layer", "type": "skos:broader" }, { "source": "Abstraction Layer", "target": "LIN-0053", "type": "skos:narrower" }, { "source": "LIN-0054", "target": "RPH-2351", "type": "skos:broader" }, { "source": "RPH-2351", "target": "LIN-0054", "type": "skos:narrower" }, { "source": "LIN-0055", "target": "Language AI", "type": "skos:broader" }, { "source": "Language AI", "target": "LIN-0055", "type": "skos:narrower" }, { "source": "LIN-0056", "target": "RPH-2703", "type": "skos:broader" }, { "source": "RPH-2703", "target": "LIN-0056", "type": "skos:narrower" }, { "source": "LIN-0057", "target": "Vector Embedding", "type": "skos:broader" }, { "source": "Vector Embedding", "target": "LIN-0057", "type": "skos:narrower" }, { "source": "LIN-0058", "target": "Knowledge Distillation", "type": "skos:broader" }, { "source": "Knowledge Distillation", "target": "LIN-0058", "type": "skos:narrower" }, { "source": "LIN-0059", "target": "RPH-3101", "type": "skos:broader" }, { "source": "RPH-3101", "target": "LIN-0059", "type": "skos:narrower" }, { "source": "LIN-0060", "target": "Language AI", "type": "skos:broader" }, { "source": "Language AI", "target": "LIN-0060", "type": "skos:narrower" }, { "source": "LIN-0061", "target": "Machine Learning", "type": "skos:broader" }, { "source": "Machine Learning", "target": "LIN-0061", "type": "skos:narrower" }, { "source": "LIN-0061", "target": "DAT-0035", "type": "augmanitai:crossDomainReference" }, { "source": "LIN-0062", "target": "Classification Method", "type": "skos:broader" }, { "source": "Classification Method", "target": "LIN-0062", "type": "skos:narrower" }, { "source": "LIN-0063", "target": "Language AI", "type": "skos:broader" }, { "source": "Language AI", "target": "LIN-0063", "type": "skos:narrower" }, { "source": "LIN-0063", "target": "CUS-0070", "type": "augmanitai:crossDomainReference" }, { "source": "LIN-0064", "target": "RPH-2355", "type": "skos:broader" }, { "source": "RPH-2355", "target": "LIN-0064", "type": "skos:narrower" }, { "source": "LIN-0064", "target": "COG-0167", "type": "augmanitai:crossDomainReference" }, { "source": "LIN-0064", "target": "AED-0077", "type": "augmanitai:crossDomainReference" }, { "source": "LIN-0065", "target": "Language AI", "type": "skos:broader" }, { "source": "Language AI", "target": "LIN-0065", "type": "skos:narrower" }, { "source": "LIN-0065", "target": "CRE-0070", "type": "augmanitai:crossDomainReference" }, { "source": "LIN-0066", "target": "Analytical Ratio", "type": "skos:broader" }, { "source": "Analytical Ratio", "target": "LIN-0066", "type": "skos:narrower" }, { "source": "LIN-0067", "target": "Language AI", "type": "skos:broader" }, { "source": "Language AI", "target": "LIN-0067", "type": "skos:narrower" }, { "source": "LIN-0067", "target": "ELR-0126", "type": "augmanitai:crossDomainReference" }, { "source": "LIN-0068", "target": "Language AI", "type": "skos:broader" }, { "source": "Language AI", "target": "LIN-0068", "type": "skos:narrower" }, { "source": "LIN-0069", "target": "Language AI", "type": "skos:broader" }, { "source": "Language AI", "target": "LIN-0069", "type": "skos:narrower" }, { "source": "LIN-0069", "target": "CUS-0072", "type": "augmanitai:crossDomainReference" }, { "source": "LIN-0069", "target": "DAT-0035", "type": "augmanitai:crossDomainReference" }, { "source": "LIN-0070", "target": "RPH-2051", "type": "skos:broader" }, { "source": "RPH-2051", "target": "LIN-0070", "type": "skos:narrower" }, { "source": "LIN-0071", "target": "Pattern Recognition", "type": "skos:broader" }, { "source": "Pattern Recognition", "target": "LIN-0071", "type": "skos:narrower" }, { "source": "LIN-0071", "target": "COP-0016", "type": "augmanitai:crossDomainReference" }, { "source": "LIN-0072", "target": "Analytical Method", "type": "skos:broader" }, { "source": "Analytical Method", "target": "LIN-0072", "type": "skos:narrower" }, { "source": "LIN-0072", "target": "CRE-0224", "type": "augmanitai:crossDomainReference" }, { "source": "LIN-0073", "target": "Detection System", "type": "skos:broader" }, { "source": "Detection System", "target": "LIN-0073", "type": "skos:narrower" }, { "source": "LIN-0073", "target": "CUS-0093", "type": "augmanitai:crossDomainReference" }, { "source": "LIN-0074", "target": "Computational Model", "type": "skos:broader" }, { "source": "Computational Model", "target": "LIN-0074", "type": "skos:narrower" }, { "source": "LIN-0075", "target": "Detection System", "type": "skos:broader" }, { "source": "Detection System", "target": "LIN-0075", "type": "skos:narrower" }, { "source": "LIN-0075", "target": "AGE-0053", "type": "augmanitai:crossDomainReference" }, { "source": "LIN-0076", "target": "Analytical Ratio", "type": "skos:broader" }, { "source": "Analytical Ratio", "target": "LIN-0076", "type": "skos:narrower" }, { "source": "LIN-0077", "target": "RPH-3202", "type": "skos:broader" }, { "source": "RPH-3202", "target": "LIN-0077", "type": "skos:narrower" }, { "source": "LIN-0078", "target": "Language AI", "type": "skos:broader" }, { "source": "Language AI", "target": "LIN-0078", "type": "skos:narrower" }, { "source": "LIN-0078", "target": "AUG-0248", "type": "augmanitai:crossDomainReference" }, { "source": "LIN-0079", "target": "RPH-2605", "type": "skos:broader" }, { "source": "RPH-2605", "target": "LIN-0079", "type": "skos:narrower" }, { "source": "LIN-0079", "target": "KNO-0035", "type": "augmanitai:crossDomainReference" }, { "source": "LIN-0080", "target": "Language AI", "type": "skos:broader" }, { "source": "Language AI", "target": "LIN-0080", "type": "skos:narrower" }, { "source": "LIN-0080", "target": "CRE-0195", "type": "augmanitai:crossDomainReference" }, { "source": "LIN-0080", "target": "IDN-0016", "type": "augmanitai:crossDomainReference" }, { "source": "LIN-0081", "target": "RPH-3202", "type": "skos:broader" }, { "source": "RPH-3202", "target": "LIN-0081", "type": "skos:narrower" }, { "source": "LIN-0081", "target": "COG-0155", "type": "augmanitai:crossDomainReference" }, { "source": "LIN-0082", "target": "Language AI", "type": "skos:broader" }, { "source": "Language AI", "target": "LIN-0082", "type": "skos:narrower" }, { "source": "LIN-0083", "target": "Language AI", "type": "skos:broader" }, { "source": "Language AI", "target": "LIN-0083", "type": "skos:narrower" }, { "source": "LIN-0083", "target": "CON-0076", "type": "augmanitai:crossDomainReference" }, { "source": "LIN-0083", "target": "COP-0065", "type": "augmanitai:crossDomainReference" }, { "source": "LIN-0084", "target": "Language AI", "type": "skos:broader" }, { "source": "Language AI", "target": "LIN-0084", "type": "skos:narrower" }, { "source": "LIN-0084", "target": "ASE-0063", "type": "augmanitai:crossDomainReference" }, { "source": "LIN-0085", "target": "Psychological Effect", "type": "skos:broader" }, { "source": "Psychological Effect", "target": "LIN-0085", "type": "skos:narrower" }, { "source": "LIN-0085", "target": "CRE-0148", "type": "augmanitai:crossDomainReference" }, { "source": "LIN-0086", "target": "Language AI", "type": "skos:broader" }, { "source": "Language AI", "target": "LIN-0086", "type": "skos:narrower" }, { "source": "LIN-0086", "target": "COG-0117", "type": "augmanitai:crossDomainReference" }, { "source": "LIN-0087", "target": "Language AI", "type": "skos:broader" }, { "source": "Language AI", "target": "LIN-0087", "type": "skos:narrower" }, { "source": "LIN-0087", "target": "ART-0068", "type": "augmanitai:crossDomainReference" }, { "source": "LIN-0088", "target": "Language AI", "type": "skos:broader" }, { "source": "Language AI", "target": "LIN-0088", "type": "skos:narrower" }, { "source": "LIN-0088", "target": "CRE-0133", "type": "augmanitai:crossDomainReference" }, { "source": "LIN-0089", "target": "Language AI", "type": "skos:broader" }, { "source": "Language AI", "target": "LIN-0089", "type": "skos:narrower" }, { "source": "LIN-0089", "target": "ADA-0015", "type": "augmanitai:crossDomainReference" }, { "source": "LIN-0089", "target": "CAI-0001", "type": "augmanitai:crossDomainReference" }, { "source": "LIN-0090", "target": "Language AI", "type": "skos:broader" }, { "source": "Language AI", "target": "LIN-0090", "type": "skos:narrower" }, { "source": "LIN-0090", "target": "COG-0145", "type": "augmanitai:crossDomainReference" }, { "source": "LIN-0090", "target": "GAM-0068", "type": "augmanitai:crossDomainReference" }, { "source": "LIN-0091", "target": "Analytical Method", "type": "skos:broader" }, { "source": "Analytical Method", "target": "LIN-0091", "type": "skos:narrower" }, { "source": "LIN-0092", "target": "Detection System", "type": "skos:broader" }, { "source": "Detection System", "target": "LIN-0092", "type": "skos:narrower" }, { "source": "LIN-0092", "target": "COP-0007", "type": "augmanitai:crossDomainReference" }, { "source": "LIN-0092", "target": "CRE-0186", "type": "augmanitai:crossDomainReference" }, { "source": "LIN-0093", "target": "Detection System", "type": "skos:broader" }, { "source": "Detection System", "target": "LIN-0093", "type": "skos:narrower" }, { "source": "LIN-0094", "target": "Language AI", "type": "skos:broader" }, { "source": "Language AI", "target": "LIN-0094", "type": "skos:narrower" }, { "source": "LIN-0095", "target": "Computational Model", "type": "skos:broader" }, { "source": "Computational Model", "target": "LIN-0095", "type": "skos:narrower" }, { "source": "LIN-0096", "target": "RPH-2505", "type": "skos:broader" }, { "source": "RPH-2505", "target": "LIN-0096", "type": "skos:narrower" }, { "source": "LIN-0096", "target": "COG-0093", "type": "augmanitai:crossDomainReference" }, { "source": "LIN-0097", "target": "Behavioral Pattern", "type": "skos:broader" }, { "source": "Behavioral Pattern", "target": "LIN-0097", "type": "skos:narrower" }, { "source": "LIN-0097", "target": "COG-0087", "type": "augmanitai:crossDomainReference" }, { "source": "LIN-0098", "target": "Language AI", "type": "skos:broader" }, { "source": "Language AI", "target": "LIN-0098", "type": "skos:narrower" }, { "source": "LIN-0098", "target": "COG-0062", "type": "augmanitai:crossDomainReference" }, { "source": "LIN-0099", "target": "Language AI", "type": "skos:broader" }, { "source": "Language AI", "target": "LIN-0099", "type": "skos:narrower" }, { "source": "LIN-0100", "target": "Language AI", "type": "skos:broader" }, { "source": "Language AI", "target": "LIN-0100", "type": "skos:narrower" }, { "source": "LIN-0100", "target": "COP-0047", "type": "augmanitai:crossDomainReference" }, { "source": "LNG-0001", "target": "Psychological Effect", "type": "skos:broader" }, { "source": "Psychological Effect", "target": "LNG-0001", "type": "skos:narrower" }, { "source": "LNG-0002", "target": "Psychological Effect", "type": "skos:broader" }, { "source": "Psychological Effect", "target": "LNG-0002", "type": "skos:narrower" }, { "source": "LNG-0003", "target": "Linguistics", "type": "skos:broader" }, { "source": "Linguistics", "target": "LNG-0003", "type": "skos:narrower" }, { "source": "LNG-0003", "target": "ELR-0005", "type": "augmanitai:crossDomainReference" }, { "source": "LNG-0004", "target": "Linguistics", "type": "skos:broader" }, { "source": "Linguistics", "target": "LNG-0004", "type": "skos:narrower" }, { "source": "LNG-0004", "target": "COP-0009", "type": "augmanitai:crossDomainReference" }, { "source": "LNG-0004", "target": "FIC-0028", "type": "augmanitai:crossDomainReference" }, { "source": "LNG-0005", "target": "RPH-2003", "type": "skos:broader" }, { "source": "RPH-2003", "target": "LNG-0005", "type": "skos:narrower" }, { "source": "LNG-0006", "target": "System Adaptation", "type": "skos:broader" }, { "source": "System Adaptation", "target": "LNG-0006", "type": "skos:narrower" }, { "source": "LNG-0007", "target": "Linguistics", "type": "skos:broader" }, { "source": "Linguistics", "target": "LNG-0007", "type": "skos:narrower" }, { "source": "LNG-0008", "target": "RPH-2052", "type": "skos:broader" }, { "source": "RPH-2052", "target": "LNG-0008", "type": "skos:narrower" }, { "source": "LNG-0008", "target": "CRE-0168", "type": "augmanitai:crossDomainReference" }, { "source": "LNG-0009", "target": "SOM-0019", "type": "skos:broader" }, { "source": "SOM-0019", "target": "LNG-0009", "type": "skos:narrower" }, { "source": "LNG-0010", "target": "Linguistics", "type": "skos:broader" }, { "source": "Linguistics", "target": "LNG-0010", "type": "skos:narrower" }, { "source": "LNG-0010", "target": "LIN-0054", "type": "augmanitai:crossDomainReference" }, { "source": "LNG-0011", "target": "Psychological Effect", "type": "skos:broader" }, { "source": "Psychological Effect", "target": "LNG-0011", "type": "skos:narrower" }, { "source": "LNG-0011", "target": "ADA-0001", "type": "augmanitai:crossDomainReference" }, { "source": "LNG-0011", "target": "ADA-0002", "type": "augmanitai:crossDomainReference" }, { "source": "LNG-0011", "target": "ADA-0004", "type": "augmanitai:crossDomainReference" }, { "source": "LNG-0012", "target": "Analytical Ratio", "type": "skos:broader" }, { "source": "Analytical Ratio", "target": "LNG-0012", "type": "skos:narrower" }, { "source": "LNG-0013", "target": "Analytical Ratio", "type": "skos:broader" }, { "source": "Analytical Ratio", "target": "LNG-0013", "type": "skos:narrower" }, { "source": "LNG-0014", "target": "Classification Spectrum", "type": "skos:broader" }, { "source": "Classification Spectrum", "target": "LNG-0014", "type": "skos:narrower" }, { "source": "LNG-0015", "target": "Linguistics", "type": "skos:broader" }, { "source": "Linguistics", "target": "LNG-0015", "type": "skos:narrower" }, { "source": "LNG-0016", "target": "Linguistics", "type": "skos:broader" }, { "source": "Linguistics", "target": "LNG-0016", "type": "skos:narrower" }, { "source": "LNG-0017", "target": "Linguistics", "type": "skos:broader" }, { "source": "Linguistics", "target": "LNG-0017", "type": "skos:narrower" }, { "source": "LNG-0018", "target": "Linguistics", "type": "skos:broader" }, { "source": "Linguistics", "target": "LNG-0018", "type": "skos:narrower" }, { "source": "LNG-0018", "target": "LIN-0015", "type": "augmanitai:crossDomainReference" }, { "source": "LNG-0019", "target": "Linguistics", "type": "skos:broader" }, { "source": "Linguistics", "target": "LNG-0019", "type": "skos:narrower" }, { "source": "LNG-0019", "target": "ETH-0003", "type": "augmanitai:crossDomainReference" }, { "source": "LNG-0020", "target": "Linguistics", "type": "skos:broader" }, { "source": "Linguistics", "target": "LNG-0020", "type": "skos:narrower" }, { "source": "LNG-0020", "target": "CUS-0090", "type": "augmanitai:crossDomainReference" }, { "source": "MKT-0001", "target": "RPH-2051", "type": "skos:broader" }, { "source": "RPH-2051", "target": "MKT-0001", "type": "skos:narrower" }, { "source": "MKT-0001", "target": "CON-0076", "type": "augmanitai:crossDomainReference" }, { "source": "MKT-0002", "target": "RPH-2104", "type": "skos:broader" }, { "source": "RPH-2104", "target": "MKT-0002", "type": "skos:narrower" }, { "source": "MKT-0002", "target": "CRE-0058", "type": "augmanitai:crossDomainReference" }, { "source": "MKT-0003", "target": "RPH-2505", "type": "skos:broader" }, { "source": "RPH-2505", "target": "MKT-0003", "type": "skos:narrower" }, { "source": "MKT-0003", "target": "CON-0035", "type": "augmanitai:crossDomainReference" }, { "source": "MKT-0004", "target": "Marketing AI", "type": "skos:broader" }, { "source": "Marketing AI", "target": "MKT-0004", "type": "skos:narrower" }, { "source": "MKT-0004", "target": "COP-0027", "type": "augmanitai:crossDomainReference" }, { "source": "MKT-0004", "target": "COG-0050", "type": "augmanitai:crossDomainReference" }, { "source": "MKT-0005", "target": "RPH-2505", "type": "skos:broader" }, { "source": "RPH-2505", "target": "MKT-0005", "type": "skos:narrower" }, { "source": "MKT-0005", "target": "ART-0090", "type": "augmanitai:crossDomainReference" }, { "source": "MKT-0006", "target": "RPH-2051", "type": "skos:broader" }, { "source": "RPH-2051", "target": "MKT-0006", "type": "skos:narrower" }, { "source": "MKT-0006", "target": "CON-0094", "type": "augmanitai:crossDomainReference" }, { "source": "MKT-0006", "target": "CON-0072", "type": "augmanitai:crossDomainReference" }, { "source": "MKT-0007", "target": "RPH-2505", "type": "skos:broader" }, { "source": "RPH-2505", "target": "MKT-0007", "type": "skos:narrower" }, { "source": "MKT-0007", "target": "ART-0018", "type": "augmanitai:crossDomainReference" }, { "source": "MKT-0008", "target": "RPH-2505", "type": "skos:broader" }, { "source": "RPH-2505", "target": "MKT-0008", "type": "skos:narrower" }, { "source": "MKT-0008", "target": "ART-0072", "type": "augmanitai:crossDomainReference" }, { "source": "MKT-0008", "target": "AUG-0383", "type": "augmanitai:crossDomainReference" }, { "source": "MKT-0008", "target": "BEH-0041", "type": "augmanitai:crossDomainReference" }, { "source": "MKT-0009", "target": "RPH-2003", "type": "skos:broader" }, { "source": "RPH-2003", "target": "MKT-0009", "type": "skos:narrower" }, { "source": "MKT-0010", "target": "RPH-2003", "type": "skos:broader" }, { "source": "RPH-2003", "target": "MKT-0010", "type": "skos:narrower" }, { "source": "MKT-0010", "target": "DES-0017", "type": "augmanitai:crossDomainReference" }, { "source": "MKT-0010", "target": "DAT-0049", "type": 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"skos:broader" }, { "source": "RPH-2503", "target": "MKT-0019", "type": "skos:narrower" }, { "source": "MKT-0019", "target": "BEH-0081", "type": "augmanitai:crossDomainReference" }, { "source": "MKT-0019", "target": "COG-0032", "type": "augmanitai:crossDomainReference" }, { "source": "MKT-0019", "target": "COG-0072", "type": "augmanitai:crossDomainReference" }, { "source": "MKT-0020", "target": "RPH-2505", "type": "skos:broader" }, { "source": "RPH-2505", "target": "MKT-0020", "type": "skos:narrower" }, { "source": "MKT-0020", "target": "CON-0012", "type": "augmanitai:crossDomainReference" }, { "source": "MKT-0021", "target": "RPH-2505", "type": "skos:broader" }, { "source": "RPH-2505", "target": "MKT-0021", "type": "skos:narrower" }, { "source": "MKT-0021", "target": "CRE-0226", "type": "augmanitai:crossDomainReference" }, { "source": "MKT-0022", "target": "RPH-2002", "type": "skos:broader" }, { "source": "RPH-2002", "target": "MKT-0022", "type": "skos:narrower" }, { "source": "MKT-0022", "target": "COP-0004", "type": "augmanitai:crossDomainReference" }, { "source": "MKT-0022", "target": "CON-0072", "type": "augmanitai:crossDomainReference" }, { "source": "MKT-0023", "target": "RPH-2505", "type": "skos:broader" }, { "source": "RPH-2505", "target": "MKT-0023", "type": "skos:narrower" }, { "source": "MKT-0024", "target": "RPH-2505", "type": "skos:broader" }, { "source": "RPH-2505", "target": "MKT-0024", "type": "skos:narrower" }, { "source": "MKT-0024", "target": "ASE-0028", "type": "augmanitai:crossDomainReference" }, { "source": "MKT-0025", "target": "RPH-3754", "type": "skos:broader" }, { "source": "RPH-3754", "target": "MKT-0025", "type": "skos:narrower" }, { "source": "MKT-0025", "target": "COP-0002", "type": "augmanitai:crossDomainReference" }, { "source": "MKT-0025", "target": "DAT-0095", "type": "augmanitai:crossDomainReference" }, { "source": "MKT-0026", "target": "RPH-2503", "type": "skos:broader" }, { "source": "RPH-2503", "target": "MKT-0026", 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"source": "Algorithm", "target": "MKT-0030", "type": "skos:narrower" }, { "source": "MKT-0031", "target": "RPH-2503", "type": "skos:broader" }, { "source": "RPH-2503", "target": "MKT-0031", "type": "skos:narrower" }, { "source": "MKT-0031", "target": "CON-0086", "type": "augmanitai:crossDomainReference" }, { "source": "MKT-0031", "target": "CON-0035", "type": "augmanitai:crossDomainReference" }, { "source": "MKT-0032", "target": "RPH-2051", "type": "skos:broader" }, { "source": "RPH-2051", "target": "MKT-0032", "type": "skos:narrower" }, { "source": "MKT-0032", "target": "COG-0028", "type": "augmanitai:crossDomainReference" }, { "source": "MKT-0032", "target": "CRE-0082", "type": "augmanitai:crossDomainReference" }, { "source": "MKT-0032", "target": "CRE-0196", "type": "augmanitai:crossDomainReference" }, { "source": "MKT-0033", "target": "RPH-2505", "type": "skos:broader" }, { "source": "RPH-2505", "target": "MKT-0033", "type": "skos:narrower" }, { "source": "MKT-0034", "target": "RPH-2051", "type": "skos:broader" }, { "source": "RPH-2051", "target": "MKT-0034", "type": "skos:narrower" }, { "source": "MKT-0034", "target": "COG-0163", "type": "augmanitai:crossDomainReference" }, { "source": "MKT-0035", "target": "RPH-2303", "type": "skos:broader" }, { "source": "RPH-2303", "target": "MKT-0035", "type": "skos:narrower" }, { "source": "MKT-0036", "target": "RPH-2154", "type": "skos:broader" }, { "source": "RPH-2154", "target": "MKT-0036", "type": "skos:narrower" }, { "source": "MKT-0036", "target": "CON-0067", "type": "augmanitai:crossDomainReference" }, { "source": "MKT-0037", "target": "Marketing AI", "type": "skos:broader" }, { "source": "Marketing AI", "target": "MKT-0037", "type": "skos:narrower" }, { "source": "MKT-0037", "target": "CRE-0063", "type": "augmanitai:crossDomainReference" }, { "source": "MKT-0038", "target": "Marketing AI", "type": "skos:broader" }, { "source": "Marketing AI", "target": "MKT-0038", "type": "skos:narrower" }, { "source": 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{ "source": "MKT-0043", "target": "COP-0004", "type": "augmanitai:crossDomainReference" }, { "source": "MKT-0043", "target": "CON-0072", "type": "augmanitai:crossDomainReference" }, { "source": "MKT-0044", "target": "RPH-2603", "type": "skos:broader" }, { "source": "RPH-2603", "target": "MKT-0044", "type": "skos:narrower" }, { "source": "MKT-0045", "target": "Marketing AI", "type": "skos:broader" }, { "source": "Marketing AI", "target": "MKT-0045", "type": "skos:narrower" }, { "source": "MKT-0045", "target": "CON-0024", "type": "augmanitai:crossDomainReference" }, { "source": "MKT-0046", "target": "RPH-2701", "type": "skos:broader" }, { "source": "RPH-2701", "target": "MKT-0046", "type": "skos:narrower" }, { "source": "MKT-0046", "target": "DES-0026", "type": "augmanitai:crossDomainReference" }, { "source": "MKT-0046", "target": "CON-0094", "type": "augmanitai:crossDomainReference" }, { "source": "MKT-0047", "target": "Marketing AI", "type": "skos:broader" }, { "source": "Marketing AI", "target": "MKT-0047", "type": "skos:narrower" }, { "source": "MKT-0047", "target": "COP-0004", "type": "augmanitai:crossDomainReference" }, { "source": "MKT-0048", "target": "RPH-2301", "type": "skos:broader" }, { "source": "RPH-2301", "target": "MKT-0048", "type": "skos:narrower" }, { "source": "MKT-0048", "target": "AED-0046", "type": "augmanitai:crossDomainReference" }, { "source": "MKT-0048", "target": "AGE-0055", "type": "augmanitai:crossDomainReference" }, { "source": "MKT-0048", "target": "ART-0023", "type": "augmanitai:crossDomainReference" }, { "source": "MKT-0049", "target": "Performance Gap", "type": "skos:broader" }, { "source": "Performance Gap", "target": "MKT-0049", "type": "skos:narrower" }, { "source": "MKT-0050", "target": "Marketing AI", "type": "skos:broader" }, { "source": "Marketing AI", "target": "MKT-0050", "type": "skos:narrower" }, { "source": "MKT-0050", "target": "AED-0005", "type": "augmanitai:crossDomainReference" }, { "source": "MKT-0050", "target": "AED-0052", "type": "augmanitai:crossDomainReference" }, { "source": "MKT-0050", "target": "ASE-0041", "type": "augmanitai:crossDomainReference" }, { "source": "MKT-0051", "target": "RPH-2003", "type": "skos:broader" }, { "source": "RPH-2003", "target": "MKT-0051", "type": "skos:narrower" }, { "source": "MKT-0052", "target": "Marketing AI", "type": "skos:broader" }, { "source": "Marketing AI", "target": "MKT-0052", "type": "skos:narrower" }, { "source": "MKT-0053", "target": "RPH-2703", "type": "skos:broader" }, { "source": "RPH-2703", "target": "MKT-0053", "type": "skos:narrower" }, { "source": "MKT-0053", "target": "CUS-0031", "type": "augmanitai:crossDomainReference" }, { "source": "MKT-0053", "target": "CUS-0002", "type": "augmanitai:crossDomainReference" }, { "source": "MKT-0053", "target": "CUS-0004", "type": "augmanitai:crossDomainReference" }, { "source": "MKT-0054", "target": "RPH-2051", "type": "skos:broader" }, { "source": "RPH-2051", "target": "MKT-0054", "type": "skos:narrower" }, { "source": "MKT-0055", "target": "RPH-2555", "type": "skos:broader" }, { "source": "RPH-2555", "target": "MKT-0055", "type": "skos:narrower" }, { "source": "MKT-0055", "target": "ART-0096", "type": "augmanitai:crossDomainReference" }, { "source": "MKT-0055", "target": "BEH-0004", "type": "augmanitai:crossDomainReference" }, { "source": "MKT-0055", "target": "DAT-0061", "type": "augmanitai:crossDomainReference" }, { "source": "MKT-0056", "target": "RPH-2003", "type": "skos:broader" }, { "source": "RPH-2003", "target": "MKT-0056", "type": "skos:narrower" }, { "source": "MKT-0056", "target": "AUG-0889", "type": "augmanitai:crossDomainReference" }, { "source": "MKT-0056", "target": "AUG-0892", "type": "augmanitai:crossDomainReference" }, { "source": "MKT-0056", "target": "AUG-0897", "type": "augmanitai:crossDomainReference" }, { "source": "MKT-0057", "target": "RPH-2505", "type": "skos:broader" }, { "source": "RPH-2505", "target": "MKT-0057", "type": "skos:narrower" }, { "source": "MKT-0057", "target": "CRE-0038", "type": "augmanitai:crossDomainReference" }, { "source": "MKT-0057", "target": "FIC-0083", "type": "augmanitai:crossDomainReference" }, { "source": "MKT-0057", "target": "ASE-0095", "type": "augmanitai:crossDomainReference" }, { "source": "MKT-0058", "target": "RPH-2051", "type": "skos:broader" }, { "source": "RPH-2051", "target": "MKT-0058", "type": "skos:narrower" }, { "source": "MKT-0058", "target": "COP-0047", "type": "augmanitai:crossDomainReference" }, { "source": "MKT-0059", "target": "Marketing AI", "type": "skos:broader" }, { "source": "Marketing AI", "target": "MKT-0059", "type": "skos:narrower" }, { "source": "MKT-0059", "target": "ART-0059", "type": "augmanitai:crossDomainReference" }, { "source": "MKT-0059", "target": "CRE-0122", "type": "augmanitai:crossDomainReference" }, { "source": "MKT-0060", "target": "Marketing AI", "type": "skos:broader" }, { "source": "Marketing AI", "target": "MKT-0060", "type": "skos:narrower" }, { "source": "MKT-0060", "target": "DAT-0085", "type": "augmanitai:crossDomainReference" }, { "source": "MKT-0061", "target": "RPH-3101", "type": "skos:broader" }, { "source": "RPH-3101", "target": "MKT-0061", "type": "skos:narrower" }, { "source": "MKT-0061", "target": "CUS-0051", "type": "augmanitai:crossDomainReference" }, { "source": "MKT-0062", "target": "RPH-2355", "type": "skos:broader" }, { "source": "RPH-2355", "target": "MKT-0062", "type": "skos:narrower" }, { "source": "MKT-0062", "target": "CUS-0071", "type": "augmanitai:crossDomainReference" }, { "source": "MKT-0063", "target": "Marketing AI", "type": "skos:broader" }, { "source": "Marketing AI", "target": "MKT-0063", "type": "skos:narrower" }, { "source": "MKT-0063", "target": "ASE-0055", "type": "augmanitai:crossDomainReference" }, { "source": "MKT-0063", "target": "COP-0036", "type": "augmanitai:crossDomainReference" }, { "source": "MKT-0063", "target": "COP-0037", "type": "augmanitai:crossDomainReference" }, { "source": "MKT-0064", "target": "Marketing AI", "type": "skos:broader" }, { "source": "Marketing AI", "target": "MKT-0064", "type": "skos:narrower" }, { "source": "MKT-0064", "target": "COP-0012", "type": "augmanitai:crossDomainReference" }, { "source": "MKT-0065", "target": "RPH-2701", "type": "skos:broader" }, { "source": "RPH-2701", "target": "MKT-0065", "type": "skos:narrower" }, { "source": "MKT-0065", "target": "ELR-0080", "type": "augmanitai:crossDomainReference" }, { "source": "MKT-0066", "target": "Analytical Ratio", "type": "skos:broader" }, { "source": "Analytical Ratio", "target": "MKT-0066", "type": "skos:narrower" }, { "source": "MKT-0066", "target": "DAT-0029", "type": "augmanitai:crossDomainReference" }, { "source": "MKT-0067", "target": "RPH-2951", "type": "skos:broader" }, { "source": "RPH-2951", "target": "MKT-0067", "type": "skos:narrower" }, { "source": "MKT-0067", "target": "ASE-0046", "type": "augmanitai:crossDomainReference" }, { "source": "MKT-0067", "target": 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{ "source": "MUS-0076", "target": "Music AI", "type": "skos:broader" }, { "source": "Music AI", "target": "MUS-0076", "type": "skos:narrower" }, { "source": "MUS-0077", "target": "Music AI", "type": "skos:broader" }, { "source": "Music AI", "target": "MUS-0077", "type": "skos:narrower" }, { "source": "MUS-0077", "target": "AED-0009", "type": "augmanitai:crossDomainReference" }, { "source": "MUS-0077", "target": "ASE-0047", "type": "augmanitai:crossDomainReference" }, { "source": "MUS-0077", "target": "DES-0006", "type": "augmanitai:crossDomainReference" }, { "source": "MUS-0078", "target": "Music AI", "type": "skos:broader" }, { "source": "Music AI", "target": "MUS-0078", "type": "skos:narrower" }, { "source": "MUS-0079", "target": "Music AI", "type": "skos:broader" }, { "source": "Music AI", "target": "MUS-0079", "type": "skos:narrower" }, { "source": "MUS-0080", "target": "Music AI", "type": "skos:broader" }, { "source": "Music AI", "target": "MUS-0080", "type": "skos:narrower" }, { "source": "MUS-0081", "target": "Knowledge Transfer", "type": "skos:broader" }, { "source": "Knowledge Transfer", "target": "MUS-0081", "type": "skos:narrower" }, { "source": "MUS-0081", "target": "AED-0016", "type": "augmanitai:crossDomainReference" }, { "source": "MUS-0081", "target": "AED-0050", "type": "augmanitai:crossDomainReference" }, { "source": "MUS-0081", "target": "AED-0054", "type": "augmanitai:crossDomainReference" }, { "source": "MUS-0082", "target": "Music AI", "type": "skos:broader" }, { "source": "Music AI", "target": "MUS-0082", "type": "skos:narrower" }, { "source": "MUS-0083", "target": "Music AI", "type": "skos:broader" }, { "source": "Music AI", "target": "MUS-0083", "type": "skos:narrower" }, { "source": "MUS-0083", "target": "ART-0012", "type": "augmanitai:crossDomainReference" }, { "source": "MUS-0083", "target": "ART-0025", "type": "augmanitai:crossDomainReference" }, { "source": "MUS-0083", "target": "ART-0081", "type": "augmanitai:crossDomainReference" }, { "source": "MUS-0084", "target": "Music AI", "type": "skos:broader" }, { "source": "Music AI", "target": "MUS-0084", "type": "skos:narrower" }, { "source": "MUS-0084", "target": "ASE-0053", "type": "augmanitai:crossDomainReference" }, { "source": "MUS-0084", "target": "BEH-0081", "type": "augmanitai:crossDomainReference" }, { "source": "MUS-0084", "target": "COG-0022", "type": "augmanitai:crossDomainReference" }, { "source": "MUS-0085", "target": "Music AI", "type": "skos:broader" }, { "source": "Music AI", "target": "MUS-0085", "type": "skos:narrower" }, { "source": "MUS-0085", "target": "FIC-0063", "type": "augmanitai:crossDomainReference" }, { "source": "MUS-0086", "target": "Abstraction Layer", "type": "skos:broader" }, { "source": "Abstraction Layer", "target": "MUS-0086", "type": "skos:narrower" }, { "source": "MUS-0087", "target": "Music AI", "type": "skos:broader" }, { "source": "Music AI", "target": "MUS-0087", "type": "skos:narrower" }, { "source": "MUS-0088", "target": "Music 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"PHO-0010", "target": "ART-0011", "type": "augmanitai:crossDomainReference" }, { "source": "PHO-0010", "target": "ART-0019", "type": "augmanitai:crossDomainReference" }, { "source": "PHO-0010", "target": "ART-0058", "type": "augmanitai:crossDomainReference" }, { "source": "PHO-0011", "target": "Photo AI", "type": "skos:broader" }, { "source": "Photo AI", "target": "PHO-0011", "type": "skos:narrower" }, { "source": "PHO-0011", "target": "CUS-0038", "type": "augmanitai:crossDomainReference" }, { "source": "PHO-0011", "target": "CUS-0033", "type": "augmanitai:crossDomainReference" }, { "source": "PHO-0012", "target": "Analytical Ratio", "type": "skos:broader" }, { "source": "Analytical Ratio", "target": "PHO-0012", "type": "skos:narrower" }, { "source": "PHO-0012", "target": "DES-0051", "type": "augmanitai:crossDomainReference" }, { "source": "PHO-0013", "target": "Photo AI", "type": "skos:broader" }, { "source": "Photo AI", "target": "PHO-0013", "type": "skos:narrower" }, { "source": "PHO-0013", "target": "ADA-0012", "type": "augmanitai:crossDomainReference" }, { "source": "PHO-0013", "target": "AGE-0007", "type": "augmanitai:crossDomainReference" }, { "source": "PHO-0013", "target": "AGE-0046", "type": "augmanitai:crossDomainReference" }, { "source": "PHO-0014", "target": "Photo AI", "type": "skos:broader" }, { "source": "Photo AI", "target": "PHO-0014", "type": "skos:narrower" }, { "source": "PHO-0014", "target": "AED-0042", "type": "augmanitai:crossDomainReference" }, { "source": "PHO-0014", "target": "BEH-0078", "type": "augmanitai:crossDomainReference" }, { "source": "PHO-0014", "target": "DES-0008", "type": "augmanitai:crossDomainReference" }, { "source": "PHO-0015", "target": "Performance Gap", "type": "skos:broader" }, { "source": "Performance Gap", "target": "PHO-0015", "type": "skos:narrower" }, { "source": "PHO-0016", "target": "RPH-3003", "type": "skos:broader" }, { "source": "RPH-3003", "target": "PHO-0016", "type": "skos:narrower" }, { "source": "PHO-0016", "target": "MUS-0026", "type": "augmanitai:crossDomainReference" }, { "source": "PHO-0017", "target": "RPH-2201", "type": "skos:broader" }, { "source": "RPH-2201", "target": "PHO-0017", "type": "skos:narrower" }, { "source": "PHO-0018", "target": "RPH-2005", "type": "skos:broader" }, { "source": "RPH-2005", "target": "PHO-0018", "type": "skos:narrower" }, { "source": "PHO-0018", "target": "ART-0072", "type": "augmanitai:crossDomainReference" }, { "source": "PHO-0018", "target": "BEH-0041", "type": "augmanitai:crossDomainReference" }, { "source": "PHO-0018", "target": "COG-0032", "type": "augmanitai:crossDomainReference" }, { "source": "PHO-0019", "target": "Analytical Ratio", "type": "skos:broader" }, { "source": "Analytical Ratio", "target": "PHO-0019", "type": "skos:narrower" }, { "source": "PHO-0019", "target": "CON-0001", "type": "augmanitai:crossDomainReference" }, { "source": "PHO-0020", "target": "Psychological Effect", "type": "skos:broader" }, { "source": "Psychological Effect", "target": "PHO-0020", "type": "skos:narrower" }, { "source": "PHO-0020", "target": "DES-0037", "type": "augmanitai:crossDomainReference" }, { "source": "PHO-0021", "target": "Photo AI", "type": "skos:broader" }, { "source": "Photo AI", "target": "PHO-0021", "type": "skos:narrower" }, { "source": "PHO-0021", "target": "CON-0058", "type": "augmanitai:crossDomainReference" }, { "source": "PHO-0022", "target": "RPH-3003", "type": "skos:broader" }, { "source": "RPH-3003", "target": "PHO-0022", "type": "skos:narrower" }, { "source": "PHO-0022", "target": "CON-0048", "type": "augmanitai:crossDomainReference" }, { "source": "PHO-0022", "target": "CON-0079", "type": "augmanitai:crossDomainReference" }, { "source": "PHO-0022", "target": "CON-0088", "type": "augmanitai:crossDomainReference" }, { "source": "PHO-0023", "target": "Photo AI", "type": "skos:broader" }, { "source": "Photo AI", "target": "PHO-0023", "type": "skos:narrower" }, { "source": "PHO-0023", "target": "CUS-0055", "type": "augmanitai:crossDomainReference" }, { "source": "PHO-0024", "target": "Photo AI", "type": "skos:broader" }, { "source": "Photo AI", "target": "PHO-0024", "type": "skos:narrower" }, { "source": "PHO-0024", "target": "DES-0060", "type": "augmanitai:crossDomainReference" }, { "source": "PHO-0025", "target": "RPH-2003", "type": "skos:broader" }, { "source": "RPH-2003", "target": "PHO-0025", "type": "skos:narrower" }, { "source": "PHO-0025", "target": "PER-0062", "type": "augmanitai:crossDomainReference" }, { "source": "PHO-0026", "target": "User Interface", "type": "skos:broader" }, { "source": "User Interface", "target": "PHO-0026", "type": "skos:narrower" }, { "source": "PHO-0026", "target": "CON-0032", "type": "augmanitai:crossDomainReference" }, { "source": "PHO-0027", "target": "RPH-2701", "type": "skos:broader" }, { "source": "RPH-2701", "target": "PHO-0027", "type": "skos:narrower" }, { "source": "PHO-0027", "target": "AGE-0031", "type": "augmanitai:crossDomainReference" }, { "source": "PHO-0027", "target": "AGE-0032", "type": "augmanitai:crossDomainReference" }, { "source": "PHO-0027", "target": "AGE-0042", "type": "augmanitai:crossDomainReference" }, { "source": "PHO-0028", "target": "Algorithm", "type": "skos:broader" }, { "source": "Algorithm", "target": "PHO-0028", "type": "skos:narrower" }, { "source": "PHO-0029", "target": "Detection System", "type": "skos:broader" }, { "source": "Detection System", "target": "PHO-0029", "type": "skos:narrower" }, { "source": "PHO-0029", "target": "AGE-0034", "type": "augmanitai:crossDomainReference" }, { "source": "PHO-0029", "target": "AGE-0099", "type": "augmanitai:crossDomainReference" }, { "source": "PHO-0029", "target": "ART-0083", "type": "augmanitai:crossDomainReference" }, { "source": "PHO-0030", "target": "RPH-2005", "type": "skos:broader" }, { "source": "RPH-2005", "target": "PHO-0030", "type": "skos:narrower" }, { "source": "PHO-0030", "target": "ART-0042", "type": "augmanitai:crossDomainReference" }, { "source": "PHO-0030", "target": "ASE-0053", "type": "augmanitai:crossDomainReference" }, { "source": "PHO-0030", "target": "COG-0022", "type": "augmanitai:crossDomainReference" }, { "source": "PHO-0031", "target": "Photo AI", "type": "skos:broader" }, { "source": "Photo AI", "target": "PHO-0031", "type": "skos:narrower" }, { "source": "PHO-0031", "target": "EDU-0040", "type": "augmanitai:crossDomainReference" }, { "source": "PHO-0032", "target": "RPH-3304", "type": "skos:broader" }, { "source": "RPH-3304", "target": "PHO-0032", "type": "skos:narrower" }, { "source": "PHO-0032", "target": "AGE-0034", "type": "augmanitai:crossDomainReference" }, { "source": "PHO-0032", "target": "AGE-0099", "type": "augmanitai:crossDomainReference" }, { "source": "PHO-0032", "target": "ART-0077", "type": "augmanitai:crossDomainReference" }, { "source": "PHO-0033", "target": "Photo AI", "type": "skos:broader" }, { "source": "Photo AI", "target": "PHO-0033", "type": "skos:narrower" }, { "source": "PHO-0033", "target": "ART-0065", "type": "augmanitai:crossDomainReference" }, { "source": "PHO-0033", "target": "ART-0066", "type": "augmanitai:crossDomainReference" }, { "source": "PHO-0033", "target": "ART-0067", "type": "augmanitai:crossDomainReference" }, { "source": "PHO-0034", "target": "Photo AI", "type": "skos:broader" }, { "source": "Photo AI", "target": "PHO-0034", "type": "skos:narrower" }, { "source": "PHO-0034", "target": "AGE-0062", "type": "augmanitai:crossDomainReference" }, { "source": "PHO-0034", "target": "AGE-0091", "type": "augmanitai:crossDomainReference" }, { "source": "PHO-0034", "target": "AUG-0982", "type": "augmanitai:crossDomainReference" }, { "source": "PHO-0035", "target": "Decision Threshold", "type": "skos:broader" }, { "source": "Decision Threshold", "target": "PHO-0035", "type": "skos:narrower" }, { "source": "PHO-0036", "target": "Photo AI", "type": "skos:broader" }, { "source": "Photo AI", "target": "PHO-0036", "type": "skos:narrower" }, { "source": "PHO-0036", "target": "PER-0045", "type": "augmanitai:crossDomainReference" }, { "source": "PHO-0037", "target": "Photo AI", "type": "skos:broader" }, { "source": "Photo AI", "target": "PHO-0037", "type": "skos:narrower" }, { "source": "PHO-0038", "target": "Photo AI", "type": "skos:broader" }, { "source": "Photo AI", "target": "PHO-0038", "type": "skos:narrower" }, { "source": "PHO-0039", "target": "Algorithm", "type": "skos:broader" }, { "source": "Algorithm", "target": "PHO-0039", "type": "skos:narrower" }, { "source": "PHO-0039", "target": "AGE-0006", "type": "augmanitai:crossDomainReference" }, { "source": "PHO-0039", "target": "AGE-0007", "type": "augmanitai:crossDomainReference" }, { "source": "PHO-0039", "target": "AGE-0051", "type": "augmanitai:crossDomainReference" }, { "source": "PHO-0040", "target": "Photo AI", "type": "skos:broader" }, { "source": "Photo AI", "target": "PHO-0040", "type": "skos:narrower" }, { "source": "PHO-0040", "target": "AGE-0002", "type": "augmanitai:crossDomainReference" }, { "source": "PHO-0040", "target": "AGE-0005", "type": "augmanitai:crossDomainReference" }, { "source": "PHO-0040", "target": "AGE-0008", "type": "augmanitai:crossDomainReference" }, { "source": "PHO-0041", "target": "Photo AI", "type": "skos:broader" }, { "source": "Photo AI", "target": "PHO-0041", "type": "skos:narrower" }, { "source": "PHO-0042", "target": "RPH-3754", "type": "skos:broader" }, { "source": "RPH-3754", "target": "PHO-0042", "type": "skos:narrower" }, { "source": "PHO-0042", "target": "AGE-0035", "type": "augmanitai:crossDomainReference" }, { "source": "PHO-0042", "target": "AGE-0061", "type": "augmanitai:crossDomainReference" }, { "source": "PHO-0042", "target": "AGE-0081", "type": "augmanitai:crossDomainReference" }, { "source": "PHO-0043", "target": "Photo AI", "type": "skos:broader" }, { "source": "Photo AI", "target": "PHO-0043", "type": "skos:narrower" }, { "source": "PHO-0044", "target": "Photo AI", "type": "skos:broader" }, { "source": "Photo AI", "target": "PHO-0044", "type": "skos:narrower" }, { "source": "PHO-0045", "target": "RPH-3002", "type": "skos:broader" }, { "source": "RPH-3002", "target": "PHO-0045", "type": "skos:narrower" }, { "source": "PHO-0046", "target": "Simulation Method", "type": "skos:broader" }, { "source": "Simulation Method", "target": "PHO-0046", "type": "skos:narrower" }, { "source": "PHO-0046", "target": "ASE-0082", "type": "augmanitai:crossDomainReference" }, { "source": "PHO-0046", "target": "COG-0032", "type": "augmanitai:crossDomainReference" }, { "source": "PHO-0046", "target": "COG-0156", "type": "augmanitai:crossDomainReference" }, { "source": "PHO-0047", "target": "RPH-3002", "type": "skos:broader" }, { "source": "RPH-3002", "target": "PHO-0047", "type": "skos:narrower" }, { "source": "PHO-0047", "target": "ASE-0087", "type": "augmanitai:crossDomainReference" }, { "source": "PHO-0047", "target": "COG-0118", "type": "augmanitai:crossDomainReference" }, { "source": "PHO-0047", "target": "COG-0129", "type": "augmanitai:crossDomainReference" }, { "source": "PHO-0048", "target": "Photo AI", "type": "skos:broader" }, { "source": "Photo AI", "target": "PHO-0048", "type": "skos:narrower" }, { "source": "PHO-0048", "target": "COG-0039", "type": "augmanitai:crossDomainReference" }, { "source": "PHO-0049", "target": "Photo AI", "type": "skos:broader" }, { "source": "Photo AI", "target": "PHO-0049", "type": "skos:narrower" }, { "source": "PHO-0049", "target": "CRE-0113", "type": "augmanitai:crossDomainReference" }, { "source": "PHO-0050", "target": "Photo AI", "type": "skos:broader" }, { "source": "Photo AI", "target": "PHO-0050", "type": "skos:narrower" }, { "source": "PHO-0050", "target": "AED-0040", "type": "augmanitai:crossDomainReference" }, { "source": "PHO-0050", "target": "AED-0051", "type": "augmanitai:crossDomainReference" }, { "source": "PHO-0050", "target": "AGE-0054", "type": "augmanitai:crossDomainReference" }, { "source": "PHO-0051", "target": "Analytical Ratio", "type": "skos:broader" }, { "source": "Analytical Ratio", "target": "PHO-0051", "type": "skos:narrower" }, { "source": "PHO-0051", "target": "KNO-0032", "type": "augmanitai:crossDomainReference" }, { "source": "PHO-0052", "target": "Photo AI", "type": "skos:broader" }, { "source": "Photo AI", "target": "PHO-0052", "type": "skos:narrower" }, { "source": "PHO-0052", "target": "COP-0002", "type": "augmanitai:crossDomainReference" }, { "source": "PHO-0052", "target": "GAM-0057", "type": "augmanitai:crossDomainReference" }, { "source": "PHO-0053", "target": "Psychological Effect", "type": "skos:broader" }, { "source": "Psychological Effect", "target": "PHO-0053", "type": "skos:narrower" }, { "source": "PHO-0053", "target": "ADA-0001", "type": "augmanitai:crossDomainReference" }, { "source": "PHO-0053", "target": "ADA-0002", "type": "augmanitai:crossDomainReference" }, { "source": "PHO-0053", "target": "ADA-0004", "type": "augmanitai:crossDomainReference" }, { "source": "PHO-0054", "target": "RPH-2701", "type": "skos:broader" }, { "source": "RPH-2701", "target": "PHO-0054", "type": "skos:narrower" }, { "source": "PHO-0054", "target": "MUS-0036", "type": "augmanitai:crossDomainReference" }, { "source": "PHO-0055", "target": "Simulation Method", "type": "skos:broader" }, { "source": "Simulation Method", "target": "PHO-0055", "type": "skos:narrower" }, { "source": "PHO-0055", "target": "AUG-0402", "type": "augmanitai:crossDomainReference" }, { "source": "PHO-0055", "target": "COG-0032", "type": "augmanitai:crossDomainReference" }, { "source": "PHO-0055", "target": "COG-0156", "type": "augmanitai:crossDomainReference" }, { "source": "PHO-0056", "target": "Photo AI", "type": "skos:broader" }, { "source": "Photo AI", "target": "PHO-0056", "type": "skos:narrower" }, { "source": "PHO-0056", "target": "AED-0025", "type": "augmanitai:crossDomainReference" }, { "source": "PHO-0056", "target": "AED-0067", "type": "augmanitai:crossDomainReference" }, { "source": "PHO-0056", "target": "AED-0077", "type": "augmanitai:crossDomainReference" }, { "source": "PHO-0057", "target": "Photo AI", "type": "skos:broader" }, { "source": "Photo AI", "target": "PHO-0057", "type": "skos:narrower" }, { "source": "PHO-0057", "target": "AGE-0023", "type": "augmanitai:crossDomainReference" }, { "source": "PHO-0057", "target": "AGE-0024", "type": "augmanitai:crossDomainReference" }, { "source": "PHO-0057", "target": "ASE-0001", "type": "augmanitai:crossDomainReference" }, { "source": "PHO-0058", "target": "Photo AI", "type": "skos:broader" }, { "source": "Photo AI", "target": "PHO-0058", "type": "skos:narrower" }, { "source": "PHO-0058", "target": "DAT-0048", "type": "augmanitai:crossDomainReference" }, { "source": "PHO-0059", "target": "Photo AI", "type": "skos:broader" }, { "source": "Photo AI", "target": "PHO-0059", "type": "skos:narrower" }, { "source": "PHO-0060", "target": "Photo AI", "type": "skos:broader" }, { "source": "Photo AI", "target": "PHO-0060", "type": "skos:narrower" }, { "source": "PHO-0060", "target": "ART-0012", "type": "augmanitai:crossDomainReference" }, { "source": "PHO-0060", "target": "ART-0025", "type": "augmanitai:crossDomainReference" }, { "source": "PHO-0060", "target": "ART-0081", "type": "augmanitai:crossDomainReference" }, { "source": "PHO-0061", "target": "Photo AI", "type": "skos:broader" }, { "source": "Photo AI", "target": "PHO-0061", "type": "skos:narrower" }, { "source": "PHO-0061", "target": "CON-0001", "type": "augmanitai:crossDomainReference" }, { "source": "PHO-0062", "target": "RPH-2701", "type": "skos:broader" }, { "source": "RPH-2701", "target": "PHO-0062", "type": "skos:narrower" }, { "source": "PHO-0062", "target": "AUG-0921", "type": "augmanitai:crossDomainReference" }, { "source": "PHO-0063", "target": "Photo AI", "type": "skos:broader" }, { "source": "Photo AI", "target": "PHO-0063", "type": "skos:narrower" }, { "source": "PHO-0064", "target": "RPH-3202", "type": "skos:broader" }, { "source": "RPH-3202", "target": "PHO-0064", "type": "skos:narrower" }, { "source": "PHO-0064", "target": "ASE-0033", "type": "augmanitai:crossDomainReference" }, { "source": "PHO-0064", "target": "AUG-0921", "type": "augmanitai:crossDomainReference" }, { "source": "PHO-0064", "target": "COG-0058", "type": "augmanitai:crossDomainReference" }, { "source": "PHO-0065", "target": "Analytical Ratio", "type": "skos:broader" }, { "source": "Analytical Ratio", "target": "PHO-0065", "type": "skos:narrower" }, { "source": "PHO-0065", "target": "AGE-0028", "type": "augmanitai:crossDomainReference" }, { "source": "PHO-0065", "target": "ELR-0073", "type": "augmanitai:crossDomainReference" }, { "source": "PHO-0065", "target": "MTH-0099", "type": "augmanitai:crossDomainReference" }, { "source": "PHO-0066", "target": "Photo AI", "type": "skos:broader" }, { "source": "Photo AI", "target": "PHO-0066", "type": "skos:narrower" }, { "source": "PHO-0067", "target": "Analytical Ratio", "type": "skos:broader" }, { "source": "Analytical Ratio", "target": "PHO-0067", "type": "skos:narrower" }, { "source": "PHO-0068", "target": "RPH-2951", "type": "skos:broader" }, { "source": "RPH-2951", "target": "PHO-0068", "type": "skos:narrower" }, { "source": "PHO-0069", "target": "Photo AI", "type": "skos:broader" }, { "source": "Photo AI", "target": "PHO-0069", "type": "skos:narrower" }, { "source": "PHO-0069", "target": "DAT-0070", "type": "augmanitai:crossDomainReference" }, { "source": "PHO-0070", "target": "Computational Model", "type": "skos:broader" }, { "source": "Computational Model", "target": "PHO-0070", "type": "skos:narrower" }, { "source": "PHO-0071", "target": "RPH-3304", "type": "skos:broader" }, { "source": "RPH-3304", "target": "PHO-0071", "type": "skos:narrower" }, { "source": "PHO-0071", "target": "ART-0082", "type": "augmanitai:crossDomainReference" }, { "source": "PHO-0072", "target": "Photo AI", "type": "skos:broader" }, { "source": "Photo AI", "target": "PHO-0072", "type": "skos:narrower" }, { "source": "PHO-0072", "target": "ELR-0162", "type": "augmanitai:crossDomainReference" }, { "source": "PHO-0073", "target": "Data Compression", "type": "skos:broader" }, { "source": "Data Compression", "target": "PHO-0073", "type": "skos:narrower" }, { "source": "PHO-0073", "target": "AGE-0062", "type": "augmanitai:crossDomainReference" }, { "source": "PHO-0073", "target": "AGE-0091", "type": "augmanitai:crossDomainReference" }, { "source": "PHO-0073", "target": "ASE-0087", "type": "augmanitai:crossDomainReference" }, { "source": "PHO-0074", "target": "RPH-2701", "type": "skos:broader" }, { "source": "RPH-2701", "target": "PHO-0074", "type": "skos:narrower" }, { "source": "PHO-0074", "target": "AED-0030", "type": "augmanitai:crossDomainReference" }, { "source": "PHO-0074", "target": "AED-0042", "type": "augmanitai:crossDomainReference" }, { "source": "PHO-0074", "target": "ASE-0030", "type": "augmanitai:crossDomainReference" }, { "source": "PHO-0075", "target": "Performance Gap", "type": "skos:broader" }, { "source": "Performance Gap", "target": "PHO-0075", "type": "skos:narrower" }, { "source": "PHO-0076", "target": "RPH-2205", "type": "skos:broader" }, { "source": "RPH-2205", "target": "PHO-0076", "type": "skos:narrower" }, { "source": "PHO-0077", "target": "Photo AI", "type": "skos:broader" }, { "source": "Photo AI", "target": "PHO-0077", "type": "skos:narrower" }, { "source": "PHO-0078", "target": "Photo AI", "type": "skos:broader" }, { "source": "Photo AI", "target": "PHO-0078", "type": "skos:narrower" }, { "source": "PHO-0078", "target": "COG-0056", "type": "augmanitai:crossDomainReference" }, { "source": "PHO-0078", "target": "COG-0082", "type": "augmanitai:crossDomainReference" }, { "source": "PHO-0078", "target": "COG-0087", "type": "augmanitai:crossDomainReference" }, { "source": "PHO-0079", "target": "Photo AI", "type": "skos:broader" }, { "source": "Photo AI", "target": "PHO-0079", "type": "skos:narrower" }, { "source": "PHO-0079", "target": "FIC-0091", "type": "augmanitai:crossDomainReference" }, { "source": "PHO-0080", "target": "Photo AI", "type": "skos:broader" }, { "source": "Photo AI", "target": "PHO-0080", "type": "skos:narrower" }, { "source": "PHO-0080", "target": "AED-0088", "type": "augmanitai:crossDomainReference" }, { "source": "PHO-0080", "target": "AGE-0026", "type": "augmanitai:crossDomainReference" }, { "source": "PHO-0080", "target": "ASE-0074", "type": "augmanitai:crossDomainReference" }, { "source": "PHO-0081", "target": "RPH-2351", "type": "skos:broader" }, { "source": "RPH-2351", "target": "PHO-0081", "type": "skos:narrower" }, { "source": "PHO-0081", "target": "ART-0087", "type": "augmanitai:crossDomainReference" }, { "source": "PHO-0081", "target": "LIN-0054", "type": "augmanitai:crossDomainReference" }, { "source": "PHO-0082", "target": "RPH-3003", "type": "skos:broader" }, { "source": "RPH-3003", "target": "PHO-0082", "type": "skos:narrower" }, { "source": "PHO-0082", "target": "ELR-0176", "type": "augmanitai:crossDomainReference" }, { "source": "PHO-0082", "target": "LIN-0076", "type": "augmanitai:crossDomainReference" }, { "source": "PHO-0082", "target": "MKT-0074", "type": "augmanitai:crossDomainReference" }, { "source": "PHO-0083", "target": "Photo AI", "type": "skos:broader" }, { "source": "Photo AI", "target": "PHO-0083", "type": "skos:narrower" }, { "source": "PHO-0083", "target": "DES-0077", "type": "augmanitai:crossDomainReference" }, { "source": "PHO-0083", "target": "DES-0009", "type": "augmanitai:crossDomainReference" }, { "source": "PHO-0084", "target": "Photo AI", "type": "skos:broader" }, { "source": "Photo AI", "target": "PHO-0084", "type": "skos:narrower" }, { "source": "PHO-0084", "target": "MUS-0070", "type": "augmanitai:crossDomainReference" }, { "source": "PHO-0085", "target": "RPH-2005", "type": "skos:broader" }, { "source": "RPH-2005", "target": "PHO-0085", "type": "skos:narrower" }, { "source": "PHO-0085", "target": "AGE-0086", "type": "augmanitai:crossDomainReference" }, { "source": "PHO-0085", "target": "AGE-0093", "type": "augmanitai:crossDomainReference" }, { "source": "PHO-0085", "target": "ART-0034", "type": "augmanitai:crossDomainReference" }, { "source": "PHO-0086", "target": "RPH-3002", "type": "skos:broader" }, { "source": "RPH-3002", "target": "PHO-0086", "type": "skos:narrower" }, { "source": "PHO-0087", "target": "Photo AI", "type": "skos:broader" }, { "source": "Photo AI", "target": "PHO-0087", "type": "skos:narrower" }, { "source": "PHO-0087", "target": "DES-0033", "type": "augmanitai:crossDomainReference" }, { "source": "PHO-0088", "target": "RPH-2205", "type": "skos:broader" }, { "source": "RPH-2205", "target": "PHO-0088", "type": "skos:narrower" }, { "source": "PHO-0088", "target": "AGE-0043", "type": "augmanitai:crossDomainReference" }, { "source": "PHO-0088", "target": "BEH-0033", "type": "augmanitai:crossDomainReference" }, { "source": "PHO-0088", "target": "BEH-0074", "type": "augmanitai:crossDomainReference" }, { "source": "PHO-0089", "target": "Analytical Ratio", "type": "skos:broader" }, { "source": "Analytical Ratio", "target": "PHO-0089", "type": "skos:narrower" }, { "source": "PHO-0089", "target": "CRE-0139", "type": "augmanitai:crossDomainReference" }, { "source": "PHO-0090", "target": "RPH-2701", "type": "skos:broader" }, { "source": "RPH-2701", "target": "PHO-0090", "type": "skos:narrower" }, { "source": "PHO-0090", "target": "AED-0019", "type": "augmanitai:crossDomainReference" }, { "source": "PHO-0090", "target": "AED-0092", "type": "augmanitai:crossDomainReference" }, { "source": "PHO-0090", "target": "AGE-0023", "type": "augmanitai:crossDomainReference" }, { "source": "PHO-0091", "target": "Photo AI", "type": "skos:broader" }, { "source": "Photo AI", "target": "PHO-0091", "type": "skos:narrower" }, { "source": "PHO-0091", "target": "CRE-0011", "type": "augmanitai:crossDomainReference" }, { "source": "PHO-0092", 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"augmanitai:crossDomainReference" }, { "source": "PHO-0098", "target": "AGE-0014", "type": "augmanitai:crossDomainReference" }, { "source": "PLY-0001", "target": "Play AI", "type": "skos:broader" }, { "source": "Play AI", "target": "PLY-0001", "type": "skos:narrower" }, { "source": "PLY-0001", "target": "ADA-0003", "type": "augmanitai:crossDomainReference" }, { "source": "PLY-0002", "target": "Play AI", "type": "skos:broader" }, { "source": "Play AI", "target": "PLY-0002", "type": "skos:narrower" }, { "source": "PLY-0003", "target": "Play AI", "type": "skos:broader" }, { "source": "Play AI", "target": "PLY-0003", "type": "skos:narrower" }, { "source": "PLY-0004", "target": "Play AI", "type": "skos:broader" }, { "source": "Play AI", "target": "PLY-0004", "type": "skos:narrower" }, { "source": "PLY-0005", "target": "Play AI", "type": "skos:broader" }, { "source": "Play AI", "target": "PLY-0005", "type": "skos:narrower" }, { "source": "PLY-0005", "target": "ADA-0012", "type": "augmanitai:crossDomainReference" }, { "source": "PLY-0005", "target": "AGE-0007", "type": "augmanitai:crossDomainReference" }, { "source": "PLY-0005", "target": "AGE-0046", "type": "augmanitai:crossDomainReference" }, { "source": "PLY-0006", "target": "Play AI", "type": "skos:broader" }, { "source": "Play AI", "target": "PLY-0006", "type": "skos:narrower" }, { "source": "PLY-0007", "target": "RPH-3601", "type": "skos:broader" }, { "source": "RPH-3601", "target": "PLY-0007", "type": "skos:narrower" }, { "source": "PLY-0007", "target": "NEO-3580", "type": "augmanitai:crossDomainReference" }, { "source": "PLY-0008", "target": "Play AI", "type": "skos:broader" }, { "source": "Play AI", "target": "PLY-0008", "type": "skos:narrower" }, { "source": "PLY-0009", "target": "Play AI", "type": "skos:broader" }, { "source": "Play AI", "target": "PLY-0009", "type": "skos:narrower" }, { "source": "PLY-0010", "target": "Play AI", "type": "skos:broader" }, { "source": "Play AI", "target": "PLY-0010", "type": "skos:narrower" }, { "source": "PLY-0010", "target": "AGE-0028", "type": "augmanitai:crossDomainReference" }, { "source": "PLY-0010", "target": "ELR-0073", "type": "augmanitai:crossDomainReference" }, { "source": "PLY-0010", "target": "MTH-0099", "type": "augmanitai:crossDomainReference" }, { "source": "PLY-0011", "target": "Play AI", "type": "skos:broader" }, { "source": "Play AI", "target": "PLY-0011", "type": "skos:narrower" }, { "source": "PLY-0011", "target": "KNO-0011", "type": "augmanitai:crossDomainReference" }, { "source": "PLY-0012", "target": "Play AI", "type": "skos:broader" }, { "source": "Play AI", "target": "PLY-0012", "type": "skos:narrower" }, { "source": "PLY-0012", "target": "CRE-0144", "type": "augmanitai:crossDomainReference" }, { "source": "PLY-0012", "target": "PHO-0047", "type": "augmanitai:crossDomainReference" }, { "source": "PLY-0013", "target": "Play AI", "type": "skos:broader" }, { "source": "Play AI", "target": "PLY-0013", "type": "skos:narrower" }, { "source": "PLY-0014", "target": "RPH-2051", "type": "skos:broader" }, { "source": "RPH-2051", "target": "PLY-0014", "type": "skos:narrower" }, { "source": "PLY-0014", "target": "GAM-0029", "type": "augmanitai:crossDomainReference" }, { "source": "PLY-0014", "target": "DES-0029", "type": "augmanitai:crossDomainReference" }, { "source": "PLY-0015", "target": "Play AI", "type": "skos:broader" }, { "source": "Play AI", "target": "PLY-0015", "type": "skos:narrower" }, { "source": "PLY-0015", "target": "CRE-0144", "type": "augmanitai:crossDomainReference" }, { "source": "PLY-0015", "target": "MSC-0003", "type": "augmanitai:crossDomainReference" }, { "source": "PLY-0015", "target": "MTH-0076", "type": "augmanitai:crossDomainReference" }, { "source": "PLY-0016", "target": "Play AI", "type": "skos:broader" }, { "source": "Play AI", "target": "PLY-0016", "type": "skos:narrower" }, { "source": "PLY-0017", "target": "Play AI", "type": "skos:broader" }, { "source": "Play AI", "target": "PLY-0017", "type": "skos:narrower" }, { "source": "PLY-0018", "target": "Play AI", "type": "skos:broader" }, { "source": "Play AI", "target": "PLY-0018", "type": "skos:narrower" }, { "source": "PLY-0019", "target": "Play AI", "type": "skos:broader" }, { "source": "Play AI", "target": "PLY-0019", "type": "skos:narrower" }, { "source": "PLY-0020", "target": "Play AI", "type": "skos:broader" }, { "source": "Play AI", "target": "PLY-0020", "type": "skos:narrower" }, { "source": "PLY-0020", "target": "IEF-0002", "type": "augmanitai:crossDomainReference" }, { "source": "PLY-0021", "target": "RPH-2703", "type": "skos:broader" }, { "source": "RPH-2703", "target": "PLY-0021", "type": "skos:narrower" }, { "source": "PLY-0022", "target": "Abstraction Layer", "type": "skos:broader" }, { "source": "Abstraction Layer", "target": "PLY-0022", "type": "skos:narrower" }, { "source": "PLY-0022", "target": "CRE-0090", "type": "augmanitai:crossDomainReference" }, { "source": "PLY-0022", "target": "CRE-0164", "type": "augmanitai:crossDomainReference" }, { "source": "PLY-0022", "target": "ETH-0018", "type": "augmanitai:crossDomainReference" }, { "source": "PLY-0023", "target": "Play AI", "type": "skos:broader" }, { "source": "Play AI", "target": "PLY-0023", "type": "skos:narrower" }, { "source": "PLY-0024", "target": "Play AI", "type": "skos:broader" }, { "source": "Play AI", "target": "PLY-0024", "type": "skos:narrower" }, { "source": "PLY-0024", "target": "BEH-0077", "type": "augmanitai:crossDomainReference" }, { "source": "PLY-0024", "target": "COG-0013", "type": "augmanitai:crossDomainReference" }, { "source": "PLY-0024", "target": "COG-0164", "type": "augmanitai:crossDomainReference" }, { "source": "PLY-0025", "target": "RPH-3101", "type": "skos:broader" }, { "source": "RPH-3101", "target": "PLY-0025", "type": "skos:narrower" }, { "source": "PLY-0025", "target": "CRE-0171", "type": "augmanitai:crossDomainReference" }, { "source": "PLY-0026", "target": "Play AI", "type": "skos:broader" }, { "source": "Play AI", "target": "PLY-0026", "type": "skos:narrower" }, { "source": "PLY-0026", "target": "ELR-0159", "type": "augmanitai:crossDomainReference" }, { "source": "PLY-0027", "target": "RPH-2701", "type": "skos:broader" }, { "source": "RPH-2701", "target": "PLY-0027", "type": "skos:narrower" }, { "source": "PLY-0028", "target": "RPH-2254", "type": "skos:broader" }, { "source": "RPH-2254", "target": "PLY-0028", "type": "skos:narrower" }, { "source": "PLY-0029", "target": "RPH-3302", "type": "skos:broader" }, { "source": "RPH-3302", "target": "PLY-0029", "type": "skos:narrower" }, { "source": "PLY-0030", "target": "Play AI", "type": "skos:broader" }, { "source": "Play AI", "target": "PLY-0030", "type": "skos:narrower" }, { "source": "PLY-0031", "target": "Play AI", "type": "skos:broader" }, { "source": "Play AI", "target": "PLY-0031", "type": "skos:narrower" }, { "source": "PLY-0031", "target": "GAM-0015", "type": "augmanitai:crossDomainReference" }, { "source": "PLY-0032", "target": "Play AI", "type": "skos:broader" }, { "source": "Play AI", "target": "PLY-0032", "type": "skos:narrower" }, { "source": "PLY-0032", "target": "GAM-0024", "type": "augmanitai:crossDomainReference" }, { "source": "PLY-0032", "target": "GAM-0072", "type": "augmanitai:crossDomainReference" }, { "source": "PLY-0032", "target": "MSC-0003", "type": "augmanitai:crossDomainReference" }, { "source": "PLY-0033", "target": "Play AI", "type": "skos:broader" }, { "source": "Play AI", "target": "PLY-0033", "type": "skos:narrower" }, { "source": "PLY-0033", "target": "CON-0045", "type": "augmanitai:crossDomainReference" }, { "source": "PLY-0033", "target": "COP-0062", "type": "augmanitai:crossDomainReference" }, { "source": "PLY-0033", "target": "KNO-0015", "type": "augmanitai:crossDomainReference" }, { "source": "PLY-0034", "target": "Play AI", "type": "skos:broader" }, { "source": "Play AI", "target": "PLY-0034", "type": "skos:narrower" }, { "source": "PLY-0034", "target": "CON-0091", "type": "augmanitai:crossDomainReference" }, { "source": "PLY-0035", "target": "RPH-2254", "type": "skos:broader" }, { "source": "RPH-2254", "target": "PLY-0035", "type": "skos:narrower" }, { "source": "PLY-0035", "target": "CRE-0050", "type": "augmanitai:crossDomainReference" }, { "source": "PLY-0036", "target": "Play AI", "type": "skos:broader" }, { "source": "Play AI", "target": "PLY-0036", "type": "skos:narrower" }, { "source": "PLY-0036", "target": "BEH-0072", "type": "augmanitai:crossDomainReference" }, { "source": "PLY-0036", "target": "CRE-0175", "type": "augmanitai:crossDomainReference" }, { "source": "PLY-0036", "target": "PER-0064", "type": "augmanitai:crossDomainReference" }, { "source": "PLY-0037", "target": "Play AI", "type": "skos:broader" }, { "source": "Play AI", "target": "PLY-0037", "type": "skos:narrower" }, { "source": "PLY-0038", "target": "RPH-2703", "type": "skos:broader" }, { "source": "RPH-2703", "target": "PLY-0038", "type": "skos:narrower" }, { "source": "PLY-0038", "target": "ELR-0082", "type": "augmanitai:crossDomainReference" }, { "source": "PLY-0039", "target": "RPH-2701", "type": "skos:broader" }, { "source": "RPH-2701", "target": "PLY-0039", "type": "skos:narrower" }, { "source": "PLY-0040", "target": "RPH-2201", "type": "skos:broader" }, { "source": "RPH-2201", "target": "PLY-0040", "type": "skos:narrower" }, { "source": "PLY-0041", "target": "Feedback Loop", "type": "skos:broader" }, { "source": "Feedback Loop", "target": "PLY-0041", "type": "skos:narrower" }, { "source": "PLY-0041", "target": "ASE-0038", "type": "augmanitai:crossDomainReference" }, { "source": "PLY-0041", "target": "BEH-0006", "type": "augmanitai:crossDomainReference" }, { "source": "PLY-0041", "target": "BEH-0023", "type": "augmanitai:crossDomainReference" }, { "source": "PLY-0042", "target": "Play AI", "type": "skos:broader" }, { "source": "Play AI", "target": "PLY-0042", "type": "skos:narrower" }, { "source": "PLY-0043", "target": "Play AI", "type": "skos:broader" }, { "source": "Play AI", "target": "PLY-0043", "type": "skos:narrower" }, { "source": "PLY-0043", "target": "CON-0073", "type": "augmanitai:crossDomainReference" }, { "source": "PLY-0044", "target": "RPH-2301", "type": "skos:broader" }, { "source": "RPH-2301", "target": "PLY-0044", "type": "skos:narrower" }, { "source": "PLY-0044", "target": "FIC-0051", "type": "augmanitai:crossDomainReference" }, { "source": "PLY-0045", "target": "RPH-2254", "type": "skos:broader" }, { "source": "RPH-2254", "target": "PLY-0045", "type": "skos:narrower" }, { "source": "PLY-0045", "target": "AED-0044", "type": "augmanitai:crossDomainReference" }, { "source": "PLY-0045", "target": "AED-0063", "type": "augmanitai:crossDomainReference" }, { "source": "PLY-0045", "target": "AED-0064", "type": "augmanitai:crossDomainReference" }, { "source": "PLY-0046", "target": "Play AI", "type": "skos:broader" }, { "source": "Play AI", "target": "PLY-0046", "type": 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"augmanitai:crossDomainReference" }, { "source": "RET-0032", "target": "Retail AI", "type": "skos:broader" }, { "source": "Retail AI", "target": "RET-0032", "type": "skos:narrower" }, { "source": "RET-0032", "target": "AED-0039", "type": "augmanitai:crossDomainReference" }, { "source": "RET-0032", "target": "AED-0071", "type": "augmanitai:crossDomainReference" }, { "source": "RET-0032", "target": "AGE-0063", "type": "augmanitai:crossDomainReference" }, { "source": "RET-0033", "target": "RPH-2505", "type": "skos:broader" }, { "source": "RPH-2505", "target": "RET-0033", "type": "skos:narrower" }, { "source": "RET-0033", "target": "PLY-0057", "type": "augmanitai:crossDomainReference" }, { "source": "RET-0034", "target": "Retail AI", "type": "skos:broader" }, { "source": "Retail AI", "target": "RET-0034", "type": "skos:narrower" }, { "source": "RET-0034", "target": "CON-0058", "type": "augmanitai:crossDomainReference" }, { "source": "RET-0035", "target": "Retail AI", "type": "skos:broader" }, { "source": "Retail AI", "target": "RET-0035", "type": "skos:narrower" }, { "source": "RET-0035", "target": "MKT-0041", "type": "augmanitai:crossDomainReference" }, { "source": "RET-0035", "target": "MKT-0028", "type": "augmanitai:crossDomainReference" }, { "source": "RET-0036", "target": "RPH-2505", "type": "skos:broader" }, { "source": "RPH-2505", "target": "RET-0036", "type": "skos:narrower" }, { "source": "RET-0036", "target": "MTH-0003", "type": "augmanitai:crossDomainReference" }, { "source": "RET-0036", "target": "MKT-0054", "type": "augmanitai:crossDomainReference" }, { "source": "RET-0036", "target": "MKT-0007", "type": "augmanitai:crossDomainReference" }, { "source": "RET-0037", "target": "RPH-2505", "type": "skos:broader" }, { "source": "RPH-2505", "target": "RET-0037", "type": "skos:narrower" }, { "source": "RET-0037", "target": "ELR-0107", "type": "augmanitai:crossDomainReference" }, { "source": "RET-0037", "target": "ART-0098", "type": "augmanitai:crossDomainReference" }, { "source": "RET-0038", "target": "RPH-2505", "type": "skos:broader" }, { "source": "RPH-2505", "target": "RET-0038", "type": "skos:narrower" }, { "source": "RET-0038", "target": "AED-0066", "type": "augmanitai:crossDomainReference" }, { "source": "RET-0039", "target": "RPH-2355", "type": "skos:broader" }, { "source": "RPH-2355", "target": "RET-0039", "type": "skos:narrower" }, { "source": "RET-0040", "target": "RPH-2703", "type": "skos:broader" }, { "source": "RPH-2703", "target": "RET-0040", "type": "skos:narrower" }, { "source": "RET-0040", "target": "MKT-0063", "type": "augmanitai:crossDomainReference" }, { "source": "RET-0040", "target": "ASE-0055", "type": "augmanitai:crossDomainReference" }, { "source": "RET-0040", "target": "PER-0003", "type": "augmanitai:crossDomainReference" }, { "source": "RET-0041", "target": "RPH-2703", "type": "skos:broader" }, { "source": "RPH-2703", "target": "RET-0041", "type": "skos:narrower" }, { "source": "RET-0042", "target": "Vector Embedding", "type": "skos:broader" }, { "source": "Vector Embedding", "target": "RET-0042", "type": "skos:narrower" }, { "source": "RET-0042", "target": "AED-0021", "type": "augmanitai:crossDomainReference" }, { "source": "RET-0042", "target": "AUG-0921", "type": "augmanitai:crossDomainReference" }, { "source": "RET-0042", "target": "CON-0044", "type": "augmanitai:crossDomainReference" }, { "source": "RET-0043", "target": "Behavioral Pattern", "type": "skos:broader" }, { "source": "Behavioral Pattern", "target": "RET-0043", "type": "skos:narrower" }, { "source": "RET-0043", "target": "MKT-0034", "type": "augmanitai:crossDomainReference" }, { "source": "RET-0043", "target": "MKT-0040", "type": "augmanitai:crossDomainReference" }, { "source": "RET-0044", "target": "Retail AI", "type": "skos:broader" }, { "source": "Retail AI", "target": "RET-0044", "type": "skos:narrower" }, { "source": "RET-0044", "target": "ASE-0044", "type": "augmanitai:crossDomainReference" }, { "source": "RET-0044", "target": "CUS-0098", "type": "augmanitai:crossDomainReference" }, { "source": "RET-0045", "target": "RPH-2505", "type": "skos:broader" }, { "source": "RPH-2505", "target": "RET-0045", "type": "skos:narrower" }, { "source": "RET-0046", "target": "Retail AI", "type": "skos:broader" }, { "source": "Retail AI", "target": "RET-0046", "type": "skos:narrower" }, { "source": "RET-0046", "target": "MKT-0061", "type": "augmanitai:crossDomainReference" }, { "source": "RET-0046", "target": "MSC-0010", "type": "augmanitai:crossDomainReference" }, { "source": "RET-0047", "target": "RPH-2505", "type": "skos:broader" }, { "source": "RPH-2505", "target": "RET-0047", "type": "skos:narrower" }, { "source": "RET-0047", "target": "PER-0032", "type": "augmanitai:crossDomainReference" }, { "source": "RET-0047", "target": "PER-0035", "type": "augmanitai:crossDomainReference" }, { "source": "RET-0048", "target": "RPH-2505", "type": "skos:broader" }, { "source": "RPH-2505", "target": "RET-0048", "type": "skos:narrower" }, { "source": "RET-0048", "target": "MKT-0034", "type": "augmanitai:crossDomainReference" }, { "source": "RET-0049", "target": "RPH-2005", "type": "skos:broader" }, { "source": "RPH-2005", "target": "RET-0049", "type": "skos:narrower" }, { "source": "RET-0050", "target": "RPH-2154", "type": "skos:broader" }, { "source": "RPH-2154", "target": "RET-0050", "type": "skos:narrower" }, { "source": "RET-0050", "target": "COP-0043", "type": "augmanitai:crossDomainReference" }, { "source": "RET-0051", "target": "Retail AI", "type": "skos:broader" }, { "source": "Retail AI", "target": "RET-0051", "type": "skos:narrower" }, { "source": "RET-0051", "target": "ASE-0076", "type": "augmanitai:crossDomainReference" }, { "source": "RET-0051", "target": "CUS-0059", "type": "augmanitai:crossDomainReference" }, { "source": "RET-0052", "target": "Psychological Effect", "type": "skos:broader" }, { "source": "Psychological Effect", "target": "RET-0052", "type": "skos:narrower" }, { "source": "RET-0053", "target": "Retail AI", "type": "skos:broader" }, { "source": "Retail AI", "target": "RET-0053", "type": "skos:narrower" }, { "source": "RET-0053", "target": "CUS-0084", "type": "augmanitai:crossDomainReference" }, { "source": "RET-0053", "target": "EDU-0077", "type": "augmanitai:crossDomainReference" }, { "source": "RET-0054", "target": "RPH-2505", "type": "skos:broader" }, { "source": "RPH-2505", "target": "RET-0054", "type": "skos:narrower" }, { "source": "RET-0055", "target": "Retail AI", "type": "skos:broader" }, { "source": "Retail AI", "target": "RET-0055", "type": "skos:narrower" }, { "source": "RET-0055", "target": "MSC-0079", "type": "augmanitai:crossDomainReference" }, { "source": "RET-0056", "target": "Cognitive Bias", "type": "skos:broader" }, { "source": "Cognitive Bias", "target": "RET-0056", "type": "skos:narrower" }, { "source": "RET-0056", "target": "MSC-0079", "type": "augmanitai:crossDomainReference" }, { "source": "RET-0057", "target": "Retail AI", "type": "skos:broader" }, { "source": "Retail AI", "target": "RET-0057", "type": "skos:narrower" }, { "source": "RET-0058", "target": "RPH-2703", "type": "skos:broader" }, { "source": "RPH-2703", "target": "RET-0058", "type": "skos:narrower" }, { "source": "RET-0058", "target": "ASE-0041", "type": "augmanitai:crossDomainReference" }, { "source": "RET-0058", "target": "MTH-0040", "type": "augmanitai:crossDomainReference" }, { "source": "RET-0058", "target": "MTH-0047", "type": "augmanitai:crossDomainReference" }, { "source": "RET-0059", "target": "RPH-3754", "type": "skos:broader" }, { "source": "RPH-3754", "target": "RET-0059", "type": "skos:narrower" }, { "source": "RET-0059", "target": "MSC-0079", "type": "augmanitai:crossDomainReference" }, { "source": "RET-0060", "target": "Algorithm", "type": "skos:broader" }, { "source": "Algorithm", "target": "RET-0060", "type": "skos:narrower" }, { "source": "RET-0060", "target": "ETH-0005", "type": "augmanitai:crossDomainReference" }, { "source": "RET-0061", "target": "Optimization Method", "type": "skos:broader" }, { "source": "Optimization Method", "target": "RET-0061", "type": "skos:narrower" }, { "source": "RET-0061", "target": "ETH-0005", "type": "augmanitai:crossDomainReference" }, { "source": "RET-0062", "target": "RPH-2201", "type": "skos:broader" }, { "source": "RPH-2201", "target": "RET-0062", "type": "skos:narrower" }, { "source": "RET-0062", "target": "COG-0015", "type": "augmanitai:crossDomainReference" }, { "source": "RET-0062", "target": "CUS-0006", "type": "augmanitai:crossDomainReference" }, { "source": "RET-0063", "target": "Retail AI", "type": "skos:broader" }, { "source": "Retail AI", "target": "RET-0063", "type": "skos:narrower" }, { "source": "RET-0063", "target": "COG-0180", "type": "augmanitai:crossDomainReference" }, { "source": "RET-0063", "target": "CON-0061", "type": "augmanitai:crossDomainReference" }, { "source": "RET-0063", "target": "COP-0058", "type": "augmanitai:crossDomainReference" }, { "source": "RET-0064", "target": "Retail AI", "type": "skos:broader" }, { "source": "Retail AI", "target": "RET-0064", "type": "skos:narrower" }, { "source": "RET-0065", "target": "RPH-3501", "type": "skos:broader" }, { "source": "RPH-3501", "target": "RET-0065", "type": "skos:narrower" }, { "source": "RET-0065", "target": "MTH-0088", "type": "augmanitai:crossDomainReference" }, { "source": "RET-0065", "target": "MTH-0005", "type": "augmanitai:crossDomainReference" }, { "source": "RET-0065", "target": "CUS-0068", "type": "augmanitai:crossDomainReference" }, { "source": "RET-0066", "target": "Optimization Method", "type": "skos:broader" }, { "source": "Optimization Method", "target": "RET-0066", "type": "skos:narrower" }, { "source": "RET-0066", "target": "COG-0189", "type": "augmanitai:crossDomainReference" }, { "source": "RET-0066", "target": "ART-0050", "type": "augmanitai:crossDomainReference" }, { "source": "RET-0066", "target": "ART-0032", "type": "augmanitai:crossDomainReference" }, { "source": "RET-0067", "target": "Cognitive Bias", "type": "skos:broader" }, { "source": "Cognitive Bias", "target": "RET-0067", "type": "skos:narrower" }, { "source": "RET-0067", "target": "DAT-0090", "type": "augmanitai:crossDomainReference" }, { "source": "RET-0068", "target": "Retail AI", "type": "skos:broader" }, { "source": "Retail AI", "target": "RET-0068", "type": "skos:narrower" }, { "source": "RET-0068", "target": "PER-0109", "type": "augmanitai:crossDomainReference" }, { "source": "RET-0068", "target": "PLY-0017", "type": "augmanitai:crossDomainReference" }, { "source": "RET-0068", "target": "PER-0110", "type": "augmanitai:crossDomainReference" }, { "source": "RET-0069", "target": "Logical Paradox", "type": "skos:broader" }, { "source": "Logical Paradox", "target": "RET-0069", "type": "skos:narrower" }, { "source": "RET-0069", "target": "ETH-0005", "type": "augmanitai:crossDomainReference" }, { "source": "RET-0069", "target": "DAT-0039", "type": "augmanitai:crossDomainReference" }, { "source": "RET-0069", "target": "DAT-0040", "type": "augmanitai:crossDomainReference" }, { "source": "RET-0070", "target": "Retail AI", "type": "skos:broader" }, { "source": "Retail AI", "target": "RET-0070", "type": "skos:narrower" }, { "source": "RET-0070", "target": "PLY-0030", "type": "augmanitai:crossDomainReference" }, { "source": "RET-0070", "target": "PLY-0032", "type": "augmanitai:crossDomainReference" }, { "source": "RET-0070", "target": "PLY-0015", "type": "augmanitai:crossDomainReference" }, { "source": "RET-0071", "target": "Retail AI", "type": "skos:broader" }, { "source": "Retail AI", "target": "RET-0071", "type": "skos:narrower" }, { "source": "RET-0072", "target": "Risk Factor", "type": "skos:broader" }, { "source": "Risk Factor", "target": "RET-0072", "type": "skos:narrower" }, { "source": "RET-0072", "target": "MKT-0077", "type": "augmanitai:crossDomainReference" }, { "source": "RET-0073", "target": "RPH-3952", "type": "skos:broader" }, { "source": "RPH-3952", "target": "RET-0073", "type": "skos:narrower" }, { "source": "RET-0073", "target": "AGE-0053", "type": "augmanitai:crossDomainReference" }, { "source": "RET-0073", "target": "ELR-0061", "type": "augmanitai:crossDomainReference" }, { "source": "RET-0074", "target": "Retail AI", "type": "skos:broader" }, { "source": "Retail AI", "target": "RET-0074", "type": "skos:narrower" }, { "source": "RET-0074", "target": "MKT-0048", "type": "augmanitai:crossDomainReference" }, { "source": "RET-0074", "target": "REL-0142", "type": "augmanitai:crossDomainReference" }, { "source": "RET-0075", "target": "RPH-2355", "type": "skos:broader" }, { "source": "RPH-2355", "target": "RET-0075", "type": "skos:narrower" }, { "source": "RET-0075", "target": "ASE-0046", "type": "augmanitai:crossDomainReference" }, { "source": "RET-0076", "target": "RPH-2355", "type": "skos:broader" }, { "source": "RPH-2355", "target": "RET-0076", "type": "skos:narrower" }, { "source": "RET-0076", "target": "BEH-0065", "type": "augmanitai:crossDomainReference" }, { "source": "RET-0077", "target": "RPH-3101", "type": "skos:broader" }, { "source": "RPH-3101", "target": "RET-0077", "type": "skos:narrower" }, { "source": "RET-0077", "target": "AED-0066", "type": "augmanitai:crossDomainReference" }, { "source": "RET-0078", "target": "Retail AI", "type": "skos:broader" }, { "source": "Retail AI", "target": "RET-0078", "type": "skos:narrower" }, { "source": "RET-0078", "target": "FIC-0075", "type": "augmanitai:crossDomainReference" }, { "source": "RET-0078", "target": "FIC-0076", "type": "augmanitai:crossDomainReference" }, { "source": "RET-0078", "target": "MKT-0029", "type": "augmanitai:crossDomainReference" }, { "source": "RET-0079", "target": "RPH-2701", "type": "skos:broader" }, { "source": "RPH-2701", "target": "RET-0079", "type": "skos:narrower" }, { "source": "RET-0079", "target": "AED-0071", "type": "augmanitai:crossDomainReference" }, { "source": "RET-0079", "target": "AGE-0063", "type": "augmanitai:crossDomainReference" }, { "source": "RET-0079", "target": "AGE-0071", "type": "augmanitai:crossDomainReference" }, { "source": "RET-0080", "target": "Analytical Ratio", "type": "skos:broader" }, { "source": "Analytical Ratio", "target": "RET-0080", "type": "skos:narrower" }, { "source": "RET-0081", "target": "Retail AI", "type": "skos:broader" }, { "source": "Retail AI", "target": "RET-0081", "type": "skos:narrower" }, { "source": "RET-0081", "target": "CON-0064", "type": "augmanitai:crossDomainReference" }, { "source": "RET-0081", "target": "GAM-0080", "type": "augmanitai:crossDomainReference" }, { "source": "RET-0081", "target": "ART-0082", "type": "augmanitai:crossDomainReference" }, { "source": "RET-0082", "target": "Risk Factor", "type": "skos:broader" }, { "source": "Risk Factor", "target": "RET-0082", "type": "skos:narrower" }, { "source": "RET-0083", "target": "Cognitive Bias", "type": "skos:broader" }, { "source": "Cognitive Bias", "target": "RET-0083", "type": "skos:narrower" }, { "source": "RET-0084", "target": "Retail AI", "type": "skos:broader" }, { "source": "Retail AI", "target": "RET-0084", "type": "skos:narrower" }, { "source": "RET-0084", "target": "PER-0032", "type": "augmanitai:crossDomainReference" }, { "source": "RET-0084", "target": "PER-0035", "type": "augmanitai:crossDomainReference" }, { "source": "RET-0085", "target": "Analytical Ratio", "type": "skos:broader" }, { "source": "Analytical Ratio", "target": "RET-0085", "type": "skos:narrower" }, { "source": "RET-0085", "target": "LNG-0012", "type": "augmanitai:crossDomainReference" }, { "source": "RET-0086", "target": "RPH-2201", "type": "skos:broader" }, { "source": "RPH-2201", "target": "RET-0086", "type": "skos:narrower" }, { "source": "RET-0086", "target": "CRE-0231", "type": "augmanitai:crossDomainReference" }, { "source": "RET-0086", "target": "ASE-0031", "type": "augmanitai:crossDomainReference" }, { "source": "RET-0086", "target": "CUS-0083", "type": "augmanitai:crossDomainReference" }, { "source": "RET-0087", "target": "RPH-2201", "type": "skos:broader" }, { "source": "RPH-2201", "target": "RET-0087", "type": "skos:narrower" }, { "source": "RET-0087", "target": "CUS-0030", "type": "augmanitai:crossDomainReference" }, { "source": "RET-0088", "target": "Feedback Loop", "type": "skos:broader" }, { "source": "Feedback Loop", "target": "RET-0088", "type": "skos:narrower" }, { "source": "RET-0088", "target": "CUS-0045", "type": "augmanitai:crossDomainReference" }, { "source": "RET-0089", "target": "RPH-2703", "type": "skos:broader" }, { "source": "RPH-2703", "target": "RET-0089", "type": "skos:narrower" }, { "source": "RET-0089", "target": "MKT-0005", "type": "augmanitai:crossDomainReference" }, { "source": "RET-0090", "target": "RPH-2703", "type": "skos:broader" }, { "source": "RPH-2703", "target": "RET-0090", "type": "skos:narrower" }, { "source": "RET-0090", "target": "MSC-0074", "type": "augmanitai:crossDomainReference" }, { "source": "RET-0091", "target": "Quality Metric", "type": "skos:broader" }, { "source": "Quality Metric", "target": "RET-0091", "type": "skos:narrower" }, { "source": "RET-0091", "target": "ELR-0107", "type": "augmanitai:crossDomainReference" }, { "source": "RET-0092", "target": "RPH-2701", "type": "skos:broader" }, { "source": "RPH-2701", "target": "RET-0092", "type": "skos:narrower" }, { "source": "RET-0092", "target": "MUS-0026", "type": "augmanitai:crossDomainReference" }, { "source": "RET-0093", "target": "RPH-3101", "type": "skos:broader" }, { "source": "RPH-3101", "target": "RET-0093", "type": "skos:narrower" }, { "source": "RET-0093", "target": "CON-0037", "type": "augmanitai:crossDomainReference" }, { "source": "RET-0094", "target": "Retail AI", "type": "skos:broader" }, { "source": "Retail AI", "target": "RET-0094", "type": "skos:narrower" }, { "source": "RET-0094", "target": "AGE-0026", "type": "augmanitai:crossDomainReference" }, { "source": "RET-0095", "target": "RPH-2505", "type": "skos:broader" }, { "source": "RPH-2505", "target": "RET-0095", "type": "skos:narrower" }, { "source": "RET-0096", "target": "Retail AI", "type": "skos:broader" }, { "source": "Retail AI", "target": "RET-0096", "type": "skos:narrower" }, { "source": "RET-0096", "target": "FIC-0076", "type": "augmanitai:crossDomainReference" }, { "source": "RET-0097", "target": "Retail AI", "type": "skos:broader" }, { "source": "Retail AI", "target": "RET-0097", "type": "skos:narrower" }, { "source": "RET-0097", "target": "ART-0030", "type": "augmanitai:crossDomainReference" }, { "source": "RET-0097", "target": "ART-0035", "type": "augmanitai:crossDomainReference" }, { "source": "RET-0097", "target": "ASE-0013", "type": "augmanitai:crossDomainReference" }, { "source": "RET-0098", "target": "Retail AI", "type": "skos:broader" }, { "source": "Retail AI", "target": "RET-0098", "type": "skos:narrower" }, { "source": "RET-0098", "target": "COP-0083", "type": "augmanitai:crossDomainReference" }, { "source": "RET-0099", "target": "Optimization Method", "type": "skos:broader" }, { "source": "Optimization Method", "target": "RET-0099", "type": "skos:narrower" }, { "source": "RET-0100", "target": "Algorithm", "type": "skos:broader" }, { "source": "Algorithm", "target": "RET-0100", "type": "skos:narrower" }, { "source": "RET-0100", "target": "CON-0064", "type": "augmanitai:crossDomainReference" }, { "source": "RET-0100", "target": "GAM-0080", "type": "augmanitai:crossDomainReference" }, { "source": "RET-0100", "target": "ART-0082", "type": "augmanitai:crossDomainReference" }, { "source": "RHR-0001", "target": "RPH-2701", "type": "skos:broader" }, { "source": "RPH-2701", "target": "RHR-0001", "type": "skos:narrower" }, { "source": "RHR-0002", "target": "ROB-0050", "type": "skos:broader" }, { "source": "ROB-0050", "target": "RHR-0002", "type": "skos:narrower" }, { "source": "RHR-0003", "target": "RPH-2604", "type": "skos:broader" }, { "source": "RPH-2604", "target": "RHR-0003", "type": "skos:narrower" }, { "source": "RHR-0004", "target": "RPH-2703", "type": "skos:broader" }, { "source": "RPH-2703", "target": "RHR-0004", "type": "skos:narrower" }, { "source": "RHR-0004", "target": "GAM-0029", "type": "augmanitai:crossDomainReference" }, { "source": "RHR-0006", "target": "Psychological Effect", "type": "skos:broader" }, { "source": "Psychological Effect", "target": "RHR-0006", "type": "skos:narrower" }, { "source": "RHR-0007", "target": "RPH-2153", "type": "skos:broader" }, { "source": "RPH-2153", "target": "RHR-0007", "type": "skos:narrower" }, { "source": "RHR-0008", "target": "RPH-2255", "type": "skos:broader" }, { "source": "RPH-2255", "target": "RHR-0008", "type": "skos:narrower" }, { "source": "RHR-0009", "target": "RPH-2255", "type": "skos:broader" }, { "source": "RPH-2255", "target": "RHR-0009", "type": "skos:narrower" }, { "source": "RHR-0010", "target": "RPH-2255", "type": "skos:broader" }, { "source": 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"EDU-0089", "type": "augmanitai:crossDomainReference" }, { "source": "ROB-0031", "target": "Robotics", "type": "skos:broader" }, { "source": "Robotics", "target": "ROB-0031", "type": "skos:narrower" }, { "source": "ROB-0031", "target": "ART-0040", "type": "augmanitai:crossDomainReference" }, { "source": "ROB-0031", "target": "ART-0067", "type": "augmanitai:crossDomainReference" }, { "source": "ROB-0031", "target": "ASE-0093", "type": "augmanitai:crossDomainReference" }, { "source": "ROB-0032", "target": "Pattern Recognition", "type": "skos:broader" }, { "source": "Pattern Recognition", "target": "ROB-0032", "type": "skos:narrower" }, { "source": "ROB-0033", "target": "RPH-2703", "type": "skos:broader" }, { "source": "RPH-2703", "target": "ROB-0033", "type": "skos:narrower" }, { "source": "ROB-0034", "target": "Psychological Effect", "type": "skos:broader" }, { "source": "Psychological Effect", "target": "ROB-0034", "type": "skos:narrower" }, { "source": "ROB-0034", "target": "DES-0057", "type": "augmanitai:crossDomainReference" }, { "source": "ROB-0035", "target": "RPH-3101", "type": "skos:broader" }, { "source": "RPH-3101", "target": "ROB-0035", "type": "skos:narrower" }, { "source": "ROB-0035", "target": "ADA-0010", "type": "augmanitai:crossDomainReference" }, { "source": "ROB-0035", "target": "AED-0034", "type": "augmanitai:crossDomainReference" }, { "source": "ROB-0035", "target": "AED-0072", "type": "augmanitai:crossDomainReference" }, { "source": "ROB-0036", "target": "Robotics", "type": "skos:broader" }, { "source": "Robotics", "target": "ROB-0036", "type": "skos:narrower" }, { "source": "ROB-0036", "target": "RET-0037", "type": "augmanitai:crossDomainReference" }, { "source": "ROB-0036", "target": "ELR-0126", "type": "augmanitai:crossDomainReference" }, { "source": "ROB-0038", "target": "Machine Learning", "type": "skos:broader" }, { "source": "Machine Learning", "target": "ROB-0038", "type": "skos:narrower" }, { "source": "ROB-0039", "target": "Machine Learning", "type": "skos:broader" }, { "source": "Machine Learning", "target": "ROB-0039", "type": "skos:narrower" }, { "source": "ROB-0039", "target": "AED-0001", "type": "augmanitai:crossDomainReference" }, { "source": "ROB-0039", "target": "AED-0002", "type": "augmanitai:crossDomainReference" }, { "source": "ROB-0039", "target": "AED-0004", "type": "augmanitai:crossDomainReference" }, { "source": "ROB-0040", "target": "RPH-2351", "type": "skos:broader" }, { "source": "RPH-2351", "target": "ROB-0040", "type": "skos:narrower" }, { "source": "ROB-0040", "target": "CRE-0121", "type": "augmanitai:crossDomainReference" }, { "source": "ROB-0041", "target": "Robotics", "type": "skos:broader" }, { "source": "Robotics", "target": "ROB-0041", "type": "skos:narrower" }, { "source": "ROB-0041", "target": "MUS-0076", "type": "augmanitai:crossDomainReference" }, { "source": "ROB-0042", "target": "Robotics", "type": "skos:broader" }, { "source": "Robotics", "target": "ROB-0042", "type": "skos:narrower" }, { "source": "ROB-0042", "target": "CRE-0180", "type": "augmanitai:crossDomainReference" }, { "source": "ROB-0043", "target": "RPH-2703", "type": "skos:broader" }, { "source": "RPH-2703", "target": "ROB-0043", "type": "skos:narrower" }, { "source": "ROB-0043", "target": "EDU-0096", "type": "augmanitai:crossDomainReference" }, { "source": "ROB-0044", "target": "Optimization Method", "type": "skos:broader" }, { "source": "Optimization Method", "target": "ROB-0044", "type": "skos:narrower" }, { "source": "ROB-0044", "target": "ART-0034", "type": "augmanitai:crossDomainReference" }, { "source": "ROB-0044", "target": "BEH-0062", "type": "augmanitai:crossDomainReference" }, { "source": "ROB-0044", "target": "COG-0046", "type": "augmanitai:crossDomainReference" }, { "source": "ROB-0045", "target": "RPH-2701", "type": "skos:broader" }, { "source": "RPH-2701", "target": "ROB-0045", "type": "skos:narrower" }, { "source": "ROB-0045", "target": "DES-0054", "type": 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"source": "RPH-2703", "target": "ROB-0051", "type": "skos:narrower" }, { "source": "ROB-0051", "target": "CAI-0006", "type": "augmanitai:crossDomainReference" }, { "source": "ROB-0052", "target": "RPH-2703", "type": "skos:broader" }, { "source": "RPH-2703", "target": "ROB-0052", "type": "skos:narrower" }, { "source": "ROB-0052", "target": "MTH-0058", "type": "augmanitai:crossDomainReference" }, { "source": "ROB-0053", "target": "Robotics", "type": "skos:broader" }, { "source": "Robotics", "target": "ROB-0053", "type": "skos:narrower" }, { "source": "ROB-0055", "target": "RPH-3501", "type": "skos:broader" }, { "source": "RPH-3501", "target": "ROB-0055", "type": "skos:narrower" }, { "source": "ROB-0055", "target": "PER-0052", "type": "augmanitai:crossDomainReference" }, { "source": "ROB-0056", "target": "Robotics", "type": "skos:broader" }, { "source": "Robotics", "target": "ROB-0056", "type": "skos:narrower" }, { "source": "ROB-0056", "target": "COG-0008", "type": 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"augmanitai:crossDomainReference" }, { "source": "ROB-0065", "target": "MSC-0076", "type": "augmanitai:crossDomainReference" }, { "source": "ROB-0066", "target": "Robotics", "type": "skos:broader" }, { "source": "Robotics", "target": "ROB-0066", "type": "skos:narrower" }, { "source": "ROB-0066", "target": "PLY-0011", "type": "augmanitai:crossDomainReference" }, { "source": "ROB-0067", "target": "Feedback Loop", "type": "skos:broader" }, { "source": "Feedback Loop", "target": "ROB-0067", "type": "skos:narrower" }, { "source": "ROB-0070", "target": "RPH-2505", "type": "skos:broader" }, { "source": "RPH-2505", "target": "ROB-0070", "type": "skos:narrower" }, { "source": "ROB-0071", "target": "Vector Embedding", "type": "skos:broader" }, { "source": "Vector Embedding", "target": "ROB-0071", "type": "skos:narrower" }, { "source": "ROB-0071", "target": "RHR-0021", "type": "augmanitai:crossDomainReference" }, { "source": "ROB-0072", "target": "Robotics", "type": "skos:broader" }, { "source": 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"target": "ART-0053", "type": "augmanitai:crossDomainReference" }, { "source": "ROB-0078", "target": "Robotics", "type": "skos:broader" }, { "source": "Robotics", "target": "ROB-0078", "type": "skos:narrower" }, { "source": "ROB-0078", "target": "AED-0016", "type": "augmanitai:crossDomainReference" }, { "source": "ROB-0078", "target": "ASE-0032", "type": "augmanitai:crossDomainReference" }, { "source": "ROB-0078", "target": "COG-0128", "type": "augmanitai:crossDomainReference" }, { "source": "ROB-0079", "target": "Optimization Method", "type": "skos:broader" }, { "source": "Optimization Method", "target": "ROB-0079", "type": "skos:narrower" }, { "source": "ROB-0080", "target": "Analytical Matrix", "type": "skos:broader" }, { "source": "Analytical Matrix", "target": "ROB-0080", "type": "skos:narrower" }, { "source": "ROB-0080", "target": "GAM-0039", "type": "augmanitai:crossDomainReference" }, { "source": "ROB-0082", "target": "Detection System", "type": "skos:broader" }, { "source": "Detection System", "target": "ROB-0082", "type": "skos:narrower" }, { "source": "ROB-0083", "target": "Feedback Loop", "type": "skos:broader" }, { "source": "Feedback Loop", "target": "ROB-0083", "type": "skos:narrower" }, { "source": "ROB-0083", "target": "NEO-0456", "type": "augmanitai:crossDomainReference" }, { "source": "ROB-0084", "target": "Interaction Protocol", "type": "skos:broader" }, { "source": "Interaction Protocol", "target": "ROB-0084", "type": "skos:narrower" }, { "source": "ROB-0084", "target": "RHR-0092", "type": "augmanitai:crossDomainReference" }, { "source": "ROB-0086", "target": "Robotics", "type": "skos:broader" }, { "source": "Robotics", "target": "ROB-0086", "type": "skos:narrower" }, { "source": "ROB-0086", "target": "DAT-0095", "type": "augmanitai:crossDomainReference" }, { "source": "ROB-0086", "target": "MSC-0003", "type": "augmanitai:crossDomainReference" }, { "source": "ROB-0087", "target": "Optimization Method", "type": "skos:broader" }, { "source": "Optimization Method", "target": "ROB-0087", "type": "skos:narrower" }, { "source": "ROB-0089", "target": "Knowledge Transfer", "type": "skos:broader" }, { "source": "Knowledge Transfer", "target": "ROB-0089", "type": "skos:narrower" }, { "source": "ROB-0089", "target": "RHR-0297", "type": "augmanitai:crossDomainReference" }, { "source": "ROB-0090", "target": "Robotics", "type": "skos:broader" }, { "source": "Robotics", "target": "ROB-0090", "type": "skos:narrower" }, { "source": "ROB-0090", "target": "ART-0084", "type": "augmanitai:crossDomainReference" }, { "source": "ROB-0091", "target": "Interaction Protocol", "type": "skos:broader" }, { "source": "Interaction Protocol", "target": "ROB-0091", "type": "skos:narrower" }, { "source": "ROB-0091", "target": "MTH-0081", "type": "augmanitai:crossDomainReference" }, { "source": "ROB-0091", "target": "COG-0172", "type": "augmanitai:crossDomainReference" }, { "source": "ROB-0092", "target": "RPH-2605", "type": "skos:broader" }, { "source": "RPH-2605", "target": "ROB-0092", "type": "skos:narrower" }, { "source": "ROB-0092", "target": "EDU-0034", "type": "augmanitai:crossDomainReference" }, { "source": "ROB-0093", "target": "Robotics", "type": "skos:broader" }, { "source": "Robotics", "target": "ROB-0093", "type": "skos:narrower" }, { "source": "ROB-0093", "target": "CON-0053", "type": "augmanitai:crossDomainReference" }, { "source": "ROB-0094", "target": "Robotics", "type": "skos:broader" }, { "source": "Robotics", "target": "ROB-0094", "type": "skos:narrower" }, { "source": "ROB-0094", "target": "RHR-0255", "type": "augmanitai:crossDomainReference" }, { "source": "ROB-0094", "target": "LIN-0043", "type": "augmanitai:crossDomainReference" }, { "source": "ROB-0095", "target": "RPH-3901", "type": "skos:broader" }, { "source": "RPH-3901", "target": "ROB-0095", "type": "skos:narrower" }, { "source": "ROB-0095", "target": "GAM-0067", "type": "augmanitai:crossDomainReference" }, { "source": "ROB-0095", "target": "KNO-0025", "type": "augmanitai:crossDomainReference" }, { "source": "ROB-0095", "target": "LIN-0072", "type": "augmanitai:crossDomainReference" }, { "source": "ROB-0096", "target": "Analytical Method", "type": "skos:broader" }, { "source": "Analytical Method", "target": "ROB-0096", "type": "skos:narrower" }, { "source": "ROB-0096", "target": "ADA-0001", "type": "augmanitai:crossDomainReference" }, { "source": "ROB-0098", "target": "Robotics", "type": "skos:broader" }, { "source": "Robotics", "target": "ROB-0098", "type": "skos:narrower" }, { "source": "ROB-0098", "target": "PLY-0057", "type": "augmanitai:crossDomainReference" }, { "source": "ROB-0099", "target": "Robotics", "type": "skos:broader" }, { "source": "Robotics", "target": "ROB-0099", "type": "skos:narrower" }, { "source": "ROB-0100", "target": "Robotics", "type": "skos:broader" }, { "source": "Robotics", "target": "ROB-0100", "type": "skos:narrower" }, { "source": "ROB-0100", "target": "AED-0039", "type": 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"source": "RPH-2002", "target": "ROB-0104", "type": "skos:narrower" }, { "source": "ROB-0104", "target": "RHR-0066", "type": "augmanitai:crossDomainReference" }, { "source": "ROB-0105", "target": "RPH-2154", "type": "skos:broader" }, { "source": "RPH-2154", "target": "ROB-0105", "type": "skos:narrower" }, { "source": "ROB-0105", "target": "GAM-0088", "type": "augmanitai:crossDomainReference" }, { "source": "ROB-0105", "target": "MTH-0076", "type": "augmanitai:crossDomainReference" }, { "source": "ROB-0106", "target": "RPH-2301", "type": "skos:broader" }, { "source": "RPH-2301", "target": "ROB-0106", "type": "skos:narrower" }, { "source": "ROB-0107", "target": "RPH-2501", "type": "skos:broader" }, { "source": "RPH-2501", "target": "ROB-0107", "type": "skos:narrower" }, { "source": "ROB-0107", "target": "CRE-0114", "type": "augmanitai:crossDomainReference" }, { "source": "ROB-0108", "target": "RPH-3002", "type": "skos:broader" }, { "source": "RPH-3002", "target": "ROB-0108", "type": "skos:narrower" }, { "source": "ROB-0108", "target": "REL-0104", "type": "augmanitai:crossDomainReference" }, { "source": "ROB-0109", "target": "Robotics", "type": "skos:broader" }, { "source": "Robotics", "target": "ROB-0109", "type": "skos:narrower" }, { "source": "ROB-0109", "target": "RHR-0059", "type": "augmanitai:crossDomainReference" }, { "source": "ROB-0110", "target": "RPH-2252", "type": "skos:broader" }, { "source": "RPH-2252", "target": "ROB-0110", "type": "skos:narrower" }, { "source": "ROB-0110", "target": "AED-0071", "type": "augmanitai:crossDomainReference" }, { "source": "ROB-0110", "target": "AGE-0063", "type": "augmanitai:crossDomainReference" }, { "source": "ROB-0110", "target": "AGE-0078", "type": "augmanitai:crossDomainReference" }, { "source": "ROB-0111", "target": "RPH-2604", "type": "skos:broader" }, { "source": "RPH-2604", "target": "ROB-0111", "type": "skos:narrower" }, { "source": "ROB-0111", "target": "RHR-0215", "type": "augmanitai:crossDomainReference" }, { "source": "ROB-0112", "target": "RPH-2201", "type": "skos:broader" }, { "source": "RPH-2201", "target": "ROB-0112", "type": "skos:narrower" }, { "source": "ROB-0112", "target": "RHR-0152", "type": "augmanitai:crossDomainReference" }, { "source": "ROB-0112", "target": "BEH-0007", "type": "augmanitai:crossDomainReference" }, { "source": "ROB-0113", "target": "RPH-3101", "type": "skos:broader" }, { "source": "RPH-3101", "target": "ROB-0113", "type": "skos:narrower" }, { "source": "ROB-0113", "target": "RHR-0167", "type": "augmanitai:crossDomainReference" }, { "source": "ROB-0113", "target": "RHR-0069", "type": "augmanitai:crossDomainReference" }, { "source": "ROB-0114", "target": "RPH-2505", "type": "skos:broader" }, { "source": "RPH-2505", "target": "ROB-0114", "type": "skos:narrower" }, { "source": "ROB-0114", "target": "CRE-0124", "type": "augmanitai:crossDomainReference" }, { "source": "ROB-0114", "target": "EDU-0045", "type": "augmanitai:crossDomainReference" }, { "source": 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"target": "RPH-3101", "type": "skos:broader" }, { "source": "RPH-3101", "target": "ROB-0118", "type": "skos:narrower" }, { "source": "ROB-0118", "target": "RHR-0296", "type": "augmanitai:crossDomainReference" }, { "source": "ROB-0119", "target": "RPH-2301", "type": "skos:broader" }, { "source": "RPH-2301", "target": "ROB-0119", "type": "skos:narrower" }, { "source": "ROB-0119", "target": "RET-0052", "type": "augmanitai:crossDomainReference" }, { "source": "ROB-0120", "target": "RPH-2701", "type": "skos:broader" }, { "source": "RPH-2701", "target": "ROB-0120", "type": "skos:narrower" }, { "source": "ROB-0120", "target": "REL-0101", "type": "augmanitai:crossDomainReference" }, { "source": "ROB-0120", "target": "RHR-0170", "type": "augmanitai:crossDomainReference" }, { "source": "ROB-0121", "target": "RPH-2355", "type": "skos:broader" }, { "source": "RPH-2355", "target": "ROB-0121", "type": "skos:narrower" }, { "source": "ROB-0121", "target": "NEO-0456", "type": 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{ "source": "ROB-0130", "target": "RHR-0015", "type": "augmanitai:crossDomainReference" }, { "source": "ROB-0131", "target": "RPH-2505", "type": "skos:broader" }, { "source": "RPH-2505", "target": "ROB-0131", "type": "skos:narrower" }, { "source": "ROB-0132", "target": "Analytical Ratio", "type": "skos:broader" }, { "source": "Analytical Ratio", "target": "ROB-0132", "type": "skos:narrower" }, { "source": "ROB-0132", "target": "RHR-0170", "type": "augmanitai:crossDomainReference" }, { "source": "ROB-0133", "target": "RPH-2505", "type": "skos:broader" }, { "source": "RPH-2505", "target": "ROB-0133", "type": "skos:narrower" }, { "source": "ROB-0133", "target": "RHR-0129", "type": "augmanitai:crossDomainReference" }, { "source": "ROB-0134", "target": "RPH-2303", "type": "skos:broader" }, { "source": "RPH-2303", "target": "ROB-0134", "type": "skos:narrower" }, { "source": "ROB-0135", "target": "Performance Gap", "type": "skos:broader" }, { "source": "Performance Gap", "target": "ROB-0135", 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"ROB-0144", "target": "REL-0190", "type": "augmanitai:crossDomainReference" }, { "source": "ROB-0145", "target": "Robotics", "type": "skos:broader" }, { "source": "Robotics", "target": "ROB-0145", "type": "skos:narrower" }, { "source": "ROB-0145", "target": "AGE-0023", "type": "augmanitai:crossDomainReference" }, { "source": "ROB-0145", "target": "AGE-0024", "type": "augmanitai:crossDomainReference" }, { "source": "ROB-0145", "target": "CAI-0014", "type": "augmanitai:crossDomainReference" }, { "source": "ROB-0146", "target": "RPH-2051", "type": "skos:broader" }, { "source": "RPH-2051", "target": "ROB-0146", "type": "skos:narrower" }, { "source": "ROB-0147", "target": "RPH-2505", "type": "skos:broader" }, { "source": "RPH-2505", "target": "ROB-0147", "type": "skos:narrower" }, { "source": "ROB-0147", "target": "ELR-0034", "type": "augmanitai:crossDomainReference" }, { "source": "ROB-0148", "target": "RPH-3003", "type": "skos:broader" }, { "source": "RPH-3003", "target": "ROB-0148", "type": "skos:narrower" }, { "source": "ROB-0148", "target": "AED-0005", "type": "augmanitai:crossDomainReference" }, { "source": "ROB-0148", "target": "AED-0052", "type": "augmanitai:crossDomainReference" }, { "source": "ROB-0148", "target": "ASE-0041", "type": "augmanitai:crossDomainReference" }, { "source": "ROB-0149", "target": "RPH-2505", "type": "skos:broader" }, { "source": "RPH-2505", "target": "ROB-0149", "type": "skos:narrower" }, { "source": "ROB-0149", "target": "RHR-0259", "type": "augmanitai:crossDomainReference" }, { "source": "ROB-0149", "target": "RHR-0083", "type": "augmanitai:crossDomainReference" }, { "source": "ROB-0149", "target": "RHR-0255", "type": "augmanitai:crossDomainReference" }, { "source": "ROB-0150", "target": "RPH-2505", "type": "skos:broader" }, { "source": "RPH-2505", "target": "ROB-0150", "type": "skos:narrower" }, { "source": "ROB-0150", "target": "RHR-0083", "type": "augmanitai:crossDomainReference" }, { "source": "ROB-0150", "target": "RHR-0066", "type": "augmanitai:crossDomainReference" }, { "source": "ROB-0150", "target": "RHR-0099", "type": "augmanitai:crossDomainReference" }, { "source": "ROB-0151", "target": "RPH-2355", "type": "skos:broader" }, { "source": "RPH-2355", "target": "ROB-0151", "type": "skos:narrower" }, { "source": "ROB-0151", "target": "RHR-0296", "type": "augmanitai:crossDomainReference" }, { "source": "ROB-0152", "target": "Robotics", "type": "skos:broader" }, { "source": "Robotics", "target": "ROB-0152", "type": "skos:narrower" }, { "source": "ROB-0152", "target": "RHR-0061", "type": "augmanitai:crossDomainReference" }, { "source": "ROB-0152", "target": "RHR-0297", "type": "augmanitai:crossDomainReference" }, { "source": "ROB-0152", "target": "RHR-0063", "type": "augmanitai:crossDomainReference" }, { "source": "ROB-0153", "target": "RPH-2505", "type": "skos:broader" }, { "source": "RPH-2505", "target": "ROB-0153", "type": "skos:narrower" }, { "source": "ROB-0154", "target": "RPH-2701", "type": "skos:broader" }, { "source": "RPH-2701", "target": "ROB-0154", "type": "skos:narrower" }, { "source": "ROB-0154", "target": "AGE-0071", "type": "augmanitai:crossDomainReference" }, { "source": "ROB-0154", "target": "ASE-0046", "type": "augmanitai:crossDomainReference" }, { "source": "ROB-0154", "target": "ASE-0096", "type": "augmanitai:crossDomainReference" }, { "source": "ROB-0155", "target": "RPH-2101", "type": "skos:broader" }, { "source": "RPH-2101", "target": "ROB-0155", "type": "skos:narrower" }, { "source": "ROB-0155", "target": "COG-0092", "type": "augmanitai:crossDomainReference" }, { "source": "ROB-0156", "target": "RPH-2254", "type": "skos:broader" }, { "source": "RPH-2254", "target": "ROB-0156", "type": "skos:narrower" }, { "source": "ROB-0157", "target": "Robotics", "type": "skos:broader" }, { "source": "Robotics", "target": "ROB-0157", "type": "skos:narrower" }, { "source": "ROB-0157", "target": "RHR-0271", "type": "augmanitai:crossDomainReference" }, { "source": "ROB-0157", "target": "RHR-0121", "type": "augmanitai:crossDomainReference" }, { "source": "ROB-0158", "target": "Robotics", "type": "skos:broader" }, { "source": "Robotics", "target": "ROB-0158", "type": "skos:narrower" }, { "source": "ROB-0158", "target": "RHR-0119", "type": "augmanitai:crossDomainReference" }, { "source": "ROB-0159", "target": "RPH-2002", "type": "skos:broader" }, { "source": "RPH-2002", "target": "ROB-0159", "type": "skos:narrower" }, { "source": "ROB-0159", "target": "RHR-0216", "type": "augmanitai:crossDomainReference" }, { "source": "ROB-0159", "target": "RHR-0168", "type": "augmanitai:crossDomainReference" }, { "source": "ROB-0159", "target": "RHR-0265", "type": "augmanitai:crossDomainReference" }, { "source": "ROB-0160", "target": "RPH-2005", "type": "skos:broader" }, { "source": "RPH-2005", "target": "ROB-0160", "type": "skos:narrower" }, { "source": "ROB-0160", "target": "PER-0069", "type": "augmanitai:crossDomainReference" }, { "source": "ROB-0161", 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"source": "RPH-2355", "target": "ROB-0224", "type": "skos:narrower" }, { "source": "ROB-0224", "target": "RHR-0247", "type": "augmanitai:crossDomainReference" }, { "source": "ROB-0224", "target": "RHR-0257", "type": "augmanitai:crossDomainReference" }, { "source": "ROB-0225", "target": "RPH-2002", "type": "skos:broader" }, { "source": "RPH-2002", "target": "ROB-0225", "type": "skos:narrower" }, { "source": "ROB-0225", "target": "RHR-0247", "type": "augmanitai:crossDomainReference" }, { "source": "ROB-0225", "target": "RHR-0257", "type": "augmanitai:crossDomainReference" }, { "source": "ROB-0225", "target": "RHR-0021", "type": "augmanitai:crossDomainReference" }, { "source": "ROB-0226", "target": "Decision Threshold", "type": "skos:broader" }, { "source": "Decision Threshold", "target": "ROB-0226", "type": "skos:narrower" }, { "source": "ROB-0226", "target": "RHR-0273", "type": "augmanitai:crossDomainReference" }, { "source": "ROB-0226", "target": "RHR-0254", "type": 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"Robotics", "type": "skos:broader" }, { "source": "Robotics", "target": "ROB-0229", "type": "skos:narrower" }, { "source": "ROB-0229", "target": "RHR-0297", "type": "augmanitai:crossDomainReference" }, { "source": "ROB-0230", "target": "RPH-2201", "type": "skos:broader" }, { "source": "RPH-2201", "target": "ROB-0230", "type": "skos:narrower" }, { "source": "ROB-0230", "target": "RHR-0021", "type": "augmanitai:crossDomainReference" }, { "source": "ROB-0231", "target": "RPH-2505", "type": "skos:broader" }, { "source": "RPH-2505", "target": "ROB-0231", "type": "skos:narrower" }, { "source": "ROB-0231", "target": "RHR-0077", "type": "augmanitai:crossDomainReference" }, { "source": "ROB-0232", "target": "Robotics", "type": "skos:broader" }, { "source": "Robotics", "target": "ROB-0232", "type": "skos:narrower" }, { "source": "ROB-0232", "target": "RHR-0297", "type": "augmanitai:crossDomainReference" }, { "source": "ROB-0232", "target": "PLY-0064", "type": "augmanitai:crossDomainReference" }, { "source": "ROB-0233", "target": "RPH-2051", "type": "skos:broader" }, { "source": "RPH-2051", "target": "ROB-0233", "type": "skos:narrower" }, { "source": "ROB-0233", "target": "RHR-0170", "type": "augmanitai:crossDomainReference" }, { "source": "ROB-0233", "target": "RHR-0226", "type": "augmanitai:crossDomainReference" }, { "source": "ROB-0234", "target": "RPH-2605", "type": "skos:broader" }, { "source": "RPH-2605", "target": "ROB-0234", "type": "skos:narrower" }, { "source": "ROB-0234", "target": "CRE-0091", "type": "augmanitai:crossDomainReference" }, { "source": "ROB-0234", "target": "RHR-0094", "type": "augmanitai:crossDomainReference" }, { "source": "ROB-0235", "target": "RPH-2002", "type": "skos:broader" }, { "source": "RPH-2002", "target": "ROB-0235", "type": "skos:narrower" }, { "source": "ROB-0235", "target": "ART-0085", "type": "augmanitai:crossDomainReference" }, { "source": "ROB-0235", "target": "ART-0086", "type": "augmanitai:crossDomainReference" }, { "source": "ROB-0235", "target": "COG-0186", "type": "augmanitai:crossDomainReference" }, { "source": "ROB-0236", "target": "RPH-2301", "type": "skos:broader" }, { "source": "RPH-2301", "target": "ROB-0236", "type": "skos:narrower" }, { "source": "ROB-0236", "target": "MSC-0052", "type": "augmanitai:crossDomainReference" }, { "source": "ROB-0237", "target": "Process Cycle", "type": "skos:broader" }, { "source": "Process Cycle", "target": "ROB-0237", "type": "skos:narrower" }, { "source": "ROB-0238", "target": "RPH-3952", "type": "skos:broader" }, { "source": "RPH-3952", "target": "ROB-0238", "type": "skos:narrower" }, { "source": "ROB-0238", "target": "RHR-0131", "type": "augmanitai:crossDomainReference" }, { "source": "ROB-0238", "target": "CUS-0045", "type": "augmanitai:crossDomainReference" }, { "source": "ROB-0239", "target": "RPH-2604", "type": "skos:broader" }, { "source": "RPH-2604", "target": "ROB-0239", "type": "skos:narrower" }, { "source": "ROB-0240", "target": "RPH-2401", "type": "skos:broader" }, { "source": "RPH-2401", "target": "ROB-0240", "type": "skos:narrower" }, { "source": "ROB-0240", "target": "ADA-0010", "type": "augmanitai:crossDomainReference" }, { "source": "ROB-0240", "target": "AED-0056", "type": "augmanitai:crossDomainReference" }, { "source": "ROB-0240", "target": "CON-0053", "type": "augmanitai:crossDomainReference" }, { "source": "ROB-0241", "target": "Pattern Recognition", "type": "skos:broader" }, { "source": "Pattern Recognition", "target": "ROB-0241", "type": "skos:narrower" }, { "source": "ROB-0241", "target": "AED-0013", "type": "augmanitai:crossDomainReference" }, { "source": "ROB-0241", "target": "AED-0077", "type": "augmanitai:crossDomainReference" }, { "source": "ROB-0241", "target": "AED-0093", "type": "augmanitai:crossDomainReference" }, { "source": "ROB-0242", "target": "RPH-2703", "type": "skos:broader" }, { "source": "RPH-2703", "target": "ROB-0242", "type": "skos:narrower" }, { "source": "ROB-0242", "target": "NEO-0456", "type": "augmanitai:crossDomainReference" }, { "source": "ROB-0242", "target": "BEH-0007", "type": "augmanitai:crossDomainReference" }, { "source": "ROB-0243", "target": "RPH-2254", "type": "skos:broader" }, { "source": "RPH-2254", "target": "ROB-0243", "type": "skos:narrower" }, { "source": "ROB-0243", "target": "ASE-0019", "type": "augmanitai:crossDomainReference" }, { "source": "ROB-0243", "target": "CUS-0001", "type": "augmanitai:crossDomainReference" }, { "source": "ROB-0243", "target": "CUS-0037", "type": "augmanitai:crossDomainReference" }, { "source": "ROB-0244", "target": "Knowledge Transfer", "type": "skos:broader" }, { "source": "Knowledge Transfer", "target": "ROB-0244", "type": "skos:narrower" }, { "source": "ROB-0244", "target": "RHR-0297", "type": "augmanitai:crossDomainReference" }, { "source": "ROB-0244", "target": "RET-0055", "type": "augmanitai:crossDomainReference" }, { "source": "ROB-0244", "target": "RHR-0096", "type": "augmanitai:crossDomainReference" }, { "source": "ROB-0245", "target": "RPH-2355", "type": "skos:broader" }, { "source": "RPH-2355", "target": "ROB-0245", "type": "skos:narrower" }, { "source": "ROB-0245", "target": "MUS-0051", "type": "augmanitai:crossDomainReference" }, { "source": "ROB-0246", "target": "RPH-2701", "type": "skos:broader" }, { "source": "RPH-2701", "target": "ROB-0246", "type": "skos:narrower" }, { "source": "ROB-0246", "target": "CON-0070", "type": "augmanitai:crossDomainReference" }, { "source": "ROB-0247", "target": "RPH-2055", "type": "skos:broader" }, { "source": "RPH-2055", "target": "ROB-0247", "type": "skos:narrower" }, { "source": "ROB-0247", "target": "RHR-0297", "type": "augmanitai:crossDomainReference" }, { "source": "ROB-0248", "target": "Computational Model", "type": "skos:broader" }, { "source": "Computational Model", "target": "ROB-0248", "type": "skos:narrower" }, { "source": "ROB-0248", "target": "GAM-0059", "type": "augmanitai:crossDomainReference" }, { "source": "ROB-0249", "target": 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"augmanitai:crossDomainReference" }, { "source": "ROB-0251", "target": "COG-0088", "type": "augmanitai:crossDomainReference" }, { "source": "ROB-0252", "target": "Robotics", "type": "skos:broader" }, { "source": "Robotics", "target": "ROB-0252", "type": "skos:narrower" }, { "source": "ROB-0252", "target": "RHR-0300", "type": "augmanitai:crossDomainReference" }, { "source": "ROB-0252", "target": "RHR-0284", "type": "augmanitai:crossDomainReference" }, { "source": "ROB-0252", "target": "RHR-0285", "type": "augmanitai:crossDomainReference" }, { "source": "ROB-0253", "target": "RPH-2701", "type": "skos:broader" }, { "source": "RPH-2701", "target": "ROB-0253", "type": "skos:narrower" }, { "source": "ROB-0253", "target": "RHR-0259", "type": "augmanitai:crossDomainReference" }, { "source": "ROB-0253", "target": "RHR-0085", "type": "augmanitai:crossDomainReference" }, { "source": "ROB-0254", "target": "RPH-2201", "type": "skos:broader" }, { "source": "RPH-2201", "target": "ROB-0254", "type": "skos:narrower" }, { "source": "ROB-0254", "target": "COG-0134", "type": "augmanitai:crossDomainReference" }, { "source": "ROB-0255", "target": "RPH-2355", "type": "skos:broader" }, { "source": "RPH-2355", "target": "ROB-0255", "type": "skos:narrower" }, { "source": "ROB-0255", "target": "EDU-0069", "type": "augmanitai:crossDomainReference" }, { "source": "ROB-0255", "target": "COG-0088", "type": "augmanitai:crossDomainReference" }, { "source": "ROB-0256", "target": "RPH-2055", "type": "skos:broader" }, { "source": "RPH-2055", "target": "ROB-0256", "type": "skos:narrower" }, { "source": "ROB-0256", "target": "RHR-0292", "type": "augmanitai:crossDomainReference" }, { "source": "ROB-0256", "target": "ART-0090", "type": "augmanitai:crossDomainReference" }, { "source": "ROB-0257", "target": "RPH-2401", "type": "skos:broader" }, { "source": "RPH-2401", "target": "ROB-0257", "type": "skos:narrower" }, { "source": "ROB-0257", "target": "RHR-0008", "type": "augmanitai:crossDomainReference" }, { "source": "ROB-0258", "target": "RPH-3802", "type": "skos:broader" }, { "source": "RPH-3802", "target": "ROB-0258", "type": "skos:narrower" }, { "source": "ROB-0258", "target": "RHR-0175", "type": "augmanitai:crossDomainReference" }, { "source": "ROB-0259", "target": "RPH-2254", "type": "skos:broader" }, { "source": "RPH-2254", "target": "ROB-0259", "type": "skos:narrower" }, { "source": "ROB-0259", "target": "AGE-0021", "type": "augmanitai:crossDomainReference" }, { "source": "ROB-0259", "target": "AGE-0032", "type": "augmanitai:crossDomainReference" }, { "source": "ROB-0259", "target": "AGE-0075", "type": "augmanitai:crossDomainReference" }, { "source": "ROB-0260", "target": "Robotics", "type": "skos:broader" }, { "source": "Robotics", "target": "ROB-0260", "type": "skos:narrower" }, { "source": "ROB-0260", "target": "MSC-0048", "type": "augmanitai:crossDomainReference" }, { "source": "ROB-0260", "target": "RHR-0092", "type": "augmanitai:crossDomainReference" }, { "source": "ROB-0260", "target": "RHR-0230", "type": "augmanitai:crossDomainReference" }, { "source": "ROB-0261", "target": "Robotics", "type": "skos:broader" }, { "source": "Robotics", "target": "ROB-0261", "type": "skos:narrower" }, { "source": "ROB-0261", "target": "IDN-0049", "type": "augmanitai:crossDomainReference" }, { "source": "ROB-0262", "target": "RPH-2303", "type": "skos:broader" }, { "source": "RPH-2303", "target": "ROB-0262", "type": "skos:narrower" }, { "source": "ROB-0262", "target": "RHR-0081", "type": "augmanitai:crossDomainReference" }, { "source": "ROB-0262", "target": "KNO-0005", "type": "augmanitai:crossDomainReference" }, { "source": "ROB-0263", "target": "Robotics", "type": "skos:broader" }, { "source": "Robotics", "target": "ROB-0263", "type": "skos:narrower" }, { "source": "ROB-0263", "target": "RHR-0248", "type": "augmanitai:crossDomainReference" }, { "source": "ROB-0264", "target": "Robotics", "type": "skos:broader" }, { "source": "Robotics", "target": "ROB-0264", "type": "skos:narrower" }, { "source": "ROB-0264", "target": "CRE-0117", "type": "augmanitai:crossDomainReference" }, { "source": "ROB-0264", "target": "ELR-0185", "type": "augmanitai:crossDomainReference" }, { "source": "ROB-0264", "target": "MKT-0014", "type": "augmanitai:crossDomainReference" }, { "source": "ROB-0265", "target": "RPH-3952", "type": "skos:broader" }, { "source": "RPH-3952", "target": "ROB-0265", "type": "skos:narrower" }, { "source": "ROB-0265", "target": "AED-0048", "type": "augmanitai:crossDomainReference" }, { "source": "ROB-0265", "target": "BEH-0013", "type": "augmanitai:crossDomainReference" }, { "source": "ROB-0266", "target": "Robotics", "type": "skos:broader" }, { "source": "Robotics", "target": "ROB-0266", "type": "skos:narrower" }, { "source": "ROB-0267", "target": "Analytical Ratio", "type": "skos:broader" }, { "source": "Analytical Ratio", "target": "ROB-0267", "type": "skos:narrower" }, { "source": "ROB-0267", "target": "RHR-0076", "type": "augmanitai:crossDomainReference" }, { "source": "ROB-0268", "target": "RPH-2301", "type": "skos:broader" }, { "source": "RPH-2301", "target": "ROB-0268", "type": "skos:narrower" }, { "source": "ROB-0268", "target": "RHR-0297", "type": "augmanitai:crossDomainReference" }, { "source": "ROB-0268", "target": "CRE-0075", "type": "augmanitai:crossDomainReference" }, { "source": "ROB-0269", "target": "Robotics", "type": "skos:broader" }, { "source": "Robotics", "target": "ROB-0269", "type": "skos:narrower" }, { "source": "ROB-0269", "target": "RHR-0297", "type": "augmanitai:crossDomainReference" }, { "source": "ROB-0270", "target": "RPH-2002", "type": "skos:broader" }, { "source": "RPH-2002", "target": "ROB-0270", "type": "skos:narrower" }, { "source": "ROB-0270", "target": "RHR-0297", "type": "augmanitai:crossDomainReference" }, { "source": "ROB-0270", "target": "RHR-0271", "type": "augmanitai:crossDomainReference" }, { "source": "ROB-0270", "target": "RHR-0213", "type": "augmanitai:crossDomainReference" }, { "source": "ROB-0271", "target": "RPH-2303", "type": "skos:broader" }, { "source": "RPH-2303", "target": "ROB-0271", "type": "skos:narrower" }, { "source": "ROB-0271", "target": "COG-0058", "type": "augmanitai:crossDomainReference" }, { "source": "ROB-0272", "target": "RPH-2254", "type": "skos:broader" }, { "source": "RPH-2254", "target": "ROB-0272", "type": "skos:narrower" }, { "source": "ROB-0272", "target": "COP-0042", "type": "augmanitai:crossDomainReference" }, { "source": "ROB-0272", "target": "CUS-0062", "type": "augmanitai:crossDomainReference" }, { "source": "ROB-0272", "target": "RHR-0152", "type": "augmanitai:crossDomainReference" }, { "source": "ROB-0273", "target": "Robotics", "type": "skos:broader" }, { "source": "Robotics", "target": "ROB-0273", "type": "skos:narrower" }, { "source": "ROB-0273", "target": "RHR-0272", "type": "augmanitai:crossDomainReference" }, { "source": "ROB-0274", "target": "RPH-2055", "type": "skos:broader" }, { "source": "RPH-2055", "target": "ROB-0274", "type": "skos:narrower" }, { "source": "ROB-0274", "target": "COG-0048", "type": "augmanitai:crossDomainReference" }, { "source": "ROB-0274", "target": "COG-0057", "type": "augmanitai:crossDomainReference" }, { "source": "ROB-0274", "target": "COP-0055", "type": "augmanitai:crossDomainReference" }, { "source": "ROB-0275", "target": "Optimization Gradient", "type": "skos:broader" }, { "source": "Optimization Gradient", "target": "ROB-0275", "type": "skos:narrower" }, { "source": "ROB-0276", "target": "RPH-3601", "type": "skos:broader" }, { "source": "RPH-3601", "target": "ROB-0276", "type": "skos:narrower" }, { "source": "ROB-0276", "target": "RHR-0216", "type": "augmanitai:crossDomainReference" }, { "source": "ROB-0276", "target": "CUS-0010", "type": "augmanitai:crossDomainReference" }, { "source": "ROB-0276", "target": "RHR-0021", "type": "augmanitai:crossDomainReference" }, { "source": "ROB-0277", "target": "RPH-2505", "type": "skos:broader" }, { "source": "RPH-2505", "target": "ROB-0277", "type": "skos:narrower" }, { "source": "ROB-0277", "target": "RHR-0083", "type": "augmanitai:crossDomainReference" }, { "source": "ROB-0277", "target": "RHR-0281", "type": "augmanitai:crossDomainReference" }, { "source": "ROB-0278", "target": "Robotics", "type": "skos:broader" }, { "source": "Robotics", "target": "ROB-0278", "type": "skos:narrower" }, { "source": "ROB-0278", "target": "AED-0018", "type": "augmanitai:crossDomainReference" }, { "source": "ROB-0278", "target": "AED-0081", "type": "augmanitai:crossDomainReference" }, { "source": "ROB-0278", "target": "ASE-0018", "type": "augmanitai:crossDomainReference" }, { "source": "ROB-0279", "target": "RPH-2355", "type": "skos:broader" }, { "source": "RPH-2355", "target": "ROB-0279", "type": "skos:narrower" }, { "source": "ROB-0279", "target": "RHR-0083", "type": "augmanitai:crossDomainReference" }, { "source": "ROB-0279", "target": "DAT-0028", "type": "augmanitai:crossDomainReference" }, { "source": "ROB-0280", "target": "RPH-2355", "type": "skos:broader" }, { "source": "RPH-2355", "target": "ROB-0280", "type": "skos:narrower" }, { "source": "ROB-0280", "target": "GAM-0043", "type": "augmanitai:crossDomainReference" }, { "source": "ROB-0281", "target": "RPH-3002", "type": "skos:broader" }, { "source": "RPH-3002", "target": "ROB-0281", "type": "skos:narrower" }, { "source": "ROB-0281", "target": "CRE-0139", "type": "augmanitai:crossDomainReference" }, { "source": "ROB-0282", "target": "RPH-2103", "type": "skos:broader" }, { "source": "RPH-2103", "target": "ROB-0282", "type": "skos:narrower" }, { "source": "ROB-0282", "target": "ASE-0018", "type": "augmanitai:crossDomainReference" }, { "source": "ROB-0282", "target": "ASE-0043", "type": "augmanitai:crossDomainReference" }, { "source": "ROB-0282", "target": "CON-0066", "type": "augmanitai:crossDomainReference" }, { "source": "ROB-0283", "target": "Robotics", "type": "skos:broader" }, { "source": "Robotics", "target": "ROB-0283", "type": "skos:narrower" }, { "source": "ROB-0283", "target": "CON-0068", "type": "augmanitai:crossDomainReference" }, { "source": "ROB-0284", "target": "Robotics", "type": "skos:broader" }, { "source": "Robotics", "target": "ROB-0284", "type": "skos:narrower" }, { "source": "ROB-0284", "target": "RHR-0021", "type": "augmanitai:crossDomainReference" }, { "source": "ROB-0284", "target": "RHR-0169", "type": "augmanitai:crossDomainReference" }, { "source": "ROB-0284", "target": "RHR-0213", "type": "augmanitai:crossDomainReference" }, { "source": "ROB-0285", "target": "Robotics", "type": "skos:broader" }, { "source": "Robotics", "target": "ROB-0285", "type": "skos:narrower" }, { "source": "ROB-0285", "target": "GAM-0002", "type": "augmanitai:crossDomainReference" }, { "source": "ROB-0286", "target": "RPH-2301", "type": "skos:broader" }, { "source": "RPH-2301", "target": "ROB-0286", "type": "skos:narrower" }, { "source": "ROB-0286", "target": 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"RPH-3901", "type": "skos:broader" }, { "source": "RPH-3901", "target": "RPH-1951", "type": "skos:narrower" }, { "source": "RPH-1951", "target": "CRE-0164", "type": "augmanitai:crossDomainReference" }, { "source": "RPH-1951", "target": "DES-0005", "type": "augmanitai:crossDomainReference" }, { "source": "RPH-1951", "target": "EDU-0069", "type": "augmanitai:crossDomainReference" }, { "source": "RPH-1952", "target": "RPH-3901", "type": "skos:broader" }, { "source": "RPH-3901", "target": "RPH-1952", "type": "skos:narrower" }, { "source": "RPH-1952", "target": "COG-0043", "type": "augmanitai:crossDomainReference" }, { "source": "RPH-1952", "target": "COG-0052", "type": "augmanitai:crossDomainReference" }, { "source": "RPH-1952", "target": "COG-0078", "type": "augmanitai:crossDomainReference" }, { "source": "RPH-1954", "target": "RPH-3901", "type": "skos:broader" }, { "source": "RPH-3901", "target": "RPH-1954", "type": "skos:narrower" }, { "source": "RPH-1954", "target": "AED-0060", "type": "augmanitai:crossDomainReference" }, { "source": "RPH-1954", "target": "AED-0061", "type": "augmanitai:crossDomainReference" }, { "source": "RPH-1954", "target": "CON-0086", "type": "augmanitai:crossDomainReference" }, { "source": "RPH-333", "target": "RPH-2003", "type": "skos:broader" }, { "source": "RPH-2003", "target": "RPH-333", "type": "skos:narrower" }, { "source": "RPH-333", "target": "CRE-0188", "type": "augmanitai:crossDomainReference" }, { "source": "RPH-333", "target": "CRE-0076", "type": "augmanitai:crossDomainReference" }, { "source": "RPH-337", "target": "Relationship Phenomena", "type": "skos:broader" }, { "source": "Relationship Phenomena", "target": "RPH-337", "type": "skos:narrower" }, { "source": "RPH-337", "target": "EDU-0085", "type": "augmanitai:crossDomainReference" }, { "source": "RPH-350", "target": "Relationship Phenomena", "type": "skos:broader" }, { "source": "Relationship Phenomena", "target": "RPH-350", "type": "skos:narrower" }, { "source": "RPH-350", "target": "ELR-0011", "type": "augmanitai:crossDomainReference" }, { "source": "RPH-350", "target": "EDU-0079", "type": "augmanitai:crossDomainReference" }, { "source": "RPH-409", "target": "RPH-3001", "type": "skos:broader" }, { "source": "RPH-3001", "target": "RPH-409", "type": "skos:narrower" }, { "source": "RPH-409", "target": "AGE-0082", "type": "augmanitai:crossDomainReference" }, { "source": "RPH-409", "target": "KNO-0012", "type": "augmanitai:crossDomainReference" }, { "source": "RPH-414", "target": "RPH-2005", "type": "skos:broader" }, { "source": "RPH-2005", "target": "RPH-414", "type": "skos:narrower" }, { "source": "RPH-428", "target": "Relationship Phenomena", "type": "skos:broader" }, { "source": "Relationship Phenomena", "target": "RPH-428", "type": "skos:narrower" }, { "source": "SAL-0001", "target": "Sales AI", "type": "skos:broader" }, { "source": "Sales AI", "target": "SAL-0001", "type": "skos:narrower" }, { "source": "SAL-0001", "target": "AUG-0319", "type": "augmanitai:crossDomainReference" }, { "source": "SAL-0001", "target": "AUG-0889", "type": "augmanitai:crossDomainReference" }, { "source": "SAL-0001", "target": "AUG-0892", "type": "augmanitai:crossDomainReference" }, { "source": "SAL-0002", "target": "RPH-2505", "type": "skos:broader" }, { "source": "RPH-2505", "target": "SAL-0002", "type": "skos:narrower" }, { "source": "SAL-0002", "target": "RET-0012", "type": "augmanitai:crossDomainReference" }, { "source": "SAL-0002", "target": "GAM-0027", "type": "augmanitai:crossDomainReference" }, { "source": "SAL-0003", "target": "RPH-2701", "type": "skos:broader" }, { "source": "RPH-2701", "target": "SAL-0003", "type": "skos:narrower" }, { "source": "SAL-0003", "target": "RHR-0190", "type": "augmanitai:crossDomainReference" }, { "source": "SAL-0003", "target": "RHR-0095", "type": "augmanitai:crossDomainReference" }, { "source": "SAL-0004", "target": "RPH-2505", "type": "skos:broader" }, { "source": "RPH-2505", "target": "SAL-0004", "type": "skos:narrower" }, { "source": "SAL-0004", "target": "CRE-0035", "type": "augmanitai:crossDomainReference" }, { "source": "SAL-0005", "target": "RPH-2701", "type": "skos:broader" }, { "source": "RPH-2701", "target": "SAL-0005", "type": "skos:narrower" }, { "source": "SAL-0006", "target": "RPH-2053", "type": "skos:broader" }, { "source": "RPH-2053", "target": "SAL-0006", "type": "skos:narrower" }, { "source": "SAL-0006", "target": "MUS-0093", "type": "augmanitai:crossDomainReference" }, { "source": "SAL-0006", "target": "CUS-0035", "type": "augmanitai:crossDomainReference" }, { "source": "SAL-0007", "target": "RPH-2104", "type": "skos:broader" }, { "source": "RPH-2104", "target": "SAL-0007", "type": "skos:narrower" }, { "source": "SAL-0007", "target": "BEH-0057", "type": "augmanitai:crossDomainReference" }, { "source": "SAL-0007", "target": "REL-0155", "type": "augmanitai:crossDomainReference" }, { "source": "SAL-0008", "target": "Sales AI", "type": "skos:broader" }, { "source": "Sales AI", "target": "SAL-0008", "type": "skos:narrower" }, { "source": "SAL-0008", "target": "BEH-0019", "type": "augmanitai:crossDomainReference" }, { "source": "SAL-0008", "target": "BEH-0034", "type": "augmanitai:crossDomainReference" }, { "source": "SAL-0008", "target": "CON-0077", "type": "augmanitai:crossDomainReference" }, { "source": "SAL-0009", "target": "Sales AI", "type": "skos:broader" }, { "source": "Sales AI", "target": "SAL-0009", "type": "skos:narrower" }, { "source": "SAL-0009", "target": "CON-0067", "type": "augmanitai:crossDomainReference" }, { "source": "SAL-0010", "target": "RPH-2701", "type": "skos:broader" }, { "source": "RPH-2701", "target": "SAL-0010", "type": "skos:narrower" }, { "source": "SAL-0010", "target": "REL-0089", "type": "augmanitai:crossDomainReference" }, { "source": "SAL-0010", "target": "COG-0088", "type": "augmanitai:crossDomainReference" }, { "source": "SAL-0011", "target": "Cognitive Bias", "type": "skos:broader" }, { "source": "Cognitive Bias", "target": "SAL-0011", "type": "skos:narrower" }, { "source": "SAL-0011", "target": "CUS-0099", "type": "augmanitai:crossDomainReference" }, { "source": "SAL-0012", "target": "RPH-2201", "type": "skos:broader" }, { "source": "RPH-2201", "target": "SAL-0012", "type": "skos:narrower" }, { "source": "SAL-0012", "target": "COG-0074", "type": "augmanitai:crossDomainReference" }, { "source": "SAL-0012", "target": "COG-0166", "type": "augmanitai:crossDomainReference" }, { "source": "SAL-0013", "target": "RPH-2701", "type": "skos:broader" }, { "source": "RPH-2701", "target": "SAL-0013", "type": "skos:narrower" }, { "source": "SAL-0013", "target": "ART-0017", "type": "augmanitai:crossDomainReference" }, { "source": "SAL-0013", "target": "ART-0032", "type": "augmanitai:crossDomainReference" }, { "source": "SAL-0013", "target": "ART-0037", "type": "augmanitai:crossDomainReference" }, { "source": "SAL-0014", "target": "RPH-2604", "type": "skos:broader" }, { "source": "RPH-2604", "target": "SAL-0014", "type": "skos:narrower" }, { "source": "SAL-0014", "target": "AED-0019", "type": "augmanitai:crossDomainReference" }, { "source": "SAL-0014", "target": "AED-0092", "type": "augmanitai:crossDomainReference" }, { "source": "SAL-0014", "target": "AGE-0023", "type": "augmanitai:crossDomainReference" }, { "source": "SAL-0015", "target": "RPH-2701", "type": "skos:broader" }, { "source": "RPH-2701", "target": "SAL-0015", "type": "skos:narrower" }, { "source": "SAL-0015", "target": "DAT-0083", "type": "augmanitai:crossDomainReference" }, { "source": "SAL-0016", "target": "Algorithm", "type": "skos:broader" }, { "source": "Algorithm", "target": "SAL-0016", "type": "skos:narrower" }, { "source": "SAL-0016", "target": "ELR-0094", "type": "augmanitai:crossDomainReference" }, { "source": "SAL-0016", "target": "MUS-0071", "type": "augmanitai:crossDomainReference" }, { "source": "SAL-0017", "target": "RPH-2201", "type": "skos:broader" }, { "source": "RPH-2201", "target": "SAL-0017", "type": "skos:narrower" }, { "source": "SAL-0017", "target": "REL-0155", "type": "augmanitai:crossDomainReference" }, { "source": "SAL-0017", "target": "BEH-0057", "type": "augmanitai:crossDomainReference" }, { "source": "SAL-0018", "target": "RPH-2201", "type": "skos:broader" }, { "source": "RPH-2201", "target": "SAL-0018", "type": "skos:narrower" }, { "source": "SAL-0018", "target": "ELR-0094", "type": "augmanitai:crossDomainReference" }, { "source": "SAL-0019", "target": "Sales AI", "type": "skos:broader" }, { "source": "Sales AI", "target": "SAL-0019", "type": "skos:narrower" }, { "source": "SAL-0019", "target": "COG-0074", "type": "augmanitai:crossDomainReference" }, { "source": "SAL-0019", "target": "COG-0088", "type": "augmanitai:crossDomainReference" }, { "source": "SAL-0020", "target": "RPH-2051", "type": "skos:broader" }, { "source": "RPH-2051", "target": "SAL-0020", "type": "skos:narrower" }, { "source": "SAL-0020", "target": "DAT-0072", "type": "augmanitai:crossDomainReference" }, { "source": "SAL-0020", "target": "DAT-0093", "type": "augmanitai:crossDomainReference" }, { "source": "SAL-0021", "target": "Quality Metric", "type": "skos:broader" }, { "source": "Quality Metric", "target": "SAL-0021", "type": "skos:narrower" }, { "source": "SAL-0021", "target": "DAT-0062", "type": "augmanitai:crossDomainReference" }, { "source": "SAL-0022", "target": "Analytical Ratio", "type": "skos:broader" }, { "source": "Analytical Ratio", "target": "SAL-0022", "type": "skos:narrower" }, { "source": "SAL-0022", "target": "CON-0064", "type": "augmanitai:crossDomainReference" }, { "source": "SAL-0022", "target": "RET-0006", "type": "augmanitai:crossDomainReference" }, { "source": "SAL-0023", "target": "RPH-3101", "type": "skos:broader" }, { "source": "RPH-3101", "target": "SAL-0023", "type": "skos:narrower" }, { "source": "SAL-0023", "target": "MKT-0017", "type": "augmanitai:crossDomainReference" }, { "source": "SAL-0024", "target": "RPH-2505", "type": "skos:broader" }, { "source": "RPH-2505", "target": "SAL-0024", "type": "skos:narrower" }, { "source": "SAL-0024", "target": "RET-0059", "type": "augmanitai:crossDomainReference" }, { "source": "SAL-0024", "target": "RET-0006", "type": "augmanitai:crossDomainReference" }, { "source": "SAL-0025", "target": "RPH-2503", "type": "skos:broader" }, { "source": "RPH-2503", "target": "SAL-0025", "type": "skos:narrower" }, { "source": "SAL-0025", "target": "ASE-0033", "type": "augmanitai:crossDomainReference" }, { "source": "SAL-0025", "target": "AUG-0383", "type": "augmanitai:crossDomainReference" }, { "source": "SAL-0025", "target": "BEH-0036", "type": "augmanitai:crossDomainReference" }, { "source": "SAL-0026", "target": "Sales AI", "type": "skos:broader" }, { "source": "Sales AI", "target": "SAL-0026", "type": "skos:narrower" }, { "source": "SAL-0026", "target": "CRE-0148", "type": "augmanitai:crossDomainReference" }, { "source": "SAL-0027", "target": "RPH-3952", "type": "skos:broader" }, { "source": "RPH-3952", "target": "SAL-0027", "type": "skos:narrower" }, { "source": "SAL-0027", "target": "RET-0054", "type": "augmanitai:crossDomainReference" }, { "source": "SAL-0028", "target": "Sales AI", "type": "skos:broader" }, { "source": "Sales AI", "target": "SAL-0028", "type": "skos:narrower" }, { "source": "SAL-0028", "target": "DES-0026", "type": "augmanitai:crossDomainReference" }, { "source": "SAL-0029", "target": "RPH-3802", "type": "skos:broader" }, { "source": "RPH-3802", "target": "SAL-0029", "type": "skos:narrower" }, { "source": "SAL-0029", "target": "ART-0017", "type": "augmanitai:crossDomainReference" }, { "source": "SAL-0029", "target": "ASE-0051", "type": "augmanitai:crossDomainReference" }, { "source": "SAL-0029", "target": "ASE-0085", "type": "augmanitai:crossDomainReference" }, { "source": "SAL-0030", "target": "Risk Factor", "type": "skos:broader" }, { "source": "Risk Factor", "target": "SAL-0030", "type": "skos:narrower" }, { "source": "SAL-0030", "target": "MKT-0075", "type": "augmanitai:crossDomainReference" }, { "source": "SAL-0030", "target": "MKT-0077", "type": "augmanitai:crossDomainReference" }, { "source": "SAL-0031", "target": "Sales AI", "type": "skos:broader" }, { "source": "Sales AI", "target": "SAL-0031", "type": "skos:narrower" }, { "source": "SAL-0031", "target": "ART-0085", "type": "augmanitai:crossDomainReference" }, { "source": "SAL-0031", "target": "ART-0086", "type": "augmanitai:crossDomainReference" }, { "source": "SAL-0031", "target": "ASE-0022", "type": "augmanitai:crossDomainReference" }, { "source": "SAL-0032", "target": "Processing Pipeline", "type": "skos:broader" }, { "source": "Processing Pipeline", "target": "SAL-0032", "type": "skos:narrower" }, { "source": "SAL-0032", "target": "ADA-0012", "type": "augmanitai:crossDomainReference" }, { "source": "SAL-0033", "target": "RPH-2355", "type": "skos:broader" }, { "source": "RPH-2355", "target": "SAL-0033", "type": "skos:narrower" }, { "source": "SAL-0033", "target": "ELR-0049", "type": "augmanitai:crossDomainReference" }, { "source": "SAL-0034", "target": "Sales AI", "type": "skos:broader" }, { "source": "Sales AI", "target": "SAL-0034", "type": "skos:narrower" }, { "source": "SAL-0034", "target": "COP-0077", "type": "augmanitai:crossDomainReference" }, { "source": "SAL-0034", "target": "COG-0164", "type": "augmanitai:crossDomainReference" }, { "source": "SAL-0035", "target": "RPH-2104", "type": "skos:broader" }, { "source": "RPH-2104", "target": "SAL-0035", "type": "skos:narrower" }, { "source": "SAL-0035", "target": "CUS-0037", "type": "augmanitai:crossDomainReference" }, { "source": "SAL-0035", "target": "BEH-0057", "type": "augmanitai:crossDomainReference" }, { "source": "SAL-0036", "target": "RPH-2104", "type": "skos:broader" }, { "source": "RPH-2104", "target": "SAL-0036", "type": "skos:narrower" }, { "source": "SAL-0036", "target": "AGE-0018", "type": "augmanitai:crossDomainReference" }, { "source": "SAL-0037", "target": "RPH-3101", "type": "skos:broader" }, { "source": "RPH-3101", "target": "SAL-0037", "type": "skos:narrower" }, { "source": "SAL-0037", "target": "GAM-0054", "type": "augmanitai:crossDomainReference" }, { "source": "SAL-0037", "target": "LIN-0004", "type": "augmanitai:crossDomainReference" }, { "source": "SAL-0037", "target": "RHR-0007", "type": "augmanitai:crossDomainReference" }, { "source": "SAL-0038", "target": "RPH-2604", "type": "skos:broader" }, { "source": "RPH-2604", "target": "SAL-0038", "type": "skos:narrower" }, { "source": "SAL-0038", "target": "RPH-1163", "type": "augmanitai:crossDomainReference" }, { "source": "SAL-0039", "target": "RPH-3101", "type": "skos:broader" }, { "source": "RPH-3101", "target": "SAL-0039", "type": "skos:narrower" }, { "source": "SAL-0040", "target": "RPH-2505", "type": "skos:broader" }, { "source": "RPH-2505", "target": "SAL-0040", "type": "skos:narrower" }, { "source": "SAL-0041", "target": "RPH-2505", "type": "skos:broader" }, { "source": "RPH-2505", "target": "SAL-0041", "type": "skos:narrower" }, { "source": "SAL-0041", "target": "CUS-0020", "type": "augmanitai:crossDomainReference" }, { "source": "SAL-0042", "target": "Sales AI", "type": "skos:broader" }, { "source": "Sales AI", "target": "SAL-0042", "type": "skos:narrower" }, { "source": "SAL-0042", "target": "RET-0066", "type": "augmanitai:crossDomainReference" }, { "source": "SAL-0042", "target": "RET-0004", "type": "augmanitai:crossDomainReference" }, { "source": "SAL-0042", "target": "BEH-0081", "type": "augmanitai:crossDomainReference" }, { "source": "SAL-0043", "target": "RPH-2104", "type": "skos:broader" }, { "source": "RPH-2104", "target": "SAL-0043", "type": "skos:narrower" }, { "source": "SAL-0043", "target": "CON-0021", "type": "augmanitai:crossDomainReference" }, { "source": "SAL-0044", "target": "Sales AI", "type": "skos:broader" }, { "source": "Sales AI", "target": "SAL-0044", "type": "skos:narrower" }, { "source": "SAL-0044", "target": "BEH-0081", "type": "augmanitai:crossDomainReference" }, { "source": "SAL-0044", "target": "ELR-0142", "type": "augmanitai:crossDomainReference" }, { "source": "SAL-0045", "target": "RPH-2104", "type": "skos:broader" }, { "source": "RPH-2104", "target": "SAL-0045", "type": "skos:narrower" }, { "source": "SAL-0045", "target": "ROB-0060", "type": "augmanitai:crossDomainReference" }, { "source": "SAL-0045", "target": "ROB-0245", "type": "augmanitai:crossDomainReference" }, { "source": "SAL-0046", "target": "Sales AI", "type": "skos:broader" }, { "source": "Sales AI", "target": "SAL-0046", "type": "skos:narrower" }, { "source": "SAL-0046", "target": "AED-0020", "type": "augmanitai:crossDomainReference" }, { "source": "SAL-0046", "target": "AUG-0383", "type": "augmanitai:crossDomainReference" }, { "source": "SAL-0046", "target": "CAI-0006", "type": "augmanitai:crossDomainReference" }, { "source": "SAL-0047", "target": "Sales AI", "type": "skos:broader" }, { "source": "Sales AI", "target": "SAL-0047", "type": "skos:narrower" }, { "source": "SAL-0047", "target": "REL-0155", "type": "augmanitai:crossDomainReference" }, { "source": "SAL-0048", "target": "Sales AI", "type": "skos:broader" }, { "source": "Sales AI", "target": "SAL-0048", "type": "skos:narrower" }, { "source": "SAL-0048", "target": "ROB-0188", "type": "augmanitai:crossDomainReference" }, { "source": "SAL-0049", "target": "RPH-2505", "type": "skos:broader" }, { "source": "RPH-2505", "target": "SAL-0049", "type": "skos:narrower" }, { "source": "SAL-0049", "target": "ART-0058", "type": "augmanitai:crossDomainReference" }, { "source": "SAL-0049", "target": "FIC-0072", "type": "augmanitai:crossDomainReference" }, { "source": "SAL-0050", "target": "Sales AI", "type": "skos:broader" }, { "source": "Sales AI", "target": "SAL-0050", "type": "skos:narrower" }, { "source": "SAL-0050", "target": "ART-0012", "type": "augmanitai:crossDomainReference" }, { "source": "SAL-0050", "target": "ART-0025", "type": "augmanitai:crossDomainReference" }, { "source": "SAL-0050", "target": "ART-0081", "type": "augmanitai:crossDomainReference" }, { "source": "SAL-0051", "target": "RPH-2951", "type": "skos:broader" }, { "source": "RPH-2951", "target": "SAL-0051", "type": "skos:narrower" }, { "source": "SAL-0051", "target": "ROB-0188", "type": "augmanitai:crossDomainReference" }, { "source": "SAL-0052", "target": "RHR-0192", "type": "skos:broader" }, { "source": "RHR-0192", "target": "SAL-0052", "type": "skos:narrower" }, { "source": "SAL-0052", "target": "ELR-0010", "type": "augmanitai:crossDomainReference" }, { "source": "SAL-0053", "target": "Detection System", "type": "skos:broader" }, { "source": "Detection System", "target": "SAL-0053", "type": "skos:narrower" }, { "source": "SAL-0053", "target": "CON-0073", "type": "augmanitai:crossDomainReference" }, { "source": "SAL-0054", "target": "RPH-2201", "type": "skos:broader" }, { "source": "RPH-2201", "target": "SAL-0054", "type": "skos:narrower" }, { "source": "SAL-0054", "target": "ROB-0188", "type": "augmanitai:crossDomainReference" }, { "source": "SAL-0055", "target": "RPH-2552", "type": "skos:broader" }, { "source": "RPH-2552", "target": "SAL-0055", "type": "skos:narrower" }, { "source": "SAL-0055", "target": "CON-0035", "type": "augmanitai:crossDomainReference" }, { "source": "SAL-0056", "target": "Feedback Loop", "type": "skos:broader" }, { "source": "Feedback Loop", "target": "SAL-0056", "type": "skos:narrower" }, { "source": "SAL-0056", "target": "CUS-0022", "type": "augmanitai:crossDomainReference" }, { "source": "SAL-0056", "target": "CON-0035", "type": "augmanitai:crossDomainReference" }, { "source": "SAL-0056", "target": "CUS-0068", "type": "augmanitai:crossDomainReference" }, { "source": "SAL-0057", "target": "Sales AI", "type": "skos:broader" }, { "source": "Sales AI", "target": "SAL-0057", "type": "skos:narrower" }, { "source": "SAL-0057", "target": "CON-0035", "type": "augmanitai:crossDomainReference" }, { "source": "SAL-0057", "target": "ASE-0091", "type": "augmanitai:crossDomainReference" }, { "source": "SAL-0058", "target": "Sales AI", "type": "skos:broader" }, { "source": "Sales AI", "target": "SAL-0058", "type": "skos:narrower" }, { "source": "SAL-0058", "target": "GAM-0006", "type": "augmanitai:crossDomainReference" }, { "source": "SAL-0059", "target": "RPH-3304", "type": "skos:broader" }, { "source": "RPH-3304", "target": "SAL-0059", "type": "skos:narrower" }, { "source": "SAL-0059", "target": "COP-0030", "type": "augmanitai:crossDomainReference" }, { "source": "SAL-0059", "target": "PHO-0095", "type": "augmanitai:crossDomainReference" }, { "source": "SAL-0060", "target": "Sales AI", "type": "skos:broader" }, { "source": "Sales AI", "target": "SAL-0060", "type": "skos:narrower" }, { "source": "SAL-0060", "target": "CON-0035", "type": "augmanitai:crossDomainReference" }, { "source": "SAL-0061", "target": "Sales AI", "type": "skos:broader" }, { "source": "Sales AI", "target": "SAL-0061", "type": "skos:narrower" }, { "source": "SAL-0061", "target": "CUS-0072", "type": "augmanitai:crossDomainReference" }, { "source": "SAL-0061", "target": "PER-0040", "type": "augmanitai:crossDomainReference" }, { "source": "SAL-0062", "target": "Sales AI", "type": "skos:broader" }, { "source": "Sales AI", "target": "SAL-0062", "type": "skos:narrower" }, { "source": "SAL-0063", "target": "Sales AI", "type": "skos:broader" }, { "source": "Sales AI", "target": "SAL-0063", "type": "skos:narrower" }, { "source": "SAL-0063", "target": "CON-0067", "type": "augmanitai:crossDomainReference" }, { "source": "SAL-0064", "target": "RPH-3101", "type": "skos:broader" }, { "source": "RPH-3101", "target": "SAL-0064", "type": "skos:narrower" }, { "source": "SAL-0064", "target": "MKT-0039", "type": "augmanitai:crossDomainReference" }, { "source": "SAL-0065", "target": "RPH-2355", "type": "skos:broader" }, { "source": "RPH-2355", "target": "SAL-0065", "type": "skos:narrower" }, { "source": "SAL-0065", "target": "ROB-0172", "type": "augmanitai:crossDomainReference" }, { "source": "SAL-0065", "target": "RPH-1855", "type": "augmanitai:crossDomainReference" }, { "source": "SAL-0065", "target": "ROB-0287", "type": "augmanitai:crossDomainReference" }, { "source": "SAL-0066", "target": "Sales AI", "type": "skos:broader" }, { "source": "Sales AI", "target": "SAL-0066", "type": "skos:narrower" }, { "source": "SAL-0066", "target": "CUS-0072", "type": "augmanitai:crossDomainReference" }, { "source": "SAL-0066", "target": "PER-0040", "type": "augmanitai:crossDomainReference" }, { "source": "SAL-0067", "target": "RPH-2505", "type": "skos:broader" }, { "source": "RPH-2505", "target": "SAL-0067", "type": "skos:narrower" }, { "source": "SAL-0067", "target": "MKT-0100", "type": "augmanitai:crossDomainReference" }, { "source": "SAL-0067", "target": "RHR-0080", "type": "augmanitai:crossDomainReference" }, { "source": "SAL-0068", "target": "Sales AI", "type": "skos:broader" }, { "source": "Sales AI", "target": "SAL-0068", "type": "skos:narrower" }, { "source": "SAL-0068", "target": "MKT-0009", "type": "augmanitai:crossDomainReference" }, { "source": "SAL-0069", "target": "RPH-2503", "type": "skos:broader" }, { "source": "RPH-2503", "target": "SAL-0069", "type": "skos:narrower" }, { "source": "SAL-0069", "target": "MKT-0065", "type": "augmanitai:crossDomainReference" }, { "source": "SAL-0069", "target": "RET-0049", "type": "augmanitai:crossDomainReference" }, { "source": "SAL-0070", "target": "RPH-2555", "type": "skos:broader" }, { "source": "RPH-2555", "target": "SAL-0070", "type": "skos:narrower" }, { "source": "SAL-0070", "target": "RET-0014", "type": "augmanitai:crossDomainReference" }, { "source": "SAL-0070", "target": "ASE-0002", "type": "augmanitai:crossDomainReference" }, { "source": "SAL-0071", "target": "RPH-2505", "type": "skos:broader" }, { "source": "RPH-2505", "target": "SAL-0071", "type": "skos:narrower" }, { "source": "SAL-0071", "target": "CON-0073", "type": "augmanitai:crossDomainReference" }, { "source": "SAL-0072", "target": "Sales AI", "type": "skos:broader" }, { "source": "Sales AI", "target": "SAL-0072", "type": "skos:narrower" }, { "source": "SAL-0072", "target": "COG-0053", "type": "augmanitai:crossDomainReference" }, { "source": "SAL-0073", "target": "RPH-3101", "type": "skos:broader" }, { "source": "RPH-3101", "target": "SAL-0073", "type": "skos:narrower" }, { "source": "SAL-0073", "target": "ROB-0267", "type": "augmanitai:crossDomainReference" }, { "source": "SAL-0073", "target": "COG-0178", "type": "augmanitai:crossDomainReference" }, { "source": "SAL-0074", "target": "Sales AI", "type": "skos:broader" }, { "source": "Sales AI", "target": "SAL-0074", "type": "skos:narrower" }, { "source": "SAL-0075", "target": "Logical Paradox", "type": "skos:broader" }, { "source": "Logical Paradox", "target": "SAL-0075", "type": "skos:narrower" }, { "source": "SAL-0075", "target": "ASE-0029", "type": "augmanitai:crossDomainReference" }, { "source": "SAL-0076", "target": "Analytical Ratio", "type": "skos:broader" }, { "source": "Analytical Ratio", "target": "SAL-0076", "type": "skos:narrower" }, { "source": "SAL-0077", "target": "Sales AI", "type": "skos:broader" }, { "source": "Sales AI", "target": "SAL-0077", "type": "skos:narrower" }, { "source": "SAL-0077", "target": "AUG-0921", "type": "augmanitai:crossDomainReference" }, { "source": "SAL-0077", "target": "MKT-0035", "type": "augmanitai:crossDomainReference" }, { "source": "SAL-0077", "target": "RET-0035", "type": "augmanitai:crossDomainReference" }, { "source": "SAL-0078", "target": "RPH-2303", "type": "skos:broader" }, { "source": "RPH-2303", "target": "SAL-0078", "type": "skos:narrower" }, { "source": "SAL-0078", "target": "AGE-0041", "type": "augmanitai:crossDomainReference" }, { "source": "SAL-0078", "target": "EDU-0016", "type": "augmanitai:crossDomainReference" }, { "source": "SAL-0078", "target": "FIC-0050", "type": "augmanitai:crossDomainReference" }, { "source": "SAL-0079", "target": "Sales AI", "type": "skos:broader" }, { "source": "Sales AI", "target": "SAL-0079", "type": "skos:narrower" }, { "source": "SAL-0079", "target": "BEH-0087", "type": "augmanitai:crossDomainReference" }, { "source": "SAL-0079", "target": "DAT-0043", "type": "augmanitai:crossDomainReference" }, { "source": "SAL-0079", "target": "DAT-0045", "type": "augmanitai:crossDomainReference" }, { "source": "SAL-0080", "target": "Sales AI", "type": "skos:broader" }, { "source": "Sales AI", "target": "SAL-0080", "type": "skos:narrower" }, { "source": "SAL-0080", "target": "COG-0074", "type": "augmanitai:crossDomainReference" }, { "source": "SAL-0081", "target": "RPH-2253", "type": "skos:broader" }, { "source": "RPH-2253", "target": "SAL-0081", "type": "skos:narrower" }, { "source": "SAL-0082", "target": "Logical Paradox", "type": "skos:broader" }, { "source": "Logical Paradox", "target": "SAL-0082", "type": "skos:narrower" }, { "source": "SAL-0082", "target": "MKT-0012", "type": "augmanitai:crossDomainReference" }, { "source": "SAL-0082", "target": "COP-0004", "type": "augmanitai:crossDomainReference" }, { "source": "SAL-0083", "target": "Analytical Ratio", "type": "skos:broader" }, { "source": "Analytical Ratio", "target": "SAL-0083", "type": "skos:narrower" }, { "source": "SAL-0083", "target": "MKT-0013", "type": "augmanitai:crossDomainReference" }, { "source": "SAL-0084", "target": "RPH-1209", "type": "skos:broader" }, { "source": "RPH-1209", "target": "SAL-0084", "type": "skos:narrower" }, { "source": "SAL-0085", "target": "Risk Factor", "type": "skos:broader" }, { "source": "Risk Factor", "target": "SAL-0085", "type": "skos:narrower" }, { "source": "SAL-0085", "target": "PER-0129", "type": "augmanitai:crossDomainReference" }, { "source": "SAL-0085", "target": "COP-0077", "type": "augmanitai:crossDomainReference" }, { "source": "SAL-0086", "target": "Sales AI", "type": "skos:broader" }, { "source": "Sales AI", "target": "SAL-0086", "type": "skos:narrower" }, { "source": "SAL-0086", "target": "CON-0015", "type": "augmanitai:crossDomainReference" }, { "source": "SAL-0087", "target": "RPH-2604", "type": "skos:broader" }, { "source": "RPH-2604", "target": "SAL-0087", "type": "skos:narrower" }, { "source": "SAL-0088", "target": "Sales AI", "type": "skos:broader" }, { "source": "Sales AI", "target": "SAL-0088", "type": "skos:narrower" }, { "source": "SAL-0088", "target": "MKT-0040", "type": "augmanitai:crossDomainReference" }, { "source": "SAL-0088", "target": "RET-0019", "type": "augmanitai:crossDomainReference" }, { "source": "SAL-0088", "target": "CRE-0053", "type": "augmanitai:crossDomainReference" }, { "source": "SAL-0089", "target": "Sales AI", "type": "skos:broader" }, { "source": "Sales AI", "target": "SAL-0089", "type": "skos:narrower" }, { "source": "SAL-0089", "target": "CON-0043", "type": "augmanitai:crossDomainReference" }, { "source": "SAL-0090", "target": "Analytical Ratio", "type": "skos:broader" }, { "source": "Analytical Ratio", "target": "SAL-0090", "type": "skos:narrower" }, { "source": "SAL-0090", "target": "ELR-0135", "type": "augmanitai:crossDomainReference" }, { "source": "SAL-0090", "target": "COP-0019", "type": "augmanitai:crossDomainReference" }, { "source": "SAL-0091", "target": "RPH-2703", "type": "skos:broader" }, { "source": "RPH-2703", "target": "SAL-0091", "type": "skos:narrower" }, { "source": "SAL-0091", "target": "BEH-0057", "type": "augmanitai:crossDomainReference" }, { "source": "SAL-0091", "target": "REL-0155", "type": "augmanitai:crossDomainReference" }, { "source": "SAL-0092", "target": "Risk Factor", "type": "skos:broader" }, { "source": "Risk Factor", "target": "SAL-0092", "type": "skos:narrower" }, { "source": "SAL-0092", "target": "MKT-0091", "type": "augmanitai:crossDomainReference" }, { "source": "SAL-0093", "target": "Sales AI", "type": "skos:broader" }, { "source": "Sales AI", "target": "SAL-0093", "type": "skos:narrower" }, { "source": "SAL-0094", "target": "RPH-2552", "type": "skos:broader" }, { "source": "RPH-2552", "target": "SAL-0094", "type": "skos:narrower" }, { "source": "SAL-0094", "target": "AED-0094", "type": "augmanitai:crossDomainReference" }, { "source": "SAL-0094", "target": "COG-0030", "type": "augmanitai:crossDomainReference" }, { "source": "SAL-0094", "target": "COG-0050", "type": "augmanitai:crossDomainReference" }, { "source": "SAL-0095", "target": "Sales AI", "type": "skos:broader" }, { "source": "Sales AI", "target": "SAL-0095", "type": "skos:narrower" }, { "source": "SAL-0095", "target": "AED-0019", "type": "augmanitai:crossDomainReference" }, { "source": "SAL-0095", "target": "ASE-0002", "type": "augmanitai:crossDomainReference" }, { "source": "SAL-0095", "target": "ASE-0027", "type": "augmanitai:crossDomainReference" }, { "source": "SAL-0096", "target": "RPH-2703", "type": "skos:broader" }, { "source": "RPH-2703", "target": "SAL-0096", "type": "skos:narrower" }, { "source": "SAL-0096", "target": "ART-0002", "type": "augmanitai:crossDomainReference" }, { "source": "SAL-0096", "target": "ART-0005", "type": "augmanitai:crossDomainReference" }, { "source": "SAL-0096", "target": "ART-0020", "type": "augmanitai:crossDomainReference" }, { "source": "SAL-0097", "target": "RPH-2604", "type": "skos:broader" }, { "source": "RPH-2604", "target": "SAL-0097", "type": "skos:narrower" }, { "source": "SAL-0097", "target": "ELR-0032", "type": "augmanitai:crossDomainReference" }, { "source": "SAL-0098", "target": "RPH-3902", "type": "skos:broader" }, { "source": "RPH-3902", "target": "SAL-0098", "type": "skos:narrower" }, { "source": "SAL-0098", "target": "CON-0050", "type": "augmanitai:crossDomainReference" }, { "source": "SAL-0099", "target": "RPH-2703", "type": "skos:broader" }, { "source": "RPH-2703", "target": "SAL-0099", "type": "skos:narrower" }, { "source": "SAL-0099", "target": "COG-0024", "type": "augmanitai:crossDomainReference" }, { "source": "SAL-0100", "target": "Sales AI", "type": "skos:broader" }, { "source": "Sales AI", "target": "SAL-0100", "type": "skos:narrower" }, { "source": "SAL-0100", "target": "ASE-0013", "type": "augmanitai:crossDomainReference" }, { "source": "SCR-0001", "target": "Screening AI", "type": "skos:broader" }, { "source": "Screening AI", "target": "SCR-0001", "type": "skos:narrower" }, { "source": "SCR-0001", "target": "ADA-0007", "type": "augmanitai:crossDomainReference" }, { "source": "SCR-0001", "target": "CON-0015", "type": "augmanitai:crossDomainReference" }, { "source": "SCR-0001", "target": "CON-0057", "type": "augmanitai:crossDomainReference" }, { "source": "SCR-0002", "target": "Screening AI", "type": "skos:broader" }, { "source": "Screening AI", "target": "SCR-0002", "type": "skos:narrower" }, { "source": "SCR-0002", "target": "CON-0090", "type": "augmanitai:crossDomainReference" }, { "source": "SCR-0003", "target": "System Adaptation", "type": "skos:broader" }, { "source": "System Adaptation", "target": "SCR-0003", "type": "skos:narrower" }, { "source": "SCR-0003", "target": "CON-0040", "type": "augmanitai:crossDomainReference" }, { "source": "SCR-0004", "target": "Screening AI", "type": "skos:broader" }, { "source": "Screening AI", "target": "SCR-0004", "type": "skos:narrower" }, { "source": "SCR-0004", "target": "CON-0045", "type": "augmanitai:crossDomainReference" }, { "source": "SCR-0005", "target": "Screening AI", "type": "skos:broader" }, { "source": "Screening AI", "target": "SCR-0005", "type": "skos:narrower" }, { "source": "SCR-0005", "target": "ADA-0012", "type": "augmanitai:crossDomainReference" }, { "source": "SCR-0005", "target": "AGE-0007", "type": "augmanitai:crossDomainReference" }, { "source": "SCR-0005", "target": "AGE-0046", "type": "augmanitai:crossDomainReference" }, { "source": "SCR-0006", "target": "Screening AI", "type": "skos:broader" }, { "source": "Screening AI", "target": "SCR-0006", "type": "skos:narrower" }, { "source": "SCR-0006", "target": "CON-0032", "type": "augmanitai:crossDomainReference" }, { "source": "SCR-0007", "target": "Screening AI", "type": "skos:broader" }, { "source": "Screening AI", "target": "SCR-0007", "type": "skos:narrower" }, { "source": "SCR-0008", "target": "Screening AI", "type": "skos:broader" }, { "source": "Screening AI", "target": "SCR-0008", "type": "skos:narrower" }, { "source": "SCR-0008", "target": "CON-0060", "type": "augmanitai:crossDomainReference" }, { "source": "SCR-0009", "target": "Systemic Drift", "type": "skos:broader" }, { "source": "Systemic Drift", "target": "SCR-0009", "type": "skos:narrower" }, { "source": "SCR-0009", "target": "FIC-0055", "type": "augmanitai:crossDomainReference" }, { "source": "SCR-0009", "target": "FIC-0027", "type": "augmanitai:crossDomainReference" }, { "source": "SCR-0010", "target": "Screening AI", "type": "skos:broader" }, { "source": "Screening AI", "target": "SCR-0010", "type": "skos:narrower" }, { "source": "SCR-0010", "target": "FIC-0040", "type": "augmanitai:crossDomainReference" }, { "source": "SCR-0011", "target": "Screening AI", "type": "skos:broader" }, { "source": "Screening AI", "target": "SCR-0011", "type": "skos:narrower" }, { "source": "SCR-0011", "target": "FIC-0012", "type": "augmanitai:crossDomainReference" }, { "source": "SCR-0011", "target": "FIC-0013", "type": "augmanitai:crossDomainReference" }, { "source": "SCR-0011", "target": "GAM-0018", "type": "augmanitai:crossDomainReference" }, { "source": "SCR-0012", "target": "RPH-3101", "type": "skos:broader" }, { "source": "RPH-3101", "target": "SCR-0012", "type": "skos:narrower" }, { "source": "SCR-0012", "target": "FIC-0046", "type": "augmanitai:crossDomainReference" }, { "source": "SCR-0013", "target": "RPH-3202", "type": "skos:broader" }, { "source": "RPH-3202", "target": "SCR-0013", "type": "skos:narrower" }, { "source": "SCR-0013", "target": "BEH-0058", "type": "augmanitai:crossDomainReference" }, { "source": "SCR-0014", "target": "Screening AI", "type": "skos:broader" }, { "source": "Screening AI", "target": "SCR-0014", "type": "skos:narrower" }, { "source": "SCR-0014", "target": "MUS-0039", "type": "augmanitai:crossDomainReference" }, { "source": "SCR-0015", "target": "Screening AI", "type": "skos:broader" }, { "source": "Screening AI", "target": "SCR-0015", "type": "skos:narrower" }, { "source": "SCR-0015", "target": "REL-0033", "type": "augmanitai:crossDomainReference" }, { "source": "SCR-0016", "target": "Analytical Ratio", "type": "skos:broader" }, { "source": "Analytical Ratio", "target": "SCR-0016", "type": "skos:narrower" }, { "source": "SCR-0016", "target": "CRE-0030", "type": "augmanitai:crossDomainReference" }, { "source": "SCR-0017", "target": "Screening AI", "type": "skos:broader" }, { "source": "Screening AI", "target": "SCR-0017", "type": "skos:narrower" }, { "source": "SCR-0017", "target": "FIC-0009", "type": "augmanitai:crossDomainReference" }, { "source": "SCR-0018", "target": "Screening AI", "type": "skos:broader" 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"SCR-0022", "target": "Psychological Effect", "type": "skos:broader" }, { "source": "Psychological Effect", "target": "SCR-0022", "type": "skos:narrower" }, { "source": "SCR-0022", "target": "DES-0056", "type": "augmanitai:crossDomainReference" }, { "source": "SCR-0023", "target": "RPH-3202", "type": "skos:broader" }, { "source": "RPH-3202", "target": "SCR-0023", "type": "skos:narrower" }, { "source": "SCR-0023", "target": "LIN-0077", "type": "augmanitai:crossDomainReference" }, { "source": "SCR-0023", "target": "RHR-0063", "type": "augmanitai:crossDomainReference" }, { "source": "SCR-0024", "target": "Screening AI", "type": "skos:broader" }, { "source": "Screening AI", "target": "SCR-0024", "type": "skos:narrower" }, { "source": "SCR-0025", "target": "Screening AI", "type": "skos:broader" }, { "source": "Screening AI", "target": "SCR-0025", "type": "skos:narrower" }, { "source": "SCR-0025", "target": "AGE-0068", "type": "augmanitai:crossDomainReference" }, { "source": "SCR-0025", "target": "AGE-0091", "type": "augmanitai:crossDomainReference" }, { "source": "SCR-0025", "target": "AGE-0092", "type": "augmanitai:crossDomainReference" }, { "source": "SCR-0026", "target": "Screening AI", "type": "skos:broader" }, { "source": "Screening AI", "target": "SCR-0026", "type": "skos:narrower" }, { "source": "SCR-0027", "target": "RPH-2201", "type": "skos:broader" }, { "source": "RPH-2201", "target": "SCR-0027", "type": "skos:narrower" }, { "source": "SCR-0027", "target": "AED-0046", "type": "augmanitai:crossDomainReference" }, { "source": "SCR-0027", "target": "AGE-0055", "type": "augmanitai:crossDomainReference" }, { "source": "SCR-0027", "target": "ART-0023", "type": "augmanitai:crossDomainReference" }, { "source": "SCR-0028", "target": "Screening AI", "type": "skos:broader" }, { "source": "Screening AI", "target": "SCR-0028", "type": "skos:narrower" }, { "source": "SCR-0028", "target": "GAM-0036", "type": "augmanitai:crossDomainReference" }, { "source": "SCR-0029", "target": "Screening AI", "type": "skos:broader" }, { "source": "Screening AI", "target": "SCR-0029", "type": "skos:narrower" }, { "source": "SCR-0029", "target": "CON-0049", "type": "augmanitai:crossDomainReference" }, { "source": "SCR-0030", "target": "Screening AI", "type": "skos:broader" }, { "source": "Screening AI", "target": "SCR-0030", "type": "skos:narrower" }, { "source": "SCR-0030", "target": "ART-0060", "type": "augmanitai:crossDomainReference" }, { "source": "SCR-0030", "target": "ART-0093", "type": "augmanitai:crossDomainReference" }, { "source": "SCR-0030", "target": "CON-0026", "type": "augmanitai:crossDomainReference" }, { "source": "SCR-0031", "target": "Screening AI", "type": "skos:broader" }, { "source": "Screening AI", "target": "SCR-0031", "type": "skos:narrower" }, { "source": "SCR-0031", "target": "FIC-0029", "type": "augmanitai:crossDomainReference" }, { "source": "SCR-0032", "target": "Analytical Ratio", "type": "skos:broader" }, { "source": "Analytical 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{ "source": "SCR-0035", "target": "Screening AI", "type": "skos:broader" }, { "source": "Screening AI", "target": "SCR-0035", "type": "skos:narrower" }, { "source": "SCR-0035", "target": "LIN-0094", "type": "augmanitai:crossDomainReference" }, { "source": "SCR-0036", "target": "Screening AI", "type": "skos:broader" }, { "source": "Screening AI", "target": "SCR-0036", "type": "skos:narrower" }, { "source": "SCR-0037", "target": "Screening AI", "type": "skos:broader" }, { "source": "Screening AI", "target": "SCR-0037", "type": "skos:narrower" }, { "source": "SCR-0037", "target": "FIC-0020", "type": "augmanitai:crossDomainReference" }, { "source": "SCR-0038", "target": "RPH-1303", "type": "skos:broader" }, { "source": "RPH-1303", "target": "SCR-0038", "type": "skos:narrower" }, { "source": "SCR-0038", "target": "CON-0022", "type": "augmanitai:crossDomainReference" }, { "source": "SCR-0038", "target": "CON-0044", "type": "augmanitai:crossDomainReference" }, { "source": "SCR-0038", 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"type": "skos:broader" }, { "source": "RPH-2052", "target": "SCR-0046", "type": "skos:narrower" }, { "source": "SCR-0046", "target": "IDN-0001", "type": "augmanitai:crossDomainReference" }, { "source": "SCR-0047", "target": "Screening AI", "type": "skos:broader" }, { "source": "Screening AI", "target": "SCR-0047", "type": "skos:narrower" }, { "source": "SCR-0047", "target": "CON-0054", "type": "augmanitai:crossDomainReference" }, { "source": "SCR-0048", "target": "RPH-2201", "type": "skos:broader" }, { "source": "RPH-2201", "target": "SCR-0048", "type": "skos:narrower" }, { "source": "SCR-0048", "target": "AGE-0014", "type": "augmanitai:crossDomainReference" }, { "source": "SCR-0048", "target": "AGE-0048", "type": "augmanitai:crossDomainReference" }, { "source": "SCR-0048", "target": "ART-0018", "type": "augmanitai:crossDomainReference" }, { "source": "SCR-0049", "target": "Screening AI", "type": "skos:broader" }, { "source": "Screening AI", "target": "SCR-0049", "type": "skos:narrower" }, { "source": "SCR-0049", "target": "CRE-0057", "type": "augmanitai:crossDomainReference" }, { "source": "SCR-0050", "target": "Screening AI", "type": "skos:broader" }, { "source": "Screening AI", "target": "SCR-0050", "type": "skos:narrower" }, { "source": "SCR-0051", "target": "RPH-3552", "type": "skos:broader" }, { "source": "RPH-3552", "target": "SCR-0051", "type": "skos:narrower" }, { "source": "SCR-0051", "target": "AGE-0043", "type": "augmanitai:crossDomainReference" }, { "source": "SCR-0051", "target": "AGE-0090", "type": "augmanitai:crossDomainReference" }, { "source": "SCR-0051", "target": "ART-0021", "type": "augmanitai:crossDomainReference" }, { "source": "SCR-0052", "target": "Screening AI", "type": "skos:broader" }, { "source": "Screening AI", "target": "SCR-0052", "type": "skos:narrower" }, { "source": "SCR-0052", "target": "REL-0090", "type": "augmanitai:crossDomainReference" }, { "source": "SCR-0053", "target": "Screening AI", "type": "skos:broader" }, { "source": "Screening AI", "target": "SCR-0053", "type": "skos:narrower" }, { "source": "SCR-0053", "target": "AED-0090", "type": "augmanitai:crossDomainReference" }, { "source": "SCR-0053", "target": "DES-0057", "type": "augmanitai:crossDomainReference" }, { "source": "SCR-0053", "target": "EDU-0045", "type": "augmanitai:crossDomainReference" }, { "source": "SCR-0054", "target": "Screening AI", "type": "skos:broader" }, { "source": "Screening AI", "target": "SCR-0054", "type": "skos:narrower" }, { "source": "SCR-0054", "target": "PER-0030", "type": "augmanitai:crossDomainReference" }, { "source": "SCR-0055", "target": "Screening AI", "type": "skos:broader" }, { "source": "Screening AI", "target": "SCR-0055", "type": "skos:narrower" }, { "source": "SCR-0055", "target": "CON-0065", "type": "augmanitai:crossDomainReference" }, { "source": "SCR-0055", "target": "CON-0067", "type": "augmanitai:crossDomainReference" }, { "source": "SCR-0055", "target": "CON-0092", "type": "augmanitai:crossDomainReference" }, { "source": "SCR-0056", "target": "Screening AI", "type": "skos:broader" }, { "source": "Screening AI", "target": "SCR-0056", "type": "skos:narrower" }, { "source": "SCR-0056", "target": "CON-0043", "type": "augmanitai:crossDomainReference" }, { "source": "SCR-0056", "target": "CON-0055", "type": "augmanitai:crossDomainReference" }, { "source": "SCR-0056", "target": "CON-0058", "type": "augmanitai:crossDomainReference" }, { "source": "SCR-0057", "target": "Screening AI", "type": "skos:broader" }, { "source": "Screening AI", "target": "SCR-0057", "type": "skos:narrower" }, { "source": "SCR-0057", "target": "PHO-0001", "type": "augmanitai:crossDomainReference" }, { "source": "SCR-0058", "target": "Analytical Ratio", "type": "skos:broader" }, { "source": "Analytical Ratio", "target": "SCR-0058", "type": "skos:narrower" }, { "source": "SCR-0058", "target": "CON-0040", "type": "augmanitai:crossDomainReference" }, { "source": "SCR-0059", "target": "Screening AI", "type": "skos:broader" }, { "source": "Screening AI", "target": "SCR-0059", "type": "skos:narrower" }, { "source": "SCR-0059", "target": "GAM-0058", "type": "augmanitai:crossDomainReference" }, { "source": "SCR-0060", "target": "Screening AI", "type": "skos:broader" }, { "source": "Screening AI", "target": "SCR-0060", "type": "skos:narrower" }, { "source": "SCR-0060", "target": "ART-0092", "type": "augmanitai:crossDomainReference" }, { "source": "SCR-0061", "target": "RPH-3001", "type": "skos:broader" }, { "source": "RPH-3001", "target": "SCR-0061", "type": "skos:narrower" }, { "source": "SCR-0062", "target": "Screening AI", "type": "skos:broader" }, { "source": "Screening AI", "target": "SCR-0062", "type": "skos:narrower" }, { "source": "SCR-0062", "target": "MUS-0083", "type": "augmanitai:crossDomainReference" }, { "source": "SCR-0063", "target": "Screening AI", "type": "skos:broader" }, { "source": "Screening AI", "target": "SCR-0063", "type": "skos:narrower" }, { "source": "SCR-0063", "target": "CON-0060", "type": "augmanitai:crossDomainReference" }, { "source": "SCR-0063", "target": "CON-0093", "type": "augmanitai:crossDomainReference" }, { "source": "SCR-0063", "target": "FIC-0012", "type": "augmanitai:crossDomainReference" }, { "source": "SCR-0064", "target": "Screening AI", "type": "skos:broader" }, { "source": "Screening AI", "target": "SCR-0064", "type": "skos:narrower" }, { "source": "SCR-0064", "target": "CON-0078", "type": "augmanitai:crossDomainReference" }, { "source": "SCR-0065", "target": "Screening AI", "type": "skos:broader" }, { "source": "Screening AI", "target": "SCR-0065", "type": "skos:narrower" }, { "source": "SCR-0065", "target": "AGE-0068", "type": "augmanitai:crossDomainReference" }, { "source": "SCR-0065", "target": "AGE-0091", "type": "augmanitai:crossDomainReference" }, { "source": "SCR-0065", "target": "AGE-0092", "type": "augmanitai:crossDomainReference" }, { "source": "SCR-0066", "target": "Screening AI", "type": "skos:broader" }, { "source": "Screening AI", "target": "SCR-0066", "type": "skos:narrower" }, { "source": "SCR-0066", "target": "FIC-0075", "type": "augmanitai:crossDomainReference" }, { "source": "SCR-0067", "target": "Screening AI", "type": "skos:broader" }, { "source": "Screening AI", "target": "SCR-0067", "type": "skos:narrower" }, { "source": "SCR-0067", "target": "FIC-0092", "type": "augmanitai:crossDomainReference" }, { "source": "SCR-0067", "target": "GAM-0058", "type": "augmanitai:crossDomainReference" }, { "source": "SCR-0067", "target": "REL-0175", "type": "augmanitai:crossDomainReference" }, { "source": "SCR-0068", "target": "Screening AI", "type": "skos:broader" }, { "source": "Screening AI", "target": "SCR-0068", "type": "skos:narrower" }, { "source": "SCR-0069", "target": "Screening AI", "type": "skos:broader" }, { "source": "Screening AI", "target": "SCR-0069", "type": "skos:narrower" }, { "source": "SCR-0069", "target": "ASE-0038", "type": "augmanitai:crossDomainReference" }, { "source": "SCR-0069", "target": "COG-0120", "type": "augmanitai:crossDomainReference" }, { "source": "SCR-0069", "target": "CON-0037", "type": "augmanitai:crossDomainReference" }, { "source": "SCR-0070", "target": "Screening AI", "type": "skos:broader" }, { "source": "Screening AI", "target": "SCR-0070", "type": "skos:narrower" }, { "source": "SCR-0070", "target": "CON-0019", "type": "augmanitai:crossDomainReference" }, { "source": "SCR-0070", "target": "DES-0068", "type": "augmanitai:crossDomainReference" }, { "source": "SCR-0070", "target": "GAM-0024", "type": "augmanitai:crossDomainReference" }, { "source": "SCR-0071", "target": "Screening AI", "type": "skos:broader" }, { "source": "Screening AI", "target": "SCR-0071", "type": "skos:narrower" }, { "source": "SCR-0071", "target": "ART-0008", "type": "augmanitai:crossDomainReference" }, { "source": "SCR-0071", "target": "ASE-0032", "type": "augmanitai:crossDomainReference" }, { "source": "SCR-0071", "target": "ASE-0095", "type": "augmanitai:crossDomainReference" }, { "source": "SCR-0072", "target": "Screening AI", "type": "skos:broader" }, { "source": "Screening AI", "target": "SCR-0072", "type": "skos:narrower" }, { "source": "SCR-0072", "target": "AED-0070", "type": "augmanitai:crossDomainReference" }, { "source": "SCR-0072", "target": "AGE-0012", "type": "augmanitai:crossDomainReference" }, { "source": "SCR-0072", "target": "AGE-0014", "type": "augmanitai:crossDomainReference" }, { "source": "SCR-0073", "target": "Screening AI", "type": "skos:broader" }, { "source": "Screening AI", "target": "SCR-0073", "type": "skos:narrower" }, { "source": "SCR-0074", "target": "RPH-3202", "type": "skos:broader" }, { "source": "RPH-3202", "target": "SCR-0074", "type": "skos:narrower" }, { "source": "SCR-0074", "target": "PER-0113", "type": "augmanitai:crossDomainReference" }, { "source": "SCR-0074", "target": "PHO-0076", "type": "augmanitai:crossDomainReference" }, { "source": "SCR-0074", "target": "ROB-0071", "type": "augmanitai:crossDomainReference" }, { "source": "SCR-0075", "target": "Screening AI", "type": "skos:broader" }, { "source": "Screening AI", "target": "SCR-0075", "type": "skos:narrower" }, { "source": "SCR-0075", "target": "AED-0004", "type": "augmanitai:crossDomainReference" }, { "source": "SCR-0075", "target": "AGE-0049", "type": "augmanitai:crossDomainReference" }, { "source": "SCR-0075", "target": "CON-0069", "type": "augmanitai:crossDomainReference" }, { "source": "SCR-0076", "target": "Screening AI", "type": "skos:broader" }, { "source": "Screening AI", "target": "SCR-0076", "type": "skos:narrower" }, { "source": "SCR-0076", "target": "COG-0168", "type": "augmanitai:crossDomainReference" }, { "source": "SCR-0077", "target": "Screening AI", "type": "skos:broader" }, { "source": "Screening AI", "target": "SCR-0077", "type": "skos:narrower" }, { "source": "SCR-0077", "target": "CON-0079", "type": "augmanitai:crossDomainReference" }, { "source": "SCR-0077", "target": "CON-0088", "type": "augmanitai:crossDomainReference" }, { "source": "SCR-0078", "target": "Screening AI", "type": "skos:broader" }, { "source": "Screening AI", "target": "SCR-0078", "type": "skos:narrower" }, { "source": "SCR-0078", "target": "CON-0032", "type": "augmanitai:crossDomainReference" }, { "source": "SCR-0079", "target": "Screening AI", "type": "skos:broader" }, { "source": "Screening AI", "target": "SCR-0079", "type": "skos:narrower" }, { "source": "SCR-0079", "target": "LIN-0009", "type": "augmanitai:crossDomainReference" }, { "source": "SCR-0079", "target": "LIN-0021", "type": "augmanitai:crossDomainReference" }, { "source": "SCR-0080", "target": "Screening AI", "type": "skos:broader" }, { "source": "Screening AI", "target": "SCR-0080", "type": "skos:narrower" }, { "source": "SCR-0080", "target": "CON-0065", "type": "augmanitai:crossDomainReference" }, { "source": "SCR-0080", "target": "CON-0067", "type": "augmanitai:crossDomainReference" }, { "source": "SCR-0080", "target": "CON-0092", "type": "augmanitai:crossDomainReference" }, { "source": "SCR-0081", "target": "RPH-2055", "type": "skos:broader" }, { "source": "RPH-2055", "target": "SCR-0081", "type": "skos:narrower" }, { "source": "SCR-0081", "target": "CON-0065", "type": "augmanitai:crossDomainReference" }, { "source": "SCR-0081", "target": "CON-0067", "type": "augmanitai:crossDomainReference" }, { "source": "SCR-0081", "target": "CON-0092", "type": "augmanitai:crossDomainReference" }, { "source": "SCR-0082", "target": "Analytical Ratio", "type": "skos:broader" }, { "source": "Analytical Ratio", "target": "SCR-0082", "type": "skos:narrower" }, { "source": "SCR-0082", "target": "CON-0070", "type": "augmanitai:crossDomainReference" }, { "source": "SCR-0083", "target": "Screening AI", "type": "skos:broader" }, { "source": "Screening AI", "target": "SCR-0083", "type": "skos:narrower" }, { "source": "SCR-0083", "target": "CON-0034", "type": "augmanitai:crossDomainReference" }, { "source": "SCR-0084", "target": "RPH-2701", "type": "skos:broader" }, { "source": "RPH-2701", "target": "SCR-0084", "type": "skos:narrower" }, { "source": "SCR-0084", "target": "FIC-0089", "type": "augmanitai:crossDomainReference" }, { "source": "SCR-0085", "target": "Screening AI", "type": "skos:broader" }, { "source": "Screening AI", "target": "SCR-0085", "type": "skos:narrower" }, { "source": "SCR-0085", "target": "MUS-0011", "type": "augmanitai:crossDomainReference" }, { "source": "SCR-0086", "target": "RPH-2701", "type": "skos:broader" }, { "source": "RPH-2701", "target": "SCR-0086", "type": "skos:narrower" }, { "source": "SCR-0086", "target": "ADA-0011", "type": "augmanitai:crossDomainReference" }, { "source": "SCR-0086", "target": "AED-0094", "type": "augmanitai:crossDomainReference" }, { "source": "SCR-0086", "target": "AED-0095", "type": "augmanitai:crossDomainReference" }, { "source": "SCR-0087", "target": "Screening AI", "type": "skos:broader" }, { "source": "Screening AI", "target": "SCR-0087", "type": "skos:narrower" }, { "source": "SCR-0087", "target": "REL-0091", "type": "augmanitai:crossDomainReference" }, { "source": "SCR-0088", "target": "Screening AI", "type": "skos:broader" }, { "source": "Screening AI", "target": "SCR-0088", "type": "skos:narrower" }, { "source": "SCR-0089", "target": "Screening AI", "type": "skos:broader" }, { "source": "Screening AI", "target": "SCR-0089", "type": "skos:narrower" }, { "source": "SOC-0001", "target": "Psychological Effect", "type": "skos:broader" }, { "source": "Psychological Effect", "target": "SOC-0001", "type": "skos:narrower" }, { "source": "SOC-0001", "target": "IDN-0035", "type": "augmanitai:crossDomainReference" }, { "source": "SOC-0002", "target": "RPH-3802", "type": "skos:broader" }, { "source": "RPH-3802", "target": "SOC-0002", "type": "skos:narrower" }, { "source": "SOC-0002", "target": "AED-0091", "type": "augmanitai:crossDomainReference" }, { "source": "SOC-0003", "target": "Social AI", "type": "skos:broader" }, { "source": "Social AI", "target": "SOC-0003", "type": "skos:narrower" }, { "source": "SOC-0003", "target": "ASE-0005", "type": "augmanitai:crossDomainReference" }, { "source": "SOC-0004", "target": "RPH-2105", "type": "skos:broader" }, { "source": "RPH-2105", "target": "SOC-0004", "type": "skos:narrower" }, { "source": "SOC-0005", "target": "Social AI", "type": "skos:broader" }, { "source": "Social AI", "target": "SOC-0005", "type": "skos:narrower" }, { "source": "SOC-0006", "target": "RPH-2603", "type": "skos:broader" }, { "source": "RPH-2603", "target": "SOC-0006", "type": "skos:narrower" }, { "source": "SOC-0007", "target": "SOM-0055", "type": "skos:broader" }, { "source": "SOM-0055", "target": "SOC-0007", "type": "skos:narrower" }, { "source": "SOC-0007", "target": "IDN-0050", "type": "augmanitai:crossDomainReference" }, { "source": "SOC-0008", "target": "RPH-3101", "type": "skos:broader" }, { "source": "RPH-3101", "target": "SOC-0008", "type": "skos:narrower" }, { "source": "SOC-0008", "target": "PER-0114", "type": "augmanitai:crossDomainReference" }, { "source": "SOC-0009", "target": "Social AI", "type": "skos:broader" }, { "source": "Social AI", "target": "SOC-0009", "type": "skos:narrower" }, { "source": "SOC-0009", "target": "CRE-0217", "type": "augmanitai:crossDomainReference" }, { "source": "SOC-0010", "target": "Social AI", "type": "skos:broader" }, { "source": "Social AI", "target": "SOC-0010", "type": "skos:narrower" }, { "source": "SOC-0010", "target": "CRE-0149", "type": "augmanitai:crossDomainReference" }, { "source": "SOC-0011", "target": "Social AI", "type": "skos:broader" }, { "source": "Social AI", "target": "SOC-0011", "type": "skos:narrower" }, { "source": "SOC-0011", "target": "AGE-0063", "type": "augmanitai:crossDomainReference" }, { "source": "SOC-0012", "target": "Social AI", "type": "skos:broader" }, { "source": "Social AI", "target": "SOC-0012", "type": "skos:narrower" }, { "source": "SOC-0012", "target": "ETH-0025", "type": "augmanitai:crossDomainReference" }, { "source": "SOC-0013", "target": "ETH-0019", "type": "skos:broader" }, { "source": "ETH-0019", "target": "SOC-0013", "type": "skos:narrower" }, { "source": "SOC-0013", "target": "CON-0053", "type": "augmanitai:crossDomainReference" }, { "source": "SOC-0014", "target": "Social AI", "type": "skos:broader" }, { "source": "Social AI", "target": "SOC-0014", "type": "skos:narrower" }, { "source": "SOC-0014", "target": "ADA-0008", "type": "augmanitai:crossDomainReference" }, { "source": "SOC-0015", "target": "Social AI", "type": "skos:broader" }, { "source": "Social AI", "target": "SOC-0015", "type": "skos:narrower" }, { "source": "SOC-0015", "target": "CON-0050", "type": "augmanitai:crossDomainReference" }, { "source": "SOC-0016", "target": "Social AI", "type": "skos:broader" }, { "source": "Social AI", "target": "SOC-0016", "type": "skos:narrower" }, { "source": "SOC-0016", "target": "MTH-0018", "type": "augmanitai:crossDomainReference" }, { "source": "SOC-0016", "target": "KNO-0001", "type": "augmanitai:crossDomainReference" }, { "source": "SOC-0017", "target": "Computational Model", "type": "skos:broader" }, { "source": "Computational Model", "target": "SOC-0017", "type": "skos:narrower" }, { "source": "SOC-0018", "target": "SOC-0037", "type": "skos:broader" }, { "source": "SOC-0037", "target": "SOC-0018", "type": "skos:narrower" }, { "source": "SOC-0018", "target": "REL-0142", "type": "augmanitai:crossDomainReference" }, { "source": "SOC-0019", "target": "RPH-2303", "type": "skos:broader" }, { "source": "RPH-2303", "target": "SOC-0019", "type": "skos:narrower" }, { "source": "SOC-0019", "target": "AGE-0096", "type": "augmanitai:crossDomainReference" }, { "source": "SOC-0020", "target": "Behavioral Pattern", "type": "skos:broader" }, { "source": "Behavioral Pattern", "target": "SOC-0020", "type": "skos:narrower" }, { "source": "SOC-0021", "target": "RPH-3902", "type": "skos:broader" }, { "source": "RPH-3902", "target": "SOC-0021", "type": "skos:narrower" }, { "source": "SOC-0021", "target": "REL-0136", "type": "augmanitai:crossDomainReference" }, { "source": "SOC-0022", "target": "TEM-0050", "type": "skos:broader" }, { "source": "TEM-0050", "target": "SOC-0022", "type": "skos:narrower" }, { "source": "SOC-0022", "target": "AUG-0487", "type": "augmanitai:crossDomainReference" }, { "source": "SOC-0023", "target": "RPH-1307", "type": "skos:broader" }, { "source": "RPH-1307", "target": "SOC-0023", "type": "skos:narrower" }, { "source": "SOC-0023", "target": "CRE-0189", "type": "augmanitai:crossDomainReference" }, { "source": "SOC-0024", "target": "NEO-0002", "type": "skos:broader" }, { "source": "NEO-0002", "target": "SOC-0024", "type": "skos:narrower" }, { "source": "SOC-0024", "target": "ETH-0012", "type": "augmanitai:crossDomainReference" }, { "source": "SOC-0025", "target": "NEO-1745", "type": "skos:broader" }, { "source": "NEO-1745", "target": "SOC-0025", "type": "skos:narrower" }, { "source": "SOC-0025", "target": "NEO-1745", "type": "augmanitai:crossDomainReference" }, { "source": "SOC-0026", "target": "Social AI", "type": "skos:broader" }, { "source": "Social AI", "target": "SOC-0026", "type": "skos:narrower" }, { "source": "SOC-0026", "target": "REL-0160", "type": "augmanitai:crossDomainReference" }, { "source": "SOC-0027", "target": "Social AI", "type": "skos:broader" }, { "source": "Social AI", "target": "SOC-0027", "type": "skos:narrower" }, { "source": "SOC-0028", "target": "RPH-2303", "type": "skos:broader" }, { "source": "RPH-2303", "target": "SOC-0028", "type": "skos:narrower" }, { "source": "SOC-0029", "target": "ETH-0005", "type": "skos:broader" }, { "source": "ETH-0005", "target": "SOC-0029", "type": "skos:narrower" }, { "source": "SOC-0029", "target": "IDN-0027", "type": "augmanitai:crossDomainReference" }, { "source": "SOC-0029", "target": "PER-0038", "type": "augmanitai:crossDomainReference" }, { "source": "SOC-0030", "target": "CRE-0149", "type": "skos:broader" }, { "source": "CRE-0149", "target": "SOC-0030", "type": "skos:narrower" }, { "source": "SOC-0031", "target": "SOC-0002", "type": "skos:broader" }, { "source": "SOC-0002", "target": "SOC-0031", "type": "skos:narrower" }, { "source": "SOC-0031", "target": "CRE-0209", "type": "augmanitai:crossDomainReference" }, { "source": "SOC-0032", "target": "Social AI", "type": "skos:broader" }, { "source": "Social AI", "target": "SOC-0032", "type": "skos:narrower" }, { "source": "SOC-0032", "target": "KNO-0011", "type": "augmanitai:crossDomainReference" }, { "source": "SOC-0033", "target": "CRE-0159", "type": "skos:broader" }, { "source": "CRE-0159", "target": "SOC-0033", "type": "skos:narrower" }, { "source": "SOC-0034", "target": "RPH-2605", "type": "skos:broader" }, { "source": "RPH-2605", "target": "SOC-0034", "type": "skos:narrower" }, { "source": "SOC-0034", "target": "KNO-0029", "type": "augmanitai:crossDomainReference" }, { "source": "SOC-0035", "target": "TEM-0153", "type": "skos:broader" }, { "source": "TEM-0153", "target": "SOC-0035", "type": "skos:narrower" }, { "source": "SOC-0035", "target": "REL-0111", "type": "augmanitai:crossDomainReference" }, { "source": "SOC-0036", "target": "Social AI", "type": "skos:broader" }, { "source": "Social AI", "target": "SOC-0036", "type": "skos:narrower" }, { "source": "SOC-0036", "target": "AUG-0840", "type": "augmanitai:crossDomainReference" }, { "source": "SOC-0036", "target": "ETH-0014", "type": "augmanitai:crossDomainReference" }, { "source": "SOC-0037", "target": "NEO-1745", "type": "skos:broader" }, { "source": "NEO-1745", "target": "SOC-0037", "type": "skos:narrower" }, { "source": "SOC-0037", "target": "BEH-0057", "type": "augmanitai:crossDomainReference" }, { "source": "SOC-0037", "target": "BEH-0081", "type": "augmanitai:crossDomainReference" }, { "source": "SOC-0037", "target": "COG-0004", "type": "augmanitai:crossDomainReference" }, { "source": "SOC-0038", "target": "Social AI", "type": "skos:broader" }, { "source": "Social AI", "target": "SOC-0038", "type": "skos:narrower" }, { "source": "SOC-0038", "target": "KNO-0030", "type": "augmanitai:crossDomainReference" }, { "source": "SOC-0039", "target": "RPH-2505", "type": "skos:broader" }, { "source": "RPH-2505", "target": "SOC-0039", "type": "skos:narrower" }, { "source": "SOC-0040", "target": "Social AI", "type": "skos:broader" }, { "source": "Social AI", "target": "SOC-0040", "type": "skos:narrower" }, { "source": "SOC-0040", "target": "IDN-0048", "type": "augmanitai:crossDomainReference" }, { "source": "SOC-0041", "target": "Social AI", "type": "skos:broader" }, { "source": "Social AI", "target": "SOC-0041", "type": "skos:narrower" }, { "source": "SOC-0041", "target": "BEH-0022", "type": "augmanitai:crossDomainReference" }, { "source": "SOC-0042", "target": "TEM-0113", "type": "skos:broader" }, { "source": "TEM-0113", "target": "SOC-0042", "type": "skos:narrower" }, { "source": "SOC-0043", "target": "RPH-3001", "type": "skos:broader" }, { "source": "RPH-3001", "target": "SOC-0043", "type": "skos:narrower" }, { "source": "SOC-0043", "target": "BEH-0087", "type": "augmanitai:crossDomainReference" }, { "source": "SOC-0044", "target": "CRE-0146", "type": "skos:broader" }, { "source": "CRE-0146", "target": "SOC-0044", "type": "skos:narrower" }, { "source": "SOC-0044", "target": "CRE-0146", "type": "augmanitai:crossDomainReference" }, { "source": "SOC-0045", "target": "PER-0048", "type": "skos:broader" }, { "source": "PER-0048", "target": "SOC-0045", "type": "skos:narrower" }, { "source": "SOC-0045", "target": "PER-0125", "type": "augmanitai:crossDomainReference" }, { "source": "SOC-0046", "target": "RPH-2605", "type": "skos:broader" }, { "source": "RPH-2605", "target": "SOC-0046", "type": "skos:narrower" }, { "source": "SOC-0046", "target": "REL-0197", "type": "augmanitai:crossDomainReference" }, { "source": "SOM-0001", "target": "RPH-2154", "type": "skos:broader" }, { "source": "RPH-2154", "target": "SOM-0001", "type": "skos:narrower" }, { "source": "SOM-0001", "target": "CON-0038", "type": "augmanitai:crossDomainReference" }, { "source": "SOM-0001", "target": "REL-0003", "type": "augmanitai:crossDomainReference" }, { "source": "SOM-0002", "target": "RPH-2503", "type": "skos:broader" }, { "source": "RPH-2503", "target": "SOM-0002", "type": "skos:narrower" }, { "source": "SOM-0003", "target": "RPH-3001", "type": "skos:broader" }, { "source": "RPH-3001", "target": "SOM-0003", "type": "skos:narrower" }, { "source": "SOM-0003", "target": "ADA-0007", "type": "augmanitai:crossDomainReference" }, { "source": "SOM-0004", "target": "RPH-3001", "type": "skos:broader" }, { "source": "RPH-3001", "target": "SOM-0004", "type": "skos:narrower" }, { "source": "SOM-0004", "target": "PLY-0062", "type": "augmanitai:crossDomainReference" }, { "source": "SOM-0005", "target": "RPH-2701", "type": "skos:broader" }, { "source": "RPH-2701", "target": "SOM-0005", "type": "skos:narrower" }, { "source": "SOM-0006", "target": "Somatics AI", "type": "skos:broader" }, { "source": "Somatics AI", "target": "SOM-0006", "type": "skos:narrower" }, { "source": "SOM-0006", "target": "PLY-0055", "type": "augmanitai:crossDomainReference" }, { "source": "SOM-0007", "target": "RPH-3354", "type": "skos:broader" }, { "source": "RPH-3354", "target": "SOM-0007", "type": "skos:narrower" }, { "source": "SOM-0007", "target": "PLY-0004", "type": "augmanitai:crossDomainReference" }, { "source": "SOM-0007", "target": "PLY-0005", "type": "augmanitai:crossDomainReference" }, { "source": "SOM-0007", "target": "PLY-0006", "type": "augmanitai:crossDomainReference" }, { "source": "SOM-0008", "target": "RPH-2254", "type": "skos:broader" }, { "source": "RPH-2254", "target": "SOM-0008", "type": "skos:narrower" }, { "source": "SOM-0008", "target": "PER-0098", "type": "augmanitai:crossDomainReference" }, { "source": "SOM-0009", "target": "RPH-3601", "type": "skos:broader" }, { "source": "RPH-3601", "target": "SOM-0009", "type": "skos:narrower" }, { "source": "SOM-0009", "target": "CON-0046", "type": "augmanitai:crossDomainReference" }, { "source": "SOM-0009", "target": "PLY-0018", "type": "augmanitai:crossDomainReference" }, { "source": "SOM-0010", "target": "Somatics AI", "type": "skos:broader" }, { "source": "Somatics AI", "target": "SOM-0010", "type": "skos:narrower" }, { "source": "SOM-0010", "target": "BEH-0029", "type": "augmanitai:crossDomainReference" }, { "source": "SOM-0011", "target": "Somatics AI", "type": "skos:broader" }, { "source": "Somatics AI", "target": "SOM-0011", "type": "skos:narrower" }, { "source": "SOM-0011", "target": "REL-0067", "type": "augmanitai:crossDomainReference" }, { "source": "SOM-0012", "target": "RPH-2255", "type": "skos:broader" }, { "source": "RPH-2255", "target": "SOM-0012", "type": "skos:narrower" }, { "source": "SOM-0013", "target": "RPH-3354", "type": "skos:broader" }, { "source": "RPH-3354", "target": "SOM-0013", "type": "skos:narrower" }, { "source": "SOM-0013", "target": "AED-0018", "type": "augmanitai:crossDomainReference" }, { "source": "SOM-0013", "target": "AED-0062", "type": "augmanitai:crossDomainReference" }, { "source": "SOM-0013", "target": "AED-0066", "type": "augmanitai:crossDomainReference" }, { "source": "SOM-0014", "target": "RPH-3002", "type": "skos:broader" }, { "source": "RPH-3002", "target": "SOM-0014", "type": "skos:narrower" }, { "source": "SOM-0014", "target": "AED-0012", "type": "augmanitai:crossDomainReference" }, { "source": "SOM-0014", "target": "BEH-0019", "type": "augmanitai:crossDomainReference" }, { "source": "SOM-0014", "target": "BEH-0034", "type": "augmanitai:crossDomainReference" }, { "source": "SOM-0015", "target": "RPH-3003", "type": "skos:broader" }, { "source": "RPH-3003", "target": "SOM-0015", "type": "skos:narrower" }, { "source": "SOM-0015", "target": "AGE-0030", "type": "augmanitai:crossDomainReference" }, { "source": "SOM-0015", "target": "ASE-0075", "type": "augmanitai:crossDomainReference" }, { "source": "SOM-0015", "target": "COG-0015", "type": "augmanitai:crossDomainReference" }, { "source": "SOM-0016", "target": "RPH-2703", "type": "skos:broader" }, { "source": "RPH-2703", "target": "SOM-0016", "type": "skos:narrower" }, { "source": "SOM-0016", "target": "REL-0206", "type": "augmanitai:crossDomainReference" }, { "source": "SOM-0017", "target": "Somatics AI", "type": "skos:broader" }, { "source": "Somatics AI", "target": "SOM-0017", "type": "skos:narrower" }, { "source": "SOM-0017", "target": "CON-0028", "type": "augmanitai:crossDomainReference" }, { "source": "SOM-0018", "target": "Behavioral Pattern", "type": "skos:broader" }, { "source": "Behavioral Pattern", "target": "SOM-0018", "type": "skos:narrower" }, { "source": "SOM-0018", "target": "AGE-0003", "type": "augmanitai:crossDomainReference" }, { "source": "SOM-0018", "target": "AGE-0004", "type": "augmanitai:crossDomainReference" }, { "source": "SOM-0018", "target": "AGE-0017", "type": "augmanitai:crossDomainReference" }, { "source": "SOM-0019", "target": "RPH-2254", "type": "skos:broader" }, { "source": "RPH-2254", "target": "SOM-0019", "type": "skos:narrower" }, { "source": "SOM-0019", "target": "ETH-0022", "type": "augmanitai:crossDomainReference" }, { "source": "SOM-0020", "target": "Somatics AI", "type": "skos:broader" }, { "source": "Somatics AI", "target": "SOM-0020", "type": "skos:narrower" }, { "source": "SOM-0021", "target": "Somatics AI", "type": "skos:broader" }, { "source": "Somatics AI", "target": "SOM-0021", "type": "skos:narrower" }, { "source": "SOM-0021", "target": "REL-0066", "type": "augmanitai:crossDomainReference" }, { "source": "SOM-0022", "target": "RPH-3501", "type": "skos:broader" }, { "source": "RPH-3501", "target": "SOM-0022", "type": "skos:narrower" }, { "source": "SOM-0023", "target": "Analytical Ratio", "type": "skos:broader" }, { "source": "Analytical Ratio", "target": "SOM-0023", "type": "skos:narrower" }, { "source": "SOM-0023", "target": "AGE-0076", "type": "augmanitai:crossDomainReference" }, { "source": "SOM-0023", "target": "COG-0051", "type": "augmanitai:crossDomainReference" }, { "source": "SOM-0023", "target": "COG-0136", "type": "augmanitai:crossDomainReference" }, { "source": "SOM-0024", "target": "RPH-2201", "type": "skos:broader" }, { "source": "RPH-2201", "target": "SOM-0024", "type": "skos:narrower" }, { "source": "SOM-0024", "target": "BEH-0056", "type": "augmanitai:crossDomainReference" }, { "source": "SOM-0024", "target": "ROB-0190", "type": "augmanitai:crossDomainReference" }, { "source": "SOM-0025", "target": "Somatics AI", "type": "skos:broader" }, { "source": "Somatics AI", "target": "SOM-0025", "type": "skos:narrower" }, { "source": "SOM-0025", "target": "ASE-0030", "type": "augmanitai:crossDomainReference" }, { "source": "SOM-0025", "target": "AUG-0821", "type": "augmanitai:crossDomainReference" }, { "source": "SOM-0025", "target": "BEH-0056", "type": "augmanitai:crossDomainReference" }, { "source": "SOM-0026", "target": "RPH-2254", "type": "skos:broader" }, { "source": "RPH-2254", "target": "SOM-0026", "type": "skos:narrower" }, { "source": "SOM-0027", "target": "RPH-3002", "type": "skos:broader" }, { "source": "RPH-3002", "target": "SOM-0027", "type": "skos:narrower" }, { "source": "SOM-0027", "target": "CON-0082", "type": "augmanitai:crossDomainReference" }, { "source": "SOM-0027", "target": "COP-0091", "type": "augmanitai:crossDomainReference" }, { "source": "SOM-0027", "target": "CRE-0083", "type": "augmanitai:crossDomainReference" }, { "source": "SOM-0028", "target": "RPH-3002", "type": "skos:broader" }, { "source": "RPH-3002", "target": "SOM-0028", "type": "skos:narrower" }, { "source": "SOM-0029", "target": "Somatics AI", "type": "skos:broader" }, { "source": "Somatics AI", "target": "SOM-0029", "type": "skos:narrower" }, { "source": "SOM-0029", "target": "ART-0058", "type": "augmanitai:crossDomainReference" }, { "source": "SOM-0029", "target": "ART-0073", "type": "augmanitai:crossDomainReference" }, { "source": "SOM-0029", "target": "ART-0076", "type": "augmanitai:crossDomainReference" }, { "source": "SOM-0030", "target": "RPH-2055", "type": "skos:broader" }, { "source": "RPH-2055", "target": "SOM-0030", "type": "skos:narrower" }, { "source": "SOM-0031", "target": "RPH-2701", "type": "skos:broader" }, { "source": "RPH-2701", "target": "SOM-0031", "type": "skos:narrower" }, { "source": "SOM-0032", "target": "Somatics AI", "type": "skos:broader" }, { "source": "Somatics AI", "target": "SOM-0032", "type": "skos:narrower" }, { "source": "SOM-0032", "target": "FIC-0044", "type": "augmanitai:crossDomainReference" }, { "source": "SOM-0033", "target": "Somatics AI", "type": "skos:broader" }, { "source": "Somatics AI", "target": "SOM-0033", "type": "skos:narrower" }, { "source": "SOM-0033", "target": "AGE-0005", "type": "augmanitai:crossDomainReference" }, { "source": "SOM-0033", "target": "AGE-0028", "type": "augmanitai:crossDomainReference" }, { "source": "SOM-0033", "target": "AGE-0029", "type": "augmanitai:crossDomainReference" }, { "source": "SOM-0034", "target": "RPH-2301", "type": "skos:broader" }, { "source": "RPH-2301", "target": "SOM-0034", "type": "skos:narrower" }, { "source": "SOM-0035", "target": "Somatics AI", "type": "skos:broader" }, { "source": "Somatics AI", "target": "SOM-0035", "type": "skos:narrower" }, { "source": "SOM-0035", "target": "REL-0069", "type": "augmanitai:crossDomainReference" }, { "source": "SOM-0036", "target": "RPH-3001", "type": "skos:broader" }, { "source": "RPH-3001", "target": "SOM-0036", "type": "skos:narrower" }, { "source": "SOM-0037", "target": "RPH-2002", "type": "skos:broader" }, { "source": "RPH-2002", "target": "SOM-0037", "type": "skos:narrower" }, { "source": "SOM-0037", "target": "AED-0045", "type": "augmanitai:crossDomainReference" }, { "source": "SOM-0037", "target": "AED-0046", "type": "augmanitai:crossDomainReference" }, { "source": "SOM-0037", "target": "AGE-0043", "type": "augmanitai:crossDomainReference" }, { "source": "SOM-0038", "target": "RPH-3601", "type": "skos:broader" }, { "source": "RPH-3601", "target": "SOM-0038", "type": "skos:narrower" }, { "source": "SOM-0038", "target": "PLY-0003", "type": "augmanitai:crossDomainReference" }, { "source": "SOM-0039", "target": "RPH-3002", "type": "skos:broader" }, { "source": "RPH-3002", "target": "SOM-0039", "type": "skos:narrower" }, { "source": "SOM-0039", "target": "ADA-0001", "type": "augmanitai:crossDomainReference" }, { "source": "SOM-0039", "target": "ADA-0002", "type": "augmanitai:crossDomainReference" }, { "source": "SOM-0039", "target": "ADA-0004", "type": "augmanitai:crossDomainReference" }, { "source": "SOM-0040", "target": "Somatics AI", "type": "skos:broader" }, { "source": "Somatics AI", "target": "SOM-0040", "type": "skos:narrower" }, { "source": "SOM-0040", "target": "AGE-0044", "type": "augmanitai:crossDomainReference" }, { "source": "SOM-0041", "target": "RPH-2254", "type": "skos:broader" }, { "source": "RPH-2254", "target": "SOM-0041", "type": "skos:narrower" }, { "source": "SOM-0041", "target": "NEO-3260", "type": "augmanitai:crossDomainReference" }, { "source": "SOM-0041", "target": "CRE-0191", "type": "augmanitai:crossDomainReference" }, { "source": "SOM-0042", "target": "RPH-2002", "type": "skos:broader" }, { "source": "RPH-2002", "target": "SOM-0042", "type": "skos:narrower" }, { "source": "SOM-0042", "target": "ART-0043", "type": "augmanitai:crossDomainReference" }, { "source": "SOM-0043", "target": "Somatics AI", "type": "skos:broader" }, { "source": "Somatics AI", "target": "SOM-0043", "type": "skos:narrower" }, { "source": "SOM-0043", "target": "SAL-0035", "type": "augmanitai:crossDomainReference" }, { "source": "SOM-0043", "target": "ROB-0036", "type": "augmanitai:crossDomainReference" }, { "source": "SOM-0043", "target": "PLY-0019", "type": "augmanitai:crossDomainReference" }, { "source": "SOM-0044", "target": "Behavioral Pattern", "type": "skos:broader" }, { "source": "Behavioral Pattern", "target": "SOM-0044", "type": "skos:narrower" }, { "source": "SOM-0044", "target": "AGE-0003", "type": "augmanitai:crossDomainReference" }, { "source": "SOM-0044", "target": "AGE-0004", "type": "augmanitai:crossDomainReference" }, { "source": "SOM-0044", "target": "AGE-0017", "type": "augmanitai:crossDomainReference" }, { "source": "SOM-0045", "target": "RPH-2154", "type": "skos:broader" }, { "source": "RPH-2154", "target": "SOM-0045", "type": "skos:narrower" }, { "source": "SOM-0045", "target": "BEH-0032", "type": "augmanitai:crossDomainReference" }, { "source": "SOM-0046", "target": "Somatics AI", "type": "skos:broader" }, { "source": "Somatics AI", "target": "SOM-0046", "type": "skos:narrower" }, { "source": "SOM-0046", "target": "CRE-0076", "type": "augmanitai:crossDomainReference" }, { "source": "SOM-0047", "target": "TEM-0138", "type": "skos:broader" }, { "source": "TEM-0138", "target": "SOM-0047", "type": "skos:narrower" }, { "source": "SOM-0047", "target": "PER-0056", "type": "augmanitai:crossDomainReference" }, { "source": "SOM-0048", "target": "Logical Paradox", "type": "skos:broader" }, { "source": "Logical Paradox", "target": "SOM-0048", "type": "skos:narrower" }, { "source": "SOM-0048", "target": "REL-0048", "type": "augmanitai:crossDomainReference" }, { "source": "SOM-0049", "target": "RPH-3003", "type": "skos:broader" }, { "source": "RPH-3003", "target": "SOM-0049", "type": "skos:narrower" }, { "source": "SOM-0050", "target": "Somatics AI", "type": "skos:broader" }, { "source": "Somatics AI", "target": "SOM-0050", "type": "skos:narrower" }, { "source": "SOM-0050", "target": "AGE-0086", "type": "augmanitai:crossDomainReference" }, { "source": "SOM-0051", "target": "TEM-0071", "type": "skos:broader" }, { "source": "TEM-0071", "target": "SOM-0051", "type": "skos:narrower" }, { "source": "SOM-0051", "target": "IDN-0033", "type": "augmanitai:crossDomainReference" }, { "source": "SOM-0052", "target": "CRE-0149", "type": "skos:broader" }, { "source": "CRE-0149", "target": "SOM-0052", "type": "skos:narrower" }, { "source": "SOM-0052", "target": "SOC-0030", "type": "augmanitai:crossDomainReference" }, { "source": "SOM-0053", "target": "BEH-0084", "type": "skos:broader" }, { "source": "BEH-0084", "target": "SOM-0053", "type": "skos:narrower" }, { "source": "SOM-0053", "target": "LIN-0043", "type": "augmanitai:crossDomainReference" }, { "source": "SOM-0054", "target": "Somatics AI", "type": "skos:broader" }, { "source": "Somatics AI", "target": "SOM-0054", "type": "skos:narrower" }, { "source": "SOM-0054", "target": "AGE-0037", "type": "augmanitai:crossDomainReference" }, { "source": "SOM-0055", "target": "IDN-0050", "type": "skos:broader" }, { "source": "IDN-0050", "target": "SOM-0055", "type": "skos:narrower" }, { "source": "SOM-0055", "target": "ETH-0012", "type": "augmanitai:crossDomainReference" }, { "source": "SOM-0056", "target": "RPH-2254", "type": "skos:broader" }, { "source": "RPH-2254", "target": "SOM-0056", "type": "skos:narrower" }, { "source": "SOM-0056", "target": "CRE-0107", "type": "augmanitai:crossDomainReference" }, { "source": "SOM-0057", "target": "Somatics AI", "type": "skos:broader" }, { "source": "Somatics AI", "target": "SOM-0057", "type": "skos:narrower" }, { "source": "SOM-0058", "target": "RPH-3101", "type": "skos:broader" }, { "source": "RPH-3101", "target": "SOM-0058", "type": "skos:narrower" }, { "source": "SOM-0058", "target": "ELR-0055", "type": "augmanitai:crossDomainReference" }, { "source": "SOM-0059", "target": "TEM-0191", "type": "skos:broader" }, { "source": "TEM-0191", "target": "SOM-0059", "type": "skos:narrower" }, { "source": "SOM-0060", "target": "RPH-2101", "type": "skos:broader" }, { "source": "RPH-2101", "target": "SOM-0060", "type": "skos:narrower" }, { "source": "SOM-0061", "target": "Somatics AI", "type": "skos:broader" }, { "source": "Somatics AI", "target": "SOM-0061", "type": "skos:narrower" }, { "source": "SOM-0061", "target": "AUG-0976", "type": "augmanitai:crossDomainReference" }, { "source": "SOM-0062", "target": "TEM-0134", "type": "skos:broader" }, { "source": "TEM-0134", "target": "SOM-0062", "type": "skos:narrower" }, { "source": "SOM-0063", "target": "Analytical Ratio", "type": "skos:broader" }, { "source": "Analytical Ratio", "target": "SOM-0063", "type": "skos:narrower" }, { "source": "SOM-0063", "target": "IDN-0057", "type": "augmanitai:crossDomainReference" }, { "source": "SOM-0064", "target": "RPH-2254", "type": "skos:broader" }, { "source": "RPH-2254", "target": "SOM-0064", "type": "skos:narrower" }, { "source": "SOM-0064", "target": "REL-0128", "type": "augmanitai:crossDomainReference" }, { "source": "SOM-0065", "target": "Analytical Ratio", "type": "skos:broader" }, { "source": "Analytical Ratio", "target": "SOM-0065", "type": "skos:narrower" }, { "source": "SOM-0065", "target": "AGE-0043", "type": "augmanitai:crossDomainReference" }, { "source": "SOM-0065", "target": "BEH-0033", "type": "augmanitai:crossDomainReference" }, { "source": "SOM-0065", "target": "BEH-0074", "type": "augmanitai:crossDomainReference" }, { "source": "SOM-0066", "target": "Somatics AI", "type": "skos:broader" }, { "source": "Somatics AI", "target": "SOM-0066", "type": "skos:narrower" }, { "source": "SOM-0066", "target": "ROB-0074", "type": "augmanitai:crossDomainReference" }, { "source": "SOM-0067", "target": "RPH-3001", "type": "skos:broader" }, { "source": "RPH-3001", "target": "SOM-0067", "type": "skos:narrower" }, { "source": "SOM-0067", "target": "MTH-0037", "type": "augmanitai:crossDomainReference" }, { "source": "SOM-0067", "target": "AUG-0164", "type": "augmanitai:crossDomainReference" }, { "source": "SOM-0068", "target": "RPH-2555", "type": "skos:broader" }, { "source": "RPH-2555", "target": "SOM-0068", "type": "skos:narrower" }, { "source": "SOM-0069", "target": "Somatics AI", "type": "skos:broader" }, { "source": "Somatics AI", "target": "SOM-0069", "type": "skos:narrower" }, { "source": "SOM-0069", "target": "REL-0128", "type": "augmanitai:crossDomainReference" }, { "source": "SOM-0070", "target": "Process Cycle", "type": "skos:broader" }, { "source": "Process Cycle", "target": "SOM-0070", "type": "skos:narrower" }, { "source": "SOM-0071", "target": "Somatics AI", "type": "skos:broader" }, { "source": "Somatics AI", "target": "SOM-0071", "type": "skos:narrower" }, { "source": "SOM-0071", "target": "CRE-0170", "type": "augmanitai:crossDomainReference" }, { "source": "SOM-0072", "target": "RPH-2254", "type": "skos:broader" }, { "source": "RPH-2254", "target": "SOM-0072", "type": "skos:narrower" }, { "source": "SOM-0072", "target": "BEH-0044", "type": "augmanitai:crossDomainReference" }, { "source": "SOM-0073", "target": "Somatics AI", "type": "skos:broader" }, { "source": "Somatics AI", "target": "SOM-0073", "type": "skos:narrower" }, { "source": "SOM-0074", "target": "SOM-0058", "type": "skos:broader" }, { "source": "SOM-0058", "target": "SOM-0074", "type": "skos:narrower" }, { "source": "SOM-0074", "target": "IDN-0018", "type": "augmanitai:crossDomainReference" }, { "source": "SOM-0075", "target": "RPH-2805", "type": "skos:broader" }, { "source": "RPH-2805", "target": "SOM-0075", "type": "skos:narrower" }, { "source": "SOM-0076", "target": "Somatics AI", "type": "skos:broader" }, { "source": "Somatics AI", "target": "SOM-0076", "type": "skos:narrower" }, { "source": "SOM-0076", "target": "RHR-0026", "type": "augmanitai:crossDomainReference" }, { "source": "SOM-0076", "target": "ROB-0145", "type": "augmanitai:crossDomainReference" }, { "source": "SOM-0077", "target": "RPH-3002", "type": "skos:broader" }, { "source": "RPH-3002", "target": "SOM-0077", "type": "skos:narrower" }, { "source": "SOM-0077", "target": "ROB-0259", "type": "augmanitai:crossDomainReference" }, { "source": "SOM-0078", "target": "RPH-3002", "type": "skos:broader" }, { "source": "RPH-3002", "target": "SOM-0078", "type": "skos:narrower" }, { "source": "SOM-0078", "target": "AGE-0034", "type": "augmanitai:crossDomainReference" }, { "source": "SOM-0078", "target": "DAT-0008", "type": "augmanitai:crossDomainReference" }, { "source": "SOM-0078", "target": "IEF-0003", "type": "augmanitai:crossDomainReference" }, { "source": "SPA-0001", "target": "RPH-2505", "type": "skos:broader" }, { "source": "RPH-2505", "target": "SPA-0001", "type": "skos:narrower" }, { "source": "SPA-0001", "target": "AGE-0032", "type": "augmanitai:crossDomainReference" }, { "source": "SPA-0001", "target": "ELR-0146", "type": "augmanitai:crossDomainReference" }, { "source": "SPA-0002", "target": "RPH-2603", "type": "skos:broader" }, { "source": "RPH-2603", "target": "SPA-0002", "type": "skos:narrower" }, { "source": "SPA-0002", "target": "COG-0074", "type": "augmanitai:crossDomainReference" }, { "source": "SPA-0002", "target": "RHR-0189", "type": "augmanitai:crossDomainReference" }, { "source": "SPA-0003", "target": "RPH-2855", "type": "skos:broader" }, { "source": "RPH-2855", "target": "SPA-0003", "type": "skos:narrower" }, { "source": "SPA-0003", "target": "ART-0091", "type": "augmanitai:crossDomainReference" }, { "source": "SPA-0003", "target": "ART-0092", "type": "augmanitai:crossDomainReference" }, { "source": "SPA-0003", "target": "ART-0093", "type": "augmanitai:crossDomainReference" }, { "source": "SPA-0004", "target": "Sports AI", "type": "skos:broader" }, { "source": "Sports AI", "target": "SPA-0004", "type": "skos:narrower" }, { "source": "SPA-0004", "target": "CRE-0206", "type": "augmanitai:crossDomainReference" }, { "source": "SPA-0005", "target": "RPH-2555", "type": "skos:broader" }, { "source": "RPH-2555", "target": "SPA-0005", "type": "skos:narrower" }, { "source": "SPA-0005", "target": "MSC-0082", "type": "augmanitai:crossDomainReference" }, { "source": "SPA-0005", "target": "MSC-0083", "type": "augmanitai:crossDomainReference" }, { "source": "SPA-0005", "target": "MSC-0087", "type": 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"target": "RPH-2355", "type": "skos:broader" }, { "source": "RPH-2355", "target": "SPA-0086", "type": "skos:narrower" }, { "source": "SPA-0087", "target": "RPH-2303", "type": "skos:broader" }, { "source": "RPH-2303", "target": "SPA-0087", "type": "skos:narrower" }, { "source": "SPA-0087", "target": "RHR-0195", "type": "augmanitai:crossDomainReference" }, { "source": "SPA-0087", "target": "RHR-0138", "type": "augmanitai:crossDomainReference" }, { "source": "SPA-0087", "target": "MKT-0035", "type": "augmanitai:crossDomainReference" }, { "source": "SPA-0088", "target": "RPH-2303", "type": "skos:broader" }, { "source": "RPH-2303", "target": "SPA-0088", "type": "skos:narrower" }, { "source": "SPA-0088", "target": "COP-0045", "type": "augmanitai:crossDomainReference" }, { "source": "SPA-0088", "target": "CUS-0010", "type": "augmanitai:crossDomainReference" }, { "source": "SPA-0089", "target": "RPH-2603", "type": "skos:broader" }, { "source": "RPH-2603", "target": "SPA-0089", "type": 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"SPA-0096", "target": "MTH-0037", "type": "augmanitai:crossDomainReference" }, { "source": "SPA-0097", "target": "RPH-2201", "type": "skos:broader" }, { "source": "RPH-2201", "target": "SPA-0097", "type": "skos:narrower" }, { "source": "SPA-0097", "target": "ROB-0280", "type": "augmanitai:crossDomainReference" }, { "source": "SPA-0097", "target": "RHR-0103", "type": "augmanitai:crossDomainReference" }, { "source": "SPA-0098", "target": "RPH-2555", "type": "skos:broader" }, { "source": "RPH-2555", "target": "SPA-0098", "type": "skos:narrower" }, { "source": "SPA-0098", "target": "COP-0036", "type": "augmanitai:crossDomainReference" }, { "source": "SPA-0098", "target": "ROB-0269", "type": "augmanitai:crossDomainReference" }, { "source": "SPA-0098", "target": "MKT-0063", "type": "augmanitai:crossDomainReference" }, { "source": "SPA-0099", "target": "RPH-2855", "type": "skos:broader" }, { "source": "RPH-2855", "target": "SPA-0099", "type": "skos:narrower" }, { "source": "SPA-0099", "target": "CUS-0026", "type": "augmanitai:crossDomainReference" }, { "source": "SPA-0100", "target": "RPH-2303", "type": "skos:broader" }, { "source": "RPH-2303", "target": "SPA-0100", "type": "skos:narrower" }, { "source": "SPA-0100", "target": "RHR-0046", "type": "augmanitai:crossDomainReference" }, { "source": "SPR-0001", "target": "Bridge AI", "type": "skos:broader" }, { "source": "Bridge AI", "target": "SPR-0001", "type": "skos:narrower" }, { "source": "SPR-0001", "target": "MKT-0061", "type": "augmanitai:crossDomainReference" }, { "source": "SPR-0002", "target": "Bridge AI", "type": "skos:broader" }, { "source": "Bridge AI", "target": "SPR-0002", "type": "skos:narrower" }, { "source": "SPR-0002", "target": "ASE-0046", "type": "augmanitai:crossDomainReference" }, { "source": "SPR-0002", "target": "LIN-0017", "type": "augmanitai:crossDomainReference" }, { "source": "SPR-0002", "target": "DAT-0023", "type": "augmanitai:crossDomainReference" }, { "source": "SPR-0003", "target": "RPH-2701", "type": "skos:broader" }, { "source": "RPH-2701", "target": "SPR-0003", "type": "skos:narrower" }, { "source": "SPR-0003", "target": "DES-0050", "type": "augmanitai:crossDomainReference" }, { "source": "SPR-0004", "target": "RPH-2701", "type": "skos:broader" }, { "source": "RPH-2701", "target": "SPR-0004", "type": "skos:narrower" }, { "source": "SPR-0004", "target": "ROB-0070", "type": "augmanitai:crossDomainReference" }, { "source": "SPR-0005", "target": "RPH-2303", "type": "skos:broader" }, { "source": "RPH-2303", "target": "SPR-0005", "type": "skos:narrower" }, { "source": "SPR-0005", "target": "GAM-0017", "type": "augmanitai:crossDomainReference" }, { "source": "SPR-0006", "target": "RPH-2505", "type": "skos:broader" }, { "source": "RPH-2505", "target": "SPR-0006", "type": "skos:narrower" }, { "source": "SPR-0006", "target": "AGE-0032", "type": "augmanitai:crossDomainReference" }, { "source": "SPR-0006", "target": "AGE-0077", "type": "augmanitai:crossDomainReference" }, { "source": "SPR-0006", "target": "AGE-0094", "type": "augmanitai:crossDomainReference" }, { "source": "SPR-0007", "target": "RPH-2701", "type": "skos:broader" }, { "source": "RPH-2701", "target": "SPR-0007", "type": "skos:narrower" }, { "source": "SPR-0007", "target": "ASE-0012", "type": "augmanitai:crossDomainReference" }, { "source": "SPR-0008", "target": "RPH-2355", "type": "skos:broader" }, { "source": "RPH-2355", "target": "SPR-0008", "type": "skos:narrower" }, { "source": "SPR-0008", "target": "AGE-0032", "type": "augmanitai:crossDomainReference" }, { "source": "SPR-0008", "target": "AGE-0095", "type": "augmanitai:crossDomainReference" }, { "source": "SPR-0008", "target": "AGE-0098", "type": "augmanitai:crossDomainReference" }, { "source": "SPR-0009", "target": "Bridge AI", "type": "skos:broader" }, { "source": "Bridge AI", "target": "SPR-0009", "type": "skos:narrower" }, { "source": "SPR-0009", "target": "COG-0150", "type": "augmanitai:crossDomainReference" }, { "source": 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"type": "augmanitai:crossDomainReference" }, { "source": "SPR-0014", "target": "RPH-2505", "type": "skos:broader" }, { "source": "RPH-2505", "target": "SPR-0014", "type": "skos:narrower" }, { "source": "SPR-0014", "target": "REL-0089", "type": "augmanitai:crossDomainReference" }, { "source": "SPR-0015", "target": "Algorithm", "type": "skos:broader" }, { "source": "Algorithm", "target": "SPR-0015", "type": "skos:narrower" }, { "source": "SPR-0015", "target": "SAL-0041", "type": "augmanitai:crossDomainReference" }, { "source": "SPR-0015", "target": "SAL-0040", "type": "augmanitai:crossDomainReference" }, { "source": "SPR-0015", "target": "SAL-0007", "type": "augmanitai:crossDomainReference" }, { "source": "SPR-0016", "target": "Bridge AI", "type": "skos:broader" }, { "source": "Bridge AI", "target": "SPR-0016", "type": "skos:narrower" }, { "source": "SPR-0016", "target": "EDU-0009", "type": "augmanitai:crossDomainReference" }, { "source": "SPR-0017", "target": "Bridge AI", "type": "skos:broader" }, { "source": "Bridge AI", "target": "SPR-0017", "type": "skos:narrower" }, { "source": "SPR-0018", "target": "Analytical Ratio", "type": "skos:broader" }, { "source": "Analytical Ratio", "target": "SPR-0018", "type": "skos:narrower" }, { "source": "SPR-0019", "target": "Logical Paradox", "type": "skos:broader" }, { "source": "Logical Paradox", "target": "SPR-0019", "type": "skos:narrower" }, { "source": "SPR-0020", "target": "Bridge AI", "type": "skos:broader" }, { "source": "Bridge AI", "target": "SPR-0020", "type": "skos:narrower" }, { "source": "SPR-0020", "target": "CON-0070", "type": "augmanitai:crossDomainReference" }, { "source": "SPR-0021", "target": "Bridge AI", "type": "skos:broader" }, { "source": "Bridge AI", "target": "SPR-0021", "type": "skos:narrower" }, { "source": "SPR-0021", "target": "CRE-0036", "type": "augmanitai:crossDomainReference" }, { "source": "SPR-0022", "target": "RPH-2505", "type": "skos:broader" }, { "source": "RPH-2505", "target": "SPR-0022", "type": "skos:narrower" }, { "source": "SPR-0022", "target": "ADA-0012", "type": "augmanitai:crossDomainReference" }, { "source": "SPR-0022", "target": "AED-0060", "type": "augmanitai:crossDomainReference" }, { "source": "SPR-0022", "target": "AED-0061", "type": "augmanitai:crossDomainReference" }, { "source": "SPR-0023", "target": "RPH-3101", "type": "skos:broader" }, { "source": "RPH-3101", "target": "SPR-0023", "type": "skos:narrower" }, { "source": "SPR-0023", "target": "CRE-0203", "type": "augmanitai:crossDomainReference" }, { "source": "SPR-0023", "target": "PHO-0013", "type": "augmanitai:crossDomainReference" }, { "source": "SPR-0023", "target": "ROB-0050", "type": "augmanitai:crossDomainReference" }, { "source": "SPR-0024", "target": "Performance Metric", "type": "skos:broader" }, { "source": "Performance Metric", "target": "SPR-0024", "type": "skos:narrower" }, { "source": "SPR-0024", "target": "ELR-0103", "type": "augmanitai:crossDomainReference" }, { "source": "SPR-0025", "target": "Psychological Effect", "type": "skos:broader" }, { "source": "Psychological Effect", "target": "SPR-0025", "type": "skos:narrower" }, { "source": "SPR-0026", "target": "Performance Metric", "type": "skos:broader" }, { "source": "Performance Metric", "target": "SPR-0026", "type": "skos:narrower" }, { "source": "SPR-0026", "target": "MKT-0064", "type": "augmanitai:crossDomainReference" }, { "source": "SPR-0027", "target": "Performance Metric", "type": "skos:broader" }, { "source": "Performance Metric", "target": "SPR-0027", "type": "skos:narrower" }, { "source": "SPR-0027", "target": "COG-0148", "type": "augmanitai:crossDomainReference" }, { "source": "SPR-0028", "target": "RPH-2552", "type": "skos:broader" }, { "source": "RPH-2552", "target": "SPR-0028", "type": "skos:narrower" }, { "source": "SPR-0028", "target": "GAM-0090", "type": "augmanitai:crossDomainReference" }, { "source": "SPR-0029", "target": "Performance Metric", "type": "skos:broader" }, { "source": "Performance Metric", "target": "SPR-0029", "type": "skos:narrower" }, { "source": "SPR-0029", "target": "ASE-0041", "type": "augmanitai:crossDomainReference" }, { "source": "SPR-0029", "target": "ASE-0040", "type": "augmanitai:crossDomainReference" }, { "source": "SPR-0030", "target": "RPH-2505", "type": "skos:broader" }, { "source": "RPH-2505", "target": "SPR-0030", "type": "skos:narrower" }, { "source": "SPR-0030", "target": "ASE-0008", "type": "augmanitai:crossDomainReference" }, { "source": "SPR-0031", "target": "RPH-2501", "type": "skos:broader" }, { "source": "RPH-2501", "target": "SPR-0031", "type": "skos:narrower" }, { "source": "SPR-0031", "target": "RHR-0248", "type": "augmanitai:crossDomainReference" }, { "source": "SPR-0032", "target": "Behavioral Pattern", "type": "skos:broader" }, { "source": "Behavioral Pattern", "target": "SPR-0032", "type": "skos:narrower" }, { "source": "SPR-0032", "target": "CRE-0202", "type": "augmanitai:crossDomainReference" }, { "source": "SPR-0032", "target": "MUS-0090", "type": "augmanitai:crossDomainReference" }, { "source": "SPR-0033", "target": "Performance Metric", "type": "skos:broader" }, { "source": "Performance Metric", "target": "SPR-0033", "type": "skos:narrower" }, { "source": "SPR-0033", "target": "DAT-0029", "type": "augmanitai:crossDomainReference" }, { "source": "SPR-0034", "target": "Performance Metric", "type": "skos:broader" }, { "source": "Performance Metric", "target": "SPR-0034", "type": "skos:narrower" }, { "source": "SPR-0034", "target": "AED-0091", "type": "augmanitai:crossDomainReference" }, { "source": "SPR-0034", "target": "ART-0086", "type": "augmanitai:crossDomainReference" }, { "source": "SPR-0034", "target": "ASE-0003", "type": "augmanitai:crossDomainReference" }, { "source": "SPR-0035", "target": "RPH-2701", "type": "skos:broader" }, { "source": "RPH-2701", "target": "SPR-0035", "type": "skos:narrower" }, { "source": "SPR-0035", "target": "SAL-0041", "type": "augmanitai:crossDomainReference" }, { "source": "SPR-0035", "target": "ASE-0021", "type": "augmanitai:crossDomainReference" }, { "source": "SPR-0035", "target": "ASE-0044", "type": "augmanitai:crossDomainReference" }, { "source": "SPR-0036", "target": "RPH-3101", "type": "skos:broader" }, { "source": "RPH-3101", "target": "SPR-0036", "type": "skos:narrower" }, { "source": "SPR-0036", "target": "IDN-0028", "type": "augmanitai:crossDomainReference" }, { "source": "SPR-0037", "target": "RPH-2701", "type": "skos:broader" }, { "source": "RPH-2701", "target": "SPR-0037", "type": "skos:narrower" }, { "source": "SPR-0037", "target": "COG-0031", "type": "augmanitai:crossDomainReference" }, { "source": "SPR-0038", "target": "Bridge AI", "type": "skos:broader" }, { "source": "Bridge AI", "target": "SPR-0038", "type": "skos:narrower" }, { "source": "SPR-0038", "target": "COG-0127", "type": "augmanitai:crossDomainReference" }, { "source": "SPR-0038", "target": "SPA-0006", "type": "augmanitai:crossDomainReference" }, { "source": "SPR-0039", "target": "Bridge AI", "type": "skos:broader" }, { "source": "Bridge AI", "target": "SPR-0039", "type": "skos:narrower" }, { "source": "SPR-0039", "target": "SCR-0026", "type": "augmanitai:crossDomainReference" }, { "source": "SPR-0040", "target": "Bridge AI", "type": "skos:broader" }, { "source": "Bridge AI", "target": "SPR-0040", "type": "skos:narrower" }, { "source": "SPR-0040", "target": "MTH-0038", "type": "augmanitai:crossDomainReference" }, { "source": "SPR-0041", "target": "Bridge AI", "type": "skos:broader" }, { "source": "Bridge AI", "target": "SPR-0041", "type": "skos:narrower" }, { "source": "SPR-0041", "target": "SPA-0039", "type": "augmanitai:crossDomainReference" }, { "source": "SPR-0042", "target": "Validation Process", "type": "skos:broader" }, { "source": "Validation Process", "target": "SPR-0042", "type": "skos:narrower" }, { "source": "SPR-0042", "target": "AED-0036", "type": "augmanitai:crossDomainReference" }, { "source": "SPR-0042", "target": "AGE-0097", "type": "augmanitai:crossDomainReference" }, { "source": "SPR-0042", "target": "BEH-0002", "type": "augmanitai:crossDomainReference" }, { "source": "SPR-0043", "target": "RPH-2201", "type": "skos:broader" }, { "source": "RPH-2201", "target": "SPR-0043", "type": "skos:narrower" }, { "source": "SPR-0043", "target": "RPH-1605", "type": "augmanitai:crossDomainReference" }, { "source": "SPR-0043", "target": "ROB-0137", "type": "augmanitai:crossDomainReference" }, { "source": "SPR-0044", "target": "RPH-2505", "type": "skos:broader" }, { "source": "RPH-2505", "target": "SPR-0044", "type": "skos:narrower" }, { "source": "SPR-0044", "target": "AGE-0097", "type": "augmanitai:crossDomainReference" }, { "source": "SPR-0045", "target": "RPH-2505", "type": "skos:broader" }, { "source": "RPH-2505", "target": "SPR-0045", "type": "skos:narrower" }, { "source": "SPR-0045", "target": "AGE-0067", "type": "augmanitai:crossDomainReference" }, { "source": "SPR-0045", "target": "AGE-0078", "type": "augmanitai:crossDomainReference" }, { "source": "SPR-0046", "target": "RPH-2355", "type": "skos:broader" }, { "source": "RPH-2355", "target": "SPR-0046", "type": "skos:narrower" }, { "source": "SPR-0046", "target": "COG-0074", "type": "augmanitai:crossDomainReference" }, { "source": "SPR-0046", "target": "FIC-0021", "type": "augmanitai:crossDomainReference" }, { "source": "SPR-0047", "target": "Analytical Ratio", "type": "skos:broader" }, { "source": "Analytical Ratio", "target": "SPR-0047", "type": "skos:narrower" }, { "source": "SPR-0048", "target": "Bridge AI", "type": "skos:broader" }, { "source": "Bridge AI", "target": "SPR-0048", "type": "skos:narrower" }, { "source": "SPR-0048", "target": "MTH-0060", "type": "augmanitai:crossDomainReference" }, { "source": "SPR-0048", "target": "RHR-0021", "type": "augmanitai:crossDomainReference" }, { "source": "SPR-0049", "target": "Bridge AI", "type": "skos:broader" }, { "source": "Bridge AI", "target": "SPR-0049", "type": "skos:narrower" }, { "source": "SPR-0049", "target": "AGE-0097", "type": "augmanitai:crossDomainReference" }, { "source": "SPR-0049", "target": "AGE-0075", "type": "augmanitai:crossDomainReference" }, { "source": "SPR-0049", "target": "PHO-0093", "type": "augmanitai:crossDomainReference" }, { "source": "SPR-0050", "target": "Analytical Ratio", "type": "skos:broader" }, { "source": "Analytical Ratio", "target": "SPR-0050", "type": "skos:narrower" }, { "source": "SPR-0050", "target": "RHR-0131", "type": "augmanitai:crossDomainReference" }, { "source": "SPR-0051", "target": "Bridge AI", "type": "skos:broader" }, { "source": "Bridge AI", "target": "SPR-0051", "type": "skos:narrower" }, { "source": "SPR-0051", "target": "RHR-0246", "type": "augmanitai:crossDomainReference" }, { "source": "SPR-0051", "target": "COP-0034", "type": "augmanitai:crossDomainReference" }, { "source": "SPR-0051", "target": "ASE-0046", "type": "augmanitai:crossDomainReference" }, { "source": "SPR-0052", "target": "RPH-2355", "type": "skos:broader" }, { "source": "RPH-2355", "target": "SPR-0052", "type": "skos:narrower" }, { "source": "SPR-0052", "target": "KNO-0005", "type": "augmanitai:crossDomainReference" }, { "source": "SPR-0053", "target": "Performance Gap", "type": "skos:broader" }, { "source": "Performance Gap", "target": "SPR-0053", "type": "skos:narrower" }, { "source": "SPR-0054", "target": "RPH-3754", "type": "skos:broader" }, { "source": "RPH-3754", "target": "SPR-0054", "type": "skos:narrower" }, { "source": "SPR-0054", "target": "COG-0074", "type": "augmanitai:crossDomainReference" }, { "source": "SPR-0055", "target": "Bridge AI", "type": "skos:broader" }, { "source": "Bridge AI", "target": "SPR-0055", "type": "skos:narrower" }, { "source": "SPR-0055", "target": "MKT-0061", "type": "augmanitai:crossDomainReference" }, { "source": "SPR-0056", "target": "Behavioral Pattern", "type": "skos:broader" }, { "source": "Behavioral Pattern", "target": "SPR-0056", "type": "skos:narrower" }, { "source": "SPR-0056", "target": "ART-0039", "type": "augmanitai:crossDomainReference" }, { "source": "SPR-0056", "target": "MKT-0061", "type": "augmanitai:crossDomainReference" }, { "source": "SPR-0056", "target": "SAL-0054", "type": "augmanitai:crossDomainReference" }, { "source": "SPR-0057", "target": "RPH-2505", "type": "skos:broader" }, { "source": "RPH-2505", "target": "SPR-0057", "type": "skos:narrower" }, { "source": "SPR-0057", "target": "ELR-0108", "type": "augmanitai:crossDomainReference" }, { "source": "SPR-0058", "target": "System Adaptation", "type": "skos:broader" }, { "source": "System Adaptation", "target": "SPR-0058", "type": "skos:narrower" }, { "source": "SPR-0058", "target": "CON-0025", "type": "augmanitai:crossDomainReference" }, { "source": "SPR-0059", "target": "Psychological Effect", "type": "skos:broader" }, { "source": "Psychological Effect", "target": "SPR-0059", "type": "skos:narrower" 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"augmanitai:crossDomainReference" }, { "source": "SPR-0062", "target": "RPH-2505", "type": "skos:broader" }, { "source": "RPH-2505", "target": "SPR-0062", "type": "skos:narrower" }, { "source": "SPR-0062", "target": "RHR-0211", "type": "augmanitai:crossDomainReference" }, { "source": "SPR-0063", "target": "RPH-2505", "type": "skos:broader" }, { "source": "RPH-2505", "target": "SPR-0063", "type": "skos:narrower" }, { "source": "SPR-0063", "target": "SOC-0039", "type": "augmanitai:crossDomainReference" }, { "source": "SPR-0064", "target": "Bridge AI", "type": "skos:broader" }, { "source": "Bridge AI", "target": "SPR-0064", "type": "skos:narrower" }, { "source": "SPR-0064", "target": "ROB-0099", "type": "augmanitai:crossDomainReference" }, { "source": "SPR-0065", "target": "RPH-3601", "type": "skos:broader" }, { "source": "RPH-3601", "target": "SPR-0065", "type": "skos:narrower" }, { "source": "SPR-0065", "target": "AED-0017", "type": "augmanitai:crossDomainReference" }, { "source": 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"type": "augmanitai:crossDomainReference" }, { "source": "SPR-0069", "target": "RPH-1260", "type": "augmanitai:crossDomainReference" }, { "source": "SPR-0070", "target": "Bridge AI", "type": "skos:broader" }, { "source": "Bridge AI", "target": "SPR-0070", "type": "skos:narrower" }, { "source": "SPR-0070", "target": "EDU-0031", "type": "augmanitai:crossDomainReference" }, { "source": "SPR-0071", "target": "Performance Gap", "type": "skos:broader" }, { "source": "Performance Gap", "target": "SPR-0071", "type": "skos:narrower" }, { "source": "SPR-0071", "target": "ASE-0046", "type": "augmanitai:crossDomainReference" }, { "source": "SPR-0071", "target": "DAT-0069", "type": "augmanitai:crossDomainReference" }, { "source": "SPR-0071", "target": "EDU-0031", "type": "augmanitai:crossDomainReference" }, { "source": "SPR-0072", "target": "RPH-3802", "type": "skos:broader" }, { "source": "RPH-3802", "target": "SPR-0072", "type": "skos:narrower" }, { "source": "SPR-0072", "target": "AED-0077", "type": "augmanitai:crossDomainReference" }, { "source": "SPR-0072", "target": "ART-0017", "type": "augmanitai:crossDomainReference" }, { "source": "SPR-0072", "target": "ART-0032", "type": "augmanitai:crossDomainReference" }, { "source": "SPR-0073", "target": "RPH-3304", "type": "skos:broader" }, { "source": "RPH-3304", "target": "SPR-0073", "type": "skos:narrower" }, { "source": "SPR-0073", "target": "ADA-0012", "type": "augmanitai:crossDomainReference" }, { "source": "SPR-0074", "target": "Performance Metric", "type": "skos:broader" }, { "source": "Performance Metric", "target": "SPR-0074", "type": "skos:narrower" }, { "source": "SPR-0074", "target": "ADA-0012", "type": "augmanitai:crossDomainReference" }, { "source": "SPR-0074", "target": "AED-0077", "type": "augmanitai:crossDomainReference" }, { "source": "SPR-0074", "target": "ART-0010", "type": "augmanitai:crossDomainReference" }, { "source": "SPR-0075", "target": "Performance Metric", "type": "skos:broader" }, { "source": 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"source": "SPR-0086", "target": "RPH-2703", "type": "skos:broader" }, { "source": "RPH-2703", "target": "SPR-0086", "type": "skos:narrower" }, { "source": "SPR-0086", "target": "ELR-0102", "type": "augmanitai:crossDomainReference" }, { "source": "SPR-0087", "target": "Analytical Ratio", "type": "skos:broader" }, { "source": "Analytical Ratio", "target": "SPR-0087", "type": "skos:narrower" }, { "source": "SPR-0088", "target": "Psychological Effect", "type": "skos:broader" }, { "source": "Psychological Effect", "target": "SPR-0088", "type": "skos:narrower" }, { "source": "SPR-0088", "target": "AED-0018", "type": "augmanitai:crossDomainReference" }, { "source": "SPR-0088", "target": "AED-0075", "type": "augmanitai:crossDomainReference" }, { "source": "SPR-0088", "target": "AED-0077", "type": "augmanitai:crossDomainReference" }, { "source": "SPR-0089", "target": "Psychological Effect", "type": "skos:broader" }, { "source": "Psychological Effect", "target": "SPR-0089", "type": 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"skos:narrower" }, { "source": "SPR-0092", "target": "AED-0092", "type": "augmanitai:crossDomainReference" }, { "source": "SPR-0093", "target": "Model Training", "type": "skos:broader" }, { "source": "Model Training", "target": "SPR-0093", "type": "skos:narrower" }, { "source": "SPR-0093", "target": "DES-0031", "type": "augmanitai:crossDomainReference" }, { "source": "SPR-0094", "target": "Algorithm", "type": "skos:broader" }, { "source": "Algorithm", "target": "SPR-0094", "type": "skos:narrower" }, { "source": "SPR-0095", "target": "RPH-3902", "type": "skos:broader" }, { "source": "RPH-3902", "target": "SPR-0095", "type": "skos:narrower" }, { "source": "SPR-0095", "target": "COG-0175", "type": "augmanitai:crossDomainReference" }, { "source": "SPR-0095", "target": "LIN-0064", "type": "augmanitai:crossDomainReference" }, { "source": "SPR-0096", "target": "Logical Paradox", "type": "skos:broader" }, { "source": "Logical Paradox", "target": "SPR-0096", "type": "skos:narrower" }, { "source": "SPR-0096", "target": "COG-0086", "type": "augmanitai:crossDomainReference" }, { "source": "SPR-0097", "target": "Logical Paradox", "type": "skos:broader" }, { "source": "Logical Paradox", "target": "SPR-0097", "type": "skos:narrower" }, { "source": "SPR-0097", "target": "CON-0030", "type": "augmanitai:crossDomainReference" }, { "source": "SPR-0098", "target": "RPH-3002", "type": "skos:broader" }, { "source": "RPH-3002", "target": "SPR-0098", "type": "skos:narrower" }, { "source": "SPR-0098", "target": "ELR-0107", "type": "augmanitai:crossDomainReference" }, { "source": "SPR-0099", "target": "RPH-3251", "type": "skos:broader" }, { "source": "RPH-3251", "target": "SPR-0099", "type": "skos:narrower" }, { "source": "SPR-0099", "target": "ROB-0244", "type": "augmanitai:crossDomainReference" }, { "source": "SPR-0099", "target": "MKT-0009", "type": "augmanitai:crossDomainReference" }, { "source": "SPR-0099", "target": "ART-0033", "type": "augmanitai:crossDomainReference" }, { "source": "SPR-0100", "target": "RPH-3304", "type": "skos:broader" }, { "source": "RPH-3304", "target": "SPR-0100", "type": "skos:narrower" }, { "source": "SPR-0100", "target": "PHO-0005", "type": "augmanitai:crossDomainReference" }, { "source": "SPR-0100", "target": "GAM-0012", "type": "augmanitai:crossDomainReference" }, { "source": "SPR-0100", "target": "EDU-0064", "type": "augmanitai:crossDomainReference" }, { "source": "SPR-0101", "target": "Bridge AI", "type": "skos:broader" }, { "source": "Bridge AI", "target": "SPR-0101", "type": "skos:narrower" }, { "source": "SPR-0101", "target": "ELR-0078", "type": "augmanitai:crossDomainReference" }, { "source": "SPR-0102", "target": "RPH-3952", "type": "skos:broader" }, { "source": "RPH-3952", "target": "SPR-0102", "type": "skos:narrower" }, { "source": "SPR-0102", "target": "GAM-0044", "type": "augmanitai:crossDomainReference" }, { "source": "SPR-0103", "target": "Bridge AI", "type": "skos:broader" }, { "source": "Bridge AI", "target": 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"target": "SPR-0106", "type": "skos:narrower" }, { "source": "SPR-0106", "target": "LIN-0037", "type": "augmanitai:crossDomainReference" }, { "source": "SPR-0106", "target": "IDN-0043", "type": "augmanitai:crossDomainReference" }, { "source": "SPR-0107", "target": "RPH-2703", "type": "skos:broader" }, { "source": "RPH-2703", "target": "SPR-0107", "type": "skos:narrower" }, { "source": "SPR-0107", "target": "GAM-0016", "type": "augmanitai:crossDomainReference" }, { "source": "SPR-0107", "target": "GAM-0026", "type": "augmanitai:crossDomainReference" }, { "source": "SPR-0108", "target": "Decision Threshold", "type": "skos:broader" }, { "source": "Decision Threshold", "target": "SPR-0108", "type": "skos:narrower" }, { "source": "SPR-0108", "target": "COG-0166", "type": "augmanitai:crossDomainReference" }, { "source": "SPR-0108", "target": "CRE-0061", "type": "augmanitai:crossDomainReference" }, { "source": "SPR-0109", "target": "Bridge AI", "type": "skos:broader" }, { "source": "Bridge AI", "target": "SPR-0109", "type": "skos:narrower" }, { "source": "SPR-0109", "target": "GAM-0027", "type": "augmanitai:crossDomainReference" }, { "source": "SPR-0110", "target": "Systemic Drift", "type": "skos:broader" }, { "source": "Systemic Drift", "target": "SPR-0110", "type": "skos:narrower" }, { "source": "SPR-0110", "target": "GAM-0044", "type": "augmanitai:crossDomainReference" }, { "source": "SPR-0110", "target": "GAM-0070", "type": "augmanitai:crossDomainReference" }, { "source": "SPR-0111", "target": "RPH-3901", "type": "skos:broader" }, { "source": "RPH-3901", "target": "SPR-0111", "type": "skos:narrower" }, { "source": "SPR-0111", "target": "MUS-0024", "type": "augmanitai:crossDomainReference" }, { "source": "SPR-0112", "target": "RPH-3754", "type": "skos:broader" }, { "source": "RPH-3754", "target": "SPR-0112", "type": "skos:narrower" }, { "source": "SPR-0112", "target": "DAT-0095", "type": "augmanitai:crossDomainReference" }, { "source": "SPR-0113", "target": 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"target": "SPR-0116", "type": "skos:narrower" }, { "source": "SPR-0116", "target": "COG-0046", "type": "augmanitai:crossDomainReference" }, { "source": "SPR-0116", "target": "RET-0010", "type": "augmanitai:crossDomainReference" }, { "source": "SPR-0117", "target": "Bridge AI", "type": "skos:broader" }, { "source": "Bridge AI", "target": "SPR-0117", "type": "skos:narrower" }, { "source": "SPR-0117", "target": "MKT-0040", "type": "augmanitai:crossDomainReference" }, { "source": "SPR-0117", "target": "SAL-0088", "type": "augmanitai:crossDomainReference" }, { "source": "SPR-0118", "target": "Bridge AI", "type": "skos:broader" }, { "source": "Bridge AI", "target": "SPR-0118", "type": "skos:narrower" }, { "source": "SPR-0118", "target": "CON-0019", "type": "augmanitai:crossDomainReference" }, { "source": "SPR-0119", "target": "RPH-2005", "type": "skos:broader" }, { "source": "RPH-2005", "target": "SPR-0119", "type": "skos:narrower" }, { "source": "SPR-0119", "target": "RHR-0037", "type": 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"target": "ELR-0083", "type": "augmanitai:crossDomainReference" }, { "source": "SPR-0123", "target": "Bridge AI", "type": "skos:broader" }, { "source": "Bridge AI", "target": "SPR-0123", "type": "skos:narrower" }, { "source": "SPR-0123", "target": "IDN-0058", "type": "augmanitai:crossDomainReference" }, { "source": "SPR-0123", "target": "LIN-0020", "type": "augmanitai:crossDomainReference" }, { "source": "SPR-0124", "target": "Pattern Recognition", "type": "skos:broader" }, { "source": "Pattern Recognition", "target": "SPR-0124", "type": "skos:narrower" }, { "source": "SPR-0124", "target": "LIN-0006", "type": "augmanitai:crossDomainReference" }, { "source": "SPR-0125", "target": "RPH-3202", "type": "skos:broader" }, { "source": "RPH-3202", "target": "SPR-0125", "type": "skos:narrower" }, { "source": "SPR-0125", "target": "LIN-0003", "type": "augmanitai:crossDomainReference" }, { "source": "SPR-0125", "target": "MTH-0089", "type": "augmanitai:crossDomainReference" }, { "source": "SPR-0126", "target": "Bridge AI", "type": "skos:broader" }, { "source": "Bridge AI", "target": "SPR-0126", "type": "skos:narrower" }, { "source": "SPR-0126", "target": "AUG-0487", "type": "augmanitai:crossDomainReference" }, { "source": "SPR-0127", "target": "Analytical Ratio", "type": "skos:broader" }, { "source": "Analytical Ratio", "target": "SPR-0127", "type": "skos:narrower" }, { "source": "SPR-0127", "target": "COP-0010", "type": "augmanitai:crossDomainReference" }, { "source": "SPR-0127", "target": "CON-0052", "type": "augmanitai:crossDomainReference" }, { "source": "SPR-0128", "target": "Psychological Effect", "type": "skos:broader" }, { "source": "Psychological Effect", "target": "SPR-0128", "type": "skos:narrower" }, { "source": "SPR-0128", "target": "BEH-0091", "type": "augmanitai:crossDomainReference" }, { "source": "SPR-0128", "target": "RHR-0100", "type": "augmanitai:crossDomainReference" }, { "source": "SPR-0129", "target": "Bridge AI", "type": "skos:broader" }, { "source": "Bridge AI", "target": "SPR-0129", "type": "skos:narrower" }, { "source": "SPR-0129", "target": "LNG-0017", "type": "augmanitai:crossDomainReference" }, { "source": "SPR-0130", "target": "Analytical Ratio", "type": "skos:broader" }, { "source": "Analytical Ratio", "target": "SPR-0130", "type": "skos:narrower" }, { "source": "SPR-0130", "target": "AED-0042", "type": "augmanitai:crossDomainReference" }, { "source": "SPR-0130", "target": "BEH-0078", "type": "augmanitai:crossDomainReference" }, { "source": "SPR-0131", "target": "Detection System", "type": "skos:broader" }, { "source": "Detection System", "target": "SPR-0131", "type": "skos:narrower" }, { "source": "SPR-0131", "target": "COG-0074", "type": "augmanitai:crossDomainReference" }, { "source": "SPR-0132", "target": "Bridge AI", "type": "skos:broader" }, { "source": "Bridge AI", "target": "SPR-0132", "type": "skos:narrower" }, { "source": "SPR-0132", "target": "ROB-0073", "type": "augmanitai:crossDomainReference" }, { "source": "SPR-0132", "target": "COG-0106", "type": "augmanitai:crossDomainReference" }, { "source": "SPR-0133", "target": "Algorithm", "type": "skos:broader" }, { "source": "Algorithm", "target": "SPR-0133", "type": "skos:narrower" }, { "source": "SPR-0133", "target": "AUG-0840", "type": "augmanitai:crossDomainReference" }, { "source": "SPR-0133", "target": "RHR-0150", "type": "augmanitai:crossDomainReference" }, { "source": "SPR-0134", "target": "RPH-2005", "type": "skos:broader" }, { "source": "RPH-2005", "target": "SPR-0134", "type": "skos:narrower" }, { "source": "SPR-0134", "target": "BEH-0043", "type": "augmanitai:crossDomainReference" }, { "source": "SPR-0134", "target": "AED-0043", "type": "augmanitai:crossDomainReference" }, { "source": "SPR-0135", "target": "Bridge AI", "type": "skos:broader" }, { "source": "Bridge AI", "target": "SPR-0135", "type": "skos:narrower" }, { "source": "SPR-0135", "target": "GAM-0090", "type": "augmanitai:crossDomainReference" }, { "source": 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"target": "GAM-0075", "type": "augmanitai:crossDomainReference" }, { "source": "SPR-0139", "target": "Bridge AI", "type": "skos:broader" }, { "source": "Bridge AI", "target": "SPR-0139", "type": "skos:narrower" }, { "source": "SPR-0139", "target": "DAT-0088", "type": "augmanitai:crossDomainReference" }, { "source": "SPR-0140", "target": "Bridge AI", "type": "skos:broader" }, { "source": "Bridge AI", "target": "SPR-0140", "type": "skos:narrower" }, { "source": "SPR-0140", "target": "CRE-0124", "type": "augmanitai:crossDomainReference" }, { "source": "SPR-0141", "target": "Psychological Effect", "type": "skos:broader" }, { "source": "Psychological Effect", "target": "SPR-0141", "type": "skos:narrower" }, { "source": "SPR-0141", "target": "SAL-0035", "type": "augmanitai:crossDomainReference" }, { "source": "SPR-0141", "target": "COG-0061", "type": "augmanitai:crossDomainReference" }, { "source": "SPR-0142", "target": "RPH-2703", "type": "skos:broader" }, { "source": "RPH-2703", "target": 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"target": "DAT-0044", "type": "augmanitai:crossDomainReference" }, { "source": "SPR-0161", "target": "GAM-0016", "type": "augmanitai:crossDomainReference" }, { "source": "SPR-0162", "target": "RPH-2303", "type": "skos:broader" }, { "source": "RPH-2303", "target": "SPR-0162", "type": "skos:narrower" }, { "source": "SPR-0162", "target": "GAM-0067", "type": "augmanitai:crossDomainReference" }, { "source": "SPR-0163", "target": "RPH-2703", "type": "skos:broader" }, { "source": "RPH-2703", "target": "SPR-0163", "type": "skos:narrower" }, { "source": "SPR-0163", "target": "DAT-0044", "type": "augmanitai:crossDomainReference" }, { "source": "SPR-0164", "target": "Optimization Method", "type": "skos:broader" }, { "source": "Optimization Method", "target": "SPR-0164", "type": "skos:narrower" }, { "source": "SPR-0164", "target": "CUS-0022", "type": "augmanitai:crossDomainReference" }, { "source": "SPR-0165", "target": "RPH-2701", "type": "skos:broader" }, { "source": "RPH-2701", "target": 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"skos:narrower" }, { "source": "SPR-0181", "target": "RHR-0108", "type": "augmanitai:crossDomainReference" }, { "source": "SPR-0181", "target": "ROB-0192", "type": "augmanitai:crossDomainReference" }, { "source": "SPR-0182", "target": "Analytical Ratio", "type": "skos:broader" }, { "source": "Analytical Ratio", "target": "SPR-0182", "type": "skos:narrower" }, { "source": "SPR-0182", "target": "REL-0067", "type": "augmanitai:crossDomainReference" }, { "source": "SPR-0182", "target": "ROB-0008", "type": "augmanitai:crossDomainReference" }, { "source": "SPR-0183", "target": "RPH-2301", "type": "skos:broader" }, { "source": "RPH-2301", "target": "SPR-0183", "type": "skos:narrower" }, { "source": "SPR-0183", "target": "COP-0091", "type": "augmanitai:crossDomainReference" }, { "source": "SPR-0183", "target": "AGE-0040", "type": "augmanitai:crossDomainReference" }, { "source": "SPR-0183", "target": "EDU-0094", "type": "augmanitai:crossDomainReference" }, { "source": "SPR-0184", "target": 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"SPR-0187", "type": "skos:narrower" }, { "source": "SPR-0187", "target": "GAM-0025", "type": "augmanitai:crossDomainReference" }, { "source": "SPR-0187", "target": "SPA-0085", "type": "augmanitai:crossDomainReference" }, { "source": "SPR-0188", "target": "Bridge AI", "type": "skos:broader" }, { "source": "Bridge AI", "target": "SPR-0188", "type": "skos:narrower" }, { "source": "SPR-0188", "target": "PLY-0064", "type": "augmanitai:crossDomainReference" }, { "source": "SPR-0189", "target": "Bridge AI", "type": "skos:broader" }, { "source": "Bridge AI", "target": "SPR-0189", "type": "skos:narrower" }, { "source": "SPR-0190", "target": "Adoption Barrier", "type": "skos:broader" }, { "source": "Adoption Barrier", "target": "SPR-0190", "type": "skos:narrower" }, { "source": "SPR-0190", "target": "CON-0046", "type": "augmanitai:crossDomainReference" }, { "source": "SPR-0191", "target": "SOC-0017", "type": "skos:broader" }, { "source": "SOC-0017", "target": "SPR-0191", "type": "skos:narrower" 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"skos:narrower" }, { "source": "SPR-0200", "target": "LIN-0086", "type": "augmanitai:crossDomainReference" }, { "source": "STE-0001", "target": "Data Compression", "type": "skos:broader" }, { "source": "Data Compression", "target": "STE-0001", "type": "skos:narrower" }, { "source": "STE-0001", "target": "MTH-0090", "type": "augmanitai:crossDomainReference" }, { "source": "STE-0001", "target": "ELR-0153", "type": "augmanitai:crossDomainReference" }, { "source": "STE-0002", "target": "RPH-2351", "type": "skos:broader" }, { "source": "RPH-2351", "target": "STE-0002", "type": "skos:narrower" }, { "source": "STE-0002", "target": "AED-0073", "type": "augmanitai:crossDomainReference" }, { "source": "STE-0002", "target": "AGE-0006", "type": "augmanitai:crossDomainReference" }, { "source": "STE-0002", "target": "AGE-0007", "type": "augmanitai:crossDomainReference" }, { "source": "STE-0003", "target": "Stereotypes", "type": "skos:broader" }, { "source": "Stereotypes", "target": "STE-0003", 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"skos:narrower" }, { "source": "SWE-0020", "target": "AED-0021", "type": "augmanitai:crossDomainReference" }, { "source": "SWE-0020", "target": "COG-0036", "type": "augmanitai:crossDomainReference" }, { "source": "SWE-0020", "target": "COG-0114", "type": "augmanitai:crossDomainReference" }, { "source": "SWE-0021", "target": "Software Engineering", "type": "skos:broader" }, { "source": "Software Engineering", "target": "SWE-0021", "type": "skos:narrower" }, { "source": "SWE-0021", "target": "STE-0012", "type": "augmanitai:crossDomainReference" }, { "source": "SWE-0022", "target": "RPH-2603", "type": "skos:broader" }, { "source": "RPH-2603", "target": "SWE-0022", "type": "skos:narrower" }, { "source": "SWE-0022", "target": "AED-0019", "type": "augmanitai:crossDomainReference" }, { "source": "SWE-0022", "target": "AED-0092", "type": "augmanitai:crossDomainReference" }, { "source": "SWE-0022", "target": "AGE-0023", "type": "augmanitai:crossDomainReference" }, { "source": "SWE-0023", "target": "Software Engineering", "type": "skos:broader" }, { "source": "Software Engineering", "target": "SWE-0023", "type": "skos:narrower" }, { "source": "SWE-0024", "target": "Analytical Ratio", "type": "skos:broader" }, { "source": "Analytical Ratio", "target": "SWE-0024", "type": "skos:narrower" }, { "source": "SWE-0025", "target": "Software Engineering", "type": "skos:broader" }, { "source": "Software Engineering", "target": "SWE-0025", "type": "skos:narrower" }, { "source": "SWE-0026", "target": "RPH-2555", "type": "skos:broader" }, { "source": "RPH-2555", "target": "SWE-0026", "type": "skos:narrower" }, { "source": "SWE-0027", "target": "RPH-2003", "type": "skos:broader" }, { "source": "RPH-2003", "target": "SWE-0027", "type": "skos:narrower" }, { "source": "SWE-0027", "target": "COG-0105", "type": "augmanitai:crossDomainReference" }, { "source": "SWE-0027", "target": "CON-0013", "type": "augmanitai:crossDomainReference" }, { "source": "SWE-0027", "target": "CUS-0097", "type": "augmanitai:crossDomainReference" }, { "source": "SWE-0028", "target": "Software Engineering", "type": "skos:broader" }, { "source": "Software Engineering", "target": "SWE-0028", "type": "skos:narrower" }, { "source": "SWE-0029", "target": "RPH-2555", "type": "skos:broader" }, { "source": "RPH-2555", "target": "SWE-0029", "type": "skos:narrower" }, { "source": "SWE-0030", "target": "Analytical Ratio", "type": "skos:broader" }, { "source": "Analytical Ratio", "target": "SWE-0030", "type": "skos:narrower" }, { "source": "SWE-0031", "target": "Software Engineering", "type": "skos:broader" }, { "source": "Software Engineering", "target": "SWE-0031", "type": "skos:narrower" }, { "source": "SWE-0031", "target": "LIN-0002", "type": "augmanitai:crossDomainReference" }, { "source": "SWE-0032", "target": "RPH-2701", "type": "skos:broader" }, { "source": "RPH-2701", "target": "SWE-0032", "type": "skos:narrower" }, { "source": "SWE-0033", "target": "Software Engineering", "type": "skos:broader" }, { "source": "Software Engineering", "target": "SWE-0033", "type": "skos:narrower" }, { "source": "SWE-0033", "target": "STE-0007", "type": "augmanitai:crossDomainReference" }, { "source": "SWE-0034", "target": "Software Engineering", "type": "skos:broader" }, { "source": "Software Engineering", "target": "SWE-0034", "type": "skos:narrower" }, { "source": "SWE-0035", "target": "Software Engineering", "type": "skos:broader" }, { "source": "Software Engineering", "target": "SWE-0035", "type": "skos:narrower" }, { "source": "SWE-0036", "target": "RPH-3304", "type": "skos:broader" }, { "source": "RPH-3304", "target": "SWE-0036", "type": "skos:narrower" }, { "source": "SWE-0036", "target": "REL-0109", "type": "augmanitai:crossDomainReference" }, { "source": "SWE-0037", "target": "Software Engineering", "type": "skos:broader" }, { "source": "Software Engineering", "target": "SWE-0037", "type": "skos:narrower" }, { "source": "SWE-0038", "target": "Software Engineering", "type": "skos:broader" }, { "source": "Software Engineering", "target": "SWE-0038", "type": "skos:narrower" }, { "source": "SWE-0039", "target": "RPH-2355", "type": "skos:broader" }, { "source": "RPH-2355", "target": "SWE-0039", "type": "skos:narrower" }, { "source": "SWE-0039", "target": "CRE-0065", "type": "augmanitai:crossDomainReference" }, { "source": "SWE-0040", "target": "RPH-2104", "type": "skos:broader" }, { "source": "RPH-2104", "target": "SWE-0040", "type": "skos:narrower" }, { "source": "SWE-0040", "target": "CRE-0112", "type": "augmanitai:crossDomainReference" }, { "source": "SWE-0041", "target": "Software Engineering", "type": "skos:broader" }, { "source": "Software Engineering", "target": "SWE-0041", "type": "skos:narrower" }, { "source": "SWE-0042", "target": "Software Engineering", "type": "skos:broader" }, { "source": "Software Engineering", "target": "SWE-0042", "type": "skos:narrower" }, { "source": "SWE-0042", "target": "COP-0071", "type": "augmanitai:crossDomainReference" }, { "source": "SWE-0043", "target": "RPH-3101", "type": "skos:broader" }, { "source": "RPH-3101", "target": "SWE-0043", "type": "skos:narrower" }, { "source": "SWE-0044", "target": "Software Engineering", "type": "skos:broader" }, { "source": "Software Engineering", "target": "SWE-0044", "type": "skos:narrower" }, { "source": "SWE-0044", "target": "MUS-0021", "type": "augmanitai:crossDomainReference" }, { "source": "SWE-0045", "target": "Software Engineering", "type": "skos:broader" }, { "source": "Software Engineering", "target": "SWE-0045", "type": "skos:narrower" }, { "source": "SWE-0046", "target": "Software Engineering", "type": "skos:broader" }, { "source": "Software Engineering", "target": "SWE-0046", "type": "skos:narrower" }, { "source": "SWE-0046", "target": "ELR-0181", "type": "augmanitai:crossDomainReference" }, { "source": "SWE-0046", "target": "RHR-0135", "type": "augmanitai:crossDomainReference" }, { "source": "SWE-0046", "target": "ROB-0205", "type": "augmanitai:crossDomainReference" }, { "source": "SWE-0047", "target": "RPH-2104", "type": "skos:broader" }, { "source": "RPH-2104", "target": "SWE-0047", "type": "skos:narrower" }, { "source": "SWE-0048", "target": "Software Engineering", "type": "skos:broader" }, { "source": "Software Engineering", "target": "SWE-0048", "type": "skos:narrower" }, { "source": "SWE-0049", "target": "RPH-2303", "type": "skos:broader" }, { "source": "RPH-2303", "target": "SWE-0049", "type": "skos:narrower" }, { "source": "SWE-0049", "target": "ELR-0027", "type": "augmanitai:crossDomainReference" }, { "source": "SWE-0050", "target": "Analytical Ratio", "type": "skos:broader" }, { "source": "Analytical Ratio", "target": "SWE-0050", "type": "skos:narrower" }, { "source": "SWE-0051", "target": "Software Engineering", "type": "skos:broader" }, { "source": "Software Engineering", "target": "SWE-0051", "type": "skos:narrower" }, { "source": "SWE-0052", "target": "Software Engineering", "type": "skos:broader" }, { "source": "Software Engineering", "target": "SWE-0052", "type": "skos:narrower" }, { "source": "SWE-0053", "target": "Software Engineering", "type": "skos:broader" }, { "source": "Software Engineering", "target": "SWE-0053", "type": "skos:narrower" }, { "source": "SWE-0053", "target": "STE-0072", "type": "augmanitai:crossDomainReference" }, { "source": "SWE-0054", "target": "Software Engineering", "type": "skos:broader" }, { "source": "Software Engineering", "target": "SWE-0054", "type": "skos:narrower" }, { "source": "SWE-0055", "target": "Software Engineering", "type": "skos:broader" }, { "source": "Software Engineering", "target": "SWE-0055", "type": "skos:narrower" }, { "source": "SWE-0055", "target": "AGE-0023", "type": "augmanitai:crossDomainReference" }, { "source": "SWE-0055", "target": "AGE-0024", "type": "augmanitai:crossDomainReference" }, { "source": "SWE-0055", "target": "ART-0060", "type": "augmanitai:crossDomainReference" }, { "source": "SWE-0056", "target": "Logical Paradox", "type": "skos:broader" }, { "source": "Logical Paradox", "target": "SWE-0056", "type": "skos:narrower" }, { "source": "SWE-0057", "target": "Cognitive Bias", "type": "skos:broader" }, { "source": "Cognitive Bias", "target": "SWE-0057", "type": "skos:narrower" }, { "source": "SWE-0057", "target": "CUS-0006", "type": "augmanitai:crossDomainReference" }, { "source": "SWE-0058", "target": "Performance Metric", "type": "skos:broader" }, { "source": "Performance Metric", "target": "SWE-0058", "type": "skos:narrower" }, { "source": "SWE-0058", "target": "SPR-0025", "type": "augmanitai:crossDomainReference" }, { "source": "SWE-0059", "target": "Analytical Ratio", "type": "skos:broader" }, { "source": "Analytical Ratio", "target": "SWE-0059", "type": "skos:narrower" }, { "source": "SWE-0060", "target": "RPH-2701", "type": "skos:broader" }, { "source": "RPH-2701", "target": "SWE-0060", "type": "skos:narrower" }, { "source": "SWE-0060", "target": "ADA-0012", "type": "augmanitai:crossDomainReference" }, { "source": "SWE-0060", "target": "AED-0044", "type": "augmanitai:crossDomainReference" }, { "source": "SWE-0060", "target": "AED-0063", "type": "augmanitai:crossDomainReference" }, { "source": "SWE-0061", "target": "Analytical Ratio", "type": "skos:broader" }, { "source": "Analytical Ratio", "target": "SWE-0061", "type": "skos:narrower" }, { "source": "SWE-0062", "target": "Software Engineering", "type": "skos:broader" }, { "source": "Software Engineering", "target": "SWE-0062", "type": "skos:narrower" }, { "source": "SWE-0062", "target": "ART-0088", "type": "augmanitai:crossDomainReference" }, { "source": "SWE-0063", "target": "Software Engineering", "type": "skos:broader" }, { "source": "Software Engineering", "target": "SWE-0063", "type": "skos:narrower" }, { "source": "SWE-0063", "target": "CRE-0036", "type": "augmanitai:crossDomainReference" }, { "source": "SWE-0064", "target": "Software Engineering", "type": "skos:broader" }, { "source": "Software Engineering", "target": "SWE-0064", "type": "skos:narrower" }, { "source": "SWE-0065", "target": "Behavioral Pattern", "type": "skos:broader" }, { "source": "Behavioral Pattern", "target": "SWE-0065", "type": "skos:narrower" }, { "source": "SWE-0066", "target": "Systemic Drift", "type": "skos:broader" }, { "source": "Systemic Drift", "target": "SWE-0066", "type": "skos:narrower" }, { "source": "SWE-0066", "target": "ROB-0050", "type": "augmanitai:crossDomainReference" }, { "source": "SWE-0067", "target": "RPH-2303", "type": "skos:broader" }, { "source": "RPH-2303", "target": "SWE-0067", "type": "skos:narrower" }, { "source": "SWE-0067", "target": "AED-0076", "type": "augmanitai:crossDomainReference" }, { "source": "SWE-0067", "target": "BEH-0071", "type": "augmanitai:crossDomainReference" }, { "source": "SWE-0067", "target": "CON-0030", "type": "augmanitai:crossDomainReference" }, { "source": "SWE-0068", "target": "Software Engineering", "type": "skos:broader" }, { "source": "Software Engineering", "target": "SWE-0068", "type": "skos:narrower" }, { "source": "SWE-0069", "target": "Software Engineering", "type": "skos:broader" }, { "source": "Software Engineering", "target": "SWE-0069", "type": "skos:narrower" }, { "source": "SWE-0069", "target": "SPR-0193", "type": "augmanitai:crossDomainReference" }, { "source": "SWE-0070", "target": "Software Engineering", "type": "skos:broader" }, { "source": "Software Engineering", "target": "SWE-0070", "type": "skos:narrower" }, { "source": "SWE-0070", "target": "COP-0045", "type": "augmanitai:crossDomainReference" }, { "source": "SWE-0071", "target": "Software Engineering", "type": "skos:broader" }, { "source": "Software Engineering", "target": "SWE-0071", "type": "skos:narrower" }, { "source": "SWE-0072", "target": "RPH-2104", "type": "skos:broader" }, { "source": "RPH-2104", "target": "SWE-0072", "type": "skos:narrower" }, { "source": "SWE-0073", "target": "Software Engineering", "type": "skos:broader" }, { "source": "Software Engineering", "target": "SWE-0073", "type": "skos:narrower" }, { "source": "SWE-0074", "target": "Performance Gap", "type": "skos:broader" }, { "source": "Performance Gap", "target": "SWE-0074", "type": "skos:narrower" }, { "source": "SWE-0075", "target": "Software Engineering", "type": "skos:broader" }, { "source": "Software Engineering", "target": "SWE-0075", "type": "skos:narrower" }, { "source": "SWE-0075", "target": "ADA-0012", "type": "augmanitai:crossDomainReference" }, { "source": "SWE-0076", "target": "RPH-2201", "type": "skos:broader" }, { "source": "RPH-2201", "target": "SWE-0076", "type": "skos:narrower" }, { "source": "SWE-0076", "target": "ROB-0288", "type": "augmanitai:crossDomainReference" }, { "source": "SWE-0077", "target": "RPH-3802", "type": "skos:broader" }, { "source": "RPH-3802", "target": "SWE-0077", "type": "skos:narrower" }, { "source": "SWE-0078", "target": "Software Engineering", "type": "skos:broader" }, { "source": "Software Engineering", "target": "SWE-0078", "type": "skos:narrower" }, { "source": "SWE-0078", "target": "REL-0052", "type": "augmanitai:crossDomainReference" }, { "source": "SWE-0079", "target": "Software Engineering", "type": "skos:broader" }, { "source": "Software Engineering", "target": "SWE-0079", "type": "skos:narrower" }, { "source": "SWE-0080", "target": "RPH-1209", "type": "skos:broader" }, { "source": "RPH-1209", "target": "SWE-0080", "type": "skos:narrower" }, { "source": "SWE-0081", "target": "Software Engineering", "type": "skos:broader" }, { "source": "Software Engineering", "target": "SWE-0081", "type": "skos:narrower" }, { "source": "SWE-0081", "target": "IDN-0017", "type": "augmanitai:crossDomainReference" }, { "source": "SWE-0082", "target": "Software Engineering", "type": "skos:broader" }, { "source": "Software Engineering", "target": "SWE-0082", "type": "skos:narrower" }, { "source": "SWE-0083", "target": "Behavioral Pattern", "type": "skos:broader" }, { "source": "Behavioral Pattern", "target": "SWE-0083", "type": "skos:narrower" }, { "source": "SWE-0084", "target": "Software Engineering", "type": "skos:broader" }, { "source": "Software Engineering", "target": "SWE-0084", "type": "skos:narrower" }, { "source": "SWE-0085", "target": "Software Engineering", "type": "skos:broader" }, { "source": "Software Engineering", "target": "SWE-0085", "type": "skos:narrower" }, { "source": "SWE-0086", "target": "Software Engineering", "type": "skos:broader" }, { "source": "Software Engineering", "target": "SWE-0086", "type": "skos:narrower" }, { "source": "SWE-0086", "target": "CUS-0090", "type": "augmanitai:crossDomainReference" }, { "source": "SWE-0086", "target": "LIN-0032", "type": "augmanitai:crossDomainReference" }, { "source": "SWE-0087", "target": "Software Engineering", "type": "skos:broader" }, { "source": "Software Engineering", "target": "SWE-0087", "type": "skos:narrower" }, { "source": "SWE-0087", "target": "AGE-0002", "type": "augmanitai:crossDomainReference" }, { "source": "SWE-0087", "target": "AGE-0003", "type": "augmanitai:crossDomainReference" }, { "source": "SWE-0087", "target": "AGE-0039", "type": "augmanitai:crossDomainReference" }, { "source": "SWE-0088", "target": "RPH-2051", "type": "skos:broader" }, { "source": "RPH-2051", "target": "SWE-0088", "type": "skos:narrower" }, { "source": "SWE-0089", "target": "Software Engineering", "type": "skos:broader" }, { "source": "Software Engineering", "target": "SWE-0089", "type": "skos:narrower" }, { "source": "SWE-0090", "target": "RPH-2552", "type": "skos:broader" }, { "source": "RPH-2552", "target": "SWE-0090", "type": "skos:narrower" }, { "source": "SWE-0090", "target": "CUS-0066", "type": "augmanitai:crossDomainReference" }, { "source": "SWE-0091", "target": "Software Engineering", "type": "skos:broader" }, { "source": "Software Engineering", "target": "SWE-0091", "type": "skos:narrower" }, { "source": "SWE-0091", "target": "DAT-0046", "type": "augmanitai:crossDomainReference" }, { "source": "SWE-0092", "target": "Software Engineering", "type": "skos:broader" }, { "source": "Software Engineering", "target": "SWE-0092", "type": "skos:narrower" }, { "source": "SWE-0093", "target": "RPH-2355", "type": "skos:broader" }, { "source": "RPH-2355", "target": "SWE-0093", "type": "skos:narrower" }, { "source": "SWE-0094", "target": "Analytical Ratio", "type": "skos:broader" }, { "source": "Analytical Ratio", "target": "SWE-0094", "type": "skos:narrower" }, { "source": "SWE-0094", "target": "DES-0086", "type": "augmanitai:crossDomainReference" }, { "source": "SWE-0095", "target": "Software Engineering", "type": "skos:broader" }, { "source": "Software Engineering", "target": "SWE-0095", "type": "skos:narrower" }, { "source": "SWE-0096", "target": "RPH-3001", "type": "skos:broader" }, { "source": "RPH-3001", "target": "SWE-0096", "type": "skos:narrower" }, { "source": "TEM-0001", "target": "Algorithm", "type": "skos:broader" }, { "source": "Algorithm", "target": "TEM-0001", "type": "skos:narrower" }, { "source": "TEM-0002", "target": "Performance Gap", "type": "skos:broader" }, { "source": "Performance Gap", "target": "TEM-0002", "type": "skos:narrower" }, { "source": "TEM-0002", "target": "PER-0101", "type": "augmanitai:crossDomainReference" }, { "source": "TEM-0003", "target": "RPH-2201", "type": "skos:broader" }, { "source": "RPH-2201", "target": "TEM-0003", "type": "skos:narrower" }, { "source": "TEM-0003", "target": "SWE-0007", "type": "augmanitai:crossDomainReference" }, { "source": "TEM-0004", "target": "RPH-3101", "type": "skos:broader" }, { "source": "RPH-3101", "target": "TEM-0004", "type": "skos:narrower" }, { "source": "TEM-0004", "target": "CON-0089", "type": "augmanitai:crossDomainReference" }, { "source": "TEM-0005", "target": "RPH-2701", "type": "skos:broader" }, { "source": "RPH-2701", "target": "TEM-0005", "type": "skos:narrower" }, { "source": "TEM-0005", "target": "NEO-3580", "type": "augmanitai:crossDomainReference" }, { "source": "TEM-0006", "target": "TEM-0110", "type": "skos:broader" }, { "source": "TEM-0110", "target": "TEM-0006", "type": "skos:narrower" }, { "source": "TEM-0006", "target": "NEO-3569", "type": "augmanitai:crossDomainReference" }, { "source": "TEM-0006", "target": "REL-0164", "type": "augmanitai:crossDomainReference" }, { "source": "TEM-0007", "target": "RPH-2002", "type": "skos:broader" }, { "source": "RPH-2002", "target": "TEM-0007", "type": "skos:narrower" }, { "source": "TEM-0007", "target": "ADA-0011", "type": "augmanitai:crossDomainReference" }, { "source": "TEM-0007", "target": "CAI-0022", "type": "augmanitai:crossDomainReference" }, { "source": "TEM-0007", "target": "ELR-0190", "type": "augmanitai:crossDomainReference" }, { "source": "TEM-0008", "target": "Psychological Effect", "type": "skos:broader" }, { "source": "Psychological Effect", "target": "TEM-0008", "type": "skos:narrower" }, { "source": "TEM-0008", "target": "NEO-3536", "type": "augmanitai:crossDomainReference" }, { "source": "TEM-0008", 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AI", "target": "WEB-0059", "type": "skos:narrower" }, { "source": "WEB-0059", "target": "SPR-0189", "type": "augmanitai:crossDomainReference" }, { "source": "WEB-0059", "target": "RHR-0023", "type": "augmanitai:crossDomainReference" }, { "source": "WEB-0059", "target": "AGE-0003", "type": "augmanitai:crossDomainReference" }, { "source": "WEB-0060", "target": "Web AI", "type": "skos:broader" }, { "source": "Web AI", "target": "WEB-0060", "type": "skos:narrower" }, { "source": "WEB-0060", "target": "IDN-0035", "type": "augmanitai:crossDomainReference" }, { "source": "WEB-0061", "target": "Psychological Effect", "type": "skos:broader" }, { "source": "Psychological Effect", "target": "WEB-0061", "type": "skos:narrower" }, { "source": "WEB-0061", "target": "SAL-0092", "type": "augmanitai:crossDomainReference" }, { "source": "WEB-0062", "target": "Performance Metric", "type": "skos:broader" }, { "source": "Performance Metric", "target": "WEB-0062", "type": "skos:narrower" }, { "source": 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"target": "TRU-0011", "type": "augmanitai:crossDomainReference" }, { "source": "WRK-0073", "target": "Workplace AI", "type": "skos:broader" }, { "source": "Workplace AI", "target": "WRK-0073", "type": "skos:narrower" }, { "source": "WRK-0073", "target": "CON-0071", "type": "augmanitai:crossDomainReference" }, { "source": "WRK-0074", "target": "Network Architecture", "type": "skos:broader" }, { "source": "Network Architecture", "target": "WRK-0074", "type": "skos:narrower" }, { "source": "WRK-0074", "target": "PER-0069", "type": "augmanitai:crossDomainReference" }, { "source": "WRK-0075", "target": "RPH-2301", "type": "skos:broader" }, { "source": "RPH-2301", "target": "WRK-0075", "type": "skos:narrower" }, { "source": "WRK-0075", "target": "AGE-0067", "type": "augmanitai:crossDomainReference" }, { "source": "WRK-0076", "target": "Analytical Ratio", "type": "skos:broader" }, { "source": "Analytical Ratio", "target": "WRK-0076", "type": "skos:narrower" }, { "source": "WRK-0076", "target": "EDU-0073", "type": "augmanitai:crossDomainReference" }, { "source": "WRK-0077", "target": "Workplace AI", "type": "skos:broader" }, { "source": "Workplace AI", "target": "WRK-0077", "type": "skos:narrower" }, { "source": "WRK-0077", "target": "REL-0066", "type": "augmanitai:crossDomainReference" }, { "source": "WRK-0078", "target": "Workplace AI", "type": "skos:broader" }, { "source": "Workplace AI", "target": "WRK-0078", "type": "skos:narrower" }, { "source": "WRK-0078", "target": "EDU-0078", "type": "augmanitai:crossDomainReference" }, { "source": "WRK-0078", "target": "ELR-0096", "type": "augmanitai:crossDomainReference" }, { "source": "WRK-0079", "target": "Workplace AI", "type": "skos:broader" }, { "source": "Workplace AI", "target": "WRK-0079", "type": "skos:narrower" }, { "source": "WRK-0079", "target": "CRE-0191", "type": "augmanitai:crossDomainReference" }, { "source": "WRK-0080", "target": "RPH-2703", "type": "skos:broader" }, { "source": "RPH-2703", "target": "WRK-0080", "type": "skos:narrower" }, { "source": "WRK-0080", "target": "CRE-0139", "type": "augmanitai:crossDomainReference" }, { "source": "WRK-0081", "target": "RPH-2505", "type": "skos:broader" }, { "source": "RPH-2505", "target": "WRK-0081", "type": "skos:narrower" }, { "source": "WRK-0081", "target": "EDU-0023", "type": "augmanitai:crossDomainReference" }, { "source": "WRK-0082", "target": "RPH-2703", "type": "skos:broader" }, { "source": "RPH-2703", "target": "WRK-0082", "type": "skos:narrower" }, { "source": "WRK-0082", "target": "AED-0027", "type": "augmanitai:crossDomainReference" }, { "source": "WRK-0082", "target": "AED-0032", "type": "augmanitai:crossDomainReference" }, { "source": "WRK-0082", "target": "AED-0047", "type": "augmanitai:crossDomainReference" }, { "source": "WRK-0083", "target": "Workplace AI", "type": "skos:broader" }, { "source": "Workplace AI", "target": "WRK-0083", "type": "skos:narrower" }, { "source": "WRK-0083", "target": "REL-0033", "type": "augmanitai:crossDomainReference" }, { "source": "WRK-0084", "target": "RPH-2703", "type": "skos:broader" }, { "source": "RPH-2703", "target": "WRK-0084", "type": "skos:narrower" }, { "source": "WRK-0084", "target": "TEM-0115", "type": "augmanitai:crossDomainReference" }, { "source": "WRK-0084", "target": "TEM-0158", "type": "augmanitai:crossDomainReference" }, { "source": "WRK-0085", "target": "Workplace AI", "type": "skos:broader" }, { "source": "Workplace AI", "target": "WRK-0085", "type": "skos:narrower" }, { "source": "WRK-0085", "target": "SOM-0069", "type": "augmanitai:crossDomainReference" }, { "source": "WRK-0086", "target": "Workplace AI", "type": "skos:broader" }, { "source": "Workplace AI", "target": "WRK-0086", "type": "skos:narrower" }, { "source": "WRK-0086", "target": "SCR-0024", "type": "augmanitai:crossDomainReference" }, { "source": "WRK-0087", "target": "RPH-2703", "type": "skos:broader" }, { "source": "RPH-2703", "target": "WRK-0087", "type": "skos:narrower" }, { "source": "WRK-0087", "target": "EDU-0037", "type": "augmanitai:crossDomainReference" }, { "source": "WRK-0088", "target": "Workplace AI", "type": "skos:broader" }, { "source": "Workplace AI", "target": "WRK-0088", "type": "skos:narrower" }, { "source": "WRK-0088", "target": "IDN-0025", "type": "augmanitai:crossDomainReference" }, { "source": "WRK-0089", "target": "Analytical Ratio", "type": "skos:broader" }, { "source": "Analytical Ratio", "target": "WRK-0089", "type": "skos:narrower" }, { "source": "WRK-0089", "target": "RHR-0057", "type": "augmanitai:crossDomainReference" }, { "source": "WRK-0089", "target": "RPH-1264", "type": "augmanitai:crossDomainReference" }, { "source": "WRK-0090", "target": "RPH-2701", "type": "skos:broader" }, { "source": "RPH-2701", "target": "WRK-0090", "type": "skos:narrower" }, { "source": "WRK-0090", "target": "AED-0042", "type": "augmanitai:crossDomainReference" }, { "source": "WRK-0090", "target": "AED-0070", "type": "augmanitai:crossDomainReference" }, { "source": "WRK-0090", "target": "ASE-0028", "type": "augmanitai:crossDomainReference" }, { "source": "WRK-0091", "target": "Workplace AI", "type": "skos:broader" }, { "source": "Workplace AI", "target": "WRK-0091", "type": "skos:narrower" }, { "source": "WRK-0091", "target": "SPR-0200", "type": "augmanitai:crossDomainReference" }, { "source": "WRK-0092", "target": "Workplace AI", "type": "skos:broader" }, { "source": "Workplace AI", "target": "WRK-0092", "type": "skos:narrower" }, { "source": "WRK-0092", "target": "CAI-0012", "type": "augmanitai:crossDomainReference" }, { "source": "WRK-0093", "target": "RPH-3001", "type": "skos:broader" }, { "source": "RPH-3001", "target": "WRK-0093", "type": "skos:narrower" }, { "source": "WRK-0093", "target": "AED-0002", "type": "augmanitai:crossDomainReference" }, { "source": "WRK-0093", "target": "AGE-0029", "type": "augmanitai:crossDomainReference" }, { "source": "WRK-0093", "target": "ASE-0096", "type": "augmanitai:crossDomainReference" }, { "source": "WRK-0094", "target": "Workplace AI", "type": "skos:broader" }, { "source": "Workplace AI", "target": "WRK-0094", "type": "skos:narrower" }, { "source": "WRK-0094", "target": "FIC-0021", "type": "augmanitai:crossDomainReference" }, { "source": "WRK-0095", "target": "Workplace AI", "type": "skos:broader" }, { "source": "Workplace AI", "target": "WRK-0095", "type": "skos:narrower" }, { "source": "WRK-0095", "target": "SPR-0094", "type": "augmanitai:crossDomainReference" }, { "source": "WRK-0096", "target": "RPH-2505", "type": "skos:broader" }, { "source": "RPH-2505", "target": "WRK-0096", "type": "skos:narrower" }, { "source": "WRK-0096", "target": "CUS-0004", "type": "augmanitai:crossDomainReference" }, { "source": "WRK-0097", "target": "RPH-3902", "type": "skos:broader" }, { "source": "RPH-3902", "target": "WRK-0097", "type": "skos:narrower" }, { "source": "WRK-0097", "target": "DAT-0068", "type": "augmanitai:crossDomainReference" }, { "source": "WRK-0098", "target": "RPH-2701", "type": "skos:broader" }, { "source": "RPH-2701", "target": "WRK-0098", "type": "skos:narrower" }, { "source": "WRK-0098", "target": "TRA-0074", "type": "augmanitai:crossDomainReference" }, { "source": "WRK-0099", "target": "Workplace AI", "type": "skos:broader" }, { "source": "Workplace AI", "target": "WRK-0099", "type": "skos:narrower" }, { "source": "WRK-0100", "target": "RPH-2703", "type": "skos:broader" }, { "source": "RPH-2703", "target": "WRK-0100", "type": "skos:narrower" }, { "source": "WRK-0100", "target": "AGE-0049", "type": "augmanitai:crossDomainReference" } ] }